Radial basis function (RBF) network adaptive power system stabilizer

Segal, Ravi; Kothari, Mangal; Madnani, S.

doi:10.1109/59.867165

Cited by 100 publications

(33 citation statements)

References 5 publications

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“…In the QPSO, the parameter β must be set as 1.782 β < to guarantee convergence of the particle [24]. Where, Mbest called mean best position is defined as the mean of the pbest positions of all particles.…”

Section: Lead-lag Compensator Based On Qpsomentioning

confidence: 99%

Novel Adaptive Neural Controller Design Based on HVDC Transmission System to Damp Low Frequency Oscillations and Sub Synchronous Resonance

Goli¹,

Goli²,

Taheri³

2015

EPE

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This paper presents the effect of the high voltage direct current (HVDC) transmission system based on voltage source converter (VSC) on the sub synchronous resonance (SSR) and low frequency oscillations (LFO) in power system. Also, a novel adaptive neural controller based on neural identifier is proposed for the HVDC which is capable of damping out LFO and sub synchronous oscillations (SSO). For comparison purposes, results of system based damping neural controller are compared with a lead-lag controller based on quantum particle swarm optimization (QPSO). It is shown that implementing adaptive damping controller not only improves the stability of power system but also can overcome drawbacks of conventional compensators with fixed parameters. In order to determine the most effective input of HVDC system to apply supplementary controller signal, analysis based on singular value decomposition is performed. To evaluate the performance of the proposed controller, transient simulations of detailed nonlinear system are considered.

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Section: Lead-lag Compensator Based On Qpsomentioning

confidence: 99%

Novel Adaptive Neural Controller Design Based on HVDC Transmission System to Damp Low Frequency Oscillations and Sub Synchronous Resonance

Goli¹,

Goli²,

Taheri³

2015

EPE

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show abstract

“…In engineering applications similar to this paper, the normalization factor is selected in the range 0.2-0.8 [19].…”

Section: Computation Of Probability Of Voltage Collapse Using Rbf Netmentioning

confidence: 99%

Determination of probabilistic risk of voltage collapse using radial basis function (RBF) network

Arya¹,

Titare

Kothari

2006

Electric Power Systems Research

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“…A number of papers have been published in the last decade. An illustrative list of example papers include (Abido & Abdel-Magid, 1999;Changaroon, Srivastava, & Thukaram, 2000;Hiyama & Lim, 1989;Hosseinzadeh & Kalam, 1999;Segal, Kothari, & Madnani, 2000;Swidenbank et al, 1999;Yuan & Chao, 1991). Both of these techniques have their own advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 96%