基于神经网络和控制图法的换流变铁心夹件电流异常诊断方法

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Abstract The online monitoring system for converter transformers is a system that evaluates the transformer condition based on characteristic parameters. This system records various parameters during the operation of the converter transformer, including gas composition in the oil, SF₆ gas pressure in the bushings, and leakage current in the core clamps. Among these, the core clamp current is a crucial indicator for determining the grounding condition and insulation strength of the transformer core. However, the internal electromagnetic environment of a converter transformer is quite complex during operation, and due to the limitations of sensor precision and operational conditions, the traditional threshold-based abnormal diagnosis methods for core clamp current have a high false alarm rate, posing challenges for refined operation and maintenance. This paper establishes a neural network-based core clamp current prediction model and uses the error between the predicted values and the online values as the observation metric. An abnormal diagnosis method for core clamp current based on the control chart method is proposed. The feasibility of this method is validated using core clamp current data from a ±800kV converter station. The experimental results show that the proposed method can avoid false alarms and accurately diagnose true alarms.

Key words： converter transformer core and clamps current neural network multi-layer perceptron control chart method anomaly diagnosis

Received: 04 December 2024

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	Articles by authors
	YU Yang
	CHEN Yi
	ZHANG Bao
	ZHONG Jinlong
	WANG Jing

Cite this article:

YU Yang,CHEN Yi,ZHANG Bao等. The diagnosis method for abnormal current in converter transformer core and clamps based on neural networks and control chart method[J]. Electrical Engineering, 2025, 26(4): 65-72.

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https://dqjs.cesmedia.cn/EN/Y2025/V26/I4/65

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