Academic literature on the topic 'Electric power-plants Load Computer simulation'
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Journal articles on the topic "Electric power-plants Load Computer simulation"
Alia Jasim Mohammed. "AN APPRAISAL OF THE TRANSIENT RESPONSE OF A D.C. SHUMT MOTOR USING MATLAB/SIMULINK UNDER NO LOADING AND FULL LOADING CONDITIONS." Diyala Journal of Engineering Sciences 4, no. 2 (December 1, 2011): 130–40. http://dx.doi.org/10.24237/djes.2011.04210.
Full textArtsiomenka, K. I. "Structural-and-Parametric Optimization of Automatic Control System for Power Units of 300 MW in Wide Range of Load Variations." ENERGETIKA. Proceedings of CIS higher education institutions and power engineering associations 62, no. 5 (October 4, 2019): 469–81. http://dx.doi.org/10.21122/1029-7448-2019-62-5-469-481.
Full textSiraev, Fanis, and Regina Khazieva. "INVESTIGATION OF A FREQUENCY-REGULATORY ELECTRIC DRIVE WITH ASYNCHRONOUS ELECTRIC MOTOR." Electrical and data processing facilities and systems 18, no. 2 (2022): 45–51. http://dx.doi.org/10.17122/1999-5458-2022-18-2-45-51.
Full textYan, Xiangwu, Ling Wang, Zhichao Chai, Shuaishuai Zhao, Zisheng Liu, and Xuewei Sun. "Electric Vehicle Battery Simulation System for Mobile Field Test of Off-Board Charger." Energies 12, no. 15 (August 6, 2019): 3025. http://dx.doi.org/10.3390/en12153025.
Full textVasilyev, N., I. Kalinin, V. Polovinkin, A. Pustoshny, O. Savchenko, and K. Sazonov. "Load simulation of icebreaker propulsion motors at laboratory and virtual tests of electric propulsion systems." Transactions of the Krylov State Research Centre 1, no. 399 (March 15, 2022): 15–30. http://dx.doi.org/10.24937/2542-2324-2022-1-399-15-30.
Full textWang, Yikai, Xin Yin, Xianggen Yin, Jian Qiao, and Liming Tan. "A Petri Net-Based Power Supply Recovery Strategy for the Electric Power System of Floating Nuclear Power Plant." Applied Sciences 12, no. 18 (September 8, 2022): 9026. http://dx.doi.org/10.3390/app12189026.
Full textKurniawan, Akhmad R., Adi Kurniawan, Sardono Sarwito, Ahlur R. N. Gumilang, and Firman Budianto. "Comparison of voltage drop in AC and DC shipboard electrical power distribution systems: A case study of 17,500 DWT tanker vessel." IOP Conference Series: Earth and Environmental Science 972, no. 1 (January 1, 2022): 012001. http://dx.doi.org/10.1088/1755-1315/972/1/012001.
Full textBulatov, Yu N., A. V. Kryukov, and K. V. Suslov. "The study of the isolated power supply system operation with controlled distributed generation plants, energy storage units and drive load." Power engineering: research, equipment, technology 23, no. 5 (January 9, 2022): 184–94. http://dx.doi.org/10.30724/1998-9903-2021-23-5-184-194.
Full textVeeramsetty, Venkataramana, Modem Sai Pavan Kumar, and Surender Reddy Salkuti. "Platform-Independent Web Application for Short-Term Electric Power Load Forecasting on 33/11 kV Substation Using Regression Tree." Computers 11, no. 8 (July 29, 2022): 119. http://dx.doi.org/10.3390/computers11080119.
Full textBulatov, Yuri, and Andrey Kryukov. "Study of cyber security of predictive control algorithms for distributed generation plants." Analysis and data processing systems, no. 2 (June 18, 2021): 19–34. http://dx.doi.org/10.17212/2782-2001-2021-2-19-34.
Full textDissertations / Theses on the topic "Electric power-plants Load Computer simulation"
Moharari, Nader S. "An electric load forecasting approach using expert systems and artificial neural networks." Diss., Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/13757.
Full textConradie, Antonie Eduard. "Performance optimization of engineering systems with particular reference to dry-cooled power plants." Thesis, Link to the online version, 1995. http://hdl.handle.net/10019.1/1326.
Full textRader, Jordan D. "Loss of normal feedwater ATWS for Vogtle Electric Generating Plant using RETRAN-02." Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31741.
Full textCommittee Chair: Abdel-Khalik, Said I.; Committee Member: Ghiaasiaan, S. Mostafa; Committee Member: Hertel, Nolan E. Part of the SMARTech Electronic Thesis and Dissertation Collection.
Nigrini, Lucas Bernardo. "Developing a neural network model to predict the electrical load demand in the Mangaung municipal area." Thesis, [Bloemfontein?] : Central University of Technology, Free State, 2012. http://hdl.handle.net/11462/176.
Full textBecause power generation relies heavily on electricity demand, consumers are required to wisely manage their loads to consolidate the power utility‟s optimal power generation efforts. Consequently, accurate and reliable electric load forecasting systems are required. Prior to the present situation, there were various forecasting models developed primarily for electric load forecasting. Modelling short term load forecasting using artificial neural networks has recently been proposed by researchers. This project developed a model for short term load forecasting using a neural network. The concept was tested by evaluating the forecasting potential of the basic feedforward and the cascade forward neural network models. The test results showed that the cascade forward model is more efficient for this forecasting investigation. The final model is intended to be a basis for a real forecasting application. The neural model was tested using actual load data of the Bloemfontein reticulation network to predict its load for half an hour in advance. The cascade forward network demonstrates a mean absolute percentage error of less than 5% when tested using four years of utility data. In addition to reporting the summary statistics of the mean absolute percentage error, an alternate method using correlation coefficients for presenting load forecasting performance results are shown. This research proposes that a 6:1:1 cascade forward neural network can be trained with data from a month of a year and forecast the load for the same month of the following year. This research presents a new time series modeling for short term load forecasting, which can model the forecast of the half-hourly loads of weekdays, as well as of weekends and public holidays. Obtained results from extensive testing on the Bloemfontein power system network confirm the validity of the developed forecasting approach. This model can be implemented for on-line testing application to adopt a final view of its usefulness.
Popoola, Olawale Muhammed. "Adaptive neuro-fuzzy inference system (ANFIS)-based modelling of residential lighting load profile." 2015. http://encore.tut.ac.za/iii/cpro/DigitalItemViewPage.external?sp=1001770.
Full textAims of this study is to develop a residential customers' lighting profile ANFIS-based model. This model is expected to address lighting load usage estimation in relation to the dynamic occupancy presence in a residential dwelling, which will take into account the climatic condition (natural lighting) of such an environment (e.g. South Africa) and its income. The objectives are as follows: 1. Develop an ANFIS-based residential lighting load profile model for middle income, low income and high-income earners. 2. Error reduction in residential lighting demand profile model. Performance evaluation and validation of the model using correlation and trend analysis, regression model, South Africa power utility application lighting program, non-weighted approach and comparison with other research studies (methodology).3. Reduction in / or elimination of repeated models for occupant presence and assumptions that residences are occupied at certain periods. 4. Derive meaning from complexities (behavioural trends) associated with lighting usage and extract patterns in such circumstances.
Manuel, Grant. "Short term load forecasting by means of neural networks and programmable logic devices for new high electrical energy users." Thesis, 2014. http://hdl.handle.net/10210/10055.
Full textLoad forecasting is a necessary and an important task for both the electrical consumer and electrical supplier. Whilst many studies emphasize the importance of determining the future demand, few papers address both the forecasting algorithm and computational resources needed to offer a turnkey solution to address the load forecasting problem. The major contribution that, this paper identified is a turnkey load forecasting algorithm. A turnkey forecasting solution is defined by a comprehensive solution that incorporates both the algorithm and processing elements needed to execute the algorithm in the most effective and efficient manner. An electrical consumer, namely the operator of a rapid railway system was faced with a problem of having to forecast the notified network demand and energy consumption. The forecast period was expected to be between a very short term window for maintenance reasons and long term for the requirements warranted by the electrical supplier. The problem was addressed by firstly reviewing the most common forms of load forecasting for which there are two types. These are statistically based methods and methods based upon artificial intelligence. The basic principle of a statistical approach is to approximate or define a curve that best defines the relationship between the load and its parameters. Regression and similar day approach methods use the defined correlation of past values in order to forecast the future behaviour. In other words the future load forecast is forecasted by observing the behaviour of the factors that influenced the load behaviour in the past. The underlying factors that influence the final load may be identified by means of a top down drill down approach. In this way both the load factors and influential variables may be identified. This paper makes use of relevance trees to create a structure of load and influential variables. For a regression forecasting model, the behaviour of the load is modelled according to weather and non-weather variables. The load may be stochastic or deterministic, linear or nonlinear. One of the biggest problems with statistical models is the lack of generality. One model may yield more acceptable results over another model simply because of the sensitivity of the model to one load element that defines the model significantly. Regression type forecast models are an example of this where the elements that define the load are broadly divided into weather and non-weather elements. It is important that the correlation curve reflects the true correlation between the load and its elements. The recursive properties of a statistical based techniques (Kalman filter) allows that the relationship be refined. For methods such as neural networks, the relationship between the load elements that define the future load behaviour is learnt by presenting a series of patterns and then a forecast model is derived. Rigorous mathematical equations are replaced with an artificial neural network where the load curve is learnt. Unlike a statistical based approach (ARMA models), the load does not first need to be defined as a stochastic or deterministic series. In terms of a stochastic approach (non stationery process), the load first would have to be brought to a stationery process. For artificial neural networks, such processes are eliminated and the future forecast is derived faster in terms of a turnkey approach (tested solution). Artificial Neural Networks (ANN) has gained momentum since the eighties. Specifically in the area of forecasting, neural networks have become a common application. In this thesis, data from a railway operator was used to train the neural network and then future data is forecasted. Two embedded processing elements were then evaluated in terms of speed, memory and ability to execute complex mathematical functions (libraries). These were namely a Complex Programmable Logic Device (CPLD) and microcontroller (MCU). The ANN forecasting algorithm was programmed on both a MCU and PLD and compared by means of timing models and hardware platform testing. The most ideal turnkey solution was found to be the ANN algorithm residing on a PLD. The accuracy and speed results surpassed that of a MCU.
Books on the topic "Electric power-plants Load Computer simulation"
Knowles, J. B. Simulation and control of electrical power stations. Taunton, Somerset, England: Research Studies Press, 1990.
Find full textSzymański, Grzegorz. Symulacja cyfrowa niebezpiecznych oddziaływań stacji i linii wysokich napięć. Poznań: Wydawn. Politechniki Poznańskiej, 1998.
Find full textInternational Joint Power Generation Conference (1998 Baltimore, Md.). Proceedings of the 1998 International Joint Power Generation Conference: Presented at the 1998 International Joint Power Generation Conference, August 23-26, 1998, Baltimore, Maryland. New York: American Society of Mechanical Engineers, 1998.
Find full textEuropean, Simulation Multiconference (1990 Nuremberg Germany). Simulation in energy systems: One track from the European Simulation Multiconference, June 10-13, 1990, the Atrium Hotel, Nuremberg, Germany. San Diego, Calif: Society for Computer Simulation International, 1990.
Find full textPiazza, Maria Carmela Di. Photovoltaic Sources: Modeling and Emulation. London: Springer London, 2013.
Find full textDamian, Flynn, and Institution of Electrical Engineers, eds. Thermal power plant simulation and control. London: Institution of Electrical Engineers, 2003.
Find full textSungiae, Cho, Lee Sang-hoon, U.S. Nuclear Regulatory Commission. Office of Nuclear Regulatory Research., Han ơguk Cho llyo k Kongsa., and Han ơguk Wo njaryo k Anjo n Kisurwo n., eds. Assessment of RELAP5/MOD2 computer code against the net load trip test data from Yong-Gwang, unit 2. Washington, DC: Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission, 1993.
Find full textAssessment of RELAP5/MOD2 against a load rejection from 100% to 50% power in the Vandellos II nuclear power plant. Washington, DC: Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission, 1993.
Find full textP, Deschutter, U.S. Nuclear Regulatory Commission. Office of Nuclear Regulatory Research, and TRACTEBEL (Firm), eds. Assessment study of RELAP5/MOD2 cycle 36.04 based on the DOEL-4 manual loss of load test of November 23, 1985. Washington, DC: Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission, 1992.
Find full textL, Dolce James, and United States. National Aeronautics and Space Administration., eds. An expert system for simulating electric loads aboard Space Station Freedom. [Washington, D.C.]: NASA, 1990.
Find full textBook chapters on the topic "Electric power-plants Load Computer simulation"
Recioui, Abdelmadjid, and Fatma Zohra Dekhandji. "Implementation of Load Control for Smart Metering in Smart Grids." In Advances in Computer and Electrical Engineering, 119–55. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-4027-5.ch006.
Full textAnwar, Adnan, Md Apel Mahmud, Md Jahangir Hossain, and Himanshu Roy Pota. "Distributed Generation Capacity Planning for Distribution Networks to Minimize Energy Loss." In Advances in Computer and Electrical Engineering, 76–95. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9911-3.ch005.
Full textKhan, Baseem, Samuel Degarege, Fsaha Mebrahtu, and Hassan Alhelou. "Energy Storage System and Its Power Electronic Interface." In Research Anthology on Smart Grid and Microgrid Development, 183–95. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-3666-0.ch009.
Full textKhan, Baseem, Samuel Degarege, Fsaha Mebrahtu, and Hassan Alhelou. "Energy Storage System and Its Power Electronic Interface." In Handbook of Research on New Solutions and Technologies in Electrical Distribution Networks, 309–21. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1230-2.ch016.
Full textAlhelou, Hassan Haes. "Fault Detection and Isolation in Power Systems Using Unknown Input Observer." In Advances in Computer and Electrical Engineering, 38–58. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-6989-3.ch002.
Full textJabari, Farkhondeh, Heresh Seyedia, Sajad Najafi Ravadanegh, and Behnam Mohammadi Ivatloo. "Stochastic Contingency Analysis Based on Voltage Stability Assessment in Islanded Power System Considering Load Uncertainty Using MCS and k-PEM." In Advances in Computer and Electrical Engineering, 12–36. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9911-3.ch002.
Full textKaliannan, Jagatheesan, Anand Baskaran, and Nilanjan Dey. "Automatic Generation Control of Thermal-Thermal-Hydro Power Systems with PID Controller Using Ant Colony Optimization." In Renewable and Alternative Energy, 761–78. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1671-2.ch023.
Full textConference papers on the topic "Electric power-plants Load Computer simulation"
Hu, Yelin, Chun Yin, Haigang Zang, Panpan Li, and Zhaoquan Chen. "Simulation on the Application of Electric Spring for Reactive Power Compensation at Load Side." In 2016 International Conference on Intelligent Control and Computer Application (ICCA 2016). Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/icca-16.2016.6.
Full textYu, Jian G. "TAROS Computer Simulation Software for DC and AC Traction Power Systems: Methods and Applications." In 2015 Joint Rail Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/jrc2015-5635.
Full textMiyano, Hiroshi, Naoto Sekimura, Masayuki Takizawa, and Masaaki Matsumoto. "Review of Pipe Wall Thinning Mechanism Study and its National Project in Japan." In ASME 2011 Pressure Vessels and Piping Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/pvp2011-58023.
Full textRavelli, S., and A. Perdichizzi. "Performance Assessment of an Integrated Gasification Combined Cycle Under Flexible Operation." In ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/gt2018-75198.
Full textWang, Li, Yang Liu, Yun Tai, Yawei Zhang, and Zhenpeng Tang. "Extension of Load Follow Operation of PWRs Without Boron Adjustment." In 2013 21st International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/icone21-15479.
Full textSadineni, Suresh B., Fady Atallah, and Robert F. Boehm. "Measurements and Simulations of Electrical Demand From Residential Buildings for Peak Load Reduction." In ASME 2011 5th International Conference on Energy Sustainability. ASMEDC, 2011. http://dx.doi.org/10.1115/es2011-54291.
Full textVenkataraman, Shankar, Reghu Ramawarrier, Vivek Kozhikkoottungal Satheesh, Nikhil Mathew Mundupalam, and Siddaling Bhure. "Computer Simulation of Diesel Fueled Engine Processes Using MATLAB and Experimental Investigations on Research Engine." In ASME 2017 Power Conference Joint With ICOPE-17 collocated with the ASME 2017 11th International Conference on Energy Sustainability, the ASME 2017 15th International Conference on Fuel Cell Science, Engineering and Technology, and the ASME 2017 Nuclear Forum. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/power-icope2017-3498.
Full textArsie, I., V. Marano, and G. Rizzo. "A Model for Thermo-Economic Analysis and Optimization of Steam Power Plants for Power and Cogeneration." In ASME 2004 Power Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/power2004-52132.
Full textXu, Xiaoqiang, Yongjia Wu, Lei Zuo, and Shikui Chen. "Multimaterial Topology Optimization of Thermoelectric Generators." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-97934.
Full textGarcia, Ivan F. Galindo, Saul Rodriguez Lozano, and Oscar P. Hernandez De La O. "Development of a Real Time Simulator to Test Load and Speed Control Systems of Hydroelectric Power Plants." In 2009 International Conference on Electrical, Communications, and Computers (CONIELECOMP). IEEE, 2009. http://dx.doi.org/10.1109/conielecomp.2009.44.
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