Innovative Load Frequency Control: Integrating Adaptive Backstepping and Disturbance Observers

Load frequency control (LFC) in large interconnected power systems is crucial for balancing electricity supply and demand while minimizing frequency deviations. Traditional methods like proportional-integral-derivative (PID)controllers and advanced techniques such as evolutionary algorithms and artificial intelligence (AI) have limitations, including computational complexity, sensitivity to parameter changes, and high resource demands. This paper introduces a novel decentralized observer-based backstepping control (DOBC) strategy to overcome these challenges. In our work, each area controller utilizes local measurements and feedback signals to regulate its own area frequency. This approach inherently reduces the reliance on centralized communication and minimizes the impact of potential communication failures, such as packet losses and delays. The proposed method synergistically combines backstepping and disturbance observer techniques, resulting in rapid and stable system responses with reduced control effort, while a noncertainty equivalent adaptive approach ensures exponential disturbance estimation and maintains system stability under time-varying disturbances. Unlike conventional sliding mode control, the proposed method eliminates chattering, making it suitable for sensitive applications. Simulations validate its effectiveness under time delays, parametric uncertainties, nonlinearities, and load disturbances. Results show superior transient response, better oscillation damping, and lower control effort compared to adaptive neuro-fuzzy inference system based fractional-order PID-acceleration controller (ANFIS-FOPIDA), Second-Order sliding mode control (SOSMC), and Deep Reinforcement Learning (DRL). The paper concludes with a rigorous stability and robustness analysis, demonstrating the method’s resilience to parametric uncertainties and time-varying disturbances. This highlights its practical applicability and advantages in modern power systems.

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Publication Details

Journal
IEEE Access
Published
2025-01-01
DOI
https://doi.org/10.1109/access.2025.3554141
Citations
6
Primary Topic
Wind Turbine Control Systems
Type
article
Field-Weighted Citation Impact
4.38
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article

Innovative Load Frequency Control: Integrating Adaptive Backstepping and Disturbance Observers

Ali Reza Abbasi, Javad Ansari, Mohamadreza Homayounzade
6 citations
IEEE Access
Wind Turbine Control Systems
4.38
article

Innovative Load Frequency Control: Integrating Adaptive Backstepping and Disturbance Observers

Ali Reza Abbasi, Javad Ansari, Mohamadreza Homayounzade
article en
6 citations

Abstract

Load frequency control (LFC) in large interconnected power systems is crucial for balancing electricity supply and demand while minimizing frequency deviations. Traditional methods like proportional-integral-derivative (PID)controllers and advanced techniques such as evolutionary algorithms and artificial intelligence (AI) have limitations, including computational complexity, sensitivity to parameter changes, and high resource demands. This paper introduces a novel decentralized observer-based backstepping control (DOBC) strategy to overcome these challenges. In our work, each area controller utilizes local measurements and feedback signals to regulate its own area frequency. This approach inherently reduces the reliance on centralized communication and minimizes the impact of potential communication failures, such as packet losses and delays. The proposed method synergistically combines backstepping and disturbance observer techniques, resulting in rapid and stable system responses with reduced control effort, while a noncertainty equivalent adaptive approach ensures exponential disturbance estimation and maintains system stability under time-varying disturbances. Unlike conventional sliding mode control, the proposed method eliminates chattering, making it suitable for sensitive applications. Simulations validate its effectiveness under time delays, parametric uncertainties, nonlinearities, and load disturbances. Results show superior transient response, better oscillation damping, and lower control effort compared to adaptive neuro-fuzzy inference system based fractional-order PID-acceleration controller (ANFIS-FOPIDA), Second-Order sliding mode control (SOSMC), and Deep Reinforcement Learning (DRL). The paper concludes with a rigorous stability and robustness analysis, demonstrating the method’s resilience to parametric uncertainties and time-varying disturbances. This highlights its practical applicability and advantages in modern power systems.

IEEE AccessVol. 13
Islamic Azad University, Tehran (IR), Islamic Azad University of Majlesi (IR), Fasa University of Medical Sciences (IR), Fasa University
Affordable and clean energy
Openalex Percentile: Top 6%
Wind Turbine Control Systems
4.38
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