@article { , title = {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-source-converter 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.}, doi = {10.1109/TPEL.2022.3209093}, eissn = {1941-0107}, issn = {0885-8993}, publicationstatus = {Published}, url = {https://nottingham-repository.worktribe.com/output/12321775}, keyword = {Artificial intelligence (AI), Machine learning, Droop control, Power converters, Inverse application, Artificial neural network (ANN), Current sharing}, year = {2022}, author = {Gao, Yuan and Wang, Songda and Hussaini, Habibu and Yang, Tao and Dragicevic, Tomislav and Bozhko, Serhiy and Wheeler, Pat and Vazquez, Sergio} }