Evolutionary approach for model order reduction

Lavania, Shilpi; Nagaria, Deepak

doi:10.1016/j.pisc.2016.04.075

Cited by 13 publications

(8 citation statements)

References 5 publications

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“…It is obvious that lesser the error criteria value, much closer is the response of reduced-order model with the original system. Equations (42), (43), and (44) show various error criteria, namely, integral of squared error (ISE), integral of time multiplied with the squared error (ITSE), and integral of absolute error (IAE). Table 3 shows the error criteria of various reduced-order models of 5001M HDGT plant.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…Then the error between the unit step response of higher-order simplified model and the PSO-RA-PA based reduced-order model are obtained. Based on the error between the simplified model response, g(t), and the PSO-RA-PA based reduced-order model response, r(t), various error criteria as expressed in Equations (42), (43), and (44) are obtained. Table 5 shows the error criteria of PSO-RA-PA based reduced-order model and RA-PA based reduced-order model.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…PSO and genetic algorithms are the most promising algorithms comparing with all the heuristic technique . PSO algorithm is found easy to implement as it does not use the concept of mutation or crossover like genetic algorithm . Hence, the PSO algorithm has been used in this paper for identifying the optimal reduced‐order model of HDGT plants.…”

Section: Pso Algorithm For Optimal Reduced‐order Modelmentioning

confidence: 99%

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

Iqbal

Xavier²

2020

Int Trans Electr Energ Syst

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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.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Pso Algorithm For Optimal Reduced‐order Modelmentioning

confidence: 99%

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

Iqbal

Xavier²

2020

Int Trans Electr Energ Syst

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“…Many papers have elaborated the concept of model order reduction and its application in control system [16,35]. Since the process under study, is a third order system.…”

Section: Model Order Reductionmentioning

confidence: 99%

“…As the process contains three storage elements, hence it is a 3 rd order system which comparatively makes the procedure of control design cumbersome. The order of this system is reduced to first order plus dead time (FOPDT) system, which in turn greatly reduce complexcities in controller design [16]. The adopted technique can also be extended to other higher order system like 4 th , 5 th and above.…”

Section: Introductionmentioning

confidence: 99%

IMC Based Fractional Order Controller for Three Interacting Tank Process

2017

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Reduced order framework for convection dominant and pure diffusive problems based on combination of deep long short‐term memory and proper orthogonal decomposition/dynamic mode decomposition methods

Kherad

Moayyedi

Fotouhi

2020

Numerical Methods in Fluids

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In many real‐world applications, mathematical models are highly complex, and numerical simulations in high‐dimensional systems are challenging. Model order reduction is a useful method to obtain a reasonable approximation by significantly reducing the computational cost of such problems. Deep learning technology is a recent improvement in artificial neural networks that can find more hidden information from the data. Deep learning has the advantage of processing data in its raw form and trains the nonlinear system with different levels of representation and predicts the data. In this article, a reduced order model framework based on a combination of deep learning [long short‐term memory (LSTM)] and proper orthogonal decomposition/dynamic mode decomposition (POD/DMD) modes is presented. Due to the robustness and stability of the LSTM recurrent neural network in predicting chaotic dynamical systems, we consider LSTM architecture to develop our data‐driven reduced order modeling (ROM). We investigate the proposed method performance by solving two well‐known canonical cases: a steady shear flow exhibiting the Kelvin‐Helmholtz instability, and two‐dimensional and unsteady mass diffusion equation. The focus of this article is to use LSTM deep recursive neural network to learn the time dynamics and POD/DMD to generate the order reduction model. The results show that the proposed method is very accurate in predicting time dynamics and input reconstruction.

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Evolutionary approach for model order reduction

Cited by 13 publications

References 5 publications

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

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

IMC Based Fractional Order Controller for Three Interacting Tank Process

Reduced order framework for convection dominant and pure diffusive problems based on combination of deep long short‐term memory and proper orthogonal decomposition/dynamic mode decomposition methods

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