Improving low-voltage ride-through capability of a multimegawatt DFIG based wind turbine under grid faults

Nadour, Mohamed; Essadki, Ahmed; Nasser, Tamou

doi:10.1186/s41601-020-00172-w

Cited by 25 publications

(15 citation statements)

References 18 publications

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“…Wind energy has become one of the vital energy sources in the world, while wind power generation systems have been widely studied and applied [1]. The planetary gearbox is one of the critical components in the transmission system of wind turbines (WTs) because of its advantages of compact structure, high power density and desirable transmission efficiency [2].…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, many intelligent methods have been investigated for gearbox fault diagnosis [5][6][7][8][9], while the proposed methods have two assumptions: (1) the training and testing data are derived from the same probability distribution; (2) enough labeled history data with fault information can be obtained [10]. However, in industrial applications, it is impractical to satisfy those two assumptions because of operating condition change, equipment wear degradation, and environmental noise interference, leading to differences of data in the probability distribution [11] and unlabeled data collected from the diagnosed devices [12].…”

Section: Introductionmentioning

confidence: 99%

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A dynamic-model-based fault diagnosis method for a wind turbine planetary gearbox using a deep learning network

Zhao

2022

Prot Control Mod Power Syst

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The planetary gearbox is a critical part of wind turbines, and has great significance for their safety and reliability. Intelligent fault diagnosis methods for these gearboxes have made some achievements based on the availability of large quantities of labeled data. However, the data collected from the diagnosed devices are always unlabeled, and the acquisition of fault data from real gearboxes is time-consuming and laborious. As some gearbox faults can be conveniently simulated by a relatively precise dynamic model, the data from dynamic simulation containing some features are related to those from the actual machines. As a potential tool, transfer learning adapts a network trained in a source domain to its application in a target domain. Therefore, a novel fault diagnosis method combining transfer learning with dynamic model is proposed to identify the health conditions of planetary gearboxes. In the method, a modified lumped-parameter dynamic model of a planetary gear train is established to simulate the resultant vibration signal, while an optimized deep transfer learning network based on a one-dimensional convolutional neural network is built to extract domain-invariant features from different domains to achieve fault classification. Various groups of transfer diagnosis experiments of planetary gearboxes are carried out, and the experimental results demonstrate the effectiveness and the reliability of both the dynamic model and the proposed method.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A dynamic-model-based fault diagnosis method for a wind turbine planetary gearbox using a deep learning network

Zhao

2022

Prot Control Mod Power Syst

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“…The fault control strategy for IIDGs has a decisive impact on the fault characteristics [22]- [24]. Due to the small inertia and fast response of IIDGs [25]- [27], some scholars begin to focus on RMG protection schemes which adjust the IIDG control strategy when a fault occurs in the RMG [28], [29]. Reference [30] proposes a protection scheme in which the IIDG injects the fifth harmonic into the short-circuit current under RMG fault conditions.…”

Section: Introductionmentioning

confidence: 99%

Renewable Microgrid Protection Strategy Coordinating with Current-based Fault Control

Wang

Mu²,

Fang³

2022

Journal of Modern Power Systems and Clean Energy

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The renewable microgrid (RMG) is a critical way to organize and utilize new energy. Its control and protection strategies during the fault are the core technologies to ensure the safe operation and stability of the system. The traditional protection principles are unsuitable for RMGs due to the flexibility of RMG operation, the complexity of RMG topology, and the variety of fault control strategies of inverter-interfaced distributed generators (IIDGs). The traditional fault component protection principle is affected by the low voltage ride-through (LVRT) control strategy and will fail in some scenarios. In order to make the fault component protection principle available in every scenario, a current-based fault control strategy is proposed. Specific fault characteristics are generated by the gridfeeding IIDGs during the fault so they can be equivalent to the open circuits, and the fault models in additional network can be simplified. By analyzing the fault characteristics, an RMG protection strategy based on the current-based fault control of IIDGs is presented. The fault directions of feeders can be distinguished and the fault feeder can be located accurately in both grid-connected and islanded RMGs. Then, the grid-feeding IIDGs can transit to LVRT mode smoothly. Thus, IIDGs are considered comprehensively in terms of coordinating with fault control and fault characteristic generation. Finally, the experimental results of the hardware platform prove the effectiveness of the proposed current-based fault control strategy, and the simulation results based on PSCAD/EMTDC verify the correctness of the protection strategy.

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“…Ukraine has numerous wind energy resources and, due to its natural and climatic characteristics, can become one of the leading countries in wind energy utilization [2][3][4][5]. The total installed capacity is about 1.3 GW [6,7].…”

Section: Introductionmentioning

confidence: 99%

Research of Emergency Modes of Wind Power Plants Using Computer Simulation

et al. 2021

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The aim of this study is to investigate changes in the wind power plant energy production parameters under the conditions of sudden wind changes and voltage drop. To achieve these goals, a simulation of operation of wind power plants was performed. Twelve wind turbines with variable rotational speed equipped with a Fuhrländer FL 2500/104 asynchronous double-fed induction generator (DFIG) were used, each with an installed capacity of 2.5 MW. A general scheme of a wind power plant has been developed using a modular-trunk power distribution scheme. The system consists of wind power modules and a central substation, which allows total power to be supplied to the power system at a voltage of 35 kV. The central substation uses two high voltage switchgears. Four modules were used, each of them consisting of three wind turbines, with a power of 7.5 MW. The simulation of the wind turbines was performed in the MATLAB® Simulink® software environment. The mode of response of the turbines to a change in wind speed, a voltage drop in the 35 kV voltage system, and a one-phase short circuit to the ground in the system of 10.5 kV voltage was explored. The results show that a sudden voltage drop and the appearance of short circuits influence the wind power plant (WPP) operation in a different way independent of regulation mode. The power generation from WPP will be limited when voltage drop occurred for both AC and Voltage regulation mode and during short circuits while WPP is set on AC regulation mode.

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Improving low-voltage ride-through capability of a multimegawatt DFIG based wind turbine under grid faults

Cited by 25 publications

References 18 publications

A dynamic-model-based fault diagnosis method for a wind turbine planetary gearbox using a deep learning network

A dynamic-model-based fault diagnosis method for a wind turbine planetary gearbox using a deep learning network

Renewable Microgrid Protection Strategy Coordinating with Current-based Fault Control

Research of Emergency Modes of Wind Power Plants Using Computer Simulation

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