Intelligence Arcing Failure Diagnosis Using Frequency Component Extraction for IoT Applications

Arcing faults in photovoltaic (PV) systems can lead to significant power loss and pose serious safety hazards. This paper introduces a novel approach for detecting DC arcing failures in PV systems, enhancing both safety and operational efficiency. The proposed methodology capitalizes on the transformation of time-domain sampled signals into the frequency domain through Fast Fourier Transform (FFT) analysis. This transformation facilitates the isolation of frequency components that fall within a specific range indicative of arcing distortion harmonics. Subsequent steps involve the calculation of the squared averages and deviation factors from these critical frequency components. The effectiveness of this approach is validated through diagnostic testing, which confirms the capability of the proposed method to accurately identify arcing incidents. The results underscore the potential of this approach to serve as a reliable tool for improving the fault diagnostic processes in PV systems, thereby ensuring their safer and more efficient operation.