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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Abstract The current of load inductance in the experimental advanced superconducting Tokamak (EAST) fast control power supply is difficult to predict due to the influence of various uncertain environmental factors. An accurate object model is optional in grey prediction.
When the traditional rolling grey prediction is applied to the EAST fast control power supply, there is a specific 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. There are low prediction accuracy and long prediction delay problems for the edge of abrupt signal in the EAST fast control power supply. Therefore, an improved grey prediction algorithm is proposed to predict the output current. The output current is sampled 4 times at equal time intervals 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 needed to realize grey prediction, and 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 intervals in a switching period, which avoids the value with non-exponential law in the original sequence and further improves the accuracy of grey prediction at the abrupt signal. The performance difference between improved grey prediction and traditional grey prediction is analyzed through convergence analysis. The improved grey prediction has a faster convergence speed and smaller prediction errors. Based on the objective function of the current tracking reference current at the next 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 the EAST fast control power supply current prediction process has higher prediction accuracy than 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. Compared with the traditional PI control, the output current overshoot is reduced by 3.125%, the control delay and dynamic time in the rising process are reduced by 40 μs, reaching the rising steady state value 0.34ms ahead of time. The control delay and dynamic time during the drop are reduced by 50 μs and 10 μs. The steady-state value of the drop is reached 0.29 ms ahead. According to the theoretical, simulation, and experimental results, the improved grey prediction variable gain single neuron PI control method proposed can improve the prediction accuracy of the 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

Received: 23 December 2022

PACS:	TM93
	TM917

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	Huang Haihong
	Chen Zhao
	Wang Haixin

Cite this article:

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[J]. Transactions of China Electrotechnical Society, 2024, 39(6): 1886-1897.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.222367 OR https://dgjsxb.ces-transaction.com/EN/Y2024/V39/I6/1886

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