Development of a reduced order model for nonlinear analysis of the wind turbine blade dynamics

Rezaei, Mahdi; Behzad, Mahdi; Haddadpour, H.; Moradi, Hamed

doi:10.1016/j.renene.2014.11.021

Cited by 39 publications

(22 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Reduced order models are an equivalent model of any higher order system and retain the dominant characteristics of the original system. Model order reduction has been identified as an effective approach for the controller development and analysis . Even though few attempts were made to develop the reduced order model of the real system, more attention is still required to identify the reduced order model of gas turbine power plant.…”

Section: Model Order Reduction Using Mixed Methodsmentioning

confidence: 99%

“…Model order reduction has been identified as an effective approach for the controller development and analysis . Even though few attempts were made to develop the reduced order model of the real system, more attention is still required to identify the reduced order model of gas turbine power plant. In order to preserve the system stability and matching the initial time moment between a higher order and reduced order models, mixed methods are preferred .…”

Section: Model Order Reduction Using Mixed Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Model order reduction of heavy duty gas turbine power plants with field test parameters

Iqbal

Sarumathi²,

Jothi

et al. 2018

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary Frequent load fluctuation and set point variations are the major issues in grid connected heavy duty gas turbine power plants. It may affect the stability of the plant and may lead to inevitable shutdown. Dynamic stability analysis of higher order heavy duty gas turbine in real‐time environment would be tedious and expensive. Various model order reduction techniques namely clustering technique‐Pade approximation (CT‐PA), modified clustering technique‐Pade approximation (MCT‐PA), and Mihailov criterion‐Pade approximation (MC‐PA) are proposed for identifying the reduced order model of 18.2 MW industrial heavy duty gas turbine with field test parameter. The step response of gas turbine plant against the load disturbance and set point variations is obtained using the reduced order models and compared with that of higher order system. The time domain specifications and error criteria have witnessed that the MC‐PA‐based reduced order model retains the original characteristics and yields an improved transient and steady‐state responses. Therefore, MC‐PA‐based reduced order model is identified as an effective reduced order model for the higher order gas turbine plant. The proposed reduced order model would be useful for analysing the behaviour of latest derivative speedtronic controlled gas turbine plant.

show abstract

Section: Model Order Reduction Using Mixed Methodsmentioning

confidence: 99%

Section: Model Order Reduction Using Mixed Methodsmentioning

confidence: 99%

Model order reduction of heavy duty gas turbine power plants with field test parameters

Iqbal

Sarumathi²,

Jothi

et al. 2018

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…Examples for such systems are wind turbine blades [1] or spring leafs for truck suspension [2]. This task can be tedious when the dynamics of structures that undergo large deformations are of special interest.…”

Section: Faster Design Studies With Model Reductionmentioning

confidence: 99%

“…The symbolsK (1) ,K (2) andK (3) describe 2nd-, 3rd-and 4th-order tensors, respectively. The symbolsK (1) ,K (2) andK (3) describe 2nd-, 3rd-and 4th-order tensors, respectively.…”

Section: Model Reduction For Geometrically Nonlinear Systemsmentioning

confidence: 99%

Global proper orthogonal decomposition for parametric model reduction of geometrically nonlinear structures

Meyer

Rixen

2019

Proc Appl Math and Mech

View full text Add to dashboard Cite

show abstract

“…In order to overcome this complexity and reduce the computational burden, it is very much essential to identify an equivalent reduced‐order model, which would retain the dominant characteristics of the original system . It is understood from the literature review that only few attempts were made to identify the reduced‐order model of the real‐time system, viz, wind turbine blade system and micro‐turbine system . Though the previous researchers used the Rowen (1983) model only for dynamic analysis and controller development for HDGT‐, there were no attempts made to identify the reduced‐order model of the HDGT plant.…”

Section: Introductionmentioning

confidence: 99%

Development of optimal reduced‐order model for gas turbine power plants using particle swarm optimization technique

Iqbal

Xavier²

2020

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary Analysis of higher‐order gas turbine plant in real time would be tedious and expensive. In order to overcome this complexity, reduced‐order model for 5001M heavy‐duty gas turbine rated 18.2 MW has been obtained by Routh approximation, clustering technique, modified pole clustering, eigen permutation, Mihailov criterion, and Padé approximation algorithms. The step responses are obtained using MATLAB/Simulink and compared based on time domain specifications and performance index criteria. It indicates that the mixed method, namely, Routh approximation–Padé approximation algorithm–based reduced‐order model, retains the original characteristics. Further, particle swarm optimization (PSO) algorithm has also been applied to develop an optimal reduced‐order model. Based on the dynamic response against the load disturbance and set point variations, PSO‐based reduced‐order model has been identified as an optimal reduced‐order model for heavy‐duty gas turbine. The reduced‐order model proposed in this paper will be suitable for analyzing the dynamic behavior of heavy‐duty gas turbine plants in real‐time environment.

show abstract

Development of a reduced order model for nonlinear analysis of the wind turbine blade dynamics

Cited by 39 publications

References 23 publications

Model order reduction of heavy duty gas turbine power plants with field test parameters

Model order reduction of heavy duty gas turbine power plants with field test parameters

Global proper orthogonal decomposition for parametric model reduction of geometrically nonlinear structures

Development of optimal reduced‐order model for gas turbine power plants using particle swarm optimization technique

Contact Info

Product

Resources

About