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Application of an adaptive model predictive control algorithm on the Pelton turbine governor control

IET RENEWABLE POWER GENERATION 2020年第10期14卷 1720-1727页

作者： Beus, Mateo Pandzic, Hrvoje Univ Zagreb Fac Elect Engn & Comp Dept Energy & Power Syst Unska 3 Zagreb Croatia

Traditionally, hydro turbine governor applications mainly rely on classical proportional-integral-derivative controllers. A classical controller can perform optimally only at the operating point chosen during the controller design. Since hydro power plants are highly non-linear systems alternative control approaches based on adaptive parameters are needed. Historically, due to the limited computation capabilities of microprocessors and programmable logic controllers (PLCs) used in hydro turbine governors, adaptive control schemes were not frequently applied. However, the latest generation of microprocessors and PLCs facilitate the application of adaptive control scheme based on predictive control algorithm for plants with faster dynamic behaviour. In that regard, this study introduces an adaptive controller based on model predictive control (MPC) algorithm developed and applied to a non-linear simulation model of a laboratory hydro power plant. The applied MPC algorithm is based on a linear prediction model whose parameters are identified offline for different operating points across the plant's operating range. The adaptive control scheme updates the prediction model parameters depending on the current operating point. Furthermore, the predictive control algorithm applied in this study is set up as a quadratic programming (QP) optimisation problem that is solved online using a QP solver in a form of Hildreth's algorithm.

关键词： hydraulic turbines linear systems fuzzy control adaptive control power generation control robust control control system synthesis predictive control three-term control quadratic programming nonlinear control systems adaptive model predictive control algorithm Pelton turbine governor control hydroturbine governor applications linear plant model fixed parameter scheme controller design hydropower plants nonlinear systems alternative control approaches adaptive parameters programmable logic controllers hydroturbine governors nonlinear simulation model laboratory hydropower plant MPC algorithm linear prediction model whose parameters adaptive control scheme updates prediction model parameters classical proportional-integral-derivative controllers quadratic programming optimisation QP solver Hildreth's algorithm QP optimisation

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adaptive MPC for constrained systems with parameter uncertainty and additive disturbance

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IET control THEORY AND APPLICATIONS 2019年第15期13卷 2500-2506页

作者： Zhang, Sixing Dai, Li Xia, Yuanqing Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

In this study, the authors propose an adaptive model predictive control (MPC) algorithm for constrained linear systems in state space subject to uncertain model parameters and disturbances. An iterative set membership identification algorithm is first presented to update the uncertain parameter set at each time step. Based on the shrunken uncertain parameter set, an MPC controller is then designed to robustly stabilise the uncertain systems subject to state and input constraints. The algorithm can efficiently reduce the size of the uncertain parameter set in min-max MPC setting, and therefore improve the control performance. The algorithm is proved to ensure constraint satisfaction, recursive feasibility and input-to-state practical stability of the closed-loop system even in the presence of system uncertainties. A numerical example and a brief comparison with traditional min-max MPC are provided to demonstrate the efficiency of the proposed algorithm.

关键词： adaptive control uncertain systems robust control closed loop systems linear systems nonlinear control systems predictive control minimax techniques traditional min-max MPC system uncertainties closed-loop system input-to-state practical stability control performance min-max MPC setting input constraints uncertain systems MPC controller shrunken uncertain parameter iterative set membership identification algorithm disturbances uncertain model parameters state space subject constrained linear systems adaptive model predictive control algorithm additive disturbance parameter uncertainty constrained systems adaptive MPC

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A novel primary frequency regulation strategy of wind farm based on wind turbine health condition

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IET RENEWABLE POWER GENERATION 2022年第10期16卷 2101-2110页

作者： Li, Shaolin Wang, Weisheng Zhang, Xing Qin, Shiyao Ying, You Hefei Univ Technol Sch Elect Engn & Automat Hefei 230009 Peoples R China China Elect Power Res Inst State Key Lab Operat & Control Renewable Energy & Beijing Peoples R China Zhejiang Windey Co Ltd Hangzhou Peoples R China

In order to optimize the primary frequency regulation performance of the wind farm and increase the service life of the wind turbines (WTs), this paper presents a novel primary frequency regulation strategy of the wind farm to maintain the system frequency considering each WT's health condition level. The proposed strategy can make WTs participate in frequency regulation according to their health conditions. Thus, the unhealthy WTs can be prevented from further deteriorating by reducing the load. In this paper, condition monitoring data from WTs is applied to estimate their health condition levels. Then, the power support is estimated according to the supplementary loop which includes inertial control and droop control. An adaptive model predictive control (AMPC) algorithm is employed to improve the control accuracy and mitigate the fluctuation caused by frequency variation. The case study is carried out in a two-area wind-diesel power system to validate the effectiveness of the proposed method. The results show that the proposed strategy optimizes the effect of primary frequency regulation while reducing the load on unhealthy WTs.

关键词： power generation control distributed power generation wind turbine health condition unhealthy WTs two-area wind-diesel power system frequency variation predictive control health conditions system frequency condition monitoring frequency control wind turbines health condition level novel primary frequency regulation strategy primary frequency regulation performance adaptive model predictive control algorithm wind farm condition monitoring data wind power plants

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Study on control strategy for personalised lane-change on highway

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JOURNAL OF ENGINEERING-JOE 2018年第16期2018卷 1724-1730页

作者： Cheng, Senlin Wang, Chuanhai Zhang, Shuangteng Zong, Ruixue Deng, Xinli Chongqing Univ Chongqing Peoples R China Eastern Kentucky Univ Richmond KY 40475 USA Chongqing Radio & Televis Univ Chongqing Peoples R China

The driving assistant system is conducive to reduce the accidents caused by operational mistakes in the complex environment of lane-change on the highway. However, the adaptability between the system and drivers has not been taken into consideration. To solve this problem, this study put forward a personalised driving assistant strategy in the process of highway lane-change. In the study, as a methodology based on the measured data of vehicle dynamic characteristics and personalised driving characteristics, it established a criterion for the personalised driving model, dissected the factors affecting driving safety and put forward a risk assessment method for individualised driving according to the criterion. Taken Matlab/Simulink software as a simulation experiment platform, it verified the rationality and feasibility of the individualised driving-aid strategy of highway lane-change with the aid of the adaptive model predictive control algorithm. The study results show that the personalised driving criterion and risk assessment method proposed in this study can effectively distinguish the driving styles and driving risks of different drivers, and the personalised driving assistance strategy not only respects different drivers' individualised operation styles but also effectively controls driving risks.

关键词： road traffic vehicle dynamics predictive control road safety safety driver information systems road vehicles risk management control strategy personalised lane-change driving assistant system personalised driving assistant strategy highway lane-change vehicle dynamic characteristics personalised driving model driving safety risk assessment method Taken Matlab/Simulink software individualised driving-aid strategy adaptive model predictive control algorithm personalised driving criterion driving styles driving risks personalised driving assistance strategy different drivers

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