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Event-triggered sliding mode observer based on particle swarm optimization for fault detection of the doubly fed induction generator for wind power systems

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Abstract

In order to accurately track the state of the doubly fed induction generator (DFIG) for wind power systems and implement its fault detection, an event-triggered sliding mode observer based on particle swarm optimization (PSO-ET-SMO) is proposed in this paper. First, the sliding mode observer (SMO) is designed according to the rotor current state space equation of the DFIG. Then, a new event-triggered mechanism (NETM) is proposed for the designed SMO, and the constructed fitness function is optimized by using Particle Swarm Optimization (PSO) algorithm, so as to obtain the optimal parameters of the SMO. Finally, to verify the effectiveness of the proposed method in this paper, the fault detection of the DFIG for wind power systems is performed by using the sequence of residuals between the rotor current output values and the sliding mode observations. The innovation of this work is that a NETM is designed by combining sliding mode reaching law, and this mechanism is introduced into the design of SMO. The simulation results show that the proposed method not only reduces sliding mode chattering, but also improves the tracking effect of SMO and detects faults accurately. At the same time, due to the action of the NETM, the transmission burden between the SMO and the DFIG is also reduced.

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Data availability statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

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Acknowledgements

This work was supported by Chinese National Natural Science Foundation (61973109), the key scientific research project of Hunan Provincial Department of Education (21A0317), the Natural Science Foundation of Hunan Province in China (No. 2022JJ30266,2021JJ30271).

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Authors and Affiliations

  1. School of Information and Electrical Engineering, Hunan University of Science and Technology, Hunan Pro., Xiangtan, 411201, China

    GuoLiang Zhong, WenXin Yu & Yanming Zhao

  2. School of Physics and Electronics, Hunan University of Science and Technology, Hunan Pro., Xiangtan, 411201, China

    JunNian Wang

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  1. GuoLiang Zhong
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  2. WenXin Yu
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Correspondence to WenXin Yu.

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Zhong, G., Yu, W., Wang, J. et al. Event-triggered sliding mode observer based on particle swarm optimization for fault detection of the doubly fed induction generator for wind power systems. J Ambient Intell Human Comput 14, 2585–2599 (2023). https://doi.org/10.1007/s12652-022-04504-6

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  • DOI: https://doi.org/10.1007/s12652-022-04504-6

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