A Multi-Information Fusion Algorithm to Fault Diagnosis of Power Converter in Wind Power Generation Systems

• Published in: IEEE transactions on industrial informatics Vol. 20; no. 2; pp. 1167 - 1179
• Main Authors: Liang, Jinping, Zhang, Ke, Al-Durra, Ahmed, Zhou, Daming
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.02.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Circuit faults; Costs; Data integration; Dempster–Shafer; Deng entropy; Entropy; Fault diagnosis; Feature extraction; feature-level fusion; Maintenance costs; multi-information fusion; power converter; Power converters; System reliability; Time-frequency analysis; Uncertainty; Weight reduction; Wind power; Wind power generation; wind power generation systems
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2023.3271358

Power electronics-based converters are the major and most vulnerable components in wind power generation systems. Converter faults will affect power quality, damage expensive equipment such as generators, or even pose a massive threat to the entire power grid. Fault diagnosis is considered as a powerful means to improve system reliability and reduce maintenance costs. Existing data-driven fault diagnosis methods related to improving feature extraction approaches to obtain high diagnostic accuracy are mostly based on single-scale feature of signals; this neglects the potentially valuable information of other scales. This article proposes an algorithm of Dempster-Shafer (DS) and Deng entropy fusion multiscale approximate entropy for wind power converter fault diagnosis. First, it calculates the multiscale approximate entropy of fault signals, mining more potentially valuable information. Second, DS is used to fuse features of various scales and effectively handles the conflicts and uncertainties between different features. Finally, Deng entropy is adopted to measure the uncertainty to adjust weight distribution, reducing the influence of highly conflicting features and mutually benefitting features of different scales. Extensive experimental results on simulated and experimental data demonstrate the effectiveness of the proposed algorithm. Compared with advanced methods, this method can diagnose the faults with higher accuracy and stronger robustness.