Voltage Stability Assessment of a Power System Incorporating Wind Turbine Using Power System Analysis Toolbox (Psat)

Abstract

As power systems become more complex and heavily loaded, voltage stability becomes an increasing serious problem. Voltage instability problems increasing day by day because of demand increase. It is very important to analyze the power system with respect to voltage stability. At present, wind power energy is increasingly penetrating electrical grids. This penetration is mainly driven by better wind technologies. The control capabilities of these new technologies are continuously improving to satisfy grid code requirements, ensuring a safe operation under normal and fault conditions. Double feed induction generators (DFIGs) and Squirrel Cage induction generators (SCIGs) are practically used. When the penetration of wind generation is high, it is important to keep these generators on line as much as possible during grid disturbances. Therefore, there is a significant interest in investigating the dynamic performance and characteristics of the system under high penetration of wind generation. To avoid this voltage collapse and to simplify the study, PSAT will be used to obtain the power flow results. This paper presents a comparative stability analysis of conventional synchronous generators and wind farms based on Squirrel Cage induction generators (SCIG). Based on an appropriate SCIG wind generator model, PV curves, and trained using Artificial Neural Network (ANN) are used to study the effect on system stability of replacing conventional generation by SCIG-based wind generation on the IEEE 14-bus benchmark system. Power System Analysis ToolBox (PSAT), Open Source MATLAB software which is used to obtain the voltage stability for various wind velocities. The voltage stability limits are derived in terms of maximum real power demand. Various case studies are analyzed .The results obtained using PSAT are given as the training data to the Artificial Neural Network(ANN). The ANN is trained using Back Propagation Algorithm (BPN) algorithm. The trained Network is used to predict the voltage stability limit at a particular wind velocity. PSAT is well suited for this kind of problem as it has the merit of solving both static and dynamic algorithm, exploiting network topology and to extract its component data and it has ability to define user defined models. Artificial Neural Network with BPN technique is preferred here for its advantages including the ability to detect all possible interactions between predictor variables and the availability of multiple training algorithms.