Main Article Content
This paper proposes a fault (line-to-line) location on Ikeja West – Benin 330kV electric power transmission lines using wavelet multi-resolution analysis and neural networks pattern recognition abilities. Three-phase line-to-line current and voltage waveforms measured during the occurrence of a fault in the power transmission-line were pre-processed first and then decomposed using wavelet multi-resolution analysis to obtain the high-frequency details and low-frequency approximations. The patterns formed based on high-frequency signal components were arranged as inputs of the neural network, whose task is to indicate the occurrence of a fault on the lines. The patterns formed using low-frequency approximations were arranged as inputs of the second neural network, whose task is to indicate the exact fault type. The new method uses both low and high-frequency information of the fault signal to achieve an exact location of the fault. The neural network was trained to recognize patterns, classify data and forecast future events. Feed forward networks have been employed along with back propagation algorithm for each of the three phases in the Fault location process. An analysis of the learning and generalization characteristics of elements in power system was carried using Neural Network toolbox in MATLAB/SIMULINK environment. Simulation results obtained demonstrate that neural network pattern recognition and wavelet multi-resolution analysis approach are efficient in identifying and locating faults on transmission lines as the average percentage error in fault location was just 0.1386%. This showed that satisfactory performance was achieved especially when compared to the conventional methods such as impedance and travelling wave methods.
Amit MP, Abhijit SP. Fault location on transmission line using wavelet transform and Artificial neural network. IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE). 2016;11(4 Ver. II):59-62.
[e-ISSN: 2278-1676, p-ISSN: 2320-3331]
Girish PA, Nitin UG. Fault classification & location of series compensated trans-mission line using artificial neural network. International Journal of Advances in Electronics and Computer Science. 2015; 2(8).
Hasabe RP, Vaidya AP. Detection and classification of faults on 220 KV transmission line using wavelet transform and neural network. International Journal of Smart Grid and Clean Energy. 2014;3(3).
Suhaas Bhargava Ayyagari. Artificial neural network based fault location for transmission lines. University of Kentucky Master’s Theses. 2011;657.
Kale VS, Bhide SR, Bedekar PP, Mohan GVK. Detection and classification of faults on parallel transmission lines using wavelet transform and neural network. World Academy of Science, Engineering and Technology. 2008;22.
Jain A, Kale VS, Thoke AS. Application of artificial neural network to transmission line faulty phase selection and fault distance location. Proc. of IASTED Inter-national Energy and Power System Conf. 2006;262-267.
Celli G, Marchesi M, Mocci F, Pilo F. Application of neural networks in power distribution systems diagnosis and control, Proc. UPEC'97, Manchester. 1997;523-526.
Cichocki A, Lobos T. Artificial neural networks for real-time estimation of basic waveforms of voltage and currents. IEEE Trans. on PAS, 2011;9(2):612-619.
Kasinathan Karthikeyan. Power system fault detection and classification by wavelet transforms and adaptive resonance theory neural networks. University of Kentucky Master's Theses. 2007;452.
Atul AK, Navita GP. Fault detection and fault classification of double circuit transmission line using artificial neural network. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056. 2015;02(08).
Aurangzeb M, Crossley PA, Gale P. Fault location using high frequency travelling waves measured at a single location on transmission line. Proceedings of 7th International conference on Developments in Power System Protection – DPSP, IEE CP479. 2001;403-406.