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

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.

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Publication Details

Journal
Energies
Published
2025-06-27
DOI
https://doi.org/10.3390/en18133382
Primary Topic
Power Transformer Diagnostics and Insulation
Type
article
Field-Weighted Citation Impact
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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

Kai Xiang Zhou, Bingyan Wang, Shiyu Ma, Yue Zhang et al.
Energies
Power Transformer Diagnostics and Insulation
article

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

Kai Xiang Zhou, Bingyan Wang, Shiyu Ma, Yue Zhang, Shuai Yang, Guangya Zhu
article en

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.

EnergiesVol. 18(13)
Sichuan University (CN), Dongfang Electric Corporation (China) (CN)
National Natural Science Foundation of China, Natural Science Foundation of Sichuan Province
Openalex Percentile: Top 86%
Power Transformer Diagnostics and Insulation
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