Fault Diagnosis in PV Integrated Distribution System using Wavelet and Data Mining Techniques

Solar energy is clean and renewable, significantly reducing carbon dioxide and other greenhouse gas emissions compared to fossil fuel-based energy sources. PV integrated distribution systems offer a wide array of benefits, from reducing environmental impact and energy costs to enhancing grid reliability and promoting technological innovation. The integration of photovoltaic (PV) systems into the distribution network introduces new challenges in maintaining reliable and efficient operation, primarily due to the intermittent nature of solar energy and the potential for various faults. This paper explores advanced methodologies for fault diagnosis in PV-integrated distribution systems by leveraging Wavelet Transform and Data Mining techniques. The proposed approach is validated through extensive simulations and real-world data analysis, demonstrating its capability to effectively diagnose common faults such as line-to-ground faults, arc faults, and partial shading. The integration of these techniques enhances the fault detection process, offering a robust, scalable, and automated solution for maintaining the integrity and performance of PV-integrated distribution systems. The proposed method yields 99.3% fault detection accuracy and it indicates a significant improvement in fault detection accuracy and response time, thereby contributing to the reliability and sustainability of renewable energy distribution networks. The proposed method is compared with methods that available in literature.