Investigation of dynamic electricity line rating based on neural networks

Abstract

The security of supply with a high level of operational safety and security has a prominent role in the domestic and international electricity networks. Due to continuous growth of consumer demand, the integration of renewable energy sources and other related changes in the market issues, a number of problems and challenges with the operation and utilization of the existing network have been identified. The need for a higher level of transmission capacity for the transmission network is one of the major challenges in the electricity network. Dynamic Line Rating (DLR) is a new generation of transfer capacity methods that can provide a cost-effective solution for the security of supply problems without re-planning the existing infrastructure background. The currently used Static Line Rating allows operators to calculate transfer capacity determined by the worst-case of the weather conditions on the wires of a particular transmission line. Whereas practical applicability shifts to security, the result of this calculation method is almost 95% of time less than the real permissible load of the overhead lines. This potential can be exploited with the DLR by always adjusting the maximum current that can be transmitted on wires. These maximum current values are calculated from the real-time environmental conditions, thus the DLR does not only provide better security of supply, but also a higher level of availability. The main issue of the article is to investigate the DLR based on the application of non-analytic computational methods different from the current calculations of the international standards (CIGRE, IEEE). The aim of this research is to create a neural network capable of recognizing patterns based on the weather data of previous years and the actual current values of the wires. In this way, it is not only possible to fine-tune, but also accelerate the applied calculation of maximum load capacity.