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.
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, 2024, 39(6): 1886-1897.
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, 2024, 39(6): 1886-1897.
[1] Jiang Ping, Xing Yanjun, Wang Peiguang.No-load cutting-in control of the doubly fed induction generator based on grey prediction PI control[J]//Energy Reports, 2021, 7: 38-48. [2] He Shengxiang, Wang Mingdong.Grey prediction PI control of direct drive permanent magnet synchronous wind turbine[C]//Proceedings of the 4th IEEE Con- ference on Energy Internet and Energy System Inte- gration, Wuhan, China, 2021: 2032-2035. [3] 张红, 袁铁江, 谭捷. 统一能源系统氢负荷中长期预测[J]. 中国电机工程学报, 2021, 41(10): 3364-3372, 3662. Zhang Hong, Yuan Tiejiang, Tan Jie.Medium and long-term forecast of hydrogen load in unified energy system[J]. Proceedings of the CSEE, 2021, 41(10): 3364-3372, 3662. [4] Pan Hongguang, Huang Xinyi, Guan Penglin, et al.Grey-prediction-based double model predictive control strategy for the speed and current control of per- manent magnet synchronous motor[J]. Asian Journal of Control, 2022, 24(6): 3494-3507. [5] Huang Xianghui, Guan Penglin, Pan Hongguang, et al.Research on grey predictive control of PMSM based on reduced-order luenberger observer[J]. Journal of Electrical Engineering & Technology, 2021, 16(5): 2635-2646. [6] Chang E C, Yang S C, Wu R C.Robust stable control design for AC power supply applications[J]. Elec- tronics, 2019, 8(4): 419-434. [7] Salhein K, Ashraf J, Zohdy M.Output temperature predictions of the geothermal heat pump system using an improved grey prediction model[J]. Energies, 2021, 14(16): 5075-5066. [8] 韦海燕, 陈静, 王惠民, 等. 新陈代谢灰色粒子滤波实现电池剩余寿命预测[J]. 电工技术学报, 2020, 35(6): 1181-1188. Wei Haiyan, Chen Jing, Wang Huimin, et al.Remaining useful life prediction of battery using metabolic grey particle filter[J]. Transactions of China Electrotechnical Society, 2020, 35(6): 1181-1188. [9] 潘海鸿, 吕治强, 李君子, 等. 基于灰色扩展卡尔曼滤波的锂离子电池荷电状态估算[J]. 电工技术学报, 2017, 32(21): 1-8. Pan Haihong, Lü Zhiqiang, Li Junzi, et al.Estimation of lithium-ion battery state of charge based on grey prediction model-extended Kalman filter[J]. Transa- ctions of China Electrotechnical Society, 2017, 32(21): 1-8. [10] 陈琳, 陈静, 王惠民, 等. 基于小波包能量熵的电池剩余寿命预测[J]. 电工技术学报, 2020, 35(8): 1827-1835. Chen Lin, Chen Jing, Wang Huimin, et al.Prediction of battery remaining useful life based on wavelet packet energy entropy[J]. Transactions of China Elec- trotechnical Society, 2020, 35(8): 1827-1835. [11] 王磊, 李兵, 董超跃, 等. 城轨牵引变流器功率开关器件的双向加速老化测试与在线损伤预测[J]. 电工技术学报, 2019, 34(增刊1): 224-234. Wang Lei, Li Bing, Dong Chaoyue, et al.Bidire- ctional accelerated aging test and online damage prediction of power switch devices in urban traction converter[J]. Transactions of China Electrotechnical Society, 2019, 34(S1): 224-234. [12] Zeng Bo, Ma Xin, Shi Juanjuan.Modeling method of the grey GM(1,1) model with interval grey action quantity and its application[J]. Complexity, 2020: 1-10. [13] Wu Weili.An intelligent gray prediction model based on fuzzy theory[J]. International Transactions on Electrical Energy Systems, 2022, 25:1-9. [14] 薛阳, 张宁, 吴海东, 等. 基于UTCI-MIC与振幅压缩灰色模型的用户侧微电网短期负荷预测方法[J]. 电网技术, 2020, 44(2): 556-563. Xue Yang, Zhang Ning, Wu Haidong, et al.Short- term load forecasting method for user side microgrid based on UTCI-MIC and amplitude compression grey model[J]. Power System Technology, 2020, 44(2): 556-563. [15] 刘鑫, 张斌, 杨来宝, 等. 灰色预测模糊自适应 PID控制的城轨列车智能驾驶系统研究[J]. 测控技术, 2020, 39(3): 109-113. Liu Xin, Zhang Bin, Yang Laibao, et al.Intelligent train operation system of urban rail transit based on grey predictive fuzzy adaptive PID control[J]. Measurement & Control Technology, 2020, 39(3): 109-113. [16] Hou Li, Li Xiaolei, Wang Mingdong.Grey-fuzzy PI optimal control of MMC-HVDC system[C]//Pro- ceedings of the 4th IEEE Conference on Energy Internet and Energy System Integration, Wuhan, China, 2021: 3258-3262. [17] 杨頔, 姚钢, 周荔丹. 功率变化环境下的四线制Vienna整流器优化联合控制方法[J]. 电工技术学报, 2021, 36(2): 305-319. Yang Di, Yao Gang, Zhou Lidan.An improved control method of 4-wire Vienna rectifier considering power fluctuation[J]. Transactions of China Electro- technical Society, 2021, 36(2): 305-319. [18] 周天豪, 杨智, 祝长生, 等. 电磁轴承高速电机转子系统的内模-PID控制[J]. 电工技术学报, 2020, 35(16): 3414-3425. Zhou Tianhao, Yang Zhi, Zhu Changsheng, et al.Internal model control-PID control of an active magnetic bearing high-speed motor rotor system[J]. Transactions of China Electrotechnical Society, 2020, 35(16): 3414-3425. [19] 刘其辉, 洪晨威, 逄思敏, 等. 基于弹性系数的双馈风电机组控制参数对次同步振荡作用分析及调整方法[J]. 电工技术学报, 2022, 37(14): 3528-3541. Liu Qihui, Hong Chenwei, Pang Simin, et al.Analysis and adjustment method of doubly-fed fan control parameters on subsynchronous oscillation based on impedance elastic sensitivity[J]. Transactions of China Electrotechnical Society, 2022, 37(14): 3528-3541. [20] 周明宽, 夏俊芳, 郑侃, 等. 基于变增益单神经元PID的秸秆旋埋还田导航系统研制[J]. 农业工程学报, 2021, 37(5): 31-40. Zhou Mingkuan, Xia Junfang, Zheng Kan, et al.Development of rotary straw burying and returning navigation system based on variable-gain single- neuron PID[J]. Transactions of the CSAE, 2021, 37(5): 31-40. [21] 周向阳, 时延君. 惯性稳定平台单神经元/PID自适应复合控制与参数优化[J]. 仪器仪表学报, 2019, 40(11): 189-196. Zhou Xiangyang, Shi Yanjun.Single neuron/PID adaptive compound control and parameter optimi- zation for the inertially stabilized platform[J]. Chinese Journal of Scientific Instrument, 2019, 40(11): 189-196. [22] 王永涛, 肖俊辰. 基于改进灰色预测单神经元PID的URV伺服控制系统研究[J]. 兵器装备工程学报, 2021, 42(8): 251-257. Wang Yongtao, Xiao Junchen.Research on URV servo control system based on improved grey prediction single neuron PID[J]. Journal of Ordnance Equipment Engineering, 2021, 42(8): 251-257. [23] 汪凤凤, 黄海宏, 王海欣. EAST快控电源灰色预测PID控制预研[J]. 电子测量与仪器学报, 2015, 29(4): 604-610. Wang Fengfeng, Huang Haihong, Wang Haixin.Pre-research on gray prediction PID control of EAST fast control power supply[J]. Journal of Electronic Measurement and instrumentation, 2015, 29(4): 604-610. [24] 黄海宏, 颜碧琛, 王海欣. 改进灰色GM(1,1)模型的EAST快控电源输出电流预测[J]. 电子测量与仪器学报, 2021, 35(5): 128-136. Huang Haihong, Yan Bichen, Wang Haixin.Predi- ction of output current of EAST fast control power supply based on improved grey GM(1,1) model[J]. Journal of Electronic Measurement and Instru- mentation, 2021, 35(5): 128-136.
2024, Vol. 39 
