Improved particle swarm optimization applied to reactive power reserve maximization

Arya, L. D.; Titare, L.S.; Kothari, D.P.

doi:10.1016/j.ijepes.2009.11.007

Cited by 59 publications

(29 citation statements)

References 20 publications

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“…Exploitation is the ability that focuses on an optimum area and refines the solution. The particle velocity and position is accelerated through an inertia weight (w in ) expressed as [20],…”

Section: Modified Swarm Optimization Algorithmmentioning

confidence: 99%

A Swarm-Based Adaptive Neural Network SMES Control for a Permanent Magnet Wind Generator

Rahim

Khan²

2014

Arab J Sci Eng

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Permanent magnet synchronous generators are becoming increasingly popular as utility-scale wind generators. While their performances are satisfactorily under normal conditions, they may be degraded under wind gusts as well as in extremely low grid voltage conditions. An adaptive control of superconducting magnetic energy storage (SMES) system for efficient wind energy transfer as well as dynamic performance improvement is proposed in this article. A radial basis function neural network has been employed to determine the controller parameter values. The nominal weights of the neural network are obtained from training of a large inputoutput data set generated through an improved swarm optimization procedure. These weights are then updated through a novel method of tracking of system outputs in time domain. Tests carried out with the adaptive controller show that the improved particle swarm-based radial basis network SMES controller delivers wind energy to grid efficiently and at the same time exhibit very good damping profile.

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Section: Modified Swarm Optimization Algorithmmentioning

confidence: 99%

A Swarm-Based Adaptive Neural Network SMES Control for a Permanent Magnet Wind Generator

Rahim

Khan²

2014

Arab J Sci Eng

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“…Step11: Calculate the difference between these two mean by using equation (16) and using this difference generates a new population using relation (17).…”

Section: Implementation Of Tlbo Algorithm To Solve Formulated Promentioning

confidence: 99%

A novel methodology for maximization of Reactive Reserve using Teaching-Learning-Based Algorithm

Tiwari¹

2013

IOSR-JEEE

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“…One idea is to summarize all component redundancies which can improve static voltage stability margin, as the system reserve [14], [15]. Another idea multiplies the component reserve with contribution factor to static voltage stability before the summarization [16], [17]. But there is still no definition and formula for dynamic reactive power reserve alleviating the instable transient voltage.…”

Section: Introductionmentioning

confidence: 99%

A dynamic reactive power reserve optimization method to enhance transient voltage stability

Wang

Zhang

Liu

et al. 2015

2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER)

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Dynamic reactive power reserve of power system is vital to improve transient voltage security. A novel definition of reactive power reserve considering transient voltage security in transient procession is proposed. Participation factor to evaluate reactive power reserve's contribution to transient voltage stability is computed through trajectory sensitivity method. Then an optimization model to enhance transient voltage stability is built and a solving algorithm is proposed. Based on an analysis of the transient voltage stability characteristics of East China Power Grid, the effectiveness of the proposed dynamical reactive power reserve optimization approach for improving transient voltage stability of large-scale AC-DC hybrid power systems is verified.

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Improved particle swarm optimization applied to reactive power reserve maximization

Cited by 59 publications

References 20 publications

A Swarm-Based Adaptive Neural Network SMES Control for a Permanent Magnet Wind Generator

A Swarm-Based Adaptive Neural Network SMES Control for a Permanent Magnet Wind Generator

A novel methodology for maximization of Reactive Reserve using Teaching-Learning-Based Algorithm

A dynamic reactive power reserve optimization method to enhance transient voltage stability

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