Secondary Voltage Control of Microgrids Using Nonlinear Multiple Models Adaptive Control

TL;DR

This paper introduces a model-free secondary voltage control method for microgrids that combines linear robust adaptive control with nonlinear neural network-based adaptive control, ensuring stability and improved voltage tracking without prior system models.

Contribution

It presents a novel data-driven adaptive control approach using nonlinear multiple models for microgrid voltage regulation, eliminating the need for prior system information.

Findings

Ensures voltage stability with BIBO-guaranteed linear controller.

Achieves improved voltage tracking with neural network-based nonlinear controller.

Demonstrates robustness and disturbance rejection in microgrid control.

Abstract

This paper proposes a novel model-free secondary voltage control (SVC) for microgrids using nonlinear multiple models adaptive control. The proposed method is comprised of two components. Firstly, a linear robust adaptive controller is designed to guarantee the voltage stability in the bounded-input-bounded-output (BIBO) manner, which is more consistent with the operation requirements of microgrids. Secondly, a nonlinear adaptive controller is developed to improve the voltage tracking performance with the help of artificial neural networks (ANNs). A switching mechanism is proposed to coordinate such two controllers for guaranteeing the closed-loop stability while achieving accurate voltage tracking. Given our method leverages a data-driven real-time identification, it only relies on the input and output data of microgrids without resorting to any prior information of primary control and grid models, thus exhibiting good robustness, ease of deployment and disturbance rejection.