A Hybrid Approach for Enhancing Line Parameter Estimation in Power Systems

Accurate modeling of power systems is crucial for various control center applications. One of the key components in a power system model is the transmission lines. The line model includes parameters such as series impedance and shunt admittance, which are typically assumed to be time-invariant. However, these parameters vary based on the ambient and operating conditions of the system. Therefore, it is essential for the different monitoring and protection applications of the power system to have an accurate visualization of the line parameter variance throughout the day. In this context, this paper develops a hybrid line parameter estimation scheme that makes use of the available Phasor Measurement Unit (PMUs) measurements and Neural Network (NN) estimations to calculate the series line parameters of the transmission lines. The developed scheme was tested in the IEEE 14-bus system under realistic ambient and operating conditions, showcasing its practical applicability and enhanced accuracy.