Model order formulation of a multivariable discrete system using a modified particle swarm optimization approach

Deepa, S. N.; Sugumaran, G.

doi:10.1016/j.swevo.2011.06.005

Cited by 28 publications

(12 citation statements)

References 14 publications

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“…The PSO has been broadly recognized as a global optimization algorithm, and is a population-based, self-versatile, stochastic optimization method [18]. The mathematical modeling and simulation swarm of birds look for food (particles) [19] and the particles move around the multi-dimension look space until they locate the ideal result. Based on the discussion above, the velocity equation for the PSO is as follows:…”

Section: Reconfiguration Methodsmentioning

confidence: 99%

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A New Method for Distribution Network Reconfiguration Analysis under Different Load Demands

et al. 2017

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Abstract:The strategies of distribution network reconfiguration are applicable for minimizing power loss and saving electrical energy in the distribution system. Network reconfiguration is usually represented by constant load demand so ignoring the variability of load demand causes uncertainty and misleading results in the minimization of power loss. This paper consists of two parts: first, the reconfiguration was accomplished using an optimization framework based on constant load to find sets of optimal switches. The minimization of active power loss was taken as an objective function while bus voltage, branch current and system radiality were taken as system constraints. The study was applied to a 33-bus test distribution network, which is exceedingly used as test examples for solving reconfiguration problems. Second, lists of the configurations set obtained from the first part, as well as other different optimization methods proposed earlier under constant load demand were taken as test switches. Additionally, the network in the presence of distributed generators was taken to analyze the reconfiguration under an active network. Two types of load demands; the variable load and voltage-dependent load, are proposed to represent the practical load demands. This paper presents a new method for good analysis as it defines the effect of loading levels and loading patterns on a distribution system performance for passive and active networks. The proposed approach tries to find the actual power loss under different characteristics of loads. Therefore, the probable benefit of this approach is the contribution to providing more flexibility for electrical utilities in terms of distribution system operation, while also opening new prospects in the automation of smart distribution systems.

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Section: Reconfiguration Methodsmentioning

confidence: 99%

“…Where the proposed approach supports the operators in distribution systems to select the best configuration that provides minimum power loss under the changes in load demand level by µ. The load demand varies as in Equations (18) and (19).…”

Section: Mpso Parametersmentioning

confidence: 99%

A New Method for Distribution Network Reconfiguration Analysis under Different Load Demands

et al. 2017

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“…In PSO, proper control of global exploration and local exploitation is crucial in finding the optimum solution efficiently (Deepa and Sugumaran 2011;Rana et al 2011). Obviously, the performance of PSO greatly depends on its parameters and its velocity/position updating strategies, which would have significant impact on the balance between global exploration and local exploitation.…”

Section: Bad Experience Lesson Learning Schemementioning

confidence: 99%

Adjustment of basal insulin infusion rate in T1DM by hybrid PSO

et al. 2014

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Basal insulin infusion rate which should be adjusted to increase or decrease insulin delivery with the varying blood sugar level plays a key role in type 1 diabetes mellitus (T1DM) patients for maintaining the blood glucose level approximately steady within reference range in order to avoid the complications developed from diabetes. This paper proposes an effective hybrid particle swarm optimization (HPSO) algorithm for solving the basal insulin infusion rate problem. In HPSO, bad experience lesson learning scheme and local search based on chaotic dynamics are proposed to make a good balance between global exploration and local exploitation. Simulation results based on a set of well-known optimization benchmark instances and the basal insulin infusion rate adjustment problem for T1DM demonstrate the effectiveness of the proposed HPSO. In silico tests on standard virtual subject via HPSO show that under nominal condition the blood glucose concentrations could be kept within a range of 80-150 mg/dL within less than 5 days; meanwhile, in case of random variations in meal timings within ±60 min or meal amounts within ±75 % deviation from the nominal values, the blood glucose concentrations could be kept within the safe regions.Keywords Particle swarm optimization (PSO) · Glycemic control · Basal insulin infusion rate optimization · Type 1 diabetes mellitus Communicated by V. Loia.

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“…., ω m , there are m frequency measurement points, and an error vector E can be constructed by Eq. 26E = H 11 (α) −H 11 (26) In Eq. 26, E is the nonlinear function of the vector α to be identified.…”

Section: Parameter Identification Of the Holder-tool Interfacementioning

confidence: 99%

Dynamic Parameter Identification of Tool-Spindle Interface Based on RCSA and Particle Swarm Optimization

Wang

et al. 2013

Shock and Vibration

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In order to ensure the stability of machining processes, the tool point frequency response functions (FRFs) should be obtained initially. By the receptance coupling substructure analysis (RCSA), the tool point FRFs can be generated quickly for any combination of holder and tool without the need of repeated measurements. A major difficulty in the sub-structuring analysis is to determine the connection parameters at the tool-holder interface. This study proposed an identification method to recognize the connection parameters at the tool-holder interface by using RCSA and particle swarm optimization (PSO). In this paper, the XHK machining center is divided into two components, which are the tool and the spindle assembly firstly. After that, the end point FRFs of the tool are achieved by mode superposition method. The end receptances of the spindle assembly with complicated structure are obtained by impacting test method. Through translational and rotational springs and dampers, the tool point FRF of the machining center is obtained by coupling the two components. Finally, PSO is adopted to identify the connection parameters at the tool-holder interface by minimizing the difference between the predicted and the measured tool point FRFs. Comparison results between the predicted and measured tool point FRFs show a good agreement and demonstrate that the identification method is valid in the identification of connection parameters at the tool-holder interface.

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Model order formulation of a multivariable discrete system using a modified particle swarm optimization approach

Cited by 28 publications

References 14 publications

A New Method for Distribution Network Reconfiguration Analysis under Different Load Demands

A New Method for Distribution Network Reconfiguration Analysis under Different Load Demands

Adjustment of basal insulin infusion rate in T1DM by hybrid PSO

Dynamic Parameter Identification of Tool-Spindle Interface Based on RCSA and Particle Swarm Optimization

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