Investigation of Dynamic Line Rating based on Black box modelling

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Dr. Németh Bálint
Department of Electric Power Engineering

In the electricity system both the production and the consumption side have significantly changed in the last few decades. However, the development of the transmission system is not as fast as it should be so that critical situations could occur in the transmission lines. The major part of the European transmission grid was built in the last century when the design principle to handle intermittent energy power units based on renewable energy sources were not as dominant as today. As a result of this phenomenon, local overloads could occur in the grid, so that there is a real demand for an increased transmission capacity in order to maintain safety and security. [1]

Dynamic Line Rating (hereinafter referred to as DLR) is a promising method to increase the capacity of the transmission lines cost effectively. The main idea of DLR is to use the existing electricity infrastructure with better efficiency by monitoring the environmental and load parameters near the conductor in real time. Theoretically, the transmission capacity of the line determined by DLR calculation method exceeds the currently used static line rating in nearly 95 % of the time. [2][3][4]

In the international literature there are two main DLR models, the IEEE and the CIGRE model. These models determine the DLR in different ways but what they have in common is that these models are empirical ones, and both of them neglect some environmental factors. The major aim of this thesis is investigate alternative, mathematical computational methods different from IEEE and CIGRE in order to reduce these neglections. In this research a new Black box DLR calculation method based on neural networks capable of training from historical data of an installed sensor and weather station had been developed. In this way, it is not only possible to tune but also to accelerate the applied calculation of real-time transmission capacity. Another aim of this dissertation is to link probability-based hybrid DLR methods and Monte Carlo simulations. By using Monte Carlo simulation, a safety factor can be associated to each environmental and load circumstances which could facilitate the decision making of the transmission system operator. By the development of the DLR models, not just increased transmission capacity, but also higher operational safety can be achieved in the power system.

[1] ENTSOE, Dynamic Line Rating for overhead lines – V6, CE TSOs current practice 2015

[2] TWENTIES project, Final report, 2013

[3] EEA Report, “Renewable energy in Europe – 2017 Update, Recent growth and knock-on effects”, 2017

[4] Cigré 601 WG B2.43 – Guide for thermal rating calculations of overhead lines, 2014


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