This research uses a Mamdani-type fuzzy logic system to present a fault detection algorithm for photovoltaic (PV) systems. Since faults can severely reduce PV system performance, a dependable diagnostic technique is essential for proactive maintenance and efficient operation. The algorithm assesses system health using three key inputs: panel current, panel voltage, and converter voltage. The method effectively detects faults by computing distortion ratios from these electrical signals and processing them through a fuzzy logic controller. Simulations validate the approach, demonstrating its capability to recognize and categorize 12 distinct fault types including PV array issues like shading, humidity, and temperature variations, as well as DC-DC converter problems such as MPPT failures, open circuits, and short circuits. The results highlight the advantages of fuzzy logic-based diagnostics, emphasizing their quick response, reliability, simplicity, and accuracy in enhancing PV system maintenance and dependability. As shown in Table 5, the technique maintains a moderately low error rate.