Local, Single-Ended, Traveling-Wave Fault Location on Distribution Systems Using Frequency and Time-Domain Data
This work proposes a signal-based fault location method combining both time and frequency-domain features. The method is based on the Traveling Wave (TW) detection and analysis, and only employs 100 microseconds of data around the TW time-of-arrival to the measuring location. Time-domain features ar...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10186525/ |
| _version_ | 1797772611318448128 |
|---|---|
| author | Miguel Jimenez-Aparicio Felipe Wilches-Bernal Matthew J. Reno |
| author_facet | Miguel Jimenez-Aparicio Felipe Wilches-Bernal Matthew J. Reno |
| author_sort | Miguel Jimenez-Aparicio |
| collection | DOAJ |
| description | This work proposes a signal-based fault location method combining both time and frequency-domain features. The method is based on the Traveling Wave (TW) detection and analysis, and only employs 100 microseconds of data around the TW time-of-arrival to the measuring location. Time-domain features are extracted using Mathematical Morphology, while frequency-domain features are obtained using the Stationary Wavelet Transform. The IEEE 34 nodes system is selected for demonstration. TW detection is performed using a method based on Dynamic Mode Decomposition. This work exhaustively analyzes the performance of the proposed method in both fault location classification and regression tasks. Combining both time and frequency-domain features is proven to be more effective than using each type of feature independently. In addition, the sensitivity to noisy measurements and lesser amount of training data are analyzed as well. The performance of this method is compared to other works in the same test system, showing better accuracy than other signal-based methods. Furthermore, the proposed method has a low fault location estimation error rate across the entire length of the feeder and shows a similar performance to slower phasor-based fault location methods. |
| first_indexed | 2024-03-12T21:54:26Z |
| format | Article |
| id | doaj.art-b89b2fd72943425983f21cb2daafe10e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T21:54:26Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b89b2fd72943425983f21cb2daafe10e2023-07-25T23:00:27ZengIEEEIEEE Access2169-35362023-01-0111742017421510.1109/ACCESS.2023.329673710186525Local, Single-Ended, Traveling-Wave Fault Location on Distribution Systems Using Frequency and Time-Domain DataMiguel Jimenez-Aparicio0https://orcid.org/0000-0001-6864-461XFelipe Wilches-Bernal1https://orcid.org/0000-0002-1135-1071Matthew J. Reno2https://orcid.org/0000-0002-4885-0480Sandia National Laboratories, Albuquerque, NM, USASandia National Laboratories, Albuquerque, NM, USASandia National Laboratories, Albuquerque, NM, USAThis work proposes a signal-based fault location method combining both time and frequency-domain features. The method is based on the Traveling Wave (TW) detection and analysis, and only employs 100 microseconds of data around the TW time-of-arrival to the measuring location. Time-domain features are extracted using Mathematical Morphology, while frequency-domain features are obtained using the Stationary Wavelet Transform. The IEEE 34 nodes system is selected for demonstration. TW detection is performed using a method based on Dynamic Mode Decomposition. This work exhaustively analyzes the performance of the proposed method in both fault location classification and regression tasks. Combining both time and frequency-domain features is proven to be more effective than using each type of feature independently. In addition, the sensitivity to noisy measurements and lesser amount of training data are analyzed as well. The performance of this method is compared to other works in the same test system, showing better accuracy than other signal-based methods. Furthermore, the proposed method has a low fault location estimation error rate across the entire length of the feeder and shows a similar performance to slower phasor-based fault location methods.https://ieeexplore.ieee.org/document/10186525/Distribution system protectionfault locationmathematical morphologymachine-learningstationary wavelet transformtraveling wave |
| spellingShingle | Miguel Jimenez-Aparicio Felipe Wilches-Bernal Matthew J. Reno Local, Single-Ended, Traveling-Wave Fault Location on Distribution Systems Using Frequency and Time-Domain Data IEEE Access Distribution system protection fault location mathematical morphology machine-learning stationary wavelet transform traveling wave |
| title | Local, Single-Ended, Traveling-Wave Fault Location on Distribution Systems Using Frequency and Time-Domain Data |
| title_full | Local, Single-Ended, Traveling-Wave Fault Location on Distribution Systems Using Frequency and Time-Domain Data |
| title_fullStr | Local, Single-Ended, Traveling-Wave Fault Location on Distribution Systems Using Frequency and Time-Domain Data |
| title_full_unstemmed | Local, Single-Ended, Traveling-Wave Fault Location on Distribution Systems Using Frequency and Time-Domain Data |
| title_short | Local, Single-Ended, Traveling-Wave Fault Location on Distribution Systems Using Frequency and Time-Domain Data |
| title_sort | local single ended traveling wave fault location on distribution systems using frequency and time domain data |
| topic | Distribution system protection fault location mathematical morphology machine-learning stationary wavelet transform traveling wave |
| url | https://ieeexplore.ieee.org/document/10186525/ |
| work_keys_str_mv | AT migueljimenezaparicio localsingleendedtravelingwavefaultlocationondistributionsystemsusingfrequencyandtimedomaindata AT felipewilchesbernal localsingleendedtravelingwavefaultlocationondistributionsystemsusingfrequencyandtimedomaindata AT matthewjreno localsingleendedtravelingwavefaultlocationondistributionsystemsusingfrequencyandtimedomaindata |