EMD'2012

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Separation of traction and non-traction power consumption by means of ... Intermediate frequency noise in and from electrical power network in modern working ...

ISSN 1822-3249 print VILNIUS GEDIMINAS TECHNICAL UNIVERSITY

EMD‘2012 THE 22nd INTERNATIONAL CONFERENCE “ELECTROMAGNETIC DISTURBANCES EMD’2012” Proceedings of the 22nd International Conference, September 20–21 2012, Vilnius, Lithuania

Organized by: VILNIUS GEDIMINAS TECHNICAL UNIVERSITY KAUNAS UNIVERSITY OF TECHNOLOGY BIALYSTOK TECHNICAL UNIVERSITY

Sponsored by: IEEE Lithuania Section AB “Lietuvos energija”, full member of EURELECTRIC SIEMENS ABB UAB „Tele 2“ AB „Lietuvos geležinkeliai“

Vilnius “Technika” 2012

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UDK 621.391(06) In-156 The 22nd International Conference “Electromagnetic disturbances EMD’2012”. Proceedings of the 22nd International Conference, September 20–21 2012, Vilnius, Lithuania (Vilnius Gediminas Technical University), edited: R. Rinkevičienė (general editor), J. Baškys, Z. Flisowski, B. Jaekel, C. Mazzetti, K. Maceika, A. W. Sowa. Vilnius: Vilnius Gediminas Technical University Press “Technika”, 2012. 154 p.

The EMD’2012 is the continuation of series Symposia concerning the Overvoltages in Power Electronics and Computer Systems which were started in 1990. The aim of the Conference EMD’2012 is to provide an international forum for presentations and discussions about the sources of electromagnetic disturbances, their influence on electronic devices and systems, measurement methods and protection against disturbances. All papers were referred by the Conference Editorial Committee, Conference Program Advisory Committee and independent peer referees. For information write to: Vilnius Gediminas Technical University, Electronics Faculty Naugarduko str. 41, LT-03227 Vilnius Lithuania VGTU Press House “Technika” scientific book No. 2018-M

ISSN 1822-3249 print ISBN: 978-609-457-260-9 eISBN: 978-609-457-261-6 © Vilnius Gediminas Technical University, 2012

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Conference organizers:

Vilnius Gediminas Technical University, Lithuania Kaunas University of Technology, Lithuania Bialystok Technical University, Poland

Honorary Chairman:

Prof. A. Čenys –Vice-rector of Vilnius Gediminas Technical University, Lithuania Prof. R. Rinkevičienė – Vilnius Gediminas Technical University, Lithuania

Conference Chairman: Conference Program Advisory Committee:

Conference Organizing Committee:

K. Aniserowicz– Bialystok Technical University, Poland Prof. A. Baškys - Vilnius Gediminas Technical University, Lithuania Prof. Z. Dmochowski – Honorary Member, Poland Prof. G. Evdokunin – St. Petersburg State Technical University, Russia Prof. J. Flisowski – Warsaw University of Technology, Poland Prof. O. Fujiwara - Nagoya Institute of Technology, Japan Dr. B. Jaekel – Siemens AG, Germany Prof. S. Kupari – Helsinki Metropolia, University of applied sciences, Finland Prof. M. Loboda – Warsaw University of Technology, Poland Prof. A. L. Markevičius – Kaunas University of Technology, Lithuania Prof. G. Mazzeti – University of Rome “La Sapienza”, Italy Prof. A. Morkvėnas – Kaunas University of Technology, Lithuania Prof. A. Sauhats – Riga Technical University, Latvia Prof. A. W. Sowa – Bialystok Technical University, Poland Prof. M. Valdma, Tallin Technical university, Estonia

Assoc. prof. K. Maceika – Vilnius Gediminas Technical University, Lithuania Assoc. prof. S. Gudžius – Kaunas University of Technology, Lithuania Dr. R. Markowska – Bialystok Technical University, Poland Assoc. prof. A. Serackis – Vilnius Gediminas Technical University, Lithuania

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Conference Editorial Committee:

Prof. J. Baškys – Vilnius Gediminas Technical University, Lithuania Prof. Z. Flisowski – Warsaw University of Technology, Poland Dr. B. Jaekel – Siemens AG, Germany Assoc. prof. K. Maceika – Vilnius Gediminas Technical University, Lithuania Prof. C. Mazzetti – University of Roma “La Sapienza”, Italy Prof. R. Rinkevičienė (general editor) – Vilnius Gediminas Technical University, Lithuania Prof. A. W. Sowa – Bialystok Technical University, Poland

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CONTENTS 1. ANKUDA Maksim, BOGOSLAV Nadya, OROBEI Igor Creation of system of fields in the primary transducer of NMR-flowmeter ..................................................................... 1

2. ANSKAITIS Aurimas, GURŠNYS Darius, MIKĖNAS Karolis, POCIUS Ričardas Visvaldas The analysis of the influence of adjacent radio stations positions on total electromagnetic field intensity observed near the projected BS ....................................................................................................................................... 6

3. BAHADORZADEH Mehdi, BOLANDPOUR Hossein Improving the shielding effectiveness of a metallic enclosure with aperture under electromagnetic pulse .................. 10

4. BENIUGA Oana, KOVAČ Karol Time domain assessment of electromagnetic disturbances due to artificial ESD in the presence of field and current sensors ........................................................................................................................................................................... 14

5. BITTERA Mikulas, SMIESKO Viktor, OCHODNICKY Oliver Modelling of coaxial cable behavior in EMI measurement ........................................................................................... 18 6. FUKALA Bogdan, PALEČEK Josef Separation of traction and non-traction power consumption by means of modeling .................................................... 22 7. GRAŽULEVIČIUS Gediminas, BARZDĖNAS Vaidotas Computer aided investigation of magnetic fields induced by CFL electronic ballasts .................................................. 26

8. GUDŽIUS Saulius, MARKEVIČIUS Linas Audronis, MORKVĖNAS Alfonsas, ROŽANSKIENĖ Arnolda, AŠMONTAS Ignas Stationary Regime Model of Asymmetrical Line............................................................................................................ 30

9. HALLON Jozef, KOVAČ Karol Technical aspects of electricity meters immunity tests against continuous RF disturbance .......................................... 35

10. JAEKEL Bernd W. Current situation in the frequency range from 2 to 150 kHz with regard to electromagnetic compatibility ................. 39

11. KATKEVICIUS Andrius, MARTAVICIUS Romanas Automated synthesis method for inhomogeneous delay systems using artificial neural networks ................................ 43

12. KOPPEL Tarmo, NIITSOO Jaan Intermediate frequency noise in and from electrical power network in modern working places .................................. 47

13. KRAMMER Anton, BITTERA Mikulas Distributed impedance as terminating load at GTEM cell ............................................................................................ 52 14. KRIAUČIŪNAS Jonas , RINKEVIČIENĖ Roma Disturbances in the controlled variable speed drive ..................................................................................................... 56

15. KRUKONIS Audrius, URBANAVICIUS Vytautas Multiconductor microstrip line modelling using FDTD method ................................................................................... 60 16. LUKOČIUS Robertas, OTAS Konstantinas, MARČIULIONIS Povilas, VIRBALIS Juozapas Arvydas, MARTYNAITIS Jonas, KELBAUSKAS Eduardas Electrical methods of apical constriction location ........................................................................................................ 64

17. LUKOČIUS Robertas, OTAS Konstantinas, MARČIULIONIS Povilas, VIRBALIS Juozapas Arvydas, MARTYNAITIS Jonas, KELBAUSKAS Eduardas Experimental investigation of electrical apical constriction finder ............................................................................... 68

18. LARKINA Vera Plasma turbulence monitoring by means of low-frequency noise satellite measurements ............................................ 72

19. MACEIKA Kazimieras Vytautas Evaluation of meteorological radar antenna radiation ................................................................................................. 75 V

20. MARKOWSKA Renata Influence of cable routing on the flashover distance between lighting protection system and electrical equipment on a building roof ............................................................................................................................................................... 78

21. MAZZETTI Carlo, KUCA Bolesław, SUL Przemysław, FIAMINGO Fabio,KISIELEWICZ Tomasz Objects having an impact on the environment due to the lightning stroke .................................................................... 82 22. METLEVSKIS Edvardas, MARTAVICIUS Romanas Calculation of characteristics of meander slow-wave system with additional shields .................................................. 87

23. MIKUCIONIS Sarunas, URBANAVICIUS Vytautas Quasi-TEM Analysis of Coupled Microstrip Lines on a Multilayered Dielectric.......................................................... 91

24. MORI Ikuko, FUJIWARA Osamu Study on severity evaluation methods for air discharges of ESD generator................................................................. 97 25. PROUDNIK Alexander, KAZEKA Alexandr, BORBOTKO Timofey Modeling of Electromagnetic Radiation Propagation Through a System Source of Radiation – Biological Object – Protection Tool ............................................................................................................................................................ 101

26. ROZEHNAL Petr, UNGER Jan, KREJCI Petr Complaints about power quality and reliability of electricity supply from renewable sources ................................... 107

27. SOWA Andrzej W. Overvoltages in low-voltage power installation with multistep system of surge protective devices ............................ 111

28. SOWA Andrzej W. , AUGUSTYNIAK Leszek Voltage surges at the power supply inputs of devices protected by metal-oxide surge arresters ................................ 114

29. STAŠIONIS Liudas, SERACKIS Artūras Experimental study of spectrum sensing algorithm with low cost SDR ....................................................................... 117

30. UNGER Jan, ROZEHNAL Petr, KREJCI Petr Analysis of quality from customer’s and renewable sources point of view.................................................................. 121 31. VALKY Gabriel, KAMENSKY Miroslav, KOVAC Karol Analysis of errors caused by input channels amplitude discrepancies in time domain EMI measuring system .......... 126

32. WIATER Jarosław Overvoltages at LAN network during high voltage surge excitation ........................................................................... 130

33. WIATER Jarosław Electric system and electronic device common surge impedance for surge excitation ............................................... 133

34. WOJTAS Stanisław Earthing effective length for low and high amplitudes of lightning impulse currents ................................................. 137

35. PLONIS Darius, MALIŠAUSKAS Vacius, MARTAVIČIUS Romanas The Investigation of Microwave Gyroelectric Modulators .......................................................................................... 142

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XXII-nd International Conference on Electromagnetic Disturbances Vilnius, Lithuania, 20-21 September 2012

EMD 2012 http://emd.vgtu.lt

EXPERIMENTAL STUDY OF SPECTRUM SENSING ALGORITHM WITH LOW COST SDR Liudas STAŠIONIS*, Artūras SERACKIS** *[email protected]; **[email protected] Department of Electronic Systems, Vilnius Gediminas Technical University because it needs low computational power and requires no prior knowledge about environment. Additionally in this paper it is proposed to use standard deviation based detector for spectrum sensing, because it has better performance comparing to energy detection method, but computational requirements doesn’t increase dramatically. Development of spectrum sensing algorithms is slowed by lack of experimental studies. Main cause of this situation is cost of dedicated hardware, needed to explore RF spectrum. In this paper is used DVB-T receiver based on Elonics E4000 Tuner as a wide-band SDR, which cost approximately is 10 €. Acquisition of RF signals for two selected spectrum sensing algorithms tests was made using this equipment.

Summary: Development of the efficient algorithm for the spectrum sensor for cognitive radio is slowed by the lack of experiments within a real environment. In this paper we suggest to use a low cost software defined radio (SDR) based on Elonics E4000 Tuner for radio spectrum acquisition. Two spectrum sensing methods are experimentally tested in this investigation. An energy detector based method is selected for wide-band signals and the standard deviation based method is selected for narrow band and frequency hopping signals detection. Keywords: Cognitive radio, SDR, Spectrum sensing, Energy detector, standard deviation. 1. INTRODUCTION

2. MAIN CONCEPTS OF SPECTRUM SENSING ALGORITHMS

Spectrum sensor is a very important element of the cognitive radio (CR). This module is used to find the existing and newly arisen signals in radio spectrum. Currently there is very little radio frequency (RF) spectrum space (up to 3GHz), which is not appointed for service providers [1]. However occupancy in the very-high and ultra-high frequency bands is 10 % – 15 % even in densely populated territories. It means that more than 85 % of RF spectrum is not used [2]. A new approach inspired by CR is suggested for modern and efficient spectrum handling. CR is a transceiver, which searches unoccupied spectrum parts and use these parts for communications [3]. Main challenge of these systems is to use free bands without interference with licensed transmitters. A licensed spectrum owner is considered as a primary user and CR as a secondary. Spectrum sensing module is responsible for analysis of RF band and uses the dedicated algorithms. Currently four most widely used methods for spectrum sensing are based on: matched filter (MF) [4], cyclo-stationary detection [5], energy detection [6] and likelihood radio test [7]. MF, cyclo-stationary and likelihood radio test methods have good signal detection performance, but main disadvantages, the need of prior knowledge about primary user signal or channel properties and high complexity, stops wide distribution of these algorithms [3]. Energy detector is the least complicated algorithm from mentioned methods,

2.1. Concept of spectrum sensing

Any spectrum sensing algorithm in dedicated sub-band must make a decision [6]:

H0 : y ( n) = t ( n);

H1 : y ( n ) = t ( n ) + s ( n ) ;

(1)

here H 0 is the hypothesis that channel is empty, H 1 is the hypothesis that channel is occupied by primary user, t ( n) are the noise components in the received signal, s( n) are the signal components of primary user. Accuracy of decision is crucial [3]. Spectrum sensing algorithms must have excellent signal detection capabilities. If spectrum sensor fails to detect the primary user signal, the unacceptable signal interference with secondary user transmitted signal may occur. 2.2. Energy detector

The spectrum sensing method base on analysis of the signal energy distribution has lowest computational complexity [3]. The average of the signal energy for the channel n can be computed as follows: 1 N -1 2 AVG 2 ( k ) = ∑ y (n) . (2) N n=0 117

It is clear from (7) expression that for this sensor average of the same channel samples should be calculated. It is possible to use this spectrum sensing method together with energy detector, because in this detector the average value is calculated for y (n) and for

The hypothesis H 0 or H 1 is confirmed by comparing the AVG ( k ) value for the channel n with threshold λ 2

H 0 : AVG 2 ( n ) ≤ λ; H1 : AVG 2 ( n ) > λ.

(3)

power of y (n) either. If in (7) equation AVG is

changed to AVG 2 , then two algorithms can be used together with low increase of complexity. More performance improvements can be made by performing parallelization of calculations, required by selected detectors. To confirm hypothesis H 0 or H 1 the

For spectrum sensing algorithms, based on energy detector, main challenge is to set the right threshold value λ . The selection of λ has the influence to the probability of false alarm [8]. Simple strategy for selecting the threshold is to set it higher, than receiver noise floor [9]. This boundary can be found by estimating average of input signal spectrum in analyzed bandwidth. The band for the analysis must be chosen wide enough to incorporate all active noise components. It is optimal, that value would be calculated for whole operational bandwidth. However the selection of the wider band for signal analysis increases the computational load of the system. Another strategy used for calculation of λ is based on noise variance σ 2 estimation [6]. The threshold can be set accordingly to equation:

(

deviation of noise σ noise should be calculated:

H 0 : σ ( n ) ≤ σ noise ; H1 : σ ( n ) > σ noise .

It is difficult to calculate noise estimate for real radio spectrum. The reason is requirement to select manually a channel where environment conditions can be estimated. It is possible to use similar algorithm like FCME for standard deviation detector:

)

λ = σ 2 1 + Q −1 ( Pfa )

N 2 ; (4)

AVG− y ( k + 1) >

here Pfa is the probability of false alarm, N is the total

number of spectrum samples in the channel. Pfa value in (4) equation must be chosen considering risk, because estimated threshold must guarantee non-interference communications with primary user and utilization of unused spectrum. Different strategy for selection of λ value is suggested in Forward Consecutive Mean Excision (FCME) algorithm [10]. The algorithm takes an assumption that the first smallest value in the channel is caused by noise and uses the following comparison:

2 1 k ∑ ( AVG − y ( i ) ) . (9) k i =0

Like for energy detector, the test is repeated for channel, until this comparison is satisfied or sub-band boundary is reached. If first situation arise then the hypothesis H 1 is confirmed, otherwise the band is marked as available for communication. 3. SETUP OF EXPIREMENTAL INVESTIGATION

For the experimental investigation a low cost USB DVB-T receiver is used. This USB dongle is based on two chips: RTL2832u demodulator and Elonics E4000 receiver. The data bandwidth up to 2.8 MHz can be processed by using this receiver. Together with DVB-T USB dongle a freeware HDSDR (High Definition Software Defined Radio) was used to record I/Q samples of RF spectrum. Elonics E4000 is a wide-band receiver, with input frequency range from 64 to 1700 MHz, sensitivity up to 97.7 dBm and noise less than 4 dB. The simplified structure of the E4000 is shown in figure 1.

y ( m + 1) > Tm ∑ y ( i ); (5) m

i =0

here Tm is scaling factor, which defines properties of method, m is the channel spectrum sample number. If this comparison is true, then it is decided that there are components of the primary user signal in the channel. Otherwise for the rest of the channel samples the comparison is repeated until boundary is reached or it satisfies the equation:

Tk = FINV (1- Pfa , 2 N , 2 Nk ) k ; (6) here FINV is the inverse of F-cumulative function. 2.3. Standard deviation detector

The spectrum sensor based on standard deviation estimates deviation of channel samples and makes decision about sub-band occupancy. The deviation is calculated accordingly to equation:

σ ( n) =

1 N

∑ ( AVG − y ( k ) ) N -1

k =0

2

(8)

Fig. 1. Simplified structure of Elonics E4000 receiver

. (7) 118

The receiver has two analog outputs in-phase “I” and quadrature “Q”, which are sampled and streamed through USB by the use of RTL2832u demodulator. In order to keep I/Q data not demodulated the dynamiclink library of demodulator was changed. The RF signal is constructed from I/Q data stream just by addition:

y ( n ) = I ( n ) + jQ ( n ) .

The threshold λ was selected manually (results are shown in figure 3 and figure 4) and using FCME algorithm (results are shown in figure 5 and figure 6). Channel width – minimum area where decision about occupancy is made – selected equal to 10 kHz, as the minimal band required for radio communication.

(10)

The receiver used together with active wide-band antenna, which consists of loop antenna and dipole. The gain of amplifier was changed from 0 dB to 20 dB. RF spectrum samples were recorded in location, which position is latitude 54.685782° and longitude 25.313168°. The location was chosen as a highly populated region and it was expected, that there would be active various nature emitters. 4. RESULTS OF EXPERIMENTAL INVESTIGATION

Four records were selected to properly represent different properties of analyzed methods like narrowband, burst, wide-band signal detection capabilities. The spectrograms of analyzed signals are illustrated in figure 2. First record taken with center frequency Fc = 430 MHz and bandwidth BW = 0.3 MHz. The record has two narrow-band signals with constant parameters (like carrier frequency, band) and one transmitter, which makes long bursts (with approximate duration of 0.5 s). The parameters of the second record are Fc = 443 MHz, BW = 0.2 MHz. Here registered transmitter, which emits narrow-band, short fixed carrier bursts (with approximate duration of 40 ms). Third record parameters are Fc = 925 MHz, BW = 0.6 MHz. A wideband (with approximate bandwidth of 0.2 MHz) signal with changing carrier (frequency hopping). The burst time of third signal is equal approximately to 10–5 ms. Fourth record parameters are Fc = 952 MHz, BW = 0.4 MHz. It is a wide-band signal (approximate bandwidth of the signal is 0.25 MHz) with constant carrier frequency. Two algorithms (energy detector and standard deviation detector) were tested using these records.

Fig. 2. Spectrograms of analyzed signals

Fig. 3. Results of energy detector with manually selected λ

Processing time of four records with energy detector was 9.68 s. This algorithm shows good performance with wide-band and narrow-band signal detection (see figure 3). Burst signals with fixed carrier frequency were located without mistakes, but the false alarm ratio was about 10 %. Signal with frequency hopping detection was less efficient (50 % – 60 % of bursts were detected). It took 22.05 s to process test records by using standard deviation detector. The detector showed excellent performance detecting wide-band, narrowband and burst signals with constant carrier frequency (see figure 5). The detector locates 10 % more burst signals with changing carrier frequency in comparison to energy detector. Processing time for both energy and standard deviation detectors, which used FCME algorithm for threshold setting, took accordingly 280.6 s and 289.4 s. It is 29 and 13 times longer than original algorithms with constant threshold.

Fig. 4. Results of standard deviation detector with manually selected threshold

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(manually and using FCME) better results than energy detector showed standard deviation method. FCME algorithm was inefficient, because neither detection capabilities, neither signal processing speed wasn't near to results achieved by using detectors, which used manually selected threshold. These methods showed excellent performance of detecting narrow-band, wideband and burst signals with fixed carrier frequency. But improvements must be made for both – energy and standard deviation detectors, because these methods have difficulties to detect all bursts of spread spectrum signal. 5. REFERENCES

1. RRT, “Lithuania Radio Spectrum Allocation Table” Available online: http://www.rrt.lt/lt/verslui/istekliai /radijo-dazniai/dazniu-valdymas.html.

Fig. 5. Results of energy detector with selected threshold using FCME algorithm

2. Taher, T. M.; Bacchus, R. B.; Zdunek, K. J.; Roberson, D. A. 2011. Long-term spectral occupancy findings in Chicago, in IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks DySPAN. 100–107.

This difference is received because FCME algorithm uses FINV function to calculate scaling factor Tk in each iteration. Energy detector with FCME algorithm was able to detected only narrow-band and burst (fixed carrier frequency) signals, but false alarm ratio was 40 % – 50 % (see figure 5). Frequency hopping signals weren’t detected at all. There were detected just traces of wideband signal.

3. Yucek, T.; Arslan, H. 2009. A survey of spectrum sensing algorithms for cognitive radio applications, Communications Surveys & Tutorials 11(1): 116–130.

4. Bhargavi, D.; Murthy, C.R. 2010. Performance comparison of energy, matched-filter and cyclostationarity-based spectrum sensing in IEEE Eleventh International Workshop on Signal Processing Advances in Wireless Communications SPAWC. 1–5.

5. Du, K.-L.; Wai H. M. 2010. Affordable cyclostationarity-based spectrum sensing for cognitive radio with smart antennas in Vehicular Technology 59(4): 1877–1886. 6. Ramly S.; Newagy F.; Yousry H.; Elezabi A. 2011. Novel Modified Energy Detection Spectrum Sensing Technique for FM Wireless Microphone Signals in International Conference on Commun. Software and Networks ICCSN‘11. 59–63. 7. Kay S. M. 1988. Fundamentals of Statistical Signal Processing: Detection Theory. NJ: Prentice-Hall. 672 p.

8. Penna F.; Pastrone C.; Spirito M. A.; Garello R. 2009. Energy Detection Spectrum Sensing with Discontinuous Primary User Signal in International Conference on Communications ICC'09. 1–5.

Fig. 6. Results of standard deviation detector with selected threshold by FCME algorithm

Standard deviation detector has located 80 % of one of narrow-band signal and another – completely (see figure 6). There were also detected burst signals with constant carrier frequency, but false alarm ratio was about 8 %. 80 % of wide-band signal components were detected, but false alarm ratio was 10 %. In frequency hopping signal there were detected about 35 % bursts with great false alarm ratio.

9. Imani S.; Dehkordi A. B.; Kamarei M. 2011. Adaptive Sub-optimal Energy Detection Based Wideband Spectrum Sensing for Cognitive Radios in International Conference on Electrical, Control and Computer Engineering INECCE’11, Pahang, Malaysia. 22–26.

10. Lehtomaki J.J.; Vartiainen J.; Juntti M.; Saarnisaari H. 2006. Spectrum Sensingwith Forward Methods in Military Communications Conference MILCOM. 1 – 7.

5. CONCLUSIONS

Two spectrum sensing methods and two threshold setting strategies have been tested by using records of real RF spectrum. In both threshold setting cases 120