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Identification of Abnormal data in online dissolved gas monitoring in oil based on PCA and Fast-MCD

Year: 2025

Type: article

Abstract: To detect the operational status of transformers by online monitoring of dissolved gases in oil is critical for ensuring its stable operation. However， faults in monitoring devices can result in abnormal data， severely impacting the effect of online monitoring. To address this issue， a method for identifying abnormal data in dissolved gas monitoring based on principal component analysis （PCA） and fast minimum covariance determinant （Fast-MCD） is proposed. First， the PCA is used to reduce the dimensionality of time-series data for dissolved gas in oil， effectively eliminating redundant features. Next， robust statistical theory is applied to the reduced data using Fast-MCD to identify anomalies. Finally， the identified anomalies are analyzed to determine the source of the fault. Case studies demonstrate that the proposed method effectively identifies faults in online dissolved gas monitoring devices， outperforming conventional methods with an accuracy of 99.1%.

Source: DOAJ (DOAJ: Directory of Open Access Journals)

Authors: WANG Ziling, WANG Ke, CHAI Weijian, LI Yexin, ZOU Guoping, AN Siguang

Institutions: State Grid Zhejiang Electric Power Company (China), China Jiliang University

Language: English

Topic: Fault Detection and Control Systems

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W6888476547

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