An Operating-Consistency and Evidence-Refinement Framework for Sensor-Data-Driven Photovoltaic Panel Risk Assessment...

Photovoltaic plants generate large amounts of electrical, thermal, environmental, and equipment-status monitoring data during long-term operation. These data provide an important basis for risk assessment and maintenance decision making, but their practical use remains challenging because photovoltaic output is strongly affected by irradiance, temperature, and environmental fluctuation. Conventional threshold-based schemes are sensitive to operating conditions, while common data-driven classifiers mainly focus on label prediction and provide limited support for operating-consistency interpretation, calibrated risk assessment, and maintenance-priority ranking. To address these issues, an Operating Consistency and Evidence Refinement Framework, named OCERF, is proposed for photovoltaic panel risk assessment. First, photovoltaic generation records, weather-sensor observations, thermal measurements, and state-level monitoring information are transformed into a unified risk-evidence matrix through missing-data handling, normalization, risk-direction alignment, and discrete-state risk encoding. Unlike direct feature concatenation, an operating-consistency residual autoencoding model is used to learn the normal relationship among electrical output, environmental response, thermal state, and efficiency-related variables, so that abnormal deviations can be identified through reconstruction residuals. Since continuous deviations may also be caused by normal environmental fluctuation, candidate-constrained discrete-state refinement is further introduced to strengthen the credibility of continuous abnormal samples. Finally, instead of using a simple weighted sum, a CD-MABAC composite ranking model integrates continuous risk, discrete enhancement, and continuous-discrete consistency, and logistic calibration is used to obtain calibrated reference-risk probabilities and risk grades. Evaluations on photovoltaic generation and weather-sensor data show improved high-risk identification, probability calibration, and ranking stability. The results indicate that OCERF can support intelligent photovoltaic monitoring, inspection prioritization, and maintenance management under limited operation and maintenance resources.