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.©2020 Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).fi=vertaisarvioitu|en=peerReviewed

Model predictive control of grid-connected voltage source converters

In this thesis, the main focus is on the design and implementation of an advanced control scheme, namely model predictive control (MPC) to the grid- connected voltage source converter (VSC) for a three phase system. MPC is a control paradigm that solves a mathematical optimization problem based on a dynamic model of the system. Due to the computationally demanding nature of MPC, the areas of applications have long been restricted to slow dynamical systems. However, with the recent advancement of microprocessor and simu- lation technologies, application of MPC is now even possible for the control of power electronics. With a very powerful concept such as on-line cost optimisation, input/output constraint handling and model-based design, MPC is able to offer the optimal actuation that allows one to achieve very fast dynamics, while also considering uncertainties such as system parameter variations and unknown disturbances. Furthermore, it is also possible to take advantage of the discrete nature of the power converters and choose from the possible switching states the optimal solution according to the minimization of a predefined cost.  Exploring these advantages of MPC and making them suitable for the control of power converters are the key focus of the thesis. The first part of the thesis investigates a multi-variable control scheme, namely a predictive voltage controller that controls both DC bus voltage and re- active current (i.e. q-axis current) in the synchronous reference frame. Explicit tuning methods of MPC are introduced to improve the closed-loop transient response as well as improving the robustness against the parameter variations such as the grid inductance. The second part of the thesis focuses on the predictive current control design. A predictive current controller for VSC with LCL (inductor-capacitor- inductor) input filter is first proposed with a robust control scheme that employs nominal and disturbance rejection control parts. The nominal control part is designed using the reduced-order model (i.e. L filter model) to control dominant dynamics of the LCL filter where as the disturbance rejection control part actively suppresses the disturbance due to unmodeled dynamics of LCL filter (i.e. resonance of the LCL filter). Following from this, a predictive resonant controller is presented to control the converter in the stationary frame axis. A resonant module with a grid frequency is embedded in the model to handle the periodicity in the measured states and the reference inputs. The proposed de- sign considers the periodic input constraints in the stationary frame as well as disturbances due to grid voltage distortion. The last part of the thesis investigates the stability aspect of a finite control set predictive control (FCS-MPC) method and presents a design framework to handle the imposed the output current constraints in the cost function. All of the presented control methods in this thesis are experimentally validated on a 1kW prototype converter that has been built by the author

Adaptive Backstepping-based H∞ Robust controller for Photovoltaic Grid-connected Inverter

To improve the robustness and stability of the photovoltaic grid-connected inverter system, a nonlinear backstepping-based H∞ controller is proposed. A generic dynamical model of grid-connected inverters is built with the consideration of uncertain parameters and external disturbances that cannot be accurately measured. According to this, the backstepping H∞ controller is designed by combining techniques of adaptive backstepping control and L2-gain robust control. The Lyapunov function is used to design the backstepping controller, and the dissipative inequality is recursively designed. The storage functions of the DC capacitor voltage and grid current are constructed, respectively, and the nonlinear H∞ controller and the parameter update law are obtained. Experimental results show that the proposed controller has the advantage of strong robustness to parameter variations and external disturbances. The proposed controller can also accurately track the references to meet the requirements of high-performance control of grid-connected inverters

A neural-network-based model predictive control of three-phase inverter with an output LC Filter

Model predictive control (MPC) has become one of the well-established modern control methods for three-phase inverters with an output $LC$ filter, where a high-quality voltage with low total harmonic distortion (THD) is needed. Although it is an intuitive controller, easy to understand and implement, it has the significant disadvantage of requiring a large number of online calculations for solving the optimization problem. On the other hand, the application of model-free approaches such as those based on artificial neural networks approaches is currently growing rapidly in the area of power electronics and drives. This paper presents a new control scheme for a two-level converter based on combining MPC and feed-forward ANN, with the aim of getting lower THD and improving the steady and dynamic performance of the system for different types of loads. First, MPC is used, as an expert, in the training phase to generate data required for training the proposed neural network. Then, once the neural network is fine-tuned, it can be successfully used online for voltage tracking purpose, without the need of using MPC. The proposed ANN-based control strategy is validated through simulation, using MATLAB/Simulink tools, taking into account different loads conditions. Moreover, the performance of the ANN-based controller is evaluated, on several samples of linear and non-linear loads under various operating conditions, and compared to that of MPC, demonstrating the excellent steady-state and dynamic performance of the proposed ANN-based control strategy

Improvement of Stability of a Grid-Connected Inverter with an LCL filter by Robust Strong Active Damping and Model Predictive Control

This study addresses development and implementation of robust control methods for a three-phase grid-connected voltage source inverter (VSI) accompanied by an inductive-capacitive-inductive (LCL) filter. A challenge of current control for the VSI is LCL filter resonance near to the control stability boundary, which interacts with the inverter control switching actions and creates the possibility of instability. In general, active damping is needed to stabilize the system and ensure robust performance in steady-state and dynamic responses. While many active damping methods have been proposed to resolve this issue, capacitor-current-feedback active damping has been most widely used for its simple implementation. There has been no clear consensus regarding design of a control system including capacitor-current-feedback active damping. This is due to the fact that simulation/experiment results are not congruent with the design analyses on which the control is designed. This study explains the incoherence between theory and practice when it comes to a capacitor-currents-feedback active damping system. Proposed capacitor-current-estimate active damping utilizing a developed posteriori Kalman estimator gives coherent simulation results as expected from the design analyses. This reveals that the highly oscillatory capacitor currents containing the inverter switching effects bring about uncertainty in the system performance. The switching effects are not incorporated in the analyses and control system design. Therefore, it is required to remove the switching noise from the capacitor currents in order to yield consistent results. It has been confirmed that the proportional-negative feedback of the capacitor current is equivalent to virtual impedance connected in parallel with the filter capacitor. In a digitally controlled system, the computation delay causes the equivalent resistance of the virtual impedance to become negative in the frequency range of fs/6 to fs/2, which produces a pair of open-loop unstable poles in RHP. This happens when the displaced resonance peak by active damping is in that region. Thus, an a priori Kalman estimator has been developed to generate one-sample-ahead state variable estimates to reconstruct the capacitor currents for active damping, which can compensate for the delay. The one-sample-ahead capacitor-current estimates are computed from the inverter-side and grid-side current estimates. The proposed method provides extended limits of the active damping gain that improve robustness against system parameter variation. It also allows strong active damping which can sufficiently attenuate the resonance. Grid condition is another significant factor affecting the stability of the system. In particular, a weak grid tends to provide high impedance. The system employing the proposed active damping method stably operates in a weak grid, ensuring robustness under grid impedance variation. The developed Kalman estimators offer an effective and easy way of determining the stability status of a system in addition to the functions of filtering and estimation. Stability analysis can be easily made since state variable estimates go to infinity when a system is unstable. As a promising approach, model predictive control (MPC) has been designed for the system. This study suggests that MPC including active damping can be employed for a grid-connected VSI with an LCL filter with good dynamic performance

Decentralized Finite Control Set Model Predictive Control Strategy of Microgrids for Unbalanced and Harmonic Power Management

In this paper, a modified decentralized finite control set model predictive control (FCS-MPC) scheme for the distributed energy resources (DERs) is proposed to improve the power management quality of the prosumers integrated microgrids under the condition of harmonic and unbalance loads. The proposed control strategy for the microgrids mainly consists of the power droop controller, the model predictive controller delay compensation, feedback correction and the unbalance compensation mechanism. The feedback correction method is used to correct the delay compensation, which effectively reduces the average switching frequency (ASF) and voltage total harmonic distortion (THD). By sharing the negative sequence reactive power of the microgrid, power distribution between the prosumers’ DERs is improved. The DERs in the prosumers can be integrated without any communication wire. The transient response and robustness to parameter changes are far superior to hierarchical cascaded control. Moreover, the proposed control strategy can better suppress harmonic and reduce and share fundamental negative sequence reactive power under microgrid unbalance and nonlinear load conditions. Finally, the effectiveness of the proposed FCSMPC control strategy is validated by time-domain simulation results and real-time tests with RT-Lab under the condition of unbalanced and nonlinear loads in the microgrids.Department of Education of Guangdong Province New and Integrated Energy System Theory and Technology Research Group; Brunel Research Initiative and Enterprise Fund BRIEF; National Natural Science Foundation of China; Operation Fund of Guangdong Key Laboratory of Clean Energy Technolog

Model Predictive Control for Power Converters and Drives: Advances and Trends

Model predictive control (MPC) is a very attractive solution for controlling power electronic converters. The aim of this paper is to present and discuss the latest developments in MPC for power converters and drives, describing the current state of this control strategy and analyzing the new trends and challenges it presents when applied to power electronic systems. The paper revisits the operating principle of MPC and identifies three key elements in the MPC strategies, namely the prediction model, the cost function, and the optimization algorithm. This paper summarizes the most recent research concerning these elements, providing details about the different solutions proposed by the academic and industrial communitiesMinisterio de Economia y Competitividad TEC2016-78430-RConsejeria de Innovacion, Ciencia y Empresa (Junta de Andalucia) P11-TIC-707