Classifying short duration voltage disturbances using fuzzy expert system
In this paper, fuzzy logic is applied for identifying and classifying the short duration voltage variations of 8, 32 and 128 cycles waveforms. A program is written in Matlab to determine the parameters such as duration, maximum and minimum root mean square voltages of a disturbance by using the fast...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2006
|
| Subjects: | |
| Online Access: | http://eprints.um.edu.my/6234/1/Classifying_Short_Duration_Voltage_Disturbances_Using_Fuzzy_Expert_System.pdf http://eprints.um.edu.my/6234/ http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4339341 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
my.um.eprints.6234 |
|---|---|
| record_format |
eprints |
| spelling |
my.um.eprints.62342013-07-01T08:46:24Z http://eprints.um.edu.my/6234/ Classifying short duration voltage disturbances using fuzzy expert system Mahdiraji, G.A. Mohamed, A. TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering In this paper, fuzzy logic is applied for identifying and classifying the short duration voltage variations of 8, 32 and 128 cycles waveforms. A program is written in Matlab to determine the parameters such as duration, maximum and minimum root mean square voltages of a disturbance by using the fast Fourier transform analysis. Based on these parameters, a fuzzy inference system has been developed with five fuzzy inputs, three fuzzy outputs and 139 fuzzy rules. The inputs are the maximum and minimum voltage magnitudes in per unit and disturbance duration in seconds. On the other hand, the outputs are namely outputl, output2 and output3 in which outputl is for classifying instantaneous sag, non sag and momentary sag, output2 is for classifying instantaneous swell, non swell and momentary swell and output3 for classifying instantaneous interruption, non interruption and momentary interruption. The proposed fuzzy expert system has been tested with 1015 recorded voltage disturbances consisting of sags, swells, interruptions, transients, voltage notching and multiple disturbance waveforms. The results have proved that the developed fuzzy system has accurately identified and classified 98.42 of the tested voltage disturbances. IEEE 2006 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.um.edu.my/6234/1/Classifying_Short_Duration_Voltage_Disturbances_Using_Fuzzy_Expert_System.pdf Mahdiraji, G.A. and Mohamed, A. (2006) Classifying short duration voltage disturbances using fuzzy expert system. In: 4th Student Conference on Research and Development, 2006. SCOReD 2006. , 2006. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4339341 |
| institution |
Universiti Malaya |
| building |
UM Library |
| collection |
Institutional Repository |
| continent |
Asia |
| country |
Malaysia |
| content_provider |
Universiti Malaya |
| content_source |
UM Research Repository |
| url_provider |
http://eprints.um.edu.my/ |
| language |
English |
| topic |
TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering |
| spellingShingle |
TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Mahdiraji, G.A. Mohamed, A. Classifying short duration voltage disturbances using fuzzy expert system |
| description |
In this paper, fuzzy logic is applied for identifying and classifying the short duration voltage variations of 8, 32 and 128 cycles waveforms. A program is written in Matlab to determine the parameters such as duration, maximum and minimum root mean square voltages of a disturbance by using the fast Fourier transform analysis. Based on these parameters, a fuzzy inference system has been developed with five fuzzy inputs, three fuzzy outputs and 139 fuzzy rules. The inputs are the maximum and minimum voltage magnitudes in per unit and disturbance duration in seconds. On the other hand, the outputs are namely outputl, output2 and output3 in which outputl is for classifying instantaneous sag, non sag and momentary sag, output2 is for classifying instantaneous swell, non swell and momentary swell and output3 for classifying instantaneous interruption, non interruption and momentary interruption. The proposed fuzzy expert system has been tested with 1015 recorded voltage disturbances consisting of sags, swells, interruptions, transients, voltage notching and multiple disturbance waveforms. The results have proved that the developed fuzzy system has accurately identified and classified 98.42 of the tested voltage disturbances. |
| format |
Conference or Workshop Item |
| author |
Mahdiraji, G.A. Mohamed, A. |
| author_facet |
Mahdiraji, G.A. Mohamed, A. |
| author_sort |
Mahdiraji, G.A. |
| title |
Classifying short duration voltage disturbances using fuzzy expert system |
| title_short |
Classifying short duration voltage disturbances using fuzzy expert system |
| title_full |
Classifying short duration voltage disturbances using fuzzy expert system |
| title_fullStr |
Classifying short duration voltage disturbances using fuzzy expert system |
| title_full_unstemmed |
Classifying short duration voltage disturbances using fuzzy expert system |
| title_sort |
classifying short duration voltage disturbances using fuzzy expert system |
| publisher |
IEEE |
| publishDate |
2006 |
| url |
http://eprints.um.edu.my/6234/1/Classifying_Short_Duration_Voltage_Disturbances_Using_Fuzzy_Expert_System.pdf http://eprints.um.edu.my/6234/ http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4339341 |
| _version_ |
1643687785652027392 |
| score |
13.211869 |