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Energy Conversion and Management 90 (2015) 336346Contents lists
available at ScienceDirect
Energy Conversion and Management
journal homepage: www.elsevier .com/locate /enconmanA smart
monitoring infrastructure design for distributed renewableenergy
systemshttp://dx.doi.org/10.1016/j.enconman.2014.10.0620196-8904/
2014 Elsevier Ltd. All rights reserved.
E-mail address: [email protected] KabalciDepartment
of Electrical and Electronics Engineering, Faculty of Engineering
and Architecture, Nevsehir Haci Bektas Veli University, Nevsehir,
Turkeya r t i c l e i n f oArticle history:Received 29 September
2014Accepted 30 October 2014Available online 5 December 2014
Keywords:Power line communication (PLC)Renewable energy
sourcesSmart gridQuadrature phase shift keying (QPSK)Automatic
meter reading (AMR)a b s t r a c t
The automatic meter reading is essentially required in renewable
grids as in conventional grids. It isintended to propose a reliable
measurement system that is validated in a photovoltaic power
systemto meet the requirement of a renewable grid. In the presented
study, the photovoltaic plants arecontrolled by using a widely
known maximum power point tracking algorithm that is named as
Perturband Observe. The distribution line at the output of inverter
is modelled according to realistic parametersof 25 km line. Besides
carrying the generated line voltage, the grid is used as a
transmission medium forthe generated power measurements of
photovoltaic plants and power consumptions of load
plantsseparately. The modem constituting the power line
communication manages the dual-channel transferand transmits the
consumed energy ratios of the load plants. One of the modems is
located at the outputof voltage source inverter and the other one
of the load plants. The power consumption values of eachload plants
are individually measured and successfully transmitted to
monitoring section in the modelledsystem. The obtained data that is
only used for monitoring in this application can also be evaluated
forautomatic meter reading applications.
2014 Elsevier Ltd. All rights reserved.1. Introduction
The renewable energy usage is rapidly increasing since
eacheconomic crisis and source deficiencies cause to increments
onper kWh cost of fossil-fuel based energy [1]. Although the
govern-ments and private sector leaders discuss on cheaper energy
gener-ation methods, those all agree to increase the renewable
energysource (RES) shares to decrease energy generation costs [2].
TheRES used in electricity generation include wind energy,
solarenergy, geothermal energy, and tidal waves. The income of
anyelectricity generation and distribution company in the market
isas important as its outcomes [3]. Since the conventional grid is
apassive model, the remote detection of the consumed energy
isprevented by the losses occurred on the distribution
line.Researchers also extensively study the microgrid and load
classifi-cation issues. Zhou et al. presented an optimal load
distributionmodel and load classifications [4,5]. The main
components of aconventional grid can be classified into five topics
that are electric-ity generation plant, transmission substations,
distribution substa-tions, control centre, and end-users. Although
the conventionalgrid has many deficiencies to be solved such as
voltage sags,blackouts, and overloads, several novel technologies
are beingresearched in order to improve the qualifications of the
grid tech-nology [6].
The smart grid concept, which is expanded since early
2000s,implies for a data communication network based on
conventionalgrid that collects and carries the measured and
modulated data oftransmission, distribution, and consumption units
[3,7]. The devel-opments seen in a conventional grid led the
researchers to powerline communication (PLC) and the smart grid
concepts. The smartgrid is assumed as a conversion of conventional
grid to a commu-nication medium that carries the data obtained from
remote sens-ing, control, and monitoring processes. These
communicationissues are expected to be performed in a secure and
sustainableway over wired and/or wireless communication
infrastructures[8]. The wireless technologies used in smart grid
are WI-Fi, WiMax,ZigBee, and Bluetooth while the wired
communication technolo-gies cover PLC, fiber optics, and copper
wires [911]. Even thoughthe wireless smart grids are more flexible
compared to PLC, thecommunication may fail because of the probable
problems suchas interference, shadowing, and/or fading [12,13].
Furthermore,weather conditions directly affect the wireless network
and causeto unexpected attenuations and several problems in
transmissionlength. Zhang et al. stated several problems related to
smarthome management systems that are based on communicationmethods
[14,15]. The PLC systems are categorized as narrow band
http://crossmark.crossref.org/dialog/?doi=10.1016/j.enconman.2014.10.062&domain=pdfhttp://dx.doi.org/10.1016/j.enconman.2014.10.062mailto:[email protected]://dx.doi.org/10.1016/j.enconman.2014.10.062http://www.sciencedirect.com/science/journal/01968904http://www.elsevier.com/locate/enconman
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E. Kabalci / Energy Conversion and Management 90 (2015) 336346
337(NB-PLC) and broad band (BB-PLC) according to operating
bands[9,10]. The proposed study in this paper deals with a
renewableenergy generation and distribution system and monitoring
thepower consumption rates of various consumers by
operatingmulti-carrier based PLC infrastructure.
The generation part of the modelled system consists of
threephotovoltaic (PV) plants that are assumed to be located in
separatefields. The converted PV energy is conducted to a DC busbar
andsupplies the input voltage of the inverter. The converted
energyis distributed to load models over a transmission line that
ismodelled with 25 km length. The power consumptions of two
loadplants are observed over transmission line constituting the
auto-matic meter reading (AMR) process. The quadrature phase
shiftkeying (QPSK) modem at the energy generation plant is
capableof demodulating the multi-channel input data on the various
car-rier frequencies. The carrier frequencies are set to 6 kHz and8
kHz in the designed QPSK modulator systems and the demodula-tors
are arranged to recover the carrier frequencies. It is possible
toreconfigure the QPSK demodulator in case of increasing the
num-ber of load plants. The renewable energy generation system
isintroduced in the second section with PV energy plants,
energyconversion part and distribution line subsections. The PLC
infra-structure is handled in the third section with modelled QPSK
mod-ulator and demodulator structures. The analysis
methods,optimization steps, and analysis results are discussed in
the fourthsection.2. Renewable energy generation system
Fig. 1 illustrates an example of smart grid applications, which
isa microgrid structure consisting the main components of a
conven-tional grid system [16]. All components of a smart grid,
such assensing, monitoring, protection, and control units can be
seen inthe figure that is originally shown in [16].
The complete schematic diagram of the modelled smart grid isseen
in Fig. 2. The modelled system can be handled in three sec-tions as
illustrated in energy generation, energy conversion andmonitoring,
and microgrid distribution and load sections. Theenergy generation
part is constituted with PV plant models, boostconverters, and DC
busbar.
The maximum power point tracking (MPPT) algorithm isalso
included to boost converter part. The energy conversion andFig. 1.
An example structure for wired and wireless smart gridmonitoring
section covers a three-phase full bridge inverterbesides QPSK modem
and monitoring part. The distribution linethat follows the energy
conversion stage carries the line voltagesto the loads. The
modelled system is tested with two different loadplants to figure
out QPSK communication over the same transmis-sion line. The drawn
current and the consumed power by loads aremeasured and are
modulated at each load plant. The modulateddata are overlapped to
line via coupling interfaces over S and Tphases namely after the
measurement and modulation processesare done. The parts of modelled
system are analysed in the follow-ing subsections in detail.
2.1. Energy generation plants
The analytical model of a PV panel is built using the
electricalequivalent circuit that is seen in Fig. 3. The specific
parameters ofPV panels are defined according to a model of Sharp
that provides170W maximum output power [1719]. The developed
modeladjusts the main parameters of PV panel such as short circuit
cur-rent (Isc), open circuit voltage (Voc), cell number, maximum
powercurrent (Ipm), and maximum power voltage (Vpm) referring to
anyPV panel. The current of PV panel is determined using Eq.
(1)[2022];
Io Ipv ID expqVo IoRs
mkBT
1
Vo IoRs
Rsh1
where, Io is output current of panel, Ipv is the generated PV
current,ID is the diode current, Vo is the output voltage, VT is
thermal volt-age, Rsh is shunt resistance, Rs is series resistance.
In addition, q isthe electron charge, m is the equivalent ideality
factor, kB is theBoltzmann constant, T is the cell temperature of
the junction.
Table 1 shows the PV panel parameters such as short
circuitcurrent, open circuit voltage, maximum power voltage, and
maxi-mum power current that are used in Simulink design. The
currentvoltage (IV) characteristic curve of the modelled PV module
isillustrated according to variable irradiation levels that vary
from200W/m2 to 1000W/m2 in Fig. 4(a).
The power characteristics generated according to same
testconditions to define the maximum power point of PV module
areshown in Fig. 4(b). The simulated curves verify that the
modelledPV module operates properly to reference module of Sharp
accord-ing to the same irradiation values. The modelled PV panels
areapplications in a generation and distribution scheme [16].
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Fig. 2. The schematic diagram of renewable grid and QPSK
modems.
Fig. 3. The electrical equivalent circuit of a PV cell.
338 E. Kabalci / Energy Conversion and Management 90 (2015)
336346arranged to construct three PV plants seen on the left-hand
side ofFig. 2. The DC output voltages of PV plants are supplied to
boostconverters, which are controlled by an MPPT algorithm to
obtainmaximum output power.Table 1PV panel and PV plant
parameters.
PV panel parametersOpen circuit voltage (Voc) Maximum power
voltage (Vpm) Short circui43.2 V 34.8 V 5.47 A
Parameters of PV Plant #1PV number in a series string Parallel
string number Maximum p16 16 556.8 V78.
Parameters of PV plant #2PV number in a series string Parallel
string number Maximum p16 16 556.8 V78.
Parameters of PV plant #3PV number in a series string Parallel
string number Maximum p16 16 556.8 V78.
Total output powerAlthough several MPPT techniques are
implemented, the hill-climbing and Perturb and Observe (P&O)
algorithms are widelypreferred among in the literature. Both the
hill climbing and P&Ogenerate a perturbation to pursue the
maximum power curve.
The P&O adjusts the operating voltage of PV array while
thehill-climbing algorithm deals with the duty cycle of the
DCDCconverter. These two methods control the PV voltage and
currentindividually. The duty cycle control performed by
hill-climbingalgorithm changes the current value of the PV array,
which causesseveral changes at the output voltage of DCDC
converter. How-ever, the P&O algorithm compares the output
power to previouslyacquired value to track the reference output
power. Since the P&Ocontrols the operating voltage, it
decreases or increases outputvoltage of DCDC converter to obtain
the reference voltage at theoutput of converter system [18,23,24].
The P&O algorithm usedin the Simulink simulation is given in
the Fig. 5 where it is basedon comparing the actual output power to
previously acquiredpower level.
If acquired power level (P(t)) is less than previously
acquiredthen algorithm increases the operating voltage (Vref) else
decreases.t current (Isc) Maximum power current (Ipm)4.9 A
ower voltage and current (VpmIpm) Maximum output power of plant
(W)4 A 43,653 W
ower voltage and current (VpmIpm) Maximum output power of plant
(W)4 A 43,653 W
ower voltage and current (VpmIpm) Maximum output power of plant
(W)4 A 43,653 W
130,959 W
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Fig. 4. Characteristics of the modelled PV module, (a) IV
characteristic at variable irradiation from 200 W/m2 to 1000 W/m2,
(b) power characteristic under same conditions.
Fig. 5. MPPT algorithm of the boost converter.
E. Kabalci / Energy Conversion and Management 90 (2015) 336346
339The previous current and voltage levels are controlled in terms
ofequivalency, if there is not any change occurred in the power
level.The duty cycle ratio of MPPT algorithm is limited up to 49%
in orderto prevent to exceed the maximum current value that is
suppliedby the converter. The switching devices of boost converters
arecontrolled at 50 kHz switching frequency. The outputs of
eachboost converter are connected to the DC busbar by coupling
thepositive and negative outputs.2.2. Energy conversion
subsection
The stabilized output voltage of DCDC converters is supplied toa
full-bridge inverter that generates the AC line voltage. A
full-bridge inverter performs the DCAC conversion process owing
toits Insulated-gate bipolar transistor (IGBT) switches. The
switchingsignals are generated with an enhanced Sinusoidal Pulse
WidthModulation (SPWM) that is introduced in [25] by author.
Althoughthe regular SPWM control eliminates the base-band
harmonics, itdisregards the side band harmonics and its multiples
that arecaused by carrier signal. However, it is theoretically
assumed thatthe bandwidth of a modulated signal is infinite [26,27]
thatexplains the main reason of harmonics measured in a
modulatedsignal. The enhanced SPWM decreases total harmonic
distortion(THD) of current and voltage by considering the side-band
har-monics that are generated by carrier signal. Besides the
regularbase band harmonics, the side band harmonics are also
eliminatedowing to Bessel filtering steps that are performed in the
modulator.In the regular SPWM scheme, a modulating reference
waveformthat is in sinusoidal form is compared to a triangular
carrier wave-form to produce the switching sequences. Eq. (2) shows
the Fourierseries that is used to define each switching angle to
eliminate har-monic contents in the side-bands;
Sswt a02Xn1
1an cosnxt bn sinnxt 2
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Table 2Transmission line parameters.
Properties Value
Frequency 50 HzResistance 0.0481 O/kmInductance 6.4938 104
H/kmCapacitance 1.2628 109 F/kmLength 25 km
Fig. 6. Coupling circuit model between grid and modem.
340 E. Kabalci / Energy Conversion and Management 90 (2015)
336346where a0 is the average DC value of the switching signal. The
Fouriercoefficients a0, an, and bn are obtained by using the
followingequations;
a0 1p
Z pp
Sswtdt 3
an 1p
Z pp
Sswt cosnxtdt 4
bn 1p
Z pp
Sswt sinnxtdt 5
Eq. (6) shows the cn coefficient that defines the nth ordered
har-monic of Ssw(t) signal,
cn an jbn 6
The amplitude distortion that should be considered in modula-tor
design is caused by the ripples of DC voltage source and drawsthe
most significant impact on the onoff spectral errors. In caseof
amplitude distortion occurs in the SPWM waveforms, thisdeclines the
amplitude of the fundamental component and causesto unexpected low
ordered harmonic contents as shown in Eq. (7).The output voltage of
the inverter is expressed in Eq. (7) dependingto the modulation
index, and Bessel functions of modulating andcarrier frequencies
that eliminates the amplitude distortion.
VOt miVdc2 cosxrt 2Vdcp
X1k1
J0k mi p2 sin k p2
cosk xct
2Vdcp
X1k1
X1l1
Jn kmip2 k sin k l p2
cosk xct l xrt
9>>>>=>>>>;7
where mi is modulation index, Vdc is dc supply voltage, xr is
mod-ulator frequency, xc is carrier frequency, J0, Jn is the Bessel
function.The term of modulation index (mi) given in Eq. (8) is the
ratio of themodulating amplitude, Vm, to the carrier signal,
Vc.
mi VmVc
8
The line frequency of the inverter is adjusted depending on
themodulating signal frequency. The line voltage of the inverter
isdetermined by the modulation index (mi) which define the
operat-ing area of inverter as linear modulation when the mi is
lower than1 or as over-modulation when the mi is higher than 1
[28,29]. TheSPWMmodulator performs as a voltage amplifier in the
linear mod-ulation range and the gain (G) is defined as given in
Eq. (9),
G 0:5miVdcVp
9
When themi is set to 1 in the control process, the gain rate
increasesup to 78.55% of the peak value of the square voltage. The
SPWMbased switching signals are arranged to operate the inverter
inthe linear region, where the inverter voltage is calculated as
givenin Eq. (10) [29,30]
VAB VBC VCA miffiffiffi3
pVd2
0 < mi 6 1 10
The carrier signal frequency that is generated in the modulator
isset to 5 kHz to switch the IGBTs of the inverter. The
impedancesof the transmission line, which are seen in Table 2, are
determinedaccording to values per kilometer, according to that are
used inindustrial applications by considering the line losses.
These values are used in a simulation environment. Fig. 6
showsthe coupling interface that is used to inject the modem data
to grid.The coupling circuit is built with an isolation transformer
and itsparallel RLC network.
3. Power line communication and QPSK modulation
The digital symbol sequences are used to adjust one or
morefeatures such as amplitude, frequency and phase parameters
ofhigh frequency sinusoidal signal, which is called carrier [28].
Thedigital modulation term defines the data transmission that is
per-formed using digital symbol sequences over the transmission
med-ium. The phase shift keying (PSK), amplitude shift keying
(ASK),and frequency shift keying (FSK) are known as the three main
dig-ital modulation types. These classifications are based on shift
key-ing process where it is called ASK if the shifted parameter
isamplitude, FSK if the shifted parameter is the frequency or PSK
ifthe shifted parameter is the phase. The PSK is the most
insensitivedigital modulation scheme to the noise and interferences
amongothers [30].
3.1. Theory of quadrature phase shift keying
The M-ary PSK modulation stands for PSK modulation type thatuses
m separate carrier phase to perform transmission space.
Thetransmission space is constituted by dividing the 2p radian
phasespace to equal M-parts. The most widely used PSK scheme
inM-ary structure is the quadrature phase shift keying (QPSK,
or4PSK) since it is not affected from Bit Error Rate (BER)
corruptionwhen the efficiency is improved. The QPSK scheme is
accepted asthe main algorithm in the systems like digital
subscriber line(DSL) modems, code division multiple access (CDMA),
3G, Wi-Fi(IEEE 802.11) and worldwide interoperability for microwave
access(WiMAX (IEEE 802.16)) [30]. The mathematical description of
aQPSK signal is given in Eq. (11);
SQPSKt A cos2pf ct hi; 0 6 t 6 T; i 1;2;3;4: 11
where hi stands for the carrier phase angle as hi 2i1pM 2i1p
4 ,Eq. (12) is obtained by re-arranging Eq. (11);
sQPSKt A cos 2pf ct 2i 1p
4
;0 6 t 6 T; i 1;2;3;4 12
When the trigonometric equality is applied to Eq. (12), the
sQPSK(t) isdefined as seen in Eqs. (13) and (14),
sQPSKt A coshi cos2pf ct A sinhi sin2pf ct 13
sQPSKt s1tu1t s2tu2t 14
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Fig. 8. Block diagram of QPSK modulator.
Fig. 9. Block diagram of QPSK demodulator.
E. Kabalci / Energy Conversion and Management 90 (2015) 336346
341where u1 and u2 are orthonormal basis functions as given in
Eqs.(15) and (16), s1(t) and s2(t) are given in Eqs. (17) and
(18),
u1t ffiffiffi2T
rcos2pf ct; 0 6 t 6 T 15
u2t ffiffiffi2T
rsin2pf ct; 0 6 t 6 T 16
s1t Z T0
sQPSKtu1tdt ffiffiffiE
pcoshi 17
s2t Z T0
sQPSKtu2tdt ffiffiffiE
psinhi 18
The symbol energy which is shown with E parameter supports
theequation of E = (1/2)A2T. The phase relation of s1(t) and s2(t)
is,
hi tan1s2s1
19
When the equations analysed from Eqs. (13)(19), QPSK signal
isdefined as the total equation as seen in Eq. (20) according to
thetime axis,
sQPSKtA It cos2pf ctA Qt sin2pf ct; 1< t
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Fig. 10. QPSK model developed in Simulink: (a) modulator, (b)
demodulator.
342 E. Kabalci / Energy Conversion and Management 90 (2015)
336346determined from DC busbar since output voltages of
independentenergy conversion plants are connected on. Each PV plant
gener-ates equal DC voltages around 550 V that are consolidated
overinductive couplers and are connected to busbar at the
stabilizedvoltage levels as seen in Fig. 11a.
The last curve of Fig. 11a shows the busbar voltage while
theleft three curves illustrate the output voltages of each PV
plant.The waveforms seen in the first three curves of Fig. 11a show
theeffects of MPPT algorithm depending to the PV plant and
varyaround 1% of the generated voltage. The three-phase line
voltagesmeasured at the output of inverter are seen in Fig. 11b
where thesettling times of the outputs are around a half cycle
(0.01 s). Themonitoring scenario of the proposed study is based on
observingthe power consumption of two separate load plants that
areassumed to be located at 25 km faraway to the PV plants.The
power consumptions of each load plants are remotely mea-sured and
are separately carried to QPSK modem by quantizing atthe
measurement points. The measured and quantized power con-sumptions
are seen in the first and second curves of Fig. 12a and
brespectively, where each figure belongs to a different load
plant.The measurement results show that the first plant
consumesaround 20 kVA (19.6 kW), while the second site consumes
around10 kVA (9.8 kW). The measured and quantized data are injected
toAC transmission line over the coupling circuit and transmitted
tomonitoring centre in order to be filtered and calibrated. The
fourthcurves of Fig. 12a and b show the demodulated data observed
inthe monitoring centre. The demodulator filter is quite
effectiveon recovering the received signal depending to its
original struc-ture. The most accurate recovered signals are
obtained at 100 Hzcut-off frequency of the modelled modem.
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Fig. 11. Line voltages, (a) DC voltages of PV plants, (b) AC
voltages of inverter.
E. Kabalci / Energy Conversion and Management 90 (2015) 336346
343The last curves of Fig. 12a and b depict the calibrated
outputthat illustrates the actual power consumption of any load
plant.The first and last curves seen in Fig. 12 are examined to
evaluatethe success of modem since the first curves show the
measuredvalue of consumed power at the load plants and the last
curveshows the recovered data in the monitoring centre. Another
mea-surement carried out to analyze the system is THD ratios of
three-phase AC transmission line, which expresses the quality of
the gen-erated voltage. The line current THD (THDi) analyses are
performedconsidering the spectrum to 50th (Fig. 13a) and to 200th
(Fig. 13b)
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Fig. 12. PLC and monitoring data, (a) first load plant, (b)
second load plant.
344 E. Kabalci / Energy Conversion and Management 90 (2015)
336346
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Fig. 13. Line current THD analysis, (a) up to 50th order, (b) up
to 200th order.
E. Kabalci / Energy Conversion and Management 90 (2015) 336346
345orders to investigate the sideband harmonics. The THD
analysesshowed that there was not any higher-ordered harmonics up
tothe 200th order of harmonics and the THD ratio of line current
ismeasured at 0.84% which is proper to IEEE-519 and
IEC-61000standards [35,36].The side band harmonics, those are
especially considered as 3rd,5th, 7th, 9th, 11th, and 13th are
measured around 0.05% and 0.02%ratios due to thedistortion
attenuation characteristic of the developedSPWMmodulation scheme.
The analyses also show that the transmis-sion line provides
high-quality ac line voltage to the load plants.
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346 E. Kabalci / Energy Conversion and Management 90 (2015)
3363465. Conclusion
The proposed system in this study deals with design of
anadvanced monitoring infrastructure that is employed in a
distrib-uted generation system of PV plants. Each PV plant is
constitutedwith a number of serial and parallel PV arrays where the
PV energyis converted to DC output voltages and is consolidated
over busbar.The energy conversion part of the proposed study is
implementedwith DCDC boost converters and a three-phase full bridge
inver-ter. The transmission line is simulated with 25 km length
betweeninverter and load plants. The power consumption rates of
loadplants are measured instantly and are delivered to a
monitoringcentre that is located at the inverter side of the grid.
This operationis realized by the designed QPSK modem that generates
data bymodulating the measured power and injects the modulated
datato transmission line to transmit to the monitoring centre.
Thereceived signal detected from the grid is filtered to recover
themeasured data, and then calibrated to its actual value that is
mea-sured at the load plant.
The proposed system is intended to observe distribution
lineagainst leakages, losses and power consumptions instantly.
Theproposed system may also be used to calculate energy demandsand
consumptions to plan the performance of a distribution lineor to
perform the billing procedures. The proposed PLC infrastruc-ture
eliminates unreliability issues, especially at long distancesthat
are caused by interference, shadowing, fading, and
similardegradation of wireless systems. The experimental study will
becarried out in the future studies to validate the obtained
valuesand the reliability of the system.
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A smart monitoring infrastructure design for distributed
renewable energy systems1 Introduction2 Renewable energy generation
system2.1 Energy generation plants2.2 Energy conversion
subsection
3 Power line communication and QPSK modulation3.1 Theory of
quadrature phase shift keying3.2 Simulink model of the QPSK
modem
4 Simulation and comprehensive analysis5
ConclusionReferences