Artificial Neural Networks-Based Multi-Objective Design Methodology for Wide-Bandgap Power Electronics Converters

Rajamony, Rajesh; Wang, Sheng; Calderon-Lopez, Gerardo; Lüdtke, Ingo; Ming, Wenlong

doi:10.1109/ojpel.2022.3204630

Cited by 8 publications

(2 citation statements)

References 31 publications

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“…There are three steps in this process: (1). Data collection from the original MPC model; (2) [49] for the application of high-frequency multi-level converters. However, none of the technical details of machine learning work was provided, for example, data collection, ANN training & validation, etc.…”

Section: B Imitation Controller For Nonlinear Controllersmentioning

confidence: 99%

Artificial Intelligence Techniques for Enhancing the Performance of Controllers in Power Converter-Based Systems—An Overview

Gao,

Wang,

Dragicevic

et al. 2023

IEEE Open J. Ind. Applicat.

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The integration of artificial intelligence (AI) techniques in power converter-based systems has the potential to revolutionize the way these systems are optimized and controlled. With the rapid advancements in AI and machine learning technologies, this paper presents the analysis and evaluation of these powerful tools as well as in computational capabilities of microprocessors that control the converter. This paper provides an overview of AI-based controllers, with a focus on online/offline supervised, unsupervised, and reinforcement-trained controllers. These controllers can be used to create surrogates for inner control loops, complete power converter controllers, and external supervisory or energy management control. The benefits of using AI-based controllers are discussed. AI-based controllers reduce the need for complex mathematical modeling and enable near-optimal real-time operation via computational efficiency. This can lead to increased efficiency, reliability, and scalability of power converter-based systems. By using physics-informed methods, a deeper understanding of the underlying physical processes in power converters can be achieved and the control performance can be made more robust. Finally, by using datadriven methods, the vast amounts of data generated by power converter-based systems can be leveraged to analyze the behavior of the surrounding system and thereby forming the basis for adaptive control. This paper discusses several other potential disruptive impacts that AI could have on a wide variety of power converter-based systems.

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Section: B Imitation Controller For Nonlinear Controllersmentioning

confidence: 99%

Artificial Intelligence Techniques for Enhancing the Performance of Controllers in Power Converter-Based Systems—An Overview

Gao,

Wang,

Dragicevic

et al. 2023

IEEE Open J. Ind. Applicat.

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show abstract

“…In this context, data-driven approaches may overcome the mentioned limits [22]. In particular, the application of artificial neural networks (ANNs) in power electronics can be found in the literature, aiming at different purposes, such as, model predictive controllers [23], [24], fault diagnostic and management [25], optimal design of converters [26], [27], and efficiency-optimized modulation [28]- [30]. According to [31], ANN approaches emerge as promising tools when considering complex problems including non-linearities and uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Optimal Modulation of Triple Active Bridge Converters by an Artificial-Neural- Network Approach

Ibrahim

Zilio

Younis

et al. 2024

IEEE Trans. Ind. Electron.

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Isolated multi-port converters can host loads and sources at different power and voltage levels to their ports by a single topology, giving potential merits in terms of power density and efficiency. However, the higher the number of ports, the higher the number of degrees of freedom in the modulation patterns. This high number of modulation variables complicates the optimization problem, making closed-form solutions impractical. This paper avoids the analytic solution to the optimization problem by proposing a data-driven solution. The presented approach is based on an artificial neural network (ANN) trained to minimize the rms value of the currents flowing through the switches and the transformer windings of a triple active bridge (TAB) converter. This minimization is achieved by determining suitable values of the duty-cycles for modulating the converter switches. The proposed ANN-based modulation is validated considering an experimental TAB prototype rated 5 kW. Index Terms-Artificial neural network (ANN), multi-port converter, triple active bridge (TAB).

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Differential Single-Phase Inverters With Active Power Decoupling: A Survey

Musona

Șerban

2023

IEEE Access

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This paper provides an overview of differential single-phase inverter topologies with active power decoupling (APD) and their main control techniques. Owing to the advantage of achieving APD without additional semiconductor devices, thus boosting inverter reliability at a minimum cost, these topologies have gained increasing interest, especially for small-scale photovoltaic applications. Therefore, in this study, we consider it essential to synthesize the main differential single-phase inverters, their operating principles, and provide a unified mathematical description for each topology. The study identified and analyzed three main structures: buck, boost, buck-boost, and derived topologies. First, a comparative analysis, including a hardware assessment in terms of the DC-link voltage requirements, voltage and current stresses on the switches, and losses, shows the performance of various inverter topologies under different operating parameters (e.g., input voltage and decoupling capacitance). Second, this paper discusses the main control strategies applied to this class of inverters to achieve both primary control (autonomous or gridconnected operation) and APD functions, while highlighting the development of control algorithms that are less dependent on parameter variations and more robust to external disturbances. Finally, the need for further research on reliability improvement in single-phase differential inverters, particularly in the context of emerging technologies, such as high-speed switches (e.g., wide-bandgap semiconductors) and advanced control techniques, is emphasized.

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Artificial Neural Networks-Based Multi-Objective Design Methodology for Wide-Bandgap Power Electronics Converters

Cited by 8 publications

References 31 publications

Artificial Intelligence Techniques for Enhancing the Performance of Controllers in Power Converter-Based Systems—An Overview

Artificial Intelligence Techniques for Enhancing the Performance of Controllers in Power Converter-Based Systems—An Overview

Optimal Modulation of Triple Active Bridge Converters by an Artificial-Neural- Network Approach

Differential Single-Phase Inverters With Active Power Decoupling: A Survey

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