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基于DRFNN滑模的多功能柔性多状态开关控制方法

廖江华 高伟 杨艳 杨耿杰

全球能源互联网(英文)2024,Vol.7Issue(2):190-205,16.
全球能源互联网(英文)2024,Vol.7Issue(2):190-205,16.DOI:10.1016/j.gloei.2024.04.007

基于DRFNN滑模的多功能柔性多状态开关控制方法

Control method based on DRFNN sliding mode for multifunctional flexible multistate switch

廖江华 1高伟 1杨艳 1杨耿杰1

作者信息

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摘要

Abstract

To address the low accuracy and stability when applying classical control theory in distribution networks with distributed generation,a control method involving flexible multistate switches(FMSs)is proposed in this study.This approach is based on an improved double-loop recursive fuzzy neural network(DRFNN)sliding mode,which is intended to stably achieve multiterminal power interaction and adaptive arc suppression for single-phase ground faults.First,an improved DRFNN sliding mode control(SMC)method is proposed to overcome the chattering and transient overshoot inherent in the classical SMC and reduce the reliance on a precise mathematical model of the control system.To improve the robustness of the system,an adaptive parameter-adjustment strategy for the DRFNN is designed,where its dynamic mapping capabilities are leveraged to improve the transient compensation control.Additionally,a quasi-continuous second-order sliding mode controller with a calculus-driven sliding mode surface is developed to improve the current monitoring accuracy and enhance the system stability.The stability of the proposed method and the convergence of the network parameters are verified using the Lyapunov theorem.A simulation model of the three-port FMS with its control system is constructed in MATLAB/Simulink.The simulation result confirms the feasibility and effectiveness of the proposed control strategy based on a comparative analysis.

关键词

配电网/柔性多状态开关/接地故障电弧抑制/双环递归模糊神经网络/准连续二阶滑模

Key words

Distribution networks/Flexible multistate switch/Grounding fault arc suppression/Double-loop recursive fuzzy neural network/Quasi-continuous second-order sliding mode

引用本文复制引用

廖江华,高伟,杨艳,杨耿杰..基于DRFNN滑模的多功能柔性多状态开关控制方法[J].全球能源互联网(英文),2024,7(2):190-205,16.

基金项目

The authors thank the Natural Science Foundation of Fujian,China(No.2021J01633). (No.2021J01633)

全球能源互联网(英文)

OAEI

2096-5117

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