Performance Analysis of a Dynamic Line Rating System Based on Project Experiences

Abstract

This paper aims to demonstrate the performance and reliability analysis of a dynamic line rating (DLR) system at the Bulgarian demonstration site of the FLEXITRANSTORE project. As part of the project, various manufacturers’ different line monitoring DLR sensors and weather stations were installed on a 110 kV double-circuit overhead line (OHL). These devices provided input parameters to the DLR system based on objective measurements. This paper used statistical tools to examine the reliability and accuracy of installed devices, thus making products from different manufacturers comparable. In addition, two independent line monitoring and DLR models have been developed: the black-box and extended white-box models. The performances of the two models were analyzed for the same input parameters and compared to the field measurements. Based on the presented results, the reliability and accuracy of the applied weather stations of different companies were almost the same. This conclusion cannot be said for DLR line monitoring sensors, where the devices could be differentiated based on reliability and measurement accuracy results. In terms of models, the usability of the extended white-box model seemed to be limited in certain weather conditions, implicating a more significant role for soft-computing-based DLR models in the future. In addition to the results, root causes for the errors and future directions that may provide a framework for further research are also presented.

Dynamic Line Rating Solution: Deployment Opportunities for the Power Transmission Grid of Vietnam

“…The DLR analysis strictly follows the steady-state heat balanced approach proposed by the IEEE and/or CIGRÉ models [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. These models are similar regarding the calculation method; however they differ in the factors that they consider for the heat balance calculation [20]. The energy balance inside the conductor without any heat storage causes the amount of heat supplied completely convert into the outside environment by heat dissipation which is expressed under the simplified equilibrium equation of heat gained (by Joule effect ( ), Solar heating ) and heat lost (by convection ( ), radiation ( )) by EEE Standard 738 [10,20]:…”

“…Dynamic rating by application of weather model allows increasing the line capacity by up to 20%, depending on the weather conditions [21]. The calculation methods are classified as indirect and direct methods [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. The indirect method (Figure 4) uses the measuring data from equipment placed surrounding line system giving all concerning parameters (wind speed, wind direction, solar radiation, and ambient temperature), which is then put into heat exchange equations ( 1), ( 5) to calculate the maximum amount of allowable intensity that the overhead line can carry in that specified time interval.…”

“…These technologies include remote sensing, measurement, communication, data analytics, high-performance computing, and automation. Implementing a DLR system requires the following steps: line identification; sensor installation; data communication; data analytics; computing and temperature forecasting; and, finally, validation [14][15][16][17][18][19][20][21][22][23][24].…”

“…Figure 6. Flowchart of the methodology application for transmission line rating implementation[14][15][16][17][18][19][20][21][22][23][24].…”

Optimizing Grid With Dynamic Line Rating of Conductors: A Comprehensive Review

“…Increased 26.6% average ampacity gain with SLR exceeding DLR only 4% of the time [118]. Canada It used on DC bi-poles to analyze wind plant installation.…”