4.6 Article

Enhancing the Conventional Controllers for Load Frequency Control of Isolated Microgrids Using Proposed Multi-Objective Formulation via Artificial Rabbits Optimization Algorithm

Journal

IEEE ACCESS
Volume 11, Issue -, Pages 3472-3493

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2023.3234043

Keywords

Artificial rabbits optimization (ARO) algorithm; isolated microgrid (IMG); load frequency control (LFC); multi-objective tuning approach; renewable energy resources (RES)

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This paper proposes a novel multi-objective tuning strategy to improve the load frequency control of isolated microgrids. The proposed approach tunes the PI and PID controllers using a multi-objective-based tuning approach and applies anti-windup to enhance their performance. The effectiveness of the proposed approach is confirmed through extensive simulations in MATLAB and Simulink.
Isolated microgrids (IMGs) power remote areas. However, IMG may lower the frequency stability and increase frequency excursions with low system inertia. Load frequency management ensures system stability. Thus, the paper proposes a novel multi-objective tuning strategy to improve IMG's load frequency control (LFC) and take the microgrid controller's control signals into account. Diesel engine generator, fuel cell, battery energy storage system, and renewable energy sources (RESs) like photovoltaic and wind systems make up the IMG. Conventional controllers such as proportional-integral (PI) and proportional integral derivative (PID) are classically tuned based on the standard error criteria as a traditional single-objective tuning approach. Due to the low inertia of the system and the stochastic nature of RES, they cannot act as required under different operating scenarios. Therefore, the PI and PID controllers are tuned using the proposed multi-objective-based tuning approach to reduce the frequency deviations. In addition, anti-windup is applied to the enhanced classic controllers to keep them distant from the nonlinear zone and beyond the source's physical constraints. The proposed tuning process also considers the maximum practical generation rates for different sources. The recent Artificial Rabbits Optimization (ARO) algorithm is applied to simultaneously adjust the controller parameters for several controlled sources in IMG. Extensive simulations in MATLAB and Simulink confirm the effectiveness of the proposed approach to keep the system stable even when facing high levels of disturbances. In addition, accomplishing sensitivity analysis, severe +/- 25% changes to the system's parameters guarantee that the proposed tuning strategy keeps the system stable.

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