Design of Adaptive Distributed Secondary Control Using Double-Hidden-Layer Recurrent-Neural-Network-Inherited Total-Sliding-Mode Scheme for Islanded Micro-Grid

IEEE Access

Rong-Jong Wai¹  Quan-Quan Zhang¹ 

[1] Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan;

关键词: Double-hidden-layer recurrent-neural-network (DRNN);    islanded micro-grid (MG);    distributed secondary control;    small-signal analysis;    total sliding-mode control (TSMC);   

DOI  :  10.1109/ACCESS.2022.3140360

来源: DOAJ

【 摘 要 】

This study proposes an adaptive double-hidden-layer recurrent-neural-network (DRNN)-based distributed secondary control (ADRNN-SC) scheme for the voltage restoration and the optimal active power sharing in an islanded micro-grid (MG). Based on the dynamic model composed of MG network model and primary control, a total-sliding-mode (TSMC)-based distributed secondary control (TSMC-SC) scheme is firstly developed for the properties of fast convergence and overall robustness during the control process, where the issues of voltage restoration and optimal active power sharing are converted to local-neighborhood synchronization and tracking problems. Meanwhile, focused on the problems of the control chattering phenomenon and the model dependence, a model-free DRNN structure is used to mimic the designed TSMC-SC law and to inherit its robust performance. The double-hidden-layer neural network (NN) in the DRNN structure needs less neuron nodes than the one with a single hidden layer at the same control performance because of its strong presentation ability. Thus, the computational complexity of the proposed ADRNN-SC scheme can be reduced. Moreover, the recurrent loop in the DRNN structure delivers the feedback signals of the output layer to the input layer, which possesses associative memory and accelerates the convergence process. Therefore, the DRNN structure can engage with a strong approximation ability and superior dynamic performance. In addition, the network parameters are online tuned adaptively to enhance the network learning ability. Furthermore, based on the small-signal model of the proposed control method embedded with communication delays, the delay margin and the influence of control parameters are also investigated. The effectiveness of the proposed control method is verified by numerical simulations.

【 授权许可】

Unknown