Power Transformer Diagnosis Based on Dissolved Gases Analysis and Copula Function

Zhang, Xiaoqin; Zhu, Hua; Li, Bo; Wu, Ruihan; Jiang, Jun

doi:10.3390/en15124192

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…Dissolved gas analysis (DGA) [3][4][5] is a critical diagnostic tool used to monitor the health of oil-immersed electrical transformers and detect latent faults. In offline DGA, oil is sampled from an operating transformer and sent to the laboratory for analysis by gas chromatography (GC), which is currently the standard method to detect the concentration of dissolved gases in oil because of its high precision and accuracy [6].…”

Section: Introductionmentioning

confidence: 99%

Rapid Online Detection of Dissolved Acetylene in Transformer Oil by Photoacoustic Spectroscopy and Membrane Degassing

Shi,

Gu,

Zhao

et al. 2024

Preprint

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Acetylene is one of the main fault gases for oil transformers. The rapid and highly sensitive detection of dissolved acetylene in the insulating oil plays an extremely important role in the diagnosis of transformer faults, as it is produced by the decomposition of hydrocarbons due to discharge and overheating. This work describes a rapid real-time online monitoring system for dissolved acetylene in oil, which integrates a highly efficient Teflon-AF2400/ceramic composite degassing module and a high-sensitive laser photoacoustic detector. Real-time online monitoring is feasible as the detection period of the device is as short as 1.5 min, and the equilibrium concentration of acetylene in the oil can be accurately determined from test data at a degassing time of 15 min. When the concentration of acetylene in the oil changes suddenly, the device can report more than 90% of the change within 30 min. The detection accuracy is improved from 0.9 µL·L− 1 to 0.3 µL·L− 1 after corrections are made to account for the influence of temperature on the oil-gas separation membrane.

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Section: Introductionmentioning

confidence: 99%

Rapid Online Detection of Dissolved Acetylene in Transformer Oil by Photoacoustic Spectroscopy and Membrane Degassing

Shi,

Gu,

Zhao

et al. 2024

Preprint

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“…DGA-based methods for analysing the health of transformer mixed insulation systems are an important element in maintaining a sustainable energy system due to the fact that a precise diagnosis of incipient faults is appropriate for sustainable development and necessary in guaranteeing a supply of electrical energy to users and consumers [22]. Besides DGA-based methods are other techniques for identifying the defects that appear in power transformers, focused on frequency response analysis (FRA), acoustic fingerprinting, ultrasound, spectrofluorimetry and Fourier transform infrared spectroscopy (FTIR) [37][38][39][40][41][42].…”

Section: Introductionmentioning

confidence: 99%

A Reviewed Turn at of Methods for Determining the Type of Fault in Power Transformers Based on Dissolved Gas Analysis

Aciu,

Enache,

Nițu

2024

Energies

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Since power transformers are the most important pieces of equipment in electricity transmission and distribution systems, special attention must be paid to their maintenance in order to keep them in good condition for a long time. This paper reviews the main steps in the process of diagnosing the health of power transformer insulation, which involves the science of analysing the gases dissolved in power transformer oil for effective identification of faults. An accurate diagnosis of incipient faults is favourable to sustainable development and necessary to maintain a reliable supply of electricity. The methods presented for fault diagnosis in mineral-oil-immersed power transformers are divided into analytical and graphical methods and have been found to be simple, economical and effective. After describing the methods, both their strengths and weaknesses were identified, and over the years, the methods were complemented to provide highly accurate information, validated by field inspections. This paper focuses on practical information and applications to manage maintenance based on accurate and up-to-date data. The contents of this paper will be of particular use to engineers who manufacture, monitor and/or use high-power transformers in the energy sector, as well as to undergraduate, master’s and PhD students interested in such applications.

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“…The transformer fault diagnosis refers to the identification of faults in the current operating state of the transformer; due to the complexity of transformer faults themselves, a combination of methods is usually used in the diagnosis process. So it is important to study the fault diagnosis method of substation for the stability and reliability of power system operation [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Research on transformer fault diagnosis based on ISOMAP and IChOA‐LSSVM

Shi

et al. 2023

IET Electric Power Appl

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Oil‐immersed transformers play an important role in the stable operation of power systems. In order to improve the accuracy of transformer fault diagnosis, a transformer fault diagnosis method based on Isomap and IChOA‐LSSVM is proposed. Firstly, Isomap is used to reduce the dimensionality of the 14‐dimensional transformer fault characteristics data to eliminate redundant data between variables. In addition, IChOA is proposed to optimise the Least Squares Support Vector Machine (LSSVM) parameters and establish an optimal diagnosis model based on LSSVM. For ChOA, three improvement methods are proposed. Circle mapping is used instead of the original population initialisation to improve population diversity. In addition, the location weighting strategy with proportional weights and the Gaussian Corsi variation strategy is proposed to improve the optimization accuracy and efficiency of ChOA. The improved ChOA is compared with the original ChOA, PSO and GWO algorithms by five benchmark test functions. Finally, the improved Chimpanzee Optimisation algorithm was used to find the parameters of the LSSVM to obtain the fault diagnosis model combining Isomap and IChOA‐LSSVM. The model is compared with PSO‐LSSVM, ChOA‐LSSVM and GWO‐LSSVM. The diagnostic accuracy is 90.83%, 81.67%, 83.33% and 80%, respectively. The results demonstrate that the proposed method can effectively improve the performance of transformer fault diagnosis.

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Power Transformer Diagnosis Based on Dissolved Gases Analysis and Copula Function

Cited by 7 publications

References 23 publications

Rapid Online Detection of Dissolved Acetylene in Transformer Oil by Photoacoustic Spectroscopy and Membrane Degassing

Rapid Online Detection of Dissolved Acetylene in Transformer Oil by Photoacoustic Spectroscopy and Membrane Degassing

A Reviewed Turn at of Methods for Determining the Type of Fault in Power Transformers Based on Dissolved Gas Analysis

Research on transformer fault diagnosis based on ISOMAP and IChOA‐LSSVM

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