Wind power system control based on least square support vector machines algorithms

Dahhani, Omar; Boumhidi, Ismail; Tekobon, Jerry; Nichita, Cristian

doi:10.1109/eeeic.2016.7555745

Cited by 1 publication

(2 citation statements)

References 14 publications

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“…where Vb is the main dc voltage, Ri is the resistance of the individual cable connecting the ith source to the bus. Hence, the current sharing among the sources, assuming they are supplying together can be expressed as in (7).…”

Section: A Analysis Of the Conventional Droop Control Methodsmentioning

confidence: 99%

“…3 in [3]), ML based classification was nearly all used for the maintenance of power electronic systems, rarely for the control; Instead, regression and optimization techniques are used for the control phase. Different ML classification methods have already been applied in the control of power electronic converter systems, for example, support vector machine [7][8][9] and neural network (NN) pattern recognition [10,11]. In this paper, a novel application of AI/ML is proposed which can generally fit with both regression and classification techniques; additionally, this idea does not need an optimization process.…”

Section: Introductionmentioning

confidence: 99%

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Inverse Application of Artificial Intelligence for the Control of Power Converters

Gao

Wang

Hussaini

et al. 2023

IEEE Trans. Power Electron.

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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.

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Section: A Analysis Of the Conventional Droop Control Methodsmentioning

confidence: 99%