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.
Benefits
The proposed innovation method for line sag monitoring using 5G has the advantage of noncontact, low cost and high measuring accuracy. The accurate real-time sag condition monitoring of an OHL offers valuable dynamic info and enable the line operating at higher power capacity. If the new method increases power capacity by 100MW, it could save £2.25m. The proposed monitoring method also saves the cost of the survey needed on site to check the sag conditions of the lines. Comparing to other direct monitoring methods, no sensors are required to be installed and no circuit outage is required, which is more cost effective.
Learnings
Outcomes
Year 2023/24
Several Radio Propagation Models were investigated and utilised to calculate distances based on received signal strength. This includes free space path loss formula, flat earth path loss formula, Okumura-Hata and CCIR models, COST231-Hata Model, and COST231-Walfish-Ikegami Model. The first three are for general radio signals, while the last two models are for 5G signals.
1. Lab Test Outcomes:
Tests were conducted in the laboratory initially to develop the idea. Tests can show that the height of the highest point at both ends of the OHL is approximately 187 cm, with a sag of 27 cm. Two sets of part samples are used to reconstruct the OHL image. The tests indicate that the two reconstructed shapes using the method can accurately depict the sag of the overhead line (OHL).
The tests were subsequently conducted in the backyard of a house as outdoor experiments, considering that all overhead lines (OHL) are located outdoors, and the radio propagation environments differ between indoor and outdoor settings. The tests once again demonstrate that the final reconstructed image (shape) closely matches the actual shape of the real overhead line (OHL), with the margin of error remaining within the 1 cm threshold.
2. Field Test Outcomes:
Field tests were conducted, where two different shapes of overhead lines (OHL) were tested. The reconstructed OHL shapes closely matched the actual OHL shapes, with only minor deviations observed on the left side. These results strongly support the feasibility of the proposed method.
Subsequently, tests were performed on a 400 kV live line. Due to safety reasons, it was not possible to annotate the live line for training purposes. The experimental methods and procedures remained consistent with previous tests. However, due to limitations at the experimental site, only a portion of the live OHL near the tower was tested. Sampling was conducted from the right side towards the center, using the relationship between received power, distance, and the known distance between the receiver and transmitter.
Three tests were conducted, resulting in similar reconstructed shapes that exhibited the same trend. As sag monitoring aims to calculate sag, which is the difference between the height at the support point and the lowest point of the OHL, it is feasible to calculate sag using the relative shape of the reconstructed OHL obtained through the proposed method. The height difference between the middle point and the rightmost point is the key factor in this calculation.
Recommendations for further work
- Use better equipment to enable the machine learning method for sag monitoring.
- Use the machine learning method to develop a mobile device application for 24/7 remote non-contact monitoring of sagging.
Lessons Learnt
Year 2022/2023
Measurements of 5G signals inside the laboratory turned out to be challenging due to reflections of radio waves from ceiling, floor, walls etc that interfered with the desired signal. For future projects, it may be useful to consider the use of equipment outdoors (converter to be battery-powered for example).
Year 2023/2024
- The cost to undertake field tests was higher than expected and emergency funding had to be requested.
- Enough time needs to be planned for outdoor experiments. As a result of adverse weather conditions, the field tests were conducted within a few weeks of the project's end date.
- Machine learning methods can be implemented with improved data collection efficiency using better equipment in the future. With this, it is also possible to develop an application on mobile device to monitor the sag 24/7 if needed, but this would require data for scenarios with sagging at different heights for training, preferably in a better lab facility where we can control the height of the OHL.
Dissemination
Year 2022/2023
One journal paper has been published recently based on the work done in literature review.
Y. Chen, X. Ding, “A survey of sag monitoring methods for power grid transmission lines,” IET Generation, Transmission & Distribution,
Available at https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/gtd2.12778 (open access).
Major dissemination will be arranged once the project is completed via workshop and publications of journals or conference paper.
Year 2023/2024
One paper has been accepted by GPECOM 2024
Hua Yan, Yunfei Chen, Xiaolin Ding, “Sag Monitoring for Overhead Transmission Lines Using 5G Radio Signals”.
