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INTELLIGENT FAULT DIAGNOSIS METHOD FOR SHIPBOARD MAIN POWER SYSTEM

An optimized electric ship operation system reduces fuel consumption and improves energy efficiency. Increasing the reliability and robustness of shipboard power systems (SPS) is crucial for working under different operating conditions to ensure the safe and satisfactory operation of the system. This study aims to provide a fault analysis method consisting of fault detection and classification, in which the wavelet-based fuzzy-logic (WFL) approach approximates the location of the fault. This work presents a novel fault detection scheme that includes fault detection, classification, and location. A wavelet approach is proposed for a fault classification methodology. Identifying the fault location using a combined wavelet and fuzzy logic method is a significant challenge. In order to effectively demonstrate this technique, various fault condition simulations are conducted using MATLAB. This study proposes a combination of discrete wavelet transform and fuzzy logic to produce significantly better performance than existing methods. However, a fuzzy feature selection technique is also introduced to reduce processing time and increase accuracy. The obtained results indicate that the proposed methodology offers high fault location accuracy. According to the experimental results, the diagnostic model proposed in this study is effective at classifying and diagnosing faults in a short time, and the response time is fast.