Projects
Projects: Projects for Investigator |
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| Reference Number | NIA_NGET0140 | |
| Title | OHL Condition Assessment | |
| Status | Completed | |
| Energy Categories | Other Power and Storage Technologies(Electricity transmission and distribution) 100%; | |
| Research Types | Applied Research and Development 100% | |
| Science and Technology Fields | PHYSICAL SCIENCES AND MATHEMATICS (Metallurgy and Materials) 50%; ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering) 50%; |
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| UKERC Cross Cutting Characterisation | Not Cross-cutting 100% | |
| Principal Investigator |
Project Contact No email address given National Grid Electricity Transmission |
|
| Award Type | Network Innovation Allowance | |
| Funding Source | Ofgem | |
| Start Date | 01 February 2014 | |
| End Date | 01 June 2016 | |
| Duration | 28 months | |
| Total Grant Value | £217,250 | |
| Industrial Sectors | Power | |
| Region | London | |
| Programme | Network Innovation Allowance | |
| Investigators | Principal Investigator | Project Contact , National Grid Electricity Transmission (100.000%) |
| Web Site | http://www.smarternetworks.org/project/NIA_NGET0140 |
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| Objectives | Refined understanding of the condition of National Grid’s OHL system by extracting maximum value from existing data Optimisation of National Grid’s future OHL conductor sampling programme Refined deterioration models that can be used to better forecast network risk This project is successful if it delivers: Report detailing the application of proposed methodology within the National Grid Electricity Transmission business. England & Wales OHL Conductor Condition Assessment Model identifying varying degrees of uncertainty, focussed on ACSR circuits, 30-50 years old. The tools and techniques to inform a National Grid policy change with regards to the OHL conductor condition assessment based on the outcome of the above two deliverables. | |
| Abstract | National Grid need to understand the condition of lead assets on the transmission system in order to ensure security of supply and to maintain a fit-for-purpose transmission system. These condition based assessments inform Asset Management decisions, and play a vital role in deciding which work is prioritised. For Over Head Line (OHL) conductors, National Grid currently takes samples on a targeted basis to understand the condition of the conductor. However the sampling technique is invasive and can be subject to operational constraints. National Grid have a need to infer condition on thw whole of the network based on existing sample results and to understand where best to target future sampling. Research Existing data can be used to assess conductor condition on other parts of the network, such as, but not limited to, similar types and ages of conductors that are located in similar environments and experience similar operational conditions. Brunel University will use a technique called Bayesian network statistical modelling, a technique that deals with probability inference: using the knowledge of prior information to predict future information. The Bayesian network will combine qualitative engineering knowledge with existing quantitative sample data to maximise the use of information. A further technique known as active machine learning - a learning algorithm that is able to interactively query the information source to obtain the desired outputs at new data points - will be explored. This measures the uncertainty in the Bayesian network model. The highest level of uncertainty in this model will form a feedback loop which will determine the locations across the network where future sampling should be undertaken. This will improve the efficiency of future sampling programmes. The techniques will be applied for National Grid’s overhead line transmission network distributed across England and Wales and especially sites connected with ACSR conductors installed during 60s and 70s. This project will engage with consumers through Ofgem’s RIIO regulatory framework and innovation reports.Note : Project Documents may be available via the ENA Smarter Networks Portal using the Website link above | |
| Publications | (none) |
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| Final Report | (none) |
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| Added to Database | 14/09/18 | |
