Author Identifier

Md Ohirul Qays Joarder Akash: http://orcid.org/0000-0003-1949-7160

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

Iftekhar Ahmad

Second Supervisor

Daryoush Habibi

Abstract

To meet net-zero carbon emission targets, the energy sector is rapidly adopting renewable energy generators (REGs), such as solar farms, wind farms and battery storage systems. Consequently, conventional coal-based synchronous generators (SGs) are being phased out of generation fleets, which raises concerns about system strength and reliability shortfall. Along with increasing load demand, the deficiency of system strength poses a significant risk to system stability and can eventually lead to blackouts by disconnecting REGs from grid systems. Researchers and power engineers have suggested to deploy synchronous condensers (SynCons) as a mitigation strategy to address these system strength and reliability challenges. However, SynCons are expensive and require thorough investigation to ensure higher reliability before installation.

In this thesis, optimal sizes, placement and reliability assessment of SynCons are investigated. This thesis presents a solution that retains low SynCons-related costs and minimizes the loss of load probability (LOLP) while keeping the system strength above satisfactory levels. This thesis also investigates the long-term techno-economic feasibility of SynCons in terms of voltage recovery time, net present value and net profit for the next thirty years. A hybrid gated recurrent unit (GRU) optimisation framework is used to address the research questions.

To ensure post-fault operational stability, SynCons’ exciter controllers play a critical role. This thesis also investigates and presents a data-driven strategy for the optimal design of a H∞ exciter controller. Further to this, a system strength forecasting method is presented in this thesis. The developed method uses an online deep learning process comprised of a hedge feedforward feedback (HFF)- based online gated recurrent unit (GRU) learning algorithm.

The proposed solutions are validated in a modified IEEE 39 bus system. The obtained results show that the developed solutions reduced voltage recovery time in post-fault conditions and SynCons overall installation costs, representing higher economic benefit, improved control technique and higher forecasting accuracy.

Access Note

Access to this thesis is embargoed until 19th December 2030

DOI

10.25958/fw22-ss97

Download

Available for download on Thursday, December 19, 2030

Share

 
COinS