Slow Manifold Analysis-Based Detection of Hot Spots in Photovoltaic Systems
Chao Cheng, Ming Liu, Hui Yi, Guangtao Ran, Hongtian Chen
Abstract
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. Whilst, they are difficult to detect because of the slight anomaly symptoms. In this paper, 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; 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 are verified via nine sets of HSs on PV experimental platforms.