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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F3: Power Electronics".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 5630

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Special Issue Editors

Dr. Lin Qiu

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Guest Editor

College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Interests: electrified transportation; power electronic grid; bio-electromagnetic

Dr. Xing Liu

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Guest Editor

Research Fellow, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, 310002, China
Interests: model predictive control; traction power systems; modular multilevel converter control

Dr. Jien Ma

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Guest Editor

College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Interests: permanent magnetic motor design; motor insulation; motor system control

Dr. Chunyang Gu

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Guest Editor

Key Laboratory of More Electric Aircraft Technology of Zhejiang Province, Department of Electrical and Electronic Engineering, University of Nottingham, Ningbo 315104, China
Interests: dual active bridge converters; more electric aircraft; motor control

Dr. Jian Zhang

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Guest Editor

College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Interests: motor design; deep-sea driving system; system reliability

Special Issue Information

Dear Colleagues,

A Special Issue of Energies entitled, “Power Electronic Converter and Its Control” is open for submissions. Model predictive control (MPC) for power converters and electrical drives is an advanced control solution that has gained attention in the research community and industry. Particularly, the MPC utilizes an explicit model of the converter to predict future plant behavior for all feasible switching state configurations and select the optimal input signals based on a user-predefined design criteria that defines the optimal performance of the system. By virtue of this property, the power converters and electrical drives are directly driven by using the desired control commands without the intervention of a pulse-width modulation block. However, from a practical standpoint, uncertainties, such as unknown physical parameters, unmodeled dynamics, and environmental disturbances, exist in the control process of the described method. Hence, new data-driven robust MPC solutions for power converters and electrical drives are urgently needed. This Special Issue plans to provide an overview of the most recent advances in the field of advanced model-free predictive controls and their applications.

The objective of this Special Issue is to share research findings and to provide selected contributions on advances in power converters and electrical drives. Potential topics include, but are not limited to:

1) Robust predictive control solutions
2) Implementation in issues of MPC (e.g., FPGA, DSP, etc.)
3) Data-driven (modeless) predictive control techniques
4) Artificial intelligence in predictive control frame
5) Related predictive control techniques in renewable energy devices or systems

Dr. Lin Qiu
Dr. Xing Liu
Dr. Jien Ma
Dr. Chunyang Gu
Dr. Jian Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

robust predictive control solutions
implementation in issues of MPC
data-driven (modeless) predictive control techniques
artificial intelligence in predictive control frame

Published Papers (5 papers)

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Research

18 pages, 4946 KiB

Open AccessArticle

Optimal Design of a Single-Phase Bidirectional Rectifier

by Vicente Esteve, Juan L. Bellido and José Jordán

Energies 2024, 17(6), 1280; https://doi.org/10.3390/en17061280 - 7 Mar 2024

Viewed by 558

Abstract

This article outlines the comprehensive design and control approach for a single-phase bidirectional rectifier (SPBR) used in bidirectional charging of electric vehicle batteries. The operational parameters of the inverter are determined through a thorough analysis of all switching sequences to accurately assess power losses, considering the type of switching device chosen in each case, enabling proper component sizing, and understanding converter efficiency. An exclusive electronic control circuit is examined, governing two converter operation modes: boost rectifier with power factor correction (PFC) and sine pulse inverter width modulation (SPWM) with a minimum number of adjustments made automatically. One problem that arises when addressing the design of an SPBR is determining the operating frequency. To address this issue, this study offers to conduct a comparative analysis of losses using various power devices and magnetic circuits to determine the optimal operating frequency for achieving maximum energy efficiency. To validate the design’s feasibility, a prototype with 10 kW output power was constructed, achieving a peak efficiency of approximately 97.5% in both directions, unity power factor (PF), and total harmonic distortion (THD) of less than 7% during full power operation. Full article

(This article belongs to the Special Issue Power Electronic Converter and Its Control)

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18 pages, 6350 KiB

Open AccessArticle

Fractional-Order Sliding-Mode Control and Radial Basis Function Neural Network Adaptive Damping Passivity-Based Control with Application to Modular Multilevel Converters

by Xuhong Yang, Wenjie Chen, Congcong Yin and Qiming Cheng

Energies 2024, 17(3), 580; https://doi.org/10.3390/en17030580 - 25 Jan 2024

Cited by 1 | Viewed by 513

Abstract

This paper proposes a hybrid control scheme that combines fractional-order sliding-mode control (FOSMC) with radial basis function neural network adaptive damping passivity-based control (RBFPBC) for modular multilevel converters (MMC) under non-ideal operating conditions. According to the passive control theory, we establish the Euler–Lagrange (EL) models of positive and negative sequences based on the unbalanced grid. A passivity-based controller that satisfies the energy dissipation law is designed. To enable rapid convergence of the system energy storage function, a radial basis function neural network (RBFNN) is introduced to adjust the injection damping adaptively. Additionally, a fractional-order sliding-mode controller (FOSMC) is designed. The fractional-order sliding mode surface used can improve tracking performance, and effectively suppressed the undesirable chattering phenomenon compared to the traditional sliding-mode control (SMC). Finally, combining the two control methods can effectively solve the issue of passivity-based control (PBC) being too dependent on parameters. The proposed hybrid control scheme enhances the ability of the system to resist disturbances, and improves its overall robustness. Simulation results demonstrate the feasibility and effectiveness of this control method. Full article

(This article belongs to the Special Issue Power Electronic Converter and Its Control)

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16 pages, 9287 KiB

Open AccessArticle

A Fault-Tolerant Control Strategy for Three-Level Grid-Connected NPC Inverters after Single-Arm Failure with Optimized SVPWM

by Jingtao Huang, Feng Bai, Qing Yang and Shiyi Ren

Energies 2023, 16(23), 7863; https://doi.org/10.3390/en16237863 - 30 Nov 2023

Viewed by 545

Abstract

Three-level NPC inverters have been widely used in grid-connected systems due to their superior performance compared with two-level inverters, but more switches lead to high fault probability. Meanwhile, the neutral point potential (NPP) fluctuation of the DC link is an inherent problem of three-level NPC inverters. To keep the three-level NPC inverter running stably after single-arm failure, a fault-tolerant control strategy based on an optimised space vector pulse width modulation (SVPWM) is proposed in this paper. Firstly, the common-mode voltage (CMV) of the postfault three-level NPC inverter is analysed and then the preliminary synthesis principles of the reference voltage vector are determined. Then, in order to ensure the NPP balance and the quality of the grid-connected currents, the reference voltage vector synthesis rules are optimised, a low-pass filter (LPF) and a hysteresis comparator are designed, respectively, to ensure the quality of grid-connected currents and effectively decrease the DC link NPP deviation. Finally, the simulation results show that the proposed fault-tolerant control strategy can realize the stable and reliable operation of the grid-connected three-level NPC inverter after single-arm failure, and the CMV can be reduced significantly, the quality of grid-connected currents is also improved. The proposed fault-tolerant strategy also shows good performance when the grid-connected currents change. Full article

(This article belongs to the Special Issue Power Electronic Converter and Its Control)

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13 pages, 1395 KiB

Open AccessArticle

A Novel Deep Reinforcement Learning-Based Current Control Method for Direct Matrix Converters

by Yao Li, Lin Qiu, Xing Liu, Jien Ma, Jian Zhang and Youtong Fang

Energies 2023, 16(5), 2146; https://doi.org/10.3390/en16052146 - 22 Feb 2023

Viewed by 1204

Abstract

This paper presents the first approach to a current control problem for the direct matrix converter (DMC), which makes use of the deep reinforcement learning algorithm. The main objective of this paper is to solve the real-time capability issues of traditional control schemes (e.g., finite-set model predictive control) while maintaining feasible control performance. Firstly, a deep Q-network (DQN) algorithm is utilized to train an agent, which learns the optimal control policy through interaction with the DMC system without any plant-specific knowledge. Next, the trained agent is used to make computationally efficient online control decisions since the optimization process has been carried out in the training phase in advance. The novelty of this paper lies in presenting the first proof of concept by means of controlling the load phase currents of the DMC via the DQN algorithm to deal with the excessive computational burden. Finally, simulation and experimental results are given to demonstrate the effectiveness and feasibility of the proposed methodology for DMCs. Full article

(This article belongs to the Special Issue Power Electronic Converter and Its Control)

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16 pages, 5973 KiB

Open AccessArticle

Model Predictive Control of DC–DC Boost Converter Based on Generalized Proportional Integral Observer

by Rongchao Niu, Hongyu Zhang and Jian Song

Energies 2023, 16(3), 1245; https://doi.org/10.3390/en16031245 - 23 Jan 2023

Cited by 2 | Viewed by 2291

Abstract

Due to the nonminimum phase characteristics and nonlinearity of boost converters, the control design is always a challenging issue. A novel model predictive control strategy is proposed for the boost converter in this work. First, the Super-Twisting algorithm is applied to current control, and the input–output plant for voltage control is derived based on the linearization technique. All the model uncertainties are defined as lumped disturbances, and a generalized proportional integral observer is designed to estimate the lumped disturbance. Second, a composite predictive approach is developed on the basis of the predictive model and disturbance estimations. By solving the cost function directly, the optimal control law is derived explicitly. Lastly, the effectiveness of the proposed control strategy is verified by both simulation and experimental results. Full article

(This article belongs to the Special Issue Power Electronic Converter and Its Control)

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