APPLICATION OF ARTIFICAIL NEURAL NETWORK FOR ENHANCED POWER SYSTEMS PROTECTION ON THE NIGERIAN 330kV NETWORK

Project Abstract

<p> </p><p>This work investigates an improved <a target="_blank" rel="nofollow" href="https//www.modishproject.com/artificail-neural-network-enhanced/">protection</a>&nbsp;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 <a target="_blank" rel="nofollow" href="https//www.modishproject.com/improved-fault-location-power-system-transimmision-lines-using-fuzzy-logic-approach/">Fourier transform</a>&nbsp;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 <a target="_blank" rel="nofollow" href="https//www.modishproject.com/artificail-neural-network-enhanced/">reclosure</a>&nbsp;scheme implemented using artificial neural <a target="_blank" rel="nofollow" href="https//www.modishproject.com/artificail-neural-network-enhanced/">network</a>. 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 <a target="_blank" rel="nofollow" href="https//www.modishproject.com/improved-fault-location-power-system-transimmision-lines-using-fuzzy-logic-approach/">distinguishing network</a>&nbsp;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.</p><br> <br><p></p>

Project 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>&nbsp;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/">&nbsp;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>&nbsp;wave approach, synchronous compensators to name a few.</p> <br><p></p>