1. College of Engineering Electrical Engineering Dept.
Power System Architecture for Cubesat A Graduation Project presentation
College of Engineering Electrical Engineering Dept.
Presented by:
Abdulaziz Hilal AlKuwari
Hassan Hisham Miqdad
Ahmed Bassam Diab
Supervisors:
Dr. Ahmed Massoud
Dr. Tamer Khattab
Spring 2014

2. Presentation overview
General Introduction About Cubesat.
Project Objectives.
System Divisions .
Time Schedule.
Simulation of QUbeSat Power System Architecture.
Test Results and Analysis
Modelling of the DC-DC Buck Converter.
Control of the DC-DC Buck Converter.
PV Model / Maximum-Power-Point-Tracking (MPPT).
Practical Implementation.
Project Cost
Future Work and Conclusion.

3. Literature Review
The satellite industry was only restricted to the space agencies and institutes till the first picosatellite was created.
Cubesat design standards have been developed by the Joint cooperation of California Polytechnic State University (Cal Poly) at
Low Earth Orbit space missions.
Life span of about 9 months on an average.

4. Literature Review (cont’d)
General Cubesat standards and requirements

5. Why Cubesat? Universities develop Cubesat because of :
The fact that the components needed to have complete satellite system are already available on shelf with lower cost.
It is a standardized system which makes it simple to design the system and launch it.

6. Some Previous Cubesat Missions (cont’d)
Number of successfully launched Cubesats versus the date of launching:

7. Mission of QU Satellite (QUbeSat)
Qatar university Cubesat (QUbeSat) mission is to map and observe the current waste disposal sites in Qatar and search for the illegally disposed waste in the desert.
gomspace.com

8. Project Objectives
The aim of the project is to design power supply architecture for the (QUbeSat) that is responsible for feeding all the satellite subsystems.
The architecture is composed of different stages including power generation, storage and finally distribution.

9. Time Schedule
The over all project life is three years!

10. System Divisions

11. Simulation Of QUbeSat Power System Architecture
The overall power system architecture of QUbeSat has been simulated using MATLAB/Simulink.
The system losses (hence efficiency) are assessed including non-idealities of the system components under different scenarios.

12. Simulation Of QUbeSat Power System Architecture(cont’d)
Solar Arrays
MPPT Converter
The five side of the satellite are connected in parallel.
Type of the PV panels is triple junction.
Transfers the maximum power from the solar array which maximizes the system efficiency.
gomspace.com

13. Simulation Of QUbeSat Power System Architecture(cont’d)
Battery Pack
Power Conditioning Converters
Lithium-ion type.
Two connected in parallel having a rated capacity of total mAh and nominal voltage of 7.4V.
Power condition converters step down/up the voltage of the battery to supply the satellite subsystems
gomspace.com

14. Simulation Of QUbeSat Power System Architecture(cont’d)
Open circuit voltage of the PV array is 5.38V.
Nominal voltage of the battery is 7.4V.
Aim of the MPPT-converter is to step up the PV array voltage to the battery level.
There were two options to design the MPPTCC boost converter and buck-boost converter. Will not use the buck boost since the option of the stepping down the voltage will not happen, during the designing stage of the MPPTCC our aspirations were to have converter woth high efficiency and required voltage level

15. Simulation Of QUbeSat Power System Architecture(cont’d)
The schematic of buck converter (including imperfections)
The schematic of boost converter (including imperfections)
Three power conditioning converter 3.3, 5 and 15 volt connected in parallel included the non idealities. At first we have used PI controller in order to regulate the voltage and have the required voltage level.

16. Simulation Of QUbeSat Power System Architecture (cont’d)
Three voltage levels to supply the loads.
The three converters simulated with the added imperfections.
The three converters are connected in parallel with the battery.

17. Simulation Of QUbeSat Power System Architecture(cont’d)
The design values (for QUbeSat)
L design
C design
3.3V Buck Converter
22uH
100uF
5V buck Converter
220 uF
15V boost Converter
100 uH
22 uF

18. Simulation Of QUbeSat Power System Architecture(cont’d)
The overall system is simulated with added imperfections to check out how the system will behave with real component imperfections

19. Test Results and Analysis
The different cases of the PV irradiation
Case 1: PV module irradiance is not enough to supply power to the system
Case 2: PV module irradiance is enough to supply power to the system
Case 3: PV module irradiance is approximately zero

20. Test Results and Analysis (cont’d)
Case 1:
The power flow diagram below where the positive sign indicates loading and negative sign indicates supplying power
The efficiency of the MPPTC
93.81%
The efficiency of 5V buck
95.69%
The efficiency of 3.3V buck
92%
The efficiency of 15V boost
97.34%

21. Test Results and Analysis (cont’d)
Case 2:
The power flow diagram below where the positive sign indicates loading and negative sign indicates supplying power
The efficiency of the MPPTC
9𝟒.1%
The efficiency of 5V buck
95.69%
The efficiency of 3.3V buck
92%
The efficiency of 15V boost
97.34%

22. Test Results and Analysis (cont’d)
Case 3:
The power flow diagram below where the positive sign indicates loading and negative sign indicates supplying power
The efficiency of the MPPTC −
The efficiency of 5V buck
95.69%
The efficiency of 3.3V buck
92%
The efficiency of 15V boost
97.34%

23. Efficiency of the Overall System
𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦= 𝑃𝑜1+𝑃𝑜2+𝑃𝑜3+𝑃𝑖𝑛2(1−𝑎) 𝑃𝑖𝑛+𝑃𝑜4(𝑎) 𝑋100%

24. Efficiency of the Overall System (cont’d)
case 1
90.33%
case 2
89.2%
case 3
94.29%
It is logical that case 3 has the highest efficiency since the input power is zero hence there is no losses in the MPPTC

25. Modeling of the DC-DC Buck Converters
The step response of the converters shows large overshoot
Loads are sensitive to small voltage variations
The response can be improved closed loop control technique
An accurate model is needed to design the controller

26. Modeling of the DC-DC Buck Converters (cont’d)
The most significant imperfections that appears in the converter are:
The voltage drop across the diode.
The voltage drop across the switch.
The on-state resistance of the diode.
The on-state resistance of the switch.
The ESR of the Capacitor.
The internal resistance of the inductor.

27. Modeling of the DC-DC Buck Converters (cont’d)
On-state Off-state

28. Modeling of the DC-DC Buck Converters (cont’d)1
State Space Representation for the On mode 2
State Space Representation for the Off mode 3
State Averaging method
( produces non-linear model) 4
Linearization by Taylor's series first order approximation 5
Transfer function

29. Modeling of the DC-DC Buck Converters (cont’d)
State space representation of the on-state mode of the buck converter
State space representation of the off-state mode of the buck converter
𝒙 = 𝑨 on 𝒙+ 𝑩 on 𝒖
𝑦= 𝑪 on 𝒙+ 𝑫 on 𝒖
𝒙 = 𝑨 off 𝒙+ 𝑩 off 𝒖
𝑦= 𝑪 off 𝒙+ 𝑫 off 𝒖

30. Modeling of the DC-DC Buck Converters (cont’d)
State Averaging
The switch is on at the time dT and off at the time (1-d)T. Thus the on- state and off state representations can be averaged as follow:

31. Modeling of the DC-DC Buck Converters (cont’d)
The Linearized Model of the Buck Converter

32. Modeling of the DC-DC Buck Converters (cont’d)

33. Modeling of the DC-DC Buck Converters (cont’d)
Result of the Buck DC- DC model
Ideal Converter
Derived Model Converter
Physical Converter

34. Control of DC-DC Buck Converters
State Feedback Control

35. Control of DC-DC Buck Converters(Cont’d)
State Feedback Control with Integral Action

36. Control of DC-DC Buck Converters(Cont’d)
The parameters used to simulate the converter
The gain vector was calculated based on the following time
domain specifications
Desired Time Domain Specification ( ) V Buck Converter (with integral)
Percent Overshoot
Peak Time
Rise Time
Settling Time
Damping factor
0.005
0.002 sec
msec
msec
0.9532

37. Control of DC-DC Buck Converters(Cont’d)

38. Control of DC-DC Converters (Cont’d)

39. PV Model
The aim of the PV model is to test the operation of the MPPT algorithm.
The available Practical PV Panel in the Lab is the 120W Polycrystalline BCT
The PV model chosen for the simulation is the Single-Diode general model.

40. PV Model (Cont’d)
The P-V characteristic curve of the Simulated Polycrystalline PV Panel for different solar insolation and temperature respectively.

41. Maximum-Power-Point-Tracking (MPPT)
MPPT can be achieved by different approaches and techniques, but the well-known and widely used algorithm is the Perturb & Observe (P & O) method because of:
Its simple implementation.
Reasonable convergence speed.
P & O technique can be well visualized in the figure shown in the next slide.

42. Maximum-Power-Point-Tracking (cont’d)
The (P&O) MPPT illustration is on Buck converter.

43. Maximum-Power-Point-Tracking (cont’d)
P&O method can be classified into:
Fixed perturb.
Adaptive perturb.
P&O with fixed perturb suffers some demerits and drawbacks:
Steady-state oscillations around the MPP due to fixed periodic tuning.
Possibility of tracking failure for rapid change of the atmospheric conditions especially the solar isolation.
The problem of steady-state oscillations can be minimized by applying small perturbation step-size, but results in slow down of the system.

44. Maximum-Power-Point-Tracking (cont’d)
Many researches went through improving the P&O method and make it adaptive-based by utilizing an automatic variable perturb tuning in order to satisfy:
Fast tracking response toward the maximum power point.
Low steady-state oscillations.
Can be achieved by applying a large perturb value at starting to reach the MPP fast, then minimize the perturb value to result in lower steady-state oscillations.

45. Maximum-Power-Point-Tracking (cont’d)
The simulation of the (P&O) algorithm has been performed on the available practical BCT PV panel to validate the functionality of the MPPT.
The converter used is Buck converter where its design parameters were chosen same as the practical design achieved in the lab.
𝑳 𝒅𝒆𝒔𝒊𝒈𝒏
𝑪 𝒅𝒆𝒔𝒊𝒈𝒏
𝑪 𝒇𝒊𝒍𝒕𝒆𝒓−𝒊𝒏
Switching Semiconductor
Switching Frequency
Load
150 µH
470.0 µF
330.0 µF
IGBT
4 KHz
12V Battery in parallel with
R= 1.2 Ω

46. Maximum-Power-Point-Tracking (cont’d)
PV module power for sudden change of solar insolation and fixed temperature (fixed-perturb of 𝜹= ).

47. Maximum-Power-Point-Tracking (cont’d)
PV module power for sudden change of solar insolation and fixed temperature (adaptive-perturb of 𝜹=𝜺+ (0.005 ×|dP/dV|)).

48. Practical Implementation
Converter Design Parameters.
Converter
𝑳 𝒅𝒆𝒔𝒊𝒈𝒏
𝑪 𝒅𝒆𝒔𝒊𝒈𝒏
𝑪 𝒇𝒊𝒍𝒕𝒆𝒓−𝒊𝒏
Switching Semiconductor
Switching Frequency
𝑹 𝑳𝒐𝒂𝒅
MPPT Buck
150 µH
470.0 µF
330.0 µF
IGBT
4 KHz
Battery + PCC
3.3 V Buck
125 µH
1000 µF
10 KHZ
6.9 Ω
5.0 V Buck
131 µH
10 KHz
5.7 Ω
15 V Boost
34.3 Ω

49. Practical Implementation (cont’d)

50. Practical Implementation (cont’d)
State feedback Control on the 5V Buck converter
Open Loop Response

51. Practical Implementation (cont’d)
Closed Loop Response

52. Practical Implementation (cont’d)
Practical Results of the (P&O) MPPT. (Taken at 2:00 PM on 30th of April 2014)
Fixed Perturb of 𝜹= (Transient and Steady-State)

53. Practical Implementation (cont’d)
Practical Results of the (P&O) MPPT. (Taken at 2:00 PM on 30th of April 2014)
Adaptive Perturb of 𝜹 = 𝜺 + (0.005 × 𝑑𝑃 𝑑𝑉 ) (Transient and Steady-State)

54. Practical Implementation (cont’d)
In addition to the (P&O), MPPT has been also practically achieved using the Artificial Intelligence (Neural Network).
A sample of 39 PV-curves were taken on the 2nd of June 2014 from 9:40 AM till 4:00 PM every 10 minutes.
Those samples were used to train the Neural Network of MATLAB Neural Network wizard which uses Levenberg Marquardt (LM) training algorithm where the performance indices are the mean- squared-error (MSE) and the Regression index (R).

55. Practical Implementation (cont’d)
Taken at 1:40 PM on 4th of June 2014

56. Practical Implementation (cont’d)
Taken at 3:00 PM on 4th of June 2014

57. Project Cost 5 panels 1 U QUbeSat 2000.00 EUR One piece
Power modules + battery
EUR

58. Conclusion
A study was conducted about previous lunched cube-satellites
MPPT converter was designed, simulated and implemented.
PC converters were designed, simulated and implemented.
DC-DC Buck converter was modeled, simulated and practically controlled.

59. Future Work Implementation the real standard QUbeSat Power management
Modeling of triple junction PV panel
Over-current protection

60. Thank You For Your Attention