APPLICATION OF ARTIFICAIL NEURAL NETWORK FOR ENHANCED POWER SYSTEMS PROTECTION ON THE NIGERIAN 330kV NETWORK
Table Of Contents
Chapter ONE
INTRODUCTION
- 1.1Introduction
- 1.2Background of Study
- 1.3Problem Statement
- 1.4Objective of Study
- 1.5Limitation of Study
- 1.6Scope of Study
- 1.7Significance of Study
- 1.8Structure of the Research
- 1.9Definition of Terms
Chapter TWO
LITERATURE REVIEW
- 2.1Overview of Artificial Neural Network (ANN)
- 2.2Power Systems Protection Basics
- 2.3Previous Studies on Power Systems Protection
- 2.4Applications of ANN in Power Systems Protection
- 2.5Challenges of Power Systems Protection
- 2.6Advantages of Using ANN in Power Systems Protection
- 2.7Comparison of ANN with Traditional Methods
- 2.8Case Studies Using ANN in Power Systems Protection
- 2.9Future Trends in Power Systems Protection
- 2.10Summary of Literature Review
Chapter THREE
RESEARCH METHODOLOGY
- 3.1Research Design
- 3.2Data Collection Methods
- 3.3Sampling Techniques
- 3.4Data Analysis Tools
- 3.5Experimental Setup
- 3.6Validation Procedures
- 3.7Ethical Considerations
- 3.8Limitations of the Methodology
Chapter FOUR
DATA PRESENTATION AND ANALYSIS
- Discussion of Findings
- 4.1Analysis of Data Collected
- 4.2Interpretation of Results
- 4.3Comparison of Results with Hypotheses
- 4.4Discussion on the Implications of Findings
- 4.5Recommendations for Future Studies
- 4.6Practical Applications of Findings
- 4.7Limitations of the Study
- 4.8Conclusion of Findings
Chapter FIVE
SUMMARY, CONCLUSION AND RECOMMENDATIONS
- and Summary
Thesis 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.
Thesis Overview
<p> </p><p><strong>INTRODUCTION</strong></p><p><strong>1.1 Background of the study</strong></p><p>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.</p><p>The power sector of <a target="_blank" rel="nofollow" href="https://www.modishproject.com/artificail-neural-network-enhanced/">Nigeria</a> 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<a target="_blank" rel="nofollow" href="https://www.modishproject.com/improved-fault-location-power-system-transimmision-lines-using-fuzzy-logic-approach/"> maintain power system stability, reliability and sustainability.</a></p><p>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 <a target="_blank" rel="nofollow" href="https://www.modishproject.com/improved-fault-location-power-system-transimmision-lines-using-fuzzy-logic-approach/">permanent fault following a short circuit.</a></p><p>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; <a target="_blank" rel="nofollow" href="https://www.modishproject.com/artificail-neural-network-enhanced/">travelling</a> wave approach, synchronous compensators to name a few.</p> <br><p></p>Blazingprojects Mobile App
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