Slow Manifold Analysis-Based Detection of Hot Spots in Photovoltaic Systems

Hot spots (HSs) in the early stage can corrupt the generation efficiency of photovoltaic (PV) systems, whose evolution may cause fire hazards as time goes on. They are difficult to detect because of slight anomaly symptoms. In this article, we propose a novel data-driven detection method of HSs, named as slow manifold analysis (SMA), for PV systems. SMA sufficiently extracts the nonlinear information hidden in monitoring data from PV modules to detect HSs. The salient strengths of the SMA-based detection method are: 1) the algorithm is of high computational efficiency, which can meet requirements of real-time detection; 2) it can fully mine the information of operation status changes caused by HSs in the early stage; and 3) the proposed method without using physical models nor expert knowledge can be directly applied to PV systems. Finally, the effectiveness of the designed scheme is verified theough nine sets of HSs on PV experimental platforms.

Automated summary

In this article, a data-driven detection method based on slow manifold analysis (SMA) is proposed for detecting hot spots in photovoltaic (PV) systems. This method can extract the nonlinear information hidden in monitoring data from PV modules and has high computational efficiency and applicability.