Efficient anomaly detection method for rooftop PV systems using big data and permutation entropy

TL;DR

This paper introduces an efficient anomaly detection method for rooftop PV systems using big data and permutation entropy, enabling fault identification without additional sensors, demonstrated on real-world data from Australian households.

Contribution

The paper presents a novel, sensor-free anomaly detection approach using weighted permutation entropy on big data from rooftop PV systems, tailored for large-scale regional deployment.

Findings

Successfully detected faulty PV systems in real-world data

Method requires no additional sensors or devices

Faulty systems identified correlated with actual issues

Abstract

The number of rooftop photovoltaic (PV) systems has significantly increased in recent years around the globe, including in Australia. This trend is anticipated to continue in the next few years. Given their high share of generation in power systems, detecting malfunctions and abnormalities in rooftop PV systems is essential for ensuring their high efficiency and safety. In this paper, we present a novel anomaly detection method for a large number of rooftop PV systems installed in a region using big data and a time series complexity measure called weighted permutation entropy (WPE). This efficient method only uses the historical PV generation data in a given region to identify anomalous PV systems and requires no new sensor or smart device. Using a real-world PV generation dataset, we discuss how the hyperparameters of WPE should be tuned for the purpose. The proposed PV anomaly detection method is then tested on rooftop PV generation data from over 100 South Australian households. The results demonstrate that anomalous systems detected by our method have indeed encountered problems and require a close inspection. The detection and resolution of potential faults would result in better rooftop PV systems, longer lifetimes, and higher returns on investment.