Tendency-Aided Data-Driven Method for Hot Spot Detection in Photovoltaic Systems

Hot spots are easy to appear in photovoltaic (PV) systems and even cause fires in severe cases. Therefore, under the framework of manifold learning, we develop a new data-driven method, named neighborhood slowest embedding (NSE), it is based on sufficiently extracting the trend change information caused by anomalies in the monitoring data to detect hot spots in PV systems. The NSE-based hot spot detection method has three salient advantages: 1) the sensitivity to hot spots is enhanced; 2) high computational efficiency to ensure real-time hot spot detection can be achieved; and 3) it can be applied to PV systems without mathematical models or expert knowledge. Finally, seven sets of hot spot experiments are carried out on a platform of PV systems to verify the effectiveness of the proposed method.

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