design and implementation of an energy-neutral solar energy system for wireless sensor ... ·...
TRANSCRIPT
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design and implementation of an energy-neutral solarenergy system for wireless sensor-actuator nodes
IEEE Global IoT Summit ’17
Jonathan Knapp and Paul G. FlikkemaJune 2017
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Outline
Introduction
Hardware Design and Development
Solar Energy Model
Software and Firmware Design
Results
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introduction
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Introduction
Introduction
∙ Wireless sensor/actuatornodes/networks
∙ Energy-neutral operation∙ Energy harvesting, forecasting, andenergy-aware design
Objectives
∙ Extend deployment lifespan∙ Reduce labor and maintenance∙ Allow for more power-intensiveoperation
Problem Statement
∙ Design and implementation of anenergy-neutral solar energy system(SES) for wireless sensor/actuatornodes
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Introduction: Motivation - SEGA
Southwest Experimental GardenArray (SEGA)
∙ Explore and quantifyecological/evolutionary responsesto climate change
∙ Geographically-distributed array ofhighly-instrumented gardens
∙ Elevation gradient∙ Each garden: networks of WiSARDwireless sensor/actuator nodes
∙ Real-Time Data Center (RTDC)
Garden Sites
Colo r a do R
iver
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Introduction - Power Management
Energy Neutral Operation (ENO)
∙ Energy harvested by the energy source∙ η
∫ T0 [Ps(t)− Pc(t)]+dt
∙ η - Scaling factor for harvester efficiency
∙ Energy consumed by the load∙∫ T0 [Pc(t)− Ps(t)]+dt
∙ Energy lost due to leakage∙∫ T0 Pleak(t)dt
Bmin ≤ B0 + η∫ T
0[Ps(t)− Pc(t)]
+dt−∫ T
0[Pc(t)− Ps(t)]
+dt−∫ T
0Pleak(t)dt ≤ Bmax
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hardware design and development
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Hardware Design and Development - Introduction
Energy Source
Microcontroller Energy Harvester/Charge Controller
Load(Wireless Sensor Node)
Storage Element
Solar Charge Controller
PowerADC Communication
Figure: Block diagram of the SES. Not shown are power management integratedcircuits for the microcontroller and the load.
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WiSARDNet (Wireless Sensor/ActuatorRelay Device Network)
∙ Modular architecture and hardwareimplementation
∙ Self-organizing, self-healing,wireless network
Design Requirements
∙ 5 V supply∙ Source current transients up to 300mA
∙ Minimum of three days operationwith no energy generation
∙ Real-time, floating-point analytics∙ Voltage and current measurements∙ UART Communication
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Load Energy Consumption
Expected Load Energy Consumption
∙ WiSARD in typical configuration (2 soil moisture and 3 temperaturesensors at 5 minute sampling)∙ 9.25 J
hour × 24 hourday = 0.22 kJ
day
∙ Worst-case scenario (doubling of transducers, sampling rate, radiocommunication, losses)∙ 0.22 kJ
day × 2× 2× 2× 2 = 3.52 kJday
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Solar Energy Generation
Expected solar energy budget
∙ Eharvested = Iavg ×Apanel × Cpanel × Charvester × Csystem × 3.6× 103 kJday
∙ Apanel = 0.002279m2, Cpanel = 0.176, Charvester = 0.8, Csystem = 0.95
∙ Average daily solar insolation by month for Flagstaff, AZJan Feb March April May June July Aug Sept Oct Nov Dec
Avg Daily Insolation (kWh/m2 day) 4.57 5.07 6.30 6.65 6.67 6.57 5.68 5.46 6.03 5.85 5.11 4.55
∙ Expected energy balance for Flagstaff, AZ using daily solar insolationaverages by month
Jan Feb March April May June July Aug Sept Oct Nov DecExpected Harvested Energy (kJ/day) 5.02 5.56 6.91 7.30 7.32 7.21 6.23 5.99 6.62 6.42 5.61 4.99Energy Surplus(+)/Deficit(-) (kJ/day) +1.50 +2.04 +3.39 +3.78 +3.8 +3.69 +2.71 +2.47 +3.1 +2.90 +2.09 +1.47
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Battery Capacity Planning
Battery Capacity Planning
∙ Joules to amp-hours (typical configuration)
∙ Total Daily Ah (Ah/day) =0.22 kJ
day day5 V×3600 seconds = 0.0123× 1.5 = 0.0185 Ah
day
∙ Joules to amp-hours (worst-case configuration)
∙ Total Daily Ah (Ah/day) =3.52 kJ
day day5 V × 3600 seconds = 0.196 Ah
day
∙ Minimum amp-hours∙ Total Daily Ah (Ah/day)×Days of autonomy×Temperature Multiplier
Depth of Discharge
∙ Minimum amp-hours (typical configuration)∙ 0.0185×3×1.40
0.8= 0.0961Ah = 97.1mAh
∙ Minimum amp-hours (worst-case configuration)∙ 0.196×3×1.40
0.8= 1.03Ah = 1030mAh
∙ Comparable price for 2200 mAH lithium-ion
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Energy Budget
Battery capacity to Joules
∙ Bfull = 3.7 V × 2.2 Ah × 0.8× 0.75× 3.6× 103 = 17.6 kJ∙ Lithium-ion nominal voltage = 3.7 V∙ Depth of discharge = 0.8∙ Temperature effect scaling factor = 0.75
Case 1
∙ Time to reach operational mode with a depleted battery∙ Bpartial = V × Ah × 3.6× 103
∙ Bdischarged = 3.0 V × 400 mAh × 3.6× 103 = 4.32 kJ∙ Boperational = 3.2 V × 500 mAh × 3.6× 103 = 5.76 kJ
∙ Boperational = Bdischarged +∫ T0 Pproduced(t) dt−��������: 0∫ T
0 Pconsumed(t) dt
∙ 5.760 kJ−4.32 kJ5.02 kJ = 0.29 days
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Energy Budget
Case 2
∙ Time to fully discharge the storage element∙ B0 = Bcharged = 17.6 kJ
∙ 0 = Bcharged +��������: 0∫ T0 Pproduced(t) dt−
∫ T0 Pconsumed(t) dt
∙ 17.6 kJ0.33 kJ/day = 53.3 days
Case 3
∙ Time to fully discharge the storage element (worst-case configuration)∙ 17.6 kJ
3.52 kJ/day = 5 days
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Hardware Design and Development - Component Selection
Block Diagram
Energy Source
Microcontroller Energy Harvester/Charge Controller
Load(Wireless Sensor Node)
Storage Element
Solar Charge Controller
PowerADC Communication
Component Selection
∙ Energy Harvester/ChargeController - TI BQ25504
∙ Energy Source - SolarMade 1.5 V 150mA solar panel
∙ Microcontroller - TI MSP432∙ Storage Element - PKCell ICR186502200 mAh lithium-ion
∙ Load - WiSARD
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Hardware Design and Development- PCB Design
PCB Layout Sections
∙ Energy harvester∙ External IO∙ Power conversion∙ On-board intelligence
Packaging
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solar energy model
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Solar Energy Model- Introduction
Objective
∙ Predict GHI∙ Convert to energy∙ Estimate battery voltage
Models
∙ Bird∙ PVLIB
∙ North-American Mesoscale (NAM)∙ National Digital Forecast Database(NDFD)
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Solar Energy Model - PCB Design
PVLIB vs. Bird January 7-9, 2016
0
200
400
600
01/07 00:00
01/08 00:00
01/09 00:00
01/10 00:00Timestamp (Arizona)
GH
I (W
/m^2
)
Observed GHIBird ModelNDFD ForecastNAM Forecast
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software and firmware design
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Software and Firmware Design- Software Implementation
Fetch battery voltages from each node and site-specific GHI for
previous 3 days from SEGA data center
Use GHI to identify nightfall and sunrise to estimate the load energy
consumption
SES Model
Use PVLIB to predict GHI and convert to expected harvestable energy over
the next 3 days
Use battery voltage at the time of the prediction for B0
Use estimated daily energy consumption and anticipated
harvestable energy to predict battery voltage at 24,48, and 72 hour intervals
from the time of the prediction
Distribute energy estimates to each SES through the SEGA CI
RTDC Software Chain
∙ Top-level bash script runs asscheduled task
∙ Fetch observed GHI and batteryvoltages for each SES
∙ Run PVLIB to predict GHI over 72hour interval
∙ Process observations andpredictions, estimate batteryvoltage at 24, 48, and 72 hours
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results
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Results - Energy-Neutral Operation
∙ WiSARD configurationsWiSARDNet ID Garden Site Sampling Interval Configuration Description
6 Flagstaff Arboretum 5 min. Lizard Model 16 temperature sensors3 Black Point 5 min. Soil Moisture 8 soil moisture sensors10 Bradshaw Ranch 5 min. MicroMet 3 temperature, 2 soil moisture sensors
∙ ENO achieved if battery maintains state of charge (above 4.0 V)
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Results- Energy-Neutral Operation
Lizard WiSARD (16 temperature sensors)
0
250
500
750
4.00
4.02
4.04
4.06
Arboretum
GH
I (W/m
^2)A
rboretum W
iS 6 (V
)
03/31 00:00
04/01 00:00
04/02 00:00
04/03 00:00
04/04 00:00
04/05 00:00
04/06 00:00
04/07 00:00
04/08 00:00
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Results- Energy-Neutral Operation
Soil Moisture WiSARD (8 soil moisture sensors)
0
250
500
750
4.13
4.14
4.15
4.16
Black P
oint GH
I (W/m
^2)B
lack Point W
iS 3 (V
)
03/31 00:00
04/01 00:00
04/02 00:00
04/03 00:00
04/04 00:00
04/05 00:00
04/06 00:00
04/07 00:00
04/08 00:00
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Results- Energy-Neutral Operation
µMet (3 temperature, 2 soil moisture sensors)
0
250
500
750
1000
4.030
4.035
4.040
4.045
Bradshaw
GH
I (W/m
^2)B
radshaw 10 (V
)
03/31 00:00
04/01 00:00
04/02 00:00
04/03 00:00
04/04 00:00
04/05 00:00
04/06 00:00
04/07 00:00
04/08 00:00
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Future Work
∙ Optimize SES firmware∙ Communication protocols (RS-232, I2C, SPI)∙ Use communication pin as interrupt, enter deeper sleep∙ More robust global variable handling∙ Enable smart task scheduling
∙ SES model software chain optimization∙ Single language
∙ SES model implementation∙ Discharge model using different discharge rates and temperatures∙ Machine learning
∙ SES hardware modification∙ Pin selectable configurations for communication and load output voltage∙ Reduce ground plane noise
Thank you!
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Results- Commercial Options
Off-the-shelf Solutions
∙ Seeed Studio/XBee Carrier∙ Adafruit charge controller PCB∙ SparkFun’s Sunny Buddy PCB
Issues
∙ Field-ready∙ Communication interfaces∙ Not low-power
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Results - Model Performance
Bird Model
0
200
400
600
01/01 00:00
01/02 00:00
01/03 00:00
01/04 00:00
01/05 00:00
01/06 00:00
01/07 00:00
01/08 00:00
01/09 00:00
01/10 00:00
01/11 00:00Timestamp (Arizona)
GH
I (W
/m2 )
ObservedBird Model
∙ The Bird model GHI prediction compared with observed GHI at TheArboretum at Flagstaff over a 10-day period in January, 2016
Period RMSE( W
m2
)MAE
( Wm2
)MBE
( Wm2
)Bird (Jan 1-3) 45.84 21.25 13.6Bird (Jan 4-6) 193.2 111.5 111.3Bird (Jan 7-9) 161.5 80.8 74.4Bird (Jan 1-10) 143.2 69.29 65.0 28
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Results - Model Performance
PVLIB vs. Bird January 1-3, 2016
0
200
400
01/01 00:00
01/02 00:00
01/03 00:00
01/04 00:00Timestamp (Arizona)
GH
I (W
/m^w
)
Observed GHIBird ModelNDFD ForecastNAM Forecast
Model RMSE( W
m2
)MAE
( Wm2
)MBE
( Wm2
)Bird 48.07 23.29 15.05NDFD 63.22 36.44 -18.62NAM 109.29 56.7 -49.27
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Results - Model Performance
PVLIB vs. Bird January 4-6, 2016
0
200
400
01/04 00:00
01/05 00:00
01/06 00:00
01/07 00:00Timestamp (Arizona)
GH
I (W
/m^2
)
Observed GHIBird ModelNDFD ForecastNAM Forecast
Model RMSE( W
m2
)MAE
( Wm2
)MBE
( Wm2
)Bird 201.8 121.7 121.5NDFD 70.13 38.69 36.49NAM 53.24 30.84 23.83
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Results - Model Performance
PVLIB vs. Bird January 7-9, 2016
0
200
400
600
01/07 00:00
01/08 00:00
01/09 00:00
01/10 00:00Timestamp (Arizona)
GH
I (W
/m^2
)
Observed GHIBird ModelNDFD ForecastNAM Forecast
Model RMSE( W
m2
)MAE
( Wm2
)MBE
( Wm2
)Bird 168.77 88.13 81.29NDFD 79.66 42.77 3.88NAM 105.42 53.49 -17.27
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Results- Model Performance
WiSARD-SES Communication Protocol
WiSARD SESUART
Communication
Sends command packet instructing SES to execute the
ADC taskDispatches ADC task, stores results, generates report
Sends command packet requesting the results of the
ADC taskReturns the generated report containing the results of the ADC task to the WiSARD
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Results- Model Performance
Quantifying model performance
∙ Typical operating range: 4.15 - 3.4 V (750 mV)∙ Acceptable error: ±37.5 mV
Date Predicted Voltage (V) Observed Voltage (V) Error (mV)02/17/2017 — 3.790 —02/18/2017 3.757 3.737 2002/19/2017 3.735 3.725 1002/20/2017 3.744 3.729 1502/21/2017 — 3.760 —02/22/2017 3.763 3.775 -1202/23/2017 3.790 3.796 -602/24/2017 3.829 3.855 -26
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Results
−10
0
10
20
10/18 00:00
10/18 03:00
10/18 06:00
10/18 09:00
10/18 12:00
10/18 15:00
10/18 18:00
10/18 21:00
10/19 00:00
10/19 03:00
10/19 06:00
10/19 09:00
10/19 12:00
10/19 15:00
10/19 18:00
10/19 21:00
10/20 00:00Sample Timestamp (Arizona)
Tem
pera
ture
(C)
WiSARD EnclosureSCC EnclosureOpen Air
Open Air
WiSARD Enclosure
SCC Enclosure
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Results - Introduction
Real-Time Data Center (RTDC)
∙ Collects observed GHI and batteryvoltage for each SES
∙ Runs GHI prediction model∙ Distributes battery voltagepredictions to each SES
SES Firmware
∙ MSP432∙ Task execution∙ UART communication with WiSARD
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Results- Model Performance
SES Firmware
∙ Written with Code Composer Studio (TI) in embedded C∙ UART communication
∙ Command, data request, interrogate, HID
∙ ADC readings for battery voltage and solar panel voltage, current∙ Error reporting∙ Parsing model results (unimplemented)
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