Three-phase inverters open-circuit faults diagnosis using an enhanced variational mode decomposition, wavelet packet analysis, and scalar indicators

Abdelkader, Rabah; Chérif, Bilal Djamal Eddine; Bendiabdellah, Azeddine; Kaddour, Abdelhafid

doi:10.1007/s00202-022-01633-1

Cited by 6 publications

(2 citation statements)

References 31 publications

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“…However, the selection of wavelet bases will directly affect the extraction effect of fault features, which increases the difficulty of applying this method. To accurately detect IGBT switching faults, a new method based on an enhanced version of the variable mode decomposition algorithm (EVMD) combined with wavelet packet analysis (WPA) and scalar indicators is proposed by the authors in [9] to detect OC faults, which also shows how effective the suggested method is at diagnosing OC faults. For three-level active neutral-point-clamped (3LANPC) inverters, the authors in [10] established a predictive current model and seamlessly integrated the residuals of the predicted current vectors between the measured and predicted currents into the backward optimization of the MPC to diagnose inverter faults, which reduces the complexity of inverter fault identification, while the authors defined the counting function within each current cycle, which enhances the robustness of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

Open-Circuit Fault Diagnosis of T-Type Three-Level Inverter Based on Knowledge Reduction

Chen,

Zhang

2024

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

Open-Circuit Fault Diagnosis of T-Type Three-Level Inverter Based on Knowledge Reduction

Chen,

Zhang

2024

Sensors

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“…Similarly to [48], the VMD + Pearson correlation coefficient-based method was also adopted in [50]. A variant form of the Pearson correlation coefficient was adopted in [20,26].…”

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confidence: 99%

A Review of Symmetry-Based Open-Circuit Fault Diagnostic Methods for Power Converters

Zhou,

Zhao,

2024

Symmetry

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Power converters can convert the electrical energy output by power source into specific forms required by target device. They are widely used in various fields such as electrification, transportation, and power systems, and are the core components of the systems. Power converters are composed of power semiconductor devices (such as IGBTs), which are prone to failure due to abnormal conditions and aging degradation, leading to power converter faults. Therefore, over the past twenty years, extensive fault diagnosis methods for power converters have been studied and proposed. Among them, symmetry-based methods have the characteristics of rapid diagnosis speed and strong robustness, but so far there’s no systemic summary of such methods. Therefore, this paper reviews the relevant research on fault diagnosis of power converters based on symmetry in recent years, analyzes the mathematical principles of these methods, and compares their diagnostic performance. In addition, two factors that may have an influence on the symmetry are pointed out. This paper provides references and suggestions for the future development and research of power converter fault diagnosis.

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Cross-correlation based fault electromagnetic signature extraction for open-circuit fault diagnosis in NPC inverters

et al. 2023

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Three-phase inverters open-circuit faults diagnosis using an enhanced variational mode decomposition, wavelet packet analysis, and scalar indicators

Cited by 6 publications

References 31 publications

Open-Circuit Fault Diagnosis of T-Type Three-Level Inverter Based on Knowledge Reduction

Open-Circuit Fault Diagnosis of T-Type Three-Level Inverter Based on Knowledge Reduction

A Review of Symmetry-Based Open-Circuit Fault Diagnostic Methods for Power Converters

Cross-correlation based fault electromagnetic signature extraction for open-circuit fault diagnosis in NPC inverters

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