Inverse Application of Artificial Intelligence for the Control of Power Converters

Abstract

This paper proposes a novel application method, Inverse Application of Artificial Intelligence (IAAI) for the control of power electronic converter systems. The proposed method can give the desired control coefficients/references in a simple way because, compared to conventional methods, IAAI only relies on a data-driven process with no need for an optimization process or substantial derivations. Noting that the IAAI approach uses artificial intelligence to provide feasible coefficients/references for the power converter control, rather than building a new controller. After illustrating the IAAI concept, a conventional application method of Artificial Neural Network (ANN) is discussed, an optimization-based design. Then, a two-sourceconverter microgrid case is studied to choose the best droop coefficients via the optimization-based approach. After that, the proposed IAAI method is employed for the same microgrid case to quickly find good droop coefficients. Furthermore, the IAAI method is applied to a modular multi-level converter (MMC) case, extending the MMC operation region under unbalanced grid faults. In the MMC case, both simulation and experimental online tests validate the operation, feasibility and practicality of IAAI.

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

“…Reference [47] proposed an inverse application method of AI that can effectively provide references and coefficients for the control of a power converter-based system. Two different cases were used for the method validation.…”

“…For ML applications, [45] proposed an ANN-based method to design the weighting factors of the MPC-based gridconnected controller. In [47], a special application of ANN is introduced to select the droop coefficients of microgrids; That is, instead of inputting system parameters for system response, the desired system response is set as the input of ANN, then the trained ANN can tell the user what is the preferable input parameters for the target system. The gain of ML approach is that, it can get rid of optimization algorithms, no local optimal problem.…”