Радіоелектронні і комп'ютерні системи (May 2025)
Enhanced fire hazard detection in solar power plants: an integrated UAV, AI, and SCADA-based approach
Abstract
The Subject of this research is the development of an intelligent, integrated system for the early detection and causal analysis of fire hazards in large-scale solar power plants (SPPs). It addresses the critical shortcomings of conventional monitoring methods, which often lack the necessary integration, speed, and diagnostic depth to reliably prevent catastrophic failures resulting from photovoltaic (PV) module defects. The goal of this study is to design, develop, and validate a comprehensive, multi-modal framework that fully automates the monitoring workflow, from data acquisition to actionable decision-making. The proposed system aims to significantly enhance plant safety by providing reliable, cause-differentiated alerts, which in turn optimizes maintenance strategies, minimizes downtime, and improves the overall economic viability of solar energy infrastructure. The Methods employed involve a synergistic architecture that combines an Unmanned Aerial Vehicle (UAV) equipped with high-resolution RGB and radiometric infrared cameras for rapid imaging, supplemented by dedicated Internet of Things (IoT) temperature sensors on PV module bypass diodes for critical component verification. A custom-trained YOLOv8 deep learning model performs automated defect detection from the captured imagery. The system’s intellectual core is a novel logical inference engine based on a Disjunctive Normal Form (DNF) equation. This formal logic model intelligently fuses four key binary features, namely, primary defect cause (damage vs. soiling), visual evidence, thermal anomaly severity, and bypass diode functional status, to produce a definitive and context-aware fire risk assessment. The entire workflow is managed and visualized using a SCADA TRACE MODE platform for centralized control and automated alerting. The study successfully validated the performance and logical integrity of the integrated system through a series of high-fidelity, scenario-based simulations. These simulations rigorously confirmed the capability of the DNF logic to accurately and reliably identify all predefined fire hazards. This included not only obvious faults but also "stealthy," damage-induced hotspots where the primary safety mechanism (the bypass diode) had failed. Concurrently, the system correctly classified mitigated risks to prevent false alarms, demonstrating its diagnostic precision. This capability allows the system to reliably differentiate between true emergencies requiring immediate module replacement and less critical issues, such as soiling that merely necessitates cleaning. The projected increase in diagnostic accuracy for identifying critical, fire-prone defects over conventional, single-modality methods is up to 40%, providing a quantitative measure of enhanced safety and reliability. Furthermore, the proposed system is projected to reduce the false-positive alarm rate by over 75% compared with IR-only automated systems. In conclusion, this study establishes a powerful new paradigm for proactive SPP safety management. The intelligent fusion of UAV and IoT sensing, AI-driven analytics, and a formal logical framework provides a robust and reliable solution for fire risk mitigation, enabling a highly efficient, condition-based maintenance strategy and significantly enhancing the safety, reliability, and performance of modern solar power infrastructure.
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