The modeling and numerical simulations of wind turbine generation system with free vortex method and simulink

Liang, Junyu; Yongxing, Qiu; Zhao, Ming; Kang, Shun; Lu, Hai

doi:10.1016/j.enconman.2015.07.006

Cited by 17 publications

(13 citation statements)

References 20 publications

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“…for three different levels of static eccentricity (0% (Healthy), 14.64% and 50%). This figure compares the results of computing the coupling parameters using the full FEM analysis with the results obtained with the proposed method considering different hierarchical levels k to compute the polynomial basis L HLI k ( ) according to (11). It must be highlighted that the proposed method uses,for each hierarchical level k, just the results of the FEM simulations for the rotor positions in the set of the GCL points shown in Tables 2 and 3.…”

Section: Resultsmentioning

confidence: 99%

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Model reduction based on sparse identification techniques for induction machines: Towards the real time and accuracy-guaranteed simulation of faulty induction machines

Sapena-Bañó

Chinesta

Puche-Panadero

et al. 2021

International Journal of Electrical Power & Energy Systems

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Section: Resultsmentioning

confidence: 99%

“…Several IMs models have been proposed in the technical literature. The well-known d-q model [11,12] is widely used in order to understand and design vector controlled drives. It is simple to be implemented in a HIL but it does not consider the geometrical complexities, the spatial distribution of the windings (i.e.…”

Section: Faulty Ims Modelsmentioning

confidence: 99%

Model reduction based on sparse identification techniques for induction machines: Towards the real time and accuracy-guaranteed simulation of faulty induction machines

Sapena-Bañó

Chinesta

Puche-Panadero

et al. 2021

International Journal of Electrical Power & Energy Systems

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“…Several models of rotating electrical machines have been proposed in the technical literature. The well-known dynamic d-q model [30,31] is simple enough to be implemented in fast HIL [32]. However, this model cannot be used for fault diagnosis purposes, because it neglects the harmonic contents generated by phase windings, and is unable to model the torque pulsations that appear in the machine shaft due to the interaction between time and space harmonics [33].…”

Section: Faulty Induction Motor Modelsmentioning

confidence: 99%

Induction machine model with finite element accuracy for condition monitoring running in real time using hardware in the loop system

Sapena-Bañó

Chinesta

Pineda-Sánchez

et al. 2019

International Journal of Electrical Power & Energy Systems

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Most industrial processes are run by induction machines (IMs). Condition monitoring of IM assures their continuity of service, and it may avoid highly costly breakdowns. Among the methods for condition monitoring, online motor current signature analysis is being attracting a rising interest, because it is non-invasive, and it can identify a wide variety of faults at early stage. To favour the development of on-line fault diagnosis techniques, it is necessary to have real-time currents with which test the new techniques and devices. Models running in real time in hardware-in-the-loop (HIL) simulators are a suitable alternative to balance the drawbacks of test benches (costly, limited machines, faults and working conditions). These models must be accurate enough to reflect the effects of a fault and they must be running in real time. A promising technique based on the equivalent circuit parameters calculation of IM by finite element analysis (FEA) is attracting a rising interest due to its reliability, performance and the possibility of being run in a HIL. Nevertheless, prior to running in a HIL, it is necessary to compute the IM parameters using FEA, which requires long simulation times and high computing resources. Consequently, covering a whole range of degrees of a giving fault could be unaffordable. What is proposed in this paper is to apply the sparse subspace learning (SSL) in combination with the hierarchical Lagrangian interpolation (HLI) to obtain the parametric solutions of the faulty IM model that cover the whole range of severity of a given fault, with a reduced number of FEA simulations. By means of this approach it is possible not only to boost the computation speed but also to achieve a significant reduction of memory requirements while retaining reasonable accuracy compared to traditional FEA, so enabling the real-time simulation of predictive models.

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“…A variety of machine modeling techniques are available in the literature, which can be broadly classified into two categories; analytical and numerical. The two-axis theory-based models [31][32][33] are being effectively utilized for control and analysis. Although those models are simple to understand, comprehensive, and fast, they are not suitable for fault simulations because of various approximations such as sinusoidal stator and rotor windings distribution, uniform air gap, no inter-bar currents, and no material saturation, etc.…”

Section: Introductionmentioning

confidence: 99%

The Cluster Computation-Based Hybrid FEM–Analytical Model of Induction Motor for Fault Diagnostics

et al. 2020

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This paper presents a hybrid finite element method (FEM)–analytical model of a three-phase squirrel cage induction motor solved using parallel processing for reducing the simulation time. The growing development in artificial intelligence (AI) techniques can lead towards more reliable diagnostic algorithms. The biggest challenge for AI techniques is that they need a big amount of data under various conditions to train them. These data are difficult to obtain from the industries because they contain low numbers of possible faulty cases, as well as from laboratories because a limited number of motors can be broken for testing purposes. The only feasible solution is mathematical models, which in the long run can become part of advanced diagnostic techniques. The benefits of analytical and FEM models for their speed and accuracy respectively can be exploited by making a hybrid model. Moreover, the concept of cloud computing can be utilized to reduce the simulation time of the FEM model. In this paper, a hybrid model being solved on multiple processors in a parallel fashion is presented. The results depict that by dividing the rotor steps among several processors working in parallel, the simulation time reduces considerably. The simulation results under healthy and broken rotor bar cases are compared with those taken from a laboratory setup for validation.

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The modeling and numerical simulations of wind turbine generation system with free vortex method and simulink

Cited by 17 publications

References 20 publications

Model reduction based on sparse identification techniques for induction machines: Towards the real time and accuracy-guaranteed simulation of faulty induction machines

Model reduction based on sparse identification techniques for induction machines: Towards the real time and accuracy-guaranteed simulation of faulty induction machines

Induction machine model with finite element accuracy for condition monitoring running in real time using hardware in the loop system

The Cluster Computation-Based Hybrid FEM–Analytical Model of Induction Motor for Fault Diagnostics

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