Signal Processing on PV Time-Series Data: Robust Degradation Analysis Without Physical Models

Abstract

A novel unsupervised machine learning approach for analyzing time-series data is applied to the topicofphotovoltaic system degradation rate estimation, sometimes referred to as energy yield degradation analysis. This approach only requires a measured power signal as an input-no irradiance data, temperature data, or system configuration information are required. We present results on a dataset that was previously analyzed and presented by National Renewable Energy Laboratory using RdTools, validating the accuracy of the new approach and showing increased robustness to data anomalies while reducing the data requirements to carry out the analysis.