Determination of probabilistic risk of voltage collapse using radial basis function (RBF) network |
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| Affiliation: | 1. Department of Electrical Engineering, Shri G.S. Institute of Technology and Science, Indore 452003, MP, India;2. Department of Electrical Engineering, Government Engineering College, Jabalpur 456010, MP, India;3. Center for Energy Studies, Indian Institute of Technology, New Delhi 110016, India;1. Electrical Engg. Department, Shri G.S. Institute of Technology and Science, Indore, M.P., India;2. Electrical Engg. Deptt., Swami Vivekanand College of Engineering, Indore, M.P., India;1. College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China;2. State Key Laboratory of Hydrology – Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China;1. State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing 400044, People''s Republic of China;2. State Grid Chongqing Electric Power Research Institute, Chongqing 401123, People''s Republic of China;1. Dept. of Electromagnetic Engineering, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden;2. Dept. of Electrical Engineering, Technical University of Denmark, Lyngby, Denmark;1. Dept. of Electrical & Computer Engineering, Texas A&M University, College Station, TX 77840, USA;2. Envision Digital, Redwood City, CA 94065, USA |
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| Abstract: | This paper describes a viewpoint for voltage stability assessment accounting uncertainties in line parameters and settings of reactive power control variables. A probabilistic risk of voltage collapse, however small it may be, is always present if system parameters and control variables are treated as random variables. Such uncertainties become important if operating point of system is near to voltage collapse point. Monte-Carlo simulation has been used to evaluate probabilities of voltage collapse for various operating conditions. Static voltage stability limit for various sampled values of system parameters and control variables have been obtained using continuation power flow methodology. Monte-Carlo simulation is a time-consuming process. Hence, a radial basis function (RBF) network has been used to get probabilistic risk of voltage collapse. Training and testing instances have been generated using Monte-Carlo simulation. The algorithm developed has been implemented on two standard test systems. |
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