Showing papers by "Graduate University of Advanced Technology published in 2010"

Comparison of ANN and principal component analysis-multivariate linear regression models for predicting the river flow based on developed discrepancy ratio statistic

Abstract: Predicting the stream flow is one of the most important steps in the water resources management. Artificial neural network (ANN) has been suggested and applied for this purpose by many of researchers. In such studies for verification and comparison of ANN results usually the popular methods such as multivariate linear regression (MLR) is used. Unfortunately, the presented methodology in some researches is faced with some problems. Thus, in this paper we have tried to find out the deficiencies of them and subsequently to present a correct the MLR methodology based on principal component analysis (PCA) for prediction of monthly stream flow. Then, assessment of different training functions on ANN operation is investigated and the best training function for optimizing the ANN parameters is selected. Afterward, the imperfections of the discrepancy ration (DR) statistic are remedied and a proper DR statistic is developed. Finally, the error distribution for testing stage of MLR and ANN models are calculated using developed DR statistic. The results of comparison show that the presented methodology in this research has improved the MLR operation. Also, comparing with the MLR, the ANN model possesses satisfactory predicting performance.

Utilizing a STATCOM to prevent the flicker propagation in a wind power system

Abstract: This paper investigates how effective a STATCOM can be in the prevention of the propagation of flicker induced by the aerodynamic aspects of wind turbines of a wind power system. A small wind farm equipped with fixed speed wind turbines connected to a distribution system is studied by using a STATCOM to improve power quality. The aerodynamic and mechanical aspects of a wind turbine are simulated using TurbSim, AeroDyn, and FAST, and the electrical parts of the wind turbine, STATCOM, network, and flickermeter are modelled by Simulink blocks. Using a flickermeter model, the instantaneous flicker at the Point of Common Coupling (PCC) is determined for the wind power system. Simulation results obtained from the model are used to observe the effects of yaw error and turbulence on the flicker level at PCC. The performance of the STATCOM is then evaluated based on these observations.