Artificial neural network based load flow analysis of radial distribution system in Kurdistan region

Today electric energy is the most commonly used source in the world. Power flow (load flow) analysis is conciderd as the backbone of any power system analysis and design; they have a great necessity for operating systems, future planning, fault analysis, and contingency analysis. For better utilization of electrical power, off-line modeling and simulation of power systems using powerful software are essential and significant task especially in developing countries and regions. Therefore, this paper performs a comparison study of conventional and non-conventional load flow techniques for a 24-Bus radial distribution system in the governorate of Sulaymaniyah. The conventional power flow techniques include the Newton-Raphson (NR), and Gauss-Seidel (GS) techniques, while the nonconventional load flow technique utilizes the artificial neural network (ANN). Modeling, simulation, and analysis of the 24-Bus feeder are performed using MATPOWER simulation tool. The MATPOWER and neural network techniques are implemented independently, and it has been proved that ANN model efficiently estimated the power flow analysis for the system mentioned above, the high regression values of nearly 0.999 indicates that the ANN model can be used as an efficient tool to perform power flow analysis.