Projects
Projects: Custom Search |
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| Reference Number | NIA2_NGET0060 | |
| Title | Robot, AI and Drone Enhanced Detection of Discharge (RAIDEDD) | |
| 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 | PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 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 April 2024 | |
| End Date | 31 March 2026 | |
| Duration | ENA months | |
| Total Grant Value | £2,300,000 | |
| 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 Electricity Transmission (0.000%) |
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| Web Site | https://smarter.energynetworks.org/projects/NIA2_NGET0060 |
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| Objectives | This project takes advantage of recent innovations and advances in technology to significantly advance capabilities for using partial discharge (PD) monitoring to understand and track the health of HV assets. There are several workstreams that will support the ability to apply condition monitoring continuously where that is appropriate or that will provide more efficient and effective monitoring than might be achieved by carrying it out manually.In particular the project will explore the use of drones and robots for collecting data routinely, using machine learning (ML) to improve the use of fixed monitoring systems and artificial intelligence (AI) in improving diagnostics of asset health from PD and environmental data. The project will focus on partial discharge from high voltage substation assets and how the collection and interpretation from monitoring PD data can be improved compared with current improvements in technology. These are intended to replace digital radiofrequency (RF) devices used quarterly which can be slow and costly. Alternate systems are available for investigative purposes but are challenging to deploy. Devices deployed on robots and drones with improved software may be suitable replacements while investigative systems designed by machine learning techniques for investigative purposes.The fusion of PD monitoring with operational and environmental data will be carried out and subjected to advanced data analytics with the intention of producing functional decision support software demonstrators. The aim of these tools will be to show the value of enhanced PD monitoring in managing assets, and managing the network more widely through outage planning and safety management. This project has five distinct work packages with their own objectives all aimed at improving PD monitoring capability:Advanced Techniques for UHF (ultra high frequency) PD Location in Electrical Substationsa. Development and benchmarking of a substation-wide online PD location system which utilises advanced analytical tools, AI and new antenna designs to significantly improve substation-wide PD location accuracy compared with that of existing systemsAdvanced Substation Partial Discharge Monitoringa. Develop a multiplexed RF PD survey monitoring system which incorporates multiplexed wideband antennas suitably positioned around a substation and integrated with weather station data and substation load datab. Implement advanced data analytical methods, ML and AI techniques to be applied with the aim of producing functional asset decision support software demonstratorsUAV PD Locatora. Modification of electronic design of integrated PD monitoring on a drone (UAV) with low weight, power consumption and footprint to provide suitable flying timeb. Antenna improvement for the drone-based PD monitorc. Development of automated flight control and post flight analysis softwarePD Locator based on agile robot platforma. Integration of RF PD monitoring on a quadrupedal robot for substation PD inspectionb. Evaluation of the relative merits of drone, robot and manual PD monitoring for routine inspectionsNew Partial Discharge Detection Systema. Development of a new RF PD system synchronised with GNSS (Global Navigation Satellite System) integrated into a drone for data collection.b. Correlation of the RF sensor detection data with real discharge events in a controlled process to ensure that the new RF sensor can be shown to detect such PD activity | |
| Abstract | Partial discharge (PD) is a phenomenon that occurs in electrical assets whereby localised energy discharges do not bridge the insulation gap between conductors. It is a sign that an asset has a defect of some kind, either from manufacture or deterioration. Initially PD may occur infrequently, occurring more often as the defect worsens until eventually the PD frequency and magnitude becomes sufficiently concerning that the asset needs to be replaced or the discharge bridges the gap causing arcing and failure. PD monitoring is carried out routinely every three months along with thermovision checks at each substation; it is time consuming, and the monitoring equipment is relatively heavy. More frequent PD checks would increase the probability of getting early warnings of asset deterioration but other than more frequent personnel checks the alternative is continuous monitoring systems that have limitations and are expensive. In the event of a dielectric failure of an asset it is sometimes necessary to establish risk management hazard zones, PD monitoring could be used to mitigate the risks and allow site work to continue.This project will address the problem in a number of different ways to improve our ability to identify, locate and diagnose PD related defects as early as possible. The project will involve a number of workstreams:Use of machine learning techniques to identify the optimal locations for PD monitoring systemsImproved diagnostic capability for understanding PD patterns taking into account influencing environmental factors using AIDemonstration of robot- and drone-mounted PD monitoring assessing the capabilities and relative merits of both solutions | |
| 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 | 18/09/24 | |
