• Journal of the Korean Society of Propulsion Engineers
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• v.8 no.3
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• pp.17-26
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• 2004

In this paper, power transmission systems converts the shaft power of a Turbo-shaft Engine with Free Power Turbine into the generator power and be composed of a method being supplied in the thrust motor driving a propellers. Being used this, Gas turbine engine works to flat rating about 110 kw (147 shp) that the thrust motor be extremely supplied from the engine of 317shp. In this test equipment, the engine is installed with the flywheel being able to the damping function when happen to the varying load between gas turbine engine output-shaft and generator. Then if the flywheel of inertial moment be not considered, the generator and motor not get the required power from the engine for raising the load. Also it is certified that the engine works the abnormal operation. Hence the flywheel of inertial moment is determined the required range to do the performance analysis with the dynamic transient from the given and tested engine data. This system is able to get the required power after a mounting test with the redesigned flywheel.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.9
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• pp.1065-1071
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• 2012

Wind energy is becoming one of the most preferable alternatives to conventional sources of electric power that rely on fossil fuels. For stable electric power generation, constant rotating speed control of a wind turbine is performed through pitch control and stall control of the turbine blades. Recently, variable pitch control has been implemented in modern wind turbines to harvest more energy at variable wind speeds that are even lower than the rated one. Although wind turbine pitch controllers are currently optimized using a step response via the Ziegler-Nichols auto-tuning process, this approach does not satisfy the requirements of variable pitch control. In this study, the variable pitch controller was optimized by a genetic algorithm using a neural network model that was constructed by the Latin Hypercube sampling method to improve the Ziegler-Nichols auto-tuning process. The optimized solution shows that the root mean square error, rise time, and settle time are respectively improved by more than 7.64%, 15.8%, and 15.3% compared with the corresponding initial solutions obtained by the Ziegler-Nichols auto-tuning process.