Classification of Many Abnormal Events in Radial Distribution Feeders Using the Complex Morlet Wavelet and Decision Trees
Monitoring of abnormal events in a distribution feeder by using a single technique is a challenging task. A number of abnormal events can cause unsafe operation, including a high impedance fault (HIF), a partial breakdown to a cable insulation, and a circuit breaker (CB) malfunction due to capacitor...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-03-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/11/3/546 |
| _version_ | 1798039731725926400 |
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| author | Mishari Metab Almalki Constantine J. Hatziadoniu |
| author_facet | Mishari Metab Almalki Constantine J. Hatziadoniu |
| author_sort | Mishari Metab Almalki |
| collection | DOAJ |
| description | Monitoring of abnormal events in a distribution feeder by using a single technique is a challenging task. A number of abnormal events can cause unsafe operation, including a high impedance fault (HIF), a partial breakdown to a cable insulation, and a circuit breaker (CB) malfunction due to capacitor bank de-energization. These abnormal events are not detectable by conventional protection schemes. In this paper, a new technique to identify distribution feeder events is proposed based on the complex Morlet wavelet (CMW) and on a decision tree (DT) classifier. First, the event is detected using CMW. Subsequently, a DT using event signatures classifies the event as normal operation, continuous and non-continuous arcing events (C.A.E. and N.C.A.E.). Additional information from the supervisory control and data acquisition (SCADA) can be used to precisely identify the event. The proposed method is meticulously tested on the IEEE 13- and IEEE 34-bus systems and has shown to correctly classify those events. Furthermore, the proposed method is capable of detecting very high impedance incipient faults (IFs) and CB restrikes at the substation level with relatively short detection time. The proposed method uses only current measurements at a low sampling rate of 1440 Hz yielding an improvement of existing methods that require much higher sampling rates. |
| first_indexed | 2024-04-11T21:57:48Z |
| format | Article |
| id | doaj.art-aa23ea4163c54fd99a83d53967e92995 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T21:57:48Z |
| publishDate | 2018-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-aa23ea4163c54fd99a83d53967e929952022-12-22T04:01:02ZengMDPI AGEnergies1996-10732018-03-0111354610.3390/en11030546en11030546Classification of Many Abnormal Events in Radial Distribution Feeders Using the Complex Morlet Wavelet and Decision TreesMishari Metab Almalki0Constantine J. Hatziadoniu1Department of Electrical and Computer Engineering, Southern Illinois University, 1230 Lincoln Dr., Carbondale 62901, IL, USADepartment of Electrical and Computer Engineering, Southern Illinois University, 1230 Lincoln Dr., Carbondale 62901, IL, USAMonitoring of abnormal events in a distribution feeder by using a single technique is a challenging task. A number of abnormal events can cause unsafe operation, including a high impedance fault (HIF), a partial breakdown to a cable insulation, and a circuit breaker (CB) malfunction due to capacitor bank de-energization. These abnormal events are not detectable by conventional protection schemes. In this paper, a new technique to identify distribution feeder events is proposed based on the complex Morlet wavelet (CMW) and on a decision tree (DT) classifier. First, the event is detected using CMW. Subsequently, a DT using event signatures classifies the event as normal operation, continuous and non-continuous arcing events (C.A.E. and N.C.A.E.). Additional information from the supervisory control and data acquisition (SCADA) can be used to precisely identify the event. The proposed method is meticulously tested on the IEEE 13- and IEEE 34-bus systems and has shown to correctly classify those events. Furthermore, the proposed method is capable of detecting very high impedance incipient faults (IFs) and CB restrikes at the substation level with relatively short detection time. The proposed method uses only current measurements at a low sampling rate of 1440 Hz yielding an improvement of existing methods that require much higher sampling rates.http://www.mdpi.com/1996-1073/11/3/546high impedance fault (HIF)incipient fault (IF)circuit breaker (CB) restrikeswavelet transformsmedium-voltage distribution system |
| spellingShingle | Mishari Metab Almalki Constantine J. Hatziadoniu Classification of Many Abnormal Events in Radial Distribution Feeders Using the Complex Morlet Wavelet and Decision Trees Energies high impedance fault (HIF) incipient fault (IF) circuit breaker (CB) restrikes wavelet transforms medium-voltage distribution system |
| title | Classification of Many Abnormal Events in Radial Distribution Feeders Using the Complex Morlet Wavelet and Decision Trees |
| title_full | Classification of Many Abnormal Events in Radial Distribution Feeders Using the Complex Morlet Wavelet and Decision Trees |
| title_fullStr | Classification of Many Abnormal Events in Radial Distribution Feeders Using the Complex Morlet Wavelet and Decision Trees |
| title_full_unstemmed | Classification of Many Abnormal Events in Radial Distribution Feeders Using the Complex Morlet Wavelet and Decision Trees |
| title_short | Classification of Many Abnormal Events in Radial Distribution Feeders Using the Complex Morlet Wavelet and Decision Trees |
| title_sort | classification of many abnormal events in radial distribution feeders using the complex morlet wavelet and decision trees |
| topic | high impedance fault (HIF) incipient fault (IF) circuit breaker (CB) restrikes wavelet transforms medium-voltage distribution system |
| url | http://www.mdpi.com/1996-1073/11/3/546 |
| work_keys_str_mv | AT misharimetabalmalki classificationofmanyabnormaleventsinradialdistributionfeedersusingthecomplexmorletwaveletanddecisiontrees AT constantinejhatziadoniu classificationofmanyabnormaleventsinradialdistributionfeedersusingthecomplexmorletwaveletanddecisiontrees |