IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i8p1767-d1118249.html
   My bibliography  Save this article

Advances in Fault Detection and Diagnosis for Thermal Power Plants: A Review of Intelligent Techniques

Author

Listed:
  • Salman Khalid

    (Department of Mechanical, Robotics, and Energy Engineering, Dongguk University-Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of Korea)

  • Jinwoo Song

    (Department of Mechanical, Robotics, and Energy Engineering, Dongguk University-Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of Korea)

  • Izaz Raouf

    (Department of Mechanical, Robotics, and Energy Engineering, Dongguk University-Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of Korea)

  • Heung Soo Kim

    (Department of Mechanical, Robotics, and Energy Engineering, Dongguk University-Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of Korea)

Abstract

Thermal power plants (TPPs) are critical to supplying energy to society, and ensuring their safe and efficient operation is a top priority. To minimize maintenance shutdowns and costs, modern TPPs have adopted advanced fault detection and diagnosis (FDD) techniques. These FDD approaches can be divided into three main categories: model-based, data-driven-based, and statistical-based methods. Despite the practical limitations of model-based methods, a multitude of data-driven and statistical techniques have been developed to monitor key equipment in TPPs. The main contribution of this paper is a systematic review of advanced FDD methods that addresses a literature gap by providing a comprehensive comparison and analysis of these techniques. The review discusses the most relevant FDD strategies, including model-based, data-driven, and statistical-based approaches, and their applications in enhancing the efficiency and reliability of TPPs. Our review highlights the novel and innovative aspects of these techniques and emphasizes their significance in sustainable energy development and the long-term viability of thermal power generation. This review further explores the recent advancements in intelligent FDD techniques for boilers and turbines in TPPs. It also discusses real-world applications, and analyzes the limitations and challenges of current approaches. The paper highlights the need for further research and development in this field, and outlines potential future directions to improve the safety, efficiency, and reliability of intelligent TPPs. Overall, this review provides valuable insights into the current state-of-the-art in FDD techniques for TPPs, and serves as a guide for future research and development.

Suggested Citation

  • Salman Khalid & Jinwoo Song & Izaz Raouf & Heung Soo Kim, 2023. "Advances in Fault Detection and Diagnosis for Thermal Power Plants: A Review of Intelligent Techniques," Mathematics, MDPI, vol. 11(8), pages 1-28, April.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:8:p:1767-:d:1118249
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/8/1767/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/8/1767/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yi-Jing Zhang & Li-Sheng Hu, 2021. "Fault Propagation Inference Based on a Graph Neural Network for Steam Turbine Systems," Energies, MDPI, vol. 14(2), pages 1-13, January.
    2. Rostek, Kornel & Morytko, Łukasz & Jankowska, Anna, 2015. "Early detection and prediction of leaks in fluidized-bed boilers using artificial neural networks," Energy, Elsevier, vol. 89(C), pages 914-923.
    3. Indrawan, Natarianto & Shadle, Lawrence J. & Breault, Ronald W. & Panday, Rupendranath & Chitnis, Umesh K., 2021. "Data analytics for leak detection in a subcritical boiler," Energy, Elsevier, vol. 220(C).
    4. Jerry L. Holechek & Hatim M. E. Geli & Mohammed N. Sawalhah & Raul Valdez, 2022. "A Global Assessment: Can Renewable Energy Replace Fossil Fuels by 2050?," Sustainability, MDPI, vol. 14(8), pages 1-22, April.
    5. Eguchi, Shogo & Takayabu, Hirotaka & Lin, Chen, 2021. "Sources of inefficient power generation by coal-fired thermal power plants in China: A metafrontier DEA decomposition approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    6. Salahshoor, Karim & Kordestani, Mojtaba & Khoshro, Majid S., 2010. "Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers," Energy, Elsevier, vol. 35(12), pages 5472-5482.
    7. Bae, Suk Joo & Mun, Byeong Min & Chang, Woojin & Vidakovic, Brani, 2019. "Condition monitoring of a steam turbine generator using wavelet spectrum based control chart," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 13-20.
    8. Hundi, Prabhas & Shahsavari, Rouzbeh, 2020. "Comparative studies among machine learning models for performance estimation and health monitoring of thermal power plants," Applied Energy, Elsevier, vol. 265(C).
    9. Pondini, Maddalena & Colla, Valentina & Signorini, Annamaria, 2017. "Models of control valve and actuation system for dynamics analysis of steam turbines," Applied Energy, Elsevier, vol. 207(C), pages 208-217.
    10. Yu, Jungwon & Yoo, Jaeyeong & Jang, Jaeyel & Park, June Ho & Kim, Sungshin, 2017. "A novel plugged tube detection and identification approach for final super heater in thermal power plant using principal component analysis," Energy, Elsevier, vol. 126(C), pages 404-418.
    11. Ghiasi, Mohammad, 2019. "Detailed study, multi-objective optimization, and design of an AC-DC smart microgrid with hybrid renewable energy resources," Energy, Elsevier, vol. 169(C), pages 496-507.
    12. Brkovic, Aleksandar & Gajic, Dragoljub & Gligorijevic, Jovan & Savic-Gajic, Ivana & Georgieva, Olga & Di Gennaro, Stefano, 2017. "Early fault detection and diagnosis in bearings for more efficient operation of rotating machinery," Energy, Elsevier, vol. 136(C), pages 63-71.
    13. Bartosz Ciupek & Karol Gołoś & Radosław Jankowski & Zbigniew Nadolny, 2021. "Effect of Hard Coal Combustion in Water Steam Environment on Chemical Composition of Exhaust Gases," Energies, MDPI, vol. 14(20), pages 1-24, October.
    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. Prashant Kumar & Salman Khalid & Heung Soo Kim, 2023. "Prognostics and Health Management of Rotating Machinery of Industrial Robot with Deep Learning Applications—A Review," Mathematics, MDPI, vol. 11(13), pages 1-37, July.
    2. Salman Khalid & Jinwoo Song & Muhammad Muzammil Azad & Muhammad Umar Elahi & Jaehun Lee & Soo-Ho Jo & Heung Soo Kim, 2023. "A Comprehensive Review of Emerging Trends in Aircraft Structural Prognostics and Health Management," Mathematics, MDPI, vol. 11(18), pages 1-42, September.

    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. Jungwon Yu & Jaeyel Jang & Jaeyeong Yoo & June Ho Park & Sungshin Kim, 2018. "A Fault Isolation Method via Classification and Regression Tree-Based Variable Ranking for Drum-Type Steam Boiler in Thermal Power Plant," Energies, MDPI, vol. 11(5), pages 1-19, May.
    2. Brkovic, Aleksandar & Gajic, Dragoljub & Gligorijevic, Jovan & Savic-Gajic, Ivana & Georgieva, Olga & Di Gennaro, Stefano, 2017. "Early fault detection and diagnosis in bearings for more efficient operation of rotating machinery," Energy, Elsevier, vol. 136(C), pages 63-71.
    3. Bi, Yubo & Wu, Qiulan & Wang, Shilu & Shi, Jihao & Cong, Haiyong & Ye, Lili & Gao, Wei & Bi, Mingshu, 2023. "Hydrogen leakage location prediction at hydrogen refueling stations based on deep learning," Energy, Elsevier, vol. 284(C).
    4. Li, Guolong & Li, Yanjun & Fang, Chengyue & Su, Jian & Wang, Haotong & Sun, Shengdi & Zhang, Guolei & Shi, Jianxin, 2023. "Research on fault diagnosis of supercharged boiler with limited data based on few-shot learning," Energy, Elsevier, vol. 281(C).
    5. Indrawan, Natarianto & Shadle, Lawrence J. & Breault, Ronald W. & Panday, Rupendranath & Chitnis, Umesh K., 2021. "Data analytics for leak detection in a subcritical boiler," Energy, Elsevier, vol. 220(C).
    6. Wei Wang & Leonid Melnyk & Oleksandra Kubatko & Bohdan Kovalov & Luc Hens, 2023. "Economic and Technological Efficiency of Renewable Energy Technologies Implementation," Sustainability, MDPI, vol. 15(11), pages 1-19, May.
    7. Yin, Sihua & Yang, Haidong & Xu, Kangkang & Zhu, Chengjiu & Zhang, Shaqing & Liu, Guosheng, 2022. "Dynamic real–time abnormal energy consumption detection and energy efficiency optimization analysis considering uncertainty," Applied Energy, Elsevier, vol. 307(C).
    8. Quintanilha, Igor M. & Elias, Vitor R.M. & da Silva, Felipe B. & Fonini, Pedro A.M. & da Silva, Eduardo A.B. & Netto, Sergio L. & Apolinário, José A. & de Campos, Marcello L.R. & Martins, Wallace A., 2021. "A fault detector/classifier for closed-ring power generators using machine learning," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    9. Toshiyuki Sueyoshi & Ruchuan Zhang & Aijun Li, 2023. "Measuring and Analyzing Operational Efficiency and Returns to Scale in a Time Horizon: Assessment of China’s Electricity Generation & Transmission at Provincial Levels," Energies, MDPI, vol. 16(2), pages 1-23, January.
    10. Zhang, Chao & Zhao, Yangsheng & Feng, Zijun & Meng, Qiaorong & Wang, Lei & Lu, Yang, 2023. "Thermal maturity and chemical structure evolution of lump long-flame coal during superheated water vapor–based in situ pyrolysis," Energy, Elsevier, vol. 263(PC).
    11. Miguel A. Rodríguez-López & Luis M. López-González & Luis M. López-Ochoa & Jesús Las-Heras-Casas, 2018. "Methodology for Detecting Malfunctions and Evaluating the Maintenance Effectiveness in Wind Turbine Generator Bearings Using Generic versus Specific Models from SCADA Data," Energies, MDPI, vol. 11(4), pages 1-22, March.
    12. Chin‐wei Huang & Hsiao‐Yin Chen, 2023. "Using nonradial metafrontier data envelopment analysis to evaluate the metatechnology and metafactor ratios for the Taiwanese hotel industry," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(4), pages 1904-1919, June.
    13. Ahmed Hussain Elmetwaly & Ramy Adel Younis & Abdelazeem Abdallah Abdelsalam & Ahmed Ibrahim Omar & Mohamed Metwally Mahmoud & Faisal Alsaif & Adel El-Shahat & Mohamed Attya Saad, 2023. "Modeling, Simulation, and Experimental Validation of a Novel MPPT for Hybrid Renewable Sources Integrated with UPQC: An Application of Jellyfish Search Optimizer," Sustainability, MDPI, vol. 15(6), pages 1-30, March.
    14. Akhlaque Ahmad Khan & Ahmad Faiz Minai & Rupendra Kumar Pachauri & Hasmat Malik, 2022. "Optimal Sizing, Control, and Management Strategies for Hybrid Renewable Energy Systems: A Comprehensive Review," Energies, MDPI, vol. 15(17), pages 1-29, August.
    15. Huang, Chung-Neng & Chen, Yui-Sung, 2017. "Design of magnetic flywheel control for performance improvement of fuel cells used in vehicles," Energy, Elsevier, vol. 118(C), pages 840-852.
    16. Lijuan Zhang & Tatyana Ponomarenko, 2023. "Directions for Sustainable Development of China’s Coal Industry in the Post-Epidemic Era," Sustainability, MDPI, vol. 15(8), pages 1-32, April.
    17. Daiva Makutėnienė & Algirdas Justinas Staugaitis & Bernardas Vaznonis & Gunta Grīnberga-Zālīte, 2023. "The Relationship between Energy Consumption and Economic Growth in the Baltic Countries’ Agriculture: A Non-Linear Framework," Energies, MDPI, vol. 16(5), pages 1-22, February.
    18. Qu, Zhijian & Xu, Juan & Wang, Zixiao & Chi, Rui & Liu, Hanxin, 2021. "Prediction of electricity generation from a combined cycle power plant based on a stacking ensemble and its hyperparameter optimization with a grid-search method," Energy, Elsevier, vol. 227(C).
    19. Feng Lu & Jipeng Jiang & Jinquan Huang & Xiaojie Qiu, 2018. "An Iterative Reduced KPCA Hidden Markov Model for Gas Turbine Performance Fault Diagnosis," Energies, MDPI, vol. 11(7), pages 1-21, July.
    20. Evgeny Chupakhin & Olga Babich & Stanislav Sukhikh & Svetlana Ivanova & Ekaterina Budenkova & Olga Kalashnikova & Alexander Prosekov & Olga Kriger & Vyacheslav Dolganyuk, 2022. "Bioengineering and Molecular Biology of Miscanthus," Energies, MDPI, vol. 15(14), pages 1-14, July.

    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:jmathe:v:11:y:2023:i:8:p:1767-:d:1118249. 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.