A novel model reduction approach for linear time-invariant systems via enhanced PSO-DV algorithm and improved MPPA method

Vasu, Ganji; Mangipudi, Sivakumar; Ramalingaraju, M

doi:10.1177/0959651819849372

Cited by 9 publications

(3 citation statements)

References 28 publications

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“…Therefore, when the PSO algorithm is applied, a certain degree of improvement needs to be made to avoid its early phenomenon and fall into the optimal local value. Various investigations have been carried out, and different methods have been proposed to simplify the higher-order transfer function [5][6]. Each method has its advantages and limitations.…”

Section: Introductionmentioning

confidence: 99%

An Improvement in LQR Controller Design based on Modified Chaotic Particle Swarm Optimization and Model Order Reduction

Abdullah¹

2021

IJIES

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The diverse engineering and scientific applications are stated through complex and high-order systems. The significant difficulties of these systems are the complications of modeling, analyzing, and controlling. It is easier to examine simpler models for more physical insights than more complex models and result in lower-ordering controllers that are easier to implement. The model order reduction (MOR) was used to simplify the computational difficulty of such complications and was later developed intensively for use with increasingly CDS. In this paper, a new Modified Chaos Particle Swarm Optimization (MCPSO) technique is employed to get a reduced-order model of a large scale system and design a Linear Quadratic Regulator (LQR) based controller. The mod uses the combination of advantages of basic PSO algorithms and chaotic algorithms. It becomes an excellent algorithm with fast convergence, few control parameters, simple execution, and avoidance of local extremes. In addition to combining the chaotic algorithm, CPSO also improved the weight parameter w, adjusting it to the dynamic attenuation direction. First, efficient reduced-order model parameters are obtained for original higher-order systems based on the MCPSO. Then linear quadratic regulator (LQR (controller parameters optimized for the reduced-order model. The goodness of the proposed method is evaluated through a numerical example. The experimental results indicate that the proposed technique’s reduced order model provides an excellent close approximation to the original system.

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Section: Introductionmentioning

confidence: 99%

An Improvement in LQR Controller Design based on Modified Chaotic Particle Swarm Optimization and Model Order Reduction

Abdullah¹

2021

IJIES

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“…Several model abatement methods have been proposed for the approximation of large-scale systems to lower order models (Chen et al, 1980a; Desai and Prasad, 2013; Haider et al, 2019; Kumar et al, 2016; Prajapati and Prasad, 2019a, 2019b, 2019b; Shamash, 1974; Sun et al, 2020; Vasu et al, 2019). The approximation of the large-scale system is carried out in such a way that it retains the essential characteristics of the original system (Ghafoor and Imran, 2017; Haider et al, 2019; Vijaya Anand et al, 2018; Vishwakarma and Prasad, 2008).…”

Section: Introductionmentioning

confidence: 99%

Reduction of linear dynamic systems using generalized approach of pole clustering method

Prajapati

Prasad

2021

Transactions of the Institute of Measurement and Control

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A new model order abatement method based on the clustering of poles and zeros of a large-scale continuous time system is proposed. The clustering of poles and zeros are used for finding the cluster centres. The abated model is identified from the cluster centres, which reflect the effectiveness of the dominant poles of the clusters. The cluster centre is determined by taking [Formula: see text] root of the sum of the inverse of [Formula: see text] power of poles (zeros) in a particular cluster. It is famous that the magnitude of the pole cluster centre plays an important role in the clustering technique for the simplification of large-scale systems. The magnitude of the cluster centres computed by the modified pole clustering method or some other methods based on the pole clustering techniques is large as compared to the proposed technique. The less magnitude of pole cluster centre reflects the better approximations and proper matching of the abated model with the original system. Therefore, the proposed method offers better approximations matching between actual and abated systems during the transient period compared to some other clustering methods, which supports the replacement of large-scale systems by proposed abated systems. The proposed technique is a generalized version of the standard pole clustering technique. The proposed method guarantees the retention of dominant poles, stability and other fundamental control properties of the actual plant in the abated model. The proposed algorithm is illustrated by the five standard systems taken from the literature. The accuracy and effectiveness of the proposed method are verified by comparing the time responses and various performance error indices.

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“…The mathematical modelling of such a complicated power system leads to large-scale models, which are formidable in carrying out dynamic simulation, stability analysis, trajectory sensitivity analysis, design of the controller, etc., for sensible economic and computation burdens. In such cases model approximation methods (Shivanagouda et al, 2016;Sikander and Prasad, 2015;Vasu et al 2017Vasu et al , 2019aVasu et al , 2019b play an important role in determining a ROM, which preserves the accuracy and dominant properties of the large-scale power system. The use of ROM instead of the large-scale original system for controller design helps in: (a) reducing the order and cost of the robust controller; (b) reducing the computational complexities; (c) reducing the memory requirements and making the simulation faster.…”

Section: Introductionmentioning

confidence: 99%

Optimal IMC-PID controller design for large-scale power systems via EDE algorithm-based model approximation method

Vasu¹,

Mangipudi²,

Ramalingaraju

2020

Transactions of the Institute of Measurement and Control

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In this paper, the authors propose an optimal IMC-PID controller design for the Load Frequency Control (LFC) of large-scale power system via model approximation method. The model approximation method uses the Enhanced Differential Evolution (EDE) algorithm to determine an optimal Reduced Order Model (ROM) for the considered large-scale power system by minimizing the performance measure called Integral Square Error (ISE) between their step responses. Later, the LFC design is carried out using an optimal ROM instead of processing with the large-scale power system model. Thus, this simplifies the design, reduces the computational efforts and also helps in determining the lower order controller. An optimal IMC design methodology is proposed by minimizing ISE between the actual output and the reference input responses of the large-scale power system using EDE algorithm. Further, PID controller gains are acquired by least square model matching with the optimal IMC transfer function. The proposed IMC-PID controller design allows a satisfied reference input tracking performance, robustness in disturbance rejection and improves the dynamic stability of the power system. The proposed method is validated by applying it to a single area power system of third-order SISO model and also extended to a centralized two-area thermal–thermal non-reheated power system of a seventh-order MIMO model. The simulation results and the comparison of error performance indices show the efficacy of the proposed method over the significant methods available in the literature.

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A novel model reduction approach for linear time-invariant systems via enhanced PSO-DV algorithm and improved MPPA method

Cited by 9 publications

References 28 publications

An Improvement in LQR Controller Design based on Modified Chaotic Particle Swarm Optimization and Model Order Reduction

An Improvement in LQR Controller Design based on Modified Chaotic Particle Swarm Optimization and Model Order Reduction

Reduction of linear dynamic systems using generalized approach of pole clustering method

Optimal IMC-PID controller design for large-scale power systems via EDE algorithm-based model approximation method

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