TABLE OF CONTENTS
Approval Page
Certification
Dedication
Acknowledgement
Abstract
List of Figures
List of Tables
Chapter 1: INTRODUCTION
1.1 Introduction
1.2 Statement of problem
1.3 Aim/objective of the study
1.4 Significance of the study
1.5 Scope of the work
Chapter 2: LITERATURE REVIEW
2.1 State of the art power system protection
2.2 Faults in Power System
2.3 Symmetrical Faults
2.3.1 Transient on a Transmission Line
2.3.2 Symmetrical Components
2.3.3. Symmetrical Component Transformation
2.4. Unsymmetrical Fault Analysis
2.4.1 Single Line to Ground Fault
2.4.2 Line to Line Fault
2.4.3 Double Line to Ground Fault
2.5. Types of Protection
2.5.1 Distance Relays
2.5.2 Pilot protection
2.6. Single Auto Reclosure Technique
2.7. System Configuration
2.8 Transmission Line Protection
2.8.1 Fault Detection & Location
2.8.2 Fault Classification
2.8.3 Enhanced Power System Protection
2.9 Artificial Neural Network
2.9.1 Multi-Layer Perceptron
2.9.2 Feed Forward Artificial Neural Network & Back Propagation Algorithm
2.9.3 Unsupervised Learning Algorithm
2.9.3.1 Self Organized Map Function
2.9.4. Clustering
Chapter 3: METHODOLOGY
3.1 Power System under Consideration
3.2 Data Pre-processing using fast Fourier transform
3.3 Overview of the Training Process
3.4 Overview of the Testing Process
3.5 Performance Evaluation
3.6 Clustering with Self-Organized Neural Network Algorithm
3.7 Neural Network Methodology for Adaptive Reclosure
3.8 Arc Modelling in Adaptive Reclosure Scheme
Chapter 4: SIMULATION RESULTS
4.1 Structure & training of neural fault detector
4.2 Discussion of Figures for A.N.N. Fault Detector
4.3 Structure & Training of A.N.N. fault location Algorithm
4.3.1 Discussion of plots from A.N.N. Fault Location Algorithm
4.4 Simulation Results for Fault Classification via Self Organising Map Function
4.4.1 Discussion of Results of Fault Classification via Self Organising Map Function
4.5 Simulation Results for Adaptive Auto Reclosure Scheme
4.5.1 Discussion of Results for Adaptive Fault Classifier Plots
4.6 Testing the Neural Network Fault Detection Algorithm
4.6.1 Discussion of Test Results of A.N.N. Fault Detector Algorithm
4.7 Test Results for Neural Network Fault Location Algorithm
4.7.1 Discussion of Simulation Results from Testing Fault Location Algorithm
4.8 Test Results for Neural Network Fault Classification Algorithm
Chapter 5: CONCLUSION
5.1 Conclusion
5.2 Recommendations
Abstract
This work investigates an improved protection solution based on the use of artificial neural network on the 330kV Nigerian Network modelled using Matlab R2014a. Measured fault voltages and currents signals decomposed using the discrete Fourier transform implemented via fast Fourier transform are fed as inputs to the neural network. The output plots of the neural network shows its successful application to fault diagnosis (fault detection, fault classification and fault location). The neural networks application to fault location shows a mean square error of 3.5331 and regression value of 0.99976 which shows a very close relationship between the output and target values fed to the neural network. Unlike conventional protection schemes, the neural network can be adapted to distances which can cover the entire length of the protected line. Numerical assessment carried out on the neural network fault locator shows a reduced time of operation of 5.15miliseconds as compared to the 0.350seconds with the use of ordinary numerical relays. This work also investigates the adaptive auto reclosure scheme implemented using artificial neural network. The adaptive reclosure scheme has been adapted for use in the Nigerian Network successfully to distinguish transient and permanent faults. Simulation results prove that the adaptive reclosure scheme was able to detect a line-to-ground transient fault and clear this fault in 0.1s while the line-to-ground permanent fault is cleared after 0.14s. The auto reclosure scheme is designed using two separate neural networks, one nework to distinguish the faults either as transient or permanent fault, and using this fault distinguishing network as input to the second network to classify decision, either as ‘safe to reclose’ represented by logic ‘1’ or ‘do not reclose’ represented as logic ‘0’. The Fault diagnostic algorithm designed using artificial neural network (A.N.N.) for the 330kV network was tested on a 132kV network. Results show and prove that the algorithm is flexible and can be adopted to other networks.
CHAPTER ONE
INTRODUCTION
1.1 Background of the study
The demand for constant power supply in Nigeria is ever increasing; however the demand is met with lots of constraint. One of them being system faults. Faults on transmission line in particular is of great interest to the power holding company of Nigeria as more investment is put into restructuring the current infrastructure and also expanding existing ones.
The power sector of Nigeria is subdivided into policy, regulations, customers, operations. The operations division brings to light the activities of the transmission company of Nigeria that controls the high voltage delivery of power from generating plants to the substations for transmission to distribution stations. T.C.N handles a 330kv system capacity of 6870MW over a total distance of 5650Km[1], their focus is to maintain power system stability, reliability and sustainability.
The major protection schemes currently employed are distance protection, over current protection, differential protection e.t.c. distance protection being the predominant suffers from inaccuracy due to restraints of relays on protection schemes i.e. reach settings. The relay cannot fully adapt to fluctuations in power system conditions especially in parallel lines as well as distinguish between transient and permanent fault following a short circuit.
This work brings to view the application of artificial neural network for enhanced power system protection in regards to fault detection, fault location, and application of the adaptive auto reclosure schemes as opposed to conventional approach; travelling wave approach, synchronous compensators to name a few.
1.2 Statement of the Problem
Among several power system components, transmission line is one of the most important components of the power system network and is mostly affected by several types of faults. Generally, 80%-90% of the fault occurs on the transmission line and the rest of substation equipment and bus bar combined. The necessary requirement of all the power system is to maintain reliability of operation which may be done by detecting, classifying and isolating various faults occurring in the system. It is required that a corrective decision should be made by the protective device to minimize the period of trouble and limit outage time, damage and related problems. If any fault or disturbances occurred in the transmission is not detected, located, and eliminated quickly, it may cause instability in the power system and causes significant changes in system quantities like over-current, under or over voltage, power factor, impedance, frequency and power. The appropriate percentage of occurrence of single line to ground fault is about 70-80%, line to line to ground faults is 10-17%, line to line fault is 8-10% and three phase is 3%. The three faults occur rarely but if it exists in a system it is quite expensive.....
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