Analysis of Hybrid Fuzzy Logic Control Based PID Through the Filter for Frequency Regulation of Electrical Power System with Real-Time Simulation

Fuzzy Control Systems for Power Quality Improvement—A Systematic Review Exploring Their Efficacy and Efficiency

“…The authors of [136] describe the combination of an interval type-2 FLC (two inputs and two outputs), a PI controller, and a dynamic selector switch to build a hybrid control system that decreases frequency ripples (maximum overshoot (MO) 0.1), contrary to type-1 fuzzy + PI (MO-0.51) and PI control (MO-0.56). The authors of [75,140] use optimization algorithms (TLBO-teaching learning-based optimization; and ABC-artificial bee colony) to adapt the parameters of the controllers. The researchers prove in [75] that their TLBO better tunes the proposed fuzzy-PID through a filter controller than a GA (ITAE = 2.74) and other methods from the literature (fuzzy PD-PI-IATE = 0.17), as the proposed method gave the lowest error (ITAE = 0.0976).…”

“…The authors of [75,140] use optimization algorithms (TLBO-teaching learning-based optimization; and ABC-artificial bee colony) to adapt the parameters of the controllers. The researchers prove in [75] that their TLBO better tunes the proposed fuzzy-PID through a filter controller than a GA (ITAE = 2.74) and other methods from the literature (fuzzy PD-PI-IATE = 0.17), as the proposed method gave the lowest error (ITAE = 0.0976). The ABC algorithm tunes the two FLCs used in [140] better than PSO (particle swam optimization), as the ISE is 3.75 for ABC-FLCs and 4.25 for PSO-FLCs, and the PI is 30.13.…”

“…Smart fuzzy control that solves uncertainty factors by dealing with the FLC's outputs and values differently is proposed in [130]. Experimental validation using test setups is presented in [75,122], whereas [102] shows real-time efficacy justification. Indeed, a large-scale real-time laboratory simulation is used in [102] to compare the methods described in the paper.…”

“…However, a classic (type-1) FLC proved deficient when working in the presence of uncertainty factors [130]. This is the reason why many researchers studied and proposed the use of enhanced hybrid fuzzy-based controllers to obtain superior results, for example, the combination of an FLC and a PID controller, where the FLC is used to tune the three constants of the PID controller [68,75,133], or the use of evolutionary algorithms, like particle swarm optimization [74,134], to improve the parameters of the control system.…”

The automatic generation controller based on whale optimization algorithm in PV-thermal power systems

“…Klasik PI ve PID kontrolörlerin kazançlarının belirlenmesi amacıyla, bakteri arama algoritması (BAA) [3], genetik algoritma (GA) [4], karga arama algoritması (KAA) [5], ateşböceği algoritması (ABA) [4,6], denge algoritması (DA) [7], balina optimizasyonu (BA) [8], parçacık sürü optimizasyonu (PSO) [9,10], gri kurt optimizasyonu (GKO) [11], Jaya algoritması [12,13], yıldırım flaş algoritması (YFA) [14], emperyalist rekabet algoritması (EKA) [15], aşırı nüfus optimizasyonu (ANO) [16] gibi sezgisel yöntemler kullanılmıştır. Klasik kontrolörlerin yanı sıra alan kontrol hatasını (AKH) azaltmak amacıyla kesir dereceli (FO)PID [17,18], TIDN [5], I -TD [19], IPD -(1 + I) [20], bulanık mantık [21,22] gibi kontrolör çeşitleri kullanılmıştır. Abd-Elazim ve Ali tarafından yapılan çalışmada [4] FV ve termal santralin bulunduğu bir güç sisteminde ateşböceği algoritması (ABA) ile PI kontrolör parametrelerinin belirlenmesi amaçlanmıştır.…”