Advanced signal processing techniques for monitoring east/west oriented solar photovoltaic systems: A case study

Abstract

Solar photovoltaic (PV) systems are increasingly recognized as crucial sustainable energy sources with diverse applications. Their implementation leverages rapid advancements in material engineering, communication systems, and computational intelligence tools. This paper focuses on selected mathematical methods for analyzing time series of power generated by PV systems, including numerical methods and algorithms for multichannel signal processing, digital filtering, and signal feature extraction. These methods monitor the characteristics of individual PV panels and identify their feature clusters. Specifically, it examines systems with east/west oriented photovoltaic panels, employing statistical methods and computational tools to analyze power signals, assess time and positioning data, evaluate symmetry coefficients, and apply machine learning tools to detect potential panel failures. Additionally, a general graphical user interface for data analysis is proposed. A detailed case study is presented, analyzing the distribution of selected features over time segments of a PV system comprising seven east-oriented and seven west-oriented panels, with data recorded over a selected set of days at a sampling rate of 15 minutes. The results reveal distinct and well-separated feature clusters for healthy PV panels. General conclusions underscore the effectiveness of signal processing tools in the statistical analysis of PV systems and the potential of feature clustering and symmetry estimation for evaluating disorders of system behaviour using communication technologies, data storage, and remote system monitoring.