A Model Predictive Control for Renewable Energy Based AC Microgrids Without Any PID Regulators

This paper presents a model predictive voltage control method for single-inductor multiple-output (SIMO) DC/DC converters operating in the continuous conduction mode (CCM). The state-space model for a SIMO DC/DC converter system is formulated and a Model Predictive Control (MPC) strategy, which has the capability to track the reference voltage automatically and reduce the impact of cross regulation problem, is proposed to regulate the system. Considering that the switches in the converter only have ON/OFF modes, the quadratic programming algorithm is employed to obtain the MPC's solution. Simulation studies are conducted in MATLAB/Simulink to verify the performance of the proposed method.

High-frequency bidirectional dual-bridge series-resonant dc–dc converters have been attracted more attention in power electronic transformer (PET) due to high power density and efficiency. In this article, a model predictive power control method with phase shift modulation is proposed, to improve the dynamic response and robustness of the converter without load current sensor. In addition, the proposed method is insensitive to the resonant parameters in the adopted converter and can keep excellent dynamic characteristics even if the resonant parameter deviates from the desired operating point, which indicates good robustness. Effectiveness of the proposed control is verified on a 400-W scale-down experimental prototype with resistance load and inverter load, respectively. Compared with traditional voltage loop control and model predictive voltage control schemes, the proposed scheme can achieve a faster dynamic response of the output voltage in load and input voltage step change.

Nowadays, a model predictive control has gained a lot of attractions for power converter. To overcome the defects of the conventional model predictive control and achieve better performance, this article presents a multivector model predictive power control (MV-MPPC) with low computational burden for the grid-tied quasi-Z-source inverter (qZSI). Compared with the conventional model predictive power control (MPPC) that adopts only one vector in one control period, the proposed MV-MPPC applies several vectors including two active vectors, one null vector, and one shoot-through (ST) vector to fulfill the expected performance for the grid-tied qZSI. By calculating the optimal duty ratios of the ST vector, the inductor current ripple can be greatly reduced. To eliminate the weighting factor, a modified sliding-mode control method for simultaneously controlling the capacitor voltage and the inductor current to their setting points is proposed. Furthermore, a quick and effective optimal sector selection method is given, which chooses the optimal sector through a look-up table instead of a bunch of complex calculations. Hence, the computational burden of the proposed MV-MPPC is decreased with significance. Both simulation and experimental results are shown to validate the effectiveness and the advantages of the proposed method.

This study proposes a model predictive stator current control (MPSCC) strategy of doubly fed induction generator (DFIG) under unbalanced grid voltage conditions. Sinusoidal and balanced stator currents injected into the power grid can be ensured due to the direct control of stator currents rather than rotor currents. Model predictive control instead of traditional vector control is adopted, which can increase the current loop bandwidth and obtain faster dynamic responses. Conventional resonant regulators such as second‐order generalised integrators or second‐order vector integrators that are usually used to eliminate unbalanced components in stator currents can also be avoided. Moreover, both extractions of negative sequence components in rotor or stator currents and calculation of commanded rotor current are avoided, which simplifies the control scheme. The proposed MPSCC strategy can also implement the grid connection by introducing the virtual stator currents without any changes in the control scheme. Finally, experimental results based on a 1 kW lab DFIG system are provided to validate the effectiveness of the proposed control strategy.

The Finite control set model predictive control (FCS-MPC) is recently introduced to control inverters without the modulation stage. The absence of the modulation stage gives an unpredictable performance of the control system. In this paper, the performance of FCS-MPC is assessed by comparing with PID control which is based on Scalar Pulse Width Modulation (PWM). The two control techniques are applied for load voltage regulation of the autonomous four-leg voltage source inverter (FLVSI). Practically, the predictive control requires a large number of calculations, resulting in high computation time and delay. In this paper, a new finite control set model predictive voltage control (MPVC) algorithm is proposed to predict the load voltages for 15 switching states instead of 16 switching states for reducing the computation time required for the control algorithm. Moreover, the algorithm is optimized by removing the repeated computations and the delay is compensated using the two-step prediction horizon principle. An accurate discrete-time state-space model of the autonomous FLVSI with output LC-filter is used for predicting the load voltages considering the neutral inductance and damping resistance of the LC filter. A simple PID control scheme with decoupled feedforward voltage and current loops is used in the DQ0 reference frame, while MPVC operates in the ABC reference frame. The simulation and experimental results are used to show the full assessment of the MPVC. The prominent outcomes show the ability of the proposed MPVC algorithm to provide high power quality under unbalanced and non-linear load conditions with high stability and robustness.

Two-level voltage source inverters (2L-VSIs) with output LC filters are widely used in uninterruptible power supplies, distributed power generation, and microgrids. This paper proposes a novel sequential modulated model predictive voltage control scheme to control the load voltages and inverter currents of the 2L-VSI in a decoupled manner without the need for weighting factors. In this proposed scheme, two cost functions, each targeting the load voltages and inverter currents of the 2L-VSI, are deployed sequentially, rather than integrated into a dual-objective cost function. This eliminates the need for a weighting factor to balance the two control objectives while retaining the benefits of a modulated model predictive voltage control scheme such as constant switching frequency, fast transient response, lower total harmonic distortion, and reduced steady-state error. The performance of the proposed control scheme is validated through MATLAB simulations on a 10 kW system with linear and nonlinear loads and compared with the classical modulated model predictive voltage control.

ABSTRACTThe expeditious advancement in renewable energy technologies enables the concept of microgrids to boost the incorporation of renewable energy into power systems. In this context, distributed generation (DG)‐based DC microgrids (MGs) are favoured because of their higher efficiency, greater reliability, and simpler development and control compared to their AC counterparts. This paper presents an artificial neural network (ANN) voltage control for a DC‐DC step‐up converter to reduce the number of sensors in the DC microgrids. The proposed approach offered cost‐effective and better voltage regulation in multi‐bus DC MG. The proposed methodology employs quasi‐stationary line (QSL) modeling to account for DC MG uncertainties and disturbances, while simultaneously developing and implementing a model predictive voltage control (MPVC) strategy to generate the comprehensive dataset. The converter's voltage error and switching signals, extracted from the generated dataset, serve as input features for offline training of an artificial neural network (ANN). Once trained, the ANN is deployed online to regulate distributed generators (DGs) within a multi‐bus DC MG. Real‐time hardware‐in‐the‐loop simulations using OPAL‐RT 4510 demonstrate that the proposed controller effectively regulates voltage with reduced sensors, ensuring improved reliability and efficiency.

This paper proposes an improved constrained networked model predictive tracking control design for the chamber pressure of a coke furnace under uncertainty and packet losses. Unlike conventional constrained model predictive control (MPC) strategies that have a limitation in the consideration of both set-point tracking and the dynamic process responses, the system state variables and output tracking errors are combined and thus can be regulated simultaneously in the new MPC scheme. Based on such advantages, there are more degrees of freedom for the subsequent controller design and improved system performance can then be obtained. Case studies on the regulation of chamber pressure of a coke furnace under process uncertainties and packet losses are investigated to verify the proposed approach in comparison with typical traditional constrained MPC schemes.

Model predictive control offers high-performance regulation for multiphase drives but is critically dependent on the accuracy of mathematical models for prediction, making it vulnerable to parameter mismatches and uncertainties. To achieve parameter-independent control across both healthy and faulty operations, this paper proposes a novel dynamic mode decomposition with control (DMDc)-based model predictive current control (MPCC) scheme for five-phase permanent magnet synchronous motors. The core innovation lies in constructing discrete-time state-space models directly from operational data via the open-loop DMDc identification, completely eliminating reliance on explicit motor parameters. Furthermore, an improved fault-tolerant strategy is developed to mitigate the torque ripple induced by inter-turn short-circuit (ITSC) faults. This strategy estimates the key fault characteristic, the product of the short-circuit ratio and current, through a spectral decomposition of the AC component in the q-axis current variations, bypassing the need for complex parameter-dependent observers. The derived compensation currents are seamlessly integrated into the predictive control loop. Experimental results comprehensively validate the effectiveness of the proposed framework, demonstrating a performance comparable to a conventional MPCC under healthy conditions and a significant reduction in torque ripple under ITSC fault conditions, all achieved without any prior knowledge of motor parameters or the retuning of controller gains.

A power compensated triple-vector model predictive direct power control (PCTV-MPDPC) strategy is proposed in this paper for non-redundant fault-tolerant three-phase rectifiers. The system fault tolerance ability and post-fault control performance are enhanced with low hardware complexity. By defining the negative conjugate of complex power as the control variable, a straight-forward pattern of power-voltage relationship analysis is obtained. Besides, a novel voltage vector selection method is put forward based on the location of the error vector between the reference and real control variables. The sector identification process can be omitted to derive a unified expression for duty ratio calculation. On top of that, an intuitive power compensation architecture is proposed to eliminate the dc-bus voltage deviation so that its utilization rate can be maximized. With the proposed PCTV-MPDPC strategy, the power ripples and input current harmonics can be significantly reduced. Superior steady-state and dynamic performance can be achieved compared with the conventional finite-control-set MPDPC (FCS-MPDPC) scheme and a recently proposed multiple-vector model predictive power control (MV-MPPC) method, along with excellent dc-bus voltage deviation suppression effect. Furthermore, simulation validation in Matlab/Simulink and experimental verification on a four-switch three-phase rectifier are carried out to demonstrate the effectiveness of the proposed PCTV-MPDPC strategy.

In this paper, a “third generation” benchmark problem that focuses on the control of wind excited response of a tall building, using the Model Predictive Control (MPC) scheme, is presented. A 76 story, 306 m tall concrete office tower proposed for the city of Melbourne, Australia, is being used to demonstrate the effectiveness of MPC. The MPC scheme is based on an explicit use of a prediction model of the system response to obtain the control actions by minimizing an objective function. Optimization objectives in MPC include minimization of the difference between the predicted and desired response trajectories, and the control effort subjected to prescribed constraints. By incorporating input/output hard constraints, the MPC scheme provides an optimal control force that satisfies the prescribed constraints.

Predictive power management strategy for PV/battery hybrid unit based islanded AC microgrid

This paper presents a force control system design by model predictive control scheme. Recently, a lot of research has been carried out on the robots that take the place of human labor. One of the challenges of such robots is deformable object manipulation to working on food processing, surgery, etc. For handling deformable objects by a rigid robot manipulator is required an advanced reaction force control methodology. Such force control methods have to consider suitable force application without excess and deficiency forces for preventing to drop and destroy in contact with unknown objects. To realize this, it is required to directly consider constraints on control input, rate of input, velocity, etc. Model predictive control is known as one of the key techniques in control fields, and it has a potential for stable force control implementation by considering constraints. This paper attempts to design a new force control system by utilizing model predictive velocity control based on an augmented state-space model with a disturbance term. Finally, the performances of the proposed method via numerical simulation are shown.

A reactive scheduling and control framework for integration of renewable energy sources with a reformer-based fuel cell system and an energy storage device

Battery is the core equipment of the electrical vehicle (EV), battery life, and performance are critical to EV. Only motor variables are considered in most model predictive control (MPC) strategy in which battery indexes are neglected. A model predictive speed control (MPSC) strategy with adjustable C-rate of battery is proposed in this paper on EV with permanent magnet synchronous machine (PMSM). The rest capacity of battery is involved in the cost function, and the operating speed is related to the rest capacity of battery which adjusts the C-rate of the discharging currents further to protect battery. As the EV runs, the C-rate is decreased step by step according to the improved cost function, and the energy is saved to prolong mileage. The benefits of the proposed method including discharging capacities and operating mileage are corroborated by the simulation and repetitive experimental results under same conditions.