Model Predictive Voltage Control Research Articles

Performance improvement of predictive voltage control for interlinking converters of integrated microgrid

Voltage quality in Renewable Energy System may be harmed by varying power demand and fluctuating energy source outputs. The model predictive voltage (MPV) control approach is suggested in this study. The proposed control strategy enables appropriate power interactions between the AC and DC subgrids while sharing power fluctuations in a coordinated way. Both the AC and DC subgrids support each other in accordance with their normalized relative changes in AC frequency and DC voltage, respectively. A typical photovoltaic (PV) wind-battery-based hybrid AC/DC microgrid is modelled and investigated, and the usefulness of the proposed scheme is validated under the renewable energy variations and load perturbations. MATLAB/Simulink has validated the efficacy of the suggested strategy. A set of comparative tests was conducted between conventional Proportional-Integral (PI) control, Sliding Mode Control (SMC) strategies, and a novel Model Predictive Voltage Control (MPV) approach under diverse conditions. The aim was to comprehensively assess the advantages of the proposed MPV method. Results demonstrate that, in contrast to traditional PI and SMC control strategies, the MPV technique exhibits distinct benefits in terms of superior control performance and achieves exceptionally low Total Harmonic Distortion (THD) values. Ultimately, the proposed solution not only raises the performance standard but also ensures the stable operation of the Renewable Energy System (RES).

Robust Constrained Model Predictive Voltage Control Scheme and its Classification

In this study, a prospective power system is suggested (PPS), predictive current control (PCC), enhanced voltage control (EVC), model predictive control (MPC), induction motor (IM) circuit. Instead of monitoring the torque and speed as in conventional MP DTC, the stator voltage is directly regulated in the suggested PVC model thanks to the predictive control theory. A voltage source inverter's use of a model predictive control approach. For all potential voltage vectors produced by the inverter, this method forecasting value of the load using a discrete model of the system. "Voltage-source converter based high voltage direct current (VSC-HVDC)" connected offshore wind farms using a "model predictive control (MPC)" based Enhanced Voltage Control Strategy (EVCS) (OWFs). All wind farm generator (WTGs) and wind farm side VSCs are effectively synchronized in the proposed Midi controller EVCS to maintain voltages within a practical range and reduce system power loss. Review of induction motor defects, including mechanical and insulating issues. The summary of static current signature of machine failures. Numerous useful feature extraction techniques have been introduced for induction motor problem detection.

An Improved Model Predictive Voltage Control With Reduced Computational Burden for T-Type Three-Phase Three-Level Inverters

An Improved Model Predictive Voltage Control With Reduced Computational Burden for T-Type Three-Phase Three-Level Inverters

Simplified Phase Model Predictive Voltage Control for Half-Centralized Open-End Winding Permanent-Magnet Motor Systems

In order to obtain a wider speed range for the permanent-magnet linear motor drive system, a half-centralized open-end winding (OEW) topology is studied. In this topology, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> movers are supplied by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> +1 voltage-source-inverters (VSIs), and all these VSIs share a common dc bus voltage. In order to reduce the computation burden, a simplified phase model predictive voltage control (S-PMPVC) is proposed in this paper. For S-PMPVC, the optimal and suboptimal base levels are defined. When the optimal base level cannot be implemented, the suboptimal base level is selected. As a result, S-PMPVC requires smaller computation burden while the performances are not degraded. By taking the advantage of the smaller computation burden, S-PMPVC can adopt higher control frequency to improve performances. The effectiveness of S-PMPVC has been verified by simulation and experimental results.

A Model Predictive Voltage Control for Dual-active-bridge DC-DC Converter Using Generalized Averaging Model

A Model Predictive Voltage Control for Dual-active-bridge DC-DC Converter Using Generalized Averaging Model

A Simplified Model Predictive Voltage Control for Three-Phase Four-Switch Inverter-Fed PMSM Drives With Capacitor Voltage Offset Suppression and Current Ripple Reduction

This article proposes a simplified model predictive voltage control (SMPVC) method without weighting factors for three-phase four-switch inverter-fed permanent magnet synchronous motor drives to suppress the capacitor voltage offset and reduce the current ripple. In this article, the control of electromagnetic torque and magnetic flux in the model predictive torque control is transformed into the control of the voltage vector, and the capacitor voltage offset is suppressed by injecting dc component obtained by an adaptive notch filter into the phase current. On the basis, the voltage vector is taken as the only control term in cost function without the weighting factors to suppress capacitor voltage offset, thus simplifying the calculation amount in cost function. Then the simplified voltage vector synthesis method is presented to synthesize the effective voltage vector to reduce the current ripple. Using the simple sector division, the switching sequence of the voltage vector can be determined quickly. The effectiveness of the proposed SMPVC method is validated by the simulation and experiment.

Disturbance Observer-Based Model Predictive Voltage Control for Electric-Vehicle Charging Station in Distribution Networks

This paper proposes a disturbance observer (DOB)-based model predictive voltage control (MPVC) method to improve the power quality of electric vehicle charging stations (EVCSs) with battery energy storage systems (BESSs) in distribution networks. As the volume of EVCS increases, we face challenges related to transformer overloading and power quality issues. In particular, voltage fluctuations in local EVCS become the most critical problem due to the highly unpredictable EV charging loads and renewable energy production. In this study, the DOB estimates the EV charging loads and PV generation power to ensure that the MPVC can compensate for them effectively and minimize the voltage fluctuation of the EVCS. The proposed MPVC with DOB does not require communication system and is obtained by solving a linear matrix inequality (LMI)-based optimization problem. Furthermore, the parameter uncertainties, caused by the inherent tolerances and aging degradation of circuit components, are considered. The effectiveness of the proposed control scheme is demonstrated based on simulations and experiments using a 10 kVA EVCS simulator.

Model Predictive Voltage Control for SPMSM Drives With Parameter Robustness Optimization

In order to improve the parameter robustness of the model predictive voltage control (MPVC) for SPMSM drives, a robust MPVC scheme is proposed in this article. First, the parameter sensitivity of MPVC is analyzed. Then, according to the reference voltage equation, the reference voltage error model is presented. In this model, only inductance information is included; the resistance and the flux-linkage parameter are avoided. Furthermore, the inductance error extraction system is designed to obtain accurate inductance information. Then, according to the inductance error and reference voltage error model, the accurate reference voltage is obtained by compensating for the reference voltage error in real time. Finally, the proposed method is verified by experiments. Experimental results prove that the proposed method can effectively enhance the robustness of the system and eliminate the negative impact of parameter mismatch.

Four-Vector Phase Model Predictive Voltage Control for Half-Centralized Open-End Winding Permanent-Magnet Linear Motor Systems

In order to achieve the purpose of extending the speed range and reducing power electronics devices for the permanent-magnet linear motor drive system, a half-centralized open-end winding (OEW) topology is studied for rail transit applications in this paper. In this topology, compared with the traditional OEW topology, the number of voltage-source-inverters (VSIs) can be reduced from four to three to drive two OEW movers and all these VSIs share a common dc bus voltage. In order to improve the steady-state performances, a four-vector phase model predictive voltage control (FV-PMPVC) is proposed in this paper. Due to the spatial distribution of phase voltage vector (PVV), an orthometric synthesis principle (OSP) is proposed to calculated the duration time for the vector synthesis, which reduces the computation burden and enhances the robustness. Comparing the existing control method, FV-PMPVC can get similar dynamic performances but better steady-state performances. Additionally, the half-centralized topology using FV-PMPVC can have same speed range while less VSIs are required. The effectiveness of FV-PMPVC is verified by experimental results.

Model Predictive Voltage Control of Uninterruptible Power Supply Based on Extended-State Observer

Finite-set model predictive controls have been widely used in inverter control because of the flexible target control and no need of a modulation unit. However, the mismatching of prediction model parameters produces prediction errors, resulting in a significant decline in the performance of finite-set model predictive controls. Aiming at the problem of model parameter mismatch, an extended-state observer was proposed to accurately estimate the disturbance of the system in this paper, and the obtained disturbance value was added to a finite-set model predictive control controller to compensate for the prediction error and achieve parameter robustness. By constructing a prediction model of the inverter output voltage in αβ coordinates, all the possible output voltage values were predicted by using different voltage vectors and system measurement values. A set of voltage vectors that minimized the cost function was selected, and its corresponding switching state was applied to the inverter in the next sampling period to achieve control of the output voltage quality. Both the simulation and experimental results showed that the finite-set model predictive voltage control method based on the extended-state observer can estimate the total disturbance quickly and accurately, suppress the influence of capacitance parameter disturbance, and improve the control effect of an inverter.

Efficient Finite-Set Model Predictive Voltage Control of Islanded-Mode Grid-Forming Inverters With Current Constraints

A grid-forming inverter (GFI) plays an important role in providing voltage and frequency support for island microgrids. This paper proposes a computationally efficient finite-set model predictive voltage control (EFS-MPVC) scheme for GFIs. To eliminate the state prediction process, a hybrid voltage-vector reference is first synthesized using the deadbeat principle, which enhances the steady-state performance and robustness to model mismatches. Then, a voltage-vector-based cost function is designed, and a fast optimal-voltage-vector (OVV) selection scheme is built to avoid multiple enumerations. Further, to reduce the additional computing efforts caused by current constraints, an offline lookup table for fast search of OVV is established. Experimental results verify that the proposed EFS-MPVC features reduced computational amount and improved control performance in comparison to conventional MPVC schemes.

Distributed Model Predictive Voltage Control for Distribution Grid Based on Relaxation and Successive Distributed Decomposition

Distributed Model Predictive Voltage Control for Distribution Grid Based on Relaxation and Successive Distributed Decomposition

Double-layer stochastic model predictive voltage control in active distribution networks with high penetration of renewables

The high penetration of renewable energy into distribution networks poses increasing challenges on voltage control. To address this issue, this paper presents a double-layer stochastic model predictive control algorithm to regulate voltage profile in active distribution networks. In the proposed algorithm, voltage regulation is achieved by coordination of an upper layer controller and a lower layer controller. In the upper layer, the number of operation of mechanical voltage regulation devices, including transformer with on-load tap changer and capacitor banks, is minimized in an hourly timescale. In the lower layer, the controller minimizes the active power curtailments and power losses with a control period of 5 min. The proposed double-layer stochastic model predictive voltage control utilizes not only the reactive power control, but also the active power curtailment to regulate bus voltages. In addition, mechanical voltage regulation devices and distributed generations are controlled in two different timescales. Case studies on a modified IEEE-33 bus system demonstrate that compared with traditional control and two-stage stochastic voltage control, the proposed algorithm can achieve an improvement of 8.05% and 7.43%, respectively.

Model Predictive Direct Power Control of Three-Port Solid-State Transformer for Hybrid AC/DC Zonal Microgrid Applications

Multi-port solid-state transformer (SST) characterized by high scalability is expected to be widely used in AC/DC hybrid microgrid. However, the DC bus voltage deviation and dynamic response speed are two key issues in its application. Therefore, a model predictive direct power control (MPDPC) of the three-port SST (TPSST) is proposed. Compared with the traditional PI-based method, the oscillation of the DC bus voltage is inhibited, and the steady-state performance is improved with the MPDPC method. Moreover, a direct power path is incorporated into the control system in order to integrate the rectifier and dual active bridge (DAB) stage. This power path delivers the power required by the load to the DAB stage and the rectifier stage for power control, thereby improving the dynamic response of the system in the process. Besides, since the MPDPC strategy is a control scheme for the whole system, the design of the control system is simplified. Finally, a low power prototype has been built and tested, and the experimental comparison with the traditional voltage control (TVC) and model predictive voltage control (MPVC) method verifies the efficiency improvement of the proposed control strategy.

A Unified Model Predictive Voltage and Current Control for Microgrids With Distributed Fuzzy Cooperative Secondary Control

A microgrid formed by a cluster of parallel distributed generation (DG) units is capable of operating in either islanded mode or grid-connected mode. Traditionally, by using model predictive control algorithms, these two operation modes can be achieved with two separate and different cost functions, which brings in control complexity and hence, compromises system reliability. In this article, a unified model predictive voltage and current control strategy is proposed for both islanded and grid-connected operations and their smooth transition. The cost function is kept unified with voltage and current taken into account without altering the control architecture. It can be used for high-quality voltage supply at the primary control level and for bidirectional power flow at the tertiary control level. In addition, by only using DGs' own and neighboring information, a distributed fuzzy cooperative algorithm is developed at the secondary layer to mitigate the voltage and frequency deviations inherent from the power droop. The fuzzy controller can optimize the secondary control coefficients for further voltage quality improvement. Comprehensive tests under various scenarios demonstrate the merits of the proposed control strategy over traditional methods.

Improved finite control set model predictive control for distributed energy resource in islanded microgrid with fault-tolerance capability

In this paper, improved finite control set model predictive voltage control (FCS-MPVC) is proposed for the distributed energy resource (DER) in AC islanded microgrid (MG). Typically, AC MGs have two or more power electronic-based DERs, which have the ability to maintain a constant voltage at the point of common coupling (PCC) as well as perform power sharing among the DERs. Though linear controllers can achieve above-mentioned tasks, they have several restrictions such as slow transient response, poor disturbance rejection capability etc. The proposed control approach uses mathematical model of power converter to anticipate the voltage response for possible switching states in every sampling period. The proposed dual-objective cost function is designed to regulate the output voltage as well as load current under fault condition. Two-step horizon prediction technique reduces the switching frequency and computational burden of the designed algorithm. Performance of the proposed control technique is demonstrated through MATLAB/Simulink simulations for single distributed generator (DG) and AC MG under linear and non-linear loading conditions. The investigated work presents an excellent steady state performance, low computational overhead, better transient performance and robustness against parametric variations in contrast to classical controllers. Total harmonic distortion (THD) for linear and non-linear load is 0.89% and 1.4% respectively as illustrated in simulation results. Additionally, the three-phase symmetrical fault current has been successfully limited to the acceptable range.

A Novel Model Predictive Voltage Control of Brushless Cascade Doubly-Fed Induction Generator in Stand-Alone Power Generation System

The aim of this paper is to present a model predictive voltage control (MPVC) strategy for stabilizing the amplitude and frequency of the output voltages in a Brushless Cascade Doubly-Fed Induction Generator (BCDFIG) under load changing and variable speed of generator shaft in stand-alone mode. BCDFIGs are a particular model of BDFIGs that consist of two induction machines called the control machine and the power machine, so that their rotors are electrically and mechanically coupled together. In this paper, unlike previous studies, which the BCDFIG rotor was integrated, the generator rotor is analyzed as a complex of two rotors of two separate induction machines. Also, the output voltages of generator are predicted and regulated in different operating conditions by using model predictive voltage control. In order to stabilize the amplitude and frequency of BCDFIG output voltages, the appropriate voltage vector is determined to apply to the stator of control machine. This generation system is simulated and simulation results prove the accuracy of proposed method. Experimental results on prototype BCDFIG are provided to validate the proposed methods. Finally, the effectiveness of the proposed controller brings better power capture optimization under variable speed wind turbine.

Effective Model Predictive Voltage Control for a Sensorless Doubly Fed Induction Generator

This article presents a novel model predictive voltage control (MP VC) for a doubly fed induction generator (DFIG) without a speed sensor. The methodology of the considered MP VC is articulated on the direct voltage control by incorporating the deadbeat control principle within the model predictive topology. The derivation of the utilized cost function is accomplished in organized steps. The finite control set (FCS) principle is adopted to avoid the utilization of the pulsewidth modulation (PWM), which contributes to simplifying the system configuration. For estimating the rotor position, a robust estimator is proposed to achieve precise tracking of the rotor alignment, and thus, a perfect co-ordinates transformation can be achieved. To visualize the significance of the intended MP VC in regard to the classic model predictive techniques, accurate analysis of the DFIG dynamics under the proposed MP VC and model predictive direct torque control (MP DTC) is presented. The test results approve and reveal the predomination of the presented MP VC over the MP DTC. Furthermore, the effectiveness of the proposed sensorless scheme is verified for different ranges of speed operation.

Model Predictive Control of LC-Filtered Voltage Source Inverters With Optimal Switching Sequence

Voltage source inverters with output LC filter enable a sinusoidal output voltage with low harmonics, suitable for islanded ac microgrid or uninterruptible power supply applications. Conventional finite-set model predictive voltage control (MPVC) applies only a single switching vector per control period, leading to a variable switching frequency and significant output ripple. This article resolves these issues by proposing an improved MPVC with optimal switching sequence (OSS-MPVC). First, an improved vector switching sequence is defined, aiming to reduce the output-voltage ripple with a constant switching frequency. Then, to tackle the difficulty in extending the OSS to high-order systems due to the coupling effect of the output filter, a generalized “one-step estimation” solution is proposed, which directly associates the control-variable gradients with the vector switching sequence. To further enhance the output-voltage tracking accuracy, intersample dynamics are taken into account in the cost function. The control delay and dead-time compensation are also considered. Simulations and experimental results verify the feasibility of the proposed method.

Deadbeat-Based Model Predictive Voltage Control for a Sensorless Five-Phase Induction Motor Drive

This paper introduces a direct model predictive voltage control (DMP VC) for a sensorless five-phase induction motor drive. The operation of the proposed sensorless DMP VC is based on the direct control of the applied stator voltages instead of controlling the torque and flux as in model predictive direct torque control (MP DTC). Thus, the simplicity of the control system is enhanced, which saves the computational time and reduces the commutation losses as well. The methodology based on which the proposed sensorless DMP VC performs its operation depends on minimizing a cost function that calculates the error between the reference and actual values of the direct and quadrature (d-q) axes components of stator voltages. The reference values of d-q components of stator voltages are obtained through incorporating the deadbeat control within the proposed model predictive system. A robust back-stepping observer is proposed for estimating the speed, stator currents, rotor flux, and rotor resistance. The validity of the proposed sensorless DMP VC is confirmed through performing detailed and extensive comparisons between the proposed DMP VC and MP DTC approach. The obtained results state that the drive is exhibiting better performance under the proposed DMP VC with less ripples content and reduced computational burden. Moreover, the proposed back-stepping observer has confirmed its effectiveness in estimating the speed and other variables for a wide range of speed operation.