Measurement Error Prediction of Power Metering Equipment Using Improved Local Outlier Factor and Kernel Support Vector Regression

The measurement error evaluation of power metering equipment (PME) is significant for the instrument design and accurate metering of electric energy, especially under extreme environmental stresses. However, actual measurement error assessment is often disturbed by the environmental noise and insufficient input information. To address this problem, an improved local outlier factor (ILOF) method is first presented to detect potential outliers. And an optimized distance function and adaptive threshold constraint method based on box plot are used to improve the outlier detection performance of ILOF. Next, an error prediction method, namely kernel support vector regression (KSVR), is presented to fuse measurement error and multiple extreme environmental stresses by using the proposed kernel approach. Integrating the ILOF and KSVR, examples from the extreme environmental region demonstrate that the proposed evaluation framework has a higher assessment performance. Compared with several state-of-art prediction methods, our framework has profound outlier identification and error prediction performance under small sample conditions.

Automated summary

This paper presents an improved local outlier factor (ILOF) method to detect potential outliers, and an optimized distance function and adaptive threshold constraint method are used to improve the outlier detection performance of ILOF. Additionally, a kernel support vector regression (KSVR) method is proposed to fuse measurement error and multiple extreme environmental stresses. The evaluation framework combining ILOF and KSVR demonstrates higher assessment performance compared to other methods.