Model-driven Per-panel Solar Anomaly Detection for Residential Arrays
Model-driven Per-panel Solar Anomaly Detection for Residential Arrays
复制标题
模型驱动的住宅阵列每面板太阳异常检测
DOI：
10.1145/3460236
复制
发表时间：
2021
影响因子：
2.3
通讯作者：
Kosanovic, Beka
中科院分区：
文献类型：
--
作者：
Feng, Menghong;Bashir, Noman;Shenoy, Prashant;Irwin, David;Kosanovic, Beka
There has been significant growth in both utility-scale and residential-scale solar installations in recent years, driven by rapid technology improvements and falling prices. Unlike utility-scale solar farms that are professionally managed and maintained, smaller residential-scale installations often lack sensing and instrumentation for performance monitoring and fault detection. As a result, faults may go undetected for long periods of time, resulting in generation and revenue losses for the homeowner. In this article, we present SunDown, a sensorless approach designed to detect per-panel faults in residential solar arrays. SunDown does not require any new sensors for its fault detection and instead uses a model-driven approach that leverages correlations between the power produced by adjacent panels to detect deviations from expected behavior. SunDown can handle concurrent faults in multiple panels and perform anomaly classification to determine probable causes. Using two years of solar generation data from a real home and a manually generated dataset of multiple solar faults, we show that SunDown has a Mean Absolute Percentage Error of 2.98% when predicting per-panel output. Our results show that SunDown is able to detect and classify faults, including from snow cover, leaves and debris, and electrical failures with 99.13% accuracy, and can detect multiple concurrent faults with 97.2% accuracy.
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