Advanced distance protection technique based on multiple classified ANFIS considering different loading conditions for long transmission lines in EPS

Abstract

The advanced application of artificial intelligent techniques (AIT) was introduced recently in protection of transmission lines in electric power systems (EPS). Adaptive neuro-fuzzy inference system (ANFIS) is a promising tool among these AIT. In this proposed research, the application of ANFIS for distance relay protection for long transmission line, under different loading conditions, in electrical power systems (EPS) will be introduced and discussed. Based on multiple classified ANFIS and considering different loading conditions for long transmission lines in EPS, the suggested and proposed technique deals with fault detection, classification, and location in long transmission lines. All these tasks will be addressed in details in this article. It considers, firstly, detecting the fault occurrence in very short time and isolate the faulty section of the long transmission lines. Secondly to classify the fault type and deduce which of the three phases are exposed to the fault. Finally, locating the fault will be achieved easily even if the procedure here is completely different from short and medium transmission lines. The input training data of the ANFIS detection units are firstly derived from the fundamental values of the voltage and current measurements [using digital signal processing via discrete Fourier transform (DFT)]. These measurements were simulated considering different loading conditions.