Abstract

An accurate definition of a system model significantly affects the performance of model-based control strategies, for example, model predictive control (MPC). In this paper, a model-free predictive control strategy is presented to mitigate all ramifications of the model’s uncertainties and parameter mismatch between the plant and controller for the control of power electronic converters in applications such as microgrids. A specific recurrent neural network structure called state-space neural network (ssNN) is proposed as a model-free current predictive control for a three-phase power converter. In this approach, NN weights are updated through particle swarm optimization (PSO) for faster convergence. After the training process, the proposed ssNN-PSO combined with the predictive controller using a performance criterion overcomes parameter variations in the physical system. A comparison has been carried out between the conventional MPC and the proposed model-free predictive control in different scenarios. The simulation results of the proposed control scheme exhibit more robustness compared to the conventional finite-control-set MPC.

Highlights

  • To avoid undesired interactions in multi-control loops, the inner loops should be designed with a higher bandwidth compared to the outer one

  • This paper aims to present a model-free and robust control strategy for converter interfaced-renewable energy sources (RESs)

  • The major contribution of this paper is to present a model-free predictive control for the robust operation of power converters

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Summary

Introduction

With the increasing penetration of converter interfaced-RESs, the inertia of the power grid reduces and poses major stability issues to the power system. To overcome these stability issues, converter interfaced-RESs should be equipped with a robust, fast, and reliable control strategy. This paper aims to present a model-free and robust control strategy for converter interfaced-RESs. The objective of the control method is to enhance its robustness to system parameter mismatch and uncertainty of RESs generation by presenting a state-space neural network-based predictive control method

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