DC active power filter based on model predictive control for DC bus overvoltage suppression of accelerator grid power supply

The China Fusion Engineering Test Reactor plans to build a 200 k V/25 A acceleration grid power supply(AGPS)for the negative-ion-based neutral beam injector prototype system.The AGPS uses a rectifier-inverter-isolated step-up structure.There is a DC bus between the rectifier and the inverter.In order to limit DC bus voltage ripple and transient fluctuations,a large number of capacitors are used,which degrades the reliability of the power supply and occupies a large amount of space.This work finds that due to the difference in the turn-off time of the rectifier and the inverter,the capacitance mainly depends on the rectifier current when the inverter is turned off.On this basis,an active power filter(APF)scheme is proposed to absorb the current.To enhance the dynamic response ability of the APF,model predictive control is adopted.In this paper,the circuit structure of the APF is introduced,the prediction model is deduced,the corresponding control strategy and signal detection method are proposed,and the simulation and experimental results show that APF can track the transient current of the DC bus and reduce the voltage fluctuation significantly.

Model Predictive Control Strategy of Multi-Port Interline DC Power Flow Controller

There are issues with flexible DC transmission system such as a lack of control freedom over power flow.In order to tackle these issues,a DC power flow controller(DCPFC)is incorporated into a multi-terminal,flexible DC power grid.In recent years,a multi-port DC power flow controller based on a modular multi-level converter has become a focal point of research due to its simple structure and robust scalability.This work proposes a model predictive control(MPC)strategy for multi-port interline DC power flow controllers in order to improve their steady-state dynamic performance.Initially,the mathematical model of a multi-terminal DC power grid with a multi-port interline DC power flow controller is developed,and the relationship between each regulated variable and control variable is determined;The power flow controller is then discretized,and the cost function and weight factor are built with numerous control objectives.Sub module sorting method and nearest level approximation modulation regulate the power flow controller;Lastly,theMATLAB/Simulink simulation platformis used to verify the correctness of the establishedmathematicalmodel and the control performance of the suggestedMPC strategy.Finally,it is demonstrated that the control strategy possesses the benefits of robust dynamic performance,multiobjective control,and a simple structure.

Distributed Model Predictive Load Frequency Control of Multi-area Power System with DFIGs

Reliable load frequency control LFC is crucial to the operation and design of modern electric power systems. Considering the LFC problem of a four-Area interconnected power system with wind turbines, this paper presents a distributed model predictive control DMPC based on coordination scheme. The proposed algorithm solves a series of local optimization problems to minimize a performance objective for each control area. The generation rate constraints GRCs, load disturbance changes, and the wind speed constraints are considered. Furthermore, the DMPC algorithm may reduce the impact of the randomness and intermittence of wind turbine effectively. A performance comparison between the proposed controller with and without the participation of the wind turbines is carried out. Analysis and simulation results show possible improvements on closed-loop performance, and computational burden with the physical constraints. © 2014 Chinese Association of Automation.

Model Predictive Control-Based Direct Torque Control for Matrix Converter-Fed Induction Motor with Reduced Torque Ripple

To reduce the torque ripple in motors resulting from the use of conventional direct torque control(DTC),a model predictive control(MPC)-based DTC strategy for a direct matrix converter-fed induction motor is proposed in this paper.Two new look-up tables are proposed,these are derived on the basis of the control of the electromagnetic torque and stator flux using all the feasible voltage vectors and their associated switching states.Finite control set model predictive control(FCS-MPC)has then been adopted to select the optimal switching state that minimizes the cost function related to the electromagnetic torque.Finally,the experimental results are shown to verify the reduced torque ripple performance of the proposed MPC-based DTC method.

Model-predictive control of power supply for particle accelerators

In this paper, model-predictive control(MPC) is proposed for controlling power source of accelerators. The system state equation is employed as the predictive model. With MPC, the difference between possible output and the ideal output is forecasted and decreased, so that the system can trace the ideal trail as closely and quickly as possible. The results of simulations and experiments show that this method can reduce influence of low frequency noise.

Predictive direct power control of three-phase PWM rectifier based on TOGI grid voltage sensor free algorithm

In predictive direct power control(PDPC)system of three-phase pulse width modulation(PWM)rectifier,grid voltage sensor makes the whole system more complex and costly.Therefore,third-order generalized integrator(TOGI)is used to generate orthogonal signals with the same frequency to estimate the grid voltage.In addition,in view of the deviation between actual and reference power in the three-phase PWM rectifier traditional PDPC strategy,a power correction link is designed to correct the power reference value.The grid voltage sensor free algorithm based on TOGI and the corrected PDPC strategy are applied to three-phase PWM rectifier and simulated on the simulation platform.Simulation results show that the proposed method can effectively eliminate the power tracking deviation and the grid voltage.The effectiveness of the proposed method is verified by comparing the simulation results.

Direct Model Predictive Control of Noninverting Buck-boost DC-DC Converter

In this paper, direct model predictive control(DMPC) of the noninverting buck-boost DC-DC converter with magnetic coupling between input and output is proposed. Unlike most of the other converters, the subject converter has the advantage of exhibiting minimum phase behavior in the boost mode. However, a major issue that arises in the classical control of the converter is the dead zone near the transition of the buck and boost mode. The reason for the dead zone is practically unrealizable duty cycles, which are close to zero or unity, of pulse width modulation(PWM) near the transition region. To overcome this issue, we propose to use DMPC. In DMPC, the switches are manipulated directly by the controller without the need of PWM.Thereby, avoiding the dead zone altogether. DMPC also offers several other advantages over classical techniques that include optimality and explicit current constraints. Simulations of the proposed DMPC technique on the converter show that the dead zone has been successfully avoided. Moreover, simulations show that the DMPC technique results in a significantly improved performance as compared to the classical control techniques in terms of response time, reference tracking, and overshoot.

Model Predictive Direct Torque Control of Permanent Magnet Synchronous Motor (PMSM) with Online Parameter Estimation Based on Extended Kalman Filter

Aiming at the torque and flux ripples in the direct torque control and the time-varying parameters for permanent magnet synchronous motor (PMSM), a model predictive direct torque control with online parameter estimation based on the extended Kalman filter for PMSM is designed. By predicting the errors of torque and flux based on the model and the current states of the system, the optimal voltage vector is selected to minimize the error of torque and flux. The stator resistance and inductance are estimated online via EKF to reduce the effect of model error and the current estimation can reduce the error caused by measurement noise. The stability of the EKF is proved in theory. The simulation experiment results show the method can estimate the motor parameters, reduce the torque, and flux ripples and improve the performance of direct torque control for permanent magnet synchronous motor (PMSM).

DISOPE distributed model predictive control of cascade systems with network communication

A novel distributed model predictive control scheme based on dynamic integrated system optimization and parameter estimation （DISOPE） was proposed for nonlinear cascade systems under network environment. Under the distributed control structure, online optimization of the cascade system was composed of several cascaded agents that can cooperate and exchange information via network communication. By iterating on modified distributed linear optimal control problems on the basis of estimating parameters at every iteration the correct optimal control action of the nonlinear model predictive control problem of the cascade system could be obtained, assuming that the algorithm was convergent. This approach avoids solving the complex nonlinear optimization problem and significantly reduces the computational burden. The simulation results of the fossil fuel power unit are illustrated to verify the effectiveness and practicability of the proposed algorithm.

Model Predictive Current Control for Low-Cost Shunt Active Power Filter

Performance of a three-phase shunt active power filter(SAPF)relies on the capability of the controller to track the reference current.Therefore,designing an accurate current controller is crucial to guarantee satisfactory SAPF operation.This paper presents a model predictive current controller(MPCC)for a low-cost,four-switch,shunt active power filter for power quality improvement.A four-switch,B4,converter topology is adopted as an SAPF,hence offering a simple,robust,and low-cost solution.In addition,to further reduce overall cost,only two interfacing filter inductors,instead of three,are used to eliminate switching current ripple.The proposed SAPF model MPCC is detailed for implementation,where simulation and experimental results validate effectiveness of the proposed control algorithm showing a 20%improvement in total harmonic distortion compared with a conventional hysteresis band current controller.

Nonlinear Model Predictive Controller for Compensations of Single Line-to-ground Fault in Resonant Grounded Power Distribution Networks

An nonlinear model predictive controller(NMPC)is proposed in this paper for compensations of single line-to-ground(SLG)faults in resonant grounded power distribution networks(RGPDNs),which reduces the likelihood of power line bushfire due to electric faults.Residual current compensation(RCC)inverters with arc suppression coils(ASCs)in RGPDNs are controlled using the proposed NMPC to provide appropriate compensations during SLG faults.The proposed NMPC is incorporated with the estimation of ASC inductance,where the estimation is carried out based on voltage and current measurements from the neutral point of the power distribution network.The compensation scheme is developed in the discrete time using the equivalent circuit of RGPDNs.The proposed NMPC for RCC inverters ensures that the desired current is injected into the neutral point during SLG faults,which is verified through both simulations and control hardware-in-the-loop(CHIL)validations.Comparative results are also presented against an integral sliding mode controller(ISMC)by demon-strating the capability of power line bushfire mitigation.

Fuzzy disturbance rejection predictive control of ultra-supercritical once-through boiler-turbine unit

In order to overcome the wide-range load tracking and unknown disturbance issues of an ultra-supercritical boiler- turbine unit, a fuzzy disturbance rejection predictive control approach is proposed using the techniques of fuzzy scheduling, model predictive control and extended state observer. Local state-space models are established on the basis of nonlinearity analysis and subspace identification. To eiJiance thedisturbance rejection capability of the controller, a extended state observer is employed to estimate unnown disturbances and model mismatches. The disturbance estimation ennaced local predictive controllers ae subsequently devised based on the local models, the performance of which is further strengthened by incorporating the fuzzy scheduling technique. The simulation results verify the merits of the proposed strategy in achieving satisfactory wide-range load tracking ad disturbance rejection performance.

A Long-Range Generalized Predictive Control Algorithm for a DFIG Based Wind Energy System

This paper presents a new Long-range generalized predictive controller in the synchronous reference frame for a wind energy system doubly-fed induction generator based. This controller uses the state space equations that consider the rotor current and voltage as state and control variables, to execute the predictive control action. Therefore, the model of the plant must be transformed into two discrete transference functions, by means of an auto-regressive moving average model, in order to attain a discrete and decoupled controller, which makes it possible to treat it as two independent single-input single-output systems instead of a magnetic coupled multiple-input multiple-output system. For achieving that, a direct power control strategy is used, based on the past and future rotor currents and voltages estimation. The algorithm evaluates the rotor current predictors for a defined prediction horizon and computes the new rotor voltages that must be injected to controlling the stator active and reactive powers. To evaluate the controller performance, some simulations were made using Matlab/Simulink. Experimental tests were carried out with a small-scale prototype assuming normal operating conditions with constant and variable wind speed profiles. Finally, some conclusions respect to the dynamic performance of this new controller are summarized.

Tube Model Predictive Control Based Cyber-attack-resilient Optimal Voltage Control Strategy in Wind Farms

Optimal voltage controls have been widely applied in wind farms to maintain voltage stability of power grids.In order to achieve optimal voltage operation,authentic grid information is widely needed in the sensing and actuating processes.However,this may induce system vulnerable to malicious cyber-attacks.To this end,a tube model predictive control-based cyber-attack-resilient optimal voltage control method is proposed to achieve voltage stability against malicious cyber-attacks.The proposed method consists of two cascaded model predictive controllers(MPC),which outperform other peer control methods in effective alleviation of adverse effects from cyber-attacks on actuators and sensors of the system.Finally,efficiency of the proposed method is evaluated in sensor and actuator cyber-attack cases based on a modified IEEE 14 buses system and IEEE 118 buses system.Index Terms-Attack-resilient control,optimal voltage control,tube-based model predictive control,wind farm-connected power system.

Multi-timescale optimization scheduling of interconnected data centers based on model predictive control

With the promotion of“dual carbon”strategy,data center(DC)access to high-penetration renewable energy sources(RESs)has become a trend in the industry.However,the uncertainty of RES poses challenges to the safe and stable operation of DCs and power grids.In this paper,a multi-timescale optimal scheduling model is established for interconnected data centers(IDCs)based on model predictive control(MPC),including day-ahead optimization,intraday rolling optimization,and intraday real-time correction.The day-ahead optimization stage aims at the lowest operating cost,the rolling optimization stage aims at the lowest intraday economic cost,and the real-time correction aims at the lowest power fluctuation,eliminating the impact of prediction errors through coordinated multi-timescale optimization.The simulation results show that the economic loss is reduced by 19.6%,and the power fluctuation is decreased by 15.23%.

Hierarchical Cluster Coordination Control Strategy for Large-scale Wind Power Based on Model Predictive Control and Improved Multi-time-scale Active Power Dispatching

The grid-connection of large-scale and high-penetration wind power poses challenges to the friendly dispatching control of the power system.To coordinate the complicated optimal dispatching and rapid real-time control,this paper proposes a hierarchical cluster coordination control(HCCC)strategy based on model predictive control(MPC)technique.Considering the time-varying characteristics of wind power generation,the proposed HCCC strategy constructs an improved multitime-scale active power dispatching model,which consists of five parts:formulation of cluster dispatching plan,rolling modification of intra-cluster plan,optimization allocation of wind farm(WF),grouping coordinated control of wind turbine group(WTG),and real-time adjustment of single-machine power.The time resolutions are sequentially given as 1 hour,30 min,15 min,5 min,and 1 min.In addition,a combined predictive model based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),wavelet thresholding(WT),and least squares support vector machine(LSSVM)is established.The fast predictive feature of this model cooperates with the HCCC strategy that effectively improves the predictive control precision.Simulation results show that the proposed HCCC strategy enables rapid response to active power control(APC),and significantly improves dispatching control accuracy and wind power accommodation capabilities.

Model predictive control of indoor thermal environment conditioned by a direct expansion air conditioning system

Temperature and humidity are two important factors that influence both indoor thermal comfort and air quality.Through varying compressor and supply fan speeds of a direct expansion(DX)air conditioning(A/C)unit,the air temperature and humidity in the conditioned space can be regulated simultaneously.However,most existing controllers are designed to minimize the tracking errors between the system outputs with their corresponding settings as quickly as possible.The energy consumption,which is directly influenced by the compressor and supply fan speeds,is not considered in the relevant controller formulations,and thus the system may not operate with the highest possible energy efficiency.To effectively control temperature and humidity while minimizing the system energy consumption,a model predictive control(MPC)strategy was developed for a DX A/C system,and the development results are presented in this paper.A physically-based dynamic model for the DX A/C system with both sensible and latent heat transfers being considered was established and validated by experiments.To facilitate the design of MPC,the physical model was further linearized.The MPC scheme was then developed by formulating the objective function which sought to minimize the tracking errors of temperature and moisture content while saving energy consumption.Based on the results of command following and disturbance rejection tests,the proposed MPC scheme was capable of controlling temperature and humidity with adequate control accuracy and sensitivity.In comparison to linear-quadratic-Gaussian(LQG)controller,better control accuracy and lower energy consumption could be realized when using the proposed MPC strategy to simultaneously control temperature and humidity.

Wind Farm Coordinated Control for Power Optimization

以降低风电场尾流损失、优化风场出力为目标,设计基于Laguerre函数非线性预测控制（nonlinear modelpredictive control,NLMPC）方案的风场集群控制器。该控制器应用风场动态尾流模型,通过NLMPC统一调整风场内各机组转速以提升风场功率。在控制器设计中,使用有效风速预测误差校正对预测模型失配及超短期风速预测误差进行补偿,引入Laguerre函数降低滚动时域优化计算负担并分析了控制器对风速预测误差的鲁棒性能。仿真研究表明,集群控制器能够在不同风速条件下提升风场功率、降低优化计算负担,且对风速预测模型失配与风场自然风速预测误差具有鲁棒性。

Model Predictive Direct Power Control of Grid-connected Converters Considering Unbalanced Filter Inductance and Grid Conditions

The grid-connected converter(GCC) is widely used as the interface between various distributed generations and the utility grid. To achieve precise power control for GCC, this paper presents a model predictive direct power control(MPDPC)with consideration of the unbalanced filter inductance and grid conditions. First, the characteristics of GCC with unbalanced filter inductance are analyzed and a modified voltage control function is derived. On this basis, to compensate for the power oscillation caused by unbalanced filter inductance, a novel power compensation method is proposed for MPDPC to eliminate the DC-side current ripple while maintaining sinusoidal grid current. Besides, to improve the control robustness against mismatched filter inductance, a filter inductance identification scheme is proposed. Through this scheme, the estimated value of filter inductance is updated in each control period and applied in the proposed MPDPC. Finally, simulation results in PSCAD/EMTDC confirm the validity of the proposed MPDPC and the filter inductance identification scheme.

PI-MPC Frequency Control of Power System in the Presence of DFIG Wind Turbines

For the recent expansion of renewable energy applications, Wind Energy System (WES) is receiving much interest all over the world. However, area load change and abnormal conditions lead to mismatches in frequency and scheduled power interchanges between areas. These mismatches have to be corrected by the LFC system. This paper, therefore, proposes a new robust frequency control technique involving the combination of conventional Proportional-Integral (PI) and Model Predictive Control (MPC) controllers in the presence of wind turbines (WT). The PI-MPC technique has been designed such that the effect of the uncertainty due to governor and turbine parameters variation and load disturbance is reduced. A frequency response dynamic model of a single-area power system with an aggregated generator unit is introduced, and physical constraints of the governors and turbines are considered. The proposed technique is tested on the single-area power system, for enhancement of the network frequency quality. The validity of the proposed method is evaluated by computer simulation analyses using Matlab Simulink. The results show that, with the proposed PI-MPC combination technique, the overall closed loop system performance demonstrated robustness regardless of the presence of uncertainties due to variations of the parameters of governors and turbines, and loads disturbances. A performance comparison between the proposed control scheme, the classical PI control scheme and the MPC is carried out confirming the superiority of the proposed technique in presence of doubly fed induction generator (DFIG) WT.