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首页> 外文期刊>Electric Power Components and Systems >Proportional-integral-differential Neural Network Based Sliding-mode Controller for Modular Multi-level High-voltage DC Converter of Offshore Wind Power
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Proportional-integral-differential Neural Network Based Sliding-mode Controller for Modular Multi-level High-voltage DC Converter of Offshore Wind Power

机译：基于比例积分微分神经网络的海上风电模块化多电平高压直流变换器滑模控制器

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摘要

This article presents an improved sliding-mode control method for a modular multi-level high-voltage DC converter. It merges the merits of the proportional-integral-differential neural network and can solve the chattering problem that exists in conventional sliding-mode control on-line. The reaching law parameters of sliding-mode control can be adjusted by the proportional-integral-differential neural network without the previously needed of off-line learning. The Lyapunov function is chosen as the energy function for real-time training of the proportional-integral-differential neural network. In addition, the stability of the control system is carefully studied, and the global optimal solution is achieved. The MATLAB (The MathWorks, Natick, Massachusetts, USA) simulation results show that the proposed method can make the system globally stable, can achieve stronger robustness under system disturbance, and be applied easily to digital signal processor based modular multi-level converter high-voltage DC control systems.
机译：本文提出了一种改进的模块化多电平高压直流转换器的滑模控制方法。它融合了比例积分微分神经网络的优点，可以解决传统的在线滑模控制中存在的抖振问题。滑模控制的到达定律参数可以通过比例积分微分神经网络进行调整，而无需进行离线学习。选择Lyapunov函数作为用于实时训练比例积分微分神经网络的能量函数。此外，还仔细研究了控制系统的稳定性，并获得了全局最优解。 MATLAB（美国马萨诸塞州内蒂克市的MathWorks公司）仿真结果表明，该方法可以使系统全局稳定，在系统干扰下可以实现更强的鲁棒性，并且可以容易地应用于基于数字信号处理器的模块化多电平转换器高直流电压控制系统。

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