IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v9y2016i5p312-d68853.html
   My bibliography  Save this article

Comparison of Dissolved Gases in Mineral and Vegetable Insulating Oils under Typical Electrical and Thermal Faults

Author

Listed:
  • Chenmeng Xiang

    (The State Key Laboratory of Power Transmission Equipment and System Security and New Technology, College of Electrical Engineering, Chongqing University, Chongqing 400044, China)

  • Quan Zhou

    (The State Key Laboratory of Power Transmission Equipment and System Security and New Technology, College of Electrical Engineering, Chongqing University, Chongqing 400044, China)

  • Jian Li

    (The State Key Laboratory of Power Transmission Equipment and System Security and New Technology, College of Electrical Engineering, Chongqing University, Chongqing 400044, China)

  • Qingdan Huang

    (Guangzhou Power Supply Company, Guangzhou 510620, China)

  • Haoyong Song

    (Guangzhou Power Supply Company, Guangzhou 510620, China)

  • Zhaotao Zhang

    (State Grid Chongqing Changshou Power Supply Company, Chongqing 401220, China)

Abstract

Dissolved gas analysis (DGA) is attracting greater and greater interest from researchers as a fault diagnostic tool for power transformers filled with vegetable insulating oils. This paper presents experimental results of dissolved gases in insulating oils under typical electrical and thermal faults in transformers. The tests covered three types of insulating oils, including two types of vegetable oil, which are camellia insulating oil, Envirotemp FR3, and a type of mineral insulating oil, to simulate thermal faults in oils from 90 °C to 800 °C and electrical faults including breakdown and partial discharges in oils. The experimental results reveal that the content and proportion of dissolved gases in different types of insulating oils under the same fault condition are different, especially under thermal faults due to the obvious differences of their chemical compositions. Four different classic diagnosis methods were applied: ratio method, graphic method, and Duval’s triangle and Duval’s pentagon method. These confirmed that the diagnosis methods developed for mineral oil were not fully appropriate for diagnosis of electrical and thermal faults in vegetable insulating oils and needs some modification. Therefore, some modification aiming at different types of vegetable oils based on Duval Triangle 3 were proposed in this paper and obtained a good diagnostic result. Furthermore, gas formation mechanisms of different types of vegetable insulating oils under thermal stress are interpreted by means of unimolecular pyrolysis simulation and reaction enthalpies calculation.

Suggested Citation

  • Chenmeng Xiang & Quan Zhou & Jian Li & Qingdan Huang & Haoyong Song & Zhaotao Zhang, 2016. "Comparison of Dissolved Gases in Mineral and Vegetable Insulating Oils under Typical Electrical and Thermal Faults," Energies, MDPI, vol. 9(5), pages 1-22, April.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:5:p:312-:d:68853
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/9/5/312/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/9/5/312/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yuanbing Zheng & Caixin Sun & Jian Li & Qing Yang & Weigen Chen, 2011. "Entropy-Based Bagging for Fault Prediction of Transformers Using Oil-Dissolved Gas Data," Energies, MDPI, vol. 4(8), pages 1-10, August.
    2. Weigen Chen & Xi Chen & Shangyi Peng & Jian Li, 2012. "Canonical Correlation Between Partial Discharges and Gas Formation in Transformer Oil Paper Insulation," Energies, MDPI, vol. 5(4), pages 1-17, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fabio Henrique Pereira & Francisco Elânio Bezerra & Shigueru Junior & Josemir Santos & Ivan Chabu & Gilberto Francisco Martha de Souza & Fábio Micerino & Silvio Ikuyo Nabeta, 2018. "Nonlinear Autoregressive Neural Network Models for Prediction of Transformer Oil-Dissolved Gas Concentrations," Energies, MDPI, vol. 11(7), pages 1-12, June.
    2. Shen, Zijia & Wang, Feipeng & Wang, Zhiqing & Li, Jian, 2021. "A critical review of plant-based insulating fluids for transformer: 30-year development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    3. Lefeng Cheng & Tao Yu, 2018. "Dissolved Gas Analysis Principle-Based Intelligent Approaches to Fault Diagnosis and Decision Making for Large Oil-Immersed Power Transformers: A Survey," Energies, MDPI, vol. 11(4), pages 1-69, April.
    4. Luc Loiselle & U. Mohan Rao & Issouf Fofana, 2020. "Gassing Tendency of Fresh and Aged Mineral Oil and Ester Fluids under Electrical and Thermal Fault Conditions," Energies, MDPI, vol. 13(13), pages 1-15, July.
    5. Mardhiah Hayati Abdul Hamid & Mohd Taufiq Ishak & Nur Sabrina Suhaimi & Jaafar Adnan & Nazrul Fariq Makmor & Nurul Izzatul Akma Katim & Rahisham Abd Rahman, 2021. "Lightning Impulse Breakdown Voltage of Rice Bran Oil for Transformer Application," Energies, MDPI, vol. 14(16), pages 1-22, August.
    6. Jingxin Zou & Weigen Chen & Fu Wan & Zhou Fan & Lingling Du, 2016. "Raman Spectral Characteristics of Oil-Paper Insulation and Its Application to Ageing Stage Assessment of Oil-Immersed Transformers," Energies, MDPI, vol. 9(11), pages 1-14, November.
    7. Yachao Wang & Feipeng Wang & Jian Li & Suning Liang & Jinghan Zhou, 2018. "Electronic Properties of Typical Molecules and the Discharge Mechanism of Vegetable and Mineral Insulating Oils," Energies, MDPI, vol. 11(3), pages 1-13, February.
    8. Bing Zeng & Jiang Guo & Wenqiang Zhu & Zhihuai Xiao & Fang Yuan & Sixu Huang, 2019. "A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM," Energies, MDPI, vol. 12(21), pages 1-18, November.
    9. Issouf Fofana & Yazid Hadjadj, 2018. "Power Transformer Diagnostics, Monitoring and Design Features," Energies, MDPI, vol. 11(12), pages 1-5, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jingxin Zou & Weigen Chen & Fu Wan & Zhou Fan & Lingling Du, 2016. "Raman Spectral Characteristics of Oil-Paper Insulation and Its Application to Ageing Stage Assessment of Oil-Immersed Transformers," Energies, MDPI, vol. 9(11), pages 1-14, November.
    2. Jian Li & Xudong Li & Lin Du & Min Cao & Guochao Qian, 2016. "An Intelligent Sensor for the Ultra-High-Frequency Partial Discharge Online Monitoring of Power Transformers," Energies, MDPI, vol. 9(5), pages 1-15, May.
    3. Ancuța-Mihaela Aciu & Claudiu-Ionel Nicola & Marcel Nicola & Maria-Cristina Nițu, 2021. "Complementary Analysis for DGA Based on Duval Methods and Furan Compounds Using Artificial Neural Networks," Energies, MDPI, vol. 14(3), pages 1-22, January.
    4. Fang Yuan & Jiang Guo & Zhihuai Xiao & Bing Zeng & Wenqiang Zhu & Sixu Huang, 2019. "A Transformer Fault Diagnosis Model Based on Chemical Reaction Optimization and Twin Support Vector Machine," Energies, MDPI, vol. 12(5), pages 1-18, March.
    5. Indranil Ghosh & Tamal Datta Chaudhuri, 2017. "Fractal Investigation and Maximal Overlap Discrete Wavelet Transformation (MODWT)-based Machine Learning Framework for Forecasting Exchange Rates," Studies in Microeconomics, , vol. 5(2), pages 105-131, December.
    6. Alper Aydogan & Fatih Atalar & Aysel Ersoy Yilmaz & Pawel Rozga, 2020. "Using the Method of Harmonic Distortion Analysis in Partial Discharge Assessment in Mineral Oil in a Non-Uniform Electric Field," Energies, MDPI, vol. 13(18), pages 1-18, September.
    7. Luis Hernández-Callejo, 2019. "A Comprehensive Review of Operation and Control, Maintenance and Lifespan Management, Grid Planning and Design, and Metering in Smart Grids," Energies, MDPI, vol. 12(9), pages 1-50, April.
    8. Xiaojun Tang & Wenjing Wang & Xuliang Zhang & Erzhen Wang & Xuanjiannan Li, 2018. "On-Line Analysis of Oil-Dissolved Gas in Power Transformers Using Fourier Transform Infrared Spectrometry," Energies, MDPI, vol. 11(11), pages 1-15, November.
    9. Shuaibing Li & Guoqiang Gao & Guangcai Hu & Bo Gao & Haojie Yin & Wenfu Wei & Guangning Wu, 2017. "Influences of Traction Load Shock on Artificial Partial Discharge Faults within Traction Transformer—Experimental Test for Pattern Recognition," Energies, MDPI, vol. 10(10), pages 1-17, October.
    10. Guoqiang Sun & Xiaoliu Ding & Zhinong Wei & Peifeng Shen & Yang Zhao & Qiugen Huang & Liang Zhang & Haixiang Zang, 2019. "Intelligent Classification Method for Grid-Monitoring Alarm Messages Based on Information Theory," Energies, MDPI, vol. 12(14), pages 1-18, July.
    11. Fabio Henrique Pereira & Francisco Elânio Bezerra & Shigueru Junior & Josemir Santos & Ivan Chabu & Gilberto Francisco Martha de Souza & Fábio Micerino & Silvio Ikuyo Nabeta, 2018. "Nonlinear Autoregressive Neural Network Models for Prediction of Transformer Oil-Dissolved Gas Concentrations," Energies, MDPI, vol. 11(7), pages 1-12, June.
    12. Lefeng Cheng & Tao Yu, 2018. "Dissolved Gas Analysis Principle-Based Intelligent Approaches to Fault Diagnosis and Decision Making for Large Oil-Immersed Power Transformers: A Survey," Energies, MDPI, vol. 11(4), pages 1-69, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:9:y:2016:i:5:p:312-:d:68853. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.