Collaborative fault detection for large-scale photovoltaic systems

Data-driven approaches have gained increasing interests in fault detection of photovoltaic systems due to the availability of sensor data. However, the noise introduced by environmental variations and measurement variabilities pose significant challenges on effective fault detection. Furthermore, the change in electrical signal magnitude of a faulty photovoltaic component is usually small, making it difficult to distinguish an anomaly from normal ones. As such, incipient faults are nearly undetectable when they cause less loss of electricity generation. This article proposes a collaborative fault detection solution based on collaborative filtering techniques. Specifically, the proposed solution first predicts photovoltaic strings' current values according to similar strings using historical data. Faults are then detected by long-term differences between the predicted and actual values. A key advantage of the proposed solution is its ability to capture similarities among different photovoltaic strings under noisy and spatial-temporally variant conditions, which significantly enhances fault detection performance. The proposed solution has been deployed in two large-scale solar farms (39.36 MWp and 51.04 MWp). The results show that the proposed solution is superior to existing data-driven solutions in terms of efficiency, effectiveness, and robustness.