Data-Driven Detection of Hot Spots in Photovoltaic Energy Systems

Abstract

Hot spots are common abnormalities in photovoltaic (PV) energy systems. Their presence can potentially cause damage to PV modules, such as performance degradation or even unexpected fire to PV energy systems. By sufficiently mining the information hidden in the test data collected from PV modules, this paper develops a space-to-space projection method, which at its core is a linear approach via preserving the locally geometrical structure with respect to time series. Based on the nonlinear model of PV modules established via the proposed projection, data-driven detection of hot spots in PV energy systems can be directly achieved with three key advantages: 1) its implementation does not depend on any mathematical model or physical knowledge of PV energy systems; 2) it is of high-computational efficiency especially in the online detection phase; and 3) it can capture the dynamic characteristic because the local structure of samplings regarding time is given sufficient consideration. The effectiveness and feasibility of the proposed approach are first presented by theoretical analysis and, then, convictively demonstrated via 15 sets of hot spot experiments on practical PV modules.