Analysis of Voltage Stability in IEEE 14-Bus Power System with Extreme Learning Machine

Abstract

Nowadays, electrical energy needs are increasing rapidly as a result of technological developments. In order to meet this growing demand, power plants have been built. In this study, voltage stability in IEEE 14-bus power system was investigated by means of Extreme Learning Machine (ELM). For this purpose, the IEEE 14-bus power system model was built in Matlab environment and load flow analysis for this model was performed by using the Newton-Raphson Method (NRM). In this power system, the voltage stability was evaluated by calculating the Line Stability Index (LSI). In the load flow analysis, the active and reactive powers of all bus were increased by 0.05 step (pu) and a total of 1000 active power, reactive power, voltage, and phase angle of the respective bus were obtained for each busbar. These values were used to calculate the values of LSI. The inputs of ELM are selected as active power, reactive power, voltage and phase angle of the respective bus. The output of the NRM was determined as LSI values. The test performance of the NRM is given by 5-fold cross-validation. In addition, the ELM’s performance was investigated for the different number of hidden layer cell numbers and different types of activation functions. The proposed method provides the best test performance in case of ELM with a hidden layer cell number of 100 and the tangent sigmoid activation function. From the obtained results, it is seen that ELM predicted LSI with a very high performance in determination of voltage stability in IEEE 14-bus power systems.

Keywords

• Voltage Stability
• Extreme Learning Machine
• Line Stability Index