Reliability analysis, diagnosis and prognosis of direct drive and medium speed generators
Lead Research Organisation:
University of Strathclyde
Department Name: Electronic and Electrical Engineering
Abstract
The growth of offshore wind energy is a significant feature of the UK's present and future energy mix. Wind energy is now a mainstream energy generation method in the UK and globally. In the UK alone, the offshore wind energy industry has installed approximately 8GW or roughly 2,000 offshore turbines in recent years. The impressive growth rate seen in the offshore wind energy industry has been partially driven by government policies and subsidies. For the UK offshore wind energy industry to meet the most recent strike price and eventually become subsidy free, the cost of generating electricity from offshore wind must continue to drop.
Additionally, recent CCC NetZero report has called for up to 75GW of installed offshore wind energy in the coming years. If the CCC targets are to be met, there will be approximately 6,500, ~10MW, new turbines types (multi MW direct drive and medium speed turbines) installed in UK waters in the coming years based on generator technology with a "blind spot" in reliability, diagnostics and prognostics.
In an industrial context, this demand for offshore wind energy cost reduction and reliability/prognostic enlightenment are the motivating factor for this proposal. The motivation in an academic context relates to a number of novel research opportunities unique to this application, which are outlined in the Case for Support.
The high level aim of this work is to complete the research that will allow for the creation of engineering support solutions to predict failure and remaining useful life of direct drive and medium speed generators from offshore wind turbines. An engineering support solution of this kind has the ability to remove unplanned downtime related to generators from on and offshore wind farms. The removal of unplanned downtime from a typical offshore wind farm has the potential to reduce the cost of generating offshore electricity by up to 4%.
The impact of that 4% cost reduction would be seen if wind farm developers were to invest the 4% cost saving in building additional offshore wind farms, increasing installed capacity by 4%. This would lead to an extra 370,000 homes in the UK being powered by sustainable offshore wind energy every year reducing carbon emissions by a further 660,000 tons annually. Alternatively, the 4% cost of generation savings (equivalent to ~ £210 million per year based on European offshore wind energy generation) from using the engineering support solution on offshore wind turbines could be passed to end users leading to reduced energy costs.
The timeliness of this cost saving work cannot be overstated for the UK offshore wind energy industry, which must reduce wind farm operation and maintenance costs by the mid 2020s to achieve the most recent strike price.
This research will be undertaken at the University of Strathclyde. It will support and be supported by other EPSRC funded initiatives such as the EPSRC Wind and Marine Energy systems and structures CDT and the SUPERGEN ORE Hub. A number of national and international wind turbine manufacturers, wind farm operators/utilities and research institutes will partner on this research.
Additionally, recent CCC NetZero report has called for up to 75GW of installed offshore wind energy in the coming years. If the CCC targets are to be met, there will be approximately 6,500, ~10MW, new turbines types (multi MW direct drive and medium speed turbines) installed in UK waters in the coming years based on generator technology with a "blind spot" in reliability, diagnostics and prognostics.
In an industrial context, this demand for offshore wind energy cost reduction and reliability/prognostic enlightenment are the motivating factor for this proposal. The motivation in an academic context relates to a number of novel research opportunities unique to this application, which are outlined in the Case for Support.
The high level aim of this work is to complete the research that will allow for the creation of engineering support solutions to predict failure and remaining useful life of direct drive and medium speed generators from offshore wind turbines. An engineering support solution of this kind has the ability to remove unplanned downtime related to generators from on and offshore wind farms. The removal of unplanned downtime from a typical offshore wind farm has the potential to reduce the cost of generating offshore electricity by up to 4%.
The impact of that 4% cost reduction would be seen if wind farm developers were to invest the 4% cost saving in building additional offshore wind farms, increasing installed capacity by 4%. This would lead to an extra 370,000 homes in the UK being powered by sustainable offshore wind energy every year reducing carbon emissions by a further 660,000 tons annually. Alternatively, the 4% cost of generation savings (equivalent to ~ £210 million per year based on European offshore wind energy generation) from using the engineering support solution on offshore wind turbines could be passed to end users leading to reduced energy costs.
The timeliness of this cost saving work cannot be overstated for the UK offshore wind energy industry, which must reduce wind farm operation and maintenance costs by the mid 2020s to achieve the most recent strike price.
This research will be undertaken at the University of Strathclyde. It will support and be supported by other EPSRC funded initiatives such as the EPSRC Wind and Marine Energy systems and structures CDT and the SUPERGEN ORE Hub. A number of national and international wind turbine manufacturers, wind farm operators/utilities and research institutes will partner on this research.
Planned Impact
Who might benefit from this research and why?
This research will benefit those involved in the offshore wind energy industry as well as those involved in the onshore wind energy industry. The benefit will however be amplified offshore due to the importance of operation and maintenance (O&M) planning and execution.
Wind Energy Companies will benefit from this research because as suggested in the letters of support for this application it will allow them to adopt a "Just in Time" O&M strategy for the latest generation of direct drive and medium speed generators. This will save costs through reduced wind turbine downtime, reduced inventory, being able to run generators until just before failure and the elimination of consequential damage to other components from the failure of one component.
Researchers in the wind, wave and tidal energy industry will benefit from this work because it will make highly sought after operational reliability and cost data available in a processed and useful way. This can then be used in similar research to this proposal as well as O&M cost modelling, cost of energy modeling, failure rate analysis and so on.
Tax Payer: One of the impacts of this research will be greatly improved operation and maintenance planning and execution, reducing offshore O&M costs. This O&M cost reduction will lead to an overall offshore wind energy cost reduction, benefiting the industry and the tax payer. Monetary values on this cost saving have been estimated in the "Summary" section of this application.
As many techniques form other research areas/industries will be applied in this research, there is potential for those areas to benefit from the outcomes and findings related to this work. These other areas included but are not limited to the signal processing area, the machine learning field and AI research.
This research will benefit those involved in the offshore wind energy industry as well as those involved in the onshore wind energy industry. The benefit will however be amplified offshore due to the importance of operation and maintenance (O&M) planning and execution.
Wind Energy Companies will benefit from this research because as suggested in the letters of support for this application it will allow them to adopt a "Just in Time" O&M strategy for the latest generation of direct drive and medium speed generators. This will save costs through reduced wind turbine downtime, reduced inventory, being able to run generators until just before failure and the elimination of consequential damage to other components from the failure of one component.
Researchers in the wind, wave and tidal energy industry will benefit from this work because it will make highly sought after operational reliability and cost data available in a processed and useful way. This can then be used in similar research to this proposal as well as O&M cost modelling, cost of energy modeling, failure rate analysis and so on.
Tax Payer: One of the impacts of this research will be greatly improved operation and maintenance planning and execution, reducing offshore O&M costs. This O&M cost reduction will lead to an overall offshore wind energy cost reduction, benefiting the industry and the tax payer. Monetary values on this cost saving have been estimated in the "Summary" section of this application.
As many techniques form other research areas/industries will be applied in this research, there is potential for those areas to benefit from the outcomes and findings related to this work. These other areas included but are not limited to the signal processing area, the machine learning field and AI research.
Organisations
People |
ORCID iD |
| James Carroll (Principal Investigator) |
Publications
Turnbull A
(2021)
Cost Benefit of Implementing Advanced Monitoring and Predictive Maintenance Strategies for Offshore Wind Farms
in Energies
| Description | 3 papers have been published in the areas of Wind Energy O&M and Data driven failrue prediction. Thos epapers are titled: 1. "A Comparative Analysis on the Variability of Temperature Thresholds through Time for Wind Turbine Generators Using Normal Behaviour Modelling", 2. "On the Development of Offshore Wind Turbine Technology: An Assessment of Reliability Rates and Fault Detection Methods in a Changing Market" and 3. "Cost Benefit of Implementing Advanced Monitoring and Predictive Maintenance Strategies for Offshore Wind Farms". This work determined ways to identify failures in wind turbine components using machine leanring techniques as outlined in paper 1 above. Paper 2 looked at determining how data driven failure predictin tehcniques would be used in new offshore wind turbine configurations such as direct drive and low speed drive trains. Paper 3 identified and quantified financial savings from data driven failure prediction. |
| Exploitation Route | To early to say, the award is still active. |
| Sectors | Energy |