Wind Turbine Fault Diagnosis by the Approach of SCADA Alarms Analysis
Wind farm operators are overwhelmed by a large amount of supervisory control and data acquisition (SCADA) alarms when faults occur. This paper presents an online root fault identification method for SCADA alarms to assist operators in wind turbine fault diagnosis. The proposed method is based on the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/1/69 |
| _version_ | 1797499721202270208 |
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| author | Lu Wei Zheng Qian Yan Pei Jingyue Wang |
| author_facet | Lu Wei Zheng Qian Yan Pei Jingyue Wang |
| author_sort | Lu Wei |
| collection | DOAJ |
| description | Wind farm operators are overwhelmed by a large amount of supervisory control and data acquisition (SCADA) alarms when faults occur. This paper presents an online root fault identification method for SCADA alarms to assist operators in wind turbine fault diagnosis. The proposed method is based on the similarity analysis between an unknown alarm vector and the feature vectors of known faults. The alarm vector is obtained from segmented alarm lists, which are filtered and simplified. The feature vector, which is a unique signature representing the occurrence of a fault, is extracted from the alarm lists belonging to the same fault. To mine the coupling correspondence between alarms and faults, we define the weights of the alarms in each fault. The similarities is measured by the weighted Euclidean distance and the weighted Hamming distance, respectively. One year of SCADA alarms and maintenance records are used to verify the proposed method. The results show that the performance of the weighted Hamming distance is better than that of the weighted Euclidean distance; 84.1% of alarm lists are labeled with the right root fault. |
| first_indexed | 2024-03-10T03:51:29Z |
| format | Article |
| id | doaj.art-a045f294529f408fab78ec834e611aac |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T03:51:29Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a045f294529f408fab78ec834e611aac2023-11-23T11:07:08ZengMDPI AGApplied Sciences2076-34172021-12-011216910.3390/app12010069Wind Turbine Fault Diagnosis by the Approach of SCADA Alarms AnalysisLu Wei0Zheng Qian1Yan Pei2Jingyue Wang3School of Instrumentation and Optoelectronic Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, ChinaSchool of Instrumentation and Optoelectronic Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, ChinaSchool of Instrumentation and Optoelectronic Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, ChinaSchool of Instrumentation and Optoelectronic Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, ChinaWind farm operators are overwhelmed by a large amount of supervisory control and data acquisition (SCADA) alarms when faults occur. This paper presents an online root fault identification method for SCADA alarms to assist operators in wind turbine fault diagnosis. The proposed method is based on the similarity analysis between an unknown alarm vector and the feature vectors of known faults. The alarm vector is obtained from segmented alarm lists, which are filtered and simplified. The feature vector, which is a unique signature representing the occurrence of a fault, is extracted from the alarm lists belonging to the same fault. To mine the coupling correspondence between alarms and faults, we define the weights of the alarms in each fault. The similarities is measured by the weighted Euclidean distance and the weighted Hamming distance, respectively. One year of SCADA alarms and maintenance records are used to verify the proposed method. The results show that the performance of the weighted Hamming distance is better than that of the weighted Euclidean distance; 84.1% of alarm lists are labeled with the right root fault.https://www.mdpi.com/2076-3417/12/1/69wind turbineSCADA alarmsfault diagnosisroot fault identificationsimilarity analysis |
| spellingShingle | Lu Wei Zheng Qian Yan Pei Jingyue Wang Wind Turbine Fault Diagnosis by the Approach of SCADA Alarms Analysis Applied Sciences wind turbine SCADA alarms fault diagnosis root fault identification similarity analysis |
| title | Wind Turbine Fault Diagnosis by the Approach of SCADA Alarms Analysis |
| title_full | Wind Turbine Fault Diagnosis by the Approach of SCADA Alarms Analysis |
| title_fullStr | Wind Turbine Fault Diagnosis by the Approach of SCADA Alarms Analysis |
| title_full_unstemmed | Wind Turbine Fault Diagnosis by the Approach of SCADA Alarms Analysis |
| title_short | Wind Turbine Fault Diagnosis by the Approach of SCADA Alarms Analysis |
| title_sort | wind turbine fault diagnosis by the approach of scada alarms analysis |
| topic | wind turbine SCADA alarms fault diagnosis root fault identification similarity analysis |
| url | https://www.mdpi.com/2076-3417/12/1/69 |
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