Enhanced Open-Circuit Fault Detection and Localization in Electric Vehicle Inverter Switches using Hybrid SGO-FAT Technique
Keywords:
Fault Detection, Active Three-Phase Neutral-Point-Clamped Inverter, Single Localization, Shell Game Optimization, Battery or Inverter Switch Faults, Feedback Artificial Tree.Abstract
This paper proposes a hybrid method for locating and identifying open-circuit problems in electric vehicle inverter switches. The proposed hybrid method is the combined execution of both the Shell Game Optimization and Feedback Artificial Tree technique, and it is commonly named as SGO-FAT technique. The proposed strategy is to provide accurate information on fault location while enhancing the resilience and speed of fault detection. The SGO technique is employed to identify system defects under steady and transient state condition of speed drive. The Feedback Artificial Tree (FAT) methodology is integrated to enhance fault localization accuracy. The proposed SGO-FAT method is to enhance the effectiveness of localizing and detecting open-circuit faults with EVs' inverter switches. The proposed technique is implemented on the MATLAB platform, and compared with current methods. The results show that the better proposed approach is at accuracy and stability, characterized by the lowest error values ISE: 5.341, ITAE: 0.3035, IAE: 0.00779 than other existing techniques, like Heap-based optimizer (HBO), Ant Colony Optimization (ACO) algorithm and Salp Swarm Algorithm (SSA).
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