The increasing wind power integration with power grid has forced the situation to improve the reliability of wind generators for stable operation. One important problem with induction generator based wind farm is its low ride through capability to the grid voltage disturbance. Any disturbance such as voltage dip may cause wind farm outages. Since wind power contribution is in predominant percentage, such outages may lead to stability problem. The proposed strategy is to use dynamic voltage controller (DVR) to compensate the voltage disturbance. The DVR provides the wind generator the ability to remain connected in grid and improve the reliability. The voltage dips due to symmetrical and unsymmetrical faults are considered for analysis. The vector control scheme is employed for fault compensation which uses software phase locked loop scheme and park dq0 transformation technique. Extensive simulation results are included to illustrate the control and operation of DVR.
T. Deepan Bharathi Kannan, B. Suresh Kumar, G. Rajesh Kannan, M. Umar and Mohammad Chand Khan
This work is aimed at developing relations between the pertinent variables that affect drilling process of stainless steel using artificial neural network. The experiments were conducted on vertical CNC machining centre. The parameters used were spindle speed and feed rate. The effect of machining parameters on entry burr height, exit burr height and surface roughness was experimentally evaluated for different spindle speeds and feed rates. A model was established between the drilling parameters and experimentally obtained data using ANN. The predicted values and measured values are fairly close, which indicates that the developed model can be effectively used to predict the burr height and surface roughness in drilling of stainless steel. Genetic algorithm (GA) technique was used in this work to identify the optimized drilling parameters. Confirmation test was conducted with the optimized parameters and it was found that confirmation test results were similar to that of GA-predicted output values.