SunDown: Model-driven Per-Panel Solar Anomaly Detection for Residential Arrays
SunDown: Model-driven Per-Panel Solar Anomaly Detection for Residential Arrays
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SunDown：模型驱动的住宅阵列每面板太阳异常检测
DOI：
10.1145/3378393.3402257
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Kosanovic, Dragoljub
中科院分区：
文献类型：
--
作者：
Feng, Menghong;Bashir, Noman;Shenoy, Prashant;Irwin, David;Kosanovic, Dragoljub
Solar arrays often experience faults that go undetected for long periods of time, resulting in generation and revenue losses. In this paper, we present SunDown, a sensorless approach for detecting per-panel faults in solar arrays. SunDown's model-driven approach leverages correlations between the power produced by adjacent panels to detect deviations from expected behavior, can handle concurrent faults in multiple panels, and performs anomaly classification to determine probable causes. Using two years of solar data from a real home and a manually generated dataset of solar faults, we show that our approach is able to detect and classify faults, including from snow, leaves and debris, and electrical failures with 99.13% accuracy, and can detect concurrent faults with 97.2% accuracy.
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