2009
DOI: 10.1016/j.ijepes.2009.01.002
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Hybrid simulation of power systems with SVC dynamic phasor model

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Cited by 39 publications
(16 citation statements)
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“…The design problems of SVCs have gained attention, so various control techniques have been discussed for SVC design, including the conventional proportional integral derivative (PID) controller [15], robust technique [16], pole placement [17], fuzzy logic control (FLC) [18][19][20], artificial neural network (ANN) [21,22], hybrid algorithm [23], model reference adaptive control [24], genetic algorithm (GA) approach [25], particle swarm optimization [26,27], imperialist competitive algorithm (ICA) [28], and bacteria foraging (BF) [29,30].…”
Section: Introductionmentioning
confidence: 99%
“…The design problems of SVCs have gained attention, so various control techniques have been discussed for SVC design, including the conventional proportional integral derivative (PID) controller [15], robust technique [16], pole placement [17], fuzzy logic control (FLC) [18][19][20], artificial neural network (ANN) [21,22], hybrid algorithm [23], model reference adaptive control [24], genetic algorithm (GA) approach [25], particle swarm optimization [26,27], imperialist competitive algorithm (ICA) [28], and bacteria foraging (BF) [29,30].…”
Section: Introductionmentioning
confidence: 99%
“…In [11], a pade approximation technique is suggested to design of SVC with delayed input signal. In [12], a hybrid scheme is used to simulate power systems equipped with SVC. A simultaneous tuning of a PSS and a SVC controller based on GA is represented in [13].…”
Section: Introductionmentioning
confidence: 99%
“…There have been many methods applying the SVC placement optimally to maintain voltage stability, such as normal form analysis [20], multi-restart benders decomposition [21], reactive power spot price index (QSPI) [22] and genetic algorithms (GA) [23]. Meanwhile, a new SVC dynamic phasor simulator was proposed in order to substitute traditional electromagnetic transient program (EMTP) method [24]. Simulation results show that curves of a new method were very close to benchmark curves of EMTP.…”
Section: Introductionmentioning
confidence: 99%