A Method for Statistical Processing of Magnetic Field Sensor Signals for Non-Invasive Condition Monitoring of Synchronous Generators
Condition monitoring of synchronous generators through non-invasive methods is widely requested by maintenance teams for not interfering the machine operation. Among the techniques used, external magnetic field monitoring is a recent strategy with great potential for detecting incipient faults. In t...
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| Format: | Article |
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MDPI AG
2022-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/22/8631 |
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| author | Luis O. S. Grillo Carlos A. C. Wengerkievicz Nelson J. Batistela Patrick Kuo-Peng Luciano M. de Freitas |
| author_facet | Luis O. S. Grillo Carlos A. C. Wengerkievicz Nelson J. Batistela Patrick Kuo-Peng Luciano M. de Freitas |
| author_sort | Luis O. S. Grillo |
| collection | DOAJ |
| description | Condition monitoring of synchronous generators through non-invasive methods is widely requested by maintenance teams for not interfering the machine operation. Among the techniques used, external magnetic field monitoring is a recent strategy with great potential for detecting incipient faults. In this context, this paper proposes the application of a simple strategy with low computational cost to process data of external magnetic field time derivative signals for the purposes of condition monitoring and fault detection in synchronous machines. The information of interest is extracted from changes in the magnetic signature of the synchronous generator, obtained from frequency spectra of monitored signals using induction magnetic field sensors. The process forms a set of time series that reflects constructive and operational characteristics of the machine. The Shewhart control chart method is applied for anomaly detection in these time series, allowing the detection of changes in the machine magnetic signature. This method is employed in an algorithm for continuous condition monitoring of synchronous generators, presenting as output a global change indicator for the multivariable problem associated with magnetic signature monitoring. Correlation matrices are used to improve the algorithm response, filtering series with similar variation patterns associated with detected events. The proposed method is validated through tests on an experimental bench that allows the controlled imposition of faults in a synchronous generator. The proposed global change indicator allows the automatic detection of stator and rotor faults with the machine synchronized with the commercial power grid. The proposed methodology is also applied on data obtained from an equipment installed in a 305 MVA synchronous generator of a hydroelectric power plant where the evolution of an incipient fault, i.e., a mechanical vibration fault, has been detected. |
| first_indexed | 2024-03-09T18:00:52Z |
| format | Article |
| id | doaj.art-45bfa918ee4f4e33a14623286eb37628 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T18:00:52Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-45bfa918ee4f4e33a14623286eb376282023-11-24T09:53:17ZengMDPI AGSensors1424-82202022-11-012222863110.3390/s22228631A Method for Statistical Processing of Magnetic Field Sensor Signals for Non-Invasive Condition Monitoring of Synchronous GeneratorsLuis O. S. Grillo0Carlos A. C. Wengerkievicz1Nelson J. Batistela2Patrick Kuo-Peng3Luciano M. de Freitas4Department of Electrical Engineering, Federal University of Santa Catarina, Florianópolis 88040-900, SC, BrazilDepartment of Electrical Engineering, Federal University of Santa Catarina, Florianópolis 88040-900, SC, BrazilDepartment of Electrical Engineering, Federal University of Santa Catarina, Florianópolis 88040-900, SC, BrazilDepartment of Electrical Engineering, Federal University of Santa Catarina, Florianópolis 88040-900, SC, BrazilENGIE Brasil Energia, Florianópolis 88025-255, SC, BrazilCondition monitoring of synchronous generators through non-invasive methods is widely requested by maintenance teams for not interfering the machine operation. Among the techniques used, external magnetic field monitoring is a recent strategy with great potential for detecting incipient faults. In this context, this paper proposes the application of a simple strategy with low computational cost to process data of external magnetic field time derivative signals for the purposes of condition monitoring and fault detection in synchronous machines. The information of interest is extracted from changes in the magnetic signature of the synchronous generator, obtained from frequency spectra of monitored signals using induction magnetic field sensors. The process forms a set of time series that reflects constructive and operational characteristics of the machine. The Shewhart control chart method is applied for anomaly detection in these time series, allowing the detection of changes in the machine magnetic signature. This method is employed in an algorithm for continuous condition monitoring of synchronous generators, presenting as output a global change indicator for the multivariable problem associated with magnetic signature monitoring. Correlation matrices are used to improve the algorithm response, filtering series with similar variation patterns associated with detected events. The proposed method is validated through tests on an experimental bench that allows the controlled imposition of faults in a synchronous generator. The proposed global change indicator allows the automatic detection of stator and rotor faults with the machine synchronized with the commercial power grid. The proposed methodology is also applied on data obtained from an equipment installed in a 305 MVA synchronous generator of a hydroelectric power plant where the evolution of an incipient fault, i.e., a mechanical vibration fault, has been detected.https://www.mdpi.com/1424-8220/22/22/8631condition monitoringcontinued magnetic signatureexternal magnetic fieldsynchronous generator |
| spellingShingle | Luis O. S. Grillo Carlos A. C. Wengerkievicz Nelson J. Batistela Patrick Kuo-Peng Luciano M. de Freitas A Method for Statistical Processing of Magnetic Field Sensor Signals for Non-Invasive Condition Monitoring of Synchronous Generators Sensors condition monitoring continued magnetic signature external magnetic field synchronous generator |
| title | A Method for Statistical Processing of Magnetic Field Sensor Signals for Non-Invasive Condition Monitoring of Synchronous Generators |
| title_full | A Method for Statistical Processing of Magnetic Field Sensor Signals for Non-Invasive Condition Monitoring of Synchronous Generators |
| title_fullStr | A Method for Statistical Processing of Magnetic Field Sensor Signals for Non-Invasive Condition Monitoring of Synchronous Generators |
| title_full_unstemmed | A Method for Statistical Processing of Magnetic Field Sensor Signals for Non-Invasive Condition Monitoring of Synchronous Generators |
| title_short | A Method for Statistical Processing of Magnetic Field Sensor Signals for Non-Invasive Condition Monitoring of Synchronous Generators |
| title_sort | method for statistical processing of magnetic field sensor signals for non invasive condition monitoring of synchronous generators |
| topic | condition monitoring continued magnetic signature external magnetic field synchronous generator |
| url | https://www.mdpi.com/1424-8220/22/22/8631 |
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