Measurement error prediction-based reliability assessment framework for electric metering devices under harsh natural environments

Abstract

Reliability assessment for electric metering devices (EMDs), which includes environmental stress analysis, measurement error (ME) prediction, and reliability estimation, can be utilized for predictive instrument maintenance, especially under harsh environmental stresses. Nevertheless, the actual evaluation process is limited by data noise and the unavailability of future environmental inputs for the degradation model. To this end, we first extract the main environmental factors affecting ME and then fuse them using the weighted principal component analysis (WPCA) method to provide future environmental input values for the prediction model. Next, a bi-directional (BD) outlier detection method based on MCD robust analysis and the Thompson tau method is proposed to detect outliers from horizontal and longitudinal perspectives. Then, an ME prediction method called multi-stress fusion nonlinear degradation (MFND) is put forward, which considers the cyclic variation of ME over time and the effect of environmental stresses on ME. Real-world datasets from a high-dry-hot area manifest that our proposed BD-based MFND reliability assessment framework enjoys pleasurable prediction performance. Compared to some well- known methods, our framework excels in outlier detection and ME prediction.