Photovoltaic Inverter Fault Prediction Technology Based on t-SNE Manifold Learning and Fast Clustering Algorithm

doi:10.11930/j.issn.1004-9649.201902032

Abstract

Abstract: As the key component of solar photovoltaic power generation system, photovoltaic inverter directly affects the safety and stable operation of power systems. Therefore, a photovoltaic inverter fault prediction technology is proposed based on t-SNE manifold learning and fast clustering algorithm. Firstly, the historical monitoring data of the PV inverter cluster are used to construct the original feature database. Secondly, the t-SNE dimensionality reduction algorithm is applied to extract the main feature matrix of the PV inverter cluster. Thirdly, the cluster center PV inverter at each sampling time is searched by the fast clustering algorithm, and the eccentricity distance of each inverter at the sampling time is calculated respectively. Then, the normalized accumulative eccentricity distance matrix is obtained. By setting a rational warning threshold, the accurate fault prediction is thus realized for photovoltaic inverters. Finally, the algorithm is tested with the collected PV inverter cluster data acquired by the distributed photovoltaic generation monitoring system. The results show that the proposed fault prediction technology can accurately predict the photovoltaic inverter faults in advance, which is helpful to ensure the healthy and stable operation of the equipment.

Key words: fault prediction, manifold learning, clustering algorithm, inverter, photovoltaic

ZHANG Xiaochen, ZHU Jinda, YANG Dongmei, CHEN Yonghua, DU Wei. Photovoltaic Inverter Fault Prediction Technology Based on t-SNE Manifold Learning and Fast Clustering Algorithm[J]. Electric Power, 2020, 53(6): 41-47.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.201902032

https://www.electricpower.com.cn/EN/Y2020/V53/I6/41

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