An On-Line Identification Method for Short-Circuit Impedance of Transformer Winding Based on Sudden Short Circuit Test

Wu, Yan; Gu, Lingyun; Zhang, Xue; Wang, Jinyu

doi:10.1007/978-981-13-3648-5_176

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…Ouyang et al (2018) used online monitoring of the obtained transformer port information, combined with PSO to identify all parameters of the transformer T-equivalent circuit to improve the identification accuracy, and by comparing the changes of the equivalent parameters in real time, it can effectively determine whether there is an internal fault in the transformer. Wu et al (2018) proposed an online identification method based upon the sudden short-circuit test to achieve the accurate calculation of short-circuit impedance and improve the testing capability and testing efficiency. Ecaterina and Ion (2019) proposed a transformer equivalent circuit parameter identification method for different equivalent circuits of double-winding transformers.…”

Section: Introductionmentioning

confidence: 99%

Research on Variable Weight Synthesizing Model for Transformer Condition Assessment

et al. 2022

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Transformer is one of the important equipment in the power grid, which helps to integrate renewable energy into the transmission and distribution network efficiently. The safe and stable operation of transformer is of great importance for the reliable transmission of electricity generated from renewable energy and for the reliable use of electricity by the end users. Therefore, it is important to assess the condition to avoid the faults of the transformer. In this paper, a variable weight synthesizing assessment model is presented that combines the G1 method, the entropy weight method, and a variable-weight method proposed in this paper to assess the condition of transformer based on the offset of the transformer equivalent circuit parameters. First, we propose deterioration indexes oriented to the maintenance management needs, which can well reflect the degree of deterioration of each transformer component. Second, the various defects of the transformer are used as the assessment indexes, and the initial weight is given to the assessment indexes according to the damage degree of the defect. The initial weight is calculated comprehensively by the G1 method and the entropy weight method. Then, each index is scored according to the offset of the equivalent circuit parameters, and the weights are adjusted appropriately according to the scores of the indicators using a variable weighting method to emphasize the severity of the defect or the “sub-health” condition of the transformer. Finally, the respective scores and combined weights of the assessment indexes are weighted to obtain a comprehensive score. The simulation shows that the model is more sensitive to abnormal and “subhealth” conditions of the transformer, which verifies the feasibility of the variable weight synthesizing model to assess the condtion of the transformer.

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Section: Introductionmentioning

confidence: 99%

Research on Variable Weight Synthesizing Model for Transformer Condition Assessment

et al. 2022

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“…By comparing the short-circuit impedance values before and after the transformer winding deformation, it can be judged whether the winding has been deformed or shifted, but the sensitivity to the small deformation of the winding is not high. The online short-circuit impedance method [8] calculates the short-circuit impedance value of the winding by online measuring the power frequency voltage circuit signal of the high and low-voltage sides of the transformer, and then, it judges whether the fault occurs. However, this method is not sensitive to the capacitive winding fault.…”

Section: Introductionmentioning

confidence: 99%

Research on On-Line Detection Method of Transformer Winding Deformation Based on VFTO

Wang

Zhou

Zeng

et al. 2021

Sensors

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At present, the detection of transformer winding deformation faults is carried out in an offline state, which requires the transformer to cooperate with the implementation of planned power outages, or it takes place after the sudden failure of the transformer when it is out of operation. It is difficult to obtain the status information of the windings online in time. Since the transformer will suffer very fast transient overvoltage (VFTO) impact during operation, combined with the principle of the frequency response method, an online detection method of transformer winding deformation based on VFTO is proposed. In order to study the frequency response characteristics of transformer winding under the impact of VFTO, the generation process of VFTO is simulated by simulation software, and the equivalent circuit model of transformer winding before and after deformation is established. The VFTO signal is injected into the transformer circuit model as an excitation source, and the changes of resonant frequencies of frequency response curve under different deformation types and different deformation degrees of winding are analyzed. The simulation results show that the frequency response curves of different winding deformation types are different. Different deformation degrees are simulated by increasing the radial capacitance by 4%, 13%, and 23%, series inductance by 2%, 4%, and 6%, and longitudinal capacitance by 3%, 6%, and 9%, and the change of resonance frequencies can comprehensively reflect the deformation information of winding. At the same time, the tests of different deformation types and deformation degrees of the simulated winding are carried out. The results show that with the deepening of the change degree of the simulated fault inductance value, the frequency response curve shifts to the low-frequency direction, confirming the feasibility of the online detection method of transformer winding deformation based on VFTO.

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Identification of parameters of power transformer models using artificial intelligence methods

Alyunov

Vyatkina

2023

E3S Web Conf.

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A large number of tasks for analyzing the state of power transformers are solved on the basis of mathematical models, the validity of which is undeniable. The disadvantage of standard methods for diagnosing current-carrying parts of transformers is the requirement to remove voltage. The applied diagnostic methods without stress relief require improvement in terms of increasing accuracy, speed and ensuring predictive response. The paper presents methods for identifying the parameters of mathematical models of power transformers using artificial neural networks.

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An On-Line Identification Method for Short-Circuit Impedance of Transformer Winding Based on Sudden Short Circuit Test

Cited by 4 publications

References 6 publications

Research on Variable Weight Synthesizing Model for Transformer Condition Assessment

Research on Variable Weight Synthesizing Model for Transformer Condition Assessment

Research on On-Line Detection Method of Transformer Winding Deformation Based on VFTO

Identification of parameters of power transformer models using artificial intelligence methods

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