基于物理信息和迁移网络的高压断路器弹簧操动机构机械参数辨识及故障诊断

doi:10.19595/j.cnki.1000-6753.tces.250521

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Abstract The new power system places high demands on the stable operation and condition monitoring of power equipment. The mechanical state of the operating mechanism of the high-voltage circuit breaker (HVCB) is crucial for ensuring the safe and stable operation of both the equipment and the power grid. Currently, the primary technical approaches to mechanical status recognition of the operating mechanism focus on identifying fault types. However, the fault state of the operating mechanism is typically caused by the combined effect of multiple components. Quantitative acquisition of mechanical parameters for key components provides a strong foundation for evaluating their states and the overall operating mechanism. This paper proposes a novel transfer network, a dual-stage convolution-transformer network with physical information (DCT-P), to identify the mechanical parameters of the closing spring, opening spring, buffer, wear level of crank arm shaft hole, and jamming conditions.
The DCT-P comprises a common feature learning network (CFLN) and a specific feature learning network (SFLN), both based on a one-dimensional convolutional neural network and a multi-head attention mechanism. CFLN and SFLN extract common knowledge between mechanical features and mechanical parameters, as well as specific knowledge related to individual mechanical parameters, respectively. In addition, based on the dynamic properties of the operating mechanism, a physics-informed loss function is defined, and a sample-weighting strategy is introduced. By learning the DCT-P, features in stroke curves and mechanical parameters can be captured.
Datasets of the mechanical characteristics of the operating mechanism are established using the mechanical characteristic simulation model. Numerical experimental results show that, compared with other models, the DCT-P model achieves superior and more balanced identification accuracy for mechanical parameters. The impact of physical information on the identification performance of DCT-P reveals that, compared to the case without physical details, the introduction of physical information reduces the mean square error and relative error by an average of 13.82% and 7.80% in the MSE and RE, respectively, while the R² obtains a relative increase of 0.89% on average. The integration of spring mechanical equations significantly enhances the physical consistency of the identified spring performance parameters. Additionally, the DCT-P model exhibits robustness to noise.
The following conclusions can be derived. (1) The DCT-P model can accurately identify the mechanical parameters of the key components in the spring operating mechanism of HVCB, with the minimum identification error reaching 0.15%. (2) Incorporating physical information into the network can improve the performance of the model and enhance the physical consistency of the identified mechanical parameters. (3) For the composite faults, based on the mechanical parameter identification results from the DCT-P model, the accuracy of simultaneously diagnosing the states of multiple components exceeds 89%. The proposed model effectively provides data support for fault diagnosis of the operating mechanism and status evaluation of key components.

Key words： High-voltage circuit breaker spring operating mechanism identification of mechanical parameter transfer learning physics-informed

Received: 01 April 2025

PACS:	TM561
	TP18

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	Zhao Chenchen
	Zhang Guogang
	Liu Jie
	Liu Lingna
	Lin Chuanqi

Cite this article:

Zhao Chenchen,Zhang Guogang,Liu Jie等. Mechanical Parameter Identification and Fault Diagnosis of Spring Operating Mechanism of High-Voltage Circuit Breaker Based on Physical Information and Transfer Network[J]. Transactions of China Electrotechnical Society, 2026, 41(6): 2073-2085.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.250521 OR https://dgjsxb.ces-transaction.com/EN/Y2026/V41/I6/2073

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