UAV power based on PSO optimized wavelet neural network system fault diagnosis model

DOI:	10.11809/bqzbgcxb2024.04.021
Keywords:	UAV; power system; improved PSO; wavelet neural network; fault diagnosis
Abstract:	Aiming at the problems of poor noise reduction and recognition ability of traditional wavelet neural network for fault signals of UAV power system and slow convergence speed and low training accuracy of the network, a fault diagnosis model of UAV power system based on improved particle swarm algorithm (PSO) optimized wavelet neural network is constructed. The model uses a combination of soft and hard threshold functions improved new threshold functions and improved PSO optimized wavelet neural network to overcome the problem of reconstructed signal discontinuity or severe distortion and optimize the initial weights and thresholds of the wavelet neural network, so that the model can achieve fast and accurate analysis and identification of fault types with better fault prediction and diagnosis capability. In this paper, by comparing the noise reduction ability of different threshold functions and the improvement effect of PSO, GA and ACO on wavelet neural network, and comparing the fault diagnosis prediction effect of BP neural network, traditional wavelet neural network and PSO optimized wavelet neural network, it is verified that the fault diagnosis model of PSO optimized wavelet neural network constructed in this paper is much better than other comparison models, with strong fault identification and noise reduction ability. It has the advantages of high fault identification and noise reduction ability, fast convergence speed and high training accuracy, and has good feasibility and effectiveness in the field of fault diagnosis of UAV power system.
Published:	2024-04-30
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