Doubly Fed Induction Generator Low Voltage Ride Through Improvement Through Modular Multilevel Converter

Souza, Victor Ramon França Bezerra de; Barros, Luciano Sales; Silva, Guilherme P. Da

doi:10.1109/access.2022.3178960

Cited by 9 publications

(5 citation statements)

References 33 publications

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“…In the future, we will conduct research from three aspects: (1) Design a more reasonable deep model to make the learned multimodal features more effective for fault diagnosis. (2) Further mine the correlation between multimodal data to integrate different types of modal features more comprehensively and without redundancy. (3) Explore how to extend our method to other multimodal data, such as vibration and audio data.…”

Section: Discussionmentioning

confidence: 99%

Fault Diagnosis for Modular Multilevel Converter Switching Devices via Multimodal Attention Fusion

Ke,

Hu,

Yang

et al. 2023

IEEE Access

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Modular multilevel converters (MMCs) have been widely applied in high voltage direct current (HVDC) transmission project, and security is their fundamental issue. Submodule (SM) switching Devices fault of MMC in HVDC is the most common problem, nevertheless, traditional fault diagnosis methods are mostly based on single modality resulting in poor feature extraction ability. To solve this pain spot, A novel multimodal attention fusion (MAF) model is proposed in this paper. Firstly, the three-phase internal circulating currents of MMC are converted into two-dimensional (2D) time-frequency image by performing Synchronous Squeezing Transform (SST). To automatically focus on the discriminative regions most related to fault features, a time series attention model(TSAM) and a visual attention model(VAM) are proposed to learn the features of the 2D time-frequency image and the internal circulating current time series data, respectively. Then, a multimodal attention model(MAM) via intermediate fusion is proposed, which utilizes the internal correlation between visual features and time series features for joint fault feature extraction. Finally, a later fusion scheme is applied to combine the fault prediction results of three attention models for fault diagnosis. The fault diagnosis accuracy of the proposed MAF model are 98.4% and 97.3% on the 31-level and 61-level MMC datasets, respectively. Experiments demonstrate that the designed model boosts feature representation power and achieves better fault diagnosis performance than the state-of-the-art baseline methods. INDEX TERMS Modular multilevel converter, fault diagnosis, attention model, multimodal fusion I. INTRODUCTION

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Section: Discussionmentioning

confidence: 99%

Fault Diagnosis for Modular Multilevel Converter Switching Devices via Multimodal Attention Fusion

Ke,

Hu,

Yang

et al. 2023

IEEE Access

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“…4/π considers the maximum magnitude of the fundamental component under severe over-modulation [19]. If the RSC is made of other matrix or multilevel converter topologies [42]- [43], the right hand side of (51) should be adjusted accordingly.…”

Section: B Desired Voltage and Rsc Voltage Capacitymentioning

confidence: 99%

Short-Circuit Modeling of DFIG-Based WTG in Sequence Domain Considering Various Fault- Ride-Through Requirements and Solutions

Chang

Koçar

Farantatos

et al. 2023

IEEE Trans. Power Delivery

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The doubly fed induction generator (DFIG) based wind turbine generator (WTG) can activate different fault ride through (FRT) control schemes and circuits to comply with various grid code or utility interconnection requirements. These FRT solutions play a key role in the resulting fault current of the DFIG-based WTG as well as the voltages and currents distributed in faulted networks. This paper accounts for their impact by proposing a new generic sequence domain model of the DFIGbased WTG for static short circuit calculations. In this paper, the FRT configurations are classified into three types. The proposed model first calculates the positive-and negative-sequence current phasors under the specified FRT control. Then based on the control, the desired currents and voltages are calculated by explicit equations to determine whether the crowbar could operate during FRT operation. By comparing with detailed electromagnetic transient (EMT) simulations using an EPRI benchmark system, the proposed model, incorporated into an iterative steady state solver, is shown to be accurate to calculate voltage and current phasors in an efficient manner. The proposed model provides a rapid tool for short circuit computations and protective relaying studies considering various grid code requirements and FRT implementations.

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“…In [22,23], a novel protection scheme was proposed in which a superconducting fault current limiter is connected in series with the rotor winding of the DFIG or the DFIG outlet, which can effectively limit the maximum value of the fault rotor current or improve the fault voltage at the outlet of the DFIG. In [24], a modular multilevel converter was employed for the DFIG to suppress the fault current, which can provide the transient damping of the DFIG rotor and stator currents during severe grid faults to achieve the fault ride through. In [25], a modulated series dynamic braking resistor control strategy was presented to enhance the capacity of DFIG fault ride through.…”

Section: Introductionmentioning

confidence: 99%

The Fault Ride-Through Characteristics of a Double-Fed Induction Generator Using a Dynamic Voltage Restorer with Superconducting Magnetic Energy Storage

Li¹,

Liang²,

Niu³

et al. 2023

Applied Sciences

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With the prevalence of renewable energy sources such as wind power in the power system, analyzing the fault characteristics of systems composed of DFIGs is becoming increasingly important. Therefore, this article analyzes, at first theoretically, the fault characteristics of a doubly fed induction generator (DFIG) during fault periods. It was found that the fault current of the DFIG exhibited the frequency offset phenomenon, which is affected by the depth of voltage dips and can negatively impact traditional distance protection. Furthermore, a method using a dynamic voltage restorer (DVR) based on superconducting magnetic energy storage (SMES) was adopted to compensate for the fault voltage of DFIG, which can mitigate the voltage dips of the DFIG. This method can not only achieve the fault ride through for DFIG but also significantly improve the frequency offset of the fault current during fault periods. Finally, a model composed of a 2.5 MW DFIG-based wind turbine and a 2.5 MW DVR-based SMES was built using a real-time digital simulator (RTDS) platform, and the simulation results showed that the fault stator voltage of DFIG can be compensated at a rated value of 0.69 kV, and the frequency of fault current can be maintained at 50 Hz These results validate the excellent performance of the method in achieving the fault ride through of DFIG and improving the frequency offset of the fault current by comparing multiple type faults while employing different protection methods.

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Doubly Fed Induction Generator Low Voltage Ride Through Improvement Through Modular Multilevel Converter

Cited by 9 publications

References 33 publications

Fault Diagnosis for Modular Multilevel Converter Switching Devices via Multimodal Attention Fusion

Fault Diagnosis for Modular Multilevel Converter Switching Devices via Multimodal Attention Fusion

Short-Circuit Modeling of DFIG-Based WTG in Sequence Domain Considering Various Fault- Ride-Through Requirements and Solutions

The Fault Ride-Through Characteristics of a Double-Fed Induction Generator Using a Dynamic Voltage Restorer with Superconducting Magnetic Energy Storage

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