WEDD-RST: An interpretive approach to detecting defects in photovoltaic panels in cyber-physical systems
Keywords:
production knowledge base; rough set theory; discretization; information granulation; reduct; classification; cyber-physical systems; photovoltaic monitoring; SCADAAbstract
The subject of this study focuses on the interpretability of fault-detection frameworks in solar cyber-physical systems. The rapid expansion of renewable energy networks generates massive volumes of continuous sensor data, requiring sophisticated monitoring strategies to prevent safety hazards such as photovoltaic fires. Nevertheless, dominant deep learning models function as opaque black boxes, providing no transparent reasoning for critical safety decisions. The goal of this work is to enhance the interpretability of anomaly detection in cyber-physical environments by formulating a rule-based production knowledge base straight from continuous sensor readings. Key tasks include developing an adaptive discretisation method, identifying minimal feature subsets, and reconciling contradictory patterns using an integral class support score. The applied methods fuses rough set theory (RST) with an innovative weighted entropy-density discretization (WEDD) algorithm. This combined pipeline fine-tunes thresholds using a dual metric of information entropy and local probability density, utilizing kernel density estimation to position cut points within natural data valleys. Deterministic rules are derived from the lower approximation, whereas probabilistic rules are generated from the boundary region. The results illustrate the substantial effectiveness of the suggested approach. Tested on a simulated SCADA dataset designed for fire hazard identification, the framework attains an overall accuracy of 96.2% and a macro F1-score of 0.960. Significantly, it delivers 100% accuracy for deterministic rules and a 98.0% recall rate for the vital Fire Hazard category. Comparative evaluations on the Iris and Wine datasets yield competitive results, achieving 93.3% and 87.6% accuracy, respectively, when compared with Decision Tree and Naive Bayes models. The generated knowledge base is a compact JSON artifact, making it well-suited for edge devices with limited computational resources. In conclusion, this research establishes the WEDD-RST approach as a rigorous approach for transforming raw sensor data into fully auditable IF-THEN rules with explicit confidence scores, offering a highly reliable solution for automated safety monitoring in cyber-physical environments.Downloads
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