SunDown: Model-driven Per-Panel Solar Anomaly Detection for Residential Arrays

• Main Author: Feng, Menghong
• Format: Others
• Published: ScholarWorks@UMass Amherst 2020
• Subjects: Solar energy; machine learning; data-driven; data science; energy efficiency; Computer and Systems Architecture; Energy Systems
• Online Access: https://scholarworks.umass.edu/masters_theses_2/894; https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1939&context=masters_theses_2

There has been significant growth in both utility-scale and residential-scale solar installa- tions 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 thesis, 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 ap- proach that leverages correlations between the power produced by adjacent panels to de- tect 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 our approach has a MAPE of 2.98% when predicting per-panel output. Our results also 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 multi- ple concurrent faults with 97.2% accuracy.