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

## TL;DR

This paper introduces an unsupervised machine learning method for PV system degradation analysis that relies solely on power data, demonstrating improved robustness and reduced data needs compared to traditional approaches.

## Contribution

The paper presents a novel data-driven approach for PV degradation analysis that does not depend on physical models or additional environmental data.

## Key findings

- Validated on NREL dataset with comparable accuracy to existing methods
- Showed increased robustness to data anomalies
- Reduced data requirements for degradation analysis

## Abstract

A novel unsupervised machine learning approach for analyzing time-series data is applied to the topic of photovoltaic (PV) 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. We present results on a data set that was previously analyzed and presented by NREL 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.