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FAULTS DIAGNOSIS ON A POWER SYSTEM TRANSMISSION LINE USING NEURAL NETWORK

CHAPTER ONE

 INTRODUCTION

 The biggest challenge in the continuity of electrical power system supply is the occurrence of faults. These faults are inevitable, but when a power system has a well coordinated protection system, it must be able to detect and isolate faults very fast to avoid damage and power outage. The faults must be cleared very quickly so as to restore the power to the isolated areas. These faults are cleared with devices which sense the fault and immediately respond and disconnect the faulty section from the good ones. To protect the power system transmission lines, faults must be detected and isolated accurately. The control centre of a power system contains large member of alarms which receives signals from different protection schemes for different types of fault. The operators in the control centre must work on the large amount of data obtained to know the required fault information. And due to the large number of calculations needed to be made so as to obtain this required information for the fault, it takes a longer time. These are the challenges to the protection of electrical power system transmission line, which are to detect, classify and isolate faults as fast as possible. But we know conventionally that when a fault occurs in the power systems, the fault current and voltage will develop a transient DC component and high frequency transient component in addition to power frequency component. This entire component will cause an increase in the magnitude of fault current and voltages with respect to fault type and location, and the system condition. Intelligent systems have been in use for fault diagnosis in power systems for some time now. Among the intelligent system is the artificial neural network (ANN) which has been applied to several power system operations and protection. There are other non intelligent system methods that can be employed in fault diagnosis. These include thevenin theorem, bus-impedance matrix and symmetrical component methods. But, in this work, symmetrical components and ANN methods were used and compared to know the best. Both methods were applied to the Matlab toolbox simpowersystem blockset modeled New – Haven/Nkalagu/Abakaliki transmission line under faulty and normal conditions. For faulty conditions, three phases, line to line, line to ground and double line to ground faults were considered. When symmetrical component was applied, fault currents, fault impedances, sequence impedances and symmetrical components of current during the faulty conditions were obtained as detected and classified faults. Fault Isolation was not able to be achieved using symmetrical component. The application of ANN to simpowersystem blockset modeled New – Haven/Nkalagu/Abakaliki transmission line is such that, the ANN has three stages, detection, classification and isolation stages. Each stage has its own ANN network selected and uses the phase voltages and currents during normal and faulty conditions as inputs of their selected networks. The ANN was able to detect the faults, classify them and isolate the faulty zone for proper protection of the transmission line. This makes it superior to other methods mentioned above. Also, it is widely used in different areas of power systems, it is simple, achieves accurate and faster result even when applied to a large network. The capability of neural network to generalize as well as tolerate faults makes it a reliable tool to be used in handling unseen faults conditions.

Project detailsContents
 
Number of Pages89 pages
Chapter one Introduction
Chapter two Literature review
Chapter three  methodology
Chapter  four  Data analysis
Chapter  five Summary,discussion & recommendations
ReferenceReference
QuestionnaireQuestionnaire
AppendixAppendix
Chapter summary1 to 5 chapters
Available documentPDF and MS-word format


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