Photovoltaic system fault detection and diagnostics using Laterally Primed Adaptive Resonance Theory neural network

Cost effective integration of solar photovoltaic (PV) systems requires increased reliability. This can be achieved with a robust fault detection and diagnostic (FDD) tool that automatically discovers faults. This paper introduces the Laterally Primed Adaptive Resonance Theory (LAPART) artificial neural network to perform this task. The present work tested the algorithm on actual and synthetic data to assess its potential for wide spread implementation. The tests were conducted on a PV system located in Albuquerque, New Mexico. The system was composed of 14 modules arranged in a configuration that produced a maximum power of 3.7kW. The LAPART algorithm learned system behavior quickly, and detected module level faults with minimal error.