Projects
Projects: Projects for Investigator |
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| Reference Number | NIA_NGTO006 | |
| Title | Mobile robot for automated identification of failures in HV substations | |
| Status | Completed | |
| Energy Categories | Other Cross-Cutting Technologies or Research 50%; Other Power and Storage Technologies(Electricity transmission and distribution) 50%; |
|
| Research Types | Applied Research and Development 100% | |
| Science and Technology Fields | PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 25%; ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering) 75%; |
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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 October 2016 | |
| End Date | 31 July 2021 | |
| Duration | ENA months | |
| Total Grant Value | £550,000 | |
| Industrial Sectors | Power | |
| Region | London | |
| Programme | Network Innovation Allowance | |
| Investigators | Principal Investigator | Project Contact , National Grid Electricity Transmission (100.000%) |
| Industrial Collaborator | Project Contact , National Grid plc (0.000%) |
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| Web Site | https://smarter.energynetworks.org/projects/NIA_NGTO006 |
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| Objectives | The project will seek to develop an autonomous device that can take regular thermographic / RF measurements of substation assets (including within risk management zones) and use the information collected by these measurements to identify patterns of unusual behavior in the substation asset. The scope of the work will be as follows:Task 1. Data collation: The first part of classifying normal behaviour is to collate data from multiple sources on the high risk assets. Data from previous tests will be made available for use and a fixed monitoring system (with appropriate sensors) will be installed at a substation. This fixed monitoring system will allow the measurement of data in a range of weather conditions and under a range of load conditions. It will therefore provide specific data that can be used in the data analytics task. New data will be also be collected as required in experiments in the high voltage (HV) lab at the University of Manchester.Task 2. Data processing: Different statistical analysis tools will be applied to interpret the data collated in task 1 in order to create metrics. From this analysis, models of normal asset behaviour will be produced. This task will investigate methods for predicting if the measurements are within the normal range. If not in range, it is assumed that the asset is at a higher risk of failure / investigations should take place. Task 3. Comparison of available localisation systems: A number of systems exist to accurately provide location data for mobile robotic platforms. The standard method of differential global positioning system (GPS) and magnetometers may not work correctly in HV substations, due to the metalwork reflections and the noisy RF environment. Trials of different localisation systems will be carried out to understand the likelihood of receiving accurate location data. The trial will also include buying a robotic platform, attaching measuring devices and operating the platform in a high voltage substation.Task 4. Providing information to the operator: This task will evaluate ways to present intuitive information to the operator of the measurement system [the human – machine interface (HMI) | |
| Abstract | NULL | |
| Publications | (none) |
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| Final Report | (none) |
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| Added to Database | 09/11/22 | |
