Intelligent identification of power quality disturbances
The increasing use of electronics in electrical equipment has made them more vulnerable to power quality disturbances. Customers are becoming more knowledgeable in these issues and demand for more detailed and precise information whenever an unfortunate event occurs. Therefore, power companies are e...
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2009
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sg-ntu-dr.10356-171622023-07-07T17:34:28Z Intelligent identification of power quality disturbances Loi, Kevin Jin Han. Wang Peng School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Electric power::Production, transmission and distribution The increasing use of electronics in electrical equipment has made them more vulnerable to power quality disturbances. Customers are becoming more knowledgeable in these issues and demand for more detailed and precise information whenever an unfortunate event occurs. Therefore, power companies are expanding the use of information processing techniques in the attempt to determine the cause and severity of power quality event so that remedy actions can be undertaken swiftly. One such process is to pinpoint the type of disturbance that has occurred. This is achieved through some form of pattern matching where signature patterns from past experiences or simulations are used to match the recorded event. However, there are many uncertainties with the network operating conditions and devices complicating the identification process. Some research works have been done in NTU in this area involving the use of advanced signal processing and fuzzy-logic based pattern matching techniques. This project is to test the proposed method for its robustness in handling the various uncertainties. Sample disturbances will be generated using Matlab and incorporated with various uncertainties. These are then used to test out the proposed method. Bachelor of Engineering 2009-06-01T03:36:56Z 2009-06-01T03:36:56Z 2009 2009 Final Year Project (FYP) http://hdl.handle.net/10356/17162 en Nanyang Technological University 67 p. application/pdf |
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DRNTU::Engineering::Electrical and electronic engineering::Electric power::Production, transmission and distribution Loi, Kevin Jin Han. Intelligent identification of power quality disturbances |
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The increasing use of electronics in electrical equipment has made them more vulnerable to power quality disturbances. Customers are becoming more knowledgeable in these issues and demand for more detailed and precise information whenever an unfortunate event occurs. Therefore, power companies are expanding the use of information processing techniques in the attempt to determine the cause and severity of power quality event so that remedy actions can be undertaken swiftly. One such process is to pinpoint the type of disturbance that has occurred. This is achieved through some form of pattern matching where signature patterns from past experiences or simulations are used to match the recorded event. However, there are many uncertainties with the network operating conditions and devices complicating the identification process. Some research works have been done in NTU in this area involving the use of advanced signal processing and fuzzy-logic based pattern matching techniques. This project is to test the proposed method for its robustness in handling the various uncertainties. Sample disturbances will be generated using Matlab and incorporated with various uncertainties. These are then used to test out the proposed method. |
author2 |
Wang Peng |
author_facet |
Wang Peng Loi, Kevin Jin Han. |
format |
Final Year Project |
author |
Loi, Kevin Jin Han. |
author_sort |
Loi, Kevin Jin Han. |
title |
Intelligent identification of power quality disturbances |
title_short |
Intelligent identification of power quality disturbances |
title_full |
Intelligent identification of power quality disturbances |
title_fullStr |
Intelligent identification of power quality disturbances |
title_full_unstemmed |
Intelligent identification of power quality disturbances |
title_sort |
intelligent identification of power quality disturbances |
publishDate |
2009 |
url |
http://hdl.handle.net/10356/17162 |
_version_ |
1772828554438901760 |