Prediction of Temperature Variability on Power Transmission Line Parameters Using Intelligent Approaches

Due to changes in meteorological factors, the instability in the power at the end of the transmission system demands considerable attention. The temperature of the transmission line varies, which has a significant impact on the line parameters. An accurate prediction of line parameters behaviour is necessary to ensure system reliability. The present study is a step towards predicting variations in line parameters with respect to temperature variation. In addition, power loss and voltage drop due to variations in resistance are also predicted. Support Vector Machine (SVM) and ElasticNet, a machine learning algorithm, predict line parameters such as resistance, inductance, capacitance, voltage drop, and power losses. Furthermore, different seasons-based SVM and ElasticNet models for these parameters are considered. Seasons-based models are divided into two types, namely, summer and winter. 220-Kilovolt transmission data and weather information are used as model inputs. Predicted results of transmission line parameters are described in the form of RMSE and MRE. Moreover, the performance results of SVM and ElasticNet are also compared to show better prediction results. The result shows that the minimum prediction error of line parameters are 0.0511, 0.301, 0.426, 0.913, and 0.1501 in RMSE and 4.212, 0.518, 2.888, 0.097, and 0.615 percentages in MRE. This research work may provide technical guidance to transmission line engineers on enhancing the performance of transmission systems.