Fault Diagnosis in Smart Grid Based on Data-Driven Computational Methods

In this paper, a Wavelet Transform (WT) based on data analysis is proposed to extract the features from real-time active power and RMS (Root Mean Square) voltage of the power grid and use a hybrid classification technique based on Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) to classify the features and diagnose different types of faults in smart grid system. Different PSO-SVM models have been used for training to detect the fault according to P-t characteristics and determine the type of fault based on V-t characteristics of the power grid. Simulations were carried out on the IEEE 9-bus test system considering the temporary and permanent open-circuit faults on the power system. The simulation results show the accuracy, effectiveness, and robustness of the proposed method.

Fault Diagnosis in Smart Grid Based on Data-Driven Computational Methods Keywords:

Fault Detection , Fault Identification , Particle Swarm Optimization (PSO) , Smart Grid (SG) , Support Vector Machine (SVM) , Wavelet Transform (WT)