一种基于传感器的新型预故障检测框架在能源系统中的应用：以光伏逆变器为例

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.