A Novel Multi-Objective Finite Control Set Model Predictive Control for IPMSM drive fed by a Five-Level Cascaded H-Bridge Inverter

In this work, a novel multi-objective voltage-vector-based finite control set model predictive control for a permanent magnet synchronous machine drive fed by a three-phase five-level cascaded H-bridge multilevel inverter is proposed. This algorithm aims to overcome the main issues relative to model predictive control implementation detected in the scientific literature for electric drives fed by cascaded H-bridge multilevel inverters. In detail, the goals are the minimization of computational cost by reducing the number of required predictions, the minimization of the switching devices state transitions, i.e. the switching losses minimization, and the common mode voltage reduction. These goals are fulfilled through an offline optimization process, thus, no additional terms and weighting factors to be tuned are required for the cost function. Experimental validations are presented to prove the effectiveness of the proposed approach. In detail, an accurate electric drive performance comparison, both in steady state and dynamic working conditions, is carried out when the proposed voltage-vector-based model predictive control and the cell-by-cell-based model predictive control are adopted. As comparison tools, current and voltage total harmonic distortion, apparent switching frequency, common mode voltage amplitude, and torque ripple are adopted

The Essential Role and the Continuous Evolution of Modulation Techniques for Voltage-Source Inverters in the Past, Present, and Future Power Electronics

The cost reduction of power-electronic devices, the increase in their reliability, efficiency, and power capability, and lower development times, together with more demanding application requirements, has driven the development of several new inverter topologies recently introduced in the industry, particularly medium-voltage converters. New more complex inverter topologies and new application fields come along with additional control challenges, such as voltage imbalances, power-quality issues, higher efficiency needs, and fault-tolerant operation, which necessarily requires the parallel development of modulation schemes. Therefore, recently, there have been significant advances in the field of modulation of dc/ac converters, which conceptually has been dominated during the last several decades almost exclusively by classic pulse-width modulation (PWM) methods. This paper aims to concentrate and discuss the latest developments on this exciting technology, to provide insight on where the state-of-the-art stands today, and analyze the trends and challenges driving its future

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

Multilevel Converters: An Enabling Technology for High-Power Applications

| Multilevel converters are considered today as the state-of-the-art power-conversion systems for high-power and power-quality demanding applications. This paper presents a tutorial on this technology, covering the operating principle and the different power circuit topologies, modulation methods, technical issues and industry applications. Special attention is given to established technology already found in industry with more in-depth and self-contained information, while recent advances and state-of-the-art contributions are addressed with useful references. This paper serves as an introduction to the subject for the not-familiarized reader, as well as an update or reference for academics and practicing engineers working in the field of industrial and power electronics.Ministerio de Ciencia y Tecnología DPI2001-3089Ministerio de Eduación y Ciencia d TEC2006-0386

Model Predictive Control for Mitigating Sensor Attacks on Multilevel Inverters

Nowadays, multilevel power inverters have become a hot research topic which are being widely used in smart grids. They are also driving devices for conveyors, compressors, motors, and can enable uninterruptible power supply for critical loads such as database centers or telecommunications base stations. In the future, smart grids will play an important role to achieve higher efficiency, smarter control and better performance. Such an ambitious goal can only be achieved by inverters with higher voltage and power levels. The smart grids are the typical cyber-physical systems that is composed of physical processes and computation units combined by sensors, actuators, and communication devices. The smart grids are apt to errors and vicious attacks on their physical construction leading to considerable damage, such as false data injection (FDI), denial of service (DOS). The vicious data injection can effectively bypass the detection of system and cause serious effects on the grid. In recent years, some advanced control approaches have been proposed to perform inverter current control. Among them, model predictive control (MPC) is a promising one that makes use of explicit system models to predict its future response and optimize system performance. It has unique advantages that can accurately forecast the future response of the system and have fast response. However, the effectiveness and the accuracy of the conventional MPC rely on whether the system model is accurate. Uncertainty and false data injection in the system model sometimes lead to unresponsive or even unstable control systems. Conventional MPC is hard to keep the system stable when the uncertainty and malicious attack happen. In existing studies, although various attacks have been investigated, the undetectable false data injection aiming at the inverter system was rarely studied. In the thesis, the model of the cascaded H-bridge inverter is established and conventional MPC to achieve load current control is applied. It shows great performance to achieve load current control and has fast dynamic control. Then considering various attack signals such as step attack signals, pulse attack signals to the sensors in the system, the conventional MPC loses the ability to achieve a stable and effective current control. According to simulation results, Kalman Filter model is built which can filter some Gaussian noises from the sensors in the system. Then from the perspective of attacker, a special FDI attack is designed that can effectively bypass the Kalman Filter. For the system that targeted by the FDI and DOS attack, a new controller is designed based on the K-Nearest Neighbor (KNN) algorithm and MPC strategy which can achieve the load current control with high output quality. Finally, the new control method based on KNN and MPC is compared with conventional MPC. The simulation results are analyzed and conclusion have been made. A modified MPC combined with KNN algorithm proposed in this thesis can detect bad data that can enter the system without triggering alarms. The case studies show the modified MPC based on KNN algorithm can achieve current control accurately when the system is injected by various attack signals showing better performance of current control with low total harmonic distortion (THD)

Model Predictive Control for Mitigating Sensor Attacks on Multilevel Inverters

Nowadays, multilevel power inverters have become a hot research topic which are being widely used in smart grids. They are also driving devices for conveyors, compressors, motors, and can enable uninterruptible power supply for critical loads such as database centers or telecommunications base stations. In the future, smart grids will play an important role to achieve higher efficiency, smarter control and better performance. Such an ambitious goal can only be achieved by inverters with higher voltage and power levels. The smart grids are the typical cyber-physical systems that is composed of physical processes and computation units combined by sensors, actuators, and communication devices. The smart grids are apt to errors and vicious attacks on their physical construction leading to considerable damage, such as false data injection (FDI), denial of service (DOS). The vicious data injection can effectively bypass the detection of system and cause serious effects on the grid. In recent years, some advanced control approaches have been proposed to perform inverter current control. Among them, model predictive control (MPC) is a promising one that makes use of explicit system models to predict its future response and optimize system performance. It has unique advantages that can accurately forecast the future response of the system and have fast response. However, the effectiveness and the accuracy of the conventional MPC rely on whether the system model is accurate. Uncertainty and false data injection in the system model sometimes lead to unresponsive or even unstable control systems. Conventional MPC is hard to keep the system stable when the uncertainty and malicious attack happen. In existing studies, although various attacks have been investigated, the undetectable false data injection aiming at the inverter system was rarely studied. In the thesis, the model of the cascaded H-bridge inverter is established and conventional MPC to achieve load current control is applied. It shows great performance to achieve load current control and has fast dynamic control. Then considering various attack signals such as step attack signals, pulse attack signals to the sensors in the system, the conventional MPC loses the ability to achieve a stable and effective current control. According to simulation results, Kalman Filter model is built which can filter some Gaussian noises from the sensors in the system. Then from the perspective of attacker, a special FDI attack is designed that can effectively bypass the Kalman Filter. For the system that targeted by the FDI and DOS attack, a new controller is designed based on the K-Nearest Neighbor (KNN) algorithm and MPC strategy which can achieve the load current control with high output quality. Finally, the new control method based on KNN and MPC is compared with conventional MPC. The simulation results are analyzed and conclusion have been made. A modified MPC combined with KNN algorithm proposed in this thesis can detect bad data that can enter the system without triggering alarms. The case studies show the modified MPC based on KNN algorithm can achieve current control accurately when the system is injected by various attack signals showing better performance of current control with low total harmonic distortion (THD)

Predictive Switching Control for Multilevel Inverter using CNN-LSTM for Voltage Regulation

Now-a-days, model predictive control (MPC) is very commonly used for three phase inverters. But conventional MPC suffers computational complexities as well as unstable switching frequency issues. To address these issues related with conventional MPC model, this paper aims to use the benefits of deep learning model for predictive switching control. In this paper, CNN-LSTM network based predictive control is proposed for three phase inverters. Along with predictive control LC filter is cascaded to reduce the harmonics. The model is simulated using SIMULINK under fixed and dynamic load condition. The result shows decreased THD under different load conditions. Finally, the result is validated with existing models and achieves better performance

Variable-Angle Phase-Shifted PWM for Multilevel Three-Cell Cascaded H-bridge Converters

Multilevel cascaded H-bridge converters have become a mature technology for applications where high-power medium ac voltages are required. Normal operation of multilevel cascaded H-bridge converters assumes that all power cells have the same dc voltage, and each power cell generates the same voltage averaged over a sampling period using a conventional phase-shifted pulse width modulation (PWM) technique. However, this modulation method does not achieve good results under unbalanced operation per H-bridge in the power converter, which may happen in grid-connected applications such as photovoltaic or battery energy storage systems. In the paper, a simplified mathematical analysis of the phase-shifted PWM technique is presented. In addition, a modification of this conventional modulation method using variable shift angles between the power cells is introduced. This modification leads to the elimination of harmonic distortion of low-order harmonics due to the switching (triangular carrier frequency and its multiples) even under unbalanced operational conditions. The analysis is particularized for a three-cell cascaded H-bridge converter, and experimental results are presented to demonstrate the good performance of the proposed modulation method

A New Control Technique for Multilevel Cascaded H-Bridge Inverters

Multi cell converters are one of the alternative topologies for medium-voltage industrial drives. For an application modulation (PWM) rectifier as front end Multi level cascaded H-bridge (CHB) converters have been presented as a good solution for high-power applications. The choice of single-phase PWM rectifiers for the input of the cells results in a reduced number of power switches and a simpler input transformer than the three phase equivalent. However, its control is not as straightforward. In this paper, the steady-state power balance in the cells of a single-phase two-cell CHB is studied. The ability to receive active power network or to deliver active power to the grid in each cell is analyzed according to the DC-link voltage and the desired output AC voltage value. Simulation results are shown to validate the presented analysis

A Novel Control Approach to Hybrid Multilevel Inverter for High-Power Applications

This paper proposes a hybrid control scheme for a newly devised hybrid multilevel inverter (HMLI) topology. The circuit configuration of HMLI is comprised of a cascaded converter module (CCM), connected in series with an H-bridge converter. Initially, a finite set model predictive control (FS-MPC) is adopted as a control scheme, and theoretical analysis is carried out in MATLAB/Simulink. Later, in the real-time implementation of the HMLI topology, a hybrid control scheme which is a variant of the FS-MPC method has been proposed. The proposed control method is computationally efficient and therefore has been employed to the HMLI topology to mitigate the high-frequency switching limitation of the conventional MPC. Moreover, a comparative analysis is carried to illustrate the advantages of the proposed work that includes low switching losses, higher efficiency, and improved total harmonic distortion (THD) in output current. The inverter topology and stability of the proposed control method have been validated through simulation results in MATLAB/Simulink environment. Experimental results via low-voltage laboratory prototype have been added and compared to realize the study in practice.publishedVersio