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

Area of Science:

• Renewable Energy Engineering
• Data Science in Energy
• Photovoltaic System Monitoring

Background:

• Photovoltaic plants generate vast operational data crucial for risk assessment and maintenance.
• Challenges exist in utilizing this data due to environmental influences and limitations of conventional methods.
• Existing data-driven approaches often lack interpretability and robust risk assessment capabilities.

Purpose of the Study:

• To introduce the Operating Consistency and Evidence Refinement Framework (OCERF) for advanced photovoltaic panel risk assessment.
• To overcome the limitations of conventional methods in handling complex operational data and environmental fluctuations.
• To enhance the accuracy and reliability of risk assessment and maintenance prioritization in photovoltaic systems.

Main Methods:

• Data transformation into a unified risk-evidence matrix, including missing-data handling and discrete-state risk encoding.
• Application of an operating-consistency residual autoencoding model to identify abnormal deviations.
• Integration of candidate-constrained discrete-state refinement and a CD-MABAC composite ranking model with logistic calibration.

Main Results:

• The OCERF framework demonstrated improved identification of high-risk photovoltaic panels.
• Enhanced probability calibration and ranking stability were achieved in risk assessment.
• The framework effectively integrates diverse data sources for comprehensive analysis.

Conclusions:

• OCERF provides a robust solution for intelligent photovoltaic monitoring and maintenance management.
• The framework supports effective inspection prioritization under resource constraints.
• OCERF advances the practical application of operational data for ensuring photovoltaic system reliability.