Journal articles on the topic 'Power state machine generation'
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1
Pop, Claudia V., and D. Foreran. "State of the art of Multiport Electrical Machines and Magnetic Gears with respect to Wind Power Generation Application." Renewable Energy and Power Quality Journal 20 (September 2022): 6–11. http://dx.doi.org/10.24084/repqj20.206.
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This paper presents a state of the art of multiport machines and magnetic gears designed for power generation applications. A multiport machine consists mainly of an electrical machine wo which a magnetic gear is integrated. Thus, one can get a device with more than two shafts, capable to operate at different levels of torque and speed. The main structures found in the literature, their operation, speed increase capability, materials, advantages and disadvantages are depicted in this review-type paper.
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Read, MG, IK Smith, and N. Stosic. "Optimisation of power generation cycles using saturated liquid expansion to maximise heat recovery." Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 231, no. 1 (December 11, 2016): 57–69. http://dx.doi.org/10.1177/0954408916679202.
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The use of two-phase screw expanders in power generation cycles can achieve an increase in the utilisation of available energy from a low-temperature heat source when compared with more conventional single-phase turbines. The efficiency of screw expander machines is sensitive to expansion volume ratio, which, for given inlet and discharge pressures, increases as the expander inlet vapour dryness fraction decreases. For single-stage screw machines with low inlet dryness, this can lead to underexpansion of the working fluid and low isentropic efficiency. The cycle efficiency can potentially be improved by using a two-stage expander, consisting of a machine for low-pressure expansion and a smaller high-pressure machine connected in series. By expanding the working fluid over two stages, the built-in volume ratios of the two machines can be selected to provide a better match with the overall expansion process, thereby increasing the efficiency. The mass flow rate though both stages must be matched, and the compromise between increasing efficiency and maximising power output must also be considered. This study is based on the use of a rigorous thermodynamic screw machine model to compare the performance of single- and two-stage expanders. The model allows optimisation of the required intermediate pressure in the two-stage expander, along with the built-in volume ratio of both screw machine stages. The results allow specification of a two-stage machine, using either two screw machines or a combination of high-pressure screw and low-pressure turbine, in order to achieve maximum efficiency for a particular power output. For the low-temperature heat recovery application considered in this paper, the trilateral flash cycle using a two-stage expander and the Smith cycle using a high-pressure screw and low-pressure turbine are both predicted to achieve a similar overall conversion efficiency to that of a conventional saturated vapour organic Rankine cycle.
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Khitrov, Andrei, and Alexander Khitrov. "Electrical subsystem of the low-power cogeneration plant with low-speed vehicle." Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference 2 (August 8, 2015): 119. http://dx.doi.org/10.17770/etr2013vol2.852.
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Nowadays diesel power plants form the basis of distributed power generation in Russia, but they have disadvantages. The alternative variant is cogeneration plant based on the rotary-vane machine. One of the types of such machines is the rotary-vane external combustion vehicle (engine) developed in Pskov State University. Electrical subsystem of the plant requires its effective work to provide both start and generation modes. Development of such subsystem structure, employment of elements for links with other subsystems in the hierarchic control system is an actual task. The paper considers structures of the electrical part of the plant, simulation and experiment results.
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Zhuang, Zhe Min, and Fen Lan Li. "Statistical Method for Rotating Machine Fault Diagnosis." Advanced Materials Research 383-390 (November 2011): 1406–10. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.1406.
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In this paper, a time-domain analysis method based on multivariate statistic is presented for wind power generation fault diagnosis. Generally, the sound and vibration signals obtained from wind power generation are time-variant since they are strongly related to the rotational speed which is not constant even in the macro steady state. Since the mostly used signal processing method, the Fourier analysis, is only suitable for stationary signals, the development of the joint time-frequency analysis is demanded. Here, Q statistic (also referred as squared prediction error, SPE) is introduced, it is used to monitor the vibration signals and three-phase currents. The control limit of the Q statistics is calculated to decide the state of the rotating machine, and the contribution plot of SPE is used to find the fault source. The method can efficiently detect faint change and the validity of the method is proved by experiments.
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Carrera, Berny, and Kwanho Kim. "Comparison Analysis of Machine Learning Techniques for Photovoltaic Prediction Using Weather Sensor Data." Sensors 20, no. 11 (June 1, 2020): 3129. http://dx.doi.org/10.3390/s20113129.
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Over the past few years, solar power has significantly increased in popularity as a renewable energy. In the context of electricity generation, solar power offers clean and accessible energy, as it is not associated with global warming and pollution. The main challenge of solar power is its uncontrollable fluctuation since it is highly depending on other weather variables. Thus, forecasting energy generation is important for smart grid operators and solar electricity providers since they are required to ensure the power continuity in order to dispatch and properly prepare to store the energy. In this study, we propose an efficient comparison framework for forecasting the solar power that will be generated 36 h in advance from Yeongam solar power plant located in South Jeolla Province, South Korea. The results show a comparative analysis of the state-of-the-art techniques for solar power generation.
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Mehar, Pramod Kumar, and Mrs Madhu Upadhyay. "Power System Stability Study on Multi Machine Systems having DFIG Based Wind Generation System." SMART MOVES JOURNAL IJOSCIENCE 6, no. 3 (March 10, 2020): 27–30. http://dx.doi.org/10.24113/ijoscience.v6i3.279.
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Power system stability is related to principles of rotational motion and the swing equation governing the electromechanical dynamic behavior. In the special case of two finite machines the equal area criterion of stability can be used to calculate the critical clearing angle on the power system, it is necessary to maintain synchronism, otherwise a standard of service to the consumers will not be achieved. With the increasing penetration of doubly fed induction generators (DFIGs), the impact of the DFIG on transient stability attracts great attention. Transient stability is largely dominated by generator types in the power system, and the dynamic characteristics of DFIG wind turbines are different from that of the synchronous generators in the conventional power plants. The analysis of the transient stability on DFIG integrated power systems has become a very important issue. This paper is a review of three types of stability condition. The first type of stability, steady state stability explains the maximum steady state power and the power angle diagram. There are several methods to improve system stability in which some methods are explained.
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Hsieh, Hsien-Yi, Jingyu Ning, Yi-Ru Chen, Hsun-Chung Wu, Hua Li Chen, Chien-Ming Wu, and Ray-Kuang Lee. "Direct Parameter Estimations from Machine Learning-Enhanced Quantum State Tomography." Symmetry 14, no. 5 (April 25, 2022): 874. http://dx.doi.org/10.3390/sym14050874.
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With the power to find the best fit to arbitrarily complicated symmetry, machine-learning (ML)-enhanced quantum state tomography (QST) has demonstrated its advantages in extracting complete information about the quantum states. Instead of using the reconstruction model in training a truncated density matrix, we develop a high-performance, lightweight, and easy-to-install supervised characteristic model by generating the target parameters directly. Such a characteristic model-based ML-QST can avoid the problem of dealing with a large Hilbert space, but cab keep feature extractions with high precision, capturing the underlying symmetry in data. With the experimentally measured data generated from the balanced homodyne detectors, we compare the degradation information about quantum noise squeezed states predicted by the reconstruction and characteristic models; both are in agreement with the empirically fitting curves obtained from the covariance method. Such a ML-QST with direct parameter estimations illustrates a crucial diagnostic toolbox for applications with squeezed states, from quantum information process, quantum metrology, advanced gravitational wave detectors, to macroscopic quantum state generation.
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Hsieh, Hsien-Yi, Jingyu Ning, Yi-Ru Chen, Hsun-Chung Wu, Hua Li Chen, Chien-Ming Wu, and Ray-Kuang Lee. "Direct Parameter Estimations from Machine Learning-Enhanced Quantum State Tomography." Symmetry 14, no. 5 (April 25, 2022): 874. http://dx.doi.org/10.3390/sym14050874.
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With the power to find the best fit to arbitrarily complicated symmetry, machine-learning (ML)-enhanced quantum state tomography (QST) has demonstrated its advantages in extracting complete information about the quantum states. Instead of using the reconstruction model in training a truncated density matrix, we develop a high-performance, lightweight, and easy-to-install supervised characteristic model by generating the target parameters directly. Such a characteristic model-based ML-QST can avoid the problem of dealing with a large Hilbert space, but cab keep feature extractions with high precision, capturing the underlying symmetry in data. With the experimentally measured data generated from the balanced homodyne detectors, we compare the degradation information about quantum noise squeezed states predicted by the reconstruction and characteristic models; both are in agreement with the empirically fitting curves obtained from the covariance method. Such a ML-QST with direct parameter estimations illustrates a crucial diagnostic toolbox for applications with squeezed states, from quantum information process, quantum metrology, advanced gravitational wave detectors, to macroscopic quantum state generation.
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Cornett, Andrew Malcolm, Peter Laurich, Enrique Gardeta, and Daniel Pelletier. "DESIGN OF A POWERFUL AND PORTABLE MULTIDIRECTIONAL WAVEMA." Coastal Engineering Proceedings, no. 35 (June 23, 2017): 29. http://dx.doi.org/10.9753/icce.v35.structures.29.
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A new multidirectional wave generator with 72 independent paddles has been designed, fabricated and commissioned at the National Research Council labs in Ottawa, Canada. The wet-back piston-mode machine is installed in a new 50 m long by 30 m wide rectangular wave basin, where water depths can be varied over the range from 0 m up to 1.3 m. The new machine is believed to be unique in the world in that it combines the power and stroke required to generate multidirectional spectral wave conditions with significant wave heights exceeding 0.4 m together with the modularity and ease of portability required to move the machine quickly and safely to new positions. The new machine can also be sub-divided to form several shorter machines if desired. The new wave generator features lightweight, composite materials, energy efficient regenerative power supplies, state-of-the-art software and control systems, including capabilities for active wave absorption (reflection compensation), second-order wave generation for improved generation of nonlinear sub- and super-harmonics, side-wall reflection, and more. The design of this new directional wavemaker is described and several of the more innovative features are highlighted in this paper.
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Li, Sheng, Xinhua Yao, and Jianzhong Fu. "Power output characterization assessment of thermoelectric generation in machine spindles for wireless sensor driving." Sensor Review 34, no. 2 (March 17, 2014): 192–200. http://dx.doi.org/10.1108/sr-03-2013-642.
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Purpose – For using wireless sensors to monitor spindle units without opening the spindle shell to replace the battery, harvesting the waste heat from spindle units of machine tools for thermoelectric generation to drive wireless sensors is studied in this paper. The paper aims to discuss these issues. Design/methodology/approach – In this paper, the thermal network method and the analogies between electrical and thermal domains are used in the simulation of power output performance of thermoelectric generation on a rotating spindle. After that, experiments are done to obtain the real power output performance of the generation and evaluate the feasibility to drive wireless sensors. Findings – The paper provides that the output voltage of the thermoelectric generations was nearly linear with the rotating speed of the spindle, the output voltage was sensitive to the fixed position of the generations, and the thermoelectric system could drive the wireless sensor well most of the time during continuous operation of the spindle. Research limitations/implications – It is found that the thermoelectric generation could not provide enough power in the early start-up stage of the spindle rotation, so a high-efficiency power manage system, which will be studied in the future research, is needed to handle this problem. Practical implications – The paper includes implications for the development of self-powered wireless sensors in the spindle unit for machine tool monitoring. Originality/value – The paper develops a model of the power output performance of thermoelectric generation on a rotating spindle and tests the feasibility to drive wireless sensors with this power.
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Baran, Sándor, and Ágnes Baran. "Calibration of wind speed ensemble forecasts for power generation." Időjárás 125, no. 4 (2021): 609–24. http://dx.doi.org/10.28974/idojaras.2021.4.4.
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In the last decades, wind power became the second largest energy source in the EU covering 16% of its electricity demand. However, due to its volatility, accurate short range wind power predictions are required for successful integration of wind energy into the electrical grid. Accurate predictions of wind power require accurate hub height wind speed forecasts, where the state-of-the-art method is the probabilistic approach based on ensemble forecasts obtained from multiple runs of numerical weather prediction models. Nonetheless, ensemble forecasts are often uncalibrated and might also be biased, thus require some form of post-processing to improve their predictive performance. We propose a novel flexible machine learning approach for calibrating wind speed ensemble forecasts, which results in a truncated normal predictive distribution. In a case study based on 100m wind speed forecasts produced by the operational ensemble prediction system of the Hungarian Meteorological Service, the forecast skill of this method is compared with the predictive performance of three different ensemble model output statistics approaches and the raw ensemble forecasts. We show that compared with the raw ensemble, post-processing always improves the calibration of probabilistic and accuracy of point forecasts, and from the four competing methods, the novel machine learning based approach results in the best overall performance.
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hao-Li, Jing. "Application of the unmanned inspection system in power generation enterprises." Journal of Physics: Conference Series 2399, no. 1 (December 1, 2022): 012040. http://dx.doi.org/10.1088/1742-6596/2399/1/012040.
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Abstract In the power generation company, it is the unmanned inspection system that can through visual image recognition, infrared monitoring, and fixed-point sensor complete the real-time monitoring of the plant area and equipment, through the system building perception layer, network layer, and application layer realizes the remote control command issued, and complete the equipment running status and working parameters of the real-time monitoring and remote control. Through the design of the bearing state feature extraction method and the fusion of machine mechanism, and artificial intelligence data processing method, the intelligent diagnosis and early warning of the whole plant equipment are realized, and the one-click inspection of power generation enterprises is achieved.
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Tom, Liya, Muhammad Khowja, Gaurang Vakil, and Chris Gerada. "Commercial Aircraft Electrification—Current State and Future Scope." Energies 14, no. 24 (December 13, 2021): 8381. http://dx.doi.org/10.3390/en14248381.
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Electric and hybrid-electric aircraft propulsion are rapidly revolutionising mobility technologies. Air travel has become a major focus point with respect to reducing greenhouse gas emissions. The electrification of aircraft components can bring several benefits such as reduced mass, environmental impact, fuel consumption, increased reliability and quicker failure resolution. Propulsion, actuation and power generation are the three key areas of focus in more electric aircraft technologies, due to the increasing demand for power-dense, efficient and fault-tolerant flight components. The necessity of having environmentally friendly aircraft systems has promoted the aerospace industry to use electrically powered drive systems, rather than the conventional mechanical, pneumatic or hydraulic systems. In this context, this paper reviews the current state of art and future advances in more electric technologies, in conjunction with a number of industrially relevant discussions. In this study, a permanent magnet motor was identified as the most efficient machine for aircraft subsystems. It is found to be 78% and 60% more power dense than switch-reluctant and induction machines. Several development methods to close the gap between existing and future design were also analysed, including the embedded cooling system, high-thermal-conductivity insulation materials, thin-gauge and high-strength electrical steel and integrated motor drive topology.
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Stamatakis, Michael E., and Maria G. Ioannides. "State Transitions Logical Design for Hybrid Energy Generation with Renewable Energy Sources in LNG Ship." Energies 14, no. 22 (November 22, 2021): 7803. http://dx.doi.org/10.3390/en14227803.
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In terms of energy generation and consumption, ships are autonomous and isolated power systems with energy requirements related to the type and kind of power demands and according to ship types: passenger ships, or commercial ships. Power supply on ships is traditionally based on engines thermal generators, which use fossil fuels, diesel, or natural gas. Due to the continuous operation of thermal generators in ships, this ends up increasing polluting gas emissions for the environment, mainly CO2. A combination of Renewable Energy Sources (RES) with traditional ship thermal engines can reduce CO2 emissions, resulting in a ‘greener’ interaction between ships and the environment. Due to the varying power needs for ship operation, considering the varying nature of load demands during long distance travels and during harbor entry, the use of RES must be evaluated. This paper presents a new control method to balance LNG ship load demands and power generation from RES, based on an accurate model and solution in real conditions. The Energy Management System (EMS) is designed and implemented in a Finite State Machine structure using the logical design of state transitions. The results prove that the reduction of consumption of fossil fuels is feasible, and, if this is combined with RES, it reduces CO2 emissions.
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Pond, James, Xu Wang, Zeqin Lu, Federico Duque Gomez, Ahsan Alam, Sebastian Gitt, Dylan McGuire, Jeff Young, and Gilles Lamant. "State-of-the-art and next-generation integrated photonic design." EPJ Web of Conferences 266 (2022): 01010. http://dx.doi.org/10.1051/epjconf/202226601010.
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The relentless need for higher bandwidth, lower power and lower cost data communications has driven tremendous innovation in integrated photonics in recent years. This innovation has been supported by state-of-the-art electronic-photonic design automation (EPDA) workflows, which enable process design kit (PDK) centred schematic driven design and layout, as well as statistically enabled electro-optical simulation. In addition, custom components can be introduced and optimized for a specific foundry process using advanced methods such as photonic inverse design and machine learning. While much of the innovation has been motivated by data communications, it has enabled a variety of different applications such as sensing, integrated LiDAR and quantum information technologies. We discuss the latest innovations in EPDA workflows and show how a silicon photonic ring-based wavelength demultiplexing (WDM) system can be easily designed, simulated and implemented. In addition, we discuss the extension of these workflows to support the design and simulation of quantum photonic devices, enabling designers to consider the effects of realistic sources and manufacturing imperfections when designing quantum building blocks to meet specific fidelity and fault tolerance thresholds.
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Ismail, Abdelrahman, Mahmoud S. Abdel-Majeed, Mohamed Y. Metwly, Ayman S. Abdel-Khalik, Mostafa S. Hamad, Shehab Ahmed, Eman Hamdan, and Noha A. Elmalhy. "Solid-State Transformer-Based DC Power Distribution Network for Shipboard Applications." Applied Sciences 12, no. 4 (February 14, 2022): 2001. http://dx.doi.org/10.3390/app12042001.
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Due to simplicity, efficiency, and the ability to accommodate energy storage devices, DC distribution networks have been seen as an optimal alternative to AC distribution networks, especially aboard future electric ships. The emerging distribution DC system entails new control and management techniques. Therefore, an integrated DC power distribution network aboard an electric ship is selected as the case study in this paper. To meet the requirements of such a large-scale mobile power system, a multiport solid-state transformer (SST) based on silicon carbide (SiC) switches/MOSFETs is proposed. Thus, the system embodiment can significantly be reduced. Moreover, at the DC distribution level, a high penetration of renewable generation with energy storage is allowed and a six-phase asymmetrical induction machine (IM) can directly be integrated. Simulations have been conducted based on a 2 MW shipboard distribution network. The effects of the propulsion system dynamics on the SST are highlighted as well. Finally, a 2 kW lab-scale prototype has been implemented to validate the theoretical findings.
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Fekri, Mohammad Navid, Ananda Mohon Ghosh, and Katarina Grolinger. "Generating Energy Data for Machine Learning with Recurrent Generative Adversarial Networks." Energies 13, no. 1 (December 26, 2019): 130. http://dx.doi.org/10.3390/en13010130.
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The smart grid employs computing and communication technologies to embed intelligence into the power grid and, consequently, make the grid more efficient. Machine learning (ML) has been applied for tasks that are important for smart grid operation including energy consumption and generation forecasting, anomaly detection, and state estimation. These ML solutions commonly require sufficient historical data; however, this data is often not readily available because of reasons such as data collection costs and concerns regarding security and privacy. This paper introduces a recurrent generative adversarial network (R-GAN) for generating realistic energy consumption data by learning from real data. Generativea adversarial networks (GANs) have been mostly used for image tasks (e.g., image generation, super-resolution), but here they are used with time series data. Convolutional neural networks (CNNs) from image GANs are replaced with recurrent neural networks (RNNs) because of RNN’s ability to capture temporal dependencies. To improve training stability and increase quality of generated data, Wasserstein GANs (WGANs) and Metropolis-Hastings GAN (MH-GAN) approaches were applied. The accuracy is further improved by adding features created with ARIMA and Fourier transform. Experiments demonstrate that data generated by R-GAN can be used for training energy forecasting models.
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de Souza, Wesley Angelino, Fernando Deluno Garcia, Fernando Pinhabel Marafão, Luiz Carlos Pereira da Silva, and Marcelo Godoy Simões. "Load Disaggregation Using Microscopic Power Features and Pattern Recognition." Energies 12, no. 14 (July 10, 2019): 2641. http://dx.doi.org/10.3390/en12142641.
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A new generation of smart meters are called cognitive meters, which are essentially based on Artificial Intelligence (AI) and load disaggregation methods for Non-Intrusive Load Monitoring (NILM). Thus, modern NILM may recognize appliances connected to the grid during certain periods, while providing much more information than the traditional monthly consumption. Therefore, this article presents a new load disaggregation methodology with microscopic characteristics collected from current and voltage waveforms. Initially, the novel NILM algorithm—called the Power Signature Blob (PSB)—makes use of a state machine to detect when the appliance has been turned on or off. Then, machine learning is used to identify the appliance, for which attributes are extracted from the Conservative Power Theory (CPT), a contemporary power theory that enables comprehensive load modeling. Finally, considering simulation and experimental results, this paper shows that the new method is able to achieve 95% accuracy considering the applied data set.
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Seo, Dongwoo, Taesang Huh, Myungil Kim, Jaesoon Hwang, and Daeyong Jung. "Prediction of Air Pressure Change Inside the Chamber of an Oscillating Water Column–Wave Energy Converter Using Machine-Learning in Big Data Platform." Energies 14, no. 11 (May 21, 2021): 2982. http://dx.doi.org/10.3390/en14112982.
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Wave power is an eco-friendly power generation method. Owing to the highly volatile nature of wave energy, the application of prediction techniques for power generation, failure diagnosis, and operational efficiency plays a key role in the successful operation of wave power plants (WPPs). To this end, we propose the following approaches: (i) deriving the correlation between highly volatile data such as wave height data and sensor data in an oscillating water column (OWC) chamber; (ii) development of an optimal training model capable of accurate prediction of the state of the wave energy converter (WEC) based on the collected sensor data. In this study, we developed a big data analysis system that can utilize the machine learning framework in KNIME (an open analysis platform), and to enable smart operation, we designed a training model using a digital twin of an OWC–WEC that is currently in operation. Using various machine learning models, the pressure of the OWC chamber was predicted, and the results obtained were tested and evaluated to confirm its validity. Furthermore, the prediction performance was comparatively analyzed, demonstrating the excellent performance of the proposed CNN-LSTM-based prediction model.
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Singh, G. K., S. N. Singh, and R. P. Saini. "Steady-State Modeling and Analysis of Grid-Connected Six-Phase Induction Generator for Renewable Energy Generation." Advanced Materials Research 516-517 (May 2012): 645–59. http://dx.doi.org/10.4028/www.scientific.net/amr.516-517.645.
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This article presents the steady-state modeling and analysis of a grid-connected six-phase induction generator for renewable energy generation powered by hydro turbine. The basis of the analysis is nodal admittance method as applied to the equivalent circuit, and used to analyze the behavior of the machine for the operating mode such as (i) when only one three-phase winding set is connected to grid, (ii) when one three-phase winding set is connected to grid and other three-phase winding set is subjected to load, and (iii) when both the three-phase winding sets are connected to grid through an interconnecting Y-/Y six-phase to three-phase transformer. Nodal admittance based matrix equations are easier to modify in order to account for mutual leakage coupling between two three-phase winding sets, core loss component, and make the analysis very easy, fast and accurate. Through analytical and practical studies, it is shown that machine can feed direct, reliable, and low cost power to grid without interface network. The analytical results are found to be in good agreement with experimental results.
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Szymański, Zygmunt. "Intelligent, Energy Saving Power Supply and Control System of Hoisting Mine Machine with Compact and Hybrid Drive System / Inteligentne, Energooszczędne Układy Zasilania I Sterowania Górniczych Maszyn Wyciągowych Z Napędem Zintegrowanym Lub Hybrydowym." Archives of Mining Sciences 60, no. 1 (March 1, 2015): 239–51. http://dx.doi.org/10.1515/amsc-2015-0016.
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Abstract In the paper present’s an analysis of suitableness an application of compact and hybrid drive system in hoisting machine. In the paper presented the review of constructional solutions of hoisting machines drive system, driving with AC and DC motor. In the paper presented conception of modern, energy sparing hoisting machine supply system, composed with compact motor, an supplied with transistor or thyristor converter supply system, and intelligent control system composed with multilevel microprocessor controller. In the paper present’s also analysis of suitableness application an selected method of artificial intelligent in hoisting machine control system, automation system, and modern diagnostic system. In the paper one limited to analysis of: fuzzy logic method, genetic algorithms method, and modern neural net II and III generation. That method enables realization of complex control algorithms of hosting machine with insurance of energy sparing exploitation conditions, monitoring of exploitation parameters, and prediction diagnostic of hoisting machine technical state, minimization a number of failure states. In the paper present’s a conception of control and diagnostic system of the hoisting machine based on fuzzy logic neural set control. In the chapter presented also a selected control algorithms and results of computer simulations realized for particular mathematical models of hoisting machine. Results of theoretical investigation were partly verified in laboratory and industrial experiments.
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Memon, Zain Anwer, Riccardo Trinchero, Paolo Manfredi, Flavio Canavero, and Igor S. Stievano. "Compressed Machine Learning Models for the Uncertainty Quantification of Power Distribution Networks." Energies 13, no. 18 (September 17, 2020): 4881. http://dx.doi.org/10.3390/en13184881.
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Today’s spread of power distribution networks, with the installation of a significant number of renewable generators that depend on environmental conditions and on users’ consumption profiles, requires sophisticated models for monitoring the power flow, regulating the electricity market, and assessing the reliability of power grids. Such models cannot avoid taking into account the variability that is inherent to the electrical system and users’ behavior. In this paper, we present a solution for the generation of a compressed surrogate model of the electrical state of a realistic power network that is subject to a large number (on the order of a few hundreds) of uncertain parameters representing the power injected by distributed renewable sources or absorbed by users with different consumption profiles. Specifically, principal component analysis is combined with two state-of-the-art surrogate modeling strategies for uncertainty quantification, namely, the least-squares support vector machine, which is a nonparametric regression belonging to the class of machine learning methods, and the widely adopted polynomial chaos expansion. Such methods allow providing compact and efficient surrogate models capable of predicting the statistical behavior of all nodal voltages within the network as functions of its stochastic parameters. The IEEE 8500-node test feeder benchmark with 450 and 900 uncertain parameters is considered as a validation example in this study. The feasibility and strength of the proposed method are verified through a systematic assessment of its performance in terms of accuracy, efficiency, and convergence, based on reference simulations obtained via classical Monte Carlo analysis.
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de Mattos Neto, Paulo S. G., João F. L. de Oliveira, Priscilla Bassetto, Hugo Valadares Siqueira, Luciano Barbosa, Emilly Pereira Alves, Manoel H. N. Marinho, Guilherme Ferretti Rissi, and Fu Li. "Energy Consumption Forecasting for Smart Meters Using Extreme Learning Machine Ensemble." Sensors 21, no. 23 (December 3, 2021): 8096. http://dx.doi.org/10.3390/s21238096.
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The employment of smart meters for energy consumption monitoring is essential for planning and management of power generation systems. In this context, forecasting energy consumption is a valuable asset for decision making, since it can improve the predictability of forthcoming demand to energy providers. In this work, we propose a data-driven ensemble that combines five single well-known models in the forecasting literature: a statistical linear autoregressive model and four artificial neural networks: (radial basis function, multilayer perceptron, extreme learning machines, and echo state networks). The proposed ensemble employs extreme learning machines as the combination model due to its simplicity, learning speed, and greater ability of generalization in comparison to other artificial neural networks. The experiments were conducted on real consumption data collected from a smart meter in a one-step-ahead forecasting scenario. The results using five different performance metrics demonstrate that our solution outperforms other statistical, machine learning, and ensembles models proposed in the literature.
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Sanchez-Iborra, Ramon. "LPWAN and Embedded Machine Learning as Enablers for the Next Generation of Wearable Devices." Sensors 21, no. 15 (July 31, 2021): 5218. http://dx.doi.org/10.3390/s21155218.
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The penetration of wearable devices in our daily lives is unstoppable. Although they are very popular, so far, these elements provide a limited range of services that are mostly focused on monitoring tasks such as fitness, activity, or health tracking. Besides, given their hardware and power constraints, wearable units are dependent on a master device, e.g., a smartphone, to make decisions or send the collected data to the cloud. However, a new wave of both communication and artificial intelligence (AI)-based technologies fuels the evolution of wearables to an upper level. Concretely, they are the low-power wide-area network (LPWAN) and tiny machine-learning (TinyML) technologies. This paper reviews and discusses these solutions, and explores the major implications and challenges of this technological transformation. Finally, the results of an experimental study are presented, analyzing (i) the long-range connectivity gained by a wearable device in a university campus scenario, thanks to the integration of LPWAN communications, and (ii) how complex the intelligence embedded in this wearable unit can be. This study shows the interesting characteristics brought by these state-of-the-art paradigms, concluding that a wide variety of novel services and applications will be supported by the next generation of wearables.
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Berghout, Tarek, Mohamed Benbouzid, Toufik Bentrcia, Xiandong Ma, Siniša Djurović, and Leïla-Hayet Mouss. "Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects." Energies 14, no. 19 (October 3, 2021): 6316. http://dx.doi.org/10.3390/en14196316.
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To ensure the continuity of electric power generation for photovoltaic systems, condition monitoring frameworks are subject to major enhancements. The continuous uniform delivery of electric power depends entirely on a well-designed condition maintenance program. A just-in-time task to deal with several naturally occurring faults can be correctly undertaken via the cooperation of effective detection, diagnosis, and prognostic analyses. Therefore, the present review first outlines different failure modes to which all photovoltaic systems are subjected, in addition to the essential integrated detection methods and technologies. Then, data-driven paradigms, and their contribution to solving this prediction problem, are also explored. Accordingly, this review primarily investigates the different learning architectures used (i.e., ordinary, hybrid, and ensemble) in relation to their learning frameworks (i.e., traditional and deep learning). It also discusses the extension of machine learning to knowledge-driven approaches, including generative models such as adversarial networks and transfer learning. Finally, this review provides insights into different works to highlight various operating conditions and different numbers and types of failures, and provides links to some publicly available datasets in the field. The clear organization of the abundant information on this subject may result in rigorous guidelines for the trends adopted in the future.
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Hassan, A. B. M. Khalid, and Kazi Firoz Ahmed. "Design and analysis of an off-grid PV plant for higher utilization efficiency in the field of pharmaceutical industry considering global pandemic state ." AIUB Journal of Science and Engineering (AJSE) 20, no. 1 (April 15, 2021): 47–58. http://dx.doi.org/10.53799/ajse.v20i1.144.
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According to the concern of WHO the less association of people in an office may restrict the likelihood of spreading this COVID-19 infection. And it applies to all kinds of organizations. On the other hand, the pharmaceutical companies are working hard to maintain uninterrupted production of vaccine and medicines. This paper focuses on the main layer which is the power system management and its utilization through the less involvement of any individual. Automation and controlling the system remotely can be a good solution. In the design process the FDA proposed structure for the Pharmaceuticals needs to be maintained as well. One of the significant necessities is most of the energy should come from environment friendly system and in Bangladesh sunlight-based energy is the best solution right now. Solar energy utilization efficiency can be increased using the data logging system and machine learning algorithms from that archived data. In this paper, a SCADA operated Off-Grid Solar PV Automation System has been proposed to increase the utilization efficiency. To predict solar power availability over time and perform efficient energy trafficking, the automation system will analyze previous data and perform situational awareness operations for uninterrupted solar power generation. The proposed automation system has been designed focusing on pharmaceutical manufacturing utilities. A comprehensive analysis of the proposed automation system for pharmaceuticals industry applications has also been presented in this paper. The continuous monitoring system for this Off-Grid Solar PV power generating unit preserves multiple data entries, which increases with time and subjected to energy trafficking. And this energy trafficking based on machine learning increases the overall solar energy utilization efficiency.
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Sejan, Mohammad Abrar Shakil, Md Habibur Rahman, Beom-Sik Shin, Ji-Hye Oh, Young-Hwan You, and Hyoung-Kyu Song. "Machine Learning for Intelligent-Reflecting-Surface-Based Wireless Communication towards 6G: A Review." Sensors 22, no. 14 (July 20, 2022): 5405. http://dx.doi.org/10.3390/s22145405.
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An intelligent reflecting surface (IRS) is a programmable device that can be used to control electromagnetic waves propagation by changing the electric and magnetic properties of its surface. Therefore, IRS is considered a smart technology for the sixth generation (6G) of communication networks. In addition, machine learning (ML) techniques are now widely adopted in wireless communication as the computation power of devices has increased. As it is an emerging topic, we provide a comprehensive overview of the state-of-the-art on ML, especially on deep learning (DL)-based IRS-enhanced communication. We focus on their operating principles, channel estimation (CE), and the applications of machine learning to IRS-enhanced wireless networks. In addition, we systematically survey existing designs for IRS-enhanced wireless networks. Furthermore, we identify major issues and research opportunities associated with the integration of IRS and other emerging technologies for applications to next-generation wireless communication.
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Shahid, Muhammad Khalil, Filmon Debretsion, Aman Eyob, Irfan Ahmed, and Tarig Faisal. "Energy Efficiency in 5G Communications – Conventional to Machine Learning Approaches." Journal of Telecommunications and Information Technology 4 (December 30, 2020): 1–9. http://dx.doi.org/10.26636/jtit.2020.146820.
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Demand for wireless and mobile data is increasing along with development of virtual reality (VR), augmented reality (AR), mixed reality (MR), and extended reality (ER) applications. In order to handle ultra-high data exchange rates while offering low latency levels, fifth generation (5G) networks have been proposed. Energy efficiency is one of the key objectives of 5G networks. The notion is defined as the ratio of throughput and total power consumption, and is measured using the number of transmission bits per Joule. In this paper, we review state-of-the-art techniques ensuring good energy efficiency in 5G wireless networks. We cover the base-station on/off technique, simultaneous wireless information and power transfer, small cells, coexistence of long term evolution (LTE) and 5G, signal processing algorithms, and the latest machine learning techniques. Finally, a comparison of a few recent research papers focusing on energy-efficient hybrid beamforming designs in massive multiple-input multiple-output (MIMO) systems is presented. Results show that machine learningbased designs may replace best performing conventional techniques thanks to a reduced complexity machine learning encoder
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Padovani, D., P. Fresia, M. Rundo, and G. Altare. "Downsizing the electric machines of energy-efficient electro-hydraulic drives for mobile hydraulics." Journal of Physics: Conference Series 2385, no. 1 (December 1, 2022): 012028. http://dx.doi.org/10.1088/1742-6596/2385/1/012028.
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Abstract The poor energy efficiency of state-of-the-art mobile hydraulics affects the carbon dioxide released into the atmosphere and the operating costs. These crucial factors require urgent improvements that can be addressed by the electrification of fluid power. This approach has already generated electro-hydraulic drives that remove flow throttling and enable energy recovery. However, the entire power managed by the actuators of conventional systems must pass through the electric machines. This characteristic is unfeasible for medium-to-high power applications since they need electric motors and electronics with high power ratings and large onboard generation of electricity. Thus, this paper applies to a hydraulic excavator’s boom the idea of splitting the power being transferred to/from the actuator between the hydraulic and electric domains (i.e., a centralized hydraulic power supply is involved). The objective is downsizing the power rating of the boom’s electric components while maintaining the high-power output of the hydraulic actuator. The results show the expected behavior of the hybrid excavator in terms of motion control, but only 57% of the boom’s peak power is now exchanged electrically. The resulting electric machine with 61% downsizing favors the system’s cost and compactness supporting the electrification process that is aligned with the low-carbon economy.
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Duong, Viet Hoang, and Nam Hoang Nguyen. "Machine learning algorithms for electrical appliances monitoring system using open-source systems." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 1 (March 1, 2022): 300. http://dx.doi.org/10.11591/ijai.v11.i1.pp300-309.
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Two main methods to minimize the impact of electricity generation on the environment are to exploit clean fuel resources and use electricity more effectively. In this paper, we aim to change the user's electricity usage by providing feedback about the electrical energy consumed by each device. The authors introduced two devices, load monitoring device (LMD) and activity monitoring device (AMD). The function of the LMD is to provide feedback on the operating status and energy consumption of electrical appliances in a home, which will help people consume electrical energy more efficiently. The parameters of LMD are used to predict the on/off state of each electrical appliance thanks to machine learning algorithms. AMD with audio sensors can assist LMD to distinguish electrical devices with the same or varying power over time. The system was tested for three weeks and achieved a state prediction accuracy of 93.60%.
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Buster, Grant, Paul Siratovich, Nicole Taverna, Michael Rossol, Jon Weers, Andrea Blair, Jay Huggins, et al. "A New Modeling Framework for Geothermal Operational Optimization with Machine Learning (GOOML)." Energies 14, no. 20 (October 19, 2021): 6852. http://dx.doi.org/10.3390/en14206852.
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Geothermal power plants are excellent resources for providing low carbon electricity generation with high reliability. However, many geothermal power plants could realize significant improvements in operational efficiency from the application of improved modeling software. Increased integration of digital twins into geothermal operations will not only enable engineers to better understand the complex interplay of components in larger systems but will also enable enhanced exploration of the operational space with the recent advances in artificial intelligence (AI) and machine learning (ML) tools. Such innovations in geothermal operational analysis have been deterred by several challenges, most notably, the challenge in applying idealized thermodynamic models to imperfect as-built systems with constant degradation of nominal performance. This paper presents GOOML: a new framework for Geothermal Operational Optimization with Machine Learning. By taking a hybrid data-driven thermodynamics approach, GOOML is able to accurately model the real-world performance characteristics of as-built geothermal systems. Further, GOOML can be readily integrated into the larger AI and ML ecosystem for true state-of-the-art optimization. This modeling framework has already been applied to several geothermal power plants and has provided reasonably accurate results in all cases. Therefore, we expect that the GOOML framework can be applied to any geothermal power plant around the world.
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Deng, Jun, Jianbo Wang, Shupeng Li, Haijing Zhang, Shutao Peng, and Tong Wang. "Adaptive Damping Design of PMSG Integrated Power System with Virtual Synchronous Generator Control." Energies 13, no. 8 (April 19, 2020): 2037. http://dx.doi.org/10.3390/en13082037.
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With the continuous development of wind power capacity, a large number of wind turbines connected by power electronic devices make the system inertia lower, which leads to the problem of system frequency stability degradation. The virtual synchronous generator (VSG) control can make wind turbines possess inertia and damping. However, the stochastic dynamic behavior of wind generation results in the stochastic changing of operating condition; this paper presents an adaptive subsynchronous oscillation (SSO) damping control method for the wind generation with VSG control. Firstly, the small signal model of the permanent magnet synchronous generator (PMSG) with VSG is built, and the model of state space is derived and built. The active power of PMSG is selected as the variable parameter vector to establish a polytopic linear variable parameter system model. Then, based on the hybrid H2/H∞ control method, each vertex state feedback matrix is solved by linear matrix inequality, and a subsynchronous oscillation adaptive damping controller with polytope is obtained. Finally, the 4-machine 2-area system connected to two PMSGs with VSG control is used as the test system for time domain simulation. The simulation results demonstrate that the LPV based adaptive damping controller could provide enough damping under the circumstances of wider changes of wind power outputs.
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Parada, Raúl, Jordi Font, and Jordi Casas-Roma. "Predicting Energy Generation Using Forecasting Techniques in Catalan Reservoirs." Energies 12, no. 10 (May 14, 2019): 1832. http://dx.doi.org/10.3390/en12101832.
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Reservoirs are natural or artificial lakes used as a source of water supply for society daily applications. In addition, hydroelectric power plants produce electricity while water flows through the reservoir. However, reservoirs are limited natural resources since water levels vary according to annual rainfalls and other natural events, and consequently, the energy generation. Therefore, forecasting techniques are helpful to predict water level, and thus, electricity production. This paper examines state-of-the-art methods to predict the water level in Catalan reservoirs comparing two approaches: using the water level uniquely, uni-variant; and adding meteorological data, multi-variant. With respect to relating works, our contribution includes a longer times series prediction keeping a high precision. The results return that combining Support Vector Machine and the multi-variant approach provides the highest precision with an R 2 value of 0.99.
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Hajiabadi, Moein, Mahdi Farhadi, Vahide Babaiyan, and Abouzar Estebsari. "Deep Learning with Loss Ensembles for Solar Power Prediction in Smart Cities." Smart Cities 3, no. 3 (August 7, 2020): 842–52. http://dx.doi.org/10.3390/smartcities3030043.
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The demand for renewable energy generation, especially photovoltaic (PV) power generation, has been growing over the past few years. However, the amount of generated energy by PV systems is highly dependent on weather conditions. Therefore, accurate forecasting of generated PV power is of importance for large-scale deployment of PV systems. Recently, machine learning (ML) methods have been widely used for PV power generation forecasting. A variety of these techniques, including artificial neural networks (ANNs), ridge regression, K-nearest neighbour (kNN) regression, decision trees, support vector regressions (SVRs) have been applied for this purpose and achieved good performance. In this paper, we briefly review the most recent ML techniques for PV energy generation forecasting and propose a new regression technique to automatically predict a PV system’s output based on historical input parameters. More specifically, the proposed loss function is a combination of three well-known loss functions: Correntropy, Absolute and Square Loss which encourages robustness and generalization jointly. We then integrate the proposed objective function into a Deep Learning model to predict a PV system’s output. By doing so, both the coefficients of loss functions and weight parameters of the ANN are learned jointly via back propagation. We investigate the effectiveness of the proposed method through comprehensive experiments on real data recorded by a real PV system. The experimental results confirm that our method outperforms the state-of-the-art ML methods for PV energy generation forecasting.
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Theis, Jonas, Luigi Vigneri, Lin Wang, and Animesh Trivedi. "Healthor: Heterogeneity-aware Flow Control in DLTs to Increase Performance and Decentralization." Distributed Ledger Technologies: Research and Practice 1, no. 2 (December 10, 2022): 1–27. http://dx.doi.org/10.1145/3555676.
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Permissionless reputation-based distributed ledger technologies (DLTs) have been proposed to overcome blockchains’ shortcomings in terms of performance and scalability, and to enable feeless messages to power the machine-to-machine economy. These DLTs allow machines with widely heterogeneous capabilities to actively participate in message generation and consensus. However, the open nature of such DLTs can lead to the centralization of decision-making power, thus defeating the purpose of building a decentralized network. In this article, we introduce Healthor, a novel heterogeneity-aware flow-control mechanism for permissionless reputation-based DLTs. Healthor formalizes node heterogeneity by defining a health value as a function of its incoming message queue occupancy. We show that health signals can be used effectively by neighboring nodes to dynamically flow control messages while maintaining high decentralization. We perform extensive simulations, and show a 23% increase in throughput, a 76% decrease in latency and four times increased node participation in consensus compared to state-of-the-art. To the best of our knowledge, Healthor is the first system to systematically explore the ramifications of heterogeneity on DLTs and proposes a dynamic, heterogeneity-aware flow control. Healthor’s source code ( https://github.com/jonastheis/healthor ) and simulation result data set ( https://zenodo.org/record/4573698 ) are both publicly available.
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Bhattacherjee, Haimanti, Debranjan Mukherjee, and Chandan Chakraborty. "Three-level Vienna Rectifier with a Brushless and Permanent Magnetless Generator for Wind Energy Conversion Systems." Power Electronics and Drives 7, no. 1 (January 1, 2022): 84–102. http://dx.doi.org/10.2478/pead-2022-0007.
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Abstract This paper proposes a system design and control technique for a newly developed brushless and permanent magnetless synchronous generator-based variable-speed wind energy generation system, transferring power to a constant voltage dc grid via a three-level Vienna rectifier (VR). The recently established generator named Brushless Induction excited Synchronous Generator (BINSYG) is a wound field synchronous generator (WFSG), whose excitation is developed by controlling an Induction Machine fitted to the same machine structure and sharing the same magnetic core. A new controller is proposed that ensures the stable operation of BINSYG for a wide variation of shaft speeds. VR achieves sinusoidal input current and can control the power factor at its input, which is particularly suitable for wind energy applications. The top and bottom capacitor voltages of the VR are balanced using redundant switching combinations. The system with its proposed control algorithm is modelled in MATLAB/Simulink for a 5 kW rated BINSYG feeding power to a 750 V dc grid. The steady-state and dynamic state simulation results are presented and the controller performance is verified for a wide range of wind speeds. Further, real-time results using the OPAL-RT testbed are presented for the same system to verify the effectiveness of the overall control strategy.
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Vivas, Eliana, Héctor Allende-Cid, and Rodrigo Salas. "A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score." Entropy 22, no. 12 (December 15, 2020): 1412. http://dx.doi.org/10.3390/e22121412.
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Electric power forecasting plays a substantial role in the administration and balance of current power systems. For this reason, accurate predictions of service demands are needed to develop better programming for the generation and distribution of power and to reduce the risk of vulnerabilities in the integration of an electric power system. For the purposes of the current study, a systematic literature review was applied to identify the type of model that has the highest propensity to show precision in the context of electric power forecasting. The state-of-the-art model in accurate electric power forecasting was determined from the results reported in 257 accuracy tests from five geographic regions. Two classes of forecasting models were compared: classical statistical or mathematical (MSC) and machine learning (ML) models. Furthermore, the use of hybrid models that have made significant contributions to electric power forecasting is identified, and a case of study is applied to demonstrate its good performance when compared with traditional models. Among our main findings, we conclude that forecasting errors are minimized by reducing the time horizon, that ML models that consider various sources of exogenous variability tend to have better forecast accuracy, and finally, that the accuracy of the forecasting models has significantly increased over the last five years.
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Gao, Jin Ji, Wei Min Wang, Li Dong He, and Hong Xu. "Fault Damage Power and Self-Recovery System of Machinery." Key Engineering Materials 413-414 (June 2009): 3–14. http://dx.doi.org/10.4028/www.scientific.net/kem.413-414.3.
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Animals possess the function of self-recovery for adapting the change in their living conditions. Can machinery, by bionic design, just like animals, also possess the function of self-recovery for making itself free of fault, or for eliminating fault automatically during operation? In this paper, a new theory of Engineering Self-recovery is discussed, which could provide theoretical basis and new methodology for creating a new generation of machinery with fault self-recovery function. The engineering self-recovery theory is different from engineering cybernetics. Engineering cybernetics endows machinery with purposive behavior which originally is one of animal’s common characteristics, while engineering self-recovery theory endows machinery with the new function of fault self-recovery which originally is another one of animal’s common characteristics. Under the guidance of engineering cybernetics, the automation of machinery has come true. While under the guidance of engineering self-recovery theory, a new generation of machinery with the new function of fault self-recovery could also be realized. The self-recovery system, which can provide fault self-recovery force to restrain fault force, is investigated in detail. Based on fault mechanism and risk analysis, and by bionic design, a fault self-recovery system, which is a dynamic system to store, supplement and transfer the self-recovery force, is endowed to a machine with the ability to maintain the machine in a health state. Also, the research in engineering application of the new concept, fault self-recovery, is on the way. As an example, the new concept centrifugal compressor with fault self-recovery function is discussed here with axial displacement and flow induced vibration fault self-recovery as examples to show the steps of fault self-recovery system construction such as fault mechanism, necessity, determination, possibility, fault tolerance, limitation and execution.
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Shirbhate, Isha M., and Sunita S. Barve. "Solar panel monitoring and energy prediction for smart solar system." International Journal of Advances in Applied Sciences 8, no. 2 (June 1, 2019): 136. http://dx.doi.org/10.11591/ijaas.v8.i2.pp136-142.
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<p>Solar Energy is established as an alternative source of energy known as renewable energy. In a developing country like India, the perspective of Solar Energy is important, as it supports a limitless source of energy. Monitoring and prediction of photo-voltaic energy generation help to reduce the energy loss and empower to utilize more energy. Solar energy prediction is challenging as it depends on the fluctuating solar radiations and climate conditions. The problem statement is to monitor solar panels and predict energy generation for energy management procedure. In this paper, the Internet of Things and Machine Learning algorithms are used as a powerful tool for developing a smart solar system. The metro-logical data such as humidity, temperature and photovoltaic panel data is used as input to forecast solar power generation. For prediction, we examine time-series of solar energy data with Hidden Markov Model. This model considers the probabilistic correlation between previous values to next value in time-series. Experimental results shows that individual panel dead state is located successfully and time-series based solar energy prediction emulate the actual power generation.</p>
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Borghini, Eugenio, and Cinzia Giannetti. "Short Term Load Forecasting Using TabNet: A Comparative Study with Traditional State-of-the-Art Regression Models." Engineering Proceedings 5, no. 1 (June 25, 2021): 6. http://dx.doi.org/10.3390/engproc2021005006.
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Electric load forecasting is becoming increasingly challenging due to the growing penetration of decentralised energy generation and power-electronics based loads such as heat pumps and electric vehicles, which adds to a transition to more variable work patterns (accentuated by the COVID-19 pandemic in 2020). In this paper, three different Machine Leaning models are analysed to predict the energy load one week ahead for a period of time including the COVID-19 pandemic. It is shown that, by using the recently proposed TabNet model architecture, it is possible to achieve an accuracy comparable to more traditional approaches based on gradient boosting and artificial neural networks without the need of performing complex feature engineering.
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Sarajcev, Petar, Antonijo Kunac, Goran Petrovic, and Marin Despalatovic. "Artificial Intelligence Techniques for Power System Transient Stability Assessment." Energies 15, no. 2 (January 11, 2022): 507. http://dx.doi.org/10.3390/en15020507.
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The high penetration of renewable energy sources, coupled with decommissioning of conventional power plants, leads to the reduction of power system inertia. This has negative repercussions on the transient stability of power systems. The purpose of this paper is to review the state-of-the-art regarding the application of artificial intelligence to the power system transient stability assessment, with a focus on different machine, deep, and reinforcement learning techniques. The review covers data generation processes (from measurements and simulations), data processing pipelines (features engineering, splitting strategy, dimensionality reduction), model building and training (including ensembles and hyperparameter optimization techniques), deployment, and management (with monitoring for detecting bias and drift). The review focuses, in particular, on different deep learning models that show promising results on standard benchmark test cases. The final aim of the review is to point out the advantages and disadvantages of different approaches, present current challenges with existing models, and offer a view of the possible future research opportunities.
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Kamath, Akshatha, Tanya Mendez, S. Ramya, and Subramanya G. Nayak. "Design and Implementation of Power-Efficient FSM based UART." Journal of Physics: Conference Series 2161, no. 1 (January 1, 2022): 012052. http://dx.doi.org/10.1088/1742-6596/2161/1/012052.
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Abstract The remarkable innovations in technology are driven mainly by the high-speed data communication requirements of the modern generation. The Universal Asynchronous Receiver Transmitter (UART) is one of the most sought-after communication protocols. This work mainly focuses on implementing and analysing the UART for data communication. The Finite State Machine (FSM) implements the baud rate generator, transmitter, and receiver modules. Cadence NCSIM was utilized for simulation, and Cadence RTL Compiler was used during synthesis using the 45 nm and 90 nm General Process Design Kit (GPDK) library files. The baud rate of 9600 bps and 50 MHz clock frequency was used to design UART. The increased speed and complexity of the VLSI chip designs has resulted in a significant increase in power consumption. The comparative analysis of power and delay for different clock periods shows an improvement in the total power and the Power Delay Product (PDP) with increasing clock periods. Better results were observed using 45 nm in comparison to the 90 nm library.
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Rusilawati, Irrine Budi Sulistiawati, Adi Soeprijanto, and Rony Seto Wibowo. "Determination of Generator Steady State Stability Limit for Multimachine System based on Network Losses Concept." MATEC Web of Conferences 164 (2018): 01041. http://dx.doi.org/10.1051/matecconf/201816401041.
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In the multimachine circumstances, it is difficult to analyze the steady state stability of each generator. In previous research, analysis of the steady state stability limit has been carried out but only look at the stability of the overall system. Therefore, to analyze the stability of each generator, the multimachine system must be changed into a Single Machine to Infinite Bus (SMIB) system by collecting all the loads into one central load in the infinite bus. The method to change from the multimachine system to SMIB system is presented in this paper. The multimachine system is converted into an equivalent impedance (req and xeq) and an equivalent load based on losses concept. After req and xeq is calculated, then by using steady state stability limit concept, the value of the maximum generation of each generator units can be determined. By means of maximum generation is the maximum output power limit that can be generated without causing unstability. ETAP simulation is used to validate the calculation results of the proposed method. The method was applied to units generator in Java Bali system 500 kV.
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Robinson, John, Sai Priya Munagala, Arun Arjunan, Nick Simpson, Ryan Jones, Ahmad Baroutaji, Loganathan T. Govindaraman, and Iain Lyall. "Electrical Conductivity of Additively Manufactured Copper and Silver for Electrical Winding Applications." Materials 15, no. 21 (October 28, 2022): 7563. http://dx.doi.org/10.3390/ma15217563.
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Efficient and power-dense electrical machines are critical in driving the next generation of green energy technologies for many industries including automotive, aerospace and energy. However, one of the primary requirements to enable this is the fabrication of compact custom windings with optimised materials and geometries. Electrical machine windings rely on highly electrically conductive materials, and therefore, the Additive Manufacturing (AM) of custom copper (Cu) and silver (Ag) windings offers opportunities to simultaneously improve efficiency through optimised materials, custom geometries and topology and thermal management through integrated cooling strategies. Laser Powder Bed Fusion (L-PBF) is the most mature AM technology for metals, however, laser processing highly reflective and conductive metals such as Cu and Ag is highly challenging due to insufficient energy absorption. In this regard, this study details the 400 W L-PBF processing of high-purity Cu, Ag and Cu–Ag alloys and the resultant electrical conductivity performance. Six Cu and Ag material variants are investigated in four comparative studies characterising the influence of material composition, powder recoating, laser exposure and electropolishing. The highest density and electrical conductivity achieved was 88% and 73% IACS, respectively. To aid in the application of electrical insulation coatings, electropolishing parameters are established to improve surface roughness. Finally, proof-of-concept electrical machine coils are fabricated, highlighting the potential for 400 W L-PBF processing of Cu and Ag, extending the current state of the art.
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Teekaraman, Yuvaraja, Irina Kirpichnikova, Hariprasath Manoharan, Ramya Kuppusamy, Ravi V. Angadi, and Amruth Ramesh Thelkar. "Diminution of Smart Grid with Renewable Sources Using Support Vector Machines for Identification of Regression Losses in Large-Scale Systems." Wireless Communications and Mobile Computing 2022 (August 8, 2022): 1–11. http://dx.doi.org/10.1155/2022/6942029.
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This article examines the effect of smart grid systems by implementing artificial intelligence (AI) technique with application of renewable energy sources (RES). The current state generation smart grid system follows a high demand on supply of equal energy load to all grid states. However, in conventional techniques, high demand is observed as manual operation is preformed and load problems are not solved within the stipulated time period due to lack of technological advancements. However, applications of AI in smart grid process reduces risk of operation as manual adjustments are converted to highly automated procedures. This type of automatic process identifies the fault location at stage 1 and diagnosis of identified faults will be processed at stage 2. The abovementioned two stage processes will be incorporated with two constant parameters as dummy load is produced to overcome high- to low-power flows. Additionally, a scrap model has been designed to reduce the wastage of power as 100 percent effective progress can be achieved for low- to high-power supplies. To detect the corresponding regression losses in the grid systems, support vector machine (SVM) which completely identifies the previous state loss in the system is integrated. Hence, to analyze the effectiveness of the SVM model, four different scenarios are evaluated and compared with heuristic algorithms, long short-term memory (LSTM), autoregressive indicated moving average (ARIMA), adaptive ARIMA, and linear regression models with distinct performance analysis that includes error in percentage values where a total efficiency of 81% is achieved for projected SVM in all power lines including large-scale systems as compared to existing approaches.
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Mahesh, P. Venkata, S. Meyyappan, and RamaKoteswara Rao Alla. "Maximum power point tracking using decision-tree machine-learning algorithm for photovoltaic systems." Clean Energy 6, no. 5 (October 1, 2022): 762–75. http://dx.doi.org/10.1093/ce/zkac057.
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Abstract This work presents a machine-learning (ML) algorithm for maximum power point tracking (MPPT) of an isolated photovoltaic (PV) system. Due to the dynamic nature of weather conditions, the energy generation of PV systems is non-linear. Since there is no specific method for effectively dealing with the non-linear data, the use of ML methods to operate the PV system at its maximum power point (MPP) is desirable. A strategy based on the decision-tree (DT) regression ML algorithm is proposed in this work to determine the MPP of a PV system. The data were gleaned from the technical specifications of the PV module and were used to train and test the DT. These algorithms predict the maximum power available and the associated voltage of the module for a defined amount of irradiance and temperature. The boost converter duty cycle was determined using predicted values. The simulation was carried out for a 10-W solar panel with a short-circuit current of 0.62 A and an open-circuit voltage of 21.50 V at 1000 W/m2 irradiance and a temperature of 25°C. The simulation findings demonstrate that the proposed method compelled the PV panel to work at the MPP predicted by DTs compared to the existing topologies such as β-MPPT, cuckoo search and artificial neural network results. From the proposed algorithm, efficiency has been improved by >93.93% in the steady state despite erratic irradiance and temperatures.
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Dhanraj, Joshuva Arockia, Ali Mostafaeipour, Karthikeyan Velmurugan, Kuaanan Techato, Prem Kumar Chaurasiya, Jenoris Muthiya Solomon, Anitha Gopalan, and Khamphe Phoungthong. "An Effective Evaluation on Fault Detection in Solar Panels." Energies 14, no. 22 (November 19, 2021): 7770. http://dx.doi.org/10.3390/en14227770.
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The world’s energy consumption is outpacing supply due to population growth and technological advancements. For future energy demands, it is critical to progress toward a dependable, cost-effective, and sustainable renewable energy source. Solar energy, along with all other alternative energy sources, is a potential renewable resource to manage these enduring challenges in the energy crisis. Solar power generation is expanding globally as a result of growing energy demands and depleting fossil fuel reserves, which are presently the primary sources of power generation. In the realm of solar power generation, photovoltaic (PV) panels are used to convert solar radiation into energy. They are subjected to the constantly changing state of the environment, resulting in a wide range of defects. These defects should be discovered and remedied as soon as possible so that PV panels efficiency, endurance, and durability are not compromised. This paper focuses on five aspects, namely, (i) the various possible faults that occur in PV panels, (ii) the online/remote supervision of PV panels, (iii) the role of machine learning techniques in the fault diagnosis of PV panels, (iv) the various sensors used for different fault detections in PV panels, and (v) the benefits of fault identification in PV panels. Based on the investigated studies, recommendations for future research directions are suggested.
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Almonacid-Olleros, Guillermo, Gabino Almonacid, David Gil, and Javier Medina-Quero. "Evaluation of Transfer Learning and Fine-Tuning to Nowcast Energy Generation of Photovoltaic Systems in Different Climates." Sustainability 14, no. 5 (March 7, 2022): 3092. http://dx.doi.org/10.3390/su14053092.
Full textAbstract:
New trends of Machine learning models are able to nowcast power generation overtaking the formulation-based standards. In this work, the capabilities of deep learning to predict energy generation over three different areas and deployments in the world are discussed. To this end, transfer learning from deep learning models to nowcast output power generation in photovoltaic systems is analyzed. First, data from three photovoltaic systems in different regions of Spain, Italy and India are unified under a common segmentation stage. Next, pretrained and non-pretrained models are evaluated in the same and different regions to analyze the transfer of knowledge between different deployments and areas. The use of pretrained models provides encouraging results which can be optimized with rearward learning of local data, providing more accurate models.
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Baig, Imran, Umer Farooq, Najam Ul Hasan, Manaf Zghaibeh, and Varun Jeoti. "A Multi-Carrier Waveform Design for 5G and beyond Communication Systems." Mathematics 8, no. 9 (September 1, 2020): 1466. http://dx.doi.org/10.3390/math8091466.
Full textAbstract:
The next generation communication network (NGCN) is expected to provide higher spectral efficiency, low latency, large throughput and massive machine-to-machine type communications. In this regard, the design of the multi-carrier waveform (MCW) is posing a major research problem for the NGCN. To overcome the stated problem, a lot of state-of-the-art work exists that proposes various MCW alternative to the standard orthogonal frequency division multiplexing (OFDM) waveform. It is true that OFDM was used in a number of real-time communication systems of fourth generation (4G) networks. However, their use in the upcoming fifth generation (5G) network is not very feasible. This is because of the strict requirements of 5G communication systems, which also extend beyond 5G systems; hence rendering the use of OFDM infeasible for newer communication standards. To satisfy the requirements of upcoming communication networks, there is a dire need for MCWs with better flexibility. In this regard, a precoding-based MCW has been proposed. The proposed MCW fulfills the requirements of the NGCN in terms of low peak-to-average power ratio (PAPR), high spectral efficiency and throughput. The MCW proposed in this work uses power-domain multiplexing such as non-orthogonal multiple access (NOMA) and phase rotation by using the selective mapping (SLM) and generalized chirp-like (GCL) precoding of the input signal to the universal filtered multi-carriers (UFMC) modulations. Statistical analysis of the PAPR is presented by using the complementary cumulative distribution function (CCDF). The MATLAB® simulations have been carried out to implement the CCDF of PAPR and results show that a PAPR gain of 5.4 dB is obtained when the proposed waveform is compared with the standard NOMA-UFMC waveform at clip rate of 10−3, using 4-QAM.
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Luo, Jianqiang, Yiqing Zou, Siqi Bu, and Ulas Karaagac. "Converter-Driven Stability Analysis of Power Systems Integrated with Hybrid Renewable Energy Sources." Energies 14, no. 14 (July 16, 2021): 4290. http://dx.doi.org/10.3390/en14144290.
Full textAbstract:
Renewable energy sources such as wind power and photovoltaics (PVs) have been increasingly integrated into the power system through power electronic converters in recent years. However, power electronic converter-driven stability has issues under specific circumstances, for instance, modal resonances might deteriorate the dynamic performance of the power systems or even threaten the overall stability. In this work, the integration impact of a hybrid renewable energy source (HRES) system on modal interaction and converter-driven stability was investigated in an IEEE 16-machine 68-bus power system. In this paper, firstly, an HRES system is introduced, which consists of full converter-based wind power generation (FCWG) and full converter-based photovoltaic generation (FCPV). The equivalent dynamic models of FCWG and FCPV are then established, followed by linearized state-space modeling. On this basis, converter-driven stability analysis was performed to reveal the modal resonance mechanisms between different renewable energy sources (RESs) and weak grids in the interconnected power systems and the multi-modal interaction phenomenon. Additionally, time-domain simulations were conducted to verify the effectiveness of dynamic models and support the converter-driven stability analysis results. To avoid detrimental modal resonances, a multi-modal and multi-parametric optimization strategy is further proposed by retuning the controller parameters of the multi-RESs in the HRES system. The overall results demonstrate the modal interaction effect between the external AC power system and the HRES system and its various impacts on converter-driven stability.