首页|基于改进GA优化的级联H桥APF神经网络电流跟踪控制

基于改进GA优化的级联H桥APF神经网络电流跟踪控制

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针对基于级联H桥有源滤波器实现谐波抑制时,传统PI控制电流跟踪的抗扰性差以及由于神经网络预测的局域最优造成的电流跟踪预测不准确、延迟大等问题.本文提出了一种改进GA优化的级联H桥APF神经网络电流跟踪控制策略,以改进选择和交叉操作的GA优化神经网络变化因子,提升了预测控制的全局搜索能力和鲁棒性能.仿真验证表明,所提出的控制策略相较于传统PI控制,提升了电流跟踪控制的鲁棒性;相较基于传统GA优化的神经网络控制方法,本文提出的方法跟踪速度提升了约0.04 s,控制误差减小约0.2 A,谐波畸变率降低0.36％.可见,响应速度和控制精度更优,具有更好的跟踪控制能力.
Cascaded H-bridge APF Neural Network Current Tracking Control Based on Improved GA Optimization
In view of such issues as weak disturbance resistance tracked by traditional PI control current,inaccurate current tracking prediction and large delay due to local optimum predicted by neutral network when the cascade-based H-bridge active power filter achieves the harmonic suppression,a kind of improved GA-optimized cascaded H-bridge APF neural network predictive current control strategy is proposed in this paper to improve the GA-optimized neural network change factor of selection and crossover operation,which improves the global search ability and robust performance of current tracking control. Simulation results show that the proposed control strategy,compared with traditional PI control,improves the robustness of current tracking control. Compared with the neural network control method based on traditional GA optimization,the tracking speed of the method proposed in this paper is improved by about 0.04 s,the control error is reduced by about 0.2 A,and the harmonic distortion rate is reduced by 0.36％. It can be seen that the response speed and control accuracy are better,and the tracking control capability is better.

cascaded H-bridgeactive power filtergenetic algorithmneural network controlcurrent tracking

沈建强、邢砾云、李冉、王策

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北华大学电气与信息工程学院,吉林 吉林 132021

级联H桥 有源电力滤波器 遗传算法 神经网络控制 电流跟踪

国家自然科学基金青年基金吉林省科技发展计划吉林省发改委项目吉林省教育科学规划课题重点项目(十四五)(2022)北华大学青年科技创新团队项目

61901007YDZJ202201ZYTS6012019C058-1ZD22091202016003

2024

10.14044/j.1674-1757.pcrpc.2024.02.010

电力电容器与无功补偿
西安电力电容器研究所

电力电容器与无功补偿

影响因子：0.99
ISSN：1674-1757
年,卷(期)：2024.45(2)
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