Optimization of Fault Identification and Location Using Adaptive Neuro-Fuzzy Inference System and Support Vector Machine for an AC Microgrid

Abstract

Conventional methods for high-impedance faults, low fault current levels, and communication delays could not properly identify the fault identification and location of an AC Microgrid. Fault identification and locating are crucial when integrating renewable energy sources with AC Microgrids. In an AC Microgrid, high-impedance faults, low-fault current levels, and communication delays are incapable approaches for fault identification and fault location. Machine learning enables rapid fault identification and location. This paper develops an adaptive neuro-fuzzy inference system and a support vector machine approach. To address these issues, lower fault current levels, detect high-impedance faults and affect communication delays. The proposed method is tested and evaluated in IEEE12BUS system, both in islanded and grid-connected modes, with execution times of 0.00202s in islanded mode and 0.0022s in grid-connected mode. The proposed adaptive neuro-fuzzy inference system method recognizes the optimal fault type. At the same time, Support vector machine identifies fault location accurately, resulting in the shortest execution time and minimal error percentage. This approach is demonstrated by using the IEEE12BUS AC Microgrid system. Hence, this approach is well-suited for real-time applications in AC Microgrid systems.

Keywords

• Alternate current (AC)
• microgrid (MG)
• renewable energy source (RES)
• support vector machine (SVM)
• adaptive neuro-fuzzy inference system (ANFIS)
• machine learning (ML)