Fuzzy Dynamic Thermal Rating of Transmission Lines

Abstract

Dynamic thermal rating (DTR) of transmission system facilities is a way to maximally realize the equipment capacities while not threatening their health. With regards to transmission lines, the allowable current of conductors is forecasted based on the environmental situations expected in some forthcoming time periods. Due to the fact that weather conditions continuously vary, sampling points are very limited against many line spans, and the measurements have an inherent error, uncertainties must be appropriately included in the DTR determination. This paper adopts the fuzzy theory as a strong and simple tool to model uncertainties in the DTR calculation. Since DTR intends to determine the line capacity for some periods ahead, the capability of fuzzy computation in predicting future phenomena is very fruitful here. In the proposed model, variables, such as ambient temperature, wind speed, wind direction, and solar hour angle are assumed to be fuzzy numbers. Numerical simulations are provided based on inputs taken from real databases and reasonable assumptions as well. The results obtained support the validation and efficiency of the proposed model.

Vulnerability assessment of the power grid against progressing wildfires

“…To increase accuracy, the model uses approximated average weather conditions for each line section instead of using weather data at a single point. Reference [9] combined the steady-state thermal balance equation with fuzzy theory to model uncertainties in DTR calculation. Ambient temperature, wind speed, wind direction, and solar hour angle were modeled as fuzzy variables.…”

Implementation of Dynamic Line Rating technique in a 130 kV regional network

“…Today, power system capacity is set with a static limit based on worst-case scenarios. One solution to significantly decrease and/or delay future investment costs and still meet the same requirements (increase in capacity of overhead lines) is Dynamic Rating (DR) [4] [5] [6] [7]. DR is therefore identified as an important topic in the quest for the development of the electrical grid [8].…”

Quantile Regression and Clustering Models of Prediction Intervals for Weather Forecasts: A Comparative Study

“…The knowledge of NWP forecast uncertainty is crucial in decision making and optimization processes involved in many applications. Wind power production [2][3][4][5][6][7][8], dynamic thermal rating of transmission lines [9][10][11][12][13], and extreme weather event prediction [14] are few applications where information about forecast uncertainty is often regarded as significant as the forecast values themselves [15,16]. In transmission line thermal rating applications, ambient temperature and wind data are used along with line current to determine the temperature of power conductors that limits the thermal capacity of the line.…”