Quickest Fault Detection in Photovoltaic Systems

被引:61
|
作者
Chen, Leian [1 ]
Li, Shang [1 ]
Wang, Xiaodong [1 ]
机构
[1] Columbia Univ, Dept Elect Engn, New York, NY 10027 USA
基金
美国国家科学基金会;
关键词
Photovoltaic (PV) array; fault detection; autoregressive model; generalized local likelihood; quickest detection; POWER POINT TRACKING; PV SYSTEMS;
D O I
10.1109/TSG.2016.2601082
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Photovoltaic (PV) systems play an important role in contemporary electricity production as a ubiquitous renewable energy source. However, the performance of a PV system is susceptible to unexpected faults that occur inside its various components. In this paper, we propose a quickest fault detection algorithm for PV systems under the sequential change detection framework. In particular, multiple meters are employed to measure different output signals of the PV system. The time correlation of the faulty signal and the signal correlation among different meters are exploited by a vector AR model in modeling the post-change signal. In order to tackle the difficulty that no prior knowledge about the fault is available, we develop a change detection algorithm based on the generalized local likelihood ratio test. Extensive simulation results demonstrate that the proposed method achieves high adaptivity and fast detection in dealing with various types of faults in PV systems.
引用
收藏
页码:1835 / 1847
页数:13
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