Development of a Low Cost Sun Sensor Using Quadphotodiode

Ishtiaq Maqsood Masdar Institute of Science and Technology

Abu-Dhabi 54224, United Arab Emirates imaqsood@masdar.ac.ae

Tahir Akram Technical University of Hamburg-Harburg

21073, Germany tahir.akram@tu-harburg.de

Abstract— This paper describes about the design and hardware implementation of a low cost model of a two axis sun sensor using a quad photo diode, which can determine the azimuth and elevation angles of Sun in sensor's body frame of reference. The sensor uses quad photodiode to obtain angular information of Sun. It weighs less than 100g (which can be further reduced by optimizing the housing design). The relevant software requires 50K bytes of memory and little processing. We have also developed sensor calibration test bed (SCTB) which will also be the part of this paper. SCTB is used to calibrate the developed Sun Senor; during calibration the whole surface of sun sensor (quad photodiode) is scanned for a field of view (FOV) equal to 60 x 60. Step size during calibration is set to one degree so we get elevation and azimuth matrices each having 3721 values. The calibration process is fully automated with the help of algorithms written in Matlab. The step size used in calibration is adjustable and we can calibrate the sensor even less than one degree using this SCTB. Azimuth and elevation matrices generated during calibration are used as error correcting tables during real-time measurements taken by the sun sensor. Sun sensor is calibrated in front of Sun simulator made by the Optical Energy Technologies USA.

Keywords— quad photodiode, calibration, Sun sensor, position sensors, Sun Tracker

I. INTRODUCTION Sun sensing is an important task in satellites for attitude determination and in solar power plants to increase the output electrical energy of the plant. The international research shows that a tracking system with single-axis can increase more than 20% electricity output, while the tracking system with double-axis can increase more than 40% electricity output [1]. A sun sensing and tracking system usually consists of the following hardware component parts: 1- Sun sensor 2- Controller or Computer 3- Actuator (Electric motor used to drive solar panel) Keeping record of previous position of actuators the controller gets angular information of the Sun from the sun sensor. After that it gives command to actuators which further drive the solar panels to receive maximum normal solar flux.

Output electrical power is proportional to the intensity of incident normal solar flux falling on the surface of solar panel. Sun sensor is also used in satellites to acquire the sun angular position. Attitude and Orbit Control System (AOCS) is a subsystem of satellite and its main purpose is to control the attitude and orbit of the satellite. In Fig.1 we see that there is a dedicated AOCS computer which is connected to central computer via the system bus. The AOCS computer receives the information from sensors and performs the necessary computation and sends commands to actuators in order to correct the attitude. Sun sensor is a part of AOCS and lies in the Sensor’s block of Fig.1. Different types of Sun sensors are used in satellite missions; Active pixel sensor (APS) and quadrant photodiode are two technologies which are currently being used to develop the Sun sensors. APS is an image sensor consisting of an integrated circuit containing an array of pixel sensors, each pixel containing a photo detector and an active amplifier. A quadrant photodiode is a 2x2 array of individual photodiode active areas, separated by a small gap and fabricated on a single chip. This maximizes the uniformity and performance matching between the four individual elements.

Figure 1. Internal architecture of AOCS [2].

Sun sensor based on Micro-Electro-Mechanical System (MEMS) technology and APS technologies are given in [3] and [4] respectively. The design of the Solar Compass Chip (SCC) based on APS technology and the algorithm that is used to calculate the Sun angles is given in [5].

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If we compare the above mentioned technologies available for sun sensor, the computational burden required by quad photodiode is lesser than those of others, resultantly decreasing burden on AOCS computer while maintaining a reasonable amount of accuracy as well. A new calibration scheme and error correction algorithm has been introduced in order to improve the accuracy of the sensor. The sensor developed is low weight, low cost and requires little memory requirement and lesser computational complexity which reduces computational load on AOCS computer. In [6] a sun sensor has been calibrated axially and diagonally and the polynomials are developed to minimize the error. This error correction is valid only for those calibrated lines on sensor surface for which polynomials are generated. These limitations have been removed in this paper to provide more accurate solution in order to find the Sun angular position. All four quadrants of quad photodiode have been scanned in order to provide more accurate result. Calibration has been done using Sensor Calibration Test Bed (SCTB). Calibration process is fully automated for sensor surface for a required FOV. Sun simulator has been used during calibration and testing to simulate the sun spectrum in space. In the following sections we will first go through the principle of operation and modeling of quad photodiode in order to detect the sun light. In the next sections we will present the details of sensor calibration and the hardware used to develop SCTB. In the last sections, we will present sun sensor algorithm and conclusion.

II. PRINCIPLE OF OPERATION The sun sensor described in this paper uses a quad cell manufactured by Advanced Photonics Incorporation (API). It provides four output electrical signals which are directly proportional to the light falling on each quadrant of the cell. A circular slit has been designed that is attached with the quad cell as shown in Fig. 2. When the light falls on the surface of the quad cell, a circular spot is made on the cell and the centre of the spot can be determined as follows: x C D A BA B C D r 1 y A D B CA B C D r 2 A, B, C, D are the four channels of quad photodiode and ‘r’ represents the maximum position on sensor surface where the centre of the sunlight spot can reach for a required FOV. Let ‘h’ be the distance (height) between slit centre and quad photodiode centre, the centre point(x, y) of the incident sunlight spot can further be used in the determination of azimuth θ and elevation angles of the Sun in sensor’s frame of reference as follows: θ tan 3

tan 4

Figure 2. Quad photodiode along with slit. In spherical coordinates, azimuth and elevation are given as follows:

tan 5 tan 6 Maximum value of the ‘r’ can be calculated based on the FOV of the sensor. From Fig. 2 we can write this mathematical expression as follows: tan FOV2 rh r h tan FOV2 For FOV = 60 we get: r 3.5 tan 30° r 2.0207mm In our case quad photodiode has circular sensitive area with radius equal to 4.825mm, radius of the sensor active surface area can be seen from quad photodiode data sheet, and the slit diameter size is 6mm. Slit size is selected by performing the experiment with different slits having radius greater than ‘r’ and noting it that we are getting signals from all quadrants for that FOV, which is also large enough to avoid diffraction. Important factors which should be considered during design are as follows: 1. Diameter of incident light spot should be lesser than the

diameter of photodiode sensitive area.

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2. Size of light spot should be greater than the spaces between detector elements to avoid from dead zones.

3. Spatial resolution will decrease as the spot size of incident light beam will increase. In other words we can say that there is trade-off between slit size and spatial resolution.

4. Values of ‘x’ and ‘y’ should be calculated when we are getting output electrical signals from all four channels (quadrants).

5. We get linear results from sensor within narrow FOV. Sensor linearity decreases as we increase the FOV. This problem can be resolved using calibration.

6. Alignment of quad cell photodiode and its placement in its housing is very critical for sensor accuracy.

III. SENSOR CALIBRATION AND SCTB

Sensor calibration is the process in which we scan the whole sensor surface for preset (known or actual) values of azimuth and elevation and store the corresponding measured values in the form of matrix. If we consider the azimuth matrix for the first quadrant, the column indices represent the actual values of azimuth and row indices represent the actual elevation while the corresponding entries represent the measured values of azimuth. Sensor calibration is done using SCTB and the sun simulator as shown in Fig. 3. In this figure we can see SCTB, back side of the sun sensor, and the sun simulator. SCTB is a 2-DoF (Degree of Freedom) table and two stepper motors have been used in order to provide azimuth and elevation rotation to the sun sensor. Minimum step size of SCTB after using the gear boxes is 45 seconds.

Figure 3. 1-Azimuth Motor, 2-Sun sensor, 3-Elevation Motor, 4-Sun

Simulator. In our case sun sensor has FOV equal to 60x60 degree therefore calibration results by 1 degree step angle will produce 61 x 61 matrix each for azimuth and elevation. We divide the matrix according to the quadrant and we get 31x31 data points for each quadrant for azimuth or elevation including axes. Although photodiode shape is circular but it is calibrated in a rectangular fashion. It should be noted that during calibration sensor placement on SCTB is very critical, sensor should be placed such that SCTB only provides it an

angular motion and there should be no linear motion provided by SCTB. Let us consider a simple example in order to understand this issue, Fig. 4 shows the sun sensor axes and SCTB axes. In order to get better calibration results, origins of both axes should meet. Another most important thing is that both axes should be parallel to each other as shown in Fig. 4.

Figure 4. Sensor axes and SCTB axes.

Any angular rotation between two axes can create bad effects on calibration results. In short we should minimize these misalignments during sensor calibration. We have designed the SCTB in order that the azimuth and elevation axes of SCTB match with that of sensor azimuth and elevation axes; furthermore we have exploited the metallic notch (present on the quad photodiode) in order to minimize the errors caused by the rotational effects.

Figure 5. First Quadrant calibration data for Azimuth

Fig. 5 and Fig. 6 show the calibration results for the first quadrant using SCTB placed in front of sun simulator. 3D graph in Fig. 5 has actual azimuth, actual elevation and measured azimuth along x, y and z axes respectively. Actual azimuth and elevation values are the values set by the SCTB

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MAX 233 IC

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ULN 2003

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during calibration process. We retrieve these actual values based on the measured azimuth and elevation values when sun sensor is in operation. In a nutshell calibration provides us an error correcting table. Sensor is calibrated in a way described in Fig. 7 while Fig. 5 and Fig. 6 are the results of calibration process. In this figure we can see that start point of calibration is upper right corner in our case. A Matlab program is implemented which drives the azimuth and elevation motors of SCTB in the manner shown in Fig. 7. Calibration has been done for all four quadrants each for azimuth and elevation.

Figure 6. First Quadrant calibration for Elevation values.

There are two stepper motors used in SCTB, one for azimuth rotation and second for elevation rotation. These two motors are driven by power electronics circuitry as shown in Fig. 8. Matlab sends command via serial port to 89C52 microcontroller. The microcontroller further controls the two motors with the help of ULN2003 ICs. ULN2003 chips further drive the single pole single through (SPST) electrostatic relays as shown in the Fig. 8. The flow of command sequence is shown in Fig. 9.

Figure 7. Sun sensor Calibration Scheme.

Relays are used to fulfill the current requirements in order to drive the SCTB. Sensor calibration is a very tedious process and consumes a lot of time, but we have designed a calibration test bed which calibrates the sensor automatically for a given field of view. The stepper motor is operated at 2.3V with 1A of current (typical) with 4 relays for each motor.

Figure 8. Power Electronics to drive SCTB motors.

Figure 9. Hardware Scheme to drive SCTB.

IV. SUN SENSOR ALGORITHM Algorithm to find the angular position of Sun is based on mathematical equations described previously i.e. (1), (2), (3) and (4). Important steps to do this task are shown in Fig. 10. There are two error correcting tables for each quadrant first for azimuth correction and second for elevation correction. Each table is a matrix of 31x31 which was obtained during sensor calibration. In this algorithm, position of the light spot is calculated from equations (1) & (2). After this, measured values for azimuth and elevation are calculated using equations (3) & (4). Furthermore the actual values of azimuth and elevation are computed from the corresponding measured values using calibration table. The detail of step 3 shown in Fig. 10 is as follows:

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Columns indices in TABLE I are actual azdegrees) while rows indices are actual elevdegrees) while the entries are the calibratioazimuth values) obtained for these angles.

TABLE I. CALIBRATION DATA FOR FIRST QUADRANTActual

Elevation angles

Actual Azimuth Ang

5 6

0 3.7640 4.7164

1 3.7976 4.7158

2 3.8281 4.7669

3 3.8428 4.7832

4 3.8698 4.7931

5 3.6681 4.7904

In order to do correction for azimuth andfrom calibration tables, we proceed as follow Let ‘XA’ and ‘XE’ be the measured azimangles respectively calculated from step 2 Let ‘MA’ and ‘ME’ are the calibration matquadrant measured azimuth and elevationTABLE I for MA). Let ‘DA’ and ‘DE’ matrices for azimuth and elevation calculated We take the case of azimuth angle, aftedifference matrix DA, the index of the coluto minimum difference entry in the differindex, A* is the corrected azimuth angle acdegree of accuracy. Similarly we can calcuangle, E* which is the index of row minimum difference entry in the difference mand E* are the corrected angles for azimuth a

Figure 10. Steps of sun sensor algori

V. HARDWARE AND TOOLS U

The hardware of the sun sensor consistscomponents. 1. Signal conditioning electronics 2. Data acquisition electronics

1-Calculate light position onsensor surface i.e. 'x' and 'y'using (1) and (2).

2-Determine the quadrant inwhich spot centre lies and thecorresponding values of azimuthand elevation using equations (3)and (4).

3-Correct the measured values of azimuth and elevation using error correcting tables of the concerned quadrant.

zimuth angles (5-7 vation angles (0-5 on data (measured

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3. Optical hardware The quad photodiode (SD 38Photovoltaic mode, as signal rather than speed. Therefore, thwhich also contributes to noiseOPA4277 has been used for very low offset voltage of 10µIn the first stage we are convoltage (transimpedence stagebeing further amplified. The ccutoff frequency of 25 Hz (whrequirements).

Figure 11. Transimpedance The Sun simulator providWatts/mm2 at the distance of glass surface which is approximthe Sun in space. The averagrange is 0.6 A/W. Now wegenerated by the one quadrancurrent of one quadrant can be I 0.6 AW 0.001357 I 14 Which is much larger than tby the operational amplifier, thdesigned so that they may amplifiers and may not underuADC. The output voltage of thcorresponding to this current acquisition unit is given by, V I 100 If we analyze the noise contdetector, it is negligible. Thdetector comprises of JohnsonShot noise contributed by thcurrent. The dark current vanithe photodiode in the photovoltcalculated from the ( 3 10 √⁄ )

80-23-21-051) has been used in to noise ratio is at premium

he dark current can be neglected e, A quad operational amplifier amplification which has also a

µV and low bias current of 1nA. nverting the photocurrent into e) and in the next stage, it is circuit has been designed at the hich can be tailored to meet the

e and Amplification Circuit.

es the intensity of 0.001357 f 14 inches from the simulator mately the same as provided by e responsivity in our detection e calculate the photo current nt of the detector. The output calculated as follows: Wmm π 4.824 mm

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n noise (Thermal noise) and the he load current and the dark ishes because we are operating taic mode. Johnson noise can be Noise equivalent power given in the datasheet of the

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photodiode, as the shot noise at this incident power is very low, so the main contribution is from the Johnson noise. This is very low as the measuring bandwidth (B = 25Hz) and NEP both are very low and can be ignored as the actual strength of photocurrent is high enough. The noise contribution from external sources has been due to the reflections and IR, as our sensor is also sensitive to the near IR region, therefore optical filter will have to be used if we go for higher accuracy.

Figure 12. Sun sensor hardware components. The signal conditioning circuit has been shown in Fig. 11. SMD (Surface Mounted Devices) technology has been used in order to reduce weight of the sun sensor. After signal conditioning, OMEGA D5141 module has been used as a data acquisition unit which incorporates a four channel ADC and a Multiplexer interfaced with Matlab through RS232. Optical hardware includes the slit and the quad photodiode.

VI. SUN SIMULATOR Sun simulator from Optical Energy USA has been used to simulate the sun light. The sun simulator has 250W Metal Halide Arc Lamp 6000K Color Temperature and Less than 3.5° Angular subtense of the simulated sun.

VII. CONCLUSIONS An accuracy of 1 degree in both azimuth and elevation has been achieved using 1 degree step size of calibration which can be further improved by decreasing the step size. A new design methodology has been discussed and implemented for the sun sensor. The quad photodiode has high responsivity and has the potential to be used in sun sensor design with low cost and lesser computational complexity. Same design steps can be followed to develop the sun sensor for GEO and LEO missions. Currently, we are trying to increase the accuracy of the sensor without decreasing the step size using neural networks. Furthermore an optical filter can be used in front of quad photodiode in the cases where the external noise is significant. FOV of the sensor can be increased by using the quad photodiode with larger sensitive area and/or by using the larger diameter slit but the care should be taken in selection of these parameters as discussed in this paper.

VIII. ACKNOWLEDGEMENTS The authors are indebted to Osama Idrees, Muhammad Usman Sadiq, Muhammad Junaid and Arslan Khalid for their suggestions.

IX. REFERENCES [1] Wu Chun-Sheng, Wang Yi-Bo, Liu Si-yang, Peng Yan-chang and Xu

Hong-Hua, “Study on automatic sun-tracking technology in PV generation,” DRPT2008 6-9 April 2008 Nanjing China.

[2] Software Frameworks and Embedded Control Systems, Springer ISBN 978-3-540-43189-3, pages 17-28

[3] Tobias Bohnke, Lars Stenmark, “Development of a moems sun sensor for space applications,” The 13th International Conference on Solid-state Sensors, Actuators and Microsystems, Seoul, Korea, June 5-9, 2005.

[4] Giancarlo Rufino, Michele Grassi, Vincenzo Pulcino, “Development and validation of a modern CMOS digital sun sensor at UNINA.”

[5] Carl Christian Liebe, “Solar Compass Chip.” IEEE sensors journal, Vol. 4, No. 6, December 2004.

[6] Pedro M. Rodrigues, Pedro M. Ramos, “Design and characterization of a sun sensor for the SSETI-ESEO project,” XVIII IMEKO world congress Metrology for a Sustainable Development September, 17 – 22, 2006, Rio de Janeiro, Brazil.

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