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Modelling and analysis of a power line communication system with QPSK modem for renewable smart grids
Electrical Power and Energy Systems 34 (2012) 19–28
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Electrical Power and Energy Systems journal homepage: www.elsevier.com/locate/ijepes
Modelling and analysis of a power line communication system with QPSK modem for renewable smart grids Ersan Kabalci a, Yasin Kabalci b, Ibrahim Develi c,⇑ a
Department of Electrical & Electronics Engineering, Nevsehir University, 50300 Nevsehir, Turkey Graduate School of Natural and Applied Sciences, Erciyes University, 38039 Kayseri, Turkey c Department of Electrical & Electronics Engineering, Erciyes University, 38039 Kayseri, Turkey b
a r t i c l e
i n f o
Article history: Received 18 April 2011 Received in revised form 17 August 2011 Accepted 20 August 2011 Available online 8 October 2011 Keywords: Power line communication Renewable energy sources Smart grid Maximum power point tracking QPSK Modem
a b s t r a c t Monitoring and metering processes are required to be performed in renewable energy conversion systems like smart grid applications of the conventional grid system. These processes concerning renewable energy sources are analyzed in this paper in order to propose a solution for solar power systems. The DC–AC conversion system proposed in this paper covers a solar power plant with a maximum power point tracking system and IGBT based three-phase inverter to generate three-phase AC line voltages. The modelled transmission line is not only used to carry the generated voltage but also to transmit the generated power rate of the solar plant in real-time. The power line communication (PLC) infrastructure employed is based on Quadrature Phase Shift Keying (QPSK) modems. In order to overlap QPSK modulated data to the three-phase transmission line at the inverter and grid sides a coupling interface is also designed in the study. The current, voltage and power data generated by the photovoltaic (PV) panels are successfully monitored over transmission lines owing to the developed system. As a result, additional monitoring costs are eliminated by using the proposed technique instead of SCADA or Ethernet-based systems. Ó 2011 Elsevier Ltd. All rights reserved.
1. Introduction Smart grid technologies are rapidly emerging due to the development of power transmission grids. The demands required of a smart grid are remote sensing, communication, control, monitoring and analysis processes. The smart grid should also meet these requirements in a sustainable, reliable and efficient way. Applications and studies on smart grids have mostly focused on phase measurements, advanced metering and remote monitoring of a source [1–3]. Smart grid applications can reduce costs and simplify several industrial requirements such as power factor correction applications, remote control of electrical machines and energy generation systems [1–5]. The term ‘‘power line communication (PLC)’’ is used to define the transmission and receiving of any data or signal over a power line. This concept also describes the main framework of a smart grid. The utilization of a power line as a transmission channel prevents additional setting up costs to connect the systems together. PLC application allows data transmission rates of up to 200 Mbps using a single phase with 220 V 50 Hz [6–8].
⇑ Corresponding author. Tel.: +90 352 437 49 01/32 205; fax: +90 352 437 57 84. E-mail addresses: [email protected] (E. Kabalci), [email protected] (Y. Kabalci), [email protected] (I. Develi). 0142-0615/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.ijepes.2011.08.021
Hybrid power generation systems based on renewable energy sources such as photovoltaic (PV) cells and wind turbines are being rapidly developed in addition to conventional energy generation systems. The distributed generation allows the installation of relatively small-scale power generators at medium and low-voltage distribution levels of the power system. Energy generation using PVs is also becoming an energy area of interest at low voltage and medium power range. Solar energy sources have the advantage of not causing pollution, having low maintenance costs, and not producing noise due to the absence of moving parts. Generally, PVs are considered as an expensive method of generating electricity. However, stand-alone PV applications are the most economic solution to provide the required power service. In gridconnected systems, single-phase or three-phase pulse-width modulation (PWM) voltage-source inverters (VSIs) are often used for interfacing the renewable energy source to the utility grid, and the current control of the grid-connected inverters plays a predominant role in feeding a grid with high-quality power. For grid-connected systems, the cost is further reduced due to the elimination of battery requirements, which is the second largest contributor to the cost of a PV system. The cost of the grid-connected system can also be minimized by decreasing the number of power conversion stages and the number of components involved in each stage [9–13]. There are several studies in the literature based on a variety of modelling and simulation software [14–17].
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The model developed in this study is built using real-time operation functions and power generation. Transmission lines are also implemented in an interacting structure with the QPSK modems over coupling circuit models. The DC power generation system is formed using the parameters of a 170 W Sharp solar panel [18]. The solar plant model includes 10 parallel connected solar panels with a Perturb and Observe (P&O) maximum power point tracking (MPPT) algorithm. The output of the boost converter is supplied to a three-phase IGBT inverter and the AC output voltages generated are transmitted to the grid side over a 10 km transmission line. The transmission line is also modelled using real-time parameters with specific line dissipations such as inductance, capacitance and resistance. The QPSK modem is assumed to observe power generated by the solar plant. The transmitting part of the modem is located at the AC side of the inverter, which is the starting point of the transmission line. The receiving part is located at the grid side where the system administrator performs monitoring and control operations. Although the system is modelled bi-directionally in terms of transmission, only the power value of the solar plant is transmitted to the grid side. It is also possible to evaluate the system by transmitting any control signal from the management side or grid to the solar plant to adjust parameters such as the modulation index or switching frequency of the inverter. This paper is organized by introducing a renewable energy conversion system and QPSK based smart grid model in the second and third sections, respectively. The simulation results are examined in detail in the fourth section.
2. Renewable energy conversion system The renewable energy generation system with PV panels, MPPT controlled boost converter, inverter, and transmission line parameters are introduced in this section, while the QPSK modem and other related parts of the smart grid are analyzed in the next section. The schematic diagram of the renewable smart grid model is shown in Fig. 1. The analytical model of the PV panel is designed using the mathematical equations given in the literature. The developed model allows the adjustment of basic parameters such as open circuit voltage (Voc), maximum power voltage (Vpm), short circuit current (Isc), and maximum power current (Ipm) according to any commercial PV model. The electrical characteristic parameters of the modelled solar panel in this study can be found in [18]. The boost converter keeps the output voltage stable at a reference value by using the comparison algorithm given in the MPPT part. The MPPT algorithm used in this converter compares the output voltage of the converter with a given reference voltage value. If the output voltage is higher than the reference voltage the MPPT algorithm decreases the duty cycle of the PWM that is used to
control the boost converter or vice versa. The MPPT algorithm ensures that the PV panels track the sun in order to obtain the maximum power point. The DC output voltage of the boost converter is supplied to the inverter to generate three-phase AC voltage. The transmission line parameters are set to real-time impedance values per kilometer to take line losses into account. 2.1. Solar plant and MPPT models The electrical power obtained from a PV cell depends on shortcircuit current (Isc), diode ideality factor (gI), shunt resistance (Rsh), and series resistance (Rs) parameters. The current–voltage (I–V) characteristic of a PV panel is determined using the following equation [19–23],
I o ¼ I pv I D
V o þ I o Rs V o þ I o Rs 1 exp gI V T Rsh
ð1Þ
where Io is the output current of the panel, Ipv is the current generated by the irradiation value, ID is the current of the output resistor, VT is the thermal voltage and Vo is the output voltage. The most widely used MPPT techniques in the literature are the hill climbing and P&O methods. The hill climbing method generates a perturbation in the duty ratio of the power converter while the P&O method involves a perturbation in the operating voltage of the PV array. In the case of a PV array connected to a power converter, perturbing the duty ratio of the power converter perturbs the PV array current and consequently perturbs the PV array voltage. The P&O algorithm is based on comparing the current output power to its previously acquired power level. If the current acquired power level is less than the previously acquired level then the algorithm increases the operating voltage or else reduces it. This situation is also valid vice versa. The switch used in the boost converter is controlled at 20 kHz switching frequency. The output of the boost converter is applied to a three-phase inverter in order to generate three-phase AC voltages [5,23–25]. 2.2. Voltage source inverter and transmission line Three-phase VSIs meet the power demand from medium to high-power applications while single-phase VSIs cover low-range power applications. The main purpose of these topologies is to provide a three-phase voltage source, where the amplitude, phase, and frequency of voltages should be controllable. Three-phase bridge inverters are widely used in industrial applications such as adjustable speed drives (ASDs), motor drives and general purpose AC supplies. A typical three-phase full bridge inverter is modelled in Simulink with an Sinusoidal PWM (SPWM) control scheme. Among
Fig. 1. Schematic diagram of the renewable smart grid model with QPSK modem.
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the various control schemes, SPWM is the most commonly used control scheme for VSIs owing to its harmonic elimination features. In the SPWM modulation technique a sinusoidal reference signal is compared to a triangular waveform to generate the switching signals of the inverter block. The sinusoidal waveform is known as the modulating signal and is also utilized to define line frequency at 50 or 60 Hz. The triangular waveform is generated in a high frequency according to the modulating signal to be compared as a modulation carrier [26–28]. The modelled full bridge inverter is commutated at a 5 kHz switching frequency acquired by a unique triangular carrier signal. The modulation index value which is the ratio of the peak value of the modulating signal (Vref) to the carrier (Vtri) defines the operating area as linear or over-modulation depending on its being less than 1 or more than 1 as given in Eq. (2). The modulation index is set to optimal value at 0.8 as proposed in [28]. In the SPWM control technique, the line voltages (VAB, VBC, VCA) are obtained in the linear modulation range given in Eq. (3) and defined using Eq. (4) for the over-modulation range.
mi ¼
V ref V tri
V AB ¼ V BC ¼ V CA ¼ mi
ð2Þ pffiffiffi 3V d 2
0 < mi 6 1
pffiffiffi pffiffiffiffiffiffiffiffiffi 3V d 4 3V d < V AB ¼ V BC ¼ V CA < 2 p 2
mi P 1
ð3Þ
ð4Þ
The output voltage and current analyses are detailed in the section of simulation results. Harmonic analyses were also performed to determine if the modelled smart grid meets the required international standards. The transmission line parameters are shown in Table 1. The resistance, inductance, and capacitance parameters
Table 1 Transmission line parameters. Unit
Value
Frequency Resistance Inductance Capacitance Length
50 Hz 0.2568 X/km 3.4 107 H/km 8 109 F/km 10 km
are defined per km in the PI section line and the length is set to 10 km. The coupling circuit contains a 1:1 isolation transformer, which is parallel connected to an R–L–C network.
3. QPSK based smart grid Data transmission over a carrier by using digital symbol sequences is known as digital modulation. The three main digital modulation types are Amplitude Shift Keying (ASK), Frequency Shift Keying (FSK), and Phase Shift Keying (PSK). The PSK is the digital modulation scheme least effected by noise. Quadrature Phase Shift Keying (QPSK or 4PSK) is the most widely used M-ary PSK scheme among others since it does not suffer from Bit Error Rate (BER) degradation while the bandwidth efficiency is increased. The QPSK scheme is the base structure of wired and wireless communication systems such as wired modems, DSL modems, CDMA, 3G, Wi-Fi (IEEE 802.11) and WiMAX (IEEE 802.16) [29–31]. 3.1. Theoretical infrastructure for QPSK modem The M-ary PSK modulation uses m separate carrier phase for transmission. The transmission space is acquired by dividing the
Fig. 2. Block diagram of QPSK Modem: (a) modulator. (b) demodulator.
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2p radian phase space to equal M-parts. QPSK signal is analytically described using the following equation;
sQPSK ðtÞ ¼ A cosð2pfc t þ hi Þ;
0 6 t 6 T;
i ¼ 1; 2; 3; 4:
ð2i 1Þp Þ; 4
0 6 t 6 T;
06t6T
ð9Þ
rffiffiffi 2 sinð2pfc tÞ; T
06t6T
ð10Þ
ð5Þ
where A is the carrier amplitude, fc is the carrier frequency and hi is p the phase angle of carrier, hi ¼ ð2i1Þ . Eq. (6) is obtained by re4 arranging Eq. (5) as;
sQPSK ðtÞ ¼ A cosð2pfc t þ
rffiffiffi 2 cosð2pfc tÞ; u1 ¼ T
u2 ¼
s1(t) and s2(t) in Eq. (8) can be defined as,
s1 ðtÞ ¼
i ¼ 1; 2; 3; 4:
Z 0
ð6Þ
Z
sQPSK ðtÞu1 ðtÞdt ¼
pffiffiffi E cosðhi Þ
ð11Þ
sQPSK ðtÞu2 ðtÞdt ¼
pffiffiffi E sinðhi Þ
ð12Þ
T
T
If trigonometric equivalence is applied to Eq. (6), sQPSK(t) term is described as,
s2 ðtÞ ¼
sQPSK ðtÞ ¼ A cosðhi Þð2pfc tÞ A sinðhi Þð2pfc tÞ
ð7Þ
The symbol energy is defined with E parameter and provides the equation of E = (1/2)A2T. The phase relation of s1(t) and s2(t) is,
sQPSK ðtÞ ¼ s1 ðtÞu1 ðtÞ cosð2pfc tÞ þ s2 ðtÞu2 ðtÞ sinð2pfc tÞ
ð8Þ
where u1 and u2 are orthonormal basis functions given by,
0
hi ¼ tan1
s2 s1
ð13Þ
As a result of equations analyzed from Eqs. (7)–(13), QPSK signal in the time axis is described as an entire equation as,
Fig. 3. Developed QPSK model in Simulink: (a) modulator. (b) demodulator.
E. Kabalci et al. / Electrical Power and Energy Systems 34 (2012) 19–28
A A sQPSK ðtÞ ¼ pffiffiffi IðtÞ cosð2pfc tÞ þ pffiffiffi Q ðtÞ sinð2pfc tÞ; 2 2
1 6 t 6 1 ð14Þ
where, the message signal is constituted odd and even digital symbol sequences such as d0, d1, d2, d3, . . . , dn1, dn while I(t) signal is being constituted by even digital symbols like d0, d2, d4, . . . , dn and Q(t) signal is being constituted by odd digital symbols like d1, d3, d5, . . . , dn1. The block diagram of a QPSK modulator is illustrated in Fig. 2a. The baseband binary bits of message signal are separated to two by using serial to parallel converter at the input of modulator. The QPSK signal of the baseband binary data is acquired by adding the modulated signals received over I and Q channels. The block diagram of a QPSK demodulator is illustrated in Fig. 2b. The demodulation process is divided into three major subsections. First subsection performs the coherent detection since the received waveform is suppressed carrier in nature. The methods by which a phase-coherent carrier is derived from the received signal are termed, carrier recovery, and will be covered first. The raw data are obtained by coherent multiplication, and used to derive clocksynchronization information in the next step of demodulation. The raw data are then passed through the channel filter, which shapes the pulse train so as to minimize inter symbol-interference distortion effects. The r1 and r2 correlator outputs which are gener-
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ated according to the received signal r(t), are each compared with a threshold of zero as seen in Fig. 2b. If r1 is higher than 0, a decision is made to provide a symbol 1 for the in-phase channel output. If r1 is lower than 0 a decision is made to provide a symbol of 0 in this case. Similarly, if r2 is higher than 0, a decision is made provide a symbol of 1 for the quadrature channel output, else if r2 is lower than 0, a decision is made provide a symbol of 0 [30,31]. 3.2. Modelling the QPSK modem in MATLAB/Simulink The Simulink design of the QPSK modem is shown in Fig. 3. The measurement signal of the maximum power point of the solar plant is converted to a 12-bit digital signal as seen in Fig. 3a. I-data and Q-data channels are realized separately using equivalent pulse generators and multiplied with sine and cosine sources. I-modulated and Q-modulated signals are added together at the output of the QPSK modulator to acquire a unique QPSK output. The demodulator side of the modem is shown in Fig. 3b. The QPSK modulated signal is separated using sine and cosine and is supplied to the following demodulator (I-channel or Q-channel) and pulse shaper block. Recovered I-data and Q-data signals are converted to serial in order to acquire output binary data. In addition, a fourth order low pass filter is used for the filtering and calibration processes. The cut-off frequency of the low pass filter is adjusted to various
Fig. 4. Simulink design of the proposed model.
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Fig. 5. Simulation results of the renewable smart grid model: (a) transmitted, demodulated and calibrated signals with filtering at 500 Hz. (b) transmitted, demodulated and calibrated signals with filtering at 250 Hz. (c) three-phase line currents. (d) three-phase line voltages. (e) THD analysis of line current up to 50th order. (f) THD analysis of line current up to 200th order.
values between 250 and 500 Hz to generate the most accurate signal. The simulation results of the QPSK modem are analyzed in the next section with other parameters of the renewable energy conversion system.
4. Simulation results The renewable smart grid modelled in Simulink is illustrated in Fig. 4. The upper part of the simulation interface includes the solar
E. Kabalci et al. / Electrical Power and Energy Systems 34 (2012) 19–28
25
Fig. 5 (continued)
plant model and boost converter with the MPPT control algorithm. The generated DC bus voltage is supplied to the full bridge inverter
which is located in the middle part of simulation from right to left side. The 10 km transmission line is depicted with a ‘‘p’’ sign for
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Fig. 5 (continued)
each phase with fixed line impedance parameters. QPSK modems are located in the lower part of the Simulink interface with equivalent coupling circuits. The modem labeled with ‘‘QPSK Modem 1’’ is used as the plant side modem while the modem denoted with
‘‘QPSK Modem 2’’ is used as the grid side modem, which is assumed to be located in the management infrastructure. Simulation results are analyzed by using the measured values from predefined monitoring points. The pre-defined monitoring points are
E. Kabalci et al. / Electrical Power and Energy Systems 34 (2012) 19–28
located at the output of the DC bus in order to observe the maximum power point of the solar plant in the plant side modem to monitor sampled modulating data. The demodulated QPSK raw data and calibrated outputs, line currents of the inverter, line voltages of the inverter, and total harmonic distortion (THD) ratios of the line currents are observed in the grid side modem. The measurement results are shown in Fig. 5 from (a) to (f). Fig. 5a and b illustrates the two different measurement results of the QPSK modem. The cut-off frequency of the low pass filter is set to 500 Hz in Fig. 5a while it is fixed to 250 Hz in the simulation shown in Fig. 5b. Each figure includes four measurement curves, which display the real-time power point of the solar plant, sampled and quantized modulating data, demodulated QPSK data and filtered & calibrated output power, respectively. The oscillations seen in the first curves of Fig. 5a and b are caused by the MPPT control algorithm of the solar plant and varies at about 3% of the generated power. The modulating signal shown in the second curves are acquired by attenuating the plant’s power by the ratio of 1:1000. While these two curves are obtained at the beginning of the transmission line, the demodulated raw data and filtered output signals are acquired at the end of the transmission line. The transmission line delay is measured around 0.01 s in both analyses. The 250 Hz filtering depicted in Fig. 5b illustrates better calibrated output according to 500 Hz filtering. Both of these measurement results provide more accurate output data according to the Binary Phase Shift Keying (BPSK) modulated PLC model introduced in [5]. The three-phase line currents of the full bridge inverter are shown in Fig. 5c. The 120° phase differences between the line currents display a regular line output. The line voltages of the inverter are shown in Fig. 5d. The settling time of the line voltages are measured around half cycle (0.01 s). The THD analysis of line currents (THDi) are seen in Fig. 5e and f. The THDi analyses were performed for two different harmonic orders to show that there is not any difference between up to the 50th and 200th order of harmonics. The current distortion limits for general distribution systems was found to be lower than 5% for systems that operate from 120 V through 69 kV [32]. The measured THDi values of energy generation and the smart grid system is 2.46% in both analyses which were performed up to the 50th (2500 Hz) and 200th order (10,000 Hz) of harmonics. The most distorted harmonic orders are determined as the 99th, 101st and 199th orders with ratios of 1.34%, 1.2% and 0.95%, respectively. The regular THD analysis performed up to the 50th harmonic exhibits weak side band harmonic such as at the 3rd and 5th orders. The amplitude of the 3rd harmonic is 0.025 while the amplitude of the 5th harmonic is 0.005.
5. Conclusion Smart grid studies and research have mostly focused on conventional interconnected grid systems. A solar energy conversion system with PLC feature was proposed in this study. The DC–AC energy conversion system generated three-phase AC output voltages at the output of the full bridge inverter. The modelled system was also evaluated to enable simulating serial or parallel connections of the panels in the model. The boost converter was controlled with an MPPT algorithm based on the Perturb and Observe control scheme. The obtained DC voltage was converted to three-phase AC voltages utilizing an SPWM controlled full bridge inverter. The THD ratios, current and voltage analysis results showed that the implemented model is suitable for use in an industrial system. The transmission line and AC grid modelled in this paper is intended to introduce a smart grid application in order to observe
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the power rate of a solar plant in a 10 km distance. The PLC infrastructure is based on a QPSK modem, which interacts with three-phase grid over modelled coupling circuits. The metering, monitoring and analysis operations of the smart grid application were simulated in the developed model. The PLC application of QPSK modulation scheme analyzed in this paper provided more accurate results compared to a similar BPSK model, which was developed and cited by the authors. In future studies the proposed model could be improved by adding several control processes to the management infrastructure, such as adjustments to the modulation index and the switching frequency of the inverter and converter. In addition, hardware implementation could also be performed owing to the reliability of real-time simulation results. Acknowledgements The authors would like to thank the anonymous reviewers for their valuable comments and suggestions, which provided valuable guidance as to how we could improve this paper. References [1] Ginot N, Mannah MA, Batard C, Machmoum M. Application of power line communication for data transmission over PWM network. IEEE Trans Smart Grid 2010;1:178–85. [2] Li F, Qiao W, Sun H, Wan H, Wang J, Xia Y, et al. Smart transmission grid: vision and framework. IEEE Trans Smart Grid 2010;1:168–77. [3] Kurohane K, Senjyu T, Yona A, Urasaki N, Goya T, Funabashi T. A hybrid smart AC/DC power system. IEEE Trans Smart Grid 2010;1:199–204. [4] Zhang P, Li F, Bhatt N. Next-generation monitoring, analysis, and control for the future smart control center. IEEE Trans Smart Grid 2010;1:186–92. [5] Kabalci E, Kabalci Y. BPSK modem based power line communication system for observing photovoltaic panels. In: 2nd International conference on nuclear and renewable energy resources (NURER 2010); 2010. p. 284–9. [6] Mannah MA, Ginot N, Batard C, Machmoum M. Data transfer through the motor feeder cable in an industrial application. In: 24th applied power electronics conference and exposition (APEC); 2009. p. 1667–72. [7] Son YS, Pulkkinen T, Moon KD, Kim C. Home energy management system based on power line communication. IEEE Trans Consum Electron 2010;56:1380–6. [8] Barmada S, Raugi M, Tucci M, Zheng T. Power line communication in a full electric vehicle: measurements, modelling and analysis. In: IEEE international symposium on power line communications and its applications (ISPLC); 2010. p. 331–6. [9] Liu F, Duan S, Liu F, Liu B, Kang Y. A variable step size INC MPPT method for PV systems. IEEE Trans Ind Electron 2008;55:2622–8. [10] Kwon JM, Kwon BH, Nam KH. Three-phase photovoltaic system with threelevel boosting MPPT control. IEEE Trans Power Electron 2008;23:2319–27. [11] Pan CT, Juan YL. A novel sensorless MPPT controller for a high-efficiency microscale wind power generation system. IEEE Trans Energy Convers 2010;25:207–16. [12] Selvaraj J, Rahim NA. Multilevel inverter for grid-connected PV system employing digital PI controller. IEEE Trans Ind Electron 2009;56:149–58. [13] Kim IS, Kim MB, Youn MJ. New maximum power point tracker using slidingmode observer for estimation of solar array current in the grid-connected photovoltaic system. IEEE Trans Ind Electron 2006;53:1027–35. [14] Podmore R, Robinson MR. The role of simulators for smart grid development. IEEE Trans Smart Grid 2010;1:205–12. [15] De La Ree J, Centeno V, Thorp JS, Phadke AG. Synchronized phasor measurement applications in power systems. IEEE Trans Smart Grid 2010;1:20–7. [16] Bose A. Smart transmission grid applications and their supporting infrastructure. IEEE Trans Smart Grid 2010;1:11–9. [17] Pavlidou N, Han Vinck AJ, Yazdani J, Honary B. Power line communications: state of the art and future trends. IEEE Commun Mag 2003;41:34–40. [18] Sharp NE-170UC1 Multipurpose Module, Sharp, USA. [19] El Ouariachi M, Mrabti T, Yaden MF, Kassmi Ka, Kassmi K. Analysis, optimization and modelling of electrical energies produced by the photovoltaic panels and systems. In: Conference on 18th mediterranean control & automation (MED); 2010. p. 1614–9. [20] Leuchter J, Rerucha V, Zobaa AF. Mathematical modeling of photovoltaic systems. In: Conference 14th international power electronics and motion control conference (EPE/PEMC); 2010. p. S4-1–4. [21] Jee-Hoon J, Shehab A. Model construction of single crystalline photovoltaic panels for real-time simulation. In: IEEE energy conversion congress and exposition (ECCE); 2010. p. 342–9.
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[22] Velasco-Quesada G, Guinjoan-Gispert F, Pique-Lopez R, Roman-Lumbreras M, Conesa-Roca A. Electrical PV array reconfiguration strategy for energy extraction improvement in grid-connected PV systems. IEEE Trans Ind Electron 2009;56:4319–31. [23] Kazmi S, Goto H, Ichinokura O, Hai-Jiao Guo. An improved and very efficient MPPT controller for PV systems subjected to rapidly varying atmospheric conditions and partial shading. In: Australasian universities power engineering conference; 2009. p. 1–6. [24] Lee DY, Noh HJ, Hyun DS, Choy I. An improved MPPT converter using current compensation method for small scaled PV-applications. In: 18th Annual IEEE applied power electronics conference and exposition; 2003. p. 540–5. [25] Piegari L, Rizzo R. Adaptive Perturb and Observe algorithm for photovoltaic maximum power point tracking. IET Renew Power Generat 2010;4: 317–28. [26] Kabalci E, Irmak E, Colak I. Design of an AC–DC–AC converter for wind turbines. Wiley Int J Energy Res 2011;35:169–75.
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Electrical Power and Energy Systems journal homepage: www.elsevier.com/locate/ijepes
Modelling and analysis of a power line communication system with QPSK modem for renewable smart grids Ersan Kabalci a, Yasin Kabalci b, Ibrahim Develi c,⇑ a
Department of Electrical & Electronics Engineering, Nevsehir University, 50300 Nevsehir, Turkey Graduate School of Natural and Applied Sciences, Erciyes University, 38039 Kayseri, Turkey c Department of Electrical & Electronics Engineering, Erciyes University, 38039 Kayseri, Turkey b
a r t i c l e
i n f o
Article history: Received 18 April 2011 Received in revised form 17 August 2011 Accepted 20 August 2011 Available online 8 October 2011 Keywords: Power line communication Renewable energy sources Smart grid Maximum power point tracking QPSK Modem
a b s t r a c t Monitoring and metering processes are required to be performed in renewable energy conversion systems like smart grid applications of the conventional grid system. These processes concerning renewable energy sources are analyzed in this paper in order to propose a solution for solar power systems. The DC–AC conversion system proposed in this paper covers a solar power plant with a maximum power point tracking system and IGBT based three-phase inverter to generate three-phase AC line voltages. The modelled transmission line is not only used to carry the generated voltage but also to transmit the generated power rate of the solar plant in real-time. The power line communication (PLC) infrastructure employed is based on Quadrature Phase Shift Keying (QPSK) modems. In order to overlap QPSK modulated data to the three-phase transmission line at the inverter and grid sides a coupling interface is also designed in the study. The current, voltage and power data generated by the photovoltaic (PV) panels are successfully monitored over transmission lines owing to the developed system. As a result, additional monitoring costs are eliminated by using the proposed technique instead of SCADA or Ethernet-based systems. Ó 2011 Elsevier Ltd. All rights reserved.
1. Introduction Smart grid technologies are rapidly emerging due to the development of power transmission grids. The demands required of a smart grid are remote sensing, communication, control, monitoring and analysis processes. The smart grid should also meet these requirements in a sustainable, reliable and efficient way. Applications and studies on smart grids have mostly focused on phase measurements, advanced metering and remote monitoring of a source [1–3]. Smart grid applications can reduce costs and simplify several industrial requirements such as power factor correction applications, remote control of electrical machines and energy generation systems [1–5]. The term ‘‘power line communication (PLC)’’ is used to define the transmission and receiving of any data or signal over a power line. This concept also describes the main framework of a smart grid. The utilization of a power line as a transmission channel prevents additional setting up costs to connect the systems together. PLC application allows data transmission rates of up to 200 Mbps using a single phase with 220 V 50 Hz [6–8].
⇑ Corresponding author. Tel.: +90 352 437 49 01/32 205; fax: +90 352 437 57 84. E-mail addresses: [email protected] (E. Kabalci), [email protected] (Y. Kabalci), [email protected] (I. Develi). 0142-0615/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.ijepes.2011.08.021
Hybrid power generation systems based on renewable energy sources such as photovoltaic (PV) cells and wind turbines are being rapidly developed in addition to conventional energy generation systems. The distributed generation allows the installation of relatively small-scale power generators at medium and low-voltage distribution levels of the power system. Energy generation using PVs is also becoming an energy area of interest at low voltage and medium power range. Solar energy sources have the advantage of not causing pollution, having low maintenance costs, and not producing noise due to the absence of moving parts. Generally, PVs are considered as an expensive method of generating electricity. However, stand-alone PV applications are the most economic solution to provide the required power service. In gridconnected systems, single-phase or three-phase pulse-width modulation (PWM) voltage-source inverters (VSIs) are often used for interfacing the renewable energy source to the utility grid, and the current control of the grid-connected inverters plays a predominant role in feeding a grid with high-quality power. For grid-connected systems, the cost is further reduced due to the elimination of battery requirements, which is the second largest contributor to the cost of a PV system. The cost of the grid-connected system can also be minimized by decreasing the number of power conversion stages and the number of components involved in each stage [9–13]. There are several studies in the literature based on a variety of modelling and simulation software [14–17].
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The model developed in this study is built using real-time operation functions and power generation. Transmission lines are also implemented in an interacting structure with the QPSK modems over coupling circuit models. The DC power generation system is formed using the parameters of a 170 W Sharp solar panel [18]. The solar plant model includes 10 parallel connected solar panels with a Perturb and Observe (P&O) maximum power point tracking (MPPT) algorithm. The output of the boost converter is supplied to a three-phase IGBT inverter and the AC output voltages generated are transmitted to the grid side over a 10 km transmission line. The transmission line is also modelled using real-time parameters with specific line dissipations such as inductance, capacitance and resistance. The QPSK modem is assumed to observe power generated by the solar plant. The transmitting part of the modem is located at the AC side of the inverter, which is the starting point of the transmission line. The receiving part is located at the grid side where the system administrator performs monitoring and control operations. Although the system is modelled bi-directionally in terms of transmission, only the power value of the solar plant is transmitted to the grid side. It is also possible to evaluate the system by transmitting any control signal from the management side or grid to the solar plant to adjust parameters such as the modulation index or switching frequency of the inverter. This paper is organized by introducing a renewable energy conversion system and QPSK based smart grid model in the second and third sections, respectively. The simulation results are examined in detail in the fourth section.
2. Renewable energy conversion system The renewable energy generation system with PV panels, MPPT controlled boost converter, inverter, and transmission line parameters are introduced in this section, while the QPSK modem and other related parts of the smart grid are analyzed in the next section. The schematic diagram of the renewable smart grid model is shown in Fig. 1. The analytical model of the PV panel is designed using the mathematical equations given in the literature. The developed model allows the adjustment of basic parameters such as open circuit voltage (Voc), maximum power voltage (Vpm), short circuit current (Isc), and maximum power current (Ipm) according to any commercial PV model. The electrical characteristic parameters of the modelled solar panel in this study can be found in [18]. The boost converter keeps the output voltage stable at a reference value by using the comparison algorithm given in the MPPT part. The MPPT algorithm used in this converter compares the output voltage of the converter with a given reference voltage value. If the output voltage is higher than the reference voltage the MPPT algorithm decreases the duty cycle of the PWM that is used to
control the boost converter or vice versa. The MPPT algorithm ensures that the PV panels track the sun in order to obtain the maximum power point. The DC output voltage of the boost converter is supplied to the inverter to generate three-phase AC voltage. The transmission line parameters are set to real-time impedance values per kilometer to take line losses into account. 2.1. Solar plant and MPPT models The electrical power obtained from a PV cell depends on shortcircuit current (Isc), diode ideality factor (gI), shunt resistance (Rsh), and series resistance (Rs) parameters. The current–voltage (I–V) characteristic of a PV panel is determined using the following equation [19–23],
I o ¼ I pv I D
V o þ I o Rs V o þ I o Rs 1 exp gI V T Rsh
ð1Þ
where Io is the output current of the panel, Ipv is the current generated by the irradiation value, ID is the current of the output resistor, VT is the thermal voltage and Vo is the output voltage. The most widely used MPPT techniques in the literature are the hill climbing and P&O methods. The hill climbing method generates a perturbation in the duty ratio of the power converter while the P&O method involves a perturbation in the operating voltage of the PV array. In the case of a PV array connected to a power converter, perturbing the duty ratio of the power converter perturbs the PV array current and consequently perturbs the PV array voltage. The P&O algorithm is based on comparing the current output power to its previously acquired power level. If the current acquired power level is less than the previously acquired level then the algorithm increases the operating voltage or else reduces it. This situation is also valid vice versa. The switch used in the boost converter is controlled at 20 kHz switching frequency. The output of the boost converter is applied to a three-phase inverter in order to generate three-phase AC voltages [5,23–25]. 2.2. Voltage source inverter and transmission line Three-phase VSIs meet the power demand from medium to high-power applications while single-phase VSIs cover low-range power applications. The main purpose of these topologies is to provide a three-phase voltage source, where the amplitude, phase, and frequency of voltages should be controllable. Three-phase bridge inverters are widely used in industrial applications such as adjustable speed drives (ASDs), motor drives and general purpose AC supplies. A typical three-phase full bridge inverter is modelled in Simulink with an Sinusoidal PWM (SPWM) control scheme. Among
Fig. 1. Schematic diagram of the renewable smart grid model with QPSK modem.
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the various control schemes, SPWM is the most commonly used control scheme for VSIs owing to its harmonic elimination features. In the SPWM modulation technique a sinusoidal reference signal is compared to a triangular waveform to generate the switching signals of the inverter block. The sinusoidal waveform is known as the modulating signal and is also utilized to define line frequency at 50 or 60 Hz. The triangular waveform is generated in a high frequency according to the modulating signal to be compared as a modulation carrier [26–28]. The modelled full bridge inverter is commutated at a 5 kHz switching frequency acquired by a unique triangular carrier signal. The modulation index value which is the ratio of the peak value of the modulating signal (Vref) to the carrier (Vtri) defines the operating area as linear or over-modulation depending on its being less than 1 or more than 1 as given in Eq. (2). The modulation index is set to optimal value at 0.8 as proposed in [28]. In the SPWM control technique, the line voltages (VAB, VBC, VCA) are obtained in the linear modulation range given in Eq. (3) and defined using Eq. (4) for the over-modulation range.
mi ¼
V ref V tri
V AB ¼ V BC ¼ V CA ¼ mi
ð2Þ pffiffiffi 3V d 2
0 < mi 6 1
pffiffiffi pffiffiffiffiffiffiffiffiffi 3V d 4 3V d < V AB ¼ V BC ¼ V CA < 2 p 2
mi P 1
ð3Þ
ð4Þ
The output voltage and current analyses are detailed in the section of simulation results. Harmonic analyses were also performed to determine if the modelled smart grid meets the required international standards. The transmission line parameters are shown in Table 1. The resistance, inductance, and capacitance parameters
Table 1 Transmission line parameters. Unit
Value
Frequency Resistance Inductance Capacitance Length
50 Hz 0.2568 X/km 3.4 107 H/km 8 109 F/km 10 km
are defined per km in the PI section line and the length is set to 10 km. The coupling circuit contains a 1:1 isolation transformer, which is parallel connected to an R–L–C network.
3. QPSK based smart grid Data transmission over a carrier by using digital symbol sequences is known as digital modulation. The three main digital modulation types are Amplitude Shift Keying (ASK), Frequency Shift Keying (FSK), and Phase Shift Keying (PSK). The PSK is the digital modulation scheme least effected by noise. Quadrature Phase Shift Keying (QPSK or 4PSK) is the most widely used M-ary PSK scheme among others since it does not suffer from Bit Error Rate (BER) degradation while the bandwidth efficiency is increased. The QPSK scheme is the base structure of wired and wireless communication systems such as wired modems, DSL modems, CDMA, 3G, Wi-Fi (IEEE 802.11) and WiMAX (IEEE 802.16) [29–31]. 3.1. Theoretical infrastructure for QPSK modem The M-ary PSK modulation uses m separate carrier phase for transmission. The transmission space is acquired by dividing the
Fig. 2. Block diagram of QPSK Modem: (a) modulator. (b) demodulator.
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2p radian phase space to equal M-parts. QPSK signal is analytically described using the following equation;
sQPSK ðtÞ ¼ A cosð2pfc t þ hi Þ;
0 6 t 6 T;
i ¼ 1; 2; 3; 4:
ð2i 1Þp Þ; 4
0 6 t 6 T;
06t6T
ð9Þ
rffiffiffi 2 sinð2pfc tÞ; T
06t6T
ð10Þ
ð5Þ
where A is the carrier amplitude, fc is the carrier frequency and hi is p the phase angle of carrier, hi ¼ ð2i1Þ . Eq. (6) is obtained by re4 arranging Eq. (5) as;
sQPSK ðtÞ ¼ A cosð2pfc t þ
rffiffiffi 2 cosð2pfc tÞ; u1 ¼ T
u2 ¼
s1(t) and s2(t) in Eq. (8) can be defined as,
s1 ðtÞ ¼
i ¼ 1; 2; 3; 4:
Z 0
ð6Þ
Z
sQPSK ðtÞu1 ðtÞdt ¼
pffiffiffi E cosðhi Þ
ð11Þ
sQPSK ðtÞu2 ðtÞdt ¼
pffiffiffi E sinðhi Þ
ð12Þ
T
T
If trigonometric equivalence is applied to Eq. (6), sQPSK(t) term is described as,
s2 ðtÞ ¼
sQPSK ðtÞ ¼ A cosðhi Þð2pfc tÞ A sinðhi Þð2pfc tÞ
ð7Þ
The symbol energy is defined with E parameter and provides the equation of E = (1/2)A2T. The phase relation of s1(t) and s2(t) is,
sQPSK ðtÞ ¼ s1 ðtÞu1 ðtÞ cosð2pfc tÞ þ s2 ðtÞu2 ðtÞ sinð2pfc tÞ
ð8Þ
where u1 and u2 are orthonormal basis functions given by,
0
hi ¼ tan1
s2 s1
ð13Þ
As a result of equations analyzed from Eqs. (7)–(13), QPSK signal in the time axis is described as an entire equation as,
Fig. 3. Developed QPSK model in Simulink: (a) modulator. (b) demodulator.
E. Kabalci et al. / Electrical Power and Energy Systems 34 (2012) 19–28
A A sQPSK ðtÞ ¼ pffiffiffi IðtÞ cosð2pfc tÞ þ pffiffiffi Q ðtÞ sinð2pfc tÞ; 2 2
1 6 t 6 1 ð14Þ
where, the message signal is constituted odd and even digital symbol sequences such as d0, d1, d2, d3, . . . , dn1, dn while I(t) signal is being constituted by even digital symbols like d0, d2, d4, . . . , dn and Q(t) signal is being constituted by odd digital symbols like d1, d3, d5, . . . , dn1. The block diagram of a QPSK modulator is illustrated in Fig. 2a. The baseband binary bits of message signal are separated to two by using serial to parallel converter at the input of modulator. The QPSK signal of the baseband binary data is acquired by adding the modulated signals received over I and Q channels. The block diagram of a QPSK demodulator is illustrated in Fig. 2b. The demodulation process is divided into three major subsections. First subsection performs the coherent detection since the received waveform is suppressed carrier in nature. The methods by which a phase-coherent carrier is derived from the received signal are termed, carrier recovery, and will be covered first. The raw data are obtained by coherent multiplication, and used to derive clocksynchronization information in the next step of demodulation. The raw data are then passed through the channel filter, which shapes the pulse train so as to minimize inter symbol-interference distortion effects. The r1 and r2 correlator outputs which are gener-
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ated according to the received signal r(t), are each compared with a threshold of zero as seen in Fig. 2b. If r1 is higher than 0, a decision is made to provide a symbol 1 for the in-phase channel output. If r1 is lower than 0 a decision is made to provide a symbol of 0 in this case. Similarly, if r2 is higher than 0, a decision is made provide a symbol of 1 for the quadrature channel output, else if r2 is lower than 0, a decision is made provide a symbol of 0 [30,31]. 3.2. Modelling the QPSK modem in MATLAB/Simulink The Simulink design of the QPSK modem is shown in Fig. 3. The measurement signal of the maximum power point of the solar plant is converted to a 12-bit digital signal as seen in Fig. 3a. I-data and Q-data channels are realized separately using equivalent pulse generators and multiplied with sine and cosine sources. I-modulated and Q-modulated signals are added together at the output of the QPSK modulator to acquire a unique QPSK output. The demodulator side of the modem is shown in Fig. 3b. The QPSK modulated signal is separated using sine and cosine and is supplied to the following demodulator (I-channel or Q-channel) and pulse shaper block. Recovered I-data and Q-data signals are converted to serial in order to acquire output binary data. In addition, a fourth order low pass filter is used for the filtering and calibration processes. The cut-off frequency of the low pass filter is adjusted to various
Fig. 4. Simulink design of the proposed model.
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Fig. 5. Simulation results of the renewable smart grid model: (a) transmitted, demodulated and calibrated signals with filtering at 500 Hz. (b) transmitted, demodulated and calibrated signals with filtering at 250 Hz. (c) three-phase line currents. (d) three-phase line voltages. (e) THD analysis of line current up to 50th order. (f) THD analysis of line current up to 200th order.
values between 250 and 500 Hz to generate the most accurate signal. The simulation results of the QPSK modem are analyzed in the next section with other parameters of the renewable energy conversion system.
4. Simulation results The renewable smart grid modelled in Simulink is illustrated in Fig. 4. The upper part of the simulation interface includes the solar
E. Kabalci et al. / Electrical Power and Energy Systems 34 (2012) 19–28
25
Fig. 5 (continued)
plant model and boost converter with the MPPT control algorithm. The generated DC bus voltage is supplied to the full bridge inverter
which is located in the middle part of simulation from right to left side. The 10 km transmission line is depicted with a ‘‘p’’ sign for
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Fig. 5 (continued)
each phase with fixed line impedance parameters. QPSK modems are located in the lower part of the Simulink interface with equivalent coupling circuits. The modem labeled with ‘‘QPSK Modem 1’’ is used as the plant side modem while the modem denoted with
‘‘QPSK Modem 2’’ is used as the grid side modem, which is assumed to be located in the management infrastructure. Simulation results are analyzed by using the measured values from predefined monitoring points. The pre-defined monitoring points are
E. Kabalci et al. / Electrical Power and Energy Systems 34 (2012) 19–28
located at the output of the DC bus in order to observe the maximum power point of the solar plant in the plant side modem to monitor sampled modulating data. The demodulated QPSK raw data and calibrated outputs, line currents of the inverter, line voltages of the inverter, and total harmonic distortion (THD) ratios of the line currents are observed in the grid side modem. The measurement results are shown in Fig. 5 from (a) to (f). Fig. 5a and b illustrates the two different measurement results of the QPSK modem. The cut-off frequency of the low pass filter is set to 500 Hz in Fig. 5a while it is fixed to 250 Hz in the simulation shown in Fig. 5b. Each figure includes four measurement curves, which display the real-time power point of the solar plant, sampled and quantized modulating data, demodulated QPSK data and filtered & calibrated output power, respectively. The oscillations seen in the first curves of Fig. 5a and b are caused by the MPPT control algorithm of the solar plant and varies at about 3% of the generated power. The modulating signal shown in the second curves are acquired by attenuating the plant’s power by the ratio of 1:1000. While these two curves are obtained at the beginning of the transmission line, the demodulated raw data and filtered output signals are acquired at the end of the transmission line. The transmission line delay is measured around 0.01 s in both analyses. The 250 Hz filtering depicted in Fig. 5b illustrates better calibrated output according to 500 Hz filtering. Both of these measurement results provide more accurate output data according to the Binary Phase Shift Keying (BPSK) modulated PLC model introduced in [5]. The three-phase line currents of the full bridge inverter are shown in Fig. 5c. The 120° phase differences between the line currents display a regular line output. The line voltages of the inverter are shown in Fig. 5d. The settling time of the line voltages are measured around half cycle (0.01 s). The THD analysis of line currents (THDi) are seen in Fig. 5e and f. The THDi analyses were performed for two different harmonic orders to show that there is not any difference between up to the 50th and 200th order of harmonics. The current distortion limits for general distribution systems was found to be lower than 5% for systems that operate from 120 V through 69 kV [32]. The measured THDi values of energy generation and the smart grid system is 2.46% in both analyses which were performed up to the 50th (2500 Hz) and 200th order (10,000 Hz) of harmonics. The most distorted harmonic orders are determined as the 99th, 101st and 199th orders with ratios of 1.34%, 1.2% and 0.95%, respectively. The regular THD analysis performed up to the 50th harmonic exhibits weak side band harmonic such as at the 3rd and 5th orders. The amplitude of the 3rd harmonic is 0.025 while the amplitude of the 5th harmonic is 0.005.
5. Conclusion Smart grid studies and research have mostly focused on conventional interconnected grid systems. A solar energy conversion system with PLC feature was proposed in this study. The DC–AC energy conversion system generated three-phase AC output voltages at the output of the full bridge inverter. The modelled system was also evaluated to enable simulating serial or parallel connections of the panels in the model. The boost converter was controlled with an MPPT algorithm based on the Perturb and Observe control scheme. The obtained DC voltage was converted to three-phase AC voltages utilizing an SPWM controlled full bridge inverter. The THD ratios, current and voltage analysis results showed that the implemented model is suitable for use in an industrial system. The transmission line and AC grid modelled in this paper is intended to introduce a smart grid application in order to observe
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the power rate of a solar plant in a 10 km distance. The PLC infrastructure is based on a QPSK modem, which interacts with three-phase grid over modelled coupling circuits. The metering, monitoring and analysis operations of the smart grid application were simulated in the developed model. The PLC application of QPSK modulation scheme analyzed in this paper provided more accurate results compared to a similar BPSK model, which was developed and cited by the authors. In future studies the proposed model could be improved by adding several control processes to the management infrastructure, such as adjustments to the modulation index and the switching frequency of the inverter and converter. In addition, hardware implementation could also be performed owing to the reliability of real-time simulation results. Acknowledgements The authors would like to thank the anonymous reviewers for their valuable comments and suggestions, which provided valuable guidance as to how we could improve this paper. References [1] Ginot N, Mannah MA, Batard C, Machmoum M. Application of power line communication for data transmission over PWM network. IEEE Trans Smart Grid 2010;1:178–85. [2] Li F, Qiao W, Sun H, Wan H, Wang J, Xia Y, et al. Smart transmission grid: vision and framework. IEEE Trans Smart Grid 2010;1:168–77. [3] Kurohane K, Senjyu T, Yona A, Urasaki N, Goya T, Funabashi T. A hybrid smart AC/DC power system. IEEE Trans Smart Grid 2010;1:199–204. [4] Zhang P, Li F, Bhatt N. Next-generation monitoring, analysis, and control for the future smart control center. IEEE Trans Smart Grid 2010;1:186–92. [5] Kabalci E, Kabalci Y. BPSK modem based power line communication system for observing photovoltaic panels. In: 2nd International conference on nuclear and renewable energy resources (NURER 2010); 2010. p. 284–9. [6] Mannah MA, Ginot N, Batard C, Machmoum M. Data transfer through the motor feeder cable in an industrial application. In: 24th applied power electronics conference and exposition (APEC); 2009. p. 1667–72. [7] Son YS, Pulkkinen T, Moon KD, Kim C. Home energy management system based on power line communication. IEEE Trans Consum Electron 2010;56:1380–6. [8] Barmada S, Raugi M, Tucci M, Zheng T. Power line communication in a full electric vehicle: measurements, modelling and analysis. In: IEEE international symposium on power line communications and its applications (ISPLC); 2010. p. 331–6. [9] Liu F, Duan S, Liu F, Liu B, Kang Y. A variable step size INC MPPT method for PV systems. IEEE Trans Ind Electron 2008;55:2622–8. [10] Kwon JM, Kwon BH, Nam KH. Three-phase photovoltaic system with threelevel boosting MPPT control. IEEE Trans Power Electron 2008;23:2319–27. [11] Pan CT, Juan YL. A novel sensorless MPPT controller for a high-efficiency microscale wind power generation system. IEEE Trans Energy Convers 2010;25:207–16. [12] Selvaraj J, Rahim NA. Multilevel inverter for grid-connected PV system employing digital PI controller. IEEE Trans Ind Electron 2009;56:149–58. [13] Kim IS, Kim MB, Youn MJ. New maximum power point tracker using slidingmode observer for estimation of solar array current in the grid-connected photovoltaic system. IEEE Trans Ind Electron 2006;53:1027–35. [14] Podmore R, Robinson MR. The role of simulators for smart grid development. IEEE Trans Smart Grid 2010;1:205–12. [15] De La Ree J, Centeno V, Thorp JS, Phadke AG. Synchronized phasor measurement applications in power systems. IEEE Trans Smart Grid 2010;1:20–7. [16] Bose A. Smart transmission grid applications and their supporting infrastructure. IEEE Trans Smart Grid 2010;1:11–9. [17] Pavlidou N, Han Vinck AJ, Yazdani J, Honary B. Power line communications: state of the art and future trends. IEEE Commun Mag 2003;41:34–40. [18] Sharp NE-170UC1 Multipurpose Module, Sharp, USA
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