Vol. 9 Issue 2
Year:2014
Issue:Oct-Dec
Title:Artificial Neural Networks Based Power System State Estimation
Author Name:Manjula S.Sureban and Shekhappa Ankaliki
Synopsis:
Increased Interconnectlon of the power system along with deregulated structure to satisfy growing demand has brought new challenges for power system state estlmatlon. The estlmatlon of power system stofe [1,3] in such interconnected system has become complex due to comp/exity in modelling and uncertalntles. This makes ANN a ideal candldate for state estimation, since if can accurately map the relationshlp between the measured varlable and other state variables of the power system with reduced computational resources as compared to weighted least square approach[2]. Using only load bus parameters for various operating condifions with an ANN other states of the power system can be accurately estimated. This paper dlscusses an approach to estimate the state of power system using ANN in MATLAB.
Year:2014
Issue:Oct-Dec
Title:Artificial Neural Networks Based Power System State Estimation
Author Name:Manjula S.Sureban and Shekhappa Ankaliki
Synopsis:
Increased Interconnectlon of the power system along with deregulated structure to satisfy growing demand has brought new challenges for power system state estlmatlon. The estlmatlon of power system stofe [1,3] in such interconnected system has become complex due to comp/exity in modelling and uncertalntles. This makes ANN a ideal candldate for state estimation, since if can accurately map the relationshlp between the measured varlable and other state variables of the power system with reduced computational resources as compared to weighted least square approach[2]. Using only load bus parameters for various operating condifions with an ANN other states of the power system can be accurately estimated. This paper dlscusses an approach to estimate the state of power system using ANN in MATLAB.
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