Fault tolerant control for Static Synchronous Series Compensator using ANN

Abstract

In the paper, a hybrid technique based fault tolerant control of Static Synchronous Series Compensator (SSSC) is utilized for power system stability improvement. The hybrid technique is a combination of artificial neural network (ANN) and Gravitational search algorithm (GSA). Here, ANN is used to evaluate the healthy and faulty sensor data of the system and which is correctly classified. The proposed algorithm is used to optimize the gain parameters of the PID controller and generate the dataset of the ANN. Here, the ANN is considered as two stages. The missed data is detected in the first stage of ANN and the second stage of ANN is used to reproduce the vector of missing sensor data. By using the proposed hybrid technique, the FACTS device is controlled and the stability of the system is improved. Then, using the proposed fault tolerant control technique, the different types of power system faults are diagonized. Also, the power flow parameters are analyzed at different fault conditions. The proposed method is implemented in MATLAB/Simulink working platform and the performance is evaluated.