Abstract:
Dynamic load models, as well as the phenomena of voltage instability, are of growing
importance to the studies of power system dynamics. If the load representation is
not of sufficient accuracy, the simulation results obtained using such models do not
correspond to the actual response of the load. This affects the assessment of power
system stability limit.
This thesis deals with modeling and description of the dynamic load characteristics
for long-term voltage stability studies. The main component of the dynamic loads
constitutes the induction motor loads. The research comprised of developing a neurofuzzy
induction motor load model which was then incorporated in a Newton-Raphson
power flow algorithm. The modified load flow incorporates a neuro-fuzzy induction
motor load model that estimates the scheduled active and reactive power adjustable
during each iteration process. The impact of the aggregated induction motor on the
convergence characteristics of load flow are investigated using the IEEE-30 bus standard
test system. Based on the load flow results, counter propagative artificial neural
networks were used to classify buses in order of weakness.
In this thesis, the neuro-fuzzy model of an induction motor load was extended into
the Continuation Load flow algorithm, where the impact of different induction motor
horse power ratings on the voltage stability margin was investigated.
The thesis finally investigates the impact of the Fuzzy Logic based Power System
Stabilizer (FLPSS) in maintaining short term voltage stability in a system with
Induction Motor Loads.
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