Fault Bus Identification in Kurdistan Power Systems using Artificial Neural Network

  • Alaa M. Abdulrahman Department of Electrical, College of Engineering, University of Sulaimani, Kurdistan Region, Iraq.
Keywords: Fault Location, Kurdistan power system Transmission Line, Artificial Neural Network.

Abstract

High voltage transmission lines are utilized to transmit electrical energy from the source to the substations. If any fault and disturbance are generated in the transmission lines and not detected, located and eliminated quickly, it may cause instability in the system. This paper presents fault location recognition in Kurdistan Regional power transmission system using artificial neural network (ANN). Load flow and short circuit calculations were performed with Power World Simulator (PWS) software. All Kurdistan region power system has been divided into 40 buses. The calculated results of the currents and the voltages at both line ends were used to train the ANN in Matlab to obtain correct fault location. The training testing and evaluation of the intelligent locator is done based on a multilayer perceptron feed forward artificial neural network with back propagation algorithm. The ANN used to locate the fault have been trained with different available sets of data from the selected power system model. Several algorithms have been carried out in order to train the network such that it locates the fault based on the input data provided. Proposed algorithm was developed by injecting the data randomly and massively to in rich the trained network. None-trained data has been used to validate the network and the network was able to locate the faults exactly.

References

A M Abdulrahman, K Iqbal (2014), "Capturing Human Body Dynamics Using RNN Based on Persistent Excitation Data Generator", Proceedings of the IEEE Symposium on Computer-Based Medical Systems, NYC.
E Bashier, M Tayeb (2013),"Faults Detection in Power Systems Using Artificial Neural Network", American Journal of Engineering Research (AJER).Volume-02, Issue-06, pp-69-75.
M H Beale, M T Hagan, H B Demuth (2010), Neural Network Toolbox™ 7, User’s Guide.
T W S Chow and S Y Cho (2007), Neural Networks and Computing, Imperial College Press.
P K Dash, A K Pradhan, and G Panda (2000), "A Novel Fuzzy Neural Network Based Distance Relaying Scheme”, IEEE Transactions on Power Delivery, Vol. 15, No. 3.
M T Hagh, K Razi, and H Taghizadeh (2007), Fault Classification and Location of Power Transmission Lines Using Artificial Neural Network, The 8th International Power Engineering Conference IPEC.
A Jain, A S Thoke, R N Patel (2009), Double Circuit Transmission Line Fault Distance Location using Artificial Neural Network, World Congress on Nature & Biologically Inspired Computing.
M Kezunovic and J Mrkic (1994), An accurate fault location algorithm using synchronized sampling, Electric Power System Research, 29, pp. 161- 169.
B Lian and M.M.A. Salama (1994), An overview of digital fault location algorithms for power transmission lines using transient waveforms, Electric Power System Research, 29, pp.17-25.
X Lin, P Mao , H Weng, B Wang, Z Q Bo and A Klimek (2007), “Study on Fault Location for High Voltage”, The 14th International Conference on Intelligent System Applications to Power Systems, ISAP Kaohsiung, Taiwan.
O F Lufty and A L Jassim (2018), "A Simplified Recurrent Neural Network Trained by Gbest-Guided Gravitational Search Algorithm to Control Nonlinear Systems", Enginnering and Technology Jouranl, Vol. 36, Part A, No. 12, pp. 1290-1301.
N Morgan and H Bourlard (1990), Continuous speech recognition using multilayer perceptrons and Hidden Markov Models", In Proceedings of the ICASSP, pages 413-416, Albuquerque, New Mexico.
D Novosel, D G Hart, E Udren and J Garitty (1996), Unsynchronized two-terminal fault location estimation, IEEE Trans. on Power Delivery, Vol. 11, No. 1, pp. 130-138.
L B Sheng and S Elangovan (1998), A fault location algorithm for transmission lines”, Electric Machines and Power Systems, 26, pp. 991-1005.
L Tekli, B Filipovi (2013), Artificial Neural Network Approach for Locating Faults in Power Transmission System, EuroCon 2013, Zagreb, Croatia
UNDP-ENRP (2016), Electricity Network Development Plan for Iraqi Kurdistan Region.
UNDP-ENRP (2017), Electricity Distribution Development Plan.
Wong, C K and M C Easton (1980), An Efficient Method for Weighted Sampling Without Replacement, SIAM Journal of Computing Vol. 9(1), pp. 111–113.
Q Zhang, Y Zhang, W Song and Y Yu (1999), Transmission line fault location for phase-to-earth fault using one-terminal data, IEE Proc. Transactions Distribution, Vol. 146, No 2, pp. 121-124.
Published
2019-08-09
How to Cite
M. Abdulrahman, A. “Fault Bus Identification in Kurdistan Power Systems Using Artificial Neural Network”. ZANCO Journal of Pure and Applied Sciences, Vol. 31, no. s3, Aug. 2019, pp. 410-6, doi:10.21271/zjpas.31.s3.59.