Wind Variability and Its Effect on Transmission Line Capacity Estimation

TL;DR

This study addresses how wind velocity averaging biases Dynamic Thermal Rating (DTR) calculations. Using high-temporal-resolution 1s wind data from a Slovenian 220 kV line, it compares vector averaging and hybrid averaging over a 5-minute window within a CIGRE-based heat-balance DTR framework, computing Nusselt numbers $Nu$ and ampacity $I_{th}$ under fixed baseline weather to isolate wind effects. It finds that averaging substantially alters $Nu$ and $I_{th}$ with strong angular dependence: in parallel wind averaging tends to underestimate cooling (and thus ampacity) depending on the method, while perpendicular wind can lead to overestimation, with the magnitude and sign of bias depending on wind speed and variability. The results underscore the importance of specifying the wind-averaging method in DTR practice and motivate broader site studies and development of short-timescale DTR models to capture wind variability more robustly.

Abstract

This study investigates the impact of wind velocity averaging on Dynamic Thermal Rating (DTR) calculations. It is based on a high-temporal-resolution (1 second) wind measurements obtained from a transmission line in Slovenia, Europe. Wind speed and direction variability are analysed, and two averaging methods, namely vector averaging, where velocity is averaged as vector, and hybrid averaging, where speed is averaged as scalar, are employed. DTR calculations are performed on both high-resolution data and averaged data (5 minute averaging window). It is demonstrated that averaging has a significant effect on both Nusselt number and ampacity, and the effect exhibits a strong angular dependency on the relative angle of the wind to the line. Therefore, two limit cases are studied: in the case of parallel wind, averaged data underestimates the ampacity, and there is a significant amount of cases where the underestimation is larger than 10 %. In the case of perpendicular wind, the two averaging methods affect the results in different ways, but both result in a substantial amount of cases where ampacity is overestimated, potentially leading to unsafe operation. The main takeaway of the study is that averaging wind velocity has a significant impact on DTR results, and special emphasis should be given to the averaging method, as different methods affect the results in different ways.

Figures (20)

• Figure 1: Scatter plot and PDFs of measured wind data for April, August and September 2024, i.e. observed data. Weibull distribution is fitted on wind speed distribution using maximum likelihood estimation (MLE).
• Figure 2: Wind rose (left) and satellite image of the considered site. The span between pylons SM111 and SM112 will be the subject of DTR computations later in the manuscript.
• Figure 3: Vector plot of 1 wind measurements and average values within one 5min window for example A with higher wind speeds and relatively constant wind direction (left) and example B with lower wind speed and significant variations in wind direction (right).
• Figure 4: Scatter plots with speed and direction PDF for example A with higher (left) and example B with lower wind speeds (right).
• Figure 5: Left: Wind direction PDFs for examples A and B along with the upper and lower limits of variability metric. Note that the calculation of the average angle is weighted by wind speed, so the average angle might not seem intuitive, especially for example B. Right: Comparison of variability metric $\xi$ and standard deviation $\sigma$.