A Novel Model Order Reduction Technique for Linear Continuous-Time Systems Using PSO-DV Algorithm

Vasu, Ganji; Mangipudi, Sivakumar; Manyala, Ramalingaraju

doi:10.1007/s40313-016-0284-9

Cited by 28 publications

(3 citation statements)

References 23 publications

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“…Uncovered shortcomings have consequentially driven scholastic examinations of better optimization alternatives through attempts of meta-heuristics hybridization, enclosing integrated algorithmic approaches, such as Particle Swarm Optimization-Differential Evolution Algorithm (PSO-DV) [16], Grey Wolf Optimizer-Chaotic Firefly Algorithm (GWO-CFA) [17], Particle Swarm Optimization-Bacterial Foraging (PSO-BF) [18], Bacterial Foraging-Modified Particle Swarm Optimization (BF-MPSO) [19], Particle Swarm Optimization-Gravitational Search Algorithm (PSO-GSA) [20], and Average Multi-Verse Optimizer and Sine Cosine Algorithm (AMVO-SCA) [21]. While such methods exploit the aggregated competencies of diverse optimization algorithms in resolving issues concerning model order reduction, their efficacious performances in the fabrication of lower-order systems are, nonetheless, offset by elevated complicatedness from existing coefficients that demand increased computational efforts and temporal investments.…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid of Nonlinear Sine Cosine Algorithm and Safe Experimentation Dynamics for model order reduction

Suid

Ahmad

2022

Automatika

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The current study introduces the hybridization of the Nonlinear Sine Cosine Algorithm (NSCA) and Safe Experimentation Dynamics (SED) as a novel optimization method for model order reduction of high-order single-input single-output (SISO) systems. Reciprocated synergism between both meta-heuristic algorithms is achieved by appropriating the nonlinear positionupdated mechanism of NSCA for enhanced exploration/exploitation competencies and proficiency of SED in maximizing stagnation avoidance within the local optima. Named the NSCA-SED algorithm, the applicability of the proposed method is assessed by scholastic adoption of a sixth-order numerical transfer function towards two independent high-order systems enclosing Double-Pendulum Overhead Crane and Flexible Manipulator. Experimentation results further suggested NSCA-SED as the superior alternative in terms of execution robustness and consistency excellence against other available optimization-based methods for tackling model order reduction. Exemplified simulations sequentially demonstrated considerable improvements by the employment of NSCA-SED over conventional SCA following respective enhanced proportions of 97.17%, 13.17% and 29.03% for Example 1, Example 2 and Example 3.

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Section: Introductionmentioning

confidence: 99%

A novel hybrid of Nonlinear Sine Cosine Algorithm and Safe Experimentation Dynamics for model order reduction

Suid

Ahmad

2022

Automatika

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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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“…Model reduction offers advantages like, simpler understanding of the system, simpler controller design and lesser simulation time (Fortuna et al, 2012;Quarteroni and Rozza, 2014;Schilders et al, 2008). Several methods have been reported in the literature for model reduction of higher order continuous and discrete systems (Fortuna et al, 2012;Schilders et al, 2008;Singh et al, 2016;Beattie et al, 2017;Ganji et al, 2017). Some methods have also been reported for reduction of interval systems (Bandyopadhyay et al, 1994;Singh andChandra, 2012a, 2011;Singh et al, 2017b;Singh and Chandra, 2011).…”

Section: Introductionmentioning

confidence: 99%

Symbiotic Organisms Search Algorithm Based Model Reduction of Higher Order Continuous Systems

Singh

2020

IJMOR

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This contribution proposes an approximation technique for reduction of higher order continuous systems (HOCS) using symbiotic organisms search (SOS) algorithm. The parameters of reduced-order model (ROM) of HOCS are obtained by minimising the normalised errors in between the Markov parameters and time moments of ROM and HOCS. The proposed technique always generates stable ROM for stable HOCS. Hurwitz criterion is utilised for ensuring the stability of ROM. The SOS algorithm is used for finding the ROM due to being simple and being free from algorithm-specific parameters. The effectiveness of proposed method is illustrated with the help of one test system. A comparative study is also accomplished for the results obtained using SOS algorithm and other optimisation techniques. The analysis indicates that the SOS algorithm outperforms when compared to other algorithms.

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A Novel Model Order Reduction Technique for Linear Continuous-Time Systems Using PSO-DV Algorithm

Cited by 28 publications

References 23 publications

A novel hybrid of Nonlinear Sine Cosine Algorithm and Safe Experimentation Dynamics for model order reduction

A novel hybrid of Nonlinear Sine Cosine Algorithm and Safe Experimentation Dynamics for model order reduction

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

Symbiotic Organisms Search Algorithm Based Model Reduction of Higher Order Continuous Systems

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