Fault Diagnosis Based on Attention Collaborative LSTM Networks for NPC Three-Level Inverters

Si, Yupeng; Wang, Rongjie; Zhang, Shiqi; Zhou, Wei; Lin, Anhui; Wang, Yichun

doi:10.1109/tim.2022.3169545

Cited by 17 publications

(3 citation statements)

References 36 publications

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“…Based on the principle of SMC, the design of a SMC controller generally includes a sliding surface and a robust control law [30]. Therefore, from (12), the commonly used linear sliding mode controller is adopted for the design of the sliding surface S U :…”

Section: Design Of the Bus Voltage Controllermentioning

confidence: 99%

“…It is the so-called issue of phase circulating current (PCC), which can reduce the conversion efficiency, produce serious oscillations, or even destroy the system's stability [9]. Therefore, more and more researchers focus on the issue of PCC, and many suppression approaches have been proposed for the parallel three-phase inverters, including droop control [10], fuzzy control [11], neural network control [12], predictive control [13], adaptive control [14], sliding mode control [15], and so on.…”

Section: Introductionmentioning

confidence: 99%

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Composite Sliding Mode Control of Phase Circulating Current for the Parallel Three-Phase Inverter Systems

Zhang,

Wang,

Han

et al. 2024

Energies

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The phase circulating current (PCC) of the parallel three-phase inverter systems dramatically affects the power quality and conversion efficiency of the power grid. In this paper, a composite suppression strategy is proposed to solve the PCC issue by using the sliding mode control (SMC) approach and improved virtual impedance droop control. Taking the commonly used 2-group parallel three-phase inverter as an example, an inter- and intra-classification model is established by analyzing the sources of PCC. In order to suppress the inter-PCC, the traditional virtual impedance droop control is given, following the improved substitute by combining SMC. And the variables of the bus voltage, Q-U loop, P-f loop, and the virtual-induced reactance are also introduced for the robust control of the impedance droop. On the other side, a SMC-based suppression approach is designed to solve the issue of the intra-PCC. Its idea is to introduce a regulation factor for the space vector pulse width modulation (SVPWM) so that the zero-sequence voltage can be eliminated and the influence of the intra-PCC can be relieved. Comparative simulations and experiments validate the effectiveness of the methods proposed in this paper.

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Section: Design Of the Bus Voltage Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Composite Sliding Mode Control of Phase Circulating Current for the Parallel Three-Phase Inverter Systems

Zhang,

Wang,

Han

et al. 2024

Energies

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show abstract

“…Recently, deep learning models with fault feature extraction capabilities have greatly improved fault diagnosis capabilities. Si et al (2022) used a deep LSTM network with attention cooperation to extract discriminative features from the original data. Compared with other methods, it has better diagnostic results under various conditions.…”

Section: Introductionmentioning

confidence: 99%

Disturbance auto-encoder generation model: Few-shot learning method for IGBT open-circuit fault diagnosis in three-phase converters

Qiu²,

Zhang³

et al. 2023

Front. Energy Res.

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With the rapid development of converters in a variety of industrial fields, the fault diagnosis of power switching devices has become an important factor in ensuring the safe and reliable operation of related systems. In recent years, machine learning has performed well in many fault diagnosis tasks. The success of these advanced methods depends on sufficient marked samples for each fault type. However, in most industrial applications, it is expensive and difficult to collect fault samples, and the fault diagnosis model trained under the limited samples cannot meet the requirements of fault diagnosis accuracy. In order to solve this problem, this study proposes a few-shot learning method based on fault sample generation to realize the open-circuit fault diagnosis of IGBT in a three-phase PWM converter. This method is the deformation of the auto-encoder called the disturbance auto-encoder generation model. By designing the model structure and training algorithm constraints, the encoder learns the nonlinear transferable disturbance from the normal operating sample pairs. Then, the disturbance is applied to the decoder to synthesize new fault samples to realize the training of the fault diagnosis model with limited samples. The biggest advantage of this method is that it can achieve 95.90% fault diagnosis accuracy by only collecting the samples in the normal operating conditions of the target system. Finally, the feasibility and advantages of the proposed method are verified by comparative experiments.

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DL-MSCNN: a general and lightweight framework for fault diagnosis with limited training samples

Shao,

Lee,

Kim

2023

J Intell Manuf

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Fault Diagnosis Based on Attention Collaborative LSTM Networks for NPC Three-Level Inverters

Cited by 17 publications

References 36 publications

Composite Sliding Mode Control of Phase Circulating Current for the Parallel Three-Phase Inverter Systems

Composite Sliding Mode Control of Phase Circulating Current for the Parallel Three-Phase Inverter Systems

Disturbance auto-encoder generation model: Few-shot learning method for IGBT open-circuit fault diagnosis in three-phase converters

DL-MSCNN: a general and lightweight framework for fault diagnosis with limited training samples

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