Fault Location Estimator Design for Power Distribution System Using Artificial Neural Networks
| dc.contributor.advisor | Dereje, Shiferaw (PhD) | |
| dc.contributor.author | Samuel, Shawul | |
| dc.date.accessioned | 2020-12-31T05:47:07Z | |
| dc.date.accessioned | 2023-11-28T14:26:41Z | |
| dc.date.available | 2020-12-31T05:47:07Z | |
| dc.date.available | 2023-11-28T14:26:41Z | |
| dc.date.issued | 2018-07 | |
| dc.description.abstract | Fault location in distribution system is critical issue to increase the availability of power supply by reducing the time of interruption for maintenance in electric utility companies. In this thesis fault location estimator for power distribution system using artificial neural network is developed for line to ground, line to line, line to line to ground and three phase to ground faults in distribution system. To develop this estimator one of rural radial power distribution feeder in Ethiopia, Oromia, Assela substation Gumguma line feeder is used as a test feeder. This feeder is simulated using ETAP software to generate data for different fault condition, with different fault resistance and loading conditions, which is the fault phase voltage and current. The generated data is preprocessed and put as an input for neural network to be trained. MATLAB R2016a neural network toolbox to train ANN and programming toolbox is used to develop graphic user interface for fault estimator. The feed forward multi layer network topologies of neural network with improved back propagation, Levenberg Marquardt learning algorithm is used to train the network. After the network (6-15-8-4) is trained the mean square error performance, regression plot and error histogram analysis was made and found to have an excellent performance with regression coefficient 0.99929 , validation performance of 0.000102 and error histogram range -0.015 to 0.019. In this thesis for practical implementation the fault records at the test feeder is handled by intelligent electronic device (IED) installed at the substation feeders. The fault record of IED can be read by PCM600 tool using laptop or manually using IEDs human machine interface, this fault recorded data feed to the graphic user interface to estimate the fault location as well as the fault type. Finally it is found that artificial neural networks are one of the alternate options in fault estimator design for distribution system where sufficient distribution network data are available with narrow fault location distance range from the substation. This has benefits in assisting for maintenance plan, saving efforts in fault location finding and economical benefits by reducing interruption time. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/12345678/24407 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Artificial neural network | en_US |
| dc.subject | fault | en_US |
| dc.subject | power distribution system | en_US |
| dc.subject | intelligent electronic device (IED) | en_US |
| dc.title | Fault Location Estimator Design for Power Distribution System Using Artificial Neural Networks | en_US |
| dc.type | Thesis | en_US |