Abstract
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.
Highlights
As an efficient way to deal with the rapidly growing distributed generation of renewable energy, the micro-grid (MG) can be employed in either a grid-connected mode or an islanded mode [1], [2]
The proposed ADRNN-SC scheme is possessed with the advantages of a double-hidden-layer NN (DNN), an recurrent neural network (RNN), a total sliding-mode control (TSMC), and the distributed secondary control manner, which endows the system with a model-free control structure, high control precision, fast dynamic performance, superior robustness to unpredictable perturbation, and no chattering phenomenon in control efforts
The proposed ADRNN-SC method can inherit the merits of fast convergence and robustness during the whole control process with a model-free control structure and without control chattering phenomenon
Summary
As an efficient way to deal with the rapidly growing distributed generation of renewable energy, the micro-grid (MG) can be employed in either a grid-connected mode or an islanded mode [1], [2]. The NN-based adaptive control can be used to inherit the fast dynamic response and robust properties of the SMC, while solving the problems of chattering phenomenon and relaxing the requirement of precise information of the MG models and primary control. The proposed ADRNN-SC scheme is possessed with the advantages of a DNN, an RNN, a TSMC, and the distributed secondary control manner, which endows the system with a model-free control structure, high control precision, fast dynamic performance, superior robustness to unpredictable perturbation, and no chattering phenomenon in control efforts. The main contributions of the proposed method can be summarized as follows: 1) In order to enhance the system robustness during the whole control process, a TSMC-based distributed secondary control (TSMC-SC) scheme is designed for dealing with the issues of voltage restoration and optimal active power sharing, which can be converted to local-neighborhood synchronization and tracking problems.
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