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Dynamic Monitoring and Optimization of Fault Diagnosis of Photo Voltaic Solar Power System Using ANN and Memetic Algorithm

机译：基于人工神经网络和模因算法的光伏太阳能发电系统动态监测与故障诊断优化

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摘要

Most of the photo voltaic (PV) arrays often work in harsh outdoor environment, and undergo various faults, such as local material aging, shading, open circuit, short circuit and so on. The generation of these faults will reduce the power generation efficiency, and when a fault occurs in a PV model, the PV model and the systems connected to it are also damaged. In this paper, an on-line distributed monitoring system based on XBee wireless sensors network is designed to monitor the output current, voltage and irradiation of each PV module, and the temperature and the irradiation of the environment. A simulation PV module model is established, based on which some common faults are simulated and fault training samples are obtained. Finally, a memetic algorithm optimized Back Propagation ANN fault diagnosis model is built and trained by the fault samples data. Experiment result shows that the system can detect the common faults of PV array with high accuracy.
机译：大多数光伏（PV）阵列通常在恶劣的室外环境中工作，并会遇到各种故障，例如局部材料老化，遮光，开路，短路等。这些故障的产生会降低发电效率，并且当PV模型中发生故障时，PV模型及其连接的系统也会受到损坏。本文设计了一种基于XBee无线传感器网络的在线分布式监控系统，以监控每个PV模块的输出电流，电压和辐射以及温度和环境的辐射。建立了模拟光伏组件模型，在此基础上对一些常见故障进行了仿真，并获得了故障训练样本。最后，通过故障样本数据建立并训练了优化的模因算法反向传播神经网络故障诊断模型。实验结果表明，该系统能够准确地检测出光伏阵列的常见故障。

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