Voltage Stability Improvement Using Artificial Neural Network Controlled Unified Power Flow Controller (UPFC)

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dc.contributor.author Mbae, Ariel Mutegi
dc.date.accessioned 2017-05-16T08:08:41Z
dc.date.available 2017-05-16T08:08:41Z
dc.date.issued 2017-05-16
dc.identifier.citation msc en_US
dc.identifier.uri http://hdl.handle.net/123456789/3048
dc.description MASTER OF SCIENCE (Electrical Engineering) en_US
dc.description.abstract The demand for clean, reliable and affordable energy is growing at unprecedented rates across the world. As we seek to increase the quantity of energy produced, the supply quality challenges will grow in tandem. The growing long distances between generation and load centers only serve to compound the voltage stability challenge. Due to their versatility, high speed control and flexibility, FACTS (Flexible Alternating Current Transmission System) devices have been increasingly used as an alternative to the conventional methods such as capacitor banks and reactors over the years. However, for maximum benefits to be reaped from the said devices, their optimal location within a power network is a critical consideration. Research on the location of the FACTS devices using such methods as small signal analysis, hopf bifurcation, time domain analysis, loss sensitivity factors, fuzzy index, voltage change index as well as voltage stability index has been well documented. This has been coupled with various FACTS devices control strategies such as genetic algorithm, particle swarm optimization, pulse width modulation and runge-Kutta method. Over and above the optimal location of the UPFC, this research sought to use artificial neural networks as a way of replacing human system control operators so as to improve on real time system control as opposed to time delays experienced in relaying and actioning of instructions. A Voltage Security Constrained load flow analysis was done on the 10-Generator 39-Bus IEEE test system. This was followed by optimal placement of the UPFC using voltage stability indices after which an analysis of the voltage stability improvement was done. After that, an artificial neural network was trained to help track voltage profiles at various buses and issue instructions to the UPFC so as to stabilize voltages. Finally, a cost-benefit analysis of the proposed system showed that indeed the developed system is an economically viable option in the improvement of voltage profiles in a power system. A 100MVA UPFC led to an overall voltage stability improvement in the network. This was quantified by an overall loss reduction by 1.217MW.The developed control system was able to track voltage profiles at various buses and issue instructions to the UPFC to either absorb or inject reactive power so as to improve on the voltage profile. en_US
dc.description.sponsorship Dr. P.K Kihato, PhD JKUAT, Kenya Dr. C.M Muriithi, PhD TUK, Kenya Dr. M.J Saulo, PhD TUM, Kenya en_US
dc.language.iso en en_US
dc.publisher JKUAT COHES en_US
dc.subject Voltage Stability Improvement en_US
dc.subject Unified Power Flow Controller en_US
dc.title Voltage Stability Improvement Using Artificial Neural Network Controlled Unified Power Flow Controller (UPFC) en_US
dc.type Thesis en_US


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  • College of Health Sciences (COHES) [759]
    Medical Laboratory; Agriculture & environmental Biotecthology; Biochemistry; Molecular Medicine, Applied Epidemiology; Medicinal PhytochemistryPublic Health;

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