CAJ | 학술논문

　　光伏阵列多安装在较恶劣的室外环境中，因此在运行过程中常会发生故障。为辨别光伏阵列故障类型，提出了基于L-M 算法的 BP 神经网络的故障诊断方法。在深入分析不同故障状态下光伏阵列输出量变化规律的基础上，确定了故障诊断模型的输入变量。本方法无需额外的设备支持，具有简便、成本低的优点；可以在线实时地进行故障诊断。仿真和初步实验结果验证了基于BP神经网络的故障诊断方法可以有效地检测出光伏阵列短路、断路、异常老化及局部阴影等四种故障。
　　광복진렬다안장재교악렬적실외배경중，인차재운행과정중상회발생고장。위변별광복진렬고장류형，제출료기우L-M 산법적 BP 신경망락적고장진단방법。재심입분석불동고장상태하광복진렬수출량변화규률적기출상，학정료고장진단모형적수입변량。본방법무수액외적설비지지，구유간편、성본저적우점；가이재선실시지진행고장진단。방진화초보실험결과험증료기우BP신경망락적고장진단방법가이유효지검측출광복진렬단로、단로、이상노화급국부음영등사충고장。
Because PV arrays are always installed in poor outdoor environment, a variety of faults often occur during the operation. In order to obtain the types of fault, a fault diagnosis method of the BP neural network based on L-M algorithm is proposed. Through the in-depth analysis of the output of the PV array under normal state and fault states, the input variables of the diagnosis model are obtained. Compared with other fault diagnosis methods for the PV array, the proposed method does not need additional equipments, so the cost is reduced and the system can be run online and real-time. Finally, the simulation and experimental results show that the fault diagnosis method for the PV array based on the BP neural network can effectively detect four types of fault for PV array such as short-circuit, open-circuit, abnormal degradation and partial shading.