A state-of-the-art review on wind power converter fault diagnosis
المؤلف | Liang, Jinping |
المؤلف | Zhang, Ke |
المؤلف | Al-Durra, Ahmed |
المؤلف | Muyeen, S.M. |
المؤلف | Zhou, Daming |
تاريخ الإتاحة | 2023-02-26T08:29:58Z |
تاريخ النشر | 2022 |
اسم المنشور | Energy Reports |
المصدر | Scopus |
الملخص | The rapid expansion of installed wind energy capacity and the continuous development of wind turbine technology has drawn attention to operation and maintenance issues. In order to keep wind power a competitive energy source, the development of high-reliability and low-maintenance wind turbine systems is imminent, the rise of fault diagnosis provides a guarantee for their satisfactory operation and maintenance. A large number of statistical studies have pointed out that converter fault is the main cause of wind turbine system failure shutdown. Up to now, wind power converters' fault diagnosis has obtained fruitful results, and those are constantly reported in power system literature. This paper presents a state-of-the-art review on wind power converters' fault diagnosis for both short-circuit faults and open-circuit faults of power switch, including model-based, signal-based and data-driven methods. It provides a wide range, involving component fault modes, the robustness and reliability issues, algorithm investigation of fault diagnosis, quantitative analysis and qualitative analysis metrics for assessing the advantages of the developed techniques, and challenges in fault diagnosis design. Main purposes of this paper are: (1) Investigating the current research status of fault diagnosis on wind power converters to update the relevant research literature; (2) Discussing the robustness and reliability issues that must be considered in real engineering and safety critical systems; (3) Providing effective performance indices involves both quantitative and qualitative analysis, so that readers can understand the novelty of the proposed method. 2022 The Authors |
راعي المشروع | This work was funded by the National Natural Science Foundation of China ( 51977177 ), Shaanxi Province Key Research and Development Plan 2021ZDLGY11-04 , Basic Research Plan of Natural Science in Shaanxi Province ( 2020JQ-152 ), the Fundamental Research Funds for the Central Universities, China ( D5000210763 ). |
اللغة | en |
الناشر | Elsevier Ltd |
الموضوع | Data-driven methods Fault diagnosis Model based methods Qualitative analysis Quantitative analysis Robustness and reliability Signal based methods Wind power converter |
النوع | Article Review |
الصفحات | 5341-5369 |
رقم المجلد | 8 |
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