A Time-Series Data Analysis Methodology for Effective Monitoring of Partially Shaded Photovoltaic Systems

Abstract

The majority of photovoltaic (PV) systems in the Netherlands are small scale, and installed on residential and commercial rooftops, where different objects in many cases may lead to the presence of shading and inevitable energy loss. Nevertheless, the energy loss due to expected shadow must be distinguished from the energy loss due to other malfunctions. In this study an algorithmic tool is presented that automates the process of analyzing monitoring data of partially shaded PV systems. The algorithm compares long-term and high-resolution yield data of a partially shaded PV system with the yield data of an unshaded PV system, as reference PV system, and automatically detects the energy loss due to the expected shadow, caused by any surrounding obstacles, and distinguishes it from any additional energy loss due to other malfunctions. This study focuses on PV systems with module-level power electronics (MLPE) since these are mostly used on PV systems on rooftops. Three different cases of shaded MLPE PV systems are presented to illustrate the versatility of the methodology. Furthermore, suggestions for further research are discussed at the end of the paper.

Keywords

• photovoltaic systems
• malfunction detection
• data analysis
• cluster analysis
• partial shadow
• malfunction detection