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

References

• Hart, G. W. (1992). Nonintrusive Appliance Load Monitoring. Proceedings of the IEEE, 80(12), 1870–1891. https://doi.org/10.1109/5.192069
• Du, L., Restrepo, J. A., Yang, Y., Harley, R. G., & Habetler, T. G. (2013). Nonintrusive, self-organizing, and probabilistic classification and identification of plugged-in electric loads. IEEE Transactions on Smart Grid, 4(3), 1371–1380. https://doi.org/10.1109/TSG.2013.2263231
• Chang, H. H., Lian, K. L., Su, Y. C., & Lee, W. J. (2014). Power-spectrum-based wavelet transform for nonintrusive demand monitoring and load identification. IEEE Transactions on Industry Applications, 50(3), 2081–2089. https://doi.org/10.1109/TIA.2013.2283318
• Giri, S., & Bergés, M. (2015). An energy estimation framework for event-based methods in Non-Intrusive Load Monitoring. Energy Conversion and Management, 90, 488–498. https://doi.org/10.1016/j.enconman.2014.11.047
• Lin, S., Zhao, L., Li, F., Liu, Q., Li, D., & Fu, Y. (2016). A nonintrusive load identification method for residential applications based on quadratic programming. Electric Power Systems Research, 133, 241–248. https://doi.org/10.1016/j.epsr.2015.12.014
• Lu, H., Xin, W., Hui, B., Bing, Q., & Aixia, Z. (2016). A residential load identification algorithm based on periodogram for non-intrusive load monitoring. In China International Conference on Electricity Distribution, CICED (Vol. 2016–September). IEEE Computer Society. https://doi.org/10.1109/CICED.2016.7576286
• Cominola, A., Giuliani, M., Piga, D., Castelletti, A., & Rizzoli, A. E. (2017). A Hybrid Signature-based Iterative Disaggregation algorithm for Non-Intrusive Load Monitoring. Applied Energy, 185, 331–344. https://doi.org/10.1016/j.apenergy.2016.10.040
• Liu, B., Luan, W., & Yu, Y. (2017). Dynamic time warping based non-intrusive load transient identification. Applied Energy, 195, 634–645. https://doi.org/10.1016/j.apenergy.2017.03.010
• Dvorkin, D., Palis, S., Silaev, M., & Tulsky, V. (2017). Balanced load identification based on the correlation of the phase currents. In 58th Annual International Scientific Confererence on Power and Electrical Engineering of Riga Technical University, RTUCON 2017 - Proceedings (Vol. 2017–November, pp. 1–4). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/RTUCON.2017.8124822
• Buddhahai, B., Wongseree, W., & Rakkwamsuk, P. (2018). A non-intrusive load monitoring system using multi-label classification approach. Sustainable Cities and Society, 39, 621–630. https://doi.org/10.1016/j.scs.2018.02.002
• Liu, Y., Wang, X., Zhao, L., & Liu, Y. (2018). Admittance-based load signature construction for non-intrusive appliance load monitoring. Energy and Buildings, 171, 209–219. https://doi.org/10.1016/j.enbuild.2018.04.049
• Zhang, Y., Yang, G., & Ma, S. (2019). Non-intrusive load monitoring based on convolutional neural network with differential input. In Procedia CIRP (Vol. 83, pp. 670–674). Elsevier B.V. https://doi.org/10.1016/j.procir.2019.04.110
• Guillén-García, E., Morales-Velazquez, L., Zorita-Lamadrid, A. L., Duque-Perez, O., Osornio-Rios, R. A., & Romero-Troncoso, R. de J. (2019). Identification of the electrical load by C-means from non-intrusive monitoring of electrical signals in non-residential buildings. International Journal of Electrical Power and Energy Systems, 104, 21–28. https://doi.org/10.1016/j.ijepes.2018.06.040
• Xiao, Y., Hu, Y., He, H., Zhou, D., Zhao, Y., & Hu, W. (2019). Non-Intrusive Load Identification Method Based on Improved KM Algorithm. IEEE Access, 7, 151368–151377. https://doi.org/10.1109/ACCESS.2019.2948079
• Ding, G., Wu, C., Wang, Y., Liang, Y., Jiang, X., & Li, X. (2019). A novel non-intrusive load monitoring method based on quantum particle swarm optimization algorithm. In Proceedings - 2019 11th International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2019 (pp. 230–234). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ICMTMA.2019.00058
• Hamdi, M., Messaoud, H., & Bouguila, N. (2020). A new approach of electrical appliance identification in residential buildings. Electric Power Systems Research, 178, 106037. https://doi.org/10.1016/j.epsr.2019.106037
• Zoha, A., Gluhak, A., Imran, M. A., & Rajasegarar, S. (2012, December). Non-intrusive Load Monitoring approaches for disaggregated energy sensing: A survey. Sensors (Switzerland). https://doi.org/10.3390/s121216838
• Xu, L., Wang, S., & Tang, R. (2019). Probabilistic load forecasting for buildings considering weather forecasting uncertainty and uncertain peak load. Applied Energy, 237, 180–195. https://doi.org/10.1016/j.apenergy.2019.01.022
• Akarslan, E., Hocaoğlu, F. O., & Ucun, I. (2017). Classification of disc damage status by discovering knowledge from experimental data in marble cutting process. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 231(13), 2407–2416. https://doi.org/10.1177/0954406216634748
• Ghiassi, M., & Burnley, C. (2010). Measuring effectiveness of a dynamic artificial neural network algorithm for classification problems. Expert Systems with Applications, 37(4), 3118–3128. https://doi.org/10.1016/j.eswa.2009.09.017