A Hybrid Intelligent Method for Compensation of Current Transformers Saturation Based on PSO-SVR

Abstract

The Current Transformers (CT) saturation may cause the protective relays mal-operation either non-recognition of internal fault or undesirable trip under external fault conditions. Therefore, compensation of CT saturation is very important for correct performance of protective schemes. Compensation of CT saturation by combination of signal processing methods and intelligent algorithms is a suitable solution to solve the problem. It decreases the probability of mal-operation and increases the reliability of the power system. In this paper, Support Vector Regression (SVR) method is employed to compensate the distorted secondary current due to CT saturation. In SVR method, despite the other methods such as MLPand ANFIS, instead of minimizing the model error, the operational risk error is considered as target function. In this method, by using Kernel tricks, a smart RBF neural network is obtained, so that all operational procedures will be optimized automatically. In this paper, an intelligent method based on Particle Swarm Optimization (PSO) algorithm is presented to determine the optimal values of SVR parameters. Due to the stability and robustness of this method in presence of noise and sudden changes in current, this method has a high accuracy. In addition, a sample power system is simulated using PSCAD software. Afterwards, current signals are extracted and fed to PSO-SVR algorithm, which is implemented in MATLAB environment. The obtained results show the preference of the proposed method in aspect of estimation accuracy as compared to some presented methods in the field of CT saturation detection and correction.