Reliability prediction of distribution network based on PCA-GA-BP neural network

Sun, Yuanyuan; Shan, Hongtao; Zhang, Wenjun; Ren, Lei; Yan, Puhong

doi:10.1063/1.5125376

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…IGBT phase-shi full-bridge with clamping diodes e prediction error of BP neural network is not sensitive to the change of weight value, the error gradient changes very little, the number of iterations is large, and the convergence speed is slow. e gradient descent method is easy to fall into local optimum in the process of backward error propagation [19]. erefore, in practical application, it is necessary to use one certain optimization algorithm to optimize the BP neural network prediction model.…”

Section: Design Of Bp Neural Network Pid Controlmentioning

confidence: 99%

A Novel PID Control Strategy Based on Improved GA-BP Neural Network for Phase-Shifted Full-Bridge Current-Doubler Synchronous Rectifying Converter

Liu

Cheng

Zhao

et al. 2021

Mathematical Problems in Engineering

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In this paper, a phase-shifted full-bridge current-doubler synchronous rectifying converter (PSFB-CDSRC) based on IGBT and its control strategies are studied. In the main circuit, a current doubling synchronous rectifying circuit based on IGBT is presented to further reduce the power loss of power devices. Moreover, in the control strategy, in view of the existing researches, the basic BP neural network PID control performance of the rectifying converter still can be further improved. Therefore, this paper combines the quasi-Newton algorithm and traditional GA to propose an improved GA-BP (IGA-BP) neural network to further improve PID control performance. The simulation results demonstrate that the maximum efficiency of 5 V/500 A rectifying converter based on the proposed circuit scheme can reach 94.1%, and the rectifying converter has a good performance of excellent waveform and wide range of load. IGA-BP neural network PID control responds fast and reaches the stable state quickly in comparison with that controlled by the GA-BP neural network control strategy, and the steady-state time can be reduced by 10.5% through using IGA-BP neural network control strategy. This study can provide a valuable guidance and reference, not only in circuit scheme but also in the optimal PID control strategy for design of the high-efficiency DC/DC rectifying converter with higher power in the future.

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Section: Design Of Bp Neural Network Pid Controlmentioning

confidence: 99%