ADAPTIVE ESTIMATION OF POWER SYSTEM HARMONICS: PERFORMANCE ANALYSIS OF GRADIENT DESCENT-BASED ADAPTIVE ALGORITHMS

Yıl 2019, Cilt: 8 Sayı: 1, 69 - 80, 28.01.2019

Engin Cemal Mengüç

https://doi.org/10.28948/ngumuh.516816

Öz

   In this study, adaptive estimation of power
system harmonics is investigated. As adaptive estimation algorithms in the
study, gradient descent based adaptive algorithms widely used in the literature
are used due to its simple computational complexity and the easily applicable
for real-time systems. These algorithms are least mean square LMS, normalized
LMS (NLMS), Sign-Data LMS, and Sign-Error LMS algorithms, respectively. Within
the scope of the study, the unknown amplitude and phase harmonics of the current
or voltage expressions available in power systems are first expressed as an
estimation problem. Then, the handled amplitude and phase information of the fundamental
and harmonic components of the power system signal are estimated by the
gradient descent based adaptive algorithms. The simulations performed in the
study indicate that the NLMS algorithm shows superior performance compared to
the other three algorithms. However, in real-time power system applications
where the high-speed data stream is available, it is also concluded that the
use of the Sign-Data LMS algorithm containing lower computational complexity will
be more appropriate instead of the LMS and NLMS algorithms.

Anahtar Kelimeler

Gradient descent algorithms, Adaptive estimation, Power system harmonics

GÜÇ SİSTEMİ HARMONİKLERİNİN ADAPTİF KESTİRİMİ: EĞİM DÜŞÜM TABANLI ADAPTİF ALGORİTMALARIN BAŞARIM ANALİZİ

Öz

   Bu çalışmada, güç sistemi harmoniklerinin
adaptif kestirimi üzerine bir inceleme yapılmıştır. Çalışmada adaptif kestirim
algoritmaları olarak, basit hesap yükü ve gerçek zamanlı sistemlere kolaylıkla
uygulanabilirliği nedeniyle literatürde yaygın kullanılan eğim düşüm tabanlı
adaptif algoritmalar kullanılmıştır. Bu algoritmalar sırasıyla en küçük
ortalama kare (LMS), normalize edilmiş LMS (NLMS), İşaret-Veri LMS ve İşaret-Hata
LMS algoritmalarıdır. Çalışma kapsamında, öncelikle güç sistemlerinde mevcut
olan akım veya gerilim ifadelerinin bilinmeyen genlik ve faz harmonikleri bir
kestirim problemi olarak ifade edilmiştir. Daha sonra ise ele alınan güç
sistemi sinyalinin temel ve harmonik bileşenlerin genlik ve faz bilgileri eğim
düşüm tabanlı adaptif algoritmalar ile kestirilmiştir. Çalışmada
gerçekleştirilen benzetimler, NLMS algoritmasının diğer üç algoritmaya kıyasla
üstün bir başarım sergilediğini göstermiştir. Fakat yüksek hızlı veri akışının
mevcut olduğu gerçek zamanlı güç sistemi uygulamalarında LMS ve NLMS
algoritmalarının yerine, daha az hesap yükü içeren İşaret-Veri LMS
algoritmasının kullanımının daha uygun olabileceği sonucuna ayrıca varılmıştır.

Anahtar Kelimeler

Eğim düşüm algoritmaları, Adaptif kestirim, Güç sistemi harmonikleri

Kaynakça

• [1] YILMAZ, A., AHMET, S., ALKAN, A., ASYALI, M.H., “Application of parametric spectral estimation methods on detection of power system harmonics,” Electric Power Syst. Res., 78, 683–693, 2008.
• [2] BARROS, J., PÉREZ, E. “Automatic detection and analysis of voltage events in power systems,” IEEE Trans. Instrum. Meas., 55 No: 5, 1487–1493, 2006.
• [3] REDDY, J.B.V., DASH, P.K., SAMANTARAY, R., MOHARANA, A.K., “Fast tracking of power quality disturbance signals using an optimized unscented filter,” IEEE Trans. Instrum. Meas., 1–10, 2008.
• [4] MOJIRI, M., GHARTEMANI, M.K., BAKHSHAI, A., “Processing of harmonics and interharmonics using an adaptive notch filter,” IEEE Trans. Power. Delivery., 25, 2, 534–542, 2010.
• [5] TAO, C., SHANXU, D., TING, R., FANGRULI, L., “A robust parametric method for power harmonics estimation based on M-estimators,” Measurement, 43, 1, 67–77, 2010.
• [6] JOORABIAN, M., MORTAZAVI, S.S., KHAYYAMI, A.A., “Harmonics estimation in a power system using a novel-hybrid least square – Adaline algorithm,” Electric. Power Syst. Res., 79, 1, 107–16, 2009.
• [7] TOMIC, J.J., KUSLJEVIC, M.D., VUJICIC, V.V., “A new Power system digital harmonic analyzer,” IEEE Trans. Power Delivery, 22, 2, 772–780, 2007.
• [8] GHODRATOLLAH, S.S., RAZZAZ, M., MOGHADDASIAN, M., MONADI, M., “Harmonics estimation in power system using adaptive perceptrons based on a genetic algorithm,” WSEAS Trans. Power Syst., 2, 11, 2007.
• [9] RAY, P.K., SUBUDHI, B., “Ensemble Kalman filter based power system harmonics estimation,” IEEE Trans Instrum. Meas., 61, 12, 3216–3224, 2012.
• [10] TSAO, T.P., WU, R.C., NING, C.C., “The optimization of spectral analysis for signal harmonics,” IEEE Trans. Power Del., 16, 149–153, 2001.
• [11] ZHANG, F., GENG, Z., YUAN, W., “The algorithm of interpolating windowed FFT for harmonic analysis of electric power system,” IEEE Trans. Power Del., 16, 2, 160–164, 2001.
• [12] LIN, H.C. LEE, C.S., “Enhanced FFT-based parametric algorithm for simultaneous multiple harmonics analysis,” Proc. Inst. Elect. Eng., Gen. Transm. Distrib., 148, 209–214, 2001.
• [13] BARROS, J., DIEGO, R.I., APRÁIZ, M., “Applications of wavelet transform for analysis of harmonic distortion in power systems: a review,” IEEE Trans. Instrum. Meas., 61, 10, 2604–2611, 2012.
• [14] DASH, P.K., SWAIN, D.P., ROUTRAY, A. LIEW, A.C., “Harmonic estimation in a power system using adaptive perceptrons,” IEE Proc. Generations, Transm. Distribution, 143, 6, 565–74, 1996.
• [15] MISHRA, S., “A hybrid least square-fuzzy bacterial foraging strategy for harmonic estimation,” IEEE Trans. Evolutionary Comput., 9, 1, 61–73, 2005.
• [16] KIM, D.H., ABRAHAM, A., CHO, J.H., “A hybrid genetic algorithm and bacterial foraging approach for global optimization,” Inf. Sci., 177, 3918–3937, 2007.
• [17] KABALCI, Y., KOCKANAT, S., KABALCI, E., “A modified ABC algorithm approach for power system harmonic estimation problems,” Electr. Power Sys. Res. 154, 160–173, 2018.
• [18] BETTAYEB, M., QIDWAI, U., “Recursive estimation of power system harmonics,” Electric Power Syst. Res., 47, 2, 143–152, 1998.
• [19] SİNGH, S.K., SİNHA, N., GOSWAMİ, A.K., SİNHA, N., “Power system harmonic estimation using biogeography hybridized recursive least square algorithm,” Int. J. Electr. Power Energy Syst. 83, 219–228, 2016.
• [20] SİNGH, S.K., KUMARİ, D., SİNHA, N., GOSWAMİ, A.K. SİNHA, N. “Gravity Search Algorithm hybridized Recursive Least Square method for power system harmonic estimation,” Eng. Sci. Technol. 20, 3, 874–884, 2017.
• [21] PRADHAN, A.K., ROUTRAY, A., BASAK ABIR, A., “Power system frequency estimation using least mean square technique,” IEEE Trans. Power Del., 20, 3, 1812–1816, 2005.
• [22] SUBUDHI, B., RAY, P.K., GHOSH, S., “Variable leaky least mean-square algorithm-based power system frequency estimation,” IET Sci. Meas. Technol., 6, 4, 288–297, 2012.
• [23] RAY, P.K., PUHAN, P.S., PANDA, G., “Real time harmonics estimation of distorted power system signal,” Electric Power Syst. Res., 75, 91–98, 2016.
• [24] HAYKIN, S., Adaptive Filter Theory, 4th ed. Englewood Cliffs, NJ, USA: Prentice-Hall, 2002.
• [25] HAYES, M., Statistical Digital Signal Processing and Modelling, Wiley, New York, 1999.