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Volume 18
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10.3390/en18133382
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Article

Identification of Dielectric Response Parameters of Pumped Storage Generator-Motor Stator Winding Insulation Based on Sparsity-Enhanced Dynamic Decomposition of Depolarization Current

by
Guangya Zhu
1,
Shiyu Ma
1,
Shuai Yang
2,*,
Yue Zhang
2,
Bingyan Wang
1 and
Kai Zhou
1
1
College of Electrical Engineering, Sichuan University, Chengdu 610065, China
2
Dongfang Electric Machinery Co., Ltd., Deyang 618000, China
*
Author to whom correspondence should be addressed.
Energies 2025, 18(13), 3382; https://doi.org/10.3390/en18133382
Submission received: 9 May 2025 / Revised: 16 June 2025 / Accepted: 19 June 2025 / Published: 27 June 2025
(This article belongs to the Special Issue Electrical Equipment State Measurement and Intelligent Calculation)
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Abstract

Accurate diagnosis of the insulation condition of stator windings in pumped storage generator-motor units is crucial for ensuring the safe and stable operation of power systems. Time domain dielectric response testing is an effective method for rapidly diagnosing the insulation condition of capacitive devices, such as those in pumped storage generator-motors. To precisely identify the conductivity and relaxation process parameters of the insulating medium and accurately diagnose the insulation condition of the stator windings, this paper proposes a method for identifying the insulation dielectric response parameters of stator windings based on sparsity-enhanced dynamic mode decomposition of the depolarization current. First, the measured depolarization current time series is processed through dynamic mode decomposition (DMD). An iterative reweighted L1 (IRL1)-based method is proposed to formulate a reconstruction error minimization problem, which is solved using the ADMM algorithm. Based on the computed modal amplitudes, the dominant modes—representing the main insulation relaxation characteristics—are separated from spurious modes caused by noise. The parameters of the extended Debye model (EDM) are then calculated from the dominant modes, enabling precise identification of the relaxation characteristic parameters. Finally, the accuracy and feasibility of the proposed method are verified through a combination of simulation experiments and laboratory tests.
Keywords: polarization/depolarization current method; dynamic mode decomposition; ADMM algorithm; extended Debye model; pumped storage generator-motor; winding insulation polarization/depolarization current method; dynamic mode decomposition; ADMM algorithm; extended Debye model; pumped storage generator-motor; winding insulation

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MDPI and ACS Style

Zhu, G.; Ma, S.; Yang, S.; Zhang, Y.; Wang, B.; Zhou, K. Identification of Dielectric Response Parameters of Pumped Storage Generator-Motor Stator Winding Insulation Based on Sparsity-Enhanced Dynamic Decomposition of Depolarization Current. Energies 2025, 18, 3382. https://doi.org/10.3390/en18133382

AMA Style

Zhu G, Ma S, Yang S, Zhang Y, Wang B, Zhou K. Identification of Dielectric Response Parameters of Pumped Storage Generator-Motor Stator Winding Insulation Based on Sparsity-Enhanced Dynamic Decomposition of Depolarization Current. Energies. 2025; 18(13):3382. https://doi.org/10.3390/en18133382

Chicago/Turabian Style

Zhu, Guangya, Shiyu Ma, Shuai Yang, Yue Zhang, Bingyan Wang, and Kai Zhou. 2025. "Identification of Dielectric Response Parameters of Pumped Storage Generator-Motor Stator Winding Insulation Based on Sparsity-Enhanced Dynamic Decomposition of Depolarization Current" Energies 18, no. 13: 3382. https://doi.org/10.3390/en18133382

APA Style

Zhu, G., Ma, S., Yang, S., Zhang, Y., Wang, B., & Zhou, K. (2025). Identification of Dielectric Response Parameters of Pumped Storage Generator-Motor Stator Winding Insulation Based on Sparsity-Enhanced Dynamic Decomposition of Depolarization Current. Energies, 18(13), 3382. https://doi.org/10.3390/en18133382

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