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In order to solve the problem of voltage fluctuation caused by the grid integration of wind power cluster, a multitime scale reactive power and voltage optimal regulation method based on model predictive control (MPC) is proposed in this paper. In the day‐ahead stage, the reduction of network active power loss is mainly achieved through the regulation of discrete reactive power compensation devices and generator units, focusing on the economic operation of the power system. In the intraday stage, considering the rapid response characteristics of continuous reactive power compensation devices, the optimal regulation aiming to minimize voltage control deviation is carried out under 15‐ and 5‐min time scale based on the MPC algorithm with the adjustment of continuous reactive power compensation devices. In the feedback correction stage, a fast calculation method considering reactive power partitioning is adopted instead of solving the 5‐min optimization model for 288 periods, avoiding excessive adjustment of reactive compensation devices. While effectively improving voltage fluctuations caused by wind power prediction deviation, it also alleviated the computational and communication burden on the system. Finally, the effectiveness of the proposed regulation method in this paper is verified with the improved IEEE‐39 test case. [ABSTRACT FROM AUTHOR]

In order to reduce the negative influence of wind speed randomness and prediction error on frequency modulation, the reliability of the wind storage system was assessed effectively. In the wind storage frequency modulation system, a state of charge (SOC) adaptive adjustment method for wind speed randomness is proposed. Firstly, through the correlation analysis between the standby capacity of frequency modulation and the output power of wind turbine, the uncertainty of its frequency modulation capacity is revealed. Secondly, in view of the uncertainty of wind turbine frequency modulation, the output power of energy storage frequency modulation is optimized with the goal of minimizing the frequency modulation power deviation of the wind storage front under the framework of model predictive control, and the improved whale optimization algorithm (WOA) is used to solve the problem. Finally, the simulation results show that, under the given 5 min continuous disturbance, the root mean square of frequency regulation of the proposed restoration method is reduced by 56.65% compared to the SOC recovery base point set to 0.5. Under continuous large perturbations, the maximum frequency deviation is reduced by 0.0455 Hz. This effectively shows that this method can not only improve the frequency modulation reliability of wind power system but also improve the continuous frequency modulation capability of energy storage system. [ABSTRACT FROM AUTHOR]

In this paper, a UAV-WPT based on a new orthogonal coupling structure is first designed. An offset weight method is proposed to optimize the design of the system resonance parameters to improve the power transmission efficiency in the offset state. The coupling mechanism's coupling ability and anti-offset performance are experimented with using Ansys software simulation. The system adopts LCC-S topology and proposes a secondary side constant current control strategy based on the model prediction control to ensure constant current output in the case of coupling coefficient deviation. Finally, an experimental platform is designed for experiments. The results show that the system can quickly realize 3A constant current output under the change of load and coupling coefficient, and at the same time, improve the working efficiency of the system. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the traditional finite set model predictive control applied to grid-connected inverter has delay and inductance varying with the working state, an improved finite set model predictive control method is proposed. By increasing the two sets of switching voltage vectors to reduce the current approximation error, the cost function value is minimized. And the inductance value is predicted in real time based on the idea of model predictive control. The improved finite set model predictive control method can realize the real-time tracking of the inductance value, improve the control precision, and effectively reduce the total harmonic distortion. Simulation and experimental results verify the feasibility and effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Flutter suppression of wind turbine blade section based on two types of model prediction control (MPC) algorithms is investigated. The pre-twist blade, exhibiting displacements of flap-wise bending and lead-lag bending, employs 2D airfoil analysis method, and is subjected to destructive divergent instability displacements. Dynamic modelling of two-dimensional (2D) blade section is based on data analysis characterized by data fitting. The MPC methods including standard MPC and terminal-weight MPC are used to realize flutter suppression for divergent instability. Vibration control and control effects of terminal-weight MPC on divergent instability are analyzed in detail, with different parameters of prediction horizon and control horizon discussed. The superiority of terminal-weight MPC can be apparently demonstrated by comparison of standard MPC and sliding mode control with disturbance observer (SMCDO). The feasibility of hardware implementation of MPC algorithm is discussed by human-computer interaction platform. The performance improvement method of the terminal-weight MPC based on offset control has been refined from the point of view of practical application, with its remarkable effect compared with the original terminal-weight MPC. [ABSTRACT FROM AUTHOR]

Piezo-actuated micro-/nanopositioning systems have been widely employed in diverse high-precision positioning applications. However, the inherent hysteresis nonlinearity seriously deteriorates the tracking performance of piezo-actuated stages. This paper presents the design, analysis, and validation of a novel control scheme termed model prediction control (MPC) with real-time modified reference based on disturbance observer (DOB) to suppress the hysteresis nonlinearity and model uncertainty, in which the nonlinear effects are treated as an unknown disturbance to the system. In order to remove the most of the interference and diminish the effect of noise, a DOB is designed for the non-minimum phase (NMP) system. Then the difference between the actual displacement and the output of the nominal model termed the residual error is estimated and used to modify the reference in real time for a better performance. By the proposed method, the model of the inherent hysteresis is not required and the controller is established based on the identified nominal model. Its effectiveness is validated through experimental investigations on a commercial nanopositioner. Experimental results show that the proposed method can improve the tracking performance of the piezo-actuated stage, as compared with the traditional MPC and DOB-based MPC. [ABSTRACT FROM AUTHOR]

Power controlling on reliability-aware GPU clusters with dynamically variable voltage and speed is investigated as combinatorial optimization problem, namely the problem of minimizing task execution time with energy consumption constraint and the problem of minimizing energy consumption with system reliability constraint. The two problems have applied in general multiprocessor computing and real-time multiprocessing systems where energy consumption and system reliability both are important. These problems which emphasize the trade-off among performance, power and reliability have not been well studied before. In this research, a novel power control model is built based on Model Prediction Control theory. Maximum Entropy Method is used to determine partial ordering relation of control variable and to identify the quality of solutions. Our controller can cap the redundant energy consumption by dynamically transforming energy states of the nodes in GPU cluster. We compare our controller with the control scheme, which does not consider the system reliability. The experimental results demonstrate that the proposed controller is more reliable and valuable. [ABSTRACT FROM AUTHOR]

Energy management strategy (EMS) for plug-in hybrid electric vehicle (PHEV) is the key to improve the energy utilization efficiency and vehicle fuel economy. In this paper, a model predictive control (MPC) based on EMS coupled with double Q-learning (DQL) is presented to allocate the power between multiple power sources for PHEV. Firstly, the powertrain framework of the PHEV and its mathematical models were analyzed in detail. Then, based on the required power and speed, an effective convergent offline learning controller was established based on DQL algorithm. Subsequently, the multi-feature input Elman neural network was implemented to predict vehicle speed in MPC, and the trained DQL controller was applied to solve the rolling optimization process in MPC to find the optimal battery output in the prediction horizon. Finally, the proposed strategy was verified in Autonomie software, and the simulation results show that the proposed strategy can achieve a superior fuel economy close to that of the offline stochastic dynamic planning strategy, meanwhile with a perfect adaptability for different state of charge (SOC) reference trajectories. [Display omitted] • An online EMS based on DQL-MPC is proposed for PHEV to improve fuel economy. • The multi-feature input Elman NN is applied to predict vehicle speed in MPC. • The proposed EMS has a perfect adaptability for different SOC reference trajectories. [ABSTRACT FROM AUTHOR]