الفهرس 
: MODELLING of MULTI-MACHINE POWER SYSTEM COMPONENTSOnly 14 pages are availabe for public view
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Abstract
Power system dynamic equivalents are critical for obtaining a quality operating performance. The
representation of power system components in detailed models makes the aggregated system model
too complicated. This way numerous off-line simulations of the nonlinear system dynamics is
conducted, and as a result, a huge cpu time and memory allocations are needed.
To alleviate a such problem armed with the recently developed approximation techniques offered
by the artificial neural network (ANN), a dynamic system model is devised to describe the complex
dynamic performance. Synchronous machines are widely used in power generation, and their stable
performance under all anticipated system disturbances is vital for sustaining overall system
performance.
In this thesis ANN models have been developed for the system generators for the purpose of
speeding up the system simulations. A two layered, feed-forward and back-propagated learning
capability ANNs was used to emulate the nonlinear models of synchronous generators in a large power
system. A combination of current state variables and the system inputs were used as network inputs,
while the step ahead predicted state variables were used as network outputs. The results verify that the
ANN generator dynamic model can emulate generator dynamics well and should therefore be suitable
as a representation method for dynamic stability analysis .