Gas generation of cellulose insulation in palm fatty acid ester and mineral oil for life prediction marker in nitrogen-sealed transformers

Tokunaga, Junko; Koide, Hidenobu; Mogami, Kenji; Hikosaka, Tomoyuki

doi:10.1109/tdei.2016.005665

Cited by 15 publications

(3 citation statements)

References 18 publications

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“…China, along with the rest of the world, is rapidly entering a UHV era. As the properties of insulating oil and its aging by-product were proved to have close relation to the operation life of power transformers [13][14][15][16][17]. The continuous rise of the voltage level of power grids and continuous increase of loads mean that insulating materials now face unprecedented challenges.…”

Section: High Antioxidation Stabilitymentioning

confidence: 99%

Review of Research Progress on the Electrical Properties and Modification of Mineral Insulating Oils Used in Power Transformers

et al. 2018

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Abstract:In November 2017, the first ±1100 kV high-voltage direct-current power transformer in the world, which was made by Siemens in Nurnberg, passed its type test. Meanwhile, in early 2017, a ±1000 kV ultra-high voltage (UHV) substation was officially put into operation in Tianjin, China. These examples illustrate that the era of UHV power transmission is coming. With the rapid increase in power transmission voltage, the performance requirements for the insulation of power transformers are getting higher and higher. The traditional mineral oils used inside power transformers as insulating and cooling agents are thus facing a serious challenge to meet these requirements. In this review, the basic properties of traditional mineral insulating oil are first introduced. Then, the variation of electrical properties such as breakdown strength, permittivity, and conductivity during transformer operation and aging is summarized. Next, the modification of mineral insulating oil is investigated with a focus on the influence of nanoparticles on the electrical properties of nano-modified insulating oil. Recent studies on the performance of mineral oil at molecular and atomic levels by molecular dynamics simulations are then described. Finally, future research hotspots and notable research topics are discussed.

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Section: High Antioxidation Stabilitymentioning

confidence: 99%

Review of Research Progress on the Electrical Properties and Modification of Mineral Insulating Oils Used in Power Transformers

et al. 2018

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“…To test the effectiveness of the proposed assistant triangle in distinguishing characteristic gases generated by vibration from those generated by discharge and thermal faults, S. M. Korobeynikov's results and 117 cases of identified fault types were put into the triangle [1,5,14,21,[46][47][48][49][50]. Figure 12 shows that data of vibration-induced characteristic gas fell in the V zone, and most of the other fault data fell in the corresponding fault zones.…”

Section: Diagnostic Methods Of Vibration-induced Characteristic Gasmentioning

confidence: 99%

Investigation on gas generation characteristics in transformer oil under vibration

Guo

Zhang

et al. 2022

IET Generation Trans & Dist

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Operating experience has shown that characteristic gases can be found in oil-immersed power transformers and high-voltage reactors under strong vibration without discharge or thermal faults occurring. This will cause misdiagnosis of Dissolved Gases Analysis (DGA). In this study, generation characteristics and mechanism of characteristic gases in transformer oil dependent on vibration are investigated. The results show that there is a vibration pressure threshold that makes characteristic gases such as hydrogen (H 2 ), methane (CH 4 ), ethylene (C 2 H 4 ), ethane (C 2 H 6 ), and acetylene (C 2 H 2 ) appear in transformer oil. The concentration of each characteristic gas increases with the increase of vibration pressure. Furthermore, misdiagnosis of insulation status which may be caused by vibration is illustrated based on Duval triangles method. Besides, vibration bubbling phenomenon in the experiment is observed. The generation mechanism of characteristic gases is analyzed with bubble dynamics under vibration. Higher vibration pressure induces cavitation processes and creates local thermal effects, which is the main reason for the generation of characteristic gases. A method based on combination of Duval triangle 1 and assistant triangle is proposed to distinguish characteristic gases generated by vibration from those generated by discharge and thermal faults. The results may provide theoretical support for fault identification and insulation state assessment of oil-immersed power transformers and high-voltage reactors.

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“…A small number of studies have put forward new ideas for establishing a transformer failure rate model [10,11]. In addition, some scholars have paid special attention to the remaining life [12][13][14][15] of the transformer. The state prediction models proposed in these studies include the neural network [4], support vector machine regression [2,3], fuzzy logic [14], nonparametric regression [10], and probabilistic graph [16].…”

Section: Introductionmentioning

confidence: 99%

Power Transformer Operating State Prediction Method Based on an LSTM Network

et al. 2018

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Abstract:The state of transformer equipment is usually manifested through a variety of information. The characteristic information will change with different types of equipment defects/faults, location, severity, and other factors. For transformer operating state prediction and fault warning, the key influencing factors of the transformer panorama information are analyzed. The degree of relative deterioration is used to characterize the deterioration of the transformer state. The membership relationship between the relative deterioration degree of each indicator and the transformer state is obtained through fuzzy processing. Through the long short-term memory (LSTM) network, the evolution of the transformer status is extracted, and a data-driven state prediction model is constructed to realize preliminary warning of a potential fault of the equipment. Through the LSTM network, the quantitative index and qualitative index are organically combined in order to perceive the corresponding relationship between the characteristic parameters and the operating state of the transformer. The results of different time-scale prediction cases show that the proposed method can effectively predict the operation status of power transformers and accurately reflect their status.

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Gas generation of cellulose insulation in palm fatty acid ester and mineral oil for life prediction marker in nitrogen-sealed transformers

Cited by 15 publications

References 18 publications

Review of Research Progress on the Electrical Properties and Modification of Mineral Insulating Oils Used in Power Transformers

Review of Research Progress on the Electrical Properties and Modification of Mineral Insulating Oils Used in Power Transformers

Investigation on gas generation characteristics in transformer oil under vibration

Power Transformer Operating State Prediction Method Based on an LSTM Network

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