Adaptive reference model predictive control for power electronics

An adaptive reference model predictive control (ARMPC) approach is proposed as an alternative means of controlling power converters in response to the issue of steady-state residual errors presented in power converters under the conventional model predictive control (MPC). Differing from other methods of eliminating steady-state errors of MPC based control, such as MPC with integrator, the proposed ARMPC is designed to track the so-called virtual references instead of the actual references. Subsequently, additional tuning is not required for different operating conditions. In this paper, ARMPC is applied to a single-phase full-bridge voltage source inverter (VSI). It is experimentally validated that ARMPC exhibits strength in substantially eliminating the residual errors in environment of model mismatch, load change, and input voltage change, which would otherwise be present under MPC control. Moreover, it is experimentally demonstrated that the proposed ARMPC shows a consistent erasion of steady-state errors, while the MPC with integrator performs inconsistently for different cases of model mismatch after a fixed tuning of the weighting factor

A Novel Reduced Components Model Predictive Controlled Multilevel Inverter for Grid-Tied Applications

This paper presents an improved single-phase Multilevel Inverter (MLI) which is conceptualized to reduce power switches along with separate DC voltage sources. Compared with recent modular topologies, the proposed MLI employs a reduced number of components. The proposed inverter consists of a combination of two circuits, i.e., the level generation and polarity generation parts. The level generation part is used to synthesize different output voltage levels, while the polarity inversion is performed by a~conventional H-bridge circuit. The performance of the proposed topology has been studied using s single-phase seven-level inverter, which utilizes seven power switches and three independent DC voltage sources. Model Predictive Control (MPC) is applied to inject a sinusoidal current into the utility grid which exhibits low Total Harmonic Distortion (THD). Tests, including a~change in grid current amplitude as well as operation under variation in Power Factor (PF), have been performed to validate the good performance obtained using MPC. The effectiveness of the proposed seven-level inverter has been verified theoretically using MATLAB Simulink. In addition, Real-Time (RT) validation using the dSPACE-CP1103 has been performed to confirm the system performance and system operation using digital platforms. Simulation and RT results show improved THD at 1.23% of injected current

Comparison between FS-MPC control strategy for an UPS inverter application in α-β and abc frames

The voltage source inverter (VSI) of an uninterruptible power supply (UPS) is a system where the main objective is to obtain a high quality output sinusoidal voltage with independence on the output load. For this reason, it includes an output LC filter. The presence of the filter increases the complexity of the controller design thus it is necessary to evaluate the performance of the control strategy in terms of the output voltage quality and computational cost of the algorithm. In this paper, both analysis are developed for the finite states model predictive control (FS-MPC) of a VSI performed in the abc and α-βframes. Both algorithms are summarized and compared in order to establish an objective criteria to choose among them when a hardware implementation is developed. Simulation results are presented for both algorithms to validate the analysis

Predictive control of a three-phase UPS inverter using two steps prediction horizon

A Model Predictive Control scheme is used for voltage control in a three-phase inverter with output LC filter. The controller uses a model of the system to calculate predictions of the future value of the system variables for a given voltage vector sequence. A cost function considering the voltage errors is defined and the voltage vectors that minimize it are selected and applied in the converter. The effect of considering different number of prediction steps is studied in this work in terms of THD. Simulation results for one and two prediction steps are presented and compared

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

Model predictive control: a review of its applications in power electronics

Model-based predictive control (MPC) for power converters and drives is a control technique that has gained attention in the research community. The main reason for this is that although MPC presents high computational burden, it can easily handle multivariable case and system constraints and nonlinearities in a very intuitive way. Taking advantage of that, MPC has been successfully used for different applications such as an active front end (AFE), power converters connected to resistor inductor RL loads, uninterruptible power supplies, and high-performance drives for induction machines, among others. This article provides a review of the application of MPC in the power electronics area

An Adaptive Model Predictive Voltage Control for LC-Filtered Voltage Source Inverters

The three-phase inductor and capacitor filter (LC)-filtered voltage source inverter (VSI) is subjected to uncertain and time-variant parameters and disturbances, e.g., due to aging, thermal effects, and load changes. These uncertainties and disturbances have a considerable impact on the performance of a VSI’s control system. It can degrade system performance or even cause system instability. Therefore, considering the effects of all system uncertainties and disturbances in the control system design is necessary. In this respect and to tackle this issue, this paper proposes an adaptive model predictive control (MPC), which consists of three main parts: an MPC, an augmented state-space model, and an adaptive observer. The augmented state-space model considers all system uncertainties and disturbances and lumps them into two disturbance inputs. The proposed adaptive observer determines the lumped disturbance functions, enabling the control system to keep the nominal system performance under different load conditions and parameters uncertainty. Moreover, it provides load-current-sensorless operation of MPC, which reduces the size and cost, and simultaneously improves the system reliability. Finally, MPC selects the proper converter voltage vector that minimizes the tracking errors based on the augmented model and outputs of the adaptive observer. Simulations and experiments on a 5 kW VSI examine the performance of the proposed adaptive MPC under different load conditions and parameter uncertainties and compare them with the conventional MPC

A Virtual Space Vectors based Model Predictive Control for Three-Level Converters

Three-phase three-level (3-L) voltage source converters (VSC), e.g., neutral-point clamped (NPC) converters, T-type converters, etc., have been deemed to be suitable for a wide range of medium- to high-power applications in microgrids (MGs) and bulk power systems. Compared to their two-level (2-L) counterparts, adopting 3-L VSCs in the MG applications not only reduces the voltage stress across the power semiconductor devices, which allows achieving higher voltage levels, but also improves the quality of the converter output waveforms, which further leads to considerably smaller output ac passive filters. Various control strategies have been proposed and implemented for 3-L VSCs. Among all the existing control methods, finite-control-set model predictive control (FCS-MPC) has been extensively investigated and applied due to its simple and intuitive design, fast-dynamic response and robustness against parameter uncertainties. However, to implement an FCS-MPC on a 3-L VSC, a multi-objective cost function, which consists of a term dedicated specifically to control the dc-link capacitor voltages such that the neutral-point voltage (NP-V) oscillations are minimized, must be designed. Nevertheless, selecting proper weighting factors for the multiple control objectives is difficult and time consuming. Additionally, adding the dc-link capacitor voltages balancing term to the cost function distributes the controller effort among different control targets, which severely impacts the primary goal of the FCS-MPC. Furthermore, to control the dc-link capacitor voltages, additional sensing circuitries are usually necessary to measure the dc-link capacitor voltages and currents, which consequently increases the system cost, volume and wiring complexity as well as reduces overall reliability. To address all the aforementioned challenges, in this dissertation research, a novel FCS-MPC method using virtual space vectors (VSVs), which do not affect the dc-link capacitor voltages of the 3-L VSCs, was proposed, implemented and validated. The proposed FCS-MPC strategy has the capability to achieve inherent balanced dc-link capacitor voltages. Additionally, the demonstrated control technique not only simplifies the controller design by allowing the use of a simplified cost function, but also improves the quality of the 3-L VSC output waveforms. Furthermore, the execution time of the proposed control algorithm was significantly reduced compared to that of the existing one. Lastly, the proposed FCS-MPC using the VSVs reduces the hardware cost and complexity as the additional dc-link capacitor voltages and current sensors are not required, which further enhances the overall system reliability

Implementation of Three Phase UPS system using Multi-Loop Fuzzy Logic Controller for Linear and Non-Linear Loads

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