Identification of the Critical Characteristics of Different Types of Voltage Sags for Synchronous Machine Torque Oscillations

Abstract—This article investigates the effects of symmetrical and unsymmetrical voltage sags on an important characteristic of torque pulsation of synchronous machines. Voltage sags may cause high torque pulsations, which can damage the shaft or equipment connected to it. Duration, magnitude,and initial point-on-wave of the voltage sag are the major characteristics that can influence the synchronous motors torque transients. The effect of the characteristics on the severity of the transient torque is clarified parametrically, and the critical characteristics of each type of voltage sag are extracted. A theoretical analysis is first presented to determine the severest conditions. In this analysis, for each type of voltage sag, the stator flux trajectory in complex coordination is traced to monitor its variation during and after sag. By this method, the effects of different sag types can easily be compared during the sag and at the sag ending instant. To justify the theoretical analysis, simulations of a typical synchronous motor are carried out that confirm the theoretical analysis.     

A Novel Stochastic Approach for Optimization of Diversion System Dimension by Considering Hydrological and Hydraulic Uncertainties

Water Resources Management - This study proposes a new stochastic approach for optimizing diversion system design and its construction schedule by considering different hydrological and hydraulic...     

Predicting acceleration response of super-tall buildings by support vector regression

Recovering missing data of defective sensors is an important challenge for reliability of structural health monitoring systems and misjudgment of structural conditions. The present study concerns predicting corrupted data of lost sensors by support vector regression (SVR). The method is tuned via optimizing their parameters by observer–teacher–learner-based optimization as a powerful meta-heuristic algorithm. Their performances are compared in predicting the acceleration responses of two real-world super-tall buildings: Milad Tower, located in Tehran, and Canton Tower in Guangzhou. Also the minimum required of sensors to predict the acceleration responses are investigated. The results are evaluated by five statistical indices exhibiting that the optimized SVR has sufficient capacity to predict acceleration responses of both towers with limited number of sensors. The proposed method is of practical interest as it does not require finite element modeling of the structure to derive its dynamic responses.