An experimentally validated long-range-enabled fuzzy–internet of things framework for real-time and cost-efficient fault detection in photovoltaic systems

Reliable and cost-effective fault detection is essential to ensure the safety, efficiency, and long-term stability of photovoltaic (PV) systems. However, most existing diagnostic techniques remain limited to simulation studies or rely on computationally intensive algorithms unsuitable for low-power, real-time embedded environments. This article presents an experimentally validated, IEC 61724-compliant, long-range (LoRa) -enabled fuzzy–Internet of Things (IoT) framework for real-time PV fault detection and diagnosis. The proposed system integrates a custom multi-sensor hardware platform with redundant measurement channels for voltage, current, irradiance, and temperature; an Arduino Mega-based fuzzy inference engine for edge-level fault classification; and LoRa–Firebase connectivity for long-range data transmission and cloud-based visualization. Unlike many existing fuzzy-logic or IoT-based PV monitoring systems that rely primarily on simulation-based validation or cloud-dependent processing, the proposed framework integrates hardware-injected multi-subsystem fault testing, embedded edge-level intelligence, and IEC 61724-1-compliant monitoring within a deployable low-power architecture. To ensure comprehensive validation, the framework was assessed under both Python-based I–V / P–V simulations and hardware fault injection tests, including shading (25%–75%), sensor disconnection, and boost converter faults (metal oxide semiconductor field effect transistor, diode, and inductor degradation). The fuzzy logic diagnostic engine employs 25 optimized rules using trapezoidal and triangular membership functions, achieving robust resilience to noise, irradiance variation, and sensor drift. Experimental results demonstrated a mean diagnostic accuracy of 98. 7% ± 1. 2% (95% CI) and an average detection delay below 0. 5 s. Compared to traditional threshold- and model-based schemes, the proposed method reduced false positives by 12%, while maintaining real-time inference (8 ms) and minimal memory usage (2 KB). The complete 50 W prototype—comprising a PV module, pulse-width modulation controller, lead-acid battery, and custom DC–DC boost converter—was implemented at a total hardware cost of ∼185 (≈ 3. 6/W). The system's hybrid fuzzy–IoT intelligence, low-power LoRa communication, and cloud-based analytics collectively establish a scalable, cost-efficient, and empirically verified architecture for intelligent PV monitoring, bridging the gap between simulation-driven research and practical field-deployable photovoltaic diagnostic systems.