Prediction of Output Power Variation of Solar Power ...

T. Hashimoto et al. / Journal of Advanced Research in Physics 2(2), 021107 (2011)

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Prediction of Output Power Variation of Solar Power Plant by Image Measurement of Cloud Movement Takeshi Hashimoto, Yohei Nagakura Dept. of Electrical & Electronics Engineering, Shizuoka University (5-1, 3-chome Johoku, Naka-ku, Hamamatsu, 432-8561, JAPAN)

Abstract — The aim of this paper is to predict that when the cloud shadow cover the solar panel and how much electric power generation fall by covering any type of cloud. Weather conditions causing a decrease in electric power generation are predicted using the three-dimensional measurement of clouds. Keywords — image measurement, photovoltaic power generation, cloud position measurement

I. INTRODUCTION Global warming has become a serious environmental issue in recent years and is closely linked to the energy crisis. Natural power sources have thus been attracting increasing attention in order to help reduce the generation of CO2, especially during summer daytime hours. As the technology involved in solar power generation has improved and costs have come down, mega solar power plants have become more common worldwide. However, the output power of such plants can drop by 1/10 in several dozen seconds. For this reason, solar energy generation is known as the unstable power generation system. To use the output power of solar cells most efficiently, the cooperation operation is available to supply the power stably. The electric power shortage caused by solar energy generation must be compensated by other generation systems. Large size generation systems such as a hydroelectric power generator and a diesel generator need time to prepare for the steady running. It is therefore necessary to be able to predict output change. In order to predict the falling output of photovoltaic power generation caused by cloud cover, it is necessary to determine the relationship between cloud type and the decreased output, and to predict the movement of clouds. The image processing method for cloud classification and three-dimensional measurement is discussed in this paper. We have been measuring cloud movement using stereo cameras. Recently, there has been much research involving cloud images, which are almost always on weather satellite or radar images. However, Himawari weather satellite and radar images are not suitable for cloud image measurement to predict falling electric power generation because temporal Manuscript received September 1, 2011. * Corresponding author [email protected]

resolution of weather satellites is low. The weather radar image depends on the density of vapor in the air. Moreover, the total sky imager cannot provide the three-dimensional position of clouds. Therefore, the prediction of momentary cloud movement is difficult. In this research, we observe clouds from the ground in order to measure them locally. Thus we can make more accurate predictions than weather satellites and radar. We can measure the three-dimensional position of clouds, the direction and the speed of cloud movement, besides the shape and size of clouds. In this report, we discuss the measured three-dimensional position of clouds and the relationship between the form of cloud and speed. Wind speed and direction and other weather factors affect clouds. Therefore, our research about the relationship between cloud shape variation and movement and weather conditions can contribute to improve prediction accuracy. II. CLOUD CLASSIFICATION A. Cloud Classification by Brightness Clouds are classified as thin and thick based on their surface brightness. In the case of thick clouds, the surface that is illuminated by the sun is bright, the other side of the cloud is dark. Figure 1 shows light-dark variation. The cloud image is checked for each pixel along the radial pattern line from the sun. The first thing to check is the brightest point in any area. The amount of decreased gray level is calculated. If a pixel that is brighter than a previous pixel is detected, that becomes the new starting point. In the case of that there is a lot of detection, the cloud is said to be thick. Otherwise, the cloud is thin. An original image (observed image) is shown in Fig. 2 and the classified image is shown in Fig. 3. In Fig. 3, the area marked A is sky, B is thin cloud, C is thick cloud, and D is unidentified cloud. We confirmed the thick and thin clouds were classified correctly. As a result of comparing between classification result based on this method and classification result based on traditional meteorology for 50 images chosen at random, difference rate of classification was less than 10%. Therefore, this method can be used to classify clouds as thin or thick in the same way as the traditional meteorology.

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T. Hashimoto et al. / Journal of Advanced Research in Physics 2(2), 021107 (2011)

(a) Thick cloud

Fig. 2: Original image (observed clouds)

(b) Thin cloud Fig. 1: Brightness change of cloud

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C. Cloud Type Classification by Cloud Contour Clouds can be classified according to their contour. To classify clouds, the Hough transformation that is a famous feature extraction technique is used. Here, the Hough transformation extracts straight lines and circles based on cloud contours. Our experimental results show as follows: In case that the radius of the circle is small and large, the cloud is a convective cloud and stratus cloud, respectively. In case that the radius is large, the cloud is a stratus cloud. An example of detected circles is shown in Fig. 5. In Fig. 5(b), many circles with a small radius are shown, so the cloud in the original image is identified as a convective cloud. In the case of stratus clouds, circles with a large radius will be detected.

Fig. 3: Classified image (A: clear sky area, B: thin cloud area, C: thick cloud area, D: unidentified cloud area)

Images

B. Prediction of Falling Generation Output based on Classification of Clouds The generation output on a cloudless day is defined as the maximum generation output of the month. In case that the generated output is the same as the maximum generation output, the generation output is 100%. Similarly, in case that the generated output is 0 W, the generation output is 0%. Figure 4 shows the relationship between cloud type and falling generated output in summer. The data up front is for the thick cloud and the data at the back is for the thin cloud. The falling output caused by the thick cloud is 70-90%, and the falling output caused by the thin cloud is 10-50%. According to the predicted falling output based on 100 images, the accuracy rate was 85%.

Thick Clouds

Fig. 4: Relationship between cloud type and generated output falling

T. Hashimoto et al. / Journal of Advanced Research in Physics 2(2), 021107 (2011)

(a) Original image (observed image)

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(a) Volumetric representation of gray level

(b) Processed image by the Hough transformation (many small circles were detected) (b) Detected sphere Fig. 5: Detected result of circle Fig. 6: Detected result of sphere

D. Cloud Type Classification by Intensity of Cloud Using the Hough transformation to which one more parameter was added, some spheres were detected in the three-dimensional space as shown in Fig. 6(b). Our previous experimental results show as follows: If a detected sphere has a large radius, the cloud is fleecy cloud or an assembled cloud mass. In the case of a comparatively small radius, the cloud is a cumulonimbus cloud. E. Comments for Cloud Type Classification The automatic cloud classification is difficult problem because of its infinite form and transparency. The previous figures are examples of our cloud classification. The more many results are necessary to establish the cloud classification. If a parameter increases, computation time of the Hough transformation will increase rapidly. This long computation time should be shortened for the quick cloud classification.

III. THREE-DIMENSIONAL MEASUREMENT OF CLOUDS A. Measurement Principle Stereovision is a famous method to measure a target using two cameras. The principle is based on triangulation. Figure 7 shows the principle of stereovision, in which α1 and α2 indicate horizontal angles and β1 and β2 indicate elevation angles, respectively. It is assumed that the target is reflected on the virtual screen by the image coordinates P1(xt1,yt1) for Camera 1. The horizontal angle α1 and elevation angle β1 can then be calculated by the camera calibration. Thus, the line of sight from Camera 1 to the target is obtained. The line of sight for Camera 2 can be obtained the same way. It is necessary that target P1 for Camera 1 corresponds to target P2 for Camera 2. This is called the corresponding point. Next, the target position is calculated as the point of intersection of the lines from each camera’s corresponding point to the target.

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T. Hashimoto et al. / Journal of Advanced Research in Physics 2(2), 021107 (2011)

Fig. 7: Stereovision

B. Experimental instruments The camera used for measurement is Axis M1114 network camera. This camera is designed for a security camera, so this camera is suitable for the long time observation outdoors. The camera’s resolution is 1280x800 pixels with a wide-angle lens. Image of clouds were taken every 10 seconds for a 10-hour period on sunny days, starting at 7 a.m.

Fig. 8: Camera arrangement and axis definition

C. Camera Arrangement A camera (camera 1) was located on the roof of the Electrical and Electronic Engineering building on Shizuoka University. Another camera (camera 2) was located on the roof of Akatsuki dormitory belongs to Shizuoka University. Both camera directions are set for the same direction. Camera 1 was online and camera 2 was connected to a radio clock for time correction, thus the images were taken at the same time. Camera locations and axis definitions are shown in Fig. 8. The base line length is about 1.2 km.

E. Measuring Weather Conditions The wind movement can be estimated by measuring the cloud movement. Because, the cloud is blown by the wind, then wind and cloud movement direction and speed are considered almost the same. Scientific consideration about the correlation between wind and cloud movement is necessary. Weather conditions at the time cloud images were taken were obtained from the homepage of the Meteorological Agency. Weather conditions were observed at the observatory in Hamamatsu. The observatory is located 1.5 km south of camera 1. Because the observatory is located at measuring area, observed data is worth. However, the altitude of clouds and observed weather condition is different. This different is discussed in the later section. Cloud movements measured three-dimensionally and weather conditions were compared.

D. Camera calibration and measurement point For three-dimensional measurement, an object with known coordinates is required. The three-dimensional position of the measured object is calculated from image coordinates’ difference between the object with known coordinates and measurement object with two cameras. In this experiment, the object with known image coordinates is the sun. Because the sun is a celestial body, the horizontal and elevation angles are calculated accurately with the position on the earth and the time. Images of the sun are taken using a solar filter. The centroid of the sun in the image is regarded as the criterion. The horizontal and elevation angles are obtained based on the difference of the centroid coordinates of the sun and the corresponding point of the cloud image. In addition, the direction of the camera is calculated from the direction of the sun. By using two or more coordinates of the sun at different positions, the direction of the camera is calculated more accurately. In three-dimensional measurement, the same measurement point is selected by each camera. The centroid of the cloud is the corresponding point in this observation. In addition, to measure the size and shape of clouds, many corresponding points are required on one cloud image. This is under study.

F. Results An example of three-dimensional cloud measurement is shown in Fig. 9. The measured cloud was a cumulus cloud. It is proper that cloud altitude was about 1,200 m because cumulus clouds generally form at an altitude of 300-1,500 m. The pixels constituting the image are discrete and the object in the image is integrated into each pixels. This is called the quantization error. An example of the quantization error is shown in Fig. 10. Two parallel lines indicate the line of sight of a pixel. The area surrounded by two lines of sight is the quantization area. Based on Fig .10, the three-dimensional position of the cloud is off by ±1.0 m according to camera direction and by ±3.0 m according to the range direction. Cloud contour changes with the angle of sight because clouds have round edges. Therefore, the position of the corresponding point, the centroid, changes. Based on previous research, it is known that the maximum camera direction error is 2.7% and the maximum range direction error is 2.0% for measuring the distance to the cloud. The three-dimensional position of clouds can be determined with errors less than 5%. If the corresponding point is centroid and clouds in the image are small, the effect of round edge is not necessarily considered.

T. Hashimoto et al. / Journal of Advanced Research in Physics 2(2), 021107 (2011)

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Fig. 9: Example of three-dimensional cloud measurement result

Fig.11: Relationship between ground level wind direction and cloud movement direction

Fig. 10: Quantitated area (framed by four lines of sight)

IV. RELATIONSHIP BETWEEN CLOUD MOVEMENT AND WEATHER CONDITIONS A. Direction of Wind and Clouds The relationship between ground level wind direction and cloud movement direction is shown in Fig. 11. The horizontal axis indicates the number of the measured cloud. The vertical axis indicates the direction and the direction angle is clockwise and north is defined as 0 degrees. The cloud movement directions were obtained from the metrological observatory in Hamamatsu every ten minutes. In Fig. 11, the ground level wind direction and the cloud movement direction is approximately the same. This result shows the possibility to be able to measure the wind direction in the sky by establishing the cloud direction measurement system. Now, the sample number is small. More samples would yield better results. B. Speed of Wind and Clouds The relationship between sky level wind speed and cloud movement speed is shown in Fig. 12. Sky level wind is observed at discontinuous altitude with irregular interval. Sky

level wind speed is plotted at the height of the closest to the altitude of the measured clouds. Cloud movement speed should be compared with sky level wind speed, because there is a big difference between ground level wind speed and sky level wind speed. The sky level wind speed was measured at meteorological observatories in Tateno and Shionomisaki, these observatories are located on the contrary direction from the Hamamatsu observatory. Tateno and Shionomisaki are located 250 km northwest and 230 km southeast from Hamamatsu, respectively. In the following discussions, these distances should be considered to have the possibility to cause the error. Maximum and minimum values were determined based on observed values at 9 a.m. and 9 p.m., respectively. The average difference between cloud movement speed and the maximum and minimum values of wind speed was compared. The difference between cloud movement speed and the maximum wind speed was 9.54 m/s and the difference between cloud movement speed and the minimum wind speed was 3.52 m/s. These differences indicate that the cloud moves slower than the sky level wind, such as the “slip”. The changing cloud shape may be one reason the difference between cloud movement speed and the minimum sky level wind speed is smaller than the difference between cloud movement speed and the maximum sky level wind speed. An assumption that the speed difference between the wind and clouds is due to the shape change of the cloud is made.

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T. Hashimoto et al. / Journal of Advanced Research in Physics 2(2), 021107 (2011)

the falling output of photovoltaic power generation is predicted, cooperative efforts to supply stable power can be realized. In addition, local weather conditions can mostly be predicted by the correlation between cloud movement and weather conditions. At present, the true values of three-dimensional position of clouds and weather conditions over our university are difficult to be observed. Therefore, the corporative works with other wind measurement system such as Doppler radar are planned. ACKNOWLEDGMENT

Fig. 12: Relationship between sky level wind and cloud movement speed

The authors thank Mr. M. Sakai, Mr. A. Okuda, Mr. H. Nagata, Mr. T. Sone, and Mr. H. Nishijima from Shizuoka University for their valuable cooperation. A part of this research was supported by Grants-in-Aid for Scientific Research, MEXT (No. 22560416). REFERENCES [1] T. Hashimoto, M. Sakai, S. Yamamoto, and T. Hashimoto, “A Basic

Fig. 13: Correlation between speed difference between wind and cloud and shape change

C. Correlation between Speed Difference between Wind and Cloud Movement and Shape Change Figure 13 shows the correlation between the speed difference between wind and cloud movement and shape change. Based on Fig. 13, the larger the scale of cloud shape change, the slower the speed of the cloud compared to the wind speed. Sky level wind speed is measured based on the cloud speed and the scale of the cloud’s shape change. This is thought to be more accurate than basing the measurement only on cloud speed. V. CONCLUSION In this paper, we used the image processing method to predict generated output in order to help improve the efficiency of photovoltaic power generation systems. Concretely, cloud classification according to the gray level of clouds was proposed. The relationship between classified clouds and falling generated output was confirmed. We could classify clouds with 90 % accuracy and falling generated output due to cloud cover of any kind was predicted with 85 % accuracy. We can measure three-dimensional cloud movement with a range direction error of less than 5% based on our experimental studies. The direction of movement and speed can be determined based on the three-dimensional measurement of clouds. As a result, the amount of time that clouds will cover solar panels can be predicted in the future. If

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