Distributed secondary control of islanded micro-grid based on adaptive fuzzy-neural-network-inherited total-sliding-mode control technique.

• A total-sliding-mode-control (TSMC)-based distributed secondary control (TSMC-DSC) scheme is designed. • Using an adaptive fuzzy-neural-network (FNN) to mimic the TSMC-DSC law. • A model-free control structure with favorable convergence speed, no chattering phenomenon, and strong robustness. • Training AFNN-DSC parameters online adaptively. • The network convergence as well as the Lyapunov stability. In this study, an adaptive fuzzy-neural-network (FNN) control scheme is proposed for an islanded micro-grid (MG) as a distributed secondary controller (DSC) to achieve the aims of voltage and frequency restoration and the optimal power sharing. Firstly, the dynamic model of an islanded MG is built, which consists of an inverter-interfaced distributed generation (DG) model and a MG architecture model. The DG model can be represented by considering the dynamics of a primary controller with an optimal active power sharing scheme. The MG architecture model is composed of power flow dynamics and loads. Then, a consensus-algorithm-based error function is defined, and a model-dependent total sliding-mode control (TSMC) technique is presented for dealing with synchronization and tracking problems. Moreover, an adaptive FNN (AFNN) scheme is designed to mimic the TSMC law to inherit its fast dynamic response with robust properties. Meanwhile, the requirement of precise information of the MG dynamic model in the TSMC law can be relaxed by the AFNN scheme. Adaptive tuning algorithms for FNN network parameters of the AFNN-based DSC (AFNN-DSC) strategy are derived by using the projection algorithm and the Lyapunov stability theorem, which can guarantee the stability of the AFNN-DSC-controlled system. The effectiveness of the proposed control method is verified by numerical simulations for real scenarios. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]