Omar Busati Alzain, Xiangjie Liu

Predictive optimization of sliding mode control using recurrent neural paradigm for nonlinear DFIG-WPGS during distorted voltage

Dynamic characteristics of the doubly-fed induction generator (DFIG)-based wind power generation (WPGS) are fully nonlinear. Therefore, issues such as stability and achieving high efficiency, especially under harmonics behavior, are challenges that assess the control strategy reliability to find the perfect dynamic solution. This discussion offers a control strategy for the separated stator-port power using a predictive sliding mode strategy with a resonant function (PSMC-R) based on a deep recurrent neural network (DRNN). DRNN is formed as a low-order Taylor series formula. PSMC-R predicts the perfect switching surface path and regulates the distorted nonlinear DFIG with several dynamic aims. This approach reduces excessive chatter while violating the sliding surface path range of the classical SMC switch-part. Also, PSMC-R handled the fundamental and 5th-/7th-type harmonic wave at the positive synchronous +dqreference level of the machine dynamic quantities without further dissociation computations of the components. Dynamic results of a 1.5 MW DFIG-WPGS are simulated by using Matlab-package and presented good dynamic characteristics, less pulsation ratio of variables, and optimal sliding chatter of PSMC-R during various operating scenarios compared to the other classical regulation approaches.

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