Addressing Uncertainty in Dynamic Thermal Line Rating Estimation through Fuzzy-based Methods
Date
2021-08-27
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Abstract
Renewable generation is growing at a high rate compared to the speed of transmission infrastructure development. The prevailing methodology assigns a fixed capacity to the transmission system selected based on conservative weather assumptions, leading to an underestimation of the actual capacity of the transmission lines. To accommodate increases in installed renewable generation capacity in the present electricity grid, transmission facilities need to be operated closer to their real-time physical capacity. Dynamic Thermal Line Rating (DTLR) calculates a capacity of a transmission line that dynamically varies according to real-time weather conditions. To effectively estimate the rating of a transmission line, the existing uncertainty in weather data needs to be incorporated. My research investigates the application of fuzzy methods and machine learning models to address the uncertainties in DTLR estimation and predictions. In real-time estimation of DTLR, weather data variations over the evaluation period should be considered. Also, for forecasting applications of DTLR, the uncertainty of weather data prediction should be addressed to quantify the error in line rating forecast. The developed methodology estimates prediction interval for line ampacity which provides a range of possible values for the line rating at any desired level of confidence. Methods and analysis developed in this research would enable utilities to make decision on short-term rating of lines in advance of real-time operation with information on confidence associated with that decision. This research also performs transient analysis to quantify the risk associated with selecting ratings at different confidence levels for multiple forecast horizons. The potential benefits of the proposed DTLR prediction method for increased wind power integration are also discussed that enables a Transmission Facility Owner (TFO) to calculate the ability to accommodate increased wind production with existing infrastructure, without the need for line upgrades. This research work also uses DTLR methods and a probabilistic prediction methodology to forecast line clearance and conductor temperature and evaluate the risk of clearance encroachment. Additional sensitivity analysis is performed to determine the applicability of ambient-adjusted predictions when considering a lightly-loaded line compared to a heavily-loaded line.
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Keywords
dynamic thermal line rating, wind power, fuzzy prediction, time series forecast, renewable energy, data analytics, machine learning
Citation
Karimi, S. (2021). Addressing Uncertainty in Dynamic Thermal Line Rating Estimation through Fuzzy-based Methods (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.