Projects
Projects: Projects for Investigator |
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| Reference Number | NIA_NIA_037 | |
| Title | Delta Detect | |
| Status | Started | |
| Energy Categories | Other Power and Storage Technologies(Electricity transmission and distribution) 100%; | |
| Research Types | Applied Research and Development 100% | |
| Science and Technology Fields | ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering) 100% | |
| UKERC Cross Cutting Characterisation | Not Cross-cutting 100% | |
| Principal Investigator |
Project Contact Electricity North West Limited |
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| Award Type | Network Innovation Allowance | |
| Funding Source | Ofgem | |
| Start Date | 01 November 2024 | |
| End Date | 30 June 2026 | |
| Duration | ENA months | |
| Total Grant Value | £1,150,000 | |
| Industrial Sectors | Power | |
| Region | North West | |
| Programme | Network Innovation Allowance | |
| Investigators | Principal Investigator | Project Contact , Electricity North West Limited |
| Web Site | https://smarter.energynetworks.org/projects/NIA_NIA_037 |
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| Objectives | The project aim is to leverage the capability of existing LV monitoring devices at secondary substations and extend their functionality to monitor HV underground networks. To do this, existing LV monitoring will be adapted, and an innovative algorithm will be developed to reverse engineer signals from LV side of the transformer onto the HV network. By monitoring critical parameters such as voltage, current, power factor and harmonics the system aims to identify abnormal conditions indicative of faults, such as insulation breakdown or equipment malfunction allowing for faults to be proactively detected, located, and repaired reducing downtime and leading to improved reliability and customer satisfaction. The project development will be supported by lab testing and academic research into the modelling of normal network background events to enhance accuracy of fault prediction and detection. The project will research into adapting the existing technology to be able to deliver benefits to the 6.6/11 kV ring main circuits.The research will study how waveforms triggered and recorded by LV monitors on the secondary mains (0.4 kV) network around the ring can be used to help sectionalise the fault.By predictively sensing faults, providing information on the section this fault is likely to lie in, it provides the DNO with an opportunity to find and effect a repair before any customers are affected.The project will undertakeresearch into adapting the existing technology and developing suitable algorithms to collect and process HV events. These algorithms would then be tested in a controlled lab trial.The project is split into three tasks:1. Research on Voltage dip and swell triggersinvestigate novel voltage dip and swell triggers to enhance fault detection capabilities in high voltage underground ring main circuits.2. Modelling of HV switching events - develop models of existing high voltage switching events and their impact on underground ring main circuits, aiding in fault prediction and localisation.3. Testing - Conduct testing and validation in a lab to assess their performance under realistic operating conditions. The objectives are:Enhance reliability and resilience of high voltage underground ring main circuits to ensure uninterrupted energy supply, especially in areas with vulnerable customers.Provide a cost-effective solution for monitoring high voltage networks that delivers tangible benefits to consumers and the electricity distribution licensees.Conduct research and demonstrate the effectiveness of adapting LV monitoring technology for high voltage networks | |
| Abstract | DeltaDetect aim is to leverage the capability of existing LV monitoring devices at secondary substations and extend their functionality to monitor HV underground networks. To do this, existing LV monitoring will be adapted, and an innovative algorithm will be developed to reverse engineer signals from LV side of the transformer onto the HV network. By monitoring critical parameters, the system aims to identify abnormal conditions indicative of faults, such as insulation breakdown or equipment malfunction allowing for faults to be proactively detected, located, and repaired reducing downtime and leading to improved reliability and customer satisfaction. | |
| Data | No related datasets |
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| Projects | No related projects |
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| Publications | No related publications |
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| Added to Database | 02/04/25 | |
