基于无线传感器网络的太阳能电站故障检测与定位：一种实验方法

This article introduces a wireless sensor network (WSN) system that enhances photovoltaic (PV) performance during shading through real-time fault detection and adaptive optimisation. The solution employs five strategically placed sensors and a Chipkit Max32-based algorithm that: 1) detects shading faults with 98.2% accuracy via dynamic voltage-current correlation, 2) localises faults at 95.7% precision using impedance mapping, and 3) recommends bypass actions to limit power loss. Experimental results demonstrate a 27.4% improvement in power output under shading versus conventional systems, with 1.8-s response times (60% faster than academic alternatives) at $23/node (62% cheaper than commercial optimisers). Key innovations include a hybrid series-parallel classification matrix and adaptive current weighting, which reduces false alarms by 41%. The system maintains more than 94% of the maximum power point during dynamic shading, as validated through comparative analysis with seven state-of-the-art methods. This work establishes a new standard for cost-effective, real-time PV monitoring while resolving critical WSN limitations in sensor density and shading response.