Data-driven failure diagnosis in transmission protection system with multiple events and data anomalies
To guarantee the reliable power supply, the expected operation of all the components in the power system is critical. Distance protection system is primarily responsible of isolating the faulty section from the healthy part of the grid. Failure in protection devices can result in multiple conflictin...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | Journal of Modern Power Systems and Clean Energy |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9028816/ |
| _version_ | 1818606054318014464 |
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| author | Amir Gholami Anurag K. Srivastava Shikhar Pandey |
| author_facet | Amir Gholami Anurag K. Srivastava Shikhar Pandey |
| author_sort | Amir Gholami |
| collection | DOAJ |
| description | To guarantee the reliable power supply, the expected operation of all the components in the power system is critical. Distance protection system is primarily responsible of isolating the faulty section from the healthy part of the grid. Failure in protection devices can result in multiple conflicting alarms at the power grid operation center and complex events analysis to manually find the root cause of the observed system state. If not handled in time, it may lead to the propagation of the faults/failures to the adjacent transmission lines and components. With availability of the synchronized measurements from phasor measurement units (PMUs), real-time system monitoring and automated failure diagnosis is feasible. With multiple adverse events and possible data anomalies, the complexity of the problem will be escalated. In this paper, a PMU based algorithm is presented and discussed to detect the root cause of the failure in transmission protection system based on the observed state, e.g. multiple line tripping, breaker failures. The failure diagnosis algorithm is further enhanced to come up with the fully functional version of the failure diagnosis tool, which is tailored for the cases in which the PMU anomalies are present. In the developed algorithm the validity of the PMU data is critical; however, such causes as communication errors or cyber-attacks might lead to the PMU data anomalies. This issue is well-addressed in this paper and some major types of anomaly detection methods suitable for PMU data are discussed. Results show that the ensemble approach has some distinct advantages in data anomaly detection compared to the previously used standalone algorithms. Additionally, the enhanced failure diagnosis method is developed to clean the inaccurate data in case of the anomaly in measured voltage magnitudes. Finally, both original and enhanced versions of the tool are tested on 96-bus test system using the real-time OPAL-RT simulator. The results show the accuracy of the enhanced tool and its advantages over the primary version of the tool. |
| first_indexed | 2024-12-16T14:04:45Z |
| format | Article |
| id | doaj.art-961e420024a743ae9d1c93b03cab4530 |
| institution | Directory Open Access Journal |
| issn | 2196-5420 |
| language | English |
| last_indexed | 2024-12-16T14:04:45Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | Journal of Modern Power Systems and Clean Energy |
| spelling | doaj.art-961e420024a743ae9d1c93b03cab45302022-12-21T22:28:56ZengIEEEJournal of Modern Power Systems and Clean Energy2196-54202019-01-017476777810.1007/s40565-019-0541-69028816Data-driven failure diagnosis in transmission protection system with multiple events and data anomaliesAmir Gholami0Anurag K. Srivastava1Shikhar Pandey2Washington State University,Pullman,USAWashington State University,Pullman,USAWashington State University,Pullman,USATo guarantee the reliable power supply, the expected operation of all the components in the power system is critical. Distance protection system is primarily responsible of isolating the faulty section from the healthy part of the grid. Failure in protection devices can result in multiple conflicting alarms at the power grid operation center and complex events analysis to manually find the root cause of the observed system state. If not handled in time, it may lead to the propagation of the faults/failures to the adjacent transmission lines and components. With availability of the synchronized measurements from phasor measurement units (PMUs), real-time system monitoring and automated failure diagnosis is feasible. With multiple adverse events and possible data anomalies, the complexity of the problem will be escalated. In this paper, a PMU based algorithm is presented and discussed to detect the root cause of the failure in transmission protection system based on the observed state, e.g. multiple line tripping, breaker failures. The failure diagnosis algorithm is further enhanced to come up with the fully functional version of the failure diagnosis tool, which is tailored for the cases in which the PMU anomalies are present. In the developed algorithm the validity of the PMU data is critical; however, such causes as communication errors or cyber-attacks might lead to the PMU data anomalies. This issue is well-addressed in this paper and some major types of anomaly detection methods suitable for PMU data are discussed. Results show that the ensemble approach has some distinct advantages in data anomaly detection compared to the previously used standalone algorithms. Additionally, the enhanced failure diagnosis method is developed to clean the inaccurate data in case of the anomaly in measured voltage magnitudes. Finally, both original and enhanced versions of the tool are tested on 96-bus test system using the real-time OPAL-RT simulator. The results show the accuracy of the enhanced tool and its advantages over the primary version of the tool.https://ieeexplore.ieee.org/document/9028816/Failure diagnosisTransmission protection systemProtection mis-operationPhasor measurement unit (PMU) data anomaly and cleaningEnsemble method |
| spellingShingle | Amir Gholami Anurag K. Srivastava Shikhar Pandey Data-driven failure diagnosis in transmission protection system with multiple events and data anomalies Journal of Modern Power Systems and Clean Energy Failure diagnosis Transmission protection system Protection mis-operation Phasor measurement unit (PMU) data anomaly and cleaning Ensemble method |
| title | Data-driven failure diagnosis in transmission protection system with multiple events and data anomalies |
| title_full | Data-driven failure diagnosis in transmission protection system with multiple events and data anomalies |
| title_fullStr | Data-driven failure diagnosis in transmission protection system with multiple events and data anomalies |
| title_full_unstemmed | Data-driven failure diagnosis in transmission protection system with multiple events and data anomalies |
| title_short | Data-driven failure diagnosis in transmission protection system with multiple events and data anomalies |
| title_sort | data driven failure diagnosis in transmission protection system with multiple events and data anomalies |
| topic | Failure diagnosis Transmission protection system Protection mis-operation Phasor measurement unit (PMU) data anomaly and cleaning Ensemble method |
| url | https://ieeexplore.ieee.org/document/9028816/ |
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