Wednesday March 28, 2018 1:00 p.m. Yost 306

Title: Data-Driven I-V Feature Extraction and Time Series Analysis for Mechanistic PV Module Degradation

Student: Xuan Ma

Advisor: Dr. Jenny Brynjarsdottir

Abstract: In the research of photovoltaic device degradation, current-voltage (I-V) data carries a large amount of information in addition to the maximum power point. Solar cell performance parameters such as short circuit current, open circuit voltage, shunt resistance, series resistance, and fill factor are essential for diagnosing the degradation of solar cells and modules. By investigating changes in these parameters over time and performing cross-comparisons between parameters, losses can be analyzed to identify specific power degradation mechanisms, thus giving useful implications to improve lifetime performance and long-term efficiency of devices. This research develops data-driven methods to extract these parameters from I-V data. In contrast to traditional work fitting diode model to I-V curves individually, the data-driven method is applied to over 2,000,000 I-V curves collected from three different climate zones over six years. And time series analysis of the extracted features are then incorporated with autoregressive-moving average model to examine degradation of the photovoltaic modules.