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

Salman Khalid, Jinwoo Song, Izaz Raouf and Heung Soo Kim ()
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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

Mathematics, 2023, vol. 11, issue 8, 1-28

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.

Keywords: thermal power plant; fault detection and diagnosis (FDD); boiler tube leakage; turbine; data-driven method; model-based method; and signal-based methods (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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