Author Identifier

Farhad Farivar: http://orcid.org/0000-0002-3577-4853

Date of Award

2025

Document Type

Thesis - ECU Access Only

Publisher

Edith Cowan University

Degree Name

Doctor of Philosophy

School

School of Engineering

First Supervisor

Octavian Bass

Second Supervisor

Daryoush Habibi

Abstract

Load frequency control (LFC) in interconnected multiarea power systems has become increasingly challenging due to the integration of renewable energy sources, random load variations, and system uncertainties. These factors contribute to frequency deviations, reduced system inertia, and compromised stability, necessitating robust control mechanisms. This research proposes an advanced sliding mode control (SMC)-based LFC framework, incorporating disturbance observer-based SMC, memory-based adaptive SMC, and event-triggered SMC to enhance system resilience, transient performance, and computational efficiency. A disturbance observer estimates lumped disturbances from tie-line power deviations, load variations, and renewable fluctuations, while a memory-based sliding mode strategy improves frequency stability by leveraging past system states. Additionally, an event-triggered SMC approach with output feedback reduces computational overhead while ensuring robust performance. The proposed framework guarantees globally stable and adaptive frequency regulation through linear matrix inequalities (LMIs) and H∞ robust performance criteria. Integrating an energy storage system (ESS) further enhances disturbance rejection and frequency response. Compared to conventional robust SMC-based LFC methods, the proposed strategy achieves superior disturbance rejection, reducing frequency overshoot, control effort, and response time. Numerical simulations and comparative studies validate the effectiveness of the framework in improving transient stability and decentralized scalability, making it a viable solution for real-world power systems. This research contributes to advancing resilient LFC strategies in renewable-integrated grids, paving the way for more adaptive and efficient grid management approaches.

DOI

10.25958/adc2-6h09

Access Note

Access to this thesis is embargoed until 5th July 2026

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