基于改进灰色预测单神经元PI的全超导托卡马克核聚变发电装置快控电源电流控制

doi:10.19595/j.cnki.1000-6753.tces.222367

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摘要

全超导托卡马克核聚变发电装置（EAST）快控电源负载电感的电流受多种不确定环境因素影响而难以预测,灰色预测无需精确对象模型,只需少量已知信息即可实现输出电流短期预测,已在EAST快控电源中有了一定研究应用。为解决灰色预测在EAST快控电源中对突变信号边沿预测精度低和预测延时时间长的问题,提出一种改进灰色预测算法实现输出电流预测。在一个开关周期内对输出电流进行等时长间隔采样4次作为原始序列,将滚动式采样预测改为逐周期采样预测,在实现灰色预测的过程中不必依赖过去几个周期的历史采样信息,只需本周期的4个原始采样值即可实现输出电流的预测。根据预测电流与参考电流误差自适应调整单神经元比例-积分(PI)控制的输出增益,实现了输出电流的快速准确控制。仿真和实验结果表明在EAST快控电源电流预测过程中所提出的改进灰色预测对比传统灰色预测具有更高的预测精度和更小的预测延时,改进灰色预测变增益单神经元PI控制能够在减小电流超调的同时加快输出电流响应速度。

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关键词 ： EAST快控电源, 改进灰色预测, 逐周期采样预测, 变增益单神经元PI

Abstract：

The current of load inductance in Experimental Advanced Superconducting Tokamak (EAST) fast control power supply is difficult to predict due to the influence of various uncertain environmental factors. Accurate object model is not essential in grey prediction.
When the traditional rolling grey prediction is applied to EAST fast control power supply, there is a certain prediction error at the abrupt signal, and at least 4 cycles of historical current data are needed to realize the current prediction of the next cycle. To solve the problems of low prediction accuracy and long prediction delay for the edge of abrupt signal in EAST fast control power supply, an improved grey prediction algorithm is proposed to predict the output current. The output current is sampled 4 times at equal time interval in a switching cycle as the original sequence. The rolling sampling prediction is changed to the cycle by cycle sampling prediction. Historical sampling information of the past several periods is not needed to realize grey prediction, but only four original sampling values of the current period are needed to realize the prediction of output current at the expense of the delay of one switching period. The original data sequence is composed of 4 times sampled at equal time interval in a switching period, which avoids the value with non-exponential law in the original sequence, and further improve the accuracy of grey prediction at the abrupt signal. Through convergence analysis, the performance difference between improved grey prediction and traditional grey prediction is analyzed. The improved grey prediction has faster convergence speed and smaller prediction error. Based on the objective function of current tracking reference current at the nextg moment, a single neuron PI parameter adaptive adjustment network structure is constructed. The output gain of the single neuron proportional-integral (PI) control is adaptively adjusted according to the error between the predicted current and the reference current, and the PI parameter is adaptively adjusted online to realize the fast and accurate control of the output current.
The simulation results show that the improved grey prediction proposed in EAST fast control power supply current prediction process has higher prediction accuracy compared with the traditional grey prediction. The improved grey prediction variable gain single neuron PI control can reduce the current overshoot and accelerate the output current response speed. The experimental results verify that compared with the traditional PI control, the output current overshoot is reduced by 3.125%, and the control delay and dynamic time in the rising process reduced 40μs, and reach the rising steady state value 0.34ms ahead of time under the improved grey prediction variable gain single neuron PI control. The control delay and dynamic time during drop are separately reduced by 50μs and 10μs. The steady state value of drop is reached 0.29ms ahead. According to the theoretical, simulation and experimental results, the following conclusions can be drawn that the improved grey prediction variable gain single neuron PI control method proposed can improve the prediction accuracy of EAST fast control power supply current, reduce the prediction delay, suppress the overshoot of output current, and speed up the dynamic response speed of output current.

Key words： Experimental advanced superconducting Tokamak(EAST) fast control power supply improved grey prediction cycle by cycle sampling prediction variable gain single neuron PI

收稿日期: 2022-12-23

PACS:

TM93

基金资助:

国家自然科学基金区域创新发展联合基金资助项目(U22A20225)

通讯作者: 陈昭男,1996年生,博士研究生,研究方向为新型电能变换技术等。E-mail：chenzhao_0202@163.com

作者简介: 黄海宏男,1973年生,教授,博士生导师,研究方向为新型大功率变流技术与电力电子技术等。E-mail：hhaihong741@126.com

引用本文:

黄海宏, 陈昭, 王海欣. 基于改进灰色预测单神经元PI的全超导托卡马克核聚变发电装置快控电源电流控制[J]. 电工技术学报, 0, (): 137-137. Huang Haihong, Chen Zhao, Wang Haixin. Current Control of Experimental Advanced Superconducting Tokamak Fast Control Power Supply Based on Improved Grey Prediction Single Neuron PI. Transactions of China Electrotechnical Society, 0, (): 137-137.

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