Chapter, 2024

Chapter 10 Physics-informed neural network-based control of power electronic converters

Control of Power Electronic Converters and Systems: Volume 4 9780323856225, Pages 309-331

Editors:

Publisher: Elsevier

DOI: 10.1016/b978-0-323-85622-5.00016-x

Contributors

Sahoo, Subham Kumar 0000-0002-7916-028X [1]

Affiliations

  1. [1] Aalborg University
    2. [NORA names: AAU Aalborg University; University; Denmark; Europe, EU; Nordic; OECD]

Abstract

This chapter introduces a physics-informed neural network (PINN) for the control of grid grid-connected converters by fusing its underlying equations into the training process, thereby reducing the requirement for qualitative training data. First, we provide a comprehensive design guideline for the PINN to circumscribe differential equations and data-driven generalizations in complex systems. Moreover, we cover recent trends in scientific computing that involve the fusion of physics and data-based learning. In comparison with traditional data-driven methods, which either incur a significant computational burden or use overly conservative surrogate models, we explore the PINN’s easy optimization per the design requirements and find it to be significantly superior in terms of computation time and data requirements (trained using only 3001 set points), with an average prediction accuracy of 98.76%. As a result, PINN reveals a new modeling orientation for power electronic converters and is well suited for commercial applications. Finally, its robustness under various grid disturbances has been validated under simulation and experimental conditions.

Keywords

accuracy, applications, average prediction accuracy, burden, chapter, commercial applications, comparison, complex systems, comprehensive design guidelines, computation time, computational burden, computer, conditions, control, converter, data, data requirements, data-based learning, data-driven method, design, design guidelines, design requirements, differential equations, disturbances, electronic converters, equations, experimental conditions, fusion, generalization, grid, grid disturbances, grid-connected converters, guidelines, learning, method, model, model orientation, network, network-based controller, neural network, neural network-based controller, optimization, orientation, physics, physics-informed neural networks, power, power electronic converters, prediction accuracy, process, requirements, results, robustness, scientific computing, simulation, surrogate, surrogate model, system, time, training, training data, training process, trends

Data Provider: Digital Science

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