A hybrid method for fault location estimation in a fixed series compensated lines. (July 2018)
- Record Type:
- Journal Article
- Title:
- A hybrid method for fault location estimation in a fixed series compensated lines. (July 2018)
- Main Title:
- A hybrid method for fault location estimation in a fixed series compensated lines
- Authors:
- Swetapadma, Aleena
Yadav, Anamika - Abstract:
- Highlights: The fault location method is based on hybrid DWT and decision tree regression. The fault location method locates the fault with an average error up to 1%. The algorithm is designed & tested for double circuit series compensated transmission lines. Abstract: This paper proposes a fault distance estimation scheme for fixed series capacitor compensated parallel transmission lines using discrete wavelet transform and decision tree regression. The purpose of the data mining based scheme is to avoid the complicated equation based methods that have been suggested by researchers to overcome the drawbacks of conventional fault location scheme. Although decision tree has inherent advantage over other methods like artificial neural network and support vector machines to work with large data sets, it has not been used in fault location estimation in series compensated (SC) transmission line so far. Decision tree is chosen to locate the faults because of its ability to work with large data set and high accuracy in associating the fault pattern to the fault distance using regression analysis. The discrete wavelet transform processed signals makes the decision process of decision tree regression easy by providing appropriate features. The proposed method is evaluated with variation of fault location, fault type, pre-fault load angle, location of series capacitor, degree of series compensation, fault inception angle, line parameters, inter-circuit faults and fault resistance.Highlights: The fault location method is based on hybrid DWT and decision tree regression. The fault location method locates the fault with an average error up to 1%. The algorithm is designed & tested for double circuit series compensated transmission lines. Abstract: This paper proposes a fault distance estimation scheme for fixed series capacitor compensated parallel transmission lines using discrete wavelet transform and decision tree regression. The purpose of the data mining based scheme is to avoid the complicated equation based methods that have been suggested by researchers to overcome the drawbacks of conventional fault location scheme. Although decision tree has inherent advantage over other methods like artificial neural network and support vector machines to work with large data sets, it has not been used in fault location estimation in series compensated (SC) transmission line so far. Decision tree is chosen to locate the faults because of its ability to work with large data set and high accuracy in associating the fault pattern to the fault distance using regression analysis. The discrete wavelet transform processed signals makes the decision process of decision tree regression easy by providing appropriate features. The proposed method is evaluated with variation of fault location, fault type, pre-fault load angle, location of series capacitor, degree of series compensation, fault inception angle, line parameters, inter-circuit faults and fault resistance. The test result of decision tree regression based location estimation scheme ensures that, it can estimate the fault distance accurately. … (more)
- Is Part Of:
- Measurement. Volume 123(2018)
- Journal:
- Measurement
- Issue:
- Volume 123(2018)
- Issue Display:
- Volume 123, Issue 2018 (2018)
- Year:
- 2018
- Volume:
- 123
- Issue:
- 2018
- Issue Sort Value:
- 2018-0123-2018-0000
- Page Start:
- 8
- Page End:
- 18
- Publication Date:
- 2018-07
- Subjects:
- Weights and measures -- Periodicals
Measurement -- Periodicals
Measurement
Weights and measures
Periodicals
530.8 - Journal URLs:
- http://www.sciencedirect.com/science/journal/02632241 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.measurement.2018.03.029 ↗
- Languages:
- English
- ISSNs:
- 0263-2241
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 5413.544700
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 11203.xml