Fault Diagnosis in Wind Turbine Current Sensors: Detecting Single and Multiple Faults with the Extended Kalman Filter Bank Approach
Currently, in modern wind farms, the doubly fed induction generator (DFIG) is commonly adopted for its ability to operate at variable wind speeds. Generally, this type of wind turbine is controlled by using two converters, one on the rotor side (RSC) and the other one on the grid side (GSC). However...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/3/728 |
| _version_ | 1797318231087644672 |
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| author | Mohammed Abbas Houcine Chafouk Sid Ahmed El Mehdi Ardjoun |
| author_facet | Mohammed Abbas Houcine Chafouk Sid Ahmed El Mehdi Ardjoun |
| author_sort | Mohammed Abbas |
| collection | DOAJ |
| description | Currently, in modern wind farms, the doubly fed induction generator (DFIG) is commonly adopted for its ability to operate at variable wind speeds. Generally, this type of wind turbine is controlled by using two converters, one on the rotor side (RSC) and the other one on the grid side (GSC). However, the control of these two converters depends mainly on current sensors measurements. Nevertheless, in the case of sensor failure, control stability may be compromised, leading to serious malfunctions in the wind turbine system. Therefore, in this article, we will present an innovative diagnostic approach to detect, locate, and isolate the single and/or multiple real-phase current sensors in both converters. The suggested approach uses an extended Kalman filter (EKF) bank structured according to a generalized observer scheme (GOS) and relies on a nonlinear model for the RSC and a linear model for the GSC. The EKF estimates the currents in the converters, which are then compared to sensor measurements to generate residuals. These residuals are then processed in the localization, isolation, and decision blocks to precisely identify faulty sensors. The obtained results confirm the effectiveness of this approach to identify faulty sensors in the abc phases. It also demonstrates its ability to overcome the nonlinearity induced by wind fluctuations, as well as resolves the coupling issue between currents in the fault period. |
| first_indexed | 2024-03-08T03:49:25Z |
| format | Article |
| id | doaj.art-0892fd4782b64159aa5ce771df9724d0 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T03:49:25Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0892fd4782b64159aa5ce771df9724d02024-02-09T15:21:39ZengMDPI AGSensors1424-82202024-01-0124372810.3390/s24030728Fault Diagnosis in Wind Turbine Current Sensors: Detecting Single and Multiple Faults with the Extended Kalman Filter Bank ApproachMohammed Abbas0Houcine Chafouk1Sid Ahmed El Mehdi Ardjoun2IRSEEM/ESIGELEC Laboratory, Normandy University of Rouen, 76000 Rouen, FranceIRSEEM/ESIGELEC Laboratory, Normandy University of Rouen, 76000 Rouen, FranceIRSEEM/ESIGELEC Laboratory, Normandy University of Rouen, 76000 Rouen, FranceCurrently, in modern wind farms, the doubly fed induction generator (DFIG) is commonly adopted for its ability to operate at variable wind speeds. Generally, this type of wind turbine is controlled by using two converters, one on the rotor side (RSC) and the other one on the grid side (GSC). However, the control of these two converters depends mainly on current sensors measurements. Nevertheless, in the case of sensor failure, control stability may be compromised, leading to serious malfunctions in the wind turbine system. Therefore, in this article, we will present an innovative diagnostic approach to detect, locate, and isolate the single and/or multiple real-phase current sensors in both converters. The suggested approach uses an extended Kalman filter (EKF) bank structured according to a generalized observer scheme (GOS) and relies on a nonlinear model for the RSC and a linear model for the GSC. The EKF estimates the currents in the converters, which are then compared to sensor measurements to generate residuals. These residuals are then processed in the localization, isolation, and decision blocks to precisely identify faulty sensors. The obtained results confirm the effectiveness of this approach to identify faulty sensors in the abc phases. It also demonstrates its ability to overcome the nonlinearity induced by wind fluctuations, as well as resolves the coupling issue between currents in the fault period.https://www.mdpi.com/1424-8220/24/3/728diagnosticDFIGwind turbineextended Kalman filtercurrent fault sensor |
| spellingShingle | Mohammed Abbas Houcine Chafouk Sid Ahmed El Mehdi Ardjoun Fault Diagnosis in Wind Turbine Current Sensors: Detecting Single and Multiple Faults with the Extended Kalman Filter Bank Approach Sensors diagnostic DFIG wind turbine extended Kalman filter current fault sensor |
| title | Fault Diagnosis in Wind Turbine Current Sensors: Detecting Single and Multiple Faults with the Extended Kalman Filter Bank Approach |
| title_full | Fault Diagnosis in Wind Turbine Current Sensors: Detecting Single and Multiple Faults with the Extended Kalman Filter Bank Approach |
| title_fullStr | Fault Diagnosis in Wind Turbine Current Sensors: Detecting Single and Multiple Faults with the Extended Kalman Filter Bank Approach |
| title_full_unstemmed | Fault Diagnosis in Wind Turbine Current Sensors: Detecting Single and Multiple Faults with the Extended Kalman Filter Bank Approach |
| title_short | Fault Diagnosis in Wind Turbine Current Sensors: Detecting Single and Multiple Faults with the Extended Kalman Filter Bank Approach |
| title_sort | fault diagnosis in wind turbine current sensors detecting single and multiple faults with the extended kalman filter bank approach |
| topic | diagnostic DFIG wind turbine extended Kalman filter current fault sensor |
| url | https://www.mdpi.com/1424-8220/24/3/728 |
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