A real time PMU data and neural network approach to analyze voltage stability

This paper proposes a Voltage Stability assessment of Power System in real time operation using Radial Basis Function Neural Network (RBF NN). One of the methods which widely used for assessing Voltage Stability is Index Stability (L-index). The indicator obtained from fundamental Kirchhoff Law which is simple in numerical calculation for steady state flow analysis. Input parameters for index calculation taken from real time measurement in the system which provides faster Recently, Phasor Measurement Units (PMUs) is a advanced method to get the information of system parameter in the wide operation with high rate data. Due to the uneconomical reason and unnecessary to place one bus for one PMU, therefore optimization placement has been applied. However, the selection of PMUs locations can be seen as a reduction of input parameters, which require for index stability calculation. To solve the lack of input parameter problem, a trained data of RBF NN has used to predict index stability. This study using 14 bus IEEE system to test the propose method and the result presents the performance of the network is sufficient to predict the index stability.