期刊
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
卷 69, 期 9, 页码 9575-9585出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2021.3114740
关键词
Measurement errors; Stress; Kernel; Temperature measurement; Degradation; Reliability; Companies; Extreme environmental stresses; improved kernel support vector regression (ILOF); kernel support vector regression (KSVR); measurement error assessment; power metering equipment (PME)
资金
- National Key R, and D Program of China [2019YFF0216800]
- Technology Project of State Grid Corporation of China [SGXJYX00ZJJS2100048, 5230HQ19000F]
- National Natural Science Foundation of China [52077067]
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.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据