Improving dynamic efficiency of photovoltaic generation systems using adaptive type 2 fuzzy‐neural network via EN 50530 test procedure

Abstract

In recent years, photovoltaic generation systems (PGSs) have been fairly popular thanks to the methods proposed for the improvement of the dynamic efficiency. For a more accurate evaluation of the actual dynamic performance in PGSs, a dynamic maximum power point tracking (MPPT) efficiency test based upon an international test procedure such as the European Procedure EN 50530 is of vital importance. The varying gradient irradiance profile in EN 50530 test procedure causes uncertainties in PGSs, which lead to unstable operating conditions for PGSs. It is a challenging task to provide stable operation for PGSs. To overcome this problem, an effective MPPT method based upon adaptive type 2 fuzzy‐neural network (AT2FNN) is proposed using EN 50530 test procedure specifically designed for the dynamic performance of PGSs. The dynamic efficiency performance of the proposed method is analyzed using comparative simulation studies with conventional MPPT methods such as perturb and observe (P&O) and incremental conductance (IC) in Matlab/Simulink environment. The average dynamic efficiency of PGSs is used as performance criteria in the present study. The numerical results obtained from comparative simulation studies indicated that the proposed method improved average dynamic efficiency by almost 4.3% and 9.2% compared to IC and P&O MPPT methods, respectively. The average dynamic efficiency performance of PGS was considerably improved by the proposed method.

Type‐2 Fuzzy Logic controller‐based stator current Model reference adaptive system speed observer for a hybrid electric vehicle to improve transient response during limp home mode

“…When it is implemented, if type-1 fuzzy sets F I G U R E 9 Speed response of IM drive with torque change have better performance than type-2, it uses the type-1 controller else type-2 controller. 24,25,27 T1FLC and T2FLC differ primarily by MF as shown in Figures 5 and 6, respectively.…”

“…T1FLC is unable to reduce the uncertainty by employing crips sets. T2FLC can have a range of MF values, three dimensions MF, and include the footprint of uncertainty (FOU), which provide an additional degree of freedom in MF uncertainty 25 Therefore, the T2FLC is employed to handle the uncertainty and large disturbance due to the sudden change in the speed and torque variations. It will also use the advantages of the T1FLC.…”

“…In T2FLC, all the antecedent and consequent parameters are to be considered as T1FL and T2FL sets, which offer the advantage of both type‐1 and type‐2 fuzzy sets. When it is implemented, if type‐1 fuzzy sets have better performance than type‐2, it uses the type‐1 controller else type‐2 controller 24,25,27 . T1FLC and T2FLC differ primarily by MF as shown in Figures 5 and 6, respectively.…”

Aralık değerli Tip-2 bulanık denetleyici ile negatif gerilim çıkışlı Temel Seri Luo çeviricinin denetimi

“…Tip-2 bulanık denetleyicilerin hesaplama yükü daha az olan aralıklı tip-2 bulanık denetim yapısı bulunmaktadır. Bu denetim yapısının çıkarım alt biriminde doğrusal bir fonksiyon ile ifade edilebilen Takagi-Sugeno-Kang (TSK) çıkarım yöntemi kullanılmaktadır [25]. Bu çalışmada aralık değerli tip-2 TSK bulanık denetim yapısı önerilmiştir.…”

Experimental evaluation of type‐2 fuzzy logic controller adapted to real environmental conditions for maximum power point tracking of solar energy systems

“…However, operating SPPs at the MPP is a very challenging task because they have nonlinear current-voltage (I-V) and power-voltage (P-V) characteristics depending on environmental conditions. [4][5][6][7] To cope with this situation and assure stability, the proposed effective approach can be defined as two-stage control scheme. 8,9 First, the MPP of PV module is determined.…”