Computational and communication architectures for MMC VSC-HVDC Construction
Lead Research Organisation:
University of Manchester
Department Name: Electrical and Electronic Engineering
Abstract
The remote inspection and asset management of offshore wind farms and their connection to shore is an industry with the potential to be worth £2billion annually by 2025 in the UK alone, according to studies for the Crown Estate.
The EPSRC funded "HOME-Offshore: Holistic Operation and Maintenance for Energy from Offshore Wind Farms" project (EP/P009743/1) is undertaking the research necessary to support this industry. The project is exploring the use of advanced sensing, design for reliability, robotics, data-mining and physics-of-failure models to improve safety and reduce operating and maintenance costs.
The transmission of power from offshore wind farms is predominantly achieved using High Voltage Direct Current (HVDC) underwater cables, and at either end of these cables is a VSC-HVDC converter. These converters are complex systems, involving power electronics, computing and communication systems.
This PhD research programme will support the HOME-Offshore project by investigating how the choice of computational and communication architecture used to implement the VSC-HVDC converter impacts power system performance and the fault tolerance of the converters.
The research will be undertaken using a low-power reduced-scale hardware model of a modular multi-level VSC-HVDC converter that has been designed and constructed as part of an ongoing PhD project within the School. The hardware model has the flexibility, using Field Programmable Gate Array (FPGA) technology, to implement and experimentally evaluate a wide range of computational and communication architectures. Faults generated from statistical models can be injected into the system, and their impact on the performance of the power system observed and analysed.
The research will inform the design of the control systems for modular multi-level VSC-HVDC converters, and in particular their robustness to sub-system failures. This will lead to improved approaches to design for reliability and fault effect modelling.
EPSRC Research Topic Classifications: Robotics & Autonomy, Wind Power
EPSRC Industrial Sector Classifications: Energy
The EPSRC funded "HOME-Offshore: Holistic Operation and Maintenance for Energy from Offshore Wind Farms" project (EP/P009743/1) is undertaking the research necessary to support this industry. The project is exploring the use of advanced sensing, design for reliability, robotics, data-mining and physics-of-failure models to improve safety and reduce operating and maintenance costs.
The transmission of power from offshore wind farms is predominantly achieved using High Voltage Direct Current (HVDC) underwater cables, and at either end of these cables is a VSC-HVDC converter. These converters are complex systems, involving power electronics, computing and communication systems.
This PhD research programme will support the HOME-Offshore project by investigating how the choice of computational and communication architecture used to implement the VSC-HVDC converter impacts power system performance and the fault tolerance of the converters.
The research will be undertaken using a low-power reduced-scale hardware model of a modular multi-level VSC-HVDC converter that has been designed and constructed as part of an ongoing PhD project within the School. The hardware model has the flexibility, using Field Programmable Gate Array (FPGA) technology, to implement and experimentally evaluate a wide range of computational and communication architectures. Faults generated from statistical models can be injected into the system, and their impact on the performance of the power system observed and analysed.
The research will inform the design of the control systems for modular multi-level VSC-HVDC converters, and in particular their robustness to sub-system failures. This will lead to improved approaches to design for reliability and fault effect modelling.
EPSRC Research Topic Classifications: Robotics & Autonomy, Wind Power
EPSRC Industrial Sector Classifications: Energy
Organisations
People |
ORCID iD |
| Peter Green (Primary Supervisor) | |
| Jack Andrews (Student) |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/R513131/1 | 01/10/2018 | 30/09/2023 | |||
| 2105488 | Studentship | EP/R513131/1 | 01/10/2018 | 31/03/2022 | Jack Andrews |