Beşir Dandil

Fuzzy neural network IP controller for robust position control of induction motor drive

In this paper, a fuzzy neural network (FNN) controller which emulates the conventional IP controller is proposed for a vector controlled induction motor drive. A Sugeno type FNN is adopted for the proposed control system and the fuzzy neural network is so designed that the FNN controller behaves an robust-nonlinear IP controller. The proposed FNN-IP controller is used for the position control of induction motor drive and the performance and the robustness of the control system is tested for nonlinear motor loads and parameter variations. The FNN-IP controller is trained off-line using experimental datas and then the trained controller is used for experimental studies. DS1104 digital signal processor control card is used to implement the control algorithm. Experimental results showing the effectiveness of the proposed control system are presented for parameter and load variations of the motor.

A virtual electrical drive control laboratory: Neuro-fuzzy control of induction motors

Neural and fuzzy courses are widely offered at graduate and undergraduate level due to the successful applications of neural and fuzzy control to nonlinear and unmodeled dynamic systems, including electrical drives. However, teaching students a neuro‐fuzzy controlled electrical drive in a laboratory environment is often difficult for schools with limited access to expensive equipment facilities. Therefore, computer simulations and dedicated software are needed to assist the students in visualizing the concepts and to provide graphical feedback during the learning process. In this article, an educational software is proposed for the neuro‐fuzzy control of induction machine drives. The tool helps students learn the application of neuro‐fuzzy control of electrical drives. The software has a flexible structure and graphical user interface. The neuro‐fuzzy architecture, the motor and load parameters can be easily changed in the developed software. Neuro‐fuzzy control performance of induction motors can be monitored graphically for various control structures and current controllers Comput Appl Eng Educ 14: 211–221, 2006; Published online in Wiley InterScience (www.interscience.wiley.com); DOI 10.1002/cae.20082

Development and Implementation of a Fuzzy-Neural Network Controller for Brushless DC Drives

Abstract In this paper, aProportional-Derivative and Integral (PD-I) type Fuzzy-Neural Network Controller (FNNC) based on Sugeno fuzzy model is proposed for brushless DC drives to achieve satisfied performance under steady state and transient conditions. The proposed FNNC uses the speed error, change of error and the error integral as inputs. While the PD-FNNC is activated in transient states, the PI-FNNC is activated in steady state region. A transition mechanism between the PI and PD type fuzzy-neural controllers modifies the control law adaptively. The gradient descent algorithm is used to train the FNN in direct adaptive control scheme. Presented experimental results show the effectiveness of the proposed control system, by comparing the performance of vazious control approaches including PD type FNNC, PI type FNNC and conventional PI controller, under nonlinear loads and parameter variations of the motor.

A hybrid neuro-fuzzy controller for brushless DC motors

In this paper, a hybrid neuro-fuzzy controller (NFC) is presented for the speed control of brushless DC motors to improve the control performance of the drive under transient and steady state conditions. In the hybrid control system, proportional-derivative (PD) type neuro-fuzzy controller (NFC) is the main tracking controller, and an integral compensator is proposed to compensate the steady state errors. A simple and smooth activation mechanism described for integral compensator modifies the control law adaptively. The presented BLDC drive has fast tracking capability, less steady state error and robust to load disturbance, and do not need complicated control method. Experimental results showing the effectiveness of the proposed control system are presented.

Adaptive neuro‐fuzzy current control for multilevel inverter fed induction motor

Purpose – The paper aims to propose an adaptive and robust on‐line trained neuro‐fuzzy current controller based on indirect field oriented control (IFOC) for the current control of multilevel inverter fed induction motor (IM).Design/methodology/approach – Torque current of IM is controlled with Sugeno type neuro‐fuzzy controller (NFC) which has the ability of self tuning against parameter variations and load disturbance. Input variables of the neuro‐fuzzy current controller are chosen error and integral of error in order to eliminate steady state error. The consequent parameters of neuro‐fuzzy current controller are trained on‐line through backpropagation learning algorithm.Findings – The validity of proposed current control algorithm is shown with experimental results carried out under different speed commands, parameter variations and load disturbances. The experimental results show that control performance of NFC in the current control of IMs is satisfactory because of its adaptive and robust structure...

Three-level Cascaded Inverter Based D-STATCOM Using Decoupled Indirect Current Control

Abstract In this paper, experimental setup of three-level cascaded inverter based 380V/±25kVAR D-STATCOM using decoupled indirect current control method (DICC) is realized. AC and DC side of D- STATCOM is modelled in dq-axis on account of D-STATCOM’s controlling. DICC is used for control of D-STATCOM’s dq-axis currents independently. Gate pulses for inverter are generated with multilevel sinusoidal pulse width modulation (SPWM) technique. In this study, controller card used for signal processing is dSPACEs DS1103. The control algorithm is prepared by the help of MATLAB/ Simulink® software. This algorithm is converted to C language by using Matlab/Real-Time Workshop and downloaded to DS1103’s program memory by dSPACE/Real-Time Interface. Implemented experimental setup is tested by changing reference reactive current (iqref) +20A to −20A and obtained results from this test are given.

Neuro-Fuzzy Control of a Dynamometer for the Emulation of Nonlinear Mechanical Loads

This paper proposes a neuro-fuzzy controller for a vector controlled load machine (dynamometer) which is mechanically coupled to a drive machine for the emulation of nonlinear loads. The main objective is to provide a system for testing of high performance electrical drives under nonlinear loads. The proposed emulation strategy is based on the model reference control approach using an on-line trained Neuro-Fuzzy Controller (NFC). The emulation is placed in the closed speed loop of the drive machine. Varieties of dynamic load models which are the nonlinear functions of the shaft speed are successfully emulated and the generalization capability of the trained NFC is shown. Simulation results showing the excellent performance of the proposed emulation strategy are presented.

Hilbert transform based simple detection and indice analyze of voltage sags using synthetic data

Electrical grid has lots of changes in its morphology and managing style since first installed nearly two hundred years ago. Today, smart grid structure plays a crucial role when creating a sustainable and reliable operation. In smart grid context, power quality issues are monitored and required measures are obtained from smart meters. Power quality term include voltage quality. When it is about voltage quality, sags take the lead among other disturbances. System operators have to track voltage sags to provide a better service quality. In this study, a fast and simple algorithm called Hilbert Transform is used to detect voltage sags in synthetic dataset. Then, a voltage sag table is built considering related IEEE and IEC standards to identify site indices SARFI-X and SIARFI-X. Purpose of the study is being a first step to voltage sag detection and defining indices with real data. Obtained results denote and feed this aim.

Machine learning based power quality event classification using wavelet — Entropy and basic statistical features

Todays industrial environment is smarter than ever before. Most production lines include electrical devices which are able to communicate each other and controlled from a single station with automation systems. Most of those elements have an internet connection link known as industrial internet. Development of smart technology with industrial internet comes with a need of monitoring. Monitoring technologies are emergent systems that focus on fault detection, grid self - healings and online tracking of power quality issues. Present study deals with one of the essential part of an electricity grid monitoring system called power quality event classification in a manner of machine learning topic. Power quality events to be processed are generated synthetically by means of a comprehensive software tool. Classification of real-like dataset is executed using extreme learning machine which is an extremely fast learning algorithm applied to single layer neural networks. Basic statistical criteria and wavelet - entropy methods are handled to achieve distinctive features of dataset. As a performance evaluation instrument, conventional artificial neural network structure is run too. Detailed results are discussed to prove the satisfactory performance of proposed pattern recognition model.

Bir Boyutlu Yerel İkili Örüntüler ve Ayrık Dalgacık Dönüşümü Tabanlı Yeni Bir Güç Kalitesi Olay Sınıflandırma Yöntemi

Bu makalede, gerilim cokmesi, yukselmesi ve kesintisi, gerilim harmonikleri ve gecici durumlardan olusan guc kalitesi bozulmalarina ait olay verilerini siniflandirmak icin akilli bir oruntu tanima sistemi incelenmistir. Onerilen sistemin altyapisini, oznitelik cikarimi ve siniflandirma asamalari olusturmaktadir. Ayirt edici ozniteliklerin cikarilmasi islemi siniflandirici performansini etkileyen en onemli unsurlar arasinda yer almaktadir. Onerilen calismada Ayrik Dalgacik Donusumu (ADD) ve Bir Boyutlu Yerel Ikili Oruntu (1B-YIO) yontemlerinden elde edilen oznitelikler kullanilmistir. Guc Kalitesi Olay (GKO) siniflandirma isleminde daha once incelenmemis yeni bir yontem olan 1B-YIO yontemi, ADD ozellikleri ile birlikte ele alinarak siniflandirici basarimi incelenmistir. Veri setini olusturan GKO isaretleri kapsamli bir yazilim araci ile uretilmistir. Matematiksel modeller kullanilarak olusturulan bu aracta GKO verilerini iceren veri seti gercege en yakin haliyle elde edilmistir. Siniflandirici olarak bircok uygulamada yaygin olarak kullanilan Uc Ogrenme Makinesi (UOM) tercih edilmistir. Basarim degerlendirmesinin etkinligini artirmak icin geleneksel Yapay Sinir Agi (YSA) tabanli siniflandiriciya ait sonuclar da elde edilmistir. Cesitli gurultu icerigi de dikkate alinarak yapilan deneysel calismalarda siniflandirici basariminin kabul edilebilir degerlere ulastigi kaydedilmistir. Calismaya ait sonuclar detayli olarak gosterilmistir.