Performance Evaluation of a Narrowband Power Line ... - IEEE Xplore

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IEEE Transactions on Consumer Electronics, Vol. 57, No. 4, November 2011

Performance Evaluation of a Narrowband Power Line Communication for Smart Grid with Noise Reduction Technique Mehdi Korki, Nasser Hosseinzadeh, Senior Member, IEEE, and Taleb Moazzeni Abstract — Performance of the narrowband power line communication (PLC) is significantly degraded by the impulsive noise with very large amplitudes and short durations. In practical applications, the simple memoryless nonlinearity techniques (Clipping, Blanking, and Clipping/Blanking) are often used in order to mitigate the effect of the impulsive noise. In this paper, we propose an optimal Clipping/Blanking technique for impulsive noise reduction in narrowband (9-490 kHz) PLC system. This optimal technique is based on the minimum bit error rate (BER) search. To this end, we have derived the transfer function of a typical low voltage (LV) PLC network using the common bottom-up approach and scattering matrix method. Our simulation results, in terms of BER versus signal to noise ratio (SNR), show that the proposed technique slightly improves the BER performance of the narrowband PLC system for smart grid applications and two-way communication between smart meters and utilities1. Index Terms — Power line communication, smart grid, Clipping/Blanking, noise reduction technique.

I. INTRODUCTION The efficiency, safety and reliability of the electricity transmission and distribution system can be improved by transforming the current electricity grids into an interactive (demand/response) service network or smart grid. In particular, Advanced Metering Infrastructure (AMI) provides consumers with the ability to use electricity more efficiently and enables operators to monitor and repair their network in real time. Smart grid communication technologies must enable the power grid control center to access each meter connected to it several times in a second, offering dynamic visibility into the power system. A cost-effective way for data communication between the meters and the control centers over the AMI system is power line communication (PLC) technology, which uses the existing power line infrastructure as the communication medium. Recently, narrowband PLC systems have gained interest for AMI applications. For instance, two projects have been devoted to the standardization of high data rate (HDR) narrowband PLC 1 M. Korki and N. Hosseinzadeh are with the Faculty of Engineering & Industrial Sciences, Swinburne Univ. of Technology, Hawthorn, VIC 3122, Melbourne, Australia (email: [email protected] & [email protected]). T. Moazzeni is with Dept. of Electrical & Computer Engineering, Univ. of Nevada Las Vegas, Las Vegas, NV 89154, USA (email: [email protected]).

Contributed Paper Manuscript received 08/28/11 Current version published 12/27/11 Electronic version published 12/27/11.

transceivers (IEEE 1901.2 and ITU-T G.hnem) [1]. Also there are some non-Standard Developing Organization (SDO)based HDR narrowband PLC solutions for communications between the meters and the control centers, such as PoweRline Intelligent Metering Evolution (PRIME) and G3-PLC [2]. Narrowband PLC technologies are operating in the frequency bands between 3 kHz and 500 kHz, which include the European CENELEC (Comité Européen de Normalisation Électrotechnique) bands (3- 148.5 kHz), the US FCC (Federal Communications Commission) band (9-490 kHz), the Japanese ARIB (Association of Radio Industries and Businesses) band (9-450 kHz), and the Chinese band (3-500 kHz) [1]. HDR narrowband PLC utilizes multicarrier technologies which are capable of data rates ranging between tens of kilobits per second and up to 500 kb/s. On the contrary, broadband PLC systems reach a data rate up to 8Mb/s using the broadband PLC standards, such as HomePlug 1.0. Recently, an updated version of HomePlug 1.0, the socalled “HomePlug AV”, has been created to improve the peak data rate to 200 Mb/s [3]. Moreover, HomePlug AV has been adopted by the P1901 working group as part of a group of standards related to smart grids, automatic meter reading (AMR), and remote management of electrical power [3]. Some implementations exist for AMI infrastructure using wireless technologies. For instance, in Victoria, Australia, the smart metering system makes use of low power radio frequency (RF) transmitters to communicate meter readings to the control centers. In this paper, we investigate the feasibility of using the existing power infrastructure for this purpose; i.e. the low voltage (LV) power lines being used as a medium for data communication between several meters located on different parts of the network. The basic structure of the AMI communications system on PLC is shown in Fig. 1. Each meter at the consumer side is equipped with an interface unit, so-called “Meter Interface Unit (MIU)” which acts as a transceiver for meter data on the low voltage power lines using a single-phase Power Line Modem (PLM). The data sent by the MIU is received by the Data Concentrator Unit (DCU) located at the distribution transformer. DCU is fitted with a three-phase PLM collecting data from all meters on different phases. The data received by DCU is then transmitted to the Utility Central Unit (UCU). The communication link in this case is usually through different communications networks such as existing cellular or cable networks. The communication links between the MIUs and their DCU, or between the DCUs and the UCU units are bi-

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M. Korki et al.: Performance Evaluation of a Narrowband Power Line Communication for Smart Grid with Noise Reduction Technique

directional links, which allow the current meter data to be checked against the historical data for any discrepancy.

 

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networks, especially when the signal to noise ratio (SNR) is at a decent level. According to [6], the noise at low frequency band is more energetic than at higher frequencies in broadband PLC systems which results in performance degradation of conventional narrowband PLC systems. The main reason of low performance of conventional narrowband PLC systems is that they usually use the simple and antiquated modulation schemes, such as frequency shift keying (FSK) and quadrature phase shift keying (QPSK) [7], which are not suitable for the hostile environment like PLC. Therefore, the employment of sophisticated modulation schemes may result in much higher performance.

 

 

3.Periodic impulsive noise asynchronous to the mains 2.Narrowband noise

Fig. 1. AMI system structure on PLC. 1.Colored noise

Since power lines were originally devised to transmit electric power from a small number of sources (the generators) to a large number of sinks (the consumers) in the frequency range of 50-60 Hz, data transmission over PLC channel undergoes severe degradation introduced by the characteristics of the PLC channel at higher frequencies. The most important PLC channel characteristics which make the performance of the PLC system worse are noise, attenuation and multipath propagation [4]. Hence, the interference scenario in PLC system is important. In contrast to many other communication channels, power lines do not represent additive white Gaussian noise (AWGN) channels [4]. The noise represented by the power line is more complicated and can be classified into five types as shown in Fig. 2 [4]. After transmitted signal passes through the impulse response h(t) of the channel, several types of noise n(t) are added, before the signal r(t) arrives at the receiver. As shown in Fig. 2, the five types of noise are denoted as colored background noise, narrowband noise, periodic impulsive noise asynchronous to the mains, periodic impulsive noise synchronous to the mains, and asynchronous impulsive noise. The noise types 1, 2 and 3 usually remain stationary over relatively longer periods, of seconds, minutes and sometimes even of some hours. Hence, all these three types of noise can be summarized into one noise class which is known as generalized background noise. On the contrary, the last two types (types 4 and 5) have a time-varying nature in the time span of milliseconds and microseconds, and can be categorized into one noise class called impulsive noise. Furthermore, impulsive noise can also be classified into sub classes, such as single pulses and bursts, which can be determined by experimental measurements [5]. Practical experiments in PLC show that the power spectral density (PSD) of the impulsive noise exceeds the PSD of the background noise by minimum of 10-15 dB and may sometimes reach more than 50 dB [4]. Therefore, the impulsive noise is the principal cause of errors in digital communication over broadband (1.8-250 MHz) PLC

4.Periodic impulsive noise synchronous to the mains 5.Asynchronous impulsive noise Noise n(t)

s(t) Transmitter

h(t)

H(f)

r(t)

Receiver PLC channel Fig. 2. Noise scenario on power lines.

Orthogonal Frequency Division Multiplexing (OFDM) is a potential candidate for communication on narrowband LV PLC [8], due to its robustness to multipath, frequencyselective fading and different kinds of interference. In contrast to the single carrier systems, OFDM systems present better performance in impulsive noise environments. This is because in OFDM systems, the longer symbol periods result in more robustness against the impulsive noise and the impulse noise energy is spread over transmitted OFDM subcarriers. Although OFDM enhances the performance of narrowband PLC systems in the presence of impulsive noise, the employment of mitigation techniques is ineluctable in order to cope with PLC channel conditions and to achieve higher data rates. In order to reduce the adverse effect of impulsive noise a memoryless nonlinearity can be applied at the receiver frontend of the conventional OFDM demodulator. This technique has been applied in wireless applications [9]. However, power line channels exhibit different channel characteristics compared to other communication channels. In practical applications the simple nonlinearity technique can be combined with the other techniques to further improve the PLC system performance. For instance, it is shown in [10] that the Clipping technique and equalizer as an impulsive noise mitigation technique can enhance the performance of implemented PLC systems in smart grid applications. In this paper, we utilize the Clipping/Blanking memoryless nonlinearity technique for mitigating the effect of impulsive noise in OFDM systems over narrowband PLC channels. We adopt the typical bottom-up modeling algorithm; i.e. the scattering matrix method to obtain the transfer function of

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PLC channel in low frequency band (9-490 kHz) for typical LV distribution networks. The impulse response of this channel transfer function is used as the channel model to simulate the performance of Clipping/Blanking for mitigating the effect of impulsive noise in OFDM over narrowband PLC systems. We also propose a method to find optimal threshold, corresponding with different values of SNR, which minimizes the bit error rate (BER). This proposed method further improves the performance of Clipping/Blanking technique in the presence of impulsive noise over narrowband PLC systems. The rest of this paper is organized as follows. In section II, noise model is reviewed and the analytical PLC channel model with some numerical results is presented. Then, the OFDM system model is presented in the presence of impulsive noise. The simulation results of Clipping/Blanking using the developed PLC channel are given in section III. Then, in section IV the proposed Clipping/Blanking technique with optimal threshold is given and some simulation results are illustrated. Finally, the paper is concluded in section V. II.

SYSTEM MODEL

A. Noise Model The major impairments to communication over power line are generalized background noise and impulsive noise. The latter is usually generated by switching on power lines. Although the impulsive noise appears during short time intervals, it is considered the main source of errors in data transmission over PLC channels due to its high power spectral density (PSD). The Middleton’s Class A model can be considered as a statistical model of impulsive noise [11]. Generally a Middleton’s Class A model is suggested for both background and impulsive noise, in order to generate a worst-case scenario in power line noise modeling [12]. This Class A model describes narrowband noise by small number of specified parameters. In general, the received noise waveform in a narrowband system is described by [13] z (t )  A(t ) cos(2f c t   (t )) ,

(1)

where A(t) is the noise envelope, fc is the noise center frequency and φ(t) is the phase angle for the envelope. The noise, z(t)=x(t)+jy(t), is a complex variable in which x(t) and y(t) are the in-phase and quadrature components of z(t), respectively. To evaluate the performance of a realistic PLC network, the Middleton’s Class A model is applied in our simulation. The probability density function (PDF) of Middleton’s Class A model is given by Amirshahi et al. [12] as pz ( z) 



e  A Am m! m 0



1 2 m 2

 z2 exp   2 2 m 

  ,  

(2)

where

 m 2  ( g 2   i 2 )

m A , 1 

(3)

is the variance which increases as m increases. Note that (2) represents a weighted sum of Gaussian distribution. Three important parameters are used in Class A model. The first, A, is called “impulsive index” and is described as A=vt·Ts where vt is the mean impulse rate and Ts is the mean impulse duration [14]. Small values of A suggest that the probability of pulses overlapping in time is small, which indicates the highly structured interference with large noise values for a small fraction of time. Large values of A imply that the probability of pulses overlapping in time is large, which results in the moderately or weakly structured interference. The next is the Gaussian-to-Impulsive variance Ratio (GIR), Γ, which can be defined as Γ=σg 2/σi 2 where σg 2 is Gaussian noise variance and σi 2 is the impulsive noise variance. Note that small value of Γ and small value of A give a highly impulsive noise type. The third parameter, σ 2=σg 2+σi 2, is the variance of the noise which indicates the average noise (Gaussian plus impulsive) power for Middleton’s Class A model. B. Channel Model In the literature, several techniques have been introduced to model the transfer function of the power lines. Generally, there are two factors in these techniques: the model parameters and the modeling algorithm. The reliability and accuracy of the model is dictated by these two factors. From the parameters estimation standpoint, two general approaches are taken into account: a top-down and a bottom-up approach. The former approach, describes the multipath propagation via an echo model in the time or frequency domain, using a model whose parameters are attained by fitting the results from measurements [15]. These models are simple and can be implemented in the simulation easily, but they depend heavily on the accuracy of the measurements. Furthermore, they cannot describe the power line network topology and loading effects. On the other hand, the bottom-up approach is based on the theoretical derivation of the model parameters [16]. Although it requires more computational effort than the former one, it describes clearly the relationship between the network behavior and the model parameters. For this reason, this modeling technique is more flexible and can better describe the network behavior as a function of the physical parameters. In this paper, we utilize the bottom-up approach to model the characteristics of the low voltage (LV) power lines and to study its properties within the frequency range 9-490 kHz for narrowband PLC systems. According to the lumped-element circuit model, the typical bottom-up modeling algorithms used in PLC are the transmission matrix or the scattering matrix [16]. While the transmission matrix relates the total voltages and currents at the ports of a two-port network, the scattering matrix relates the voltage waves incident on the ports to those reflected from the ports. The scattering matrix method is more accurate than the transmission matrix method. This is because the scattering matrix method represents the attenuation discontinuities at the branching points more accurately. In this paper, we use the

M. Korki et al.: Performance Evaluation of a Narrowband Power Line Communication for Smart Grid with Noise Reduction Technique

scattering matrix method to find the channel transfer function of a typical LV power line. Consider a typical LV power line topology as shown in Fig. 3 [17]. In this topology each solid line represents a main branch which is mostly made up of threephase bare aluminum conductor steel reinforced (ACSR) overhead lines with a length of approximately 100 meters between two power poles. The dashed lines represent the singlephase branch cables made of stranded copper conductors with PVC insulations, terminating at residential properties.

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hardwood cross arms. As depicted in Fig. 4, the multiple earthedneutral system is widely used and neutral conductors are connected to earth electrodes at every other pole and at each consumer cutout. Single-phase insulated service cables are connected to line and neutral conductors at most poles and attempts are made to roughly balance the system by connecting loads to alternate phases when this is possible. The conductors of three-phase configuration are usually bare ACSR. The details of formulas for primary line parameters relevant to copper and ACSR conductors can be found in [16], [18], and [19].

P1 50cm 50cm 50cm

A

P2

B

C

N

15m

Ground Wire

P3

Ground plate

Fig. 4. Typical low voltage distribution line structure.

Fig. 3. Typical low voltage power line network topology.

On the basis of the lumped-element circuit model [16], the two parameters of the transmission line modeling, the propagation constant γ and the characteristic impedance Z0, can be given as [16]

    j  ( R  jL)(G  jC ) , Z0  (

R  j L , ) G  j C

(4) (5)

where ω is the angular frequency, α and β are the attenuation constant (in Np/m) and phase constant (in rad/m) of the transmission line, respectively. The electrical properties of a transmission line at a given frequency are completely characterized by its four distributed parameters R, L, G, and C, which represent resistance, inductance, shunt conductance and shunt capacitance of a unit length (m) of the transmission line. The formulas of these parameters can be expressed in terms of the physical dimensions and the medium constitutive parameters for both ACSR and stranded copper cables. A typical threephase distribution line structure is shown in Fig. 4, which is used in many places in the world including Australia. Three-phase LV distribution in Australia is often handled via flat configuration, 4-wire, 400-415V, overhead lines. These threephase overhead lines are generally supported by wood poles with

We obtain the transfer function for the typical topology shown in Fig. 3, using the scattering matrix method. For the example topology studied in this paper, the values between 5 m and 10 m of length for single-phase copper cables, and 10 mm2 and 181.6 mm2 of cross-sectional area for copper cable and ACSR line are used, respectively. The impedance of the transmitters is matched to the characteristic impedance of the transmission line. The loading effect of the appliances inside the residential properties is modeled using different RLC circuit configurations as suggested by Bausch et al. [20]. Consequently, the transfer functions of the three-phase power line channel over the 9~490 kHz frequency band for the transmission pairs (P1 to P2 and P1 to P3 in Fig. 3) using the scattering matrix method is depicted in Fig. 5. It is found that the attenuation between points P1 and P3 is generally 13-15 dB more than the attenuation between points P1 and P2, in the frequency range of 150-250 kHz. Attenuation over 450 kHz is quite high, between 55 dB and 65 dB, for both transmission pairs. It also can be seen from Fig. 5 that there are strong attenuation notches at about 100 kHz, which are mainly caused by the impedances of the branch loads inside the residential properties. C. OFDM System Model Orthogonal Frequency Division Multiplexing is used extensively in broadband wired and wireless communication systems because it is an effective solution to combat intersymbol interference (ISI) caused by a dispersive channel. Also, OFDM is widely used in broadband PLC, according to Anatory et al. [21], and has been investigated as a technique

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for increasing the bandwidth of narrowband power line communications by Lakshmi et al. [22], and Galda et al. [23]. Recently a consortium of several companies has been working on the so-called PoweRline Intelligent Metering Evolution (PRIME) project, a system for communication in a power line grid [8]. PRIME is a physical and medium access control (MAC) layer standard based on up-to-date technologies. The MAC layer provides core functionalities of system access, bandwidth allocation, connection establishment/maintenance and topology resolution, but these functionalities are not the focus of this paper. The physical layer of PRIME specifications is based on OFDM multiplexing in CENELEC A-band and reaches up to 130 kbps raw data rate. It uses a frequency range 41-89 kHz. In this paper, we choose PRIME specifications to evaluate the performance of the channel model derived in this section in terms of bit error rate (BER) versus signal to noise ratio (SNR). 120

From P1 to P2 From P1 to P3

Amplitude (dBuV)

110

PDF is given in (2). The PDF of wm(n) is given by 

e  A Aq q! q 0

p w ( w)  

1 2 q 2

 w2 exp   2 2 q 

  .  

Therefore, from (8), wm(n) is the Middleton’s Class A noise with zero mean and the following variance [12]

 w2 

e  A g 2 



Aq  q   q!  A    . q 0

(9)

The transmitted symbols are recovered from the received sequence by performing an N point discrete Fourier transform (DFT) as follows N 1

1

j

2nik N

 rm (n)e , N n 0  H (ik ) S m (k )  Wm (ik )

Rm (ik ) 

100

(8)

(10)

90

where Wm(ik) is the Fourier transform of wm(n) and is expressed as

80 70 60

Wm (ik ) 

50 40 0

1

2

3

4

5

Frequency (Hz) x 10 Fig. 5. Transfer function of three-phase power line channel model using scattering matrix method. (points P1, P2 and P3 are shown in Fig. 3) 5

According to the OFDM system of PRIME [8], as shown in Fig. 6, with N subcarrier composed of K data subcarriers and N-2K null subcarriers, the time domain OFDM signal at the mth OFDM symbol after the inverse discrete Fourier transform (IDFT) (as denoted in Fig. 7) is expressed as s m ( n) 

1 N

K 1 



 S m (k )e

k 0 

j

2nik N

 S m * ( k )e

j

2n ( N ik ) N

  , (6) 

where Sm(k) is the mth OFDM symbol and ik is the kth data subcarrier. The asterisk represents complex conjugate. A cyclic prefix (CP) of length NCP is appended to the beginning of each time domain OFDM signal. Assuming perfect timing synchronization, after CP removal, the received signal at the mth OFDM symbol over PLC channel with impulsive noise can be given as

1 N

N 1



wm (n)e

j

2nik N

.

(11)

n 0

Based on Central Limit Theorem, Wm(ik) is Gaussian with zero mean and standard deviation σw [12]. In fact, DFT procedure spreads the effect of impulsive noise over multiple subcarriers in a way that the noise on each subband exhibits a Gaussian behaviour. This is one of the major benefits of OFDM system over an impulsive noise, since the impulse noise energy is spread over all OFDM subcarriers. Nevertheless, if mitigation techniques are not employed for impulsive noise, it can still have a considerable effect on the performance of OFDM systems in LV narrowband PLC. Hence, the effect of impulsive noise can be mitigated by applying a memoryless nonlinearity technique in the time domain.

Fig. 6. Using subcarrier for OFDM system of PRIME.

L 1

rm (n)   h(l ) s m (n  l )  wm (n) ,

(7)

l 0

where h(l) denotes the channel impulse response with length of L, wm(n)=Re{zm(n)} and zm(n) is the impulsive noise, whose

III. CLIPPING/BLANKING WITH FIXED THRESHOLD In the literature, different techniques have been proposed for impulsive noise reduction [24]-[26]. In practical applications, the memoryless nonlinearity techniques (Clipping, Blanking, and

M. Korki et al.: Performance Evaluation of a Narrowband Power Line Communication for Smart Grid with Noise Reduction Technique

Clipping/Blanking) are usually applied for impulsive noise reduction. This is because they are simple to implement [9]. It is shown in [9] that combined nonlinearity technique (Clipping/Blanking) performs better than Clipping or Blanking nonlinearity techniques for wireless applications. This is also true for broadband and narrowband power line communication (PLC) [26], [27]. To this end, we use combined nonlinearity technique (Clipping/Blanking) to reduce the effect of impulsive noise in narrowband PLC. In OFDM system, the OFDM demodulator is usually preceded with the Clipping/Blanking nonlinearity as shown in Fig. 7. It should be noted that the Clipping/Blanking nonlinearity technique changes the amplitude of the signal only and the phase of the signal is not modified. Using the received signal before Clipping/Blanking, the Clipping/Blanking is executed as rm (n), rm (n)  T1  j arg( r ( n )) r m (n)  T1e , T1  rm (n)  T2 ,  rm (n)  T2 0, _

m

(12)

where T1 is the Clipping threshold and T2 is the Blanking threshold.

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Since, the performance of narrowband PLC is strongly affected by the impulsive noise with very large amplitudes and short durations [27], we set A=0.001 and Γ=0.001 as the parameters of the Middleton’s Class A noise in order to simulate a highly impulsive environment. The results of this simulation with Clipping/Blanking for three-phase power line channel using the computed transfer function by scattering matrix method is shown in Fig. 8. It is clear from Fig. 8 that the Clipping/Blanking can improve the BER performance of the narrowband PLC in the highly impulsive noise environment significantly for both transmission pairs (P1 to P2 and P1 to P3 in Fig. 3). In this simulation, the fixed threshold values are T1=T and T2=1.4T. The fixed threshold (T) is optimized at 22 dB and 35 dB SNR for the first (P1 to P2) and second (P1 to P3) transmission pairs, respectively. TABLE I OFDM MODULATION PARAMETERS

Parameter

Value

Modulation for carriers Total raw data rate Frequency range Coding scheme Interleaving Cyclic prefix (samples) or NCP Cyclic prefix (µs) Number of data subcarriers (K) Number of pilot subcarriers FFT interval (samples) or number of subcarriers (N) FFT interval (µs) Symbol interval (samples) Symbol interval (µs) Subcarrier bandwidth (Hz)

DQPSK 42.9 kbps 41-89 kHz ½ rate convolutional code block 48 192 96 1 512 2048 560 2240 488.28125

0

10

P1 to P2 with AWGN only P1 to P2 with AWGN+Impulsive P1 to P2 with Clipping/Blanking P1 to P3 with AWGN only P1 to P3 with AWGN+Impulsive P1 to P3 with Clipping/Blanking

-1

10

Fig. 7. Block diagram of the OFDM-based PLC system with impulsive noise reduction.

-2

As shown in Fig. 7, at the transmitter side the random generated bits are mapped into DQPSK symbols and then modulated using OFDM modulator (IFFT). This signal is then passed through the impulse response of the PLC channel h which has been derived in section II. After that, the impulsive noise which is Middleton’s Class A model is added to the OFDM signal. At the receiver side signal rm is first passed through Clipping/Blanking nonlinearity and then demodulated using OFDM demodulator (FFT). Then, the signal is demapped and the output bits, after deinterleaving and decoding, are given for evaluating the BER performance. Table I summarizes the parameters for the modulation used in our simulation. We consider the corresponding channel gain in the frequency domain H(ik) with E[│H(ik)│2]=1.0. In each simulation, the average power of the transmitted OFDM signals is also unity and SNR is defined as 2

SNR  E[ S m (k ) ]  g 2 .

(13)

BER

10

-3

10

-4

10

-5

10

-6

10

10

15

20

25

30

SNR (dB)

35

40

45

Fig. 8. BER vs. SNR for LV PLC system with Clipping/Blanking.

In Fig. 9, BER versus Clipping/Blanking threshold values (T1 and T2) between both transmission pairs (P1 to P2 and P1 to P3 in Fig. 3) are depicted for different values of SNR. It can be seen from Fig. 9 that there exists an optimal threshold value for Clipping/Blanking nonlinearity, corresponding with an SNR value, at which the BER is minimized. Therefore, we propose an optimal Clipping/Blanking nonlinearity technique in which

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the threshold values are chosen corresponding with the minimum BER values for different SNR values. In this proposed technique, the threshold values are chosen on the basis of a Matlab algorithm that searches for the threshold value with minimum BER for each SNR value, as shown in Fig. 10. The simulation result of the proposed optimal-threshold Clipping/Blanking technique for impulsive noise mitigation in narrowband PLC system, in terms of BER versus SNR, is illustrated in Fig. 11. It is observed from Fig. 11 that, the proposed optimal-threshold Clipping/Blanking technique performs better than the Clipping/Blanking with fixed threshold values. In order to observe the influence of the OFDM modulation parameters on the proposed noise reduction technique, we investigate the BER performance (between points P1 and P2 in Fig. 3) for the effect of channel coding and different modulation schemes. In Fig. 12, the BER performance of narrowband PLC system without channel coding in highly impulsive noise PLC channel (A=0.001and Γ=0.001) is depicted. It is observed from Fig. 12 that with Clipping/Blanking (with fixed threshold) DBPSK, DQPSK, and D8PSK illustrate 26 dB, 28 dB, and 32 dB BER performance at 10-3, respectively. The BER performance of all modulation schemes at 10-3 increases around 0.4~0.6 dB when the optimal-threshold Clipping/Blanking technique is applied. Hence, the proposed technique enhances the system performance slightly compared to the conventional Clipping/Blanking technique. The BER performance of narrowband PLC system with ½ rate convolutional coding in highly impulsive noise PLC channel is shown in Fig. 13. In this case, the overall BER performance of the system at 10-3 is approximately improved by 7 dB. There is also additional performance enhancement between 0.5 dB and 0.7 dB due to the effect of optimalthreshold Clipping/Blanking technique. Therefore, the BER performance for DBPSK, DQPSK, and D8PSK is 18.8 dB, 20.6 dB, and 25.8 dB at the above-mentioned point, respectively. 0

10

Fig. 10. Pseudo code of the minimum BER search algorithm.

IV. CONCLUSION In this paper, we investigated the performance of Clipping/Blanking nonlinearity technique in the narrowband OFDM-based PLC system. We derived the transfer function of the PLC channel using the typical bottom-up approach; i. e. scattering matrix method. The Middleton’s Class A model was used to simulate the impulsive noise in narrowband PLC system. We used Matlab simulation to evaluate the BER performance of the PLC system. We showed that applying the Clipping/Blanking technique at the receiver front-end of the OFDM demodulator decreases the BER significantly and it leads to performance enhancement of PLC system over 9~490 kHz frequency band. Furthermore, we proposed an optimal Clipping/Blanking technique based on the minimum BER search. We demonstrated that the BER performance of the narrowband OFDM-based PLC system is slightly improved by using optimal Clipping/Blanking as compared to the Clipping/Blanking technique with fixed threshold. The results of this paper can be applied in narrowband PLC systems for smart grid applications in order to reduce the effect of impulsive noise. 0

10 -1

10

-1

10 -2

10

-2

-3

10

BER

BER

10

-3

10

-4

10

-5

10

-6

10

0

0.1

0.2

0.3

0.4

Clipping/Blanking threshold (T)

0.5

P1 to P2 with AWGN only

-4

P1 to P2, SNR=20 P1 to P2, SNR=18 P1 to P2, SNR=22 P1 to P3, SNR=20 P1 to P3, SNR=30 P1 to P3, SNR=40

10

P1 to P2 with AWGN+Impulsive P1 to P2 with Clipping/Blanking P1 to P2 with Opimal Clipping/Blanking

-5

P1 to P3 with AWGN only

10

P1 to P3 with AWGN+Impulsive P1 to P3 with Clipping/Blanking

0.6

Fig. 9. BER at the output of the OFDM (Viterbi Decoder) receiver with Clipping/Blanking as a function of threshold value (T1=T and T2=1.4T).

P1 to P3 with Optimal Clipping/Blanking

-6

10

15

20

Fig. 11. BER vs. Clipping/Blanking.

25

SNR

30

SNR (dB) for

LV

35

40

PLC

system

45

with

optimal

M. Korki et al.: Performance Evaluation of a Narrowband Power Line Communication for Smart Grid with Noise Reduction Technique [6]

0

10

-1

10

[7]

-2

10

[8]

BER

-3

10

-4

10

-5

10

-6

10

[9]

DBPSK with Clipping/Blanking DBPSK with optimal Clipping/Blanking DQPSK with Clipping/Blanking DQPSK with optimal Clipping/Blanking D8PSK with Clipping/Blanking D8PSK with optimal Clipping/Blanking

[10]

-7

10

15

20

SNR (dB)

25

30

Fig. 12. BER vs. SNR (between point P1 and P2 in Fig. 3) for uncoded LV PLC system using noise reduction techniques in highly impulsive noise environment (A=0.001 and Γ=0.001).

[11] [12]

[13] DBPSK with Clipping/Blanking DBPSK with optimal Clipping/Blanking DQPSK with Clipping/Blanking DQPSK with optimal Clipping/Blanking D8PSK with Clipping/Blanking D8PSK with optimal Clipping/Blanking

0

10

-1

10

-2

10

[14]

[15]

BER

-3

10

[16]

-4

10

[17]

-5

10

-6

10

[18]

-7

10

15

20

SNR (dB)

25

30

Fig. 13. BER vs. SNR (between point P1 and P2 in Fig. 3) for coded LV PLC system using noise reduction techniques in highly impulsive noise environment. (Convolutional code)

REFERENCES [1] [2]

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S. Galli, A. Scaglione, and Z. Wang, “For the grid and through the grid: the role of power line communications in the smart grid,” Proceedings of the IEEE, vol. 99, no. 6, pp. 998-1027, June 2011. D. P. Shaver, “Narrowband PLC solutions for AMI achieve long distance communications and flexibility with immediate market impact,” IEEE Int. Conf. on Consumer Electron. (ICCE 2011), Las Vegas, USA, pp. 601-602. S. H. Russ and S. Alsharif, “Packet loss behavior of HomePlug AV traffic at video bit rates,” IEEE Trans. Consumer Electronics, vol. 57, no. 2, pp. 823-826, May 2011. M. Zimmermann and K. Dostert, “Analysis and modeling of impulsive noise in broad-band powerline communications,” IEEE Trans. Electromagn. Compat., vol. 44, no. 1, pp. 249-258, Feb. 2002. V. Degardin, M. Lienard, A. Zeddam, F. Gauthier, and P. Degauquel, “Classification and characterization of impulsive noise on indoor power line used for data communications,” IEEE Trans. Consumer Electronics, vol. 48, no. 4, pp. 913-918, Nov. 2002.

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[26] K. Al. Mawali, A. Z. Sadik, and Z. M. Hussain, “Joint timedomain/frequency-domain impulsive noise reduction in OFDMbased power line communications,” In Proc. of the Australian Telecommunication Networks and Applications Conference, (ATNAC 2008), Adelaide, Australia, Dec. 2008. [27] A. Mengi, and A. J. H. Vinck, “Successive impulsive noise suppression in OFDM,” International Symposium on Power Line Communications and Its Applications, (ISPLC 2010), pp. 33-37, Brazil, 2010. BIOGRAPHIES

Nasser Hosseinzadeh (M’86-SM’11, CIGRE-APC1) is currently with Swinburne University of Technology, Melbourne. Earlier, he worked as a senior lecturer at Central Queensland University in Australia, as a lecturer at Monash University, Malaysia and as an assistant professor at Shiraz University, Iran. His special fields of interest include power system analysis and planning, wind energy systems, power system stability, applications of intelligent control in power engineering, and engineering education. Dr. Hosseinzadeh is a Member of IEEE and also is on the Australian Panel C1 (System Development and Economics) of CIGRE.

Mehdi Korki was born in Boushehr, Iran, in 1978. He received his B.Sc. degree in electrical engineering from Shiraz University in February 2001. From 2001 to 2009 he has worked as a professional engineer for Telecommunication Company of Boushehr (T.C.B), Iran. He is currently studying for a Master of Engineering (by research) at Swinburne University of Technology. His research interests are in the field of Power Line Communication (PLC), digital communications, optical transmission systems, fiber optic cables, and multiple access systems.

Taleb Moazzeni was born in Jam, Iran, in 1978. He received his B.S. and M.S degrees in Electrical Engineering from Shiraz University and Petroleum University of Technology, Tehran, respectively. He has received his Ph.D. degree on signal processing algorithms from the University of Nevada, Las Vegas (UNLV) in 2010. From 2006 to 2007, he was a scholar visitor working at the UNLV on multiple divisions multiple access schemes.