Analysis of low power wireless links in smart grid environments

Computer Networks 57 (2013) 1192–1203

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Analysis of low power wireless links in smart grid environments Necati Kilic ⇑, V. Cagri Gungor Department of Computer Engineering, Bahcesehir University, Ciragan Caddesi 34353 Besiktas, Istanbul, Turkey

a r t i c l e

i n f o

Article history: Received 12 June 2012 Received in revised form 22 November 2012 Accepted 14 December 2012 Available online 23 December 2012 Keywords: Smart grid Wireless sensor networks Low power wireless links Smart grid applications

a b s t r a c t Recently, wireless sensor networks (WSNs) have been used in various smart grid applications, including remote power system monitoring and control, power fraud detection, wireless automatic metering, fault diagnostics, demand response, outage detection, overhead transmission line monitoring, load control, and distribution automation. However, harsh smart grid environment propagation characteristics cause great challenges in the reliability of WSN communications in smart grid applications. To this end, the analysis of wireless link reliability and channel characterizations can help network designers to foresee the performance of the deployed WSN for specific smart grid propagation environments, and guide the network engineers to make design decisions for the channel modulation, encoding schemes, output power, and frequency band. This paper presents a detailed analysis of low power wireless link reliability in different smart grid environments, such as 500 kV outdoor substation environment, indoor main power control room, and underground network transformer vaults. Specifically, the proposed analysis aims to evaluate the impact of different sensor radio parameters, such as modulation, encoding, transmission power, packet size, as well as the channel propagation characteristics of different smart grid propagation environments on the performance of the deployed sensor network in smart grid. Overall, the main objective of this paper is to help network designers quantifying the impact of the smart grid propagation environment and sensor radio characteristics on low power wireless link reliability in harsh smart grid environments. Ó 2012 Elsevier B.V. All rights reserved.

1. Introduction The rapid population growth of the modern world together with the continuously increasing availability of devices using electric power causes a huge increase in electric power demand. Until now, all the burden of satisfying the constantly growing power demand has rested primarily on the shoulders of the problematic and over-aged power grid infrastructure. Importantly, overstressed power grid infrastructures had many instances of congestion and fluctuation issues, which leaded to major blackouts in many regions. Grid infrastructure is affected not only with being overstressed, but also with the lack of ubiquitous ⇑ Corresponding author. Tel.: +90 5053614781. E-mail addresses: [email protected] (N. Kilic), cagri. [email protected] (V.C. Gungor). 1389-1286/$ - see front matter Ó 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.comnet.2012.12.009

communications, automated diagnostics, and monitoring of the system [11,13,14]. Recently, the idea of future electric power systems, i.e., the Smart Grid, has emerged to overcome these problems in the power grid. The smart grid can be defined as a modern electric power grid infrastructure for improved efficiency, reliability, and safety [18,29], with smooth integration of renewable and distributed energy sources, through automated and distributed controls and modern communication and sensing technologies [5,10,13,21,22,25,31]. In general, there are three fundamental processes in electric power systems, which are generation, transmission & distribution and utilization of the power. Lately, wireless sensor networks (WSNs) have been recognized as an essential technology that has the potential to improve all these processes in the power grid. In WSN-based smart grid systems, wireless sensor nodes

N. Kilic, V.C. Gungor / Computer Networks 57 (2013) 1192–1203

are installed on the critical power grid equipment [14,24,28]. Collected sensor information from these equipment can be used to diagnose potential problems rapidly and hence, autonomous and reliable operation can be achieved. The existing and potential applications of WSNbased Smart Grid span a very wide range, including wireless automatic metering, remote power system monitoring and control, electricity fraud detection, fault diagnostics, demand response, outage detection, overhead transmission line monitoring, load control, and distribution automation [1,3,8,11,12,30]. However, the realization of smart grid depends on the efficient communication capabilities of sensor network in harsh and complex electricity network environments that bring out great challenges for reliability of deployed sensor network [16]. To this end, the research efforts related to analysis of reliability of low power wireless links in harsh smart grid environments are important to achieve a reliable WSN deployment in the smart grid [15]. These analyses and channel characterizations can help network designers to foresee the performance of the deployed WSN for specific propagation environment, noise, channel modulation, encoding, output power, and frequency band. Although there exist analysis of low power wireless links in urban areas, office buildings, and factories [7,13,17,20,32,33], a detailed link reliability analysis in outdoor and indoor smart power grid environments are yet to be efficiently studied and explored. Wireless links within the smart power grid environments are different in terms of noise levels, path loss, fading compared to urban and outdoor environments. The tough task of providing reliable and energy-efficient communication for WSNs in smart grid becomes even more challenging when the links are subject to high interference due to non-linear power equipment and fading as a result of obstacles. In our previous work [14], we performed some field tests to measure wireless channel characteristics, background noise and attenuation in smart grid environments. The tests also verified that the links are exposed to varying spectrum characteristics due to noise generated by the equipment, electromagnetic interference, fading and dynamic topology changes [4]. According to the field measurements, the average noise level is 88 dBm in indoor power control room, 93 dBm in 500 kV substation, 92 in underground transformer vault. We observed that the average noise levels are significantly higher than that of outdoor noise levels which was found to be at around 105 dBm. Note that, in our measurements, the 500 kV substation environment contained several obstacles and had high path loss (g). Similarly, the underground transformer vault had significantly high noise levels due to the dense transformer clusters in the enclosed environment. On the other hand, there were fewer obstructions within the main power control room and it had relatively lower path loss [14]. In addition, we observed that the background noise levels continuously keep changing over time which may be caused by frequent temperature changes, noise generated by the power equipment, electromagnetic interferences. This paper presents an in-depth analysis of low power wireless link reliability in different smart grid

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environments, such as 500 kV outdoor substation environment, indoor main power control room, and underground network transformer vaults at Georgia Power, Atlanta, GA, USA. Specifically, in our analysis, we consider important sensor radio parameters, such as transmission power, packet size, as well as channel parameters of different smart grid propagation environments, such as path loss exponent, log-normal variance, and noise. We also evaluate the impact of different sensor radio hardware features (such as modulation and encoding schemes) on the performance of the deployed sensor network in smart grid. In summary, the following major contributions have been made in this paper:  The impacts of radio propagation characteristics of smart grid environments on the critical communication properties, such as received signal strength and packet reception rate have been investigated. Specifically, the effects of channel parameters, such as path loss exponent, log-normal variance, and noise, on the performance of the network have been evaluated for different smart grid environments.  The effects of sensor radio parameters, such as modulation, encoding, transmission power, packet size, on the low power wireless link reliability have been analytically studied for harsh smart grid environments. To this end, the performance of different sensor network platforms (such as Mica, Mica2, and TMote Sky nodes) has been evaluated.  Our analyses are based on field tests using IEEE 802.15.4 compliant wireless sensor nodes deployed in different smart power grid environments at Georgia Power, Atlanta, GA, USA [14]. Upon request, the complete experimental data will be made available. This can help the research community develop novel WSN protocols for smart grid applications. The remainder of the paper is organized as follows. WSN-based smart grid application areas are explored for power generation systems, T&D networks, and consumer facilities in Section 2. An overview of the related work on wireless link quality measurements in WSNs is presented in Section 3. Wireless channel background and analytical models have been described in Section 4. The comprehensive analysis on statistical characterization of the wireless channel in different electric power system environments is presented in Section 5. An overview of performance results is presented in Section 6. Finally, the paper is concluded in Section 7. 2. WSN-based smart grid applications In general, WSN-based smart grid applications can be classified into three classes, such as consumer side, transmission & distribution side, and generation side:  Consumer side (demand side) applications are comprised of wireless automatic meter reading (WAMR), building automation, residential energy management, and automated solar panels management. These applications offer several advantages to consumers

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and electric utilities. For example, WAMR systems can reduce the operational costs of the electric utility by eliminating the need for human readers and enable dynamic pricing models [14]. Besides, through WSNbased building automation applications, the energy consumed by the home appliances can be monitored and managed, and hence unnecessary energy consumption can be avoided [8].  Transmission & Distribution (T&D) side applications span a wide range, including conductor temperature and dynamic thermal rating systems, equipment fault diagnostics, outage detection, overhead transmission line monitoring, and underground cable system monitoring. In general, WSN-based smart grid applications can enable uninterrupted distribution and transmission of the power through equipment fault diagnostics and control systems [9]. Moreover, WSNs can be used to provide a cost effective and reliable underground cable monitoring mechanism [28]. Another important application in T&D systems can be temperature monitoring of the power cables, since the load capacity of the these cables is closely related to the temperature of the conductor material used in the cable. WSNs can be used in such applications to measure the temperature of the cables and thus, to provide a flexible as well as a cost-effective solution [28].  Generation side applications also span a wide range, including real-time generation monitoring, remote monitoring of wind farms and solar farms, power quality monitoring, distributed generation. Remote monitoring of wind farms is another potential smart grid application that can be supported with WSNs. The performance of wind farms can be affected by many external problems, such as high pressure, extreme temperatures, orientation of the wind, and flock of birds. Capturing the various sensor data around the wind turbines can help preventing these external problems or diminish their influences [9]. Overall, WSN-based smart grid technologies can be used in many other real-world applications. It seems that more innovative smart grid applications will be available in the future considering the rapid advances in WSN and smart grid technologies.

Different sensor platforms have been used, and each of them has had their corresponding radio frequency bands, channel modulation, encoding schemes and output power levels. With different foci, all these experimental studies are complementary to our work. In [32] and [33], Zuniga et al. make use of the analytical tools to derive expressions for the Packet Reception Rate (PRR) as a function of distance for different environmental settings, and to determine the width of the transitional region. They also presented analytical link layer models so that the research community can enhance the network simulation tools. With the help of these improvements, the standard sensor network simulator, i.e., TOSSIM, had the capability to capture the real-life dynamics and edge conditions, eliminating the burden of waiting until the network deployment. All these previous studies provide valuable and solid foundations for several sensor network protocols and have guided design decisions and tradeoffs for a wide range of sensor network applications. However, none of them presents an in-depth theoretical analysis of low power wireless link reliability in different smart power grid environments. This paper aims at fulfilling this gap and analytically quantifies the impact of the smart grid propagation environment and radio characteristics on low power wireless link reliability in power grid environments. 4. Low power wireless link reliability in smart grid When a radio signal propagates through the wireless environment, it is affected by reflection, diffraction and scattering [20,32,33]. In addition to these, in WSNs, low antenna heights of the sensor nodes, near ground communication channels, and obstructions exacerbate these effects. In this study, we modelled the wireless channel using lognormal shadowing path loss model through a combination of empirical measurements and analytical methods. This model is used for WSN coverage models and experimental studies have shown that it provides more accurate channel models than Nakagami and Rayleigh models for indoor wireless environments with obstructions [20,32,33]. In this model, signal to noise ratio c(d) at distance d from the transmitter is given by the equation [20,32,33]:

cðdÞdB ¼ Pt  PLðd0 Þ  10glog10 3. Related work on link-quality measurements in wireless sensor networks Several experimental studies on the link-quality of wireless sensor networks [1,6,7,13,14,17,23,26] have shown that low power wireless links can be highly unreliable and that this must be explicitly considered when deploying WSNs in real-world applications. These studies have shown the existence of three packet reception regions in a wireless link: connected, transitional, and disconnected. Depending on the characteristics of radio propagation environment, the width of transitional region could be large and variable. The problem of co-existence between IEEE 802.11b and IEEE 802.15.4 networks has also been studied [2,19,27].

d  X r  Pg d0

ð1Þ

where Pt is the transmit power in dBm, PL(d0) is the path loss at a reference distance d0, g is the path-loss exponent, Xr is a zero mean Gaussian random variable with standard deviation r, and Pg is the noise power (noise floor) in dBm. The design space of sensor platforms and their radio hardware have advanced significantly. Note that as shown in Fig. 1 and Table 1 the earlier sensor network platforms, such as TR1000, CC1000, and TDA5250, had relatively primitive modulation schemes, including frequency shift keying (FSK), amplitude shift keying (ASK), and on–off keying (OOK). Recently, many sensor platforms have gravitated towards an international sensor network standard (IEEE 802.15.4) [35]. Different from earlier platforms, the modulation scheme of the IEEE 802.15.4 is orthogonal

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Fig. 1. Timeline of the wireless sensor transceivers.

Table 1 Off-the-shelf radio transceivers for wireless sensor networks. Radio

TR1000

CC1000

Manufacturer RFM TI Platform WeC, Rene, Mica2Dot, Dot, Mica Mica2, BTnode

CC2420

TDA5250

CC2520

CC2430

JN5139

MC13191

EM250

TI Egs, Ubimote 2

TI Ubimote 1

Jennic –

Freescale –

Ember –

IEEE 802.15.4 250

IEEE 802.15.4 250

IEEE 802.15.4 250

IEEE 802.15.4 250

IEEE 802.15.4 250

O-QPSK Spread Spectrum 2.4 GHz

O-QPSK –

Standard

N/A

N/A

TI Infineon MicaZ, SunSPOT, eyesIFX TelosB, Imote2, Tmote Sky IEEE 802.15.4 N/A

Data rate (kbps) Modulation Encoding

2.4 to 115.2 OOK ASK SECDED

38.4 to 76.8

250

64

O-QPSK –

ASK FSK O-QPSK Manchester –

O-QPSK –

Radio frequency Supply voltage (V) TX Min (mA/dBm) TX Max (mA/dBm) RX (mA) Sleep (lA) Startup time (ms) Noise floor (dBm)

916 MHz

2.5 GHz

868 MHz

2.4 GHz

2.4 GHz

O-QPSK NRZ/ RTZ 2.4 GHz

2.7–3.5

FSK NCFSK Manchester/ NRZ 315 433 868 915 MHz 2.1–3.6

2.1–3.6

2.1–5.5

1.8–3.8

2.0–3.6

2.2–3.6

0.3 to 3.6

2.1–3.6

N/A

5.3/20

8.5/25

4.9/22

16.2/18

–

–

30/0

19/32

12/1

26.7/10

17.4/0

11.9/9

33.6/5

27/0

38/3

38/0

33/5

1.8–4.5 5 12

7.4–9.6 0.2 to 1 15.5 to 5

19.7 1 0.3 to 0.6

8.6–9.5 18.5 9 1 0.77 to 1.43 0.3

27 0.5 0.192

37 60 nA 2.75

37–45 1 10

29 1 1 to 2

94

109

92

97

92

97

up to 106 98

quadrature phase shift keying (O-QPSK) with direct sequence spread spectrum (DSSS), which provides much more sophisticated mechanism for the sensor networks [12,27]. In this section, we show the expressions for bit error rates for different sensor node platforms, such as Mica, Mica2, and MicaZ. Since the modulation schemes used in these nodes are significantly different, it is necessary to investigate the effect of propagation characteristics on these nodes separately. For example, Mica2 nodes are implemented with coherent FSK modulation scheme. The bit error rate of this scheme is given by [24]:

PFSK ¼Q b

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi BN ðEb=NoÞ ; Eb=No ¼ w R

ð2Þ

where Q() is the standard Gaussian error function (implemented in the simulation tool), BN is the noise bandwidth, R is the data rate (BN and R vary for different transceivers and can be found in the datasheet of the specific sensor radio hardware), and w is the received SNR.

98

2.4 GHz

On the other hand, the modulation scheme used in TMote Sky nodes is offset quadrature phase shift keying (O-QPSK) with direct sequence spread spectrum (DSSS). O-QPSK with DSSS (Direct Sequence Spread Spectrum) modulation scheme is given in by [24]:

POQPSK ¼Q b

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ððEb=NoÞDS Þ

ð3Þ

where

ðEb=NoÞDS ¼

ð2N  Eb=NoÞ ðN þ 4Eb=NoðK  1Þ=3Þ

ð4Þ

where K is the number of users who are transferring simultaneously and N is the number of chips per bit. In addition, the modulation scheme used in Mica nodes is ASK modulation and the bit error rate Pb for ASK modulation scheme is given by the following equation [32,33]:

PASK ¼Q b

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ððEb=NoÞ=2Þ

ð5Þ

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Table 2 The parameters and notations. Parameter Transitional region (ds) and (de) Boundary equations

Radio Modulation scheme

Description Starting distance of the transitional region Ending distance of the transitional region

FSK ASK O-QPSK

Encoding scheme Pt Pn

NRZ SECDED Output Power Noise Floor

f l BN R N

Frame size Preamble length Noise bandwidth of the transceiver chip Data rate of the transceiver chip in bits Number of chips per bit

Topology #nodes DX DY Topology

Number of nodes Terrain dimension: X Terrain dimension: Y Topology layout

Combining the above mentioned bit error rates together with the corresponding encoding schemes (NRZ and SECDED); the packet reception rate (PRR) for different sensor motes can be calculated as shown in Table 2 based on valuable foundations shown in [24,20,14,32,33]. 5. Performance evaluations In this section, the performance results of our reliability analysis of low power wireless links are shown for different power distribution environments, including indoor power control room, outdoor 500 kV substation, and underground network transformer vault environments. Our analysis is based on experimentally determined lognormal channel parameters obtained in our previous study [14], where the wireless channel in different smart grid environments has been modeled through a comprehensive set of real-world field tests using IEEE 802.15.4 compliant wireless sensor nodes in different electric power system environments at Georgia Power, Atlanta, GA, USA. Experimentally determined log-normal channel parameters for different power system environments are given in Table 3 [14]. In these experiments, LOS (Line of Sight) and NLOS (Non-Line of Sight) links have been considered. The LOS setups have a direct line of sight between two sensor nodes, whereas in NLOS setups some physical obstructions exist between the communicating nodes. Unless specified otherwise, the parameters used in our performance analysis are listed in Table 2 [14,20,24,32,33]. Overall, in our analysis, we consider important sensor radio parameters, such as modulation, encoding, transmission power, packet size, as well as channel parameters of different smart grid propagation environments, such as

Values P n þcU P t þPLðd0 Þþ2r 10g

ds ¼ 10

P n þcU P t þPLðd0 Þ2r 10g

de ¼ 10

pffiffiffiffiffiffiffiffiffi cðdÞ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Pb ¼ Q cðdÞ=2 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Pb ¼ Q 2N cðdÞ

Pb ¼ Q

PRR = (1  Pb)8l(1  Pb)(8(f  l)) PRR = (1  Pb)8l((1  Pb)8 + 8Pb (1  Pb)7)(3(fl)) 7 dB Subst.: 93 dB Undrgr. Vault: 92 dB 30 Bytes 2 Bytes 30 kHz (for Mica 2) 19.2 kbps (for Mica2) 16 chips/bit

Main Pwr.: 88 dB

40 40 m 0m Line topology (1 m gap)

path loss exponent, log–normal variance, and noise. We also evaluate the impact of different sensor radio hardware features (such as the modulation and encoding schemes) on the performance of the deployed sensor network in smart grid. Table 2 presents a comprehensive list of equations used in our analyses for different modulation and encoding schemes, and a summary of the notations used in our analyses based on valuable foundations shown in [14,20,24,32,33]. For the performance evaluations, we have extended the simulation environment developed by the ANRG [34] to simulate the link layer model in different smart grid environments. This comprehensive tool enabled us to focus on the reliability of wireless links in different smart grid propagation characteristics. In this study, to have meaningful statistical results and to show deviations in different network configurations, we have executed the simulation experiments in multiple runs and get multiple results for each distance by changing random seeds in the simulation platform. Thus, we get multiple points as the output that are generally within the theoretical upper and lower limits, which are also shown by the dashed lines in Figs. 2a–f and 5a. Fig. 2a–f shows how the received power changes as the distance between the sender and receiver nodes vary. For this analysis, we used the line topology, in which an equally spaced 40 sensor nodes (1 m gap between each successive node) are deployed along a straight line of 40 m. In Fig. 2a–f, there are three non-linear lines, where outer lines represent the boundaries of the received power Pr at varying distances d. Non-linear line shown by the middle function represents the average Pr at varying distances d. This analysis shows that the signal strength of

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N. Kilic, V.C. Gungor / Computer Networks 57 (2013) 1192–1203 Table 3 Path loss exponent and shadowing deviation in smart grid environments. Propagation environment

Path loss (g)

Shadowing deviation (r)

Noise floor (Pn)

500 kv Substation (LOS) 500 kv Substation (NLOS) Underground Transformer Vault (LOS) Underground Transformer Vault (NLOS) Main Power Room (LOS) Main Power Room (NLOS)

2.42 3.51 1.45 3.15 1.64 2.38

3.12 2.95 2.45 3.19 3.29 2.25

93 93 92 92 88 88

the received power decays exponentially with respect to distance and for a given distance d and it can show a random behavior depending on the propagation characteristics. In our experiments, we also observed that the width of the transitional region vary significantly depending on the smart grid propagation characteristics. To better analyze all these changes, Fig. 3a–f shows the impact of the path loss exponent and the log-normal shadowing standard deviation on the transitional region. The variables ds and de are indicating the starting and the ending distances of the transitional regions in different propagation environments. Noise floor Pn ranges between 93 dBm, 92 dBm, 88 dBm for three different environments. Fig. 3a–f shows the physical interaction of the channel and the radio models under smart grid environments. The transitional region starts when Pr values (which are shown by PrL) begin entering the lower Pr limit (PrL = cU + Pn), and ends when Pr values (which are shown by PrU) begin leaving the upper Pr limit (PrU = cL + Pn). In general, it is observed that if the log–normal shadowing standard deviation increases, the transitional region increases, and the received signal quality sharply decreases with increasing distance and high path loss. In addition, we have conducted another analysis to observe the change in the received signal strength Pr when the path loss exponent is kept constant for LOS (Line of Sight) and NLOS (Non-Line of Sight) setups of the same environments. The LOS setups have a direct line of sight between two sensor nodes, whereas in NLOS setups some physical obstructions exist between the communicating nodes. In the first observation g = 2.42, in the second one g = 3.15, and in the third g = 2.38. This analysis shows that an increase in the shadowing deviation (r) expands the transitional region. Related results can be inspected in Fig. 4a, c and e. Furthermore, we have also observed the change of the signal strength whilst this time the shadowing deviation is kept constant and the path loss exponent is changed for each LOS - NLOS pairs. The results of the observations are presented in Fig. 4b, d and f. Shadowing deviation values are fixed to 3.12, 2.45, and 3.29 for substation, transformer vault, and main power room, respectively in Fig. 4b, d and f. We have seen that the path loss exponent is inversely proportional to transitional region’s size. Every time the path loss exponent is decreased, the size of the transitional region of the particular environment increases. For example, in Fig. 4b, the transitional region starts at around 3 m and ends at 7 m for g = 3.51. On the other hand, for the same environment, when the path loss expo-

nent is set to a lower value of g = 2.42, the starting and the ending distances change to approximately 4.5 m and 17.5 m, respectively. This is due to the exponential impact of the path loss exponent on the transitional region. Fig. 5a analyzes the correlation between the packet reception rate PRR and the distance. We observe that for different pair of nodes, even though the distance remains constant, the packet reception rate may change in the transitional region. This is of course due to the unstable characteristics of the transitional region. Actually, the PRR values can vary greatly. For instance, in Fig. 5a, PRR values are volatile at 3 m distance fluctuating between 0% and 100%. The figure compares the beginning and the ending distances of the transitional zone in terms of analytical and simulation measurements. In this figure, the blue vertical dashed lines represent the observed distances obtained by the simulations where the transitional region begins and ends. On the other hand, the black vertical lines represent the analytical measurements for the same regional boundaries. Importantly, another analysis is performed to measure the impact of the modulation scheme on the transitional region. In this analysis we keep all the parameters constant, but change the modulation schemes to amplitude shift keying (ASK) as in Mica, and frequency shift keying(FSK) as in Mica2 and Mica2Dot, and Offset-Quadrature Phase-Shift Keying (O-QPSK) as in MicaZ and Tmote Sky. Changing the modulation scheme also changes the packet reception rate, since the bit error rates of these schemes are different. Fig. 5c and e shows that the packet reception rate (PRR) values for different modulation schemes for different SNR and distance values, respectively. For example, in Fig. 5c, we observe that the PRR reaches its maximum after 14 dB for O-QPSK, 18 dB for FSK, and 26 dB for ASK. Until around 6 dB, none of the packets are received using ASK modulation scheme. According to our analyses for different SNR and distance values, the efficiency of the modulations schemes is O-QPSK > FSK > ASK. This observation is also consistent with the results in the related literature [20]. Fig. 5b also shows the comparison of six different smart grid environments in terms of packet reception rates (PRR) at various distances when O-QPSK is used. In Fig. 5b, it is shown that the size of the transitional region in descending order is underground transformer vault (LOS), main power room (LOS), 500 kV substation (LOS), main power room (NLOS), underground transformer vault (NLOS), 500 kV substation (NLOS). Furthermore, the rate of the decrease in packet reception rates for these environments is in the reverse order of the transitional region sizes.

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Propagation Model for n = 3.51, σ = 2.95, PL(d0) = 55 dB, Pt = 0 dBm (i.e. NLOS Outdoor 500−kV substation environment)

−50

−50

−60

−60

−70

−70

Pr (dBm)

Pr (dBm)

Propagation Model for n = 2.42, σ = 3.12, PL(d0) = 55 dB, Pt = 0 dBm (i.e. LOS Outdoor 500−kV substation environment)

−80 −90 −100

−90 −100

−110

−110 0

5

10

15

20

25

30

35

40

0

10

15

20

25

30

35

distance (m)

(a)

(b)

Propagation Model for n = 1.45, σ = 2.45, PL(d0) = 55 dB, Pt = 0 dBm (i.e. LOS Underground Transformer Vault)

Propagation Model for n = 3.15, σ = 3.19, PL(d0) = 55 dB, Pt = 0 dBm (i.e. NLOS Underground Transformer Vault)

−50

−50

−60

−60

−70

−70

−80 −90 −100

40

−80 −90 −100

−110

−110 0

5

10

15

20

25

30

35

40

0

20

25

(d)

−70

−70

Pr (dBm)

−60

−80 −90

−110

−110 25

30

35

40

−90 −100

20

35

−80

−100

15

30

Propagation Model for n = 2.38, σ = 2.25, PL(d0) = 55 dB, Pt = 0 dBm (i.e. NLOS Main Power Room)

−60

10

15

(c)

−50

5

10

distance (m)

−50

0

5

distance (m)

Propagation Model for n = 1.64, σ = 3.29, PL(d0) = 55 dB, Pt = 0 dBm (i.e. LOS Main Power Room)

Pr (dBm)

5

distance (m)

Pr (dBm)

Pr (dBm)

−80

40

0

5

10

15

20

25

distance (m)

distance (m)

(e)

(f)

30

35

40

Fig. 2. Received power vs. distance analysis in experimental smart grid sites. (a) Outdoor 500-kV substation environment (LOS). (b) Outdoor 500-kV substation environment (NLOS). (c) Underground Transformer Vault (LOS). (d) Underground Transformer Vault (NLOS). (e) Main Power Room (LOS), (f) Main Power Room (NLOS) transformer vault environments.

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−50

−50 ds

500 kV Substation LOS η=2.42, σ=3.12, Pn=−93

de

−60

−60

−65

−65

−70 −75 −80

μ−2σ

μ

μ+2σ

−90

5

−75 μ−2σ μ μ+2σ

15

20

25

30

35

Pn + γU Radio Bounds Pn + γL

−90

10

de

−85

noise floor Pn

0

ds

−70

−80

Pn + γU Radio Bounds Pn + γL

−85

500 kV Substation NLOS η=3.51, σ=2.95, Pn=−93

−55

Pr (dBm)

Pr (dBm)

−55

40

noise floor Pn

0

5

10

Underground Transformer Vault LOS η=1.45, σ=2.45, Pn =−92

−55 −60

ds

−55

25

30

35

40

Pr (dBm)

−70 −75

μ−2σ μ

μ+2σ

−75 −80

−85

Radio Bounds Pn + γL

−85

noise floor Pn

10

μ−2σ μ

30

40

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Fig. 3. Observation of the transitional region for six different smart grid site (a), (b), (c), (d), (e), and (f).

In Fig. 5d, we also observed the impact of the packet size on the transitional region. We evaluate the change in packet size for the frames of 30, 60, 90, and 128 bytes. Our analysis clearly shows that the frame size must be kept small to mitigate the effects of the packet losses especially in harsh smart grid environments. The last

analysis as shown in Fig. 5f depicts the effects of different output power levels Pt on the performance of the link. Our results show that basically, the decrease in output power leads to decrease in the size of the transitional region, as well as an expected decrease in the maximum communication range.

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−50

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Fig. 4. The impact of smart grid propagation characteristics on the transitional region range for different smart grid environments (a), (c), (e), (b), (d), and (f).

6. Overview of performance results Extensive performance evaluations address the challenges in the design of smart grid communication protocols, and show that the overall performance of the network suffers from propagation characteristics in smart

grid environments. An overview of our performance results can be outlined as follows:  As shown in Fig. 2a–f, we have observed that the signal strength of the received power decays exponentially with respect to distance and for a given distance d, it

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can show a random behavior depending on the environmental characteristics. In other words, it can greatly vary at the same distance for the same environment. Since the grid environment is very unstable, it is challenging to estimate link quality dynamically. Another analysis depicted in Fig. 3a–f shows the impact of smart grid propagation characteristics on received power strength. It is observed that if the log-normal shadowing standard deviation r increases, the transitional region increases. It is also shown that the received signal quality decreases with increasing distance and high path loss. Fig. 4a, c, e also verify our observations in Fig. 4a–f. Keeping the path loss exponent constant for line-ofsight (LOS) and non-line-of-sight (NLOS) setups of the same environments, the impact of the sigma r on the size of the transitional region and received signal strengths can be seen clearly. Analyses in Fig. 4b, d, f show the variations on the transitional region characteristics whilst the shadowing deviation is kept constant and the path loss exponent g is changed for each LOS - NLOS pairs. This analysis also verifies the findings in the previous ones. For example, in Fig. 4b, the transitional region starts at around 3 m and ends at 7 m for g = 3.51. On the other hand, for the same environment, when the path loss exponent is set to a lower value of g = 2.42, the starting and the ending distances change to approximately 4.5 m and 17.5 m, respectively. In Fig. 5a, we have compared the beginning and the ending distances of the transitional zone in terms of analytical and simulation measurements (blue dashed lines). We have observed that the analytical and the simulation outputs do not differ significantly. In addition, we have seen that the PRR values are volatile. For example, at 3 m distance they can fluctuate between 0% and 100%. The high variation in PRR values at the same distance is of course due to the unstable characteristics of the transitional region. In Fig. 5c–e, we can clearly see that the modulation scheme is also an important factor that should be taken into account when deploying WSN in smart grid environment. The reason is the fact that it can affect the PRR values dramatically as shown in Fig. 5c–e for different SNR and communication distances. According to our analyses, the efficiency of the modulations schemes is O-QPSK > FSK > ASK. In Fig. 5b, our analysis also reveals that the size of the transitional region in descending order is underground transformer vault (LOS), main power room (LOS), 500 kV substation (LOS), main power room (NLOS), underground transformer vault (NLOS), 500 kV substation (NLOS). Furthermore, the rate of the decrease in packet reception rates for these environments is in the reverse order of the transitional region sizes. In Fig. 5d, our analysis shows that the frame size must be kept small to mitigate the effects of the packet losses especially in harsh smart grid environments. The last analysis as shown in Fig. 5f depicts the effects of different output power levels Pt on the performance of the link. Our results show that basically, the decrease in

output power leads to decrease in the size of the transitional region, as well as an expected decrease in the maximum communication range. 7. Conclusions Recently, wireless sensor networks (WSNs) have been recognized as a promising technology that has the potential to improve the performance of the power grid. This paper presents an in-depth analysis of low power wireless links in different smart grid operating environments, such as 500 kV outdoor substation environment, indoor main power control room, and underground network transformer vaults. Specifically, in our analysis, we consider important sensor radio parameters, such as transmission power, packet size, as well as channel parameters of different smart grid propagation environments, such as path loss exponent, log-normal variance, and noise. Overall, our analysis helps network designers quantifying the impact of the smart grid propagation environment and radio characteristics on low power wireless link reliability. Clearly, there exist many important research challenges that are needed to be well investigated on the realization of WSN-based smart grid applications. Hopefully, this paper is going to help better elaborate the performance of the WSNs for smart grid environments and motivate the researchers to further study this promising research area. While the analytical results in this work provide valuable insights about the reliability of WSNs in smart grid environments and guide design decisions and tradeoffs for WSN applications in different electric power system environments, future work still remains in the area of development of adaptive and novel communication solutions for smart grid applications. Acknowledgements This work was supported by the European Union FP7 Marie Curie International Reintegration Grant (IRG) under Grant PIRG05-GA-2009-249206 with the research project entitled ‘‘Spectrum-Aware and Reliable Wireless Sensor Networks for Europe’s Future Electricity Networks and Power Systems’’. The authors also would like to thank ANRG [33] for their comprehensive tool, which enabled them to focus on the reliability of wireless links in different smart grid propagation characteristics. References [1] I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, Wireless sensor networks: a survey, Computer Networks 38 (4) (2002) 393– 422. 538. [2] L. Angrisani et al., Experimental study of coexistence issues between IEEE 802.11b and IEEE 802.15.4 wireless networks, IEEE Transactions on Instrumentation and Measurement 57 (8) (2008) 1514–1523. [3] A.O. Bicen, V.C. Gungor, O.B. Akan, Delay-sensitive and multimedia communication in cognitive radio sensor networks, Ad Hoc Networks Journal 10 (5) (2012) 816–830. [4] A.O. Bicen, V.C. Gungor, O.B. Akan, Spectrum-aware and cognitive sensor networks for smart grid applications, IEEE Communications Magazine 50 (5) (2012) 58–165. [5] B.E. Bilgin, V.C. Gungor, Performance evaluations of zigbee in different smart grid environments, Computer Networks Journal 56 (8) (2012) 2196–2205.

N. Kilic, V.C. Gungor / Computer Networks 57 (2013) 1192–1203 [6] C.A. Boano, N. Tsiftes, T. Voigt, J. Brown, U. Roedig, The impact of temperature on outdoor industrial sensornet applications, IEEE Transactions on Industrial Informatics 6 (3) (2010) 451–459. [7] A. Bose, Smart transmission grid applications and their supporting infrastructure, IEEE Transactions on Smart Grid 1 (1) (2010) 11–19. [8] F. Bouhafs, M. Mackay, M. Merabti, Links to the future: communication requirements and challenges in the smart grid, IEEE Power and Energy Magazine 10 (1) (2012) 24–32. [9] M. Erol-Kantarci, H.T. Mouftah, Wireless multimedia sensor and actor networks for the next generation power grid, Ad Hoc Networks 9 (4) (2011) 542–551. [10] M. Erol-Kantarci, H.T. Mouftah, Wireless sensor networks for costefficient residential energy management in the smart grid, IEEE Transactions on Smart Grid 2 (2) (2011). [11] Z.M. Fadlullah et al., Toward intelligent machine-to-machine communications in smart grid, IEEE Communications Magazine 49 (4) (2011) 60–65. [12] V.C. Gungor, D. Sahin, Cognitive radio networks for smart grid applications: a promising technology to overcome spectrum inefficiency, IEEE Vehicular Technology Magazine 7 (2) (2012) 41– 46. [13] V.C. Gungor, D. Sahin, T. Kocak, S. Ergut, C. Buccella, C. Cecati, G.P. Hancke, Smart grid technologies: communication technologies and standards, IEEE Transactions on Industrial Informatics 7 (4) (2011) 529–539. [14] V.C. Gungor, B. Lu, G.P. Hancke, Opportunities and challenges of wireless sensor networks in smart grid, IEEE Transactions on Industrial Electronics 57 (10) (2010) 3557–3564. [15] V.C. Gungor, G. Hancke, Industrial wireless sensor networks: challenges, design principles, and technical approaches, IEEE Transactions on Industrial Electronics 56 (10) (2009) 4258– 4265. [16] V.C. Gungor, F.C. Lambert, A survey on communication networks for electric system automation, Computer Networks 50 (7) (2006) 877– 897. [17] H.-J. Korber, H. Wattar, G. Scholl, Modular wireless real-time sensor/ actuator network for factory automation applications, IEEE Transactions on Industrial Informatics 3 (2) (2007) 111–119. [18] R.A. Leon, V. Vittal, G. Manimaran, Application of sensor network for secure electric energy infrastructure, in: IEEE Transactions on Power Delivery 22 (2) (2007) 1021–1028. [19] S. Pollin, Harmful coexistence between 802.15.4 and 802.11: a measurement-based study in: Proc. of IEEE CrownCom, 2008. [20] T. Rappapport, Wireless Communications: Principles and Practice, Prentice Hall, 2002. [21] N. Saputro, K. Akkaya, S. Uludag, A survey of routing protocols for smart grid communications, Computer Networks 56 (11) (2012) 2742–2771. [22] T. Sauter, M. Lobashov, End-to-end communication architecture for smart grids, IEEE Transactions on Industrial Electronics 58 (4) (2011) 1218–1228. [23] L. Tang, K.-C. Wang, Y. Huang, F. Gu, Channel characterization and link quality assessment of IEEE 802.15.4-compliant radio for factory environments, IEEE Transactions on Industrial Informatics 3 (2) (2007) 99–110. [24] M.C. Vuran, I.F. Akyildiz, Error control in wireless sensor networks: a cross layer analysis, IEEE/ACM Transactions on Networking 17 (4) (2009) 1186–1199. [25] W. Wang, Y. Xu, M. Khanna, A survey on the communication architectures in smart grid, Computer Networks 55 (15) (2011) 3604–3629. [26] A. Willig et al., Measurements of a wireless link in an industrial environment using an IEEE 802.11-compliant physical layer, IEEE Transactions on Industrial Electronics 49 (6) (2002) 1265–1282. [27] K. Wu, H. Tan, Hoi-Lun Ngan, Y. Liu, L.M. Ni, Chip error rattern analysis in IEEE 802.15.4, IEEE Transactions on Mobile Computing 11 (4) (2012) 543–552. [28] Y. Yang, F. Lambert, D. Divan, A survey on technologies for implementing sensor networks for power delivery systems, in: IEEE Power Engineering Society General Meeting, 2007, pp. 1–8. [29] R. Yu et al., Cognitive radio based hierarchical communications infrastructure for smart grid, IEEE Network 25 (5) (2011) 6–14.

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Necati Kilic received his B.S. degree in computer engineering from Bahcesehir University, Istanbul, Turkey, in 2010, and his M.S. degree in computer engineering from Bahcesehir University, Istanbul, Turkey, 2012 under the supervision of Dr. T. Kocak and Dr. V.C. Gungor. He worked as both teaching and research assistant during his M.S. studies in the Computer Networks and Mobile Communications Laboratory at the Department of Computer Engineering, Bahcesehir University. His research on Smart Transportation Systems was supported by Türk Telekom Group R&D under the award number 11316-02. Currently, he is working in Turkish Airlines as a student pilot, and at the same time pursuing his Ph.D. degree at the Department of Computer Engineering, Bahcesehir University. His current research interests are in smart grid communications, wireless sensor networks, and intelligent transportation systems.

Vehbi Cagri Gungor received his B.S. and M.S.degrees in electrical and electronics engineering from Middle East Technical University, Ankara, Turkey, in 2001 and 2003, respectively. He received his Ph.D. degree in electrical and computer engineering from the Broadband and Wireless Networking Laboratory, Georgia Institute of Technology, Atlanta, GA, USA, in 2007. Currently, he is an Assistant Professor and Co-Director of the Computer Networks and Mobile Communications Lab at the Department of Computer Engineering, Bahcesehir University, Istanbul, Turkey. Before joining to Bahcesehir University, he was working at Eaton Corporation, Innovation Center, WI, USA as a Project Leader. His current research interests are in smart grid communications, next-generation wireless networks, wireless ad hoc and sensor networks, cognitive radio networks, and IP networks. He has authored several papers in refereed journals and international conference proceedings, and has been serving as an Editor and program committee member to numerous journals and conferences in these areas. He is also the co-recipient of the IEEE Transactions on Industrial Informatics 2012 Best Paper Award, IEEE ISCN 2006 Best Paper Award, the European Union FP7 Marie Curie IRG Award in 2009, and the San-Tez Project Awards issued by Alcatel-Lucent and the Turkish Ministry of Science, Industry and Technology in 2010. He is also the Principal Investigator of the Smart Grid Communications R&D project funded by Turk Telekom and European Union.