The Investigation of Harmonic Signals Success on Load Identification and a New Model Proposal

Year 2020, Volume: 7 Issue: 1, 452 - 460, 28.06.2020

Emre Akarslan Rasim Doğan

https://doi.org/10.35193/bseufbd.736206

Cited By: 2

Abstract

With the development of technology and smart systems, the control of the demand side has become an important issue today. Especially in smart homes, accurate determination of the loads presented in the system at any time is critical in terms of managing them. In this study, a novel load identification model is proposed that uses harmonic current signals as input. 1000W heater, 1200W heater, 1000W vacuum cleaner, 2200W kettle and 2100W iron are selected as electrical loads, and their current and voltage signals are recorded when these loads are used in individual or various combinations. Then, by using the features extracted from these signals and artificial neural networks, the loads presented at any time in the system are determined. Finally, the effect of each harmonic current signal used as a feature on load identification success is examined and the most effective features are determined. The simulation results present that the proposed model provides very successful results in load identification.  

Keywords

Load identification, Harmonic components, Artificial neural networks

Harmonik Sinyallerin Yük Tanımadaki Başarısının İncelenmesi ve Yeni Bir Model Önerisi

Abstract

Günümüzde gelişen teknoloji ve akıllı sistemlerin yaygınlaşması ile birlikte yük tarafının kontrolü önemli bir konu haline gelmiştir. Özellikle akıllı evlerde herhangi bir anda sistemde mevcut yüklerin doğru bir şekilde belirlenmesi, onların yönetilebilmesi açısından kritiktir. Bu çalışmada, harmonik akım sinyallerini girdi olarak kullanan yeni bir yük tanıma modeli önerilmiştir. 1000W ısıtıcı, 1200W ısıtıcı, 1000W süpürge, 2200W su ısıtıcısı ve 2100W ütünün elektrik yükü olarak seçildiği çalışmada, bu yüklerin bireysel ya da çeşitli kombinasyonlarda birlikte kullanıldığı durumlarda akım ve gerilim sinyalleri toplanmıştır. Sonrasında bu sinyallerden çıkarılan öznitelikler ve yapay sinir ağları kullanılarak, sistemde herhangi bir anda bulunan yükler belirlenmeye çalışılmıştır. Son olarak öznitelik olarak kullanılan her bir harmonik akım sinyalinin yük tanıma başarısına etkisi incelenmiş ve en etkili öznitelikler belirlenmiştir. Gerçekleştirilen simülasyon sonuçları önerilen modelin yük tanımada çok başarılı sonuçlar sağladığını göstermiştir

Keywords

Yük tanıma, Harmonik bileşenler, Yapay sinir ağları

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