A novel sensor-driven framework for preemptive failure detection in energy systems: Application to photovoltaic inverters

Abstract

Effective asset monitoring in energy systems is essential for minimizing the levelized cost of energy, as failures can lead to significant energy losses and expensive repairs. This paper introduces a modular industrial framework for detecting failures preemptively in energy systems. The framework consists of three main modules: preprocessing of autonomous sensor data, mitigating external influences, and flagging failure risks. The first module applies data cleaning, transformation, calibration, and feature engineering techniques to refine raw sensor data for subsequent analysis. The second module minimizes the influence of external variables such as environmental and operational variables on the sensor signals. The third module utilizes advanced ensemble methods to detect anomalies indicative of potential failures. This study underscores the critical role of preprocessing in enhancing data quality and validates the framework’s effectiveness through a real-world case study involving photovoltaic (PV) inverters. The results demonstrate the framework’s ability to accurately identify inverters at risk of failure, enabling timely maintenance and reducing downtime.