This study proposes a new fault diagnosis algorithm for a photovoltaic (PV) solar system implemented on the bases of diode distribution and two classifiers based on ensemble bagged trees and neural pattern recognition techniques. The proposed approach is designed by adding two diodes connected to the upper and lower terminals of each PV string. The two diodes are connected to prevent the reverse direction of faulted string currents, thereby preventing high injecting fault currents. Given that the string fault current is prevented owing to diode interaction, this dynamic interaction provides new states during the fault periods considered to detect string faults. Owing to the distributed diodes, system voltage is not reduced during fault disturbances and voltage is not considered an input to the proposed detection techniques. Monitoring currents at the upper and lower ends of each string is required as input for the proposed approaches to detect string fault types. The proposed approaches are used to detect string fault types, such as cell-to-string negative terminal, cell-to-cell in the same string, and string-to-string faults. The proposed model is applied for 400 kW, and the PV system consists of 4 arrays interconnected with 1200 V AC grid built-in MATLAB/Simulink. Experimental results validate the newly suggested approaches.

这项研究提出了一种新的光伏（PV）太阳系故障诊断算法，该算法基于二极管分布和基于集成袋装树和神经模式识别技术的两个分类器实现。通过添加两个连接到每个PV串的上端子和下端子的二极管来设计提出的方法。连接两个二极管以防止出现故障的串电流反向，从而防止注入高故障电流。假设由于二极管的相互作用而阻止了串故障电流，则这种动态相互作用在被认为是检测串故障的故障期间提供了新的状态。由于采用了分布式二极管，因此在故障干扰期间不会降低系统电压，并且电压也不会被视为所提出的检测技术的输入。要求在每个串的上端和下端监视电流，作为提出的检测串故障类型的方法的输入。所提出的方法用于检测字符串故障类型，例如电池到电池串的负极端子，同一字符串中的电池到电池以及字符串到电池串的故障。所提出的模型适用于400 kW，光伏系统由4个阵列组成，这些阵列与1200 V AC电网内置的MATLAB / Simulink互连。实验结果验证了新提出的方法。光伏系统由4个阵列组成，这些阵列与1200 V交流电网内置的MATLAB / Simulink互连。实验结果验证了新提出的方法。光伏系统由4个阵列组成，这些阵列与1200 V交流电网内置的MATLAB / Simulink互连。实验结果验证了新提出的方法。