A robust neural network model for monitoring online voltage stability

Abstract

Incessant assessment of voltage stability is a lively aspect to safeguard the electrical power system (EPS) operation. The conservative methods for online assessment in terms of stability check for voltage are extremely time engrossing and also absurd for supervising any application online. In line to this, a Salp Swarm Algorithm-based artificial neural network (SSA–ANN) model is opted for online monitoring of voltage stability in this manuscript. Artificial neural network is an influential and promising predictive tool. To upsurge the efficacy and accuracy and minimalize the training time. SSA is used for tuning the metaparameters such as the activation functions and number of nodes along with the learning rate. The method anticipated for the aforesaid problem utilizes the magnitude of voltage and its corresponding phase angle which are attained from the PMU as the inputs to the neural network model and the output is the voltage stability margin index (VSMI). The efficiency of the proposed model is verified with ICA–ANN and GA–ANN underrobust test cases and compared with the same data set to attest its preeminence.