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Adaptive stochastic resonance in an improved Izhikevich neuron model driven by multiplicative and additive Gaussian noise and its application in fault diagnosis of wind turbines

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  • Xu, Lianbing
  • Zhang, Gang
  • Huang, Xiaoxiao
  • He, Lin

Abstract

Fault diagnosis of wind turbines under complex environmental disturbances remains a major challenge in the development of renewable energy sources, and stochastic resonance has been recognized as an effective fault diagnosis method. This paper investigates the stochastic resonance phenomena of an improved Izhikevich neuron model driven by multiplicative and additive Gaussian noise. Firstly, the relevant metrics such as mean first-passage time (MFPT) and output signal-to-noise ratio (SNR) of the improved Izhikevich neuron model are derived by means of theoretical analysis. Then, the Adaptive Genetic Algorithm (AGA) is used to find the optimal parameters of the system, and the numerical simulation results show that the improved Izhikevich neuron model performs optimally and detects transcranial magneto-acoustical stimulation (TMAS) signals. Finally, in real fault diagnosis of wind turbines, the MSNRG of the improved Izhikevich neuron model is 1.05 dB and 1.522 dB higher than those of the HR neuron model and the CBSR system, respectively. Additionally, the amplitude is 9.994 and 43.882 times greater, and the MEG is 9.997 dB and 16.423 dB higher. The results demonstrate the theoretical importance and practical value of the improved Izhikevich neuron model for fault diagnosis of wind turbines operating in complex environments.

Suggested Citation

  • Xu, Lianbing & Zhang, Gang & Huang, Xiaoxiao & He, Lin, 2025. "Adaptive stochastic resonance in an improved Izhikevich neuron model driven by multiplicative and additive Gaussian noise and its application in fault diagnosis of wind turbines," Renewable Energy, Elsevier, vol. 253(C).
    • Handle: RePEc:eee:renene:v:253:y:2025:i:c:s0960148125012285
    DOI: 10.1016/j.renene.2025.123566
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