Hierarchical Extreme Learning Machine Enabled Dynamic Line Rating Forecasting

Abstract

Dynamic line rating (DLR) is a technology introduced to rectify an overhead line's current carrying capacity based on climatic conditions. Reducing congestion costs, increasing the penetration of renewable energy, and network stability are the most important benefits of this modification. The DLR forecasting is a significant issue and can be very useful in network management decisions for real-time and day-ahead. So far, however, very few studies have talked about the literature on day-ahead DLR forecasting. This article develops various DLR forecasting models based on historical meteorological data and machine learning techniques. Accordingly, multilayer perceptron, group method data handling, support vector regression, back-propagation neural network, extreme learning machine (ELM), and hierarchical ELM (H-ELM) techniques compete for short-term DLR forecasting of two 400 kV overhead transmission lines, installed in the Khaf and Ghadamgah regions in Iran. The meteorological parameters such as air temperature, wind speed, wind direction, and solar radiation in the studied areas are utilized as input variables for selected methods. Different evaluation indicators assessments the forecasting results of each technique. Finally, evaluations emphasize the effectiveness and capability of the H-ELM method with the lowest forecasting error values and the highest correlation coefficient value compared to other methods. Experiments exposed the DLR forecasting models' generalizability at various points of the line without the extension of monitoring devices and communication infrastructures.