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Projects: Projects for Investigator
Reference Number	NIA2_NGET0013
Title	Overhead Line Sagging Monitoring Using 5G Signals
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 No email address given National Grid Electricity Transmission
Award Type	Network Innovation Allowance
Funding Source	Ofgem
Start Date	01 July 2022
End Date	31 October 2024
Duration	ENA months
Total Grant Value	£350,000
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%)
Web Site	https://smarter.energynetworks.org/projects/NIA2_NGET0013
Objectives	The project investigates the feasibility of using 5G signals to monitor OHL line sag condition. It proposes an innovative method that using 5G signals to monitor the sag condition of the OHL line in a direct but non-intrusive manner. 5G radio waves travelling through the overhead line will create an image of the line due to reflection and diffraction. The method proposed is to propagate 5G signals through the OHLs to be monitored, and capture the image via the receiver and process the image to create the sag profile of the line. This allows monitoring sag condition of an OHL in a direct but non-intrusive way without installing any sensors. The high frequency characteristic of 5G signals enables the high resolution of the measurement. The wide coverage of the 5G signals in future will enable the application of wide area monitoring. A dedicated active transmitter will be used to generate the 5G signals to illuminate the overhead line; the received signals will be processed by machine learning algorithms to extract the sag profile from the received 5G signals; Then the sag profile will be further processed to calculate the clearance-to-ground data of the OHL; the developed line sag monitoring methods will be tested on site to evaluate the accuracy of the methods. Data Quality Statement (DQS): The project will be delivered under the NIA framework in line with OFGEM, ENA and NGGT / NGET internal policy. Data produced as part of this project will be subject to quality assurance to ensure that the information produced with each deliverable is accurate to the best of our knowledge and sources of information are appropriately documented. All deliverables and project outputs will be stored on our internal sharepoint platform ensuring access control, backup and version management. Relevant project documentation and reports will also be made available on the ENA Smarter Networks Portal and dissemination material will be shared with the relevant stakeholders.   Measurement Quality Statement (MQS):  The methodology used in this project will be subject to the suppliers own quality assurance regime. Quality assurance processes and the source of data, measurement processes and equipment as well as data processing will be clearly documented and verifiable. The measurements, designs and economic assessments will also be clearly documented in the relevant deliverables and final project report and will be made available for review. The scope of the project includes the following:Literature review on OHL sag monitoring.Develop a methodology to monitor the line sag using 5G signals. This includes developing an algorithm to capture the image of the line, computer software package performing data acquisition and processing, and machine learning to extract sag profiles of the line monitored and calculate the clearance of the line.Validate the developed method on a select span of an OHL in NGET network and compare with results from LiDAR data survey results available if possible, and evaluate the accuracy of the developed 5G line sag monitoring.Recommendations for practices and further development works required. The objectives of the project are as follows:Develop a direct but nonintrusive method to monitor the line sag condition using 5G signals.Extract sag profiles of the monitored OHL using machine learning to deliver results.Test the developed method on site and prove the accuracy of monitoring results.
Abstract	All overhead lines in the GB transmission network must maintain statutory clearances to ground. To maintain these clearances the line sag needs to be monitored. Also, if the line sag can be monitored easily and with great frequency (dynamically), it is possible to provide valuable inputs to the dynamic thermal rating of the overhead line. Current methods use either sensors installed on the line to directly measure temperature/sag or weather stations nearby to indirectly calculate temperature/sag. This project aims to design a new method by exploiting the fifth generation (5G) cellular signals to directly monitor and measure the line sagging but without sensor installation on the line.
Publications	(none)
Final Report	(none)
Added to Database	14/10/22