power management

1. Dynamic Power Management Controller to the Self-Sustained Wind-
Solar DC microgridin the Transport Applications
Vulisi Narendra Kumar
NIT Meghalaya
narendrakumar.abc@gmail.com
Gayadhar Panda
NIT Meghalaya
narendrakumar@gmail.com
Moushumi Patowary
NIT Meghalaya
moushum13@gmail.com
Abstract— The exhaustion of petroleum fuels day-by-day imposes
the use of renewable energy sources in the transportation sector.
The wind and solar generations are the well-established
technologies in the field of renewable generation.Nevertheless,
these sources suffer from the sporadic nature of electricity
generation.Therefore, wind-solar sources are assisted by the
energy storage systems (ESS) (i.e., battery and supercapacitor
bank). In recent years, thebrushless DC (BLDC) motor became
popular in the transport applications due to its reliable and
efficient behaviour. To achieve the best-operating
conditionsandgood efficiency in the DC microgrid, the high gain
converters are used in this paper. Also, a power management
algorithm is implemented to manage the proper power sharing
among the DC microgrid partakers. Finally, Hardware-in-Loop
validation of the proposedpower management control will be
carried out using Virtex-7 FPGA kit co-simulated using Xilinx
system generator.
Keywords—Wind turbine; Energy Storage Systems (ESS);
FPGA; PV array; supercapacitor bank;Maximum Power Point
Tracking (MPPT); Brush Less DC (BLDC)
Motor
I. INTRODUCTION
The automotive sectorshifting its orientation from internal
combustion engine (ICE) driven vehicles to hybrid and battery
electric vehicles (HEVs and BEVs) to eradicate the carbon
emission into the environment [1]. In this regard, the renewable
source generation based transport vehicles are becoming
prominent in the automation sector. Even though renewable
sources (wind-solar) are intermittent in nature yet it has to
attract features like generating clean electricity, low installation
costand less maintenance. Nowadays the concept of the
renewable source-based vehicles is becoming popular in the
world transportation industry.As of 2015, Germany’s
renewable energy consumption by the Transportation sectoris
5.3% (i.e., 33.611 GWh).Also, countries like Australia
conducting solar car racing for every two years to promote the
need of renewable source-based electric vehicles (EV) in the
automotive industry.At present, the permanent magnet
Synchronous generators (PMSG) are widely used in wind
generation [2], [3]. The benefits of the PMSG are its
compactness, rugged construction and highefficient
operation.The modelling and simulation of PV array are dealt
in [4]. To get the good working conditions and to reduce the
cost of renewable sources, it is desired to operate them at
maximum power point tracking (MPPT). There are different
types of MPPT algorithms are available in theliterature.
Among them,Kalman MPP algorithm shows better
performance than existing MPPT techniques in extracting the
maximum power is presented in [5] is used in this paper.
Energy storage system (ESS) is the combination of battery
and supercapacitor bank, supplies continuous energysupply to
the loads during off generating period. The supercapacitor
charges and discharges within the fraction of seconds. The
supercapacitor designing and applications for a hybrid system
is presented in [6], [7]. Whereas the battery charges and
discharges slowly but it can store the maximum amount of
energy. The designing, sizing and usage of the batteryare dealt
in [8], [9].The combination of battery and supercapacitor
provides quick and long-running operating conditions.
The DC-DC boost converters are used to match the low
voltage levels of therenewable source with the common DC
bus bar. The limitations of conventional DC-DC converters
are its large peak currents at the input, the large voltage drop
across the switch and diode reverse recovery current.
Therefore theDC microgrid suffers from high stress and less
efficiency [10], [11]. To overcome this drawback, a new
topology of high-gain, high-efficient converters are
required.Different types of high-gain high-efficient boost
converter topologies were proposed byusing a high-
frequencystep-uptransformer, coupled inductor, interleaved
coupled inductor, active & passive clamping circuits and
intermediate storage capacitor[12]-[14]. The converter used
withhigh-frequency transformer results in high voltage spikes
and harmonics at the input side. The drawback is removed
with the help of coupled inductor based converter but it also
suffers from the low efficiency due tolarge leakage reactance.
To overcome this demerit an active and passive clamping
circuits are employed in converters. To achieve the high
voltage levels with high efficiency an intermediate storage
capacitor also used. The high-gain high-efficient
Converterproposed in [15] uses a coupled inductor in
aninterleaved manner, an intermediate capacitor and a passive
clamp circuit. It results high step-up of voltages with small
duty ratio, high efficiency and low cost.
The DC-DC bi-directional boost converters are used to
match the low voltage battery and supercapacitor to the high
voltage common DC bus. The conventional bi-directional
converters also sufferfrom more losses [16]. Different high-
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2. gain high-efficient bi-directional DC-DC converter classes are
proposed by using four switches and three switches used bi-
directional dc-dc converter [17],[18]. This converter also
suffers fromhigh losses. To achieve high efficiency by
reducing the number of switches.A bi-directional dc-dc
converter with two switches is presented in [19].
The main challenge in DC microgrid is to implement a
suitable power management algorithm to maintain stability
[20], [21]. A dynamic power management algorithm is
proposed in this paper is capable of maintaining the proper
power sharing among the microgrid participants. The main
functions of dynamicpower management are to provide a
constant supply voltage at common bus bar by keepingthe
battery and supercapacitor SOC within the limits.Lastly, the
real-timeimplementation of the dynamic power
managementwas done by the help ofVIRTEX-7 FPGA kit
through Xilinx system generator.
II. SYSTEM DESCRIPTION
The wind-solar sources based DC microgridaccompanying
with energy storage systems connected to the common DC bus
bar through the high-gain high-efficiency processing stages is
shown in Fig.1. Wind -solar sources are the primary generating
sources in the DC microgrid system.The ESS assists the extra
power needed when the generation is less than the load
requirement. Also, stores the excess power available at the
common DC bus bar. As described in the introduction, the
combination of battery and supercapacitor maintains the
constant voltage at DC bus bar evenunder the variations of load
or weather conditions. To improve the efficiency of DC
microgrid by minimizing the stress,the high-gain high-efficient
DC-DC convertersare used in place of conventional converters.
Also, the ESS is connected to common DC bus bar through the
bi-directional high-gain high-efficient converters for gaining
the high voltage levels with reduced losses. The power balance
equationfor the DC microgrid system is given by
Pwn+Ppv=Pload+Psc+Pbat (1)
Where Pwn is the instantaneous power generated by wind
source; Ppvis the instantaneous power generated by PV
source;Ploadis the power demanded by theloadin watts; Pscand
Pbatare the amount of power flowing from/to the
supercapacitor and battery bank respectively.All the powers
are in watts. The system specifications are listed in below
Table. I.
III. DYNAMIC CONTROL STRATEGY
Upon finalizing the converter topologies and type of energy
source/storage devices, what follows next is the control of
these converters. The wind turbine DC-DC converter is
controlled by using Kalman MPPT algorithm. Time update
and measurement update for the Kalman MPPT is given in
Table I.
Fig 1. DC microgrid system with high gain interfacing converters for traction applications.
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3. TABLE I. KALMAN ALGORITHM
Measurement Update(Correct) Time Update (Predict)
ሾሿ ൌ ሾሿെሾሾሿെ ൅ ሿെͳ
෡ƒ…– ሾሿ ൌ
෡ƒ…– ሾሿെ ൅ ሾሿሾ”‡ˆ ሾሿ
െ
෡ƒ…– ሾሿെሿ
ሾሿ ൌ ൣͳ െ ሾሿ൧ሾሿെ
෡ƒ…– ሾ൅ͳሿ=
෡ƒ…– ሾሿ ൅
ሾሿെሾെͳሿ
ሾሿെሾെͳሿ
ሾ൅ͳሿെ ൌ ሾሿ ൅
In this method, initially the Kalman gain ሾሿ is computed.
Later, the estimated voltage
෡ƒ…– ሾሿ and error covariance ሾሿ
are updated respectively. In the time update the succeeding
voltage
෡ƒ…– ሾ൅ͳሿ and error covariance ሾ൅ͳሿെ are predicted
corresponding to
෡ƒ…– ሾሿሾሿ . Therefore the estimated voltage
෡ƒ…– ሾ൅ͳሿ is closer to MPP than the actual value
෡ƒ…– ሾሿ. Where
Q is the process noise of the plant.
The ESS provides required energy supply/absorption to
maintain constant voltage at the DC link bus.Hence, The main
role of the controller is to operate the converters of the
particular energy storing device depending upon the system
operating conditions. To perform this task a dual loop control
is used to calculate the amount of current supplied/absorbed
to/from the ESS to maintain aconstant voltage at the bus bar. A
voltage controller with PI control is engaged to generate
reference current that is to be supplied/absorbed by the ESS.
Further, a current control is preferredforbattery and
supercapacitor bank for power flow. The error in DC link
voltage (Verror)is supplied to the voltage controller and the
reference current generated is given by
Iref(s)=( kp+ (ki/s)) ×Verror (2)
where kp is the proportional gain and kiis the integral gain.
The generated current reference(Iref) is separated and supplied
to the battery reference current and supercapacitor reference
current using alow-pass filter (LPF). The reference currents of
the battery (Ibat)and supercapacitor (Isc)are thus given by
Ibat(s)=Iref(s) ×H(s) (3)
H(s) =ω/(s+ω) (4)
Isc(s)=Iref(s)-Ibat(s) (5)
Where H(s) is the transfer function of the low-pass filter
with a cut-off frequency of ωrad/s. It is chosen as 100 Hz,
therefore high frequency the alterations are compensated by the
supercapacitor to improve the lifetime of the battery.
Depending on the reference currents generated, the power is
managed by the ESS depending on the SOC of theparticular
device. The cutoff frequency of The flow chart for the control
algorithm is given in Fig.3.
TABLE I. SYSTEM PARAMETERS
Parameter Specification
PV array voltage (MPP) 50 V
PV cell current (MPP) 50 A
Wind Turbine DC voltage (MPP) 150 V
Wind Turbine DC current (MPP) 10 A
Supercapacitor voltage 34 V
Supercapacitor module capacitance 58 F
Battery voltage (nominal) 40 V
Battery current(nominal) 20 A
BLDC motor rating 3 hp
BLDC motor input voltage 400 V
TABLE II. CONTROL PARAMETERS USED FOR ANALYSIS
Parameter Specification
VDC,Ref 400 V
Switching frequency 20 KHz
ω 2×3.14×100 rad/s
Sampling period 5 μs
The reference current is generated after checking SOC of
the ESS. This reference current is supplied to the inner current
loop for generating the pulses for the converters of the
ESS.Important control parameters considered for this analysis
are shown in Table II.
Fig. 2. Flow chart of the controller employed for ESS
Fig. 3. Energy management Controller employed for ESS.
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4. IV. HARDWARE-IN-LOOP ANALYSIS OF THE CONTROLLER
FPGA is the good tool for checking the Real-time
implementation of the control algorithm. It eliminates the
complexity and huge cost incurred by hardware components.
The task of themodelling, converting and programming ofthe
controller is performed by Xilinx system generator (XSG) of
the FPGA kit. XSG is a part of theMATLAB/SIMULINK
software. JTAG cable performs the task of hardware-in-loop
by connecting PC and FPGA kit.
Xilinx blocks of the dynamic power
managementcontrollerareshown in Fig. 4. Xilinx blocks used in
above figure are constant, addition, gateway-in, gateway-out,
m-code, delay, counter, assert, cast, cmult and relational
blocks.Xilinx modelling of the supercapacitor charging current
controller and battery discharging current controller isshown in
Fig. 4.The “Battery discharge current controller” and
“Supercapacitor charging current controller also resembles the
Fig. 4. The bilinear transformation is used to convert the low
pass filter from Laplace domain into discrete domain. The
discrete low pass filter has been realised using direct form-I.
The important FPGA resources utilized during the
implementation of the given controller is tabulated in Table
III.Fig. 5 displays the Zynq ZC702 FPGA-based hardware-in-
loop experimental setup used for analysis.
TABLE III. FPGA RESOURCE UTILIZATION
Resource Available Utilized % of utilisation
Slice LUT’s 53200 3037 5.71
Slice Registers 106400 1286 1.21
Block RAM tiles 140 2 1.43
V. RESULTS AND DISCUSSION
The DC microgrid system under considerationas shown in
Fig. 1 is modelled and analysedwith the help of
MATLAB/SIMULINK. In order to assure the performance of
the controller under different system variations in solar
irradiance, wind speed and load torque of the BLDC motor
done as follows.
x Solar irradiance is maintained at 1000 W/m2
from 0 to
3 s and it is reducedto 800 W/m2
within the duration of
3 to 5 s.Finally, it is maintained up to 10 s.
x The wind speed of 12 m/s is increased linearly to 14
m/s with in the duration of 1s to 4 s and is maintained
constant up to 10 s.
x The Load torque of 1 Nm is applied to the BLDC
motor from 0 to 1 s. a step change of 2 Nm load
torque is applied at 2.5 s and 3.5 Nm at 3.5 s. Again it
is reduced to 2 Nm at 9s and maintained up to 10 s.
Case 1: In this case all the microgrid participants are
considered and SOC of the ESS also confined to the limits.
Case 2: In this case PV source is neglected and all other
microgrid participants are considered. Also, SOC of the ESS
also within the limits.
Fig. 4. Xilinx modelling of the important sections of the controller
Fig. 5.Experimental test bench for hardware-in-loop analysis of the
controller
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5. The power flow among the wind turbine, PV array, battery
bank, supercapacitor bank and BLDC motor as shown in Fig.
6. It is confirmed from the Fig. 6,that there is a stable power
flow among the microgrid partakersadhering to the load torque,
wind power and PV power variations due to the dynamicaction
of the controller. Fig.7 displaysthe power-sharing amongst the
microgrid partakers in the absence of PV generation. It can be
observed that the power demanded by the load is satisfied by
the battery and supercapacitor.Similarly, the battery and
supercapacitor bank voltage, current and SOC variations can be
observed from Figs. 8 9 respectively. It can be observed
from Figs. 8 9 that there is a suitable power flow among the
ESS (excluding at the starting) such that always high-frequency
variations (above 100 Hz) taken care by supercapacitor to
reduce the stress on the battery. The terminal voltage and
currents extracted from the wind turbine and solar array by
using Kalman MPPT is shown in Fig. 11. The voltage and the
current at the common DC bus bar are shown in Fig.12.
Therefore, it is assured that the voltage at the DC link bus bar
is maintained constant irrespective of the load torque change.
The mechanical parameters of the solar car are shown in Fig.
13, from the figure we can assure that the speed of the rotor is
changing inversely proportional to the load torque. The high-
gain high-efficient converters are capable ofboosting the low
voltage of the PV array, wind turbine and the ESS to 400 V
with improved efficiency. Also, the controller is able to
maintain the DC microgrid instableconditionirrespective of the
other system parameter abnormalities.
Fig 11.Wind parameters under case 2 (a) Voltage (b) Current
Fig.10.PV parameters under case 1 (a) Voltage (b) Current
Fig. 9.Supercapacitor parameters under case 2 (a) voltage (b) current (c) SOC
Fig.8.Battery bank parameters under case 2 (a) voltage (b) current (c) SOC
Fig. 7. Power flow among the microgrid participants under case 2.
Fig.6.Power flow among the microgrid participants under case 1.
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6. VI. CONCLUSION
Anextreme increase in environmental pollutants
discharging from ICE based vehicles necessitates
theintegration of clean/renewable energy into the automotive
industry. In this viewpoint,a DC microgrid incorporating PV
array and wind turbine as generating sourceaccompanied with
ESS containing supercapacitor and battery bank has been
proposed in this paper. The high stresses and high losses
associatedwith conventional DC-DC converters for high-
voltage applications are overcame by high-gain high-efficient
DC-DC converters.In order to control the power flow from/to
the ESS under different system conditions are done by dual
loop controller.In order to validate the performance of the
controller,the changes is done in load torque and PV
irradiance.Finally, the controlleris modelled and confirmed
using hardware-in-loop co-simulation employing ZYNQ
ZC702 FPGA evaluation kit for its performance and the
synthesis details are tabulated.
ACKNOWLEDGMENT
This work is supported by the REC Transmission
ProjectsCompany Limited Grant RECTPCL/CSR/2016-
17/693.
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Fig 13.DC link bus bar (a) voltage (b) Current
Fig 12.BLDC motor electrical parameters (a) Voltage (b) Current
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