Identify and Locating the Faults in the Photovoltaic Array Using Neural Network

Gigih Surya Adi Pratama, Hendik Eko Hadi Suharyanto, Y. C. Arif
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引用次数: 2

Abstract

In making the PV array system work optimally without a hitch, it is important to recognize and know where the fault occurs. The current and voltage represent the conditions of a PV array, so that, in this paper, the proposed method is based on the current and voltage values for each string, four identified conditions, namely free fault conditions, partial shading, short circuit and open circuit. Neural network is used as a tool for predicting the type and location of faults, fault samples are obtained from simulations through PSIM and the learning process is carried out through MATLAB/Simulink, the algorithms used in the learning process are also compared to see which are the best. As a result, neural network was able to identify the type and location of faults on the PV array. This proves that the condition of a PV array can be explained through its voltage and current values. Keyword: PV array, partial shading, short circuit, open circuit, neural network Normal 0 false false false EN-US X-NONE X-NONE
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基于神经网络的光伏阵列故障识别与定位
为了使光伏阵列系统无故障地最佳工作,识别和了解故障发生的位置非常重要。电流和电压代表了光伏阵列的状态,因此,本文提出的方法是基于每个串的电流和电压值,识别出四种状态,即无故障状态、部分遮阳状态、短路状态和开路状态。利用神经网络作为预测故障类型和定位的工具,通过PSIM仿真获得故障样本,并通过MATLAB/Simulink进行学习过程,比较学习过程中使用的算法,找出最佳算法。结果表明,神经网络能够识别光伏阵列故障的类型和位置。这证明了光伏阵列的状态可以通过其电压和电流值来解释。关键词:光伏阵列,部分遮阳,短路,开路,神经网络正常0假假假EN-US X-NONE X-NONE
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