11-10-2016, 02:30 PM
1458638574-05abstract.pdf (Size: 128.98 KB / Downloads: 138)
ABSTRACT
High Voltage Direct Current transmission systems have a very
important place in today’s power system Scenario. HVDC link is inherently
non-linear in nature. Obtaining an accurate mathematical model of the
system is not possible.
Power transmitted in a HVDC plant depends upon the efficiency of the
controller and is the most important and complex part. Traditionally
Proportional plus Integral Controllers are employed for rectifier pole current
control and inverter side extinction angle control but they suffer from the
problems of fixed gains and can operate well over a limited operating range.
So, this thesis deals with enhancing the performance of PI controllers for
HVDC by employing the intelligence of expert techniques available in
literature which can deal with non-linear optimization problems.
In the first part of the thesis, a hybrid technique incorporating the
features of Fuzzy Logic and Neural Networks to self-tune the PI controller is
implemented. The error in the value of current and rate at which this error
varies were considered as the base parameters.
In the second part of the thesis a Genetic Algorithm-Neural Network
technique is adapted to self –tune PI controller gains. Results from
Simulations illustrate the potential of the proposed control scheme as GA-NN
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technique successfully adapts to different system conditions and is able to
minimize the total current error.
Frequently occurring transient stability because of severe power system
disturbances can be overcome by interconnection of HVDC links with
interconnected systems. A graphical based control strategy is proposed to
enhance the transient stability of the interconnected system in the final part
of the thesis.