Fault Diagnosis in Micro Grid Using Wavelet Transform in Combination with Machine Learning

Microgrids, which are defined by their decentralized methods of generating and distributing electricity, have a vital role in improving the resilience and sustainability of energy systems. Ensuring the stability and efficiency of micro grids relies heavily on the identification of faults. Conventional fault detection techniques often lack the necessary sophistication to effectively manage the intricacy and variety of contemporary micro grid systems. This paper focuses on the issue of fault identification in micro grids. It presents a new method that combines wavelet transform and data mining approaches to tackle this difficulty. The goal is to provide a reliable approach that can accurately detect and categorize defects, resulting in reduced downtime and enhanced system performance. The wavelet transform allows for the analysis of signals by breaking them down into several frequency components, thereby offering a comprehensive depiction of the dynamics of a system. The suggested technique provides various benefits over existing fault detection methods, which often depend on unsophisticated threshold-based approaches. As a consequence, there is an enhancement in precision and dependability, which allows for prompt detection and resolution of errors, while reducing interruptions in the provision of energy. Moreover, the use of sophisticated data analytics improves the capability to distinguish between typical operational circumstances and distinct fault situations, hence minimizing inaccurate alerts and enhancing the efficiency of the system. The effectiveness of the suggested technique in improving the accuracy of fault detection and assuring the dependable functioning of micro grids is confirmed via simulation results.