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Nonzero-Sum Game-Based Voltage Recovery Consensus Optimal Control for Nonlinear Microgrids System | IEEE Journals & Magazine | IEEE Xplore

Nonzero-Sum Game-Based Voltage Recovery Consensus Optimal Control for Nonlinear Microgrids System


Abstract:

Since most of the existing models based on the microgrids (MGs) are nonlinear, which could cause the controller oscillate, resulting in the excessive line loss, and the n...Show More

Abstract:

Since most of the existing models based on the microgrids (MGs) are nonlinear, which could cause the controller oscillate, resulting in the excessive line loss, and the nonlinear could also lead to the controller design difficulty of MGs system. Therefore, this article researches the distributed voltage recovery consensus optimal control problem for the nonlinear MGs system with N -distributed generations (DGs), in the case of providing stringent real power sharing. First, based on the distributed cooperative control concept of multiagent systems and the critic neural networks (NNs), a novel distributed secondary voltage recovery consensus optimal control protocol is constructed via applying the backstepping technique and nonzero-sum (NZS) differential game strategy to realize the voltage recovery of island MGs. Meanwhile, the model identifier is established to reconstruct the unknown NZS games systems based on a three-layer NN. Then, a critic NN weight adaptive adjustment tuning law is proposed to ensure the convergence of the cost functions and the stability of the closed-loop system. Furthermore, according to Lyapunov stability theory, it is proven that all signals are uniform ultimate boundedness in the closed loop system and the voltage recovery synchronization error converges to an arbitrarily small neighborhood of the origin near. Finally, some simulation results in MATLAB illustrate the validity of the proposed control strategy.
Published in: IEEE Transactions on Neural Networks and Learning Systems ( Volume: 34, Issue: 11, November 2023)
Page(s): 8617 - 8629
Date of Publication: 11 March 2022

ISSN Information:

PubMed ID: 35275823

Funding Agency:


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