Adaptive Control of Solid State Transformer Using Type-2 Fuzzy Neural System

Açıkgöz, Hakan; Keçecioğlu, Ö. Fatih; Karadöl, İsrafil; Ganı, Ahmet; Şekkeli, Mustafa

doi:10.24846/v26i2y201705

Cited by 11 publications

(8 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…T2BMD'lerde kullanılan üyelik fonksiyonlarının üyelik dereceleri [0,1] arasındaki bulanık sayılarla ifade edilmektedir. Bu durum üyelik fonksiyonlarındaki belirsizliklerin çözümünde önemli bir araç olabilmektedir [15][16][17][18][19][20].…”

Section: Igi̇ri̇şunclassified

“…J x ise x girişinin birincil üyelik fonksiyonu olarak belirtilir. Tüm birincil üyelik fonksiyonları arasında kalan alan Belirsizliğin Ayak İzi (FOU) olarak ifade edilmektedir [14][15][16]. Şekil 2'de gösterilen Gauss üyelik fonksiyonları arasında kalan diğer bir deyişle alt ve üst üyelik fonksiyonları ile sınırlandırılmış bu alan aşağıdaki gibi tanımlanabilir: [10][11]16].…”

Section: Aralikli Ti̇p-2 Bulanik Mantik Denetleyi̇ci̇ Yapisinin Tasarimiunclassified

See 1 more Smart Citation

Aralıklı Tip-2 Bulanık Mantık Denetleyici Tabanlı Yükselten Tip DA-DA Çevirici Yapısının Denetimi ve Deneysel Analizi

Açıkgöz¹,

Keçecioğlu

2020

Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi

View full text Add to dashboard Cite

Bu çalışmada, yükselten tip DA-DA çeviricinin çıkış geriliminin denetimi için Aralıklı Tip-2 Bulanık Mantık Denetleyici (AT2BMD) yapısı önerilmiştir. Bu denetleyici yapısı ile yük değişimine karşı DA-DA çeviricinin dinamik cevabının iyileştirilmesi amaçlanmıştır. Bu amaçla ilk olarak yükselten tip DA-DA çevirici yapısı deneysel olarak geliştirilmiştir. Daha sonra önerilen denetleyici yapısının performansının ve güvenilirliğinin incelenmesi için birbirinden farklı iki deneysel çalışma durumu oluşturulmuştur. AT2BMD ile Tip-1 BMD (T1BMD) bu çalışma durumları altında karşılaştırılmıştır. Bu denetleyici yapılarının deneysel çalışmalara uygulanabilmesi için dSPACE DS1104 denetleyici kartı kullanılmıştır. Elde edilen sonuçlara göre önerilen denetleyici yapısının her iki deneysel çalışmada da T1BMD'den daha tatmin edici dinamik cevaplar sağladığı açıkça gözlenmiştir.

show abstract

Section: Igi̇ri̇şunclassified

Section: Aralikli Ti̇p-2 Bulanik Mantik Denetleyi̇ci̇ Yapisinin Tasarimiunclassified

Aralıklı Tip-2 Bulanık Mantık Denetleyici Tabanlı Yükselten Tip DA-DA Çevirici Yapısının Denetimi ve Deneysel Analizi

Açıkgöz¹,

Keçecioğlu

2020

Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi

View full text Add to dashboard Cite

show abstract

“…In addition, the effects of uncertainties in the rule base can be minimized through the Membership Functions (MFs) that have lower and upper limits used in T2FLCs 11,12 . Since T2FLCs have the remarkable features mentioned above, they have been successfully used in a variety of applications such as pulse‐width modulation (PWM) rectifier, 13,14 solid‐state transformer 15 mobile robots, 16 noisy regression problems, 17 modular and reconfigurable robots, 18 microstepping motor drives, 19 and autonomous mobile robots 20 . Recently, many researchers have designed controller structures based on different features that make them more attractive.…”

Section: Introductionmentioning

confidence: 99%

Real‐time adaptive speed control of vector‐controlled induction motor drive based on online‐trained Type‐2 Fuzzy Neural Network Controller

Açıkgöz

2020

Int Trans Electr Energ Syst

Self Cite

View full text Add to dashboard Cite

Background: The speed control of induction motor having superior features such as high efficiency, robust construction, low maintenance and low cost is carried out more effectively with the developments in control theory. With innovations in power electronics and microprocessor technology, it has been made possible to use the vector control method for applications requiring high performance in induction motor drives. Aim and Objective: In this study, Type-2 Fuzzy Neural Network (T2FNN) controller which is durable, adaptable and has fast dynamic response capabilities against parameter changes, is proposed to obtain a robust speed response from induction motor. Materials and Methods: Matlab/RTI model is developed through DS1103 controller card to experimentally test the speed performance of the proposed controller based induction motor. The proposed controller is trained on-line to improve the robustness of the induction motor against disturbances. After that, experimental studies are built to investigate the speed control behavior and effectiveness of the induction motor. Results and Discussion: The performance of T2FNN controller is compared with PI and Type-1 Fuzzy Neural Network (T1FNN) controllers. The experimental results clearly indicate that the proposed controller has a faster and more stable dynamic response capability. Conclusion: The proposed controller is significantly improved the dynamic response of the induction motor compared to T1FNN and PI controllers. In addition, the settling time, overshoot and recovery time promoting percentages of T1FNN and PI controllers by T2FNN controller are in satisfactory levels during all steady-state and the transient conditions.

show abstract

“…These controllers are compared under voltage sag, swell, flicker, and voltage harmonics. Finally, [17] presents a type-2 neuro-fuzzy controller (T2NFC) structure for an EPT structure consisting of three stages. In this study, the T2NFC is compared with the NFC and PI controller under the same conditions.…”

Section: Introductionmentioning

confidence: 99%

Real-time implementation of electronic power transformer based on intelligent controller

Açıkgöz¹,

Keçecioğlu²,

Şekkeli³

2019

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Along with the rapid advances in power electronics and semiconductor technology, electronic power transformers (EPTs), which are expected to replace conventional power transformers in the future, are being developed and designed. This study presents the dynamic performance of the intelligent controller structure in the control of an EPT. The EPT structure, consisting of the input, isolation, and output stages, is designed for experimental and simulation studies. A three-phase pulse width modulation (PWM) rectifier is used to rectify the grid voltages at the input stage of EPT. The DC-bus voltage of the three-phase PWM rectifier is controlled by a neuro-fuzzy controller (NFC) against the disturbances that may occur in the input stage. In the isolation stage of the EPT, a DC-DC converter circuit is used. Thus, DC voltage obtained from the input part is reduced to the appropriate voltage values. In the output stage, the two-level three-phase inverter circuit is used to provide the necessary voltages for users. The control algorithms for input and output stages of the EPT have been developed using a dSPACE DS1104 controller card, which can work in real time with MATLAB/Simulink. Experimental and simulation studies are realized to demonstrate the voltage sag, swell, harmonic, and reactive power compensation performances of the EPT controlled by NFC.

show abstract

Adaptive Control of Solid State Transformer Using Type-2 Fuzzy Neural System

Cited by 11 publications

References 11 publications

Aralıklı Tip-2 Bulanık Mantık Denetleyici Tabanlı Yükselten Tip DA-DA Çevirici Yapısının Denetimi ve Deneysel Analizi

Aralıklı Tip-2 Bulanık Mantık Denetleyici Tabanlı Yükselten Tip DA-DA Çevirici Yapısının Denetimi ve Deneysel Analizi

Real‐time adaptive speed control of vector‐controlled induction motor drive based on online‐trained Type‐2 Fuzzy Neural Network Controller

Real-time implementation of electronic power transformer based on intelligent controller

Contact Info

Product

Resources

About