Abstract
Conference Title: 2015 4th International Conference on Electric Power and Energy Conversion Systems (EPECS) Conference Start Date: 2015, Nov. 24 Conference End Date: 2015, Nov. 26 Conference Location: Sharjah, United Arab Emirates This paper presents online tools based decision tree (DT) and artificial neural network (ANN) to identify the critical operating scenarios and enhance the system dynamic performance considering stochastic behavior of wind energy. The stochastic behavior of wind speed requires a fast generation rescheduling to meet the load demand that may force the system towards stability limits. The objective is the online estimation of the critical fault clearing time (CCT) using decision tree as indicator for the power system transient stability. The system state is used to initial control actions against instability using ANN during critical operating points. The training of DT to estimate the CCT is carried out under various system operating conditions considering stochastic behavior of integrated wind energy. During abnormal operating conditions, a generation rescheduling is used as a preventive control for enhancing system transient stability. The paper conducts also with the impact of wind energy size and location on power system transient stability. The offline trained DT and ANN are conducted to predict the appropriately generated power to enhance system stability which can be updated in real time to account for changes in operating conditions and system topology. The test system used during the analysis is 66-bus power system.