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A new approach for fault location on transmission lines using a combination of wavelet-based filtering, prony fitting, and artificial neural networks (ann). The authors, m.m. Tawfik and m.m. Morcos, present their methodology and test it using data from the alternative transient program (atp). Keywords: fault locator, artificial neural network, back-propagation, prony fitting, wavelet.
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ower Engineering Letters is a new section of IEEE Power Engi- neering Review, running for the first time in the January 1998 is- sue This section of the magazine offers a vehicle that will speed publication of new results, discoveries, and developments. The section affords authors the opportunity to publish contributions within a few months of submission to ensure rapid dissemination of ideas and timely archiving of developments in our rapidly changing field. Original and significant contributions in applications, case studies, and research in all fields of power engineering are invited. Of specific interest are contributions defining emerging problems and special
eas of established power topics.
A Novel Approach for Fault Location on Transmission Lines, by M M Tawfik, M M Morcos
Lagrangian Relaxation, by A G. Bakirtzis Wavelet Transform and Neural Network Approach to Developing Adaptive Single-Pole Auto-Reclosing Schemes for EHV Trans- mission Systems, by I.K. Yu, Y.H Song Submit contributions, criticism, and queries to the Power Engmeer-
6198 or (902) 494-6199, FAX (902) 429-3011, E-mail elha- wary@dal ca.
for the peer review of all Letters appearing in this section. Editonal board members include.
housie University, Halifax, NS, Canada Maria Teresa Correia de Barros, Universidade Tecnica de Lisboa, Portugal
R.K. Green, Jr., Central and Southwest Services, Dallas, TX, U S A
Shin-Ichi Iwamoto, Waseda University, Tokyo, Japan J H Jones, Southem Companies Services, Birmingham, Ala- bama, U S.A Prabha Kundur, Power Tech Labs, Surrey, BC, Canada Fred N Lee, University of Oklahooma, Norman, OK, U.S.A. Peter McLaren, University of Manitoba, Winnipeg, Manitoba, Canada James Momoh, Howard University, Washington DC, U S.A.
sisco, CA, U.S.A. Michel Poloujadoff, Universite Pierre et Marie Curie, Pans, France Narayan Rau, IS0 New England Inc., Holyoke, MA U.S.A. A. Schwab, Universitat Karlsruhe, Karlsruhe, Germany Walter L Snyder, Jr., Siemens Energy & Automation, Inc. Brooklyn Park, MN, U.S A.
A Novel Approach for Fault Location on Transmission L i n e s
M.M. Tawfik, M.M. Morcos
neering, Kansas State University, Manhattan, KS 66506
fault locator estimation is based on the noise generated by the fault in the sending-end current signal. The proposed scheme consists of a wavelet-based filter module, a Prony-based signal processor, and an ANN-based estimator Input data has been generated using the Altema- tive Transient Program (ATP). A three-phase, frequency-dependent (FD) transmission line model was used. The scheme is tested using data employed in the ANN training as well as new data sets. The proposed locator has a good level of accuracy.
propagation, Prony fitting, wavelet
open access and deregulation may have an impact on the reliability and security of power systems. New methodologies for various protection and control schemes are a must to maintain system reliability and secu- rity within an accepted level. Artificial intelligence (AI) techniques are among the top candidates to realize this new methodology.
require either prefault measurements or remote-end measurements. These measurements sometimes add technical complexity to the scheme or even introduce an error component to the estimation if not processed properly.
very encouraging to extend their use to fault location. This noise is sim- ply the distortion in the voltage and current waveforms due to the trav- eling wave moving over the line between the fault location and the sending end.
;
,
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Most present fault locators estimate the location of the fault based "
I I Figure 1. Waveformsfor different fault locutions
IEEE Power Enganeenng Revaew, November 1998
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The utilization of artificial neural networks (ANN) and preproces- sors has been discussed elsewhere [2]. Prony fitting was chosen to be the preprocessor tool. Prony provides modal information about the sig- nal under consideration that can be further used to make inference about the system disturbance or conditions. The general case of a fault
sion line is represented by a frequency dependent model using ATP. A modified version of the benchmark case DCN3 is used. The current
samples per cycle or higher. Samples for one cycle after the fault are used to estimate the location. No prefault or remote-end measurement is required. The fault locator has three different modules. The first module is a wavelet-based filter used for noise reduction. The second is a Prony- based signal processor to estimate the modal content of the filtered sig- nal. The third module is an ANN-based estimator to estimate the fault
the noise content in the signal. One advantage of the wavelet filter is that the filtering process will not affect the details of the signal but will limit the magnitude of the high-frequency harmonic components. The wavelet filtering process is done in three steps. The first step is signal decomposition, the second is noise removal, and the third is signal re- construction. In the first step, two filters decompose the signal. One filter is to pass the high-amplitude, low-frequency component, which is the ap- proximation of the signal. The other filter is to pass the low-amplitude, high-frequency component which is the detail of the signal. The pro- cess is repeated using the signal approximation component. As a second step, the noise content of the signal is suppressed; the content is represented by the detail components of different levels. A threshold is set for the harmonics. This threshold is then used to clip dif- ferent detail components. The last step would be reconstructing the signal using the thresh- olded detail coefficients, the approximation coefficient and the inverse of the filters used in decomposition. The MATLAB Wavelet Toolbox was used to implement the wavelet-based filter module.
ing the wavelet filter, the samples are processed using Prony fitting. The output of the Prony processing is then passed to the ANN as input
[3]. The Power System Identification (PSI) Toolbox was used to ana- lyze the filtered time fault signal. A decimation of three was used for the input samples to the Prony. The fault location was changed, and every time the signal was filtered and then processed using Prony. Prony fitting for different fault cases usually results in a number of modes. The most dominant mode was a nonoscillatory mode represent- ing the dc component of the fault current. The second dominant mode had a frequency very close to 60 Hz. These two modes were disre- garded since the proposed technique is based on higher order harmonic
to 2 kHz. Since some of the modes represent random transients, only those with relatively high energy content were considered. These modes usually have relatively high amplitudes and small damping ra-
tios which means that they were present for long intervals from the original signal. Figure 3 illustrates the changes in the frequency of four modes of in- terest. The changes are due to the variation in the location of the fault on the line. These frequency changes can be used to locate the fault. The four modes shown in Figure 3 were selected to be used as a measure for the fault location. The change in frequency was the best choice as an indication for the location of the fault. However, the infor- mation provided by the damping was useful to identify these modes out of the Prony modes for each fault case.
the main network. The backpropagation algorithm was used in the pro- cess. Initial values for the network weights are assigned. Input pattems are then applied to the network and the error in the output is estimated. The value of the error is then backpropagated and all the weights are ad- justed. The training process is repeated in the form of epochs and the sum of the squared errors (SSE) between the ANN outputs and the de- sired outputs is estimated for each epoch. The training process should
mum number of epochs is reached. Using the frequency of each of the four modes as an input to the ANN and the associated fault location, the training process was carried out. Fault locations starting at 10 miles from the sending end, with in-
The network was simulated and the fault location for each of the cases used in training was estimated. The maximum error in the estima- tion was less than 4 miles, which is about 2.8 percent. The error in esti- mating the fault location for the different cases is shown in Figure 4. The generalization test is made to check the ability of the network to generalize the knowledge acquired during the training process. Testing the network with new input patterns that were not included in the train- ing process does this. If the error in the output for these new input pat- tems was small, the network training is successful. If the error is large, the network needs to be retrained, and more input pattems are required in the training process. The ANN was tested using new cases for fault locations starting at
within the same range of 2.8 percent, which indicates that the training was successful and the network is able to generalize its knowledge. Re-
fault location is contributed by the error of the Prony fitting. A higher accuracy for Prony fitting will lead to a smaller error in the estimation
the accuracy of Prony. A preliminary study of the effect of changing the fault resistance has
frequency of the modes. Hence, it is expected that the accuracy of the scheme to be almost independent of the fault resistance. Work is under- way to include the effect of the fault arc voltage in the simulation.
Figure 2. Scheme configuration
IEEE Power Engineering Review, November 1998
Figure 3. Changes in frequency vs fault location