Aragón, R. G., Cornejo, M. E., Medina, J., Moreno-García, J., & Ramírez-Poussa, E. (2024). Decision support system for photovoltaic fault detection avoiding meteorological conditions. arXiv preprint arXiv:2410.02812v1.
This paper aims to develop a decision support system for photovoltaic (PV) fault detection that circumvents the reliance on meteorological conditions, addressing the limitations of existing weather-dependent methods.
The researchers designed a system based on fuzzy logic and ordered weighted averaging (OWA) operators. The system analyzes the relative differences in energy production between PV facilities, leveraging historical data of correct and incorrect performance days to establish membership functions and a state machine for classifying daily performance and identifying anomalies.
The proposed decision support system offers a practical and reliable solution for unsupervised PV fault detection, particularly beneficial for small and medium-sized installations where continuous monitoring is cost-prohibitive. The system's ability to avoid reliance on meteorological data enhances its robustness and applicability in diverse geographical locations.
This research contributes a valuable tool for optimizing PV energy production and maintenance by enabling early fault detection and reducing downtime. The system's scalability and portability make it suitable for a wide range of PV installations, promoting wider adoption of solar energy.
The study acknowledges the potential for improvement in refining the classification of correct and incorrect performance days and optimizing interval values for enhanced accuracy. Future research directions include incorporating probabilistic OWA operators, integrating meteorological variables for comprehensive analysis, and developing predictive maintenance capabilities.
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