Journal articles: 'Power management technique'

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Dolan, Michael J., Euan M. Davidson, Ivana Kockar, Graham W. Ault, and Stephen D. J. McArthur. "Distribution Power Flow Management Utilizing an Online Optimal Power Flow Technique." IEEE Transactions on Power Systems 27, no. 2 (May 2012): 790–99. http://dx.doi.org/10.1109/tpwrs.2011.2177673.

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Ohk, Seung-Ryeol, YongSin Kim, and Young-Jin Kim. "Phase-Based Low Power Management Combining CPU and GPU for Android Smartphones." Electronics 11, no. 16 (August 9, 2022): 2480. http://dx.doi.org/10.3390/electronics11162480.

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Smartphones have limited battery capacity, so efficient power management is required for high-performance applications and to increase usage time. In recent years, efficient power management of smartphones has become very important as the demand for power use of smartphones has grown due to deep learning, games, virtual reality, and augmented reality applications. Existing low-power techniques of smartphones focus only on lowering power consumption without considering actual power consumption based on utilization of the central processing unit (CPU) and graphics processing unit (GPU), which are major components of smartphones. In addition, they do not take into consideration the strict use of resources within the component and what instructions are being processed to operate them. In this paper, we propose a low-power technique that manages power by calculating the actual power consumption of smartphones at execution time and classifying the detailed resource operating states of CPUs and GPUs. The proposed technique was implemented by linking the kernel and native app on a Galaxy S7 smartphone equipped with Android. In experiments with 15 workloads, the proposed technique achieves an energy reduction of 18.11% compared to the low-power technique of the interactive governor built into the Galaxy S7 with a small FPS reduction of 3.12%.

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Zhou, Fang, Yong Zhong, and Pei Zhang. "Research on Thermal Management System for the Vehicle Application of Lithium-Ion Power Batteries." Advanced Materials Research 347-353 (October 2011): 984–88. http://dx.doi.org/10.4028/www.scientific.net/amr.347-353.984.

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Thermal management technique is one of the key techniques for the vehicle application of lithium-ion power batteries. Based on the analysis of thermal characteristics of the lithium-ion power batteries, the establishment of thermal model and numerical simulation for the lithium-ion power batteries were discussed. Finally, a procedure for designing battery thermal management system (BTMS) was proposed, and the key techniques during designing a BTMS were studied, including selection of heat transfer medium, design of cooling/heating structure and so on. This research provides a technique support for designing a good and effective BTMS, as well as improving the working performance and security of the lithium-ion power batteries and the electric vehicles.

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N. S. S. Rama Krishna et al.,, N. S. S. Rama Krishna et al ,. "A Review on Power Management Technique Hybrid Micro-Grid Systems." International Journal of Mechanical and Production Engineering Research and Development 8, no. 4 (2018): 203–10. http://dx.doi.org/10.24247/ijmperdaug201823.

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Hwang, Young-Si, Sung-Kwan Ku, and Ki-Seok Chung. "A predictive dynamic power management technique for embedded mobile devices." IEEE Transactions on Consumer Electronics 56, no. 2 (May 2010): 713–19. http://dx.doi.org/10.1109/tce.2010.5505992.

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Hu, Xiaotong, Tianqi Liu, Chuan He, Yiwei Ma, Yu Su, He Yin, Fei Wang, Leon M. Tolbert, Shunliang Wang, and Yilu Liu. "Real-time power management technique for microgrid with flexible boundaries." IET Generation, Transmission & Distribution 14, no. 16 (August 21, 2020): 3161–70. http://dx.doi.org/10.1049/iet-gtd.2019.1576.

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Akkar, Hanan A. R., and Sameh J. Mohammed. "Artificial Intelligent Technique for Power Management Lighting Based on FPGA." Engineering and Technology Journal 38, no. 2A (February 25, 2020): 232–39. http://dx.doi.org/10.30684/etj.v38i2a.305.

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The modern technological advances gave rise to new intelligent ways of performance and management in various fields of our lives. The employment of the artificial intelligent techniques proved influential in enhancing the technological developments and in meeting the demands for new, more efficient, more reliable and faster ways of performing activities and tasks. Lighting systems are an important part of human life. For this reason, it is important to reduce and manage energy consumption properly. Light dimming paves the way for massive energy saving in lighting applications. The options include simply reducing the output during the night and achieve maximum saving with variable dimming. Advantage can be taken of off-peak times (no light needed) to reduce energy consumption significantly. Pulse Width Modulation (PWM) technique is used as dimming method. The proposed system offers intelligent management of lighting to reduce power consumption, extend lamp life and reduce maintenance. In this work, we will be using multiple sensors such as light dependent resistor (LDR) and Motion Sensor (PIR) for LED dimming system to achieve intelligent LED lighting system to manage energy consumption. The data collected by sensors is processed by Artificial Neural Network (ANN), which is implemented by using Field Programmable Gate Arrays (FPGAs), Spartan 3A starter kit that controls the light intensity of LED from changing the duty cycle of the PWM signals. FPGA was used to implement the design, because of the re-programmability of the FPGAs, which can support the re-configuration necessary to implement the design.

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Hwang, Young-Si, and Ki-Seok Chung. "Dynamic Power Management Technique for Multicore Based Embedded Mobile Devices." IEEE Transactions on Industrial Informatics 9, no. 3 (August 2013): 1601–12. http://dx.doi.org/10.1109/tii.2012.2232299.

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Bhaduri, Rupam, G. Rahul Saravana, and C. Vaskar. "Supervisory Controller for Power Management of Microgrid Using Hybrid Technique." Transactions on Electrical and Electronic Materials 21, no. 1 (October 28, 2019): 30–47. http://dx.doi.org/10.1007/s42341-019-00152-4.

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Rajora, Harshita. "Congestion Management in Deregulated Power System Using Different Control Technique." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (November 30, 2022): 2072–77. http://dx.doi.org/10.22214/ijraset.2022.47759.

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Abstract: In today’s scenario there is a challenge for power companies to meet the expected demand load due to continuous increases in load demand causes unpredictable failure in the components of power system including transmission line, generator, transformer and various other equipment this leads to over loading in the power system and the line become congested if this failure not removed on time the system reaches to emergency state, therefore we are using various techniques to control or manage these situations these methods includes Generator Rescheduling(GR),Load Shedding, Particle Swarm Optimizer(PSO), Grey Wolf Optimization (GWO),Harmony search algorithm etc. Optimal load shedding is effective control action for congestion management. The various algorithm applied on IEEE 30 bus system.

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Podder, Amit Kumer, Sayemul Islam, Nallapaneni Manoj Kumar, Aneesh A. Chand, Pulivarthi Nageswara Rao, Kushal A. Prasad, T. Logeswaran, and Kabir A. Mamun. "Systematic Categorization of Optimization Strategies for Virtual Power Plants." Energies 13, no. 23 (November 27, 2020): 6251. http://dx.doi.org/10.3390/en13236251.

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Due to the rapid growth in power consumption of domestic and industrial appliances, distributed energy generation units face difficulties in supplying power efficiently. The integration of distributed energy resources (DERs) and energy storage systems (ESSs) provides a solution to these problems using appropriate management schemes to achieve optimal operation. Furthermore, to lessen the uncertainties of distributed energy management systems, a decentralized energy management system named virtual power plant (VPP) plays a significant role. This paper presents a comprehensive review of 65 existing different VPP optimization models, techniques, and algorithms based on their system configuration, parameters, and control schemes. Moreover, the paper categorizes the discussed optimization techniques into seven different types, namely conventional technique, offering model, intelligent technique, price-based unit commitment (PBUC) model, optimal bidding, stochastic technique, and linear programming, to underline the commercial and technical efficacy of VPP at day-ahead scheduling at the electricity market. The uncertainties of market prices, load demand, and power distribution in the VPP system are mentioned and analyzed to maximize the system profits with minimum cost. The outcome of the systematic categorization is believed to be a base for future endeavors in the field of VPP development.

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Lipták, Peter, Ivan Kopecký, Ján Štrba, and Ivan Dlugoš. "The Need Of Special Technique In Crisis Management." TRANSACTIONS of the VŠB – Technical University of Ostrava, Safety Engineering Series 10, no. 2 (September 1, 2015): 41–47. http://dx.doi.org/10.1515/tvsbses-2015-0012.

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Abstract Operation of special equipment is possible in crisis situations. It is because special equipment is designed for operation in non-standard often extreme conditions and situation, it is available, it has a high degree of crossing capability and it is able to provide basic living conditions even in field, e.g. provision of power for hospital, transportation of wounded and injured persons, supplies, medical care in field conditions, delivery of potable and utility water etc. The authors in the paper deal with a possibility to provide electric energy through advanced renewable sources, especially in meeting tasks in areas with no public mains, possible supplies of potable and non-potable water, embedding such assets into mobile systems. The authors in publication summarize results of research within the „Use of renewable sources of energy in practice project“. System of modeling and computer-aided simulation of renewable sources of energy has been proposed within this project. Application of a system for designing of power systems in logistic containers is expected. The knowledge on power balance of logistic containers operated by the SR Armed Forces in missions abroad is summarized in the last chapter of the paper. Experiences with operating the equipment in crisis and extreme conditions have proved a possibility to export and deploy machines and weaponry equipment of the SR Armed Forces into extreme climatic conditions, e.g. out of European conditions etc.

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Ab. Wahab, N., Z. Mat Yasin, N. A. Salim, and N. F. A. Aziz. "Artificial neural network based technique for energy management prediction." Indonesian Journal of Electrical Engineering and Computer Science 17, no. 1 (January 1, 2020): 94. http://dx.doi.org/10.11591/ijeecs.v17.i1.pp94-101.

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<p>The energy management of electrical machine is significant to ensure efficient power consumption. Mismanagement of energy consumption could give impact on low efficiency of energy consumption that leads to power wastage. This paper presents analysis of power consumption and electricity costing of the electrical machineries and equipment in High Voltage (HV) and Electrical Machine (EM) Laboratories at Faculty of Electrical Engineering (FKE), Universiti Teknologi MARA (UiTM) Shah Alam, Selangor, Malaysia. The electrical data are collected using Fluke Meter 1750. Based on the analysis, it is found that the estimated annually electricity cost for HV Laboratory and EM Laboratory are RM 392.00 and RM 3197.76 respectively. For prediction of energy consumption of the two laboratories, Artificial Neural Network (ANN) algorithm is applied as computational tool using feedforward network type. The results show that the ANN is successfully modelled to predict the energy consumption.</p>

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Mengi, Onur Ozdal, and Ismail Hakki Altas. "A New Energy Management Technique for PV/Wind/Grid Renewable Energy System." International Journal of Photoenergy 2015 (2015): 1–19. http://dx.doi.org/10.1155/2015/356930.

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An intelligent energy management system (IEMS) for maintaining the energy sustainability in renewable energy systems (RES) is introduced here. It consists of wind and photovoltaic (PV) solar panels are established and used to test the proposed IEMS. Since the wind and solar sources are not reliable in terms of sustainability and power quality, a management system is required for supplying the load power demand. The power generated by RES is collected on a common DC bus as a renewable green power pool to be used for supplying power to loads. The renewable DC power bus is operated in a way that there is always a base power available for permanent loads. Then the additional power requirement is supplied from either wind or PV or both depending upon the availability of these power sources. The decision about operating these systems is given by an IEMS with fuzzy logic decision maker proposed in this study. Using the generated and required power information from the wind/PV and load sides, the fuzzy reasoning based IEMS determines the amount of power to be supplied from each or both sources. Besides, the IEMS tracks the maximum power operating point of the wind energy system.

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Hassan, Tehseen Ul, and Fei Gao. "An Active Power Control Technique for Downlink Interference Management in a Two-Tier Macro–Femto Network." Sensors 19, no. 9 (April 29, 2019): 2015. http://dx.doi.org/10.3390/s19092015.

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The femtocell has evolved as a great solution for improving coverage and traffic offloading from the current LTE cellular networks, and it accomplishes the dreams of the high data rate for indoor mobile users. However, the exponentially expanding LTE femtocells cause interference in the network, as they share the same licensed spectrum with a macrocell. To tackle this issue, numerous interference mitigation techniques have been proposed in the literature. In this paper, we proposed an Active Power Control (APC) technique, which not only reduces Inter-Cell Interference (ICI) in a Macro User Equipment (MUE), generated from the downlink transmission power of an inadequately deployed femtocell, but also reduces unnecessary power consumption to achieve a green femtocell network. The simulation results show that the proposed APC technique effectively reduces ICI and optimizes the throughput performance of the MUE. Compared with the existing power control techniques, the APC technique provides a balanced trade-off in attaining necessary Quality-of-Service (QoS) of the Femto User Equipment (FUE) and reducing ICI to the victim MUE existing in the close proximity of the femtocell.

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Sharmila, N., K. R. Nataraj, and K. R. Rekha. "An efficient dynamic power management model for a stand-alone DC Microgrid using CPIHC technique." International Journal of Power Electronics and Drive Systems (IJPEDS) 12, no. 3 (September 1, 2021): 1439. http://dx.doi.org/10.11591/ijpeds.v12.i3.pp1439-1449.

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The power generation using solar photovoltaic (PV) system in microgrid requires energy storage system due to their dilute and intermittent nature. The system requires efficient control techniques to ensure the reliable operation of the microgrid. This work presents dynamic power management using a decentralized approach. The control techniques in microgrid including droop controllers in cascade with proportional-integral (PI) controllers for voltage stability and power balance have few limitations. PI controllers alone will not ensure microgrid’s stability. Their parameters cannot be optimized for varying demand and have a slow transient response which increases the settling time. The droop controllers have lower efficiency. The load power variation and steady-state voltage error make the droop control ineffective. This paper presents a control scheme for dynamic power management by incorporating the combined PI and hysteresis controller (CPIHC) technique. The system becomes robust, performs well under varying demand conditions, and shows a faster dynamic response. The proposed DC microgrid has solar PV as an energy source, a lead-acid battery as the energy storage system, constant and dynamic loads. The simulation results show the proposed CPIHC technique efficiently manages the dynamic power, regulates DC link voltage and battery’s state of charge (SoC) compared to conventional combined PI and droop controller (CPIDC).

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Simonovic, Mirela, and Lazar Saranovac. "Power management implementation in FreeRTOS on LM3S3748." Serbian Journal of Electrical Engineering 10, no. 1 (2013): 199–208. http://dx.doi.org/10.2298/sjee1301199s.

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Power consumption has become a major concern of embedded systems today. With the aim to reduce power consumption during the runtime, operating systems are dealing with power management. In this work, the FreeRTOS port is extended with power management features on LM3S3748 microcontroller. Tickless idle technique is implemented to provide more powersaving during the processor idle periods.

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Satheesh Kumar, S., and A. Immanuel Selvakumar. "Maximum power point tracking and power flow management of hybrid renewable energy system with partial shading capability: A hybrid technique." Transactions of the Institute of Measurement and Control 42, no. 12 (March 20, 2020): 2276–96. http://dx.doi.org/10.1177/0142331220909671.

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A grid connected hybrid energy system combining wind turbine (WT) and photovoltaic (PV) array generating system with energy storage system to supply continuous power to the load using hybrid technique is exhibited in this dissertation. The proposed hybrid technique is the joint execution of both the binary chaotic crow search optimizer (BCCSO) with grey wolf optimizer and random forest algorithm (GWORFA) and hence it is named as BCCSO-GWORFA technique. The main aim of the proposal is to optimally track the maximum power point tracking (MPPT) and to maintain the power flow of the grid connected HRES. Here, the BCCSO-based MPPT procedure optimizes the exact duty cycles required for the DC-DC converter of the PV under partial shading conditions and WT system under variable speed conditions based on the voltage and current parameters. On the other hand, the grey wolf optimizer (GWO) learning procedure-based random forest algorithm (RFA) predicts the control signals of the voltage source inverter (VSI) based on the active and reactive power variations available in the load side. To predict the control parameters, the proposed technique considers power balance constraints like RES accessibility, storage element state of charge, and load side power demand. The proposed strategy is implemented in MATLAB/Simulink working platform. The performance of the HRES is assessed by utilizing the comparison analysis with the existing techniques. The comparison results invariably prove the proposed hybrid technique effectiveness and confirm its potential to solve the related issues with efficiency of 99.5%.

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Durairaj, Danalakshmi, Łukasz Wróblewski, A. Sheela, A. Hariharasudan, and Mariusz Urbański. "Random forest based power sustainability and cost optimization in smart grid." Production Engineering Archives 28, no. 1 (February 12, 2022): 82–92. http://dx.doi.org/10.30657/pea.2022.28.10.

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Abstract Presently power control and management play a vigorous role in information technology and power management. Instead of non-renewable power manufacturing, renewable power manufacturing is preferred by every organization for controlling resource consumption, price reduction and efficient power management. Smart grid efficiently satisfies these requirements with the integration of machine learning algorithms. Machine learning algorithms are used in a smart grid for power requirement prediction, power distribution, failure identification etc. The proposed Random Forest-based smart grid system classifies the power grid into different zones like high and low power utilization. The power zones are divided into number of sub-zones and map to random forest branches. The sub-zone and branch mapping process used to identify the quantity of power utilized and the non-utilized in a zone. The non-utilized power quantity and location of power availabilities are identified and distributed the required quantity of power to the requester in a minimal response time and price. The priority power scheduling algorithm collect request from consumer and send the request to producer based on priority. The producer analysed the requester existing power utilization quantity and availability of power for scheduling the power distribution to the requester based on priority. The proposed Random Forest based sustainability and price optimization technique in smart grid experimental results are compared to existing machine learning techniques like SVM, KNN and NB. The proposed random forest-based identification technique identifies the exact location of the power availability, which takes minimal processing time and quick responses to the requestor. Additionally, the smart meter based smart grid technique identifies the faults in short time duration than the conventional energy management technique is also proven in the experimental results.

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Veerappan Kousik, Nalliyanna Goundar, Yuvaraj Natarajan, Kallam Suresh, Rizwan Patan, and Amir H. Gandomi. "Improving Power and Resource Management in Heterogeneous Downlink OFDMA Networks." Information 11, no. 4 (April 10, 2020): 203. http://dx.doi.org/10.3390/info11040203.

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In the past decade, low power consumption schemes have undergone degraded communication performance, where they fail to maintain the trade-off between the resource and power consumption. In this paper, management of resource and power consumption on small cell orthogonal frequency-division multiple access (OFDMA) networks is enacted using the sleep mode selection method. The sleep mode selection method uses both power and resource management, where the former is responsible for a heterogeneous network, and the latter is managed using a deactivation algorithm. Further, to improve the communication performance during sleep mode selection, a semi-Markov sleep mode selection decision-making process is developed. Spectrum reuse maximization is achieved using a small cell deactivation strategy that potentially identifies and eliminates the sleep mode cells. The performance of this hybrid technique is evaluated and compared against benchmark techniques. The results demonstrate that the proposed hybrid performance model shows effective power and resource management with reduced computational cost compared with benchmark techniques.

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Elshenawy, Mahmoud, Ashraf Fahmy, Adel Elsamahy, Shaimaa A. Kandil, and Helmy M. El Zoghby. "Optimal Power Management of Interconnected Microgrids Using Virtual Inertia Control Technique." Energies 15, no. 19 (September 24, 2022): 7026. http://dx.doi.org/10.3390/en15197026.

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Two interconnected AC microgrids are proposed based on three renewable energy sources (RESs): wind, solar, and biogas. The wind turbine drives a permanent magnet synchronous generator (PMSG). A solar photovoltaic system (SPVS) with an appropriate inverter was incorporated. The biogas genset (BG) consists of a biogas engine coupled with a synchronous generator. Two interconnected AC microgrids, M1 and M2, were considered for study in this work. The microgrid M2 is connected to a diesel engine (DE) characterized by a continuous power supply. The distribution power loss of the interconnected AC microgrids comprises in line loss. The M1 and M2 losses are modeled as an objective function (OF). The power quality enhancement of the interconnected microgrids will be achieved by minimizing this OF. This research also created a unique frequency control method called virtual inertia control (VIC), which stabilizes the microgrid frequency using an optimal controller. In this paper, the following five controllers are studied: a proportional integral controller (PI), a fractional order PI controller (FOPI), a fuzzy PI controller (FPI), a fuzzy fractional order PI controller (FFOPI), and a VIC based on FFOPI controller. The five controllers were tuned using particle swarm optimization (PSO) to minimize the (OF). The main contribution of this paper is the comprehensive study of the performance of interconnected AC microgrids under step load disturbances, step changes in wind/solar input power, and eventually grid following/forming contingencies as well as the virtual inertia control of renewable energy resources used in the structure of the microgrids.

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Nageswari, M. K., and T. Sunil Kumar Reddy. "An efficient power management technique for data centres using smart grid." International Journal of Smart Grid and Green Communications 1, no. 2 (2016): 156. http://dx.doi.org/10.1504/ijsggc.2016.078951.

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Hasan, Maha Yousif, and Dheyaa Jasim Kadhim. "A new smart approach of an efficient energy consumption management by using a machine-learning technique." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 1 (January 1, 2022): 68. http://dx.doi.org/10.11591/ijeecs.v25.i1.pp68-78.

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Many consumers of electric power have excesses in their electric power consumptions that exceed the permissible limit by the electrical power distribution stations, and then we proposed a validation approach that works intelligently by applying machine learning (ML) technology to teach electrical consumers how to properly consume without wasting energy expended. The validation approach is one of a large combination of intelligent processes related to energy consumption which is called the efficient energy consumption management (EECM) approaches, and it connected with the internet of things (IoT) technology to be linked to Google Firebase Cloud where a utility center used to check whether the consumption of the efficient energy is satisfied. It divides the measured data for actual power (A_p ) of the electrical model into two portions: the training portion is selected for different maximum actual powers, and the validation portion is determined based on the minimum output power consumption and then used for comparison with the actual required input power. Simulation results show the energy expenditure problem can be solved with good accuracy in energy consumption by reducing the maximum rate (A_p ) in a given time (24) hours for a single house, as well as electricity’s bill cost, is reduced.

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Ota, R. R., J. C. Pati, and A. K. Ojha. "Geometric programming technique to optimize power distribution system." OPSEARCH 56, no. 1 (February 25, 2019): 282–99. http://dx.doi.org/10.1007/s12597-019-00363-6.

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Joshi, Gauri, and Prabhat Ranjan. "Optimizing Power and Buffer Congestion on Wireless Sensor Nodes Using CAP (Coordinated Adaptive Power) Management Technique." International Journal of Wireless & Mobile Networks 3, no. 2 (April 30, 2011): 225–41. http://dx.doi.org/10.5121/ijwmn.2011.3218.

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Lee, Chie-In, Yan-Ting Lin, Yu-Her Chen, and Wei-Cheng Lin. "A novel low-power transceiver topology for noncontact vital sign detection including the power management technique." Microelectronics Journal 44, no. 12 (December 2013): 1309–15. http://dx.doi.org/10.1016/j.mejo.2013.08.021.

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Sureshkumar, Kumaravel, and Vijayakumar Ponnusamy. "Hybrid renewable energy systems for power flow management in smart grid using an efficient hybrid technique." Transactions of the Institute of Measurement and Control 42, no. 11 (March 3, 2020): 2068–87. http://dx.doi.org/10.1177/0142331220904818.

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Efficient approach for power flow management of hybrid renewable energy system connected smart grid system is proposed in this paper. Here, the proposed approach is the combination of both the modified elephant herding optimization algorithm with tabu search algorithm named as MEHOTSA. In the proposed technique, the modified elephant herding optimization algorithm plays out the assessment procedure to establish the exact control signals for the system and builds up the control signals database for the offline way in light of the power variety between source side and the load side. The multi-objective function is shaped by the grid required active power and reactive power varieties generated based on the accessible source power. The accomplished dataset is used to work the Tabu search algorithm on the online way and it leads the control procedure in less execution time. The proposed technique-based control model enhances the control parameters of the power controller in light of the power flow varieties. By utilizing the proposed methodology, the power flow management of the smart grid system is controlled dependent on the source side and load side parameters varieties. Additionally, the proposed methodology is in charge of controlling the energy sources so as to produce the power demanded by the grid, utilizing optimally both renewable energy sources and energy storage devices. Finally, the proposed model be actualized in MATLAB/Simulink platform and the performance are compared with other techniques.

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Salameh, Khouloud, Mohammed Awad, Aisha Makarfi, Abdul-Halim Jallad, and Richard Chbeir. "Demand Side Management for Smart Houses: A Survey." Sustainability 13, no. 12 (June 15, 2021): 6768. http://dx.doi.org/10.3390/su13126768.

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Continuous advancements in Information and Communication Technology and the emergence of the Big Data era have altered how traditional power systems function. Such developments have led to increased reliability and efficiency, in turn contributing to operational, economic, and environmental improvements and leading to the development of a new technique known as Demand Side Management or DSM. In essence, DSM is a management activity that encourages users to optimize their electricity consumption by controlling the operation of their electrical appliances to reduce utility bills and their use during peak times. While users may save money on electricity costs by rescheduling their power consumption, they may also experience inconvenience due to the inflexibility of getting power on demand. Hence, several challenges must be considered to achieve a successful DSM. In this work, we analyze the power scheduling techniques in Smart Houses as proposed in most cited papers. We then examine the advantages and drawbacks of such methods and compare their contributions based on operational, economic, and environmental aspects.

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Jiang, Lei, Su Huai Luo, and Jia Ming Li. "Intelligent Electrical Appliance Event Recognition Using Multi-Load Decomposition." Advanced Materials Research 805-806 (September 2013): 1039–45. http://dx.doi.org/10.4028/www.scientific.net/amr.805-806.1039.

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The management of electricity system in home environments plays an important role in generating energy consumption and improving efficiency of energy usage. At present, nonintrusive appliance load monitoring (NIALM) techniques are the most effective approach for estimating the electrical power consumption of individual appliances. This paper presents our contribution in intelligent electrical appliance decomposition in home environment. It is a modified power appliance disaggregation technique based on power harmonic features and support vector machine (SVM). It has higher recognition accuracy and faster computational speed. The experimental results of the power decomposition technique on real date are presented with promising results.

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Symon V.A., Tibbie Pon, and I. Jacob Ragland. "Improving Efficiency of Power Systems by Demand Side Management Method." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 1 (February 1, 2017): 100. http://dx.doi.org/10.11591/ijece.v7i1.pp100-106.

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In the smart grid infrastructure based power systems, it is necessary to consider the demand side management to enhance the energy reduction and system control. In many countries the resources are very less so the available resources have to be used in an efficient manner without any loss. The total loss cannot be avoided but it can be reduced. In the proposed system, the Particle Swarm Optimization (PSO) technique is used to distribute the power in the smart grid. Here, the grids are arranged in such a way that the losses in it are reduced. The load connected to the grid is rearranged according to their use. It uses a new and stochastic scheduling technique to handle the uncertainties in the power system. Solar and wind power are taken in account for twenty four hours and the values are given to the PSO algorithm. The experiment was conducted by MATLAB and the results show that the efficiency level of wind and solar power systems was increased by an appreciable level. The proposed technique is compared with the normal system without using Demand Side Management (DSM) and it shows that the proposed system gives better results than the existing systems.

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Schultz, William C. "Animation with Power Point: A Fog Cutter." Journal of Educational Technology Systems 25, no. 2 (December 1996): 141–60. http://dx.doi.org/10.2190/c2hj-qrkj-2f6p-bcbt.

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This article demonstrates how to take a traditional approach to the development of a lesson in a management science course and replace it with an approach that takes advantage of course technology available today. The traditional approach contains elements that are hard to present to students. The new approach takes advantage of presentation software. Examples, selected from presentations for a management science course at Buffalo State College, feature use of animation techniques that were developed with Power Point. Use of this animation technique in Power Point presentations offers a very sharp tool for cutting through the often foggy, dull, and at times even boring details of the mathematical analysis associated with management science and decision theory. Animation offers a dramatic and exciting contrast to the alternative of scanning through several pages of printed graphs or tables in a textbook.

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Lueangvilai, Attakorn, Christina Robertson, and Christopher J. Martinez. "A Dynamic Frequency Controlling Technique for Power Management in Existing Commercial Microcontrollers." Journal of Computing Science and Engineering 6, no. 2 (June 30, 2012): 79–88. http://dx.doi.org/10.5626/jcse.2012.6.2.79.

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Hassan, Tehseen, Fei Gao, Babur Jalal, and Sheeraz Arif. "Interference Management in Femtocells by the Adaptive Network Sensing Power Control Technique." Future Internet 10, no. 3 (March 1, 2018): 25. http://dx.doi.org/10.3390/fi10030025.

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Haque, Shatil, William A. Stinnett, Douglas J. Nelson, and Guo-Quan Lu. "Thermal management of power electronics modules packaged by a stacked-plate technique." Microelectronics Reliability 39, no. 9 (September 1999): 1343–49. http://dx.doi.org/10.1016/s0026-2714(99)00055-4.

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Janson, Alan, Vinayak Bhogan, Akash Name, and Julie Dsouza. "Efficient Power Management Technique Of Multicore Architecture For Real Time Visual Data." International Journal of Scientific & Engineering Research 5, no. 8 (August 25, 2014): 422–30. http://dx.doi.org/10.14299/ijser.2014.08.009.

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Lee, Chie-In, Yan-Ting Lin, Jhih-Jie Chen, and Wei-Cheng Lin. "Direct conversion Doppler radar vital sign detection system using power management technique." Microwave and Optical Technology Letters 56, no. 9 (June 24, 2014): 2167–74. http://dx.doi.org/10.1002/mop.28505.

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Duncan, Dauda, Adamu Murtala Zungeru, Mmoloki Mangwala, Bakary Diarra, Joseph Chuma, and Bokani Mtengi. "Optimal Power Management for Seismic Nodes." International Journal of Engineering Research in Africa 56 (October 4, 2021): 162–81. http://dx.doi.org/10.4028/www.scientific.net/jera.56.162.

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Estimating the state-of-charge of a lead-acid battery at remote seismic nodes is a key factor in managing the available power. Optimal management enables the continuous acquisition of seismic data of an area. This paper presents the management of lead-acid batteries at remote seismic nodes, using the Neural Network model's historical data to estimate the battery's state-of-charge. Powersim (PSIM) simulation tool was used to implement photovoltaic energy harvesting system with a buck mode converter and maximum power point tracking algorithm to acquire historical data. A backpropagation neural network technique for training the historical dataset of hourly points in 500 days on the Matlab platform is adopted, and a feedforward neural network is employed due to the irregularities of the input data. The neural network model's hidden layer contains the transfer function of the Tansig Function to produce the model output of state-of-charge estimations. Besides, this paper is based on the management of estimating the state-of-charge of the lead-acid battery near-realtime instead of relying on the vendor's lifecycle information. The simulated results show the simplicity and optimal estimations of state-of-charge of the lead-acid battery with RMSE of 0.023%.

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Manjula, Venkatachalam, and Ahamed Khan Mahabub Basha. "Adaptive technique for transient stability constraints optimal power flow." International Journal of Operational Research 39, no. 1 (2020): 1. http://dx.doi.org/10.1504/ijor.2020.108833.

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Annapandi, P., R. Banumathi, N. S. Pratheeba, and A. Amala Manuela. "Power flow management scheme of hybrid renewable energy source to maximize power transfer capability using I2HOSOA approach." Journal of Intelligent & Fuzzy Systems 39, no. 3 (October 7, 2020): 4159–81. http://dx.doi.org/10.3233/jifs-200266.

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Due to the intermittent nature of renewable sources, the generation of power is varied which is the main problem in renewable energy system. Miss-matching between the power generation and load power causes a deviation from the desired voltage and frequency in power supply. Therefore, a new efficient smart grid system is required for an optimal power flow management. In this paper, a hybrid approach is presented for power flow management of HRES connected smart grid system. The novelty of the proposed approach is the combined execution of IHHO with SOA named as I2HOSOA technique. In the established work, the HHO is integrated by crossover and mutation function, it is known as IHHO. The main contribution of the proposed strategy is to control the power flow based on source and load side parameters variations. In the proposed approach, the control signals of the voltage source are developed by the IHHO based on the variety of power exchange between the source and load side. Similarly, the online control signals are located by the SOA procedure by utilizing the parallel execution against the active and reactive power varieties. The multi-objective function is shaped by the grid required active and reactive power varieties created based on accessible source power. Here, the control parameters of the power controller are enhanced by the proposed technique based control models in light of the power flow varieties. The comparison between established and existing methods is analyzed in terms of reactive current injection, grid code, current amplitude limitation control, active power control, zero active power oscillations, and injection of active and reactive power. Furthermore, the statistical evaluation of established, and existing methods of mean, median, and standard deviation, is evaluated. Finally, the proposed model is executed in MATLAB/Simulink working platform and the execution is compared with the existing techniques.

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Petter, Stacie, and Yasamin Hadavi. "With Great Power Comes Great Responsibility." ACM SIGMIS Database: the DATABASE for Advances in Information Systems 52, SI (December 9, 2021): 10–23. http://dx.doi.org/10.1145/3505639.3505643.

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Partial least squares (PLS) offers multiple advantages as a composite-based structural equation modeling (SEM) technique. PLS enables scholars to examine the measurement model and structural model simultaneously and often requires fewer assumptions than factor-based SEM techniques. For these reasons and more, PLS offers great power for researchers who wish to use a SEM-based approach to evaluate a research model. However, with the great power of PLS also comes great responsibility. Scholars should determine if PLS is appropriate to use within their context, and scholars should explain their rationale for employing PLS for data analysis. Recognizing the power and responsibility associated with PLS is important since many scholars have called for an abandonment of PLS within the information systems discipline and beyond. We reviewed articles from four premier journals within the information systems field from 2017-2020 that use PLS as an analysis technique. Based on this review, we identify recommendations for scholars seeking to embrace the power and responsibility of using composite-based SEM to analyze research models.

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Mohanan, Mahesh, and Yun Ii Go. "Optimized Power System Management Scheme for LSS PV Grid Integration in Malaysia Using Reactive Power Compensation Technique." Global Challenges 4, no. 4 (April 2020): 1900093. http://dx.doi.org/10.1002/gch2.201900093.

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Izzat Abu Bakar, Amirul, Mohamad Khairuzzaman Mohamad Zamani, Ismail Musirin, and Nor Azura Md Ghani. "Load Management for Voltage Control Study Using Parallel Immunized-computational Intelligence Technique." Bulletin of Electrical Engineering and Informatics 7, no. 2 (June 1, 2018): 176–82. http://dx.doi.org/10.11591/eei.v7i2.1172.

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The increase of power demand is a crucial issue in the power system community in many parts of the world. Malaysia has also witnessed the familiar scenario due to the current development throughout the country has invited the urgency of increase in the power supply. Since Malaysia practices vertical system; where the electricity is supplied by only one utility, load management is an important issue so that the delivery of electricity is implemented without discrimination. Parallel Computational Intelligence will be developed which can alleviate and avoid all the unsolved issues, highlighting the weakness of current schemes. Parallel Computational Intelligence is developed to manage the optimal load in making sure the system maintains the stability condition, within the voltage limits. This paper presents evolutionary programming (EP) technique for optimizing the voltage profile. In this study, 3 algorithms which are Gaussian, Cauchy and Parallel EP were developed to solve optimal load management problem on IEEE 26-bus Reliability Test System (RTS). Results obtained from the study revealed that the application of Parallel EP has significantly reduced the time for the optimization process to complete.

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Kumar, D. Sai. "Demand Side Management Techniques for Peak Reduction." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 30, 2021): 2911–13. http://dx.doi.org/10.22214/ijraset.2021.36979.

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Industrial growth is the back bone for the development of any nation. Industries are mainly dependent on electrical energy. But from the various studies, the sources for electrical energy are decreasing gradually, and in turn, the gap is increasing between the supplier and the load. The solution for this scenario is optimal utilization of resources. To overcome this problem , the concept Demand Side Management (DSM) has emerged in Power System Planning and Management. The principle objective of DSM is mutual understanding between the supplier and the consumer for maximizing benefits and minimizing inconvenience. The aim of this research work is selection and application of appropriate DSM techniques to industrial and domestic loads for peak load management and energy conservation, that is to control the maximum demand during the peak hours and saving the energy by using the energy efficient and intelligent appliances like air conditioners and water heaters. DSM includes techniques like the End Use Equipment Control, the Load Priority Technique, he Peak Clipping & Valley filling, the Differential Tariff and Resizing of the equipment. Depending upon the application, all the techniques may be applied sequentially, or only a few of them can be applied. There is a lot of ambiguity in the selection of DSM techniques, because the application of each DSM technique depends on the case study and the problem associated with the respective case study. After comprehensive understanding of a particular case, a thorough investigation and subsequent data analysis pave the way for the selection of appropriate DSM technique/techniques

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Subramanian, S., R. Anandhakumar, and S. Ganesan. "Generator maintenance management using bio‐inspired search algorithm." International Journal of Energy Sector Management 5, no. 4 (November 22, 2011): 522–44. http://dx.doi.org/10.1108/17506221111186350.

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PurposeThe purpose of this paper is to solve the maintenance management problems of generating units under the reliability criterion.Design/methodology/approachThe problem has been formulated as a combinatorial optimization task, with explicit and simultaneous treatment of multiple objectives: maximization of reliability, minimization of fuel costs and minimization of constraint violations. This paper formulates a general generator maintenance management (GMM) problem using a reliability criterion and a novel bio‐inspired search technique, namely, artificial bee colony (ABC) algorithm is applied to determine the optimal generator maintenance schedule.FindingsA novel meta‐heuristic search technique based algorithm has been developed to determine the optimal maintenance schedule of generating units to improve the system reliability.Originality/valueThe contribution of the paper is that an efficient bio‐inspired algorithm based solution technique has been developed to solve a very important problem for a power utility, i.e. the economical and reliable operation of a power system.

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Chekira, Ouadiâ, Ali Boharb, Younes Boujoudar, Hassan El Moussaoui, Tijani Lamhamdi, and Hassane El Markhi. "An improved energy management control strategy for a standalone solar photovoltaic/battery system." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 2 (August 1, 2022): 647. http://dx.doi.org/10.11591/ijeecs.v27.i2.pp647-658.

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This <span>paper proposes efficient energy management in hybrid microgrid-comprising of photovoltaic (PV) and battery storage systems. The proposed technique. The hybrid system's power balance is based on smart control to meet the demands of isolated off-grid direct current (DC) loads as well as to stabilize the voltage to DC Bus. The Perturb and Observe technique (P&O) is used to achieve maximum power point tracking by adjusting the duty cycle of the Bidirectional converter, which links the Li-ion battery to the DC Bus of stand-alone power systems (SPS). The proposed controller regulates the power flow of the battery for efficiency voltage control in a microgrid. The energy management system proposed has been approved using MATLAB/Simulink under variable solar irradiation conditions. The simulation results show that the technique used increases the battery cycle-life and better energy management and voltage control performance compared with previous examples</span>.

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Khan, S. A., and M. Koç. "Thermal management of power electronics using nanofluids and nucleate boiling heat transfer technique." Energy Reports 8 (November 2022): 766–71. http://dx.doi.org/10.1016/j.egyr.2022.08.162.

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HamaAli, Goran, Diary Sulaiman, and Muhammed Ibrahim. "Power and thermal management in SRAM and DRAM using adaptive body biasing technique." IEICE Electronics Express 16, no. 19 (2019): 20190432. http://dx.doi.org/10.1587/elex.16.20190432.

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Cheng, Ching-Hwa. "Using a Voltage Domain Programmable Technique for Low-Power Management Cell-Based Design." Journal of Low Power Electronics and Applications 1, no. 2 (September 14, 2011): 303–26. http://dx.doi.org/10.3390/jlpea1020303.

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Kim, Junkyeong, Ju-Won Kim, Changgil Lee, and Seunghee Park. "Concrete Strength Development Monitoring Technique for Automatic Construction Management of Nuclear Power plants." IABSE Symposium Report 102, no. 7 (September 1, 2014): 2947–52. http://dx.doi.org/10.2749/222137814814069679.

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Manharbhai, Patel Miteshkumar, Shrikant Ahirwar, and Paritosh Goldar. "Soft Computing Optimization Technique for Efficient Radio Resource and Power Management at BaseStation." International Journal of Engineering Trends and Technology 26, no. 5 (August 25, 2015): 256–62. http://dx.doi.org/10.14445/22315381/ijett-v26p244.

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Journal articles on the topic 'Power management technique'

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