Momentum-based wavelet and double wavelet neural networks for power system applications
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In order to minimize the power loss and to control the voltage in the power systems, the proposed momentum-based wavelet neural network and proposed momentum-based double wavelet neural network are proposed in this paper. The training data are obtained by using linear programming method by solving several abnormal conditions. The control variables considered are generator voltages and transformer taps, and the dependent variables are generator reactive powers and load bus voltages. The IEEE 14-bus system and IEEE 30-bus system are tested using the linear programming, Levenberg–Marquardt artificial neural network, proposed momentum-based wavelet neural network and proposed momentum-based double wavelet neural network to validate the effectiveness of the proposed MDWNN method. The trained neural networks are capable of controlling the voltage, and reactive power in power systems is proved by the results with the high level of precision and speed.
KeywordsLinear programming (LP) Levenberg–Marquardt artificial neural network (ANN) Momentum-based wavelet neural network (MWNN) Momentum-based double wavelet neural network (MDWNN) Load flow Reactive power Voltage profile
The authors express their sincere thanks to the authorities of Anna University for providing necessary facilities to carry out this research work. The authors are grateful to University Grants Commission, New Delhi for providing the financial grant under Maulana Abul Kalam Azad Scheme to carry out this research work.
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