Güç Sistemlerinde Harmonik Kestirim Problemlerinin Çözümü için Sezgisel Yöntemler

Yıl 2025, Cilt: 29 Sayı: 1, 149 - 157, 25.04.2025

Mustafa Saka

https://doi.org/10.19113/sdufenbed.1526998

Öz

Globalleşen dünya ile birlikte kaliteli elektrik enerjisine olan ihtiyaç da her geçen gün artmaktadır. Güç sistemlerinde yer alan lineer olmayan bileşenler nedeniyle, harmonikler oluşmakta ve sinyal formunda bozulmalar meydana gelmektedir. Harmoniklerin neden olabileceği olumsuz etkileri azaltmak amacıyla, sinyallerin genlik, faz ve frekans bileşenlerinin kestirimi oldukça önemlidir. Bu çalışmada, sentetik bir güç sinyaline ait genlik, faz ve frekans parametreleri, Gri Kurt Optimizasyonu (GWO), Güve Alev Optimizasyonu (MFO), Balık Kartalı Optimizasyon Algoritması (OOA) ve Balina Optimizasyon Algoritması (WOA) sezgisel optimizasyon yöntemleri ile bulunmuştur. Güç sistemlerinin sahip olduğu bileşenlerden dolayı sinyaller gürültü içerebilmektedir. Bu nedenle, seçilen test sinyalinin 20db sinyal gürültü oranı (SNR) içeren durumu da ayrıca dikkate alınmıştır. Bu sayede, farklı sezgisel yöntemlerin harmonik kestirim problemlerindeki etkisi analiz edilmiştir. Seçilen yöntemler ile tahmin edilen parametrelerin yüzde hata oranları karşılaştırmalı olarak sunulmuştur. Dahası, sezgiler teknikler için performans indeksleri ayrı ayrı hesaplanmıştır. Elde edilen sonuçlar analiz edildiğinde, MFO ile sentetik güç sinyaline ait genlik, frekans ve faz parametrelerinin en yüksek doğrulukta belirlendiği ve en düşük performans indeksine ulaşıldığı gözlemlenmiştir.

Anahtar Kelimeler

Harmonikler , Harmonik kestirim , SNR , Sezgisel teknikler , Optimizasyon

Heuristic Methods for Solving of Harmonic Estimation Problems in Power Systems

Öz

With the globalizing world, the need for quality electrical energy is increasing day by day. Due to the nonlinear components in power systems, harmonics and distortions occur in the signal form. In order to reduce the negative effects of harmonics, estimation of amplitude, phase and frequency components of signals is very important. In this study, amplitude, phase and frequency parameters of a synthetic power signal have been found via Grey Wolf Optimizer (GWO), Moth Flame Optimization (MFO), Osprey Optimization Algorithm (OOA) and Whale Optimization Algorithm (WOA) heuristic optimization methods. Due to the components of power systems, signals may contain noise. For this reason, the case containing 20db signal-to-noise ratio (SNR) was taken into account of the selected test signal. In this way, the effect of different heuristic methods on harmonic estimation problems has been analyzed. The percentage error rates of the estimated parameters with the selected methods have been presented comparatively. Moreover, the performance indexes for the heuristic techniques have been computed separately. When the obtained results have been analyzed, it has been observed that the amplitude, frequency and phase parameters of the synthetic power signal have been determined with the highest accuracy with MFO and the lowest performance index has been achieved with MFO.

Anahtar Kelimeler

Harmonics , Harmonic estimation , SNR , Heuristic techniques , Optimization

Kaynakça

• [1] Gençol K., 2023. An efficient iterative optimization-based algorithm for the real-time estimation of harmonics under power system frequency deviations, Engineering Science and Technology, an International Journal, 47, 1-14.
• [2] SJain S. K., Singh, S. N., 2011. Harmonics estimation in emerging power system: Key issues and challenges, Electric Power Systems Research, 81(9), 1754-1766.
• [3] Kalair, A., Abas, N., Kalair, A. R., Saleem, Z., Khan, N., 2017. Review of harmonic analysis, modeling and mitigation techniques, Renewable and Sustainable Energy Reviews, 78, 1152-1187.
• [4] Wiczynski, G., 2008. Analysis of voltage fluctuations in power networks, IEEE Transactions on Instrumentation and Measurement, 57(11), 2655-2664.
• [5] Kabalci, Y., Kockanat, S., Kabalci, E., 2017. Harmonic estimator design using hybrid particle swarm optimization, 2017 International Artificial Intelligence and Data Processing Symposium (IDAP), Malatya, Turkiye, 1-4.
• [6] Lu, Z., Ji, T. Y., Tang, W. H., Wu, Q. H., 2008. Optimal harmonic estimation using a particle swarm optimizer, IEEE Transactions on Power Delivery, 23(2), 1166-1174.
• [7] Bettayeb M., Qidwai, U., 2003. A hybrid least squares-GA-based algorithm for harmonic estimation, IEEE Transactions on Power Delivery, 18(2), 377-382.
• [8] Kabalci, Y., Kockanat, S., Kabalci, E., 2018. A modified ABC algorithm approach for power system harmonic estimation problems, Electric Power Systems Research, 154, 160-173.
• [9] Singh, S. K., Sinha, N., Goswami, A. K., Sinha, N., 2016. Robust estimation of power system harmonics using a hybrid firefly based recursive least square algorithm, International Journal of Electrical Power & Energy Systems, 80, 287-296.
• [10] Avalos, O., Cuevas, E., Becerra, H. G., Gálvez, J., Hinojosa, S., Zaldívar, D., 2021. Kernel recursive least square approach for power system harmonic estimation, Electric Power Components and Systems, 48:16-17, 1708-1721.
• [11] Coban M., Saka, M., 2024. Directly power system harmonics estimation using Equilibrium Optimizer, Electric Power Systems Research, 234, 1-14.
• [12] Mirjalili, S., Mirjalili, S. M., Lewis, A., 2014. Grey wolf optimizer, Advances in Engineering Software, 69, 46-61.
• [13] Mirjalili, S., 2015. Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm, Knowledge-Based Systems, 89, 228-249.
• [14] Dehghani M., Trojovský, P., 2023. Osprey optimization algorithm: A new bio-inspired metaheuristic algorithm for solving engineering optimization problems, Frontiers in Mechanical Engineering, 8, 1-43.
• [15] Mirjalili S., Lewis, A., 2016. The whale optimization algorithm, Advances in Engineering Software, 95, 51-67.
• [16] Altintasi, C., 2021. Sine cosine algorithm approaches for directly estimation of power system harmonics & interharmonics parameters, IEEE Access, 9, 73169-73181.