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Application Of Artificail Neural Network For Enhanced Power Systems Protection On The Nigerian 330kv Network
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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.
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CHAPTER ONE - [ Total Page(s): 1 ]CHAPTER ONEINTRODUCTION1.1 Background of the studyThe 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, customer ... Continue reading---
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CHAPTER ONE - [ Total Page(s): 1 ]CHAPTER ONEINTRODUCTION1.1 Background of the studyThe 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, customer ... Continue reading---
ABSRACT -- [Total Page(s) 1]
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ABSRACT -- [Total Page(s) 1]
Page 1 of 1