Graph-based semi-supervised learning for fault detection and classification in solar photovoltaic arrays
Graph-based semi-supervised learning for fault detection and classification in solar photovoltaic arrays
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DOI：
10.1109/ecce.2013.6646901
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发表时间：
2013-10
期刊：
影响因子：
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通讯作者：
Ye Zhao;B. Lehman;R. Ball;J. de Palma
中科院分区：
文献类型：
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作者：
Ye Zhao;B. Lehman;R. Ball;J. de Palma
太阳能光伏 (PV) 阵列中的故障检测是提高光伏系统可靠性和安全性的一项重要任务。故障分类可以识别可能的故障类型，从而加快光伏系统的恢复。然而，由于光伏阵列的非线性输出特性，使用传统的保护装置可能难以检测到多种故障。先前已提出监督学习方法来检测和分类太阳能光伏阵列。这些方法依赖于大量的标记数据来训练模型，因此存在缺陷：1）太阳能光伏阵列的标记数据很难获得或成本高昂； 2）模型需要随着环境条件的变化而更新。为了解决这些问题，本文提出了一种基于图的半监督学习（SSL）的故障检测和分类方法。所提出的方法仅使用一些标记数据点，但依赖于大量廉价的未标记数据点。该方法在实时操作中展现了自学习能力。仿真和实验结果验证了所提出的方法。
Fault detection in solar photovoltaic (PV) arrays is an essential task for increasing reliability and safety in PV systems. Fault classification allows identification of the possible fault type so that to expedite PV system recovery. However, because of the non-linear output characteristics of PV arrays, a variety of faults may be difficult to detect using conventional protection devices. Supervised learning methods have been previously proposed to detect and classify solar PV arrays. These methods rely on numerous labeled data for training models and, therefore, have drawbacks: 1) The labeled data on solar PV arrays is difficult or expensive to obtain; 2) The model requires updates as environmental conditions change. To solve these issues, this paper proposes a fault detection and classification method using graph-based semi-supervised learning (SSL). The proposed method only uses a few labeled data points, but relies instead on a large amount of inexpensive unlabeled data points. The method demonstrates self-learning ability in real-time operation. Simulation and experimental results verify the proposed method.