Open-Circuit Fault Diagnosis of T-Type Three-Level Inverter Based on Knowledge Reduction
Compared with traditional two-level inverters, multilevel inverters have many solid-state switches and complex composition methods. Therefore, diagnosing and treating inverter faults is a prerequisite for the reliable and efficient operation of the inverter. Based on the idea of intelligent compleme...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/3/1028 |
| _version_ | 1797318197515386880 |
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| author | Xiaojuan Chen Zhaohua Zhang |
| author_facet | Xiaojuan Chen Zhaohua Zhang |
| author_sort | Xiaojuan Chen |
| collection | DOAJ |
| description | Compared with traditional two-level inverters, multilevel inverters have many solid-state switches and complex composition methods. Therefore, diagnosing and treating inverter faults is a prerequisite for the reliable and efficient operation of the inverter. Based on the idea of intelligent complementary fusion, this paper combines the genetic algorithm–binary granulation matrix knowledge-reduction method with the extreme learning machine network to propose a fault-diagnosis method for multi-tube open-circuit faults in T-type three-level inverters. First, the fault characteristics of power devices at different locations of T-type three-level inverters are analyzed, and the inverter output power and its harmonic components are extracted as the basis for power device fault diagnosis. Second, the genetic algorithm–binary granularity matrix knowledge-reduction method is used for optimization to obtain the minimum attribute set required to distinguish the state transitions in various fault cases. Finally, the kernel attribute set is utilized to construct extreme learning machine subclassifiers with corresponding granularity. The experimental results show that the classification accuracy after attribute reduction is higher than that of all subclassifiers under different attribute sets, reflecting the advantages of attribute reduction and the complementarity of different intelligent diagnosis methods, which have stronger fault-diagnosis accuracy and generalization ability compared with the existing methods and provides a new way for hybrid intelligent diagnosis. |
| first_indexed | 2024-03-08T03:48:54Z |
| format | Article |
| id | doaj.art-83eafe30e93e4212b3d82e8ee1f29188 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T03:48:54Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-83eafe30e93e4212b3d82e8ee1f291882024-02-09T15:22:38ZengMDPI AGSensors1424-82202024-02-01243102810.3390/s24031028Open-Circuit Fault Diagnosis of T-Type Three-Level Inverter Based on Knowledge ReductionXiaojuan Chen0Zhaohua Zhang1School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, ChinaSchool of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, ChinaCompared with traditional two-level inverters, multilevel inverters have many solid-state switches and complex composition methods. Therefore, diagnosing and treating inverter faults is a prerequisite for the reliable and efficient operation of the inverter. Based on the idea of intelligent complementary fusion, this paper combines the genetic algorithm–binary granulation matrix knowledge-reduction method with the extreme learning machine network to propose a fault-diagnosis method for multi-tube open-circuit faults in T-type three-level inverters. First, the fault characteristics of power devices at different locations of T-type three-level inverters are analyzed, and the inverter output power and its harmonic components are extracted as the basis for power device fault diagnosis. Second, the genetic algorithm–binary granularity matrix knowledge-reduction method is used for optimization to obtain the minimum attribute set required to distinguish the state transitions in various fault cases. Finally, the kernel attribute set is utilized to construct extreme learning machine subclassifiers with corresponding granularity. The experimental results show that the classification accuracy after attribute reduction is higher than that of all subclassifiers under different attribute sets, reflecting the advantages of attribute reduction and the complementarity of different intelligent diagnosis methods, which have stronger fault-diagnosis accuracy and generalization ability compared with the existing methods and provides a new way for hybrid intelligent diagnosis.https://www.mdpi.com/1424-8220/24/3/1028inverterfault diagnosisknowledge reductionopen circuit |
| spellingShingle | Xiaojuan Chen Zhaohua Zhang Open-Circuit Fault Diagnosis of T-Type Three-Level Inverter Based on Knowledge Reduction Sensors inverter fault diagnosis knowledge reduction open circuit |
| title | Open-Circuit Fault Diagnosis of T-Type Three-Level Inverter Based on Knowledge Reduction |
| title_full | Open-Circuit Fault Diagnosis of T-Type Three-Level Inverter Based on Knowledge Reduction |
| title_fullStr | Open-Circuit Fault Diagnosis of T-Type Three-Level Inverter Based on Knowledge Reduction |
| title_full_unstemmed | Open-Circuit Fault Diagnosis of T-Type Three-Level Inverter Based on Knowledge Reduction |
| title_short | Open-Circuit Fault Diagnosis of T-Type Three-Level Inverter Based on Knowledge Reduction |
| title_sort | open circuit fault diagnosis of t type three level inverter based on knowledge reduction |
| topic | inverter fault diagnosis knowledge reduction open circuit |
| url | https://www.mdpi.com/1424-8220/24/3/1028 |
| work_keys_str_mv | AT xiaojuanchen opencircuitfaultdiagnosisofttypethreelevelinverterbasedonknowledgereduction AT zhaohuazhang opencircuitfaultdiagnosisofttypethreelevelinverterbasedonknowledgereduction |