TRANSACTIONS ON ENVIRONMENT AND ELECTRICAL ENGINEERING ISSN 2450-5730 Vol 2, No 2 (2017) © Turgay Yalcin, Muammer Ozdemir, Pawel Kostyla, Zbigniew Leonowicz Investigation of Supra-Harmonics through Signal Processing Methods in Smart Grids Turgay Yalcin1, Muammer Ozdemir1 1Ondokuz Mayis University, Electrical&Electronic Engineering Faculty, Samsun, TURKEY turgay.yalcin@omu.edu.tr , ozdemirm@omu.edu.tr Pawel Kostyla2, Zbigniew Leonowicz2 2Wroclaw University of Technology Faculty of Electrical Engineering, Wroclaw, Poland pawel.kostyla@pwr.wroc.pl, leonowicz@ieee.org Abstract— Nowadays supra-harmonic distortion studies are gaining attention day by day in power quality research area. When handling communication systems especially Power Line Carrier (PLC) systems in frequency range 2- 150 kHz, they are suitable for causing electromagnetic interference (EMI) to other systems. This study shows results of analysis employing advanced method called ensemble empirical mode decomposition (EEMD) to describe supra-harmonic distortion. Unlike the traditional method (short time fourier transform- STFT), EEMD gives extensive representation for supra-harmonic components Index Terms—ensemble empirical mode decomposition; power quality; short time fourier analysis; supra-harmonics; I. INTRODUCTION A novel and significance increasing day by day hazardous risk to smart grid systems called supra ‐ harmonics or emissions in 2 kHz ‐150 kHz frequency band. This threat can affect capacitors, lose communication contacts with smart meters. The important single fault source operations from photovoltaic inverters (PVs). Naturally fuel cells, battery chargers also wind turbines, can produce this serious threat [1, 2, 3]. Supra‐ harmonics also disturb domestic appliances, semi-conductor manufacturing devices, medical equipments, security systems even transportation controls. PLC produces low‐impedance for emissions in subharmonics frequency range. The highest levels are commonly by virtue of PLC. As a result, power grids are worked out to transfer power at 50 Hz however, line also carries 2 kHz‐ 150 kHz electromagnetic components [4, 5, 6, 7, 17]. In this study, National Instruments PWr cRIO Data Recorder was used to acquire distortions in power systems. Sampling frequency was selected 1 MHz for measurements. Table I. gives the information of PV systems components properties. Fig 1 shows PV System at the Faculty of Electrical Engineering Wroclaw University of Technology. TABLE I. PV SYSTEM COMPONENTS PROPERTIES Monocrystalline c-Si Polycrystalline p-Si The panel thin-film CIGS Efficiency 14,90% 15,50% 11,80% Maximum power 190W 240W 90W Maximum voltage 36,5V 30V 60,8V Maximum current 5,2A 8A 1,48A Dimensions 1580 × 808 × 35mm 1680 × 1040 × 35mm 1196 × 636 × 36mm Weight 17,2 kg 20 kg 14,5 kg The angle of inclination to the horizontal  =400  = 400  =400 Azimuth  =1350  =2250  =1350 Fig 1. Photovoltaic System at the Faculty of Electrical Engineering Wroclaw University of Technology II. SIGNAL PROCESSING METHODS A. Short Time Fourier Transform Traditionally STFT is applied to measured data from domestic appliances and Sunny Mini Central PV inverter. The results from the STFT are presented in a spectrogram. Spectrograms used for signal processing owing to show supra- harmonics. STFT has drawbacks about representation magnitude and frequency bands [8]. In algorithm we used STFT with hamming sliding window (5 ms) for decompositon. . Respectively fig 2 and 5 shows STFT spectrograms and 2 Hz - 120 kHz frequency band for current signals of domestic appliances LCD TV and Laser Printer. mailto:turgay.yalcin@omu.edu.tr mailto:ozdemirm@omu.edu.tr mailto:pawel.kostyla@pwr.wroc.pl mailto:leonowicz@ieee.org Fig 2. Spectrogram of the current LCD TV In figure 2 is illustrated constant or continuous frequency emissions at 17. 6 kHz, 53 kHz and 88 kHz bands. B. Ensemble Emprical Mode Decomposition (EEMD) EMD has been profitably performed for non-stationary signal processing. The EMD could decompose the complicated signal function into a number of Intrinsic Mode Functions (IMFs)[9,10,11]. The algorithm has major drawbacks of mode mixing, end effects and etc[12,13,14,15,16]. Therefore, in this work we performed EEMD method for generating IMFs in order to analyze supra-harmonics. We focused on pattern frequency band which is dominated in power grid. The mathematical background of EEMD algorithm (Fig 3): i. Add noise, wn(t), to target signal s1(t). s2(t)=s1(t)+wn(t). ii. Used EMD [9, 10, 11, 12, 16] algorithm for decomposing the final signal s2(t). iii. Continue steps (i) and (ii) till the trial numbers. When new imf combination Cij(t) is succeeded, predict the ensemble mean of the last intrinsic mode function (IMF). (Selected Nstd: 0 and Number of Ensemble: 1 we used EEMD like EMD.) The aimed output:    tn i ijj tctcEEMD 1 )()]([ (1) tn: trial numbers, i: iteration number and j: imf scale [13, 14, 15]. Fig. 3. The representation of the EEMD algorithm[16] Fig 4. Frequency IMFs components for current of LCD TV Figure 4 shows the frequency spectrum of IMF 3- IMF 5. When we look for the spectrum it is clearly illustrated that IMF 3 represents the frequency component (17.58 kHz). Moreover, IMF 4 also shows frequency (52.73 kHz) band. 0 1 2 3 4 5 6 7 8 9 x 10 4 -4 -2 0 2 4 Current Plot for LCD TV Time Samples C u r r e n t [A ] Spectrogram of LCD TV Time F r e q u e n c y ( H z ) 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0 2 4 6 8 10 x 10 4 -40 -20 0 20 40 60 17.67 kHz 17.33 kHz 53 kHz 49 kHz 88.04 kHz 0 1 2 3 4 5 6 7 8 9 10 11 x 10 4 0 2 4 6 8 10 12 14 Single-Sided Amplitude Spectrum of Imfs of Current Signal (LCD TV) Frequency (Hz) |Y (f )| imf 3 imf 4 imf 5 17.58 kHz 87.89 kHz 52.73 kHz Fig 5. Spectrogram of the current Laser Printer In figure 5 is illustrated continuous and fluctuant frequency emission at 15 kHz and 44.67 kHz bands. Figure 6 shows the frequency spectrum of IMF 3- IMF 5. When considering the spectrum it is comprehensively shown that IMF 3 represents the frequency component (15.14 kHz). Furthermore, IMF 4 also shows frequency (44.92 kHz) band. Fig 6. Frequency IMFs components for current of Laser Printer Fig 7. Spectrogram of the current power line with PV (Sampling Frequency: 1 MHz) In figure 7 is illustrated Continuous and fluctuant frequency emissions at 16 kHz output PLC frequency and Daylight emission at 32 kHz PV inverter signature bands. 0 1 2 3 4 5 6 7 8 9 x 10 4 -5 0 5 Current Plot for Laser Printer Time Samples C u r r e n t [ A ] Spectrogram of Laser Printer Time F r e q u e n c y ( H z ) 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0 1 2 3 4 5 x 10 4 -40 -20 0 20 40 60 15 kHz 44 .67 kHz 39 kHz 12.33 kHz 0 2 4 6 8 10 12 x 10 4 0 1 2 3 4 5 6 7 8 9 Single-Sided Amplitude Spectrum of Imfs of Current Signal (Laser Printer) Frequency (Hz) |Y (f )| imf 3 imf 4 imf 5 15.14 kHz 44.92 kHz 74. 71 kHz 0 1 2 3 4 5 6 7 8 9 x 10 4 -30 -20 -10 0 10 20 30 Current of PV Time Samples C u r r e n t [ A ] Spectrogram of PV Time F r e q u e n c y ( H z ) 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0 1 2 3 4 5 x 10 4 -40 -20 0 20 40 60 80 48. 33 kHz 32. 33 kHz 16. 33 kHz 16 kHz TRANSACTIONS ON ENVIRONMENT AND ELECTRICAL ENGINEERING ISSN 2450-5730 Vol 2, No 2 (2017) © Turgay Yalcin, Muammer Ozdemir, Pawel Kostyla, Zbigniew Leonowicz Fig 8. IMF components of PV current signal Fig 9. Frequency components of IMFs Figure 8 shows the IMF components of PV signal. For measurements for PV, fig 9 shows frequency spectrum of IMF 2- IMF 5. When we look for the spectrum it is clearly illustrated that IMF 3 has PLC frequency component (15.63 kHz). Moreover, IMF 4 also shows PV converter frequency (32.23 kHz) band. Table II. shows the relationship between signal and the IMFs components. IMFs are sorted from higher frequency to lower. Algorithm routine stops till the end of getting monotonic function IMF 9 called residual IMF 7 (R=0.9998) strongly related with the signal. 0 1 2 3 4 5 6 7 8 9 10 x 10 4 0 20 40 60 80 100 120 140 Single-Sided Amplitude Spectrum of Imfs Current Signal (PV) Frequency (Hz) | Y ( f ) | imf2 imf3 imf4 imf5 32.23 kHz 31.25 kHz 63.48 kHz 15.63 kHz 47.85 kHz TABLE II. CORRELATION COEFFICIENT BETWEEN SIGNAL AND IMFS R (correlation coefficient) signal,imf1 0,0006 signal,imf2 -0,0014 signal,imf3 -0,0005 signal,imf4 0,0087 signal,imf5 0,0049 signal,imf6 0,0079 signal,imf7 0,9998 signal,imf8 0,0511 signal,imf9 0,1524 III. CONCLUSIONS Instead of traditional methods such as STFT, EEMD gives more accurate results, determining the PV inverter and PLC frequency bands with high exactitude. It was shown that EEMD method can be used for spectral analysis of supra- harmonics and can be also applied for pattern recognition of supra-harmonics in smart grids with PV systems. With the help of the proposed method supra- harmonic analysis and pattern detection of them easily inquire into. For future study, this results will discuss with another signal processing methods. Filter design for measuring supra-harmonics in smart grids will be investigated in the light of the analysis. REFERENCES [1] E.O.A. Larsson, M.H.J. Bollen, M.G. Wahlberg, C.M. Lundmark, and S.K. 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London A, 2002. http://www.powersensorsltd.com/Download/AppNotes/ About the Authors Turgay YALCIN received the B.Sc. Erciyes University (ERU), Kayseri, Turkey in 2006 and M.Sc. degrees in electrical engineering from Ondokuz Mayıs University (OMU), Samsun, Turkey, in 2010 and he is currently pursuing the Ph.D. degree in electrical engineering from Ondokuz Mayıs University (OMU). His areas of interest are Identification of Power Quality Disturbances, Signal Processing Methods and Machine Learning Algorithms. Muammer OZDEMIR received the B.Sc. and M.Sc. degrees in electrical engineering from Black Sea Technical University (KTÜ), Trabzon, Turkey, in 1988 and 1991, respectively, and the Ph.D. degree in electrical engineering from The University of Texas at Austin (UT), Austin, TX, USA, in 2002.Currently, he is an Assistant Professor with the Department of Electrical and Electronics Engineering, Ondokuz Mayıs University (OMU), Samsun, Turkey. His areas of interest are power systems harmonics, power quality, and power system analysis. Pawel Kostyla in 1998 was awarded the title of Doctor of Science at the Wroclaw University of Science and Technology. From 1999 until now he has been working as an Assistant Professor at the Department of Electrical Engineering. He holds the position of Laboratory Manager of the Theoretical Electrical Engineering. Author and coauthor of publications from the author's interest areas such as artificial neural networks and methods of digital signal processing in automation and electrical engineering, algorithms of digital signal processing and electrical measurements, development and testing of new methods of measuring electrical parameters, quality of electricity. Zbigniew Leonowicz (IEEE M’03– SM’12) became a Member (M) of IEEE in 2003 and a Senior Member (SM) in 2012. He received the M.Sc., Ph.D. and Habilitate Doctorate (Dr Sc.) degrees, all in Electrical Engineering, in 1997, 2001, and 2012, respectively. He has been with the Department of Electrical Engineering, Wroclaw University of Technology, since 1997 where he currently holds position of Associate Professor. His current research interests are in the areas of power quality, control and protection of power systems, renewables, industrial ecology and applications of signal processing methods in power systems. [12] T.Yalcin, O.Ozgonenel, “Feature vector extraction by using empirical mode decomposition from power quality disturbances”, IEEE SIU, Fethiye, Mugla, 2012. [13] O.Ozgonenel, T. Yalcin, I. Guney, U. Kurt, “A New Classification for Power Quality Events in Distribution System”, Electric Power System Research (EPSR), 95, 2013, pp. 192-199. [14] Z.Wu, N.E. Huang, “Ensemble empirical mode decomposition: a noise- assisted data analysis method”, Adv. Adapt. Data. Anal., 1, 2009, pp.1–41. [15] Z. Wang, Q. Zhu, J. Kiely, R. Luxton, “Hilbert Huang transform impedance measurement data for cellular toxicity monitoring” International Conference on Networking, Sensing and Control, 2009, pp. 767-772. [16] ,T Yalcin, M Ozdemir, “An Implementation of Exploratory Start for Power Quality Disturbance Pattern Recognition”, TRANSACTIONS ON ENVIRONMENT AND ELECTRICAL ENGINEERING, Vol 1, No 3, 2016, pp 86-93. [17] T. Yalcin, M Ozdemir, P. Kostyla, Z Leonowicz, “Analysis of Supra‐ Harmonics in Smart Grids”, EEEIC 2017., 6-9 June 2017 , DOI: 10.1109/EEEIC.2017.7977812 http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=%22Authors%22:.QT.Zhiyan%20Wang.QT.&newsearch=true http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=%22Authors%22:.QT.Quan%20Zhu.QT.&newsearch=true http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=%22Authors%22:.QT.J.%20Kiely.QT.&newsearch=true http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=%22Authors%22:.QT.R.%20Luxton.QT.&newsearch=true https://doi.org/10.1109/EEEIC.2017.7977812 I. Introduction II. Sıgnal Processıng METhods A. Short Time Fourier Transform B. Ensemble Emprical Mode Decomposition (EEMD) III. Conclusıons Turgay YALCIN received the B.Sc. Erciyes University (ERU), Kayseri, Turkey in 2006 and M.Sc. degrees in electrical engineering from Ondokuz Mayıs University (OMU), Samsun, Turkey, in 2010 and he is currently pursuing the Ph.D. degree in electrical eng... Muammer OZDEMIR received the B.Sc. and M.Sc. degrees in electrical engineering from Black Sea Technical University (KTÜ), Trabzon, Turkey, in 1988 and 1991, respectively, and the Ph.D. degree in electrical engineering from The University of Texas at A...