2018
DOI: 10.1016/j.compeleceng.2017.07.005
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A novel technique to design cuckoo search based FOPID controller for AVR in power systems

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Cited by 116 publications
(107 citation statements)
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“…During comparison as reported in Table 2 and as shown in Figure 4, the proposed TLBO-PID and TLBO-FOPID controllers have superior performance over the other optimized controllers for each case of . In comparison to PSO-PID [2,16], GA-PID [16], TLBO-PID [8], CAS-PID [14], CS-FOPID [16], CAS-FOPID [14], PSO-FOPID [10], it may be found that the proposed controllers exhibit better performance characteristics which are , , , objective function value, and only little worse . As a result, the proposed TLBO-FOPID controller has the ability to achieve the most accurate and most stable unit step response in comparison with all the PID and the other FOPID controllers in all cases (i.e.…”
Section: Transient Response Analysismentioning
confidence: 99%
“…During comparison as reported in Table 2 and as shown in Figure 4, the proposed TLBO-PID and TLBO-FOPID controllers have superior performance over the other optimized controllers for each case of . In comparison to PSO-PID [2,16], GA-PID [16], TLBO-PID [8], CAS-PID [14], CS-FOPID [16], CAS-FOPID [14], PSO-FOPID [10], it may be found that the proposed controllers exhibit better performance characteristics which are , , , objective function value, and only little worse . As a result, the proposed TLBO-FOPID controller has the ability to achieve the most accurate and most stable unit step response in comparison with all the PID and the other FOPID controllers in all cases (i.e.…”
Section: Transient Response Analysismentioning
confidence: 99%
“…The fine tuning of fractional order PID control is challenging compared to conventional PID control due to the existence of extra two parameters and to meet some special constraints like gain margin, phase margin, gain crossover frequency, and sensitivity conditions. Nevertheless, evident from a literature shows that the development of the meta-heuristic methods are created the tuning of constraints very ease [21][22][23] in last few years such as genetic algorithm [24,25], Big bang big crunch algorithm [26], particle swarm optimization [27], bacterial foraging optimization algorithm [22,23,28], artificial bee colony algorithm [29,30], stochastic multi-parameters divergence optimization [31], multi objective optimization design [32], cuckoo search algorithm [33,34], bacterial foraging chemotaxis gravitational search algorithm [35], differential evolution [36], chaotic ant swarm optimization [37], gases brownian motion optimization [38], bat algorithm [39], tabu search algorithm [40] than analytical approach.…”
Section: Introductionmentioning
confidence: 99%
“…GOA; proton değişim zarı yakıt hücreleri yığınının elektrik karakterizasyonu [27], kanser sınıflandırması [28], dağıtılmış yörünge optimizasyonu [29], veri kümelenmesi [30], kısa süreli güç yükü tahmini [31] ve özellik seçimi [32] Son zamanlarda, PID kontrolörün performansını arttırmak için türev ve integral derecelerinin tamsayı olmadığı kesirli hesaplama kavramının kullanımına dair artan bir ilgi bulunmaktadır [33]. Podlubny tarafından önerilen kesir dereceli PID (FOPID) kontrolör [34,35], PID kontrolörün kesirli hesaplama kullanılarak genelleştirilmesidir. Bir FOPID kontrolörü beş parametreyle yani oransal kazanç, integral kazancı, türev kazancı, integral derecesi ve türev derecesi ile karakterize edilir.…”
Section: Gi̇ri̇ş (Introduction)unclassified