Power flow variation based on extreme learning machine algorithm in power system
Labed Imen, Labed Djamel · International Journal of Power Electronics and Drive Systems (IJPEDS) · 2019
The main focus of this paper is a study that empowers us to understand how the temperature variation affects the transmission line resistance and as a result the power flow analysis with a specific end goal to assess losses in the electrical network. The paper is composed of two sections; the first part is a power flow study under normal conditions utilizing the neural network approach while the second investigated extreme learning machine algorithm efficiency and exactitude. Extreme learning machine algorithm has been used to settle several complications in power system: load forecasting, fault diagnosis, economic dispatch, security, transient stability; Thus, we proposed to study this technique to figure out this sort of complex issue. The study was conducted for IEEE 30 bus test system. The simulation results are exposed and analyzed in detail at the end of this paper.