PhD Studentship (Funded): Advanced Digital Twin Technology for Enhancing Real-Time Power System Stability and Islanding, PhD in Electrical and Electronic Engineering

The increasing reliance on electrical energy in contemporary society has made the stability and reliability of power systems a paramount concern. Rapid population growth, the inefficiency of traditional power plants, the proliferation of electrical devices, and the high cost of establishing new power plants have led to power systems operating near their stability limits. This situation heightens the system's susceptibility to disturbances, making the prediction and management of system instability or collapse essential for preventing large-scale blackouts and ensuring an uninterrupted energy supply. 

Power system stability is a multifaceted issue, involving various interconnected and dependent parameters. The nonlinear and dynamic nature of power systems, combined with different rates of change in stability aspects, complicates accurate prediction. System collapse typically begins in localized areas and spreads, making rapid detection challenging with conventional methods. Effective islanding, which isolates faulty areas to prevent total system collapse, requires swift action that is often difficult to achieve with traditional approaches. 

This PhD project aims to address these challenges by developing a digital twin for real-time power system stability prediction and islanding. A digital twin is a virtual model of a physical system that can simulate, monitor, and optimize the system's performance in real-time. By leveraging advanced data analytics and artificial intelligence (AI), the digital twin will enable precise prediction of potential collapse areas and identification of islands, facilitating timely interventions. 

The digital twin will simulate the power system in real-time, continuously analyzing data from various sources, including sensors, smart meters, and other monitoring devices. This real-time monitoring will allow the digital twin to detect anomalies and predict system instability before it escalates. By dividing the power system into various zones, such as collapse-prone and stable areas, the digital twin will enhance situational awareness and decision-making. 

The project will focus on developing AI algorithms to process and analyze the vast amounts of data collected, enabling the digital twin to learn from historical and real-time data to improve its predictive capabilities. The digital twin will also facilitate real-time control actions to isolate faulty areas and maintain system stability, reducing the risk of widespread blackouts and ensuring a continuous energy supply to consumers.

By integrating digital twin technology with real-time monitoring and AI-driven predictive analytics, this project promises to revolutionize power system stability management. The outcomes of this research will provide significant benefits, including enhanced reliability and resilience of power systems, reduced operational costs, and improved energy security for society. 

The studentship will be awarded on the basis of merit for 3.5 years of full-time study to commence on 1st February 2025. The collaboration involves a project partner who is providing funding [and other material support to the project], this means there are special terms that apply to the project, these will be discussed with Candidates at Interview and fully set out in the offer letter.  The collaboration with the named project partner is subject to contract.  Please note full details of the project partner’s contribution and involvement with the project is still to be confirmed and may change during the course of contract negotiations.  Full details will be confirmed at offer stage. 

International applicants need to be aware that you will have to cover the cost of your student visa, healthcare surcharge and other costs of moving to the UK to do a PhD.

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