2012
DOI: 10.1016/j.ijepes.2012.06.048
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Bacteria Foraging Optimization Algorithm based SVC damping controller design for power system stability enhancement

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Cited by 51 publications
(23 citation statements)
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“…According to (13), it is clear that the minimum and maximum values of CI i are equal to 0 pu and 1 pu, respectively. Also, the value of CI i becomes less for more SVC investment costs.…”
Section: Cost Indexmentioning
confidence: 99%
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“…According to (13), it is clear that the minimum and maximum values of CI i are equal to 0 pu and 1 pu, respectively. Also, the value of CI i becomes less for more SVC investment costs.…”
Section: Cost Indexmentioning
confidence: 99%
“…Based on the characteristics of FACTS devices, different objectives have been considered in optimal locations of FACTS devices problems, such as static voltage stability enhancement [1][2][3][4][5], loss reduction [5][6][7], voltage profile improvement [5,8], enhancement of the network load ability [8,9], violation diminution of the line thermal constraints [10], reduction of the power plants fuel cost using optimal power flow [11], mitigating small signal oscillations [12,13], adequate damping in power system network with changing system conditions [14], and minimization of cost function with an economical approach [15]. Although all of the mentioned objectives improve the performance of the power system, considering unlimited FACTS devices by neglecting their cost cannot be legitimized despite the assumption in [8].…”
Section: Introductionmentioning
confidence: 99%
“…In last few years, many researchers have posed techniques for designing SVC to enhance the damping of electromechanical oscillations of power systems, regulate the transmission voltage, and improve power quality [3][4][5][6][7][8]. The main advantage of SVCs over simple mechanically-switched compensation schemes is their near-instantaneous response to changes in the system voltage [2].…”
Section: Introductionmentioning
confidence: 99%
“…Fuzzy Logic Control (FLC) [5][6][7][8][9][10], Artificial Neural Network (ANN) [11,12], robust controller [13], and adaptive controller [14] have been employed to solve the problem of speed control of SRM. Moreover, optimization techniques like Genetic Algorithm (GA) [15], Particle Swarm Optimization (PSO) [16][17][18], Bacteria Foraging [19][20][21][22][23] and BAT algorithm [24] have attracted the attention in designing controller and speed control of various motors.…”
Section: Introductionmentioning
confidence: 99%