The rapid development of photovoltaic (PV) technology and the growing number and size of PV power plants require increasingly efficient and intelligent health monitoring strategies to ensure reliable operation and high energy availability. Among the various techniques, Artificial Neural Network (ANN) has exhibited the functional capacity to perform the identification and classification of PV faults. In the present review, a systematic study on the application of ANN and hybridized ANN models for PV fault detection and diagnosis (FDD) is conducted. For each application, the targeted PV faults, the detectable faults, the type and amount of data used, the model configuration and the FDD performance are extracted, and analyzed. The main trends, challenges and prospects for the application of ANN for PV FDD are extracted and presented.

光伏（PV）技术的飞速发展以及光伏电站的数量和规模不断增长，要求越来越高效和智能的健康监控策略，以确保可靠的运行和高能源利用率。在各种技术中，人工神经网络（ANN）发挥了进行PV故障的识别和分类的功能。在这篇综述中，对ANN和混合ANN模型在光伏故障检测与诊断（FDD）中的应用进行了系统的研究。对于每种应用，提取并分析目标PV故障，可检测故障，使用的数据类型和数量，模型配置和FDD性能。提取并介绍了人工神经网络在光伏FDD中的应用的主要趋势，挑战和前景。