Overhead Line Ampacity Forecasting With a Focus on Safety

Abstract

Predictions of the ampacity of overhead lines can be framed in a general context that aims to make electric grids highly efficient and reliable. In this paper, a methodology is presented that provides ampacity forecasts, which are valid for both the very short term, such as a few minutes or hours, and longer terms, up to 24 hours ahead. The former can be useful for grid operations, while the latter may be valuable in electricity markets. A time series methodology and mesoscale weather forecasts have been combined in machine learning algorithms for producing reliable ampacity forecasts for a span located in complex terrain. In a prior step, the developed algorithms made point forecasts, but finally, a computationally inexpensive algorithm produces probabilistic forecasts. These probabilistic forecasts are nonparametric, as they are not based on predefined probability distributions, and they demonstrate how a low risk in overhead lines is closely related to the reliability of ampacity forecasts.

Research on key technology of transmission and OPGW line hidden danger prediction based on neural network

“…With the development of the "Belt and Road" and the construction of the "Global Energy Internet," the number of extra-high voltage and long-distance overhead transmission lines is increasing, and the safety of overhead transmission lines in complex environments is becoming increasingly prominent [5][6]. Overhead transmission lines have been in a complex operating environment for a long time and have become the weakest link in the power network and the traditional transmission line mechanism model is difficult to adapt to the risk assessment of equipment in complex environments, which brings challenges to the transmission line condition assessment and fault prediction [7][8]. With the massive accumulation of transmission line-related data and the development of big data technology, big data analysis gradually presents advantages in equipment condition assessment and fault prediction [9].…”