A new SPARLS algorithm for tuning power system stabilizer

Ragavendiran, A.; Gnanadass, R.; Ramakrishnan, K. K.

doi:10.1016/j.ijepes.2014.12.059

Cited by 10 publications

(1 citation statement)

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“…A modified fruit fly optimization algorithm combined with a probabilistic approach to coordinate and optimize the parameters of PSS and SVC damping controller for improving the probabilistic small signal stability of power systems is proposed in [14]. A space recursive least square algorithm developed for tuning of PSS parameters on SMIB power system based PID is proposed in [15] to meet the vulnerable conditions. An objective function and algorithm to obtain a set of optimal PSS parameters that include a feedback signal of a remote machine and local and remote input signal ratios for each machine in a multi-machine power system under various operating conditions in proposed in [16].…”

Section: Introductionmentioning

confidence: 99%

Improving Power System Stability Using Transfer Function: A Comparative Analysis

Shahgholian

Fattollahi

2017

Eng. Technol. Appl. Sci. Res.

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In this paper, a small-signal dynamic model of a single-machine infinite-bus (SMIB) power system that includes IEEE type-ST1 excitation system and PSS based on transfer function structure is presented. The changes in the operating condition of a power system on dynamic performance have been examined. The dynamic performance of the closed-loop system is analyzed base on its eigenvalues. The effectiveness of the parameters changes on dynamic stability is verified by simulation results. Three types of PSS have been considered for analysis: (a) the derivative PSS, (b) the lead-lag PSS or conventional PSS, and (c) the proportional-integral-derivative PSS. The objective function is formulated to increase the damping ratio of the electromechanical mode eigenvalues. Simulation results show that the PID-PSS performs better for less overshoot and less settling time compared with the CPSS and DPSS under different load operation and the significant system parameter variation conditions.

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Section: Introductionmentioning

confidence: 99%