Title :
Genetic algorithm based artificial neural network model for voltage stability monitoring
Author :
Sajan, K.S. ; Tyagi, Barjeev ; Kumar, Vishal
Author_Institution :
Electr. Dept., Indian Inst. of Technol., Roorkee, Roorkee, India
Abstract :
In this paper, hybrid Artificial Neural Network and Genetic Algorithm (ANN-GA) approach for online monitoring of long-term voltage instability has been proposed. Thegenetic algorithm (GA) has been used to improve the accuracy of ANN by tuning its meta-parameters such as number of nodes in hidden layer, input and output activation function and learning rate. The proposed approach uses the voltage magnitude and phase angle obtained from phasor measurement units (PMUs) as the input vectors and the outputs is the Voltage Stability Margin Index (VSMI)vector. The effectiveness of the proposed approach is testedon New England 39-bus test system. The results of the proposed ANN-GA approach for voltage stability monitoring is compared with ANN model on same data set.
Keywords :
genetic algorithms; neural nets; phasor measurement; power system stability; voltage regulators; ANN-GA; New England 39-bus test system; PMU; VSMI vector; artificial neural network; genetic algorithm; metaparameters; phasor measurement units; voltage stability margin index vector; voltage stability monitoring; Artificial neural networks; Genetic algorithms; Mathematical model; Phasor measurement units; Power system stability; Stability criteria; Artificial Neural Network (ANN); Genetic Algorithm (GA); Phasor Measurement Units (PMUs); Voltage Stability Margin Index (VSMI);
Conference_Titel :
Power Systems Conference (NPSC), 2014 Eighteenth National
DOI :
10.1109/NPSC.2014.7103798
