Study on optimization of sampling inspection strategy for distribution transformer

Chen, Jinmeng; Li, Yang; Zhang, Jingqing; Chen, Lingtian

doi:10.1109/appeec.2016.7779654

Cited by 2 publications

(2 citation statements)

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“…Sampling inspection is the main method used to control the quality risk for the users, and it is conducted according to regulations of the power grid in purchasing. Many testing items and procedures exist in the inspections of DTs [2]- [4]. Due to the abnormal operation of measuring instruments, the interference of environmental factors, the negligence of the inspectors and other special circumstance factors, measuring errors and implausible testing reports would possibly occur, thus leading to misjudgment in DT inspection.…”

Section: Introductionmentioning

confidence: 99%

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Flagging Implausible Inspection Reports of Distribution Transformers via Anomaly Detection

Xiang¹,

Liu

Zhang³

2020

IEEE Access

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Distribution transformer is the key equipment in the distribution power grid, and sampling inspection is the main method used to ensure quality control. Inspection comprises many testing processes. Due to various reasons, the testing results may occasionally have errors and be implausible. Only a few errors can be detected empirically by inspectors. To solve this problem, anomaly detection is proposed in this paper to determine implausible inspection reports and assist in re-inspecting. The well-known and representative anomaly detection algorithms, which are now used in many application domains are introduced. These algorithms include single Gaussian distribution and multivariate Gaussian distribution, local outlier factor, one-class SVM. Based on the distribution transformer inspection reports collected, anomaly samples are constructed manually. Train datasets and test datasets are then formulated. By comparing the testing results, the one-class SVM algorithm can detect anomaly samples in testing datasets correctly. Thus, it can be used to distinguish the abnormal samples (i.e., reports with measurement errors suspected) in the inspection reports of distribution transformer, which can help inspectors complete their inspecting work correctly and effectively.

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Section: Introductionmentioning

confidence: 99%

“…The typical anomaly detection algorithms are introduced and applied to flag the abnormal report samples in DT inspection. 4. By comparing the testing results, the one-class SVM algorithm can detect anomaly samples correctly and flag the implausible inspection reports.…”

Section: Introductionmentioning

confidence: 99%