Fuzzy Logic Technique based Fault Diagnosis for Electrical Distribution Network

The power system needs to operate reliably and efficiently, with the distribution system being a critical component. Distribution systems are prone to faults and malfunctions for several reasons. These issues can have a significant impact on the power grid, but their effects can be minimized quickly and accurately through fault diagnostics. Electric utilities face an ongoing challenge in promptly and precisely identifying and locating faults in the distribution system. AI tools can be an efficient method for accurately identifying these faults. There are several possible diagnostic methods, such as fuzzy logic, ANN (artificial neural network), ANFIS (adaptive neuro-fuzzy inference system), support vector machines, genetic algorithms, and matching approaches. Each technique has its own advantages and disadvantages. The paper has employed fuzzy logic for fault detection, identification, and location. This paper includes the study of multiple faults on different buses. Also, the identification and location of multiple faults are included. The simulation and algorithm development have been carried out in MATLAB/Simulink. Thus, fault detection, identification, and location have been done through a fuzzy logic algorithm.