IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i17p6435-d905683.html
   My bibliography  Save this article

An Offline and Online Approach to the OLTC Condition Monitoring: A Review

Author

Listed:
  • Firas B. Ismail

    (Power Generation Unit, Institute of Power Engineering (IPE), Universiti Tenaga Nasional, Kajang 43000, Malaysia)

  • Maisarah Mazwan

    (Power Generation Unit, Institute of Power Engineering (IPE), Universiti Tenaga Nasional, Kajang 43000, Malaysia)

  • Hussein Al-Faiz

    (Power Generation Unit, Institute of Power Engineering (IPE), Universiti Tenaga Nasional, Kajang 43000, Malaysia)

  • Marayati Marsadek

    (Power Generation Unit, Institute of Power Engineering (IPE), Universiti Tenaga Nasional, Kajang 43000, Malaysia)

  • Hasril Hasini

    (Power Generation Unit, Institute of Power Engineering (IPE), Universiti Tenaga Nasional, Kajang 43000, Malaysia)

  • Ammar Al-Bazi

    (School of Mechanical, Aerospace and Automotive Engineering, Coventry University, Coventry CV1 5FB, UK)

  • Young Zaidey Yang Ghazali

    (Asset Management Department, Tenaga Nasional Berhad, Kuala Lumpur 59200, Malaysia)

Abstract

Transformer failures have a significant cost impact on the operation of an electrical network. In many utilities, transformers have been operating for many years past their expected usable life. As power demand has surged, transformers in some areas are being loaded beyond their rated capacity to meet the demand. One of the vital components in a transformer is the on-load tap changer (OLTC), which regulates the voltage in the distribution network. This study aims to review several condition-monitoring techniques (online and offline) that can monitor the health of the OLTC and assure the safety of the transformer’s OLTC from irreparable damage by detecting the defect at an earlier stage, which is preceded by the specification of typical faults. This paper also discussed the common faults of the OLTC and the root causes of these faults. The OLTC is prone to mechanical faults due to its frequently changing mechanism in the tap operation. The OLTC are also prone to oil as well as thermal faults. As a result, it is critical to monitor OLTC conditions while they are in use. Proper management of condition monitoring (CM) for the OLTC is useful and necessary to increase availability and achieve optimised operating. Condition monitoring (CM) and diagnostics methods (DM) have been developing since the 1950s. CM and DM have been implemented to diagnose and detect an incipient fault, especially for the OLTC. Many techniques, online and offline, are being used to monitor the condition of the OLTC to prevent failure and minimize outages. These DM and CM will prolong the operational cycle and avoid a major disaster for the OLTC, which is an unfavorable scenario.

Suggested Citation

  • Firas B. Ismail & Maisarah Mazwan & Hussein Al-Faiz & Marayati Marsadek & Hasril Hasini & Ammar Al-Bazi & Young Zaidey Yang Ghazali, 2022. "An Offline and Online Approach to the OLTC Condition Monitoring: A Review," Energies, MDPI, vol. 15(17), pages 1-18, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:17:p:6435-:d:905683
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/17/6435/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/17/6435/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Luiz Cheim & Michel Duval & Saad Haider, 2020. "Combined Duval Pentagons: A Simplified Approach," Energies, MDPI, vol. 13(11), pages 1-12, June.
    Full references (including those not matched with items on IDEAS)

    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. George Kimani Irungu & Aloys Oriedi Akumu, 2020. "Application of Dissolved Gas Analysis in Assessing Degree of Healthiness or Faultiness with Fault Identification in Oil-Immersed Equipment," Energies, MDPI, vol. 13(18), pages 1-24, September.
    2. Dimitris A. Barkas & Stavros D. Kaminaris & Konstantinos K. Kalkanis & George Ch. Ioannidis & Constantinos S. Psomopoulos, 2022. "Condition Assessment of Power Transformers through DGA Measurements Evaluation Using Adaptive Algorithms and Deep Learning," Energies, MDPI, vol. 16(1), pages 1-17, December.
    3. Youcef Benmahamed & Omar Kherif & Madjid Teguar & Ahmed Boubakeur & Sherif S. M. Ghoneim, 2021. "Accuracy Improvement of Transformer Faults Diagnostic Based on DGA Data Using SVM-BA Classifier," Energies, MDPI, vol. 14(10), pages 1-17, May.
    4. Tomasz Piotrowski & Pawel Rozga & Ryszard Kozak & Zbigniew Szymanski, 2020. "Using the Analysis of the Gases Dissolved in Oil in Diagnosis of Transformer Bushings with Paper-Oil Insulation—A Case Study," Energies, MDPI, vol. 13(24), pages 1-12, December.
    5. 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.
    6. James Dukarm & Zachary Draper & Tomasz Piotrowski, 2020. "Diagnostic Simplexes for Dissolved-Gas Analysis," Energies, MDPI, vol. 13(23), pages 1-16, December.
    7. Michel Duval & Constantin Ene, 2021. "Identification of Stray Gassing of Dodecylbenzene in Bushings," Energies, MDPI, vol. 14(9), pages 1-7, April.
    8. Vahid Behjat & Reza Emadifar & Mehrdad Pourhossein & U. Mohan Rao & Issouf Fofana & Reza Najjar, 2021. "Improved Monitoring and Diagnosis of Transformer Solid Insulation Using Pertinent Chemical Indicators," Energies, MDPI, vol. 14(13), pages 1-13, July.
    9. El-Sayed M. El-kenawy & Fahad Albalawi & Sayed A. Ward & Sherif S. M. Ghoneim & Marwa M. Eid & Abdelaziz A. Abdelhamid & Nadjem Bailek & Abdelhameed Ibrahim, 2022. "Feature Selection and Classification of Transformer Faults Based on Novel Meta-Heuristic Algorithm," Mathematics, MDPI, vol. 10(17), pages 1-28, September.

    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:15:y:2022:i:17:p:6435-:d:905683. 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.