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With the large-scale integration of renewable energy sources like wind and solar into power grids, the decline in traditional synchronous machines has reduced system inertia, compromising grid stability. Energy storage technology is now widely used to address these challenges. Among these, modular multilevel converter-based energy storage systems have emerged as a promising solution due to their direct grid connection, excellent transient and steady-state performance and flexible applications. Moreover, applying virtual synchronous generators to these systems allows them to mimic the behaviour of traditional synchronous generators, improving frequency and voltage stability. This makes investigating virtual synchronous generators for modular converter-based energy storage systems highly relevant and practically important.

The modular power converter-based energy storage system has emerged as a cutting-edge technology in renewable energy integration and grid stability. Nonetheless, adaptive and optimal control for these systems in virtual synchronous generators still needs to be explored in the existing literature. This research will explore an adaptive and optimal virtual synchronous generator technology for modular-based energy storage systems. An advanced model reference adaptive controller will be designed and advanced intelligent control technology will be employed to optimise the parameters of the reference model in the adaptive controller, effectively adapting to variations in power grid dispatching. The project is expected to develop a novel adaptive observer to compensate for negative sequence voltage under unbalanced grid voltage. A comparative study will be conducted to compare the proposed methods with traditional approaches such as PQ control, droop control, and primary virtual synchronous generator control.



Modulation and control methods will be developed to ensure stable operation, efficient energy management and seamless integration of the modular power converter-based energy storage system with the grid. They will also aim to maintain grid stability, adapt to dynamic grid conditions and improve the overall performance of the virtual synchronous generator especially under unbalanced grid voltage and varying power demands. This will contribute to better handling of issues like frequency regulation, voltage support and fault tolerance in renewable energy systems. Simulation platforms will be used to model and validate the performance of the proposed adaptive and optimal control methods for the virtual synchronous generator technology. A laboratory-scale prototype will be implemented to demonstrate the feasibility of the proposed control methods.



The research activities include the design, development and testing of adaptive control strategies and advanced modulation techniques for modular power converter-based energy storage systems, with particular emphasis on addressing the limitations of existing technologies for virtual synchronous generators such as limited adaptability to grid fluctuations, poor handling of unbalanced grid conditions and suboptimal parameter tuning. The measurable objectives of the proposed research are:

1. To develop and validate an adaptive virtual synchronous generator control method that enhances grid stability and resilience.

2. To design an advanced model reference adaptive controller and optimise its parameters using intelligent control algorithms.

3. To develop a novel adaptive observer capable of compensating for negative sequence voltage under unbalanced grid voltage conditions.

4. To compare the performance of the proposed control methods with traditional approaches, demonstrating improvements in efficiency, stability and adaptability.

5. To implement and test a laboratory-scale prototype to verify the applicability of the developed technology.

Funding Information

To be eligible for consideration for a Home DfE or EPSRC Studentship (covering tuition fees and maintenance stipend of approx. £19,237 per annum), a candidate must satisfy all the eligibility criteria based on nationality, residency and academic qualifications.

To be classed as a Home student, candidates must meet the following criteria and the associated residency requirements:

• Be a UK National,
or • Have settled status,
or • Have pre-settled status,
or • Have indefinite leave to remain or enter the UK.

Candidates from ROI may also qualify for Home student funding.

Previous PhD study MAY make you ineligible to be considered for funding.

Please note that other terms and conditions also apply.

Please note that any available PhD studentships will be allocated on a competitive basis across a number of projects currently being advertised by the School.

A small number of international awards will be available for allocation across the School. An international award is not guaranteed to be available for this project, and competition across the School for these awards will be highly competitive.

Academic Requirements:

The minimum academic requirement for admission is normally an Upper Second Class Honours degree from a UK or ROI Higher Education provider in a relevant discipline, or an equivalent qualification acceptable to the University.