Ferhat Ucar

Extreme learning machine based robotic arm modeling

Robotic arms are very powerful machines that can be used in many various applications in industry. So that, a suitable dynamic model is derived to verify that performs the tasks. But, dynamic equation is an important issue due to its complexity. Thus, an alternative model can be derived for the robotic arms. This paper is proposed Extreme Learning Machine (ELM) model for the angular acceleration of a robotic arm. The performance of the ELM model is performed by using Pumadyn datasets. At the same time, the validation of the proposed model is compared with Artificial Neural Network (ANN). Experimental results show that the proposed model is suitable and it provides low computation complexity.

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.

Classification of power quality events using extreme learning machine

Industrial plants and residential areas need to utilize electrical energy effectively. For this purpose smart grids were performed within power system voltage and current signals are processed and monitored in advanced. Thus controller systems provide such solutions that will keep the grid sustainability both faulty and normal conditions. In this study, single phase voltage data set consists of power quality events is composed in software and classified by an intelligent classifier. Distinctive features are extracted by discrete wavelet transform method. Feature vector size reduction is held via entropy values determining of discrete wavelet details. Extreme learning machine is used as classifier and its advantages in performance are evaluated with conventional artificial neural networks.