Transmission line loss determination of electricity by using convolutional neural network - DURU, ADİL DENİZ (My University, 2022)
© 2022 IEEE.Businesses are concerned about energy losses. Electronic gadgets have become more prevalent as a result of their adoption. The frequency with which home electricity usage data is collected has grown, allowing for sophisticated data analysis that was previously unavailable. Indeed, adopting Smart Grid (SG) networks, which are freshly improved networks of linked devices, may considerably enhance the existing energy infrastructure\"s dependability, economy, and durability. The SG involves sharing a lot of data, including information on specific users\" power use. And using this information, machine learning and deep learning algorithms may be able to detect power theft users reliably. This paper presented a Convolutional Neural Network (CNN)-based model for automated network-based vulnerability scanning that has excellent classification performance in many categories. Testing from iteration two to four iterations, this study examines research to discover the ideal configuration of the sequential model (SM) for categorization. The method is validated using a two-layer design, including an efficiency of 0.92, the whole first layer is composed of 128 nodes while the second level consists of 64 nodes. This allows for the development of a higher-level classifier for electrical signals, which may be employed in a number of applications. CNN was used to create electrical signal detectors, and SM was used to extract data from an electricity usage dataset. Furthermore, the number of features in the data set can be reduced using the Blue Monkey (BM) approach, and these results are then used to develop high-performance models. In this regard, the focus of this study has been on lowering the amount of needed features in the dataset in order to establish a rising classification algorithm for electrical signals. Experiments have applied the proposed systems\" fantastic performance, with just 666 characteristics required to combine the CNN and BM methods. Comparative to 1035 traits when CNN was used alone. This shows that the CNN and BM models are better than the CNN model in terms of lowering sufficient know while maintaining the same reliability.
Keywords: Sosyal ve Beşeri Bilimler; Sosyoloji; Kütüphanecilik; Tarımsal Bilimler; Ziraat; Tarım Makineleri; Tarımda Enerji; Biyoyakıt Teknolojisi; Bilgi Sistemleri, Haberleşme ve Kontrol Mühendisliği; Kontrol ve Sistem Mühendisliği; Bilgisayar Bilimleri; Algoritmalar; İnşaat Mühendisliği; Mühendislik ve Teknoloji; Social Sciences and Humanities; Sociology; Library Sciences; Agricultural Sciences; Agriculture; Farm Machinery; Energy in Agriculture; Biofuels Technology; Information Systems, Communication and Control Engineering; Control and System Engineering; Computer Sciences; algorithms; Civil Engineering; Engineering and Technology; Mühendislik, Bilişim ve Teknoloji (ENG); Sosyal Bilimler (SOC); Bilgisayar Bilimi; Mühendislik; Sosyal Bilimler Genel; OTOMASYON & KONTROL SİSTEMLERİ; BİLGİSAYAR BİLİMİ, YAPAY ZEKA; TELEKOMÜNİKASYON; ENERJİ VE YAKITLAR; BİLGİ BİLİMİ VE KÜTÜPHANE BİLİMİ; MÜHENDİSLİK, İNŞAAT; Engineering, Computing & Technology (ENG); Social Sciences (SOC); COMPUTER SCIENCE; ENGINEERING; SOCIAL SCIENCES, GENERAL; AUTOMATION & CONTROL SYSTEMS; COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE; TELECOMMUNICATIONS; ENERGY & FUELS; INFORMATION SCIENCE & LIBRARY SCIENCE; ENGINEERING, CIVIL; Yapay Zeka; Fizik Bilimleri; Bilgisayar Ağları ve İletişim; Bilgisayar Bilimi Uygulamaları; Bilgi Sistemleri ve Yönetimi; Sosyal Bilimler ve Beşeri Bilimler; Yenilenebilir Enerji, Sürdürülebilirlik ve Çevre; İnşaat ve Yapı Mühendisliği; Kontrol ve Optimizasyon; Artificial Intelligence; Physical Sciences; Computer Networks and Communications; Computer Science Applications; Information Systems and Management; Social Sciences & Humanities; Renewable Energy, Sustainability and the Environment; Civil and Structural Engineering; Control and Optimization; Convolutional Neural Network (CNN); Deep Learning (DL); Electricity consumption dataset; Power Consumption; Power Consumption; Deep Learning (DL); Convolutional Neural Network (CNN); Electricity consumption dataset