JP2007184354A - Solar photovoltaic power generation system - Google Patents

Solar photovoltaic power generation system Download PDF

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Publication number
JP2007184354A
JP2007184354A JP2006000578A JP2006000578A JP2007184354A JP 2007184354 A JP2007184354 A JP 2007184354A JP 2006000578 A JP2006000578 A JP 2006000578A JP 2006000578 A JP2006000578 A JP 2006000578A JP 2007184354 A JP2007184354 A JP 2007184354A
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Prior art keywords
cloud
solar panel
power generation
distribution
power
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JP2006000578A
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Japanese (ja)
Inventor
Yasuhiro Kojima
Tomihiro Takano
Kiyoshi Tsuru
Yasuo Yoshida
康夫 吉田
康弘 小島
潔 都留
富裕 高野
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Mitsubishi Electric Corp
三菱電機株式会社
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    • FMECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24HEATING; RANGES; VENTILATING
    • F24SSOLAR HEAT COLLECTORS; SOLAR HEAT SYSTEMS
    • F24S50/00Arrangements for controlling solar heat collectors
    • F24S50/20Arrangements for controlling solar heat collectors for tracking
    • FMECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24HEATING; RANGES; VENTILATING
    • F24SSOLAR HEAT COLLECTORS; SOLAR HEAT SYSTEMS
    • F24S2201/00Prediction; Simulation
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02EREDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00Energy generation through renewable energy sources
    • Y02E10/40Solar thermal energy, e.g. solar towers
    • Y02E10/47Mountings or tracking

Abstract

<P>PROBLEM TO BE SOLVED: To provide a solar photovoltaic power generation system which can observe clouds which influence solar photovoltaic power generation over the sky, and also can accurately predict a distribution of clouds at any time in the future and thereby can predict a change in power to be generated in advance. <P>SOLUTION: The solar photovoltaic power generation system comprises a solar light panel which receives solar light and generates power; a 360° camera which images the whole sky at a point where the solar light panel is installed; an image processor which detects the distribution and movement of clouds from the image of the whole sky; and a power predicter which predicts a distribution of clouds at a predetermined point in the future according to the detected distribution and movement of clouds, and then predicts the power to be generated by the solar light panel at the predetermined point in the future based on the predicted distribution of clouds. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention relates to a photovoltaic power generation system that predicts future cloud distribution based on the movement of clouds in the sky to be observed and predicts photovoltaic power generation based on the predicted cloud distribution.

The conventional photovoltaic power generation system outputs the power generation amount that fluctuates depending on the weather conditions to the interconnected system as it is, so when the size of the photovoltaic power generation system increases, the frequency greatly fluctuates with the generated power fluctuation, or There is a problem that the voltage fluctuates greatly.
Also, in distributed power supply systems that self-sufficiency energy in regions such as small microgrids, power quality such as frequency fluctuation tolerance and voltage fluctuation tolerance of the entire system due to fluctuations in the power generation of the photovoltaic power generation system There is a problem that can not be satisfied.
Therefore, in order to solve this problem of power quality, there is a problem that even if a secondary battery for power leveling is installed or an SVC for suppressing voltage fluctuation is installed, the equipment capacity becomes large.
There is also a proposal for predicting photovoltaic power generation by capturing the movement of clouds as an image. In this proposed system, a color CCD camera masked at the center is installed on an equatorial mount that is set to always face the sun, the sun is automatically tracked, and the resulting image is analyzed to analyze the cloud. The movement time of the cloud is calculated to determine the time when the cloud reaches the position of the sun, and the generated power of the photovoltaic power generation system is predicted (see, for example, Non-Patent Document 1).

Yamamoto and 4 others, "Fundamental research on output fluctuation prediction of photovoltaic power generation by cloud image analysis", The Institute of Electrical Engineers of Japan, The Institute of Electrical Engineers of Japan, August 1999, Vol.119, No.8 / 9 , P909-915

However, this proposed system not only requires a rotating system that causes the camera to follow the sun accurately, but it cannot predict the power generated in the vicinity because an image of the vicinity of the sun cannot be obtained with a mask. There is a problem that the influence of indirect light which has a great influence on the photovoltaic power generation system together with direct light cannot be considered.
In addition, there is a problem that only a short time prediction is possible because clouds in a direction far from the sun cannot be recognized.

  An object of the present invention is to provide a solar power generation system that can observe clouds that affect solar power generation over the entire sky, accurately predict the cloud distribution at the time of prediction, and predict fluctuations in generated power. It is.

  A solar power generation system according to the present invention includes a solar panel that receives sunlight to generate power, a 360-degree omnidirectional camera that captures an image of the entire sky at a location where the solar panel is installed, An image processing unit that detects a cloud distribution and a cloud movement from a sky image, and predicts a cloud distribution at a predetermined future time point based on the cloud distribution and the cloud movement, and the predicted cloud A power prediction processing unit that predicts the generated power of the solar panel at the future time point based on the distribution of

  The effect of the photovoltaic power generation system according to the present invention is that it has a 360-degree omnidirectional camera that captures an image of the entire sky above the solar panel, and can be used for clouds around the sun that affect the nearest power generation and future power generation. Clouds that may have an impact can also be identified. For example, in a system such as a microgrid aiming at self-sufficiency of energy in a certain area, it becomes easier to balance the supply and demand. The frequency accuracy in the grid can be improved.

Embodiment 1 FIG.
FIG. 1 is a configuration diagram of a photovoltaic power generation system according to Embodiment 1 of the present invention.
A solar power generation system 1 according to Embodiment 1 of the present invention includes a solar panel 10 that receives sunlight from the sun 8 to generate power, and a power conditioner 11 that transmits power generated by the solar panel 10 to the system 5. The 360-degree omnidirectional camera 12 that captures an image of the sky above the point where the solar panel 10 is installed from the omnidirectional image transmitted from the 360-degree omnidirectional camera 12 and the 360-degree omnidirectional camera 12 The image processing unit 6 to be performed, the power prediction processing unit 7 for predicting the photovoltaic power generation power at the future time point of the solar panel 10 based on the processing result of the image processing unit 6, the distribution of the clouds 9 and the generated power of the solar panel 10 Are registered in association with each other. The cloud 9 depicted in FIG. 1 moves so as to block the sun 8.
The image processing unit 6, the power prediction processing unit 7, and the database 4 are configured by a computer including a CPU, a ROM, a RAM, and an interface circuit.

In the following description, the position of the whole sky from the zenith to the horizon is expressed by the horizon coordinate system. The altitude of the horizon coordinate system is 0 degree in the horizon direction and +90 degrees in the zenith direction. Also, the azimuth angles measured westward of the horizon coordinate system are 0 degrees south, 90 degrees west, 180 degrees north, and 270 degrees east.
Then, the whole sky image is used as a processing unit of the following processing, for example, with a portion surrounded by an altitude of 5 degrees and an azimuth angle of 10 degrees. The whole sky image is illustrated as a pattern arranged in a grid pattern.

Next, the operation of the photovoltaic power generation system 1 according to Embodiment 1 will be described.
The 360-degree omnidirectional camera 12 captures the entire sky at the point where the solar panel 10 is installed as an image from the zenith to the horizon in one frame. This 360-degree omnidirectional camera 12 is a camera that can shoot a 360-degree sky from the horizon to the zenith without distortion by a combination of lenses (not shown). Even without tracking the sun 8, the cloud 8 can be seen in the sun 8 and the sky. If there is, an image as the cloud 9 can be taken.
In addition, since the 360-degree omnidirectional camera 12 is equipped with the infrared transmission filter 13, the image to be captured loses color information and has only brightness information.

  The movement of the clouds 9 around the sun 8 is most important for the prediction of the generated power of the solar power generation system 1, but the image around the sun 8 is likely to cause halation, and a method for solving this is important. is there. Conventionally, the mask is always overlapped with the sun 8 by masking the part toward the sun 8 and tracking the sun 8.

In the photovoltaic power generation system 1 according to the first embodiment, the infrared transmission filter 13 is attached to the 360-degree omnidirectional camera 12, and the visible light is blocked and only the infrared is captured, so that the sun 8 is viewed in the field of view and 360-degree omnidirectional. By preventing the halation occurring in the camera 12, the cloud 9 around the sun 8 is prevented from being invisible due to the halation.
As a result, the diaphragm can be fixed for image processing, so that the shape of the cloud 9 can be adjusted even when the sky is cloudy. If halation cannot be completely prevented, the image processing unit 6 and the power prediction processing unit 7 perform complement processing. This sky image is continuously captured and input to the image processing unit 6.

FIG. 2 is a flowchart relating to an image processing routine executed by the image processing unit 6 according to the first embodiment. FIG. 3 is a diagram for explaining a process of separating the sky, the cloud 9, and the halation based on the brightness level of the image area. FIG. 4 is a diagram for explaining a labeling process for collecting regions separated into clouds 9 as a cloud of cloud 9. FIG. 5 is an image in which the movement vector of the cloud 9 is entered.
Next, an image processing routine executed by the image processing unit 6 according to the first embodiment will be described with reference to FIGS.
In step S1, the uptake in the period [Delta] T 1 takes in the whole sky image captured by the 360-degree omnidirectional camera 12, to partition the Zenten image in a grid of a plurality of regions. In the following description, the brightness of a region used means an average value of the region, but is not limited thereto. .
In step S2, the sky, thin cloud 9, thick cloud 9 or halation separation process is performed on the captured whole sky image for each region. The separation process of the sky, the thin cloud 9, the thick cloud 9, or the halation is performed using the brightness information of the area as shown in FIG.

In the infrared image, since the sky has low brightness and the cloud 9 has high brightness, the cloud 9 and the sky can be separated. Moreover, since the thick cloud 9 has low brightness and the thin cloud 9 has high brightness, the thick cloud 9 and the thin cloud 9 can be separated. Further, when the brightness is higher than the threshold value, it can be separated as halation.
Therefore, three first to third lightness thresholds relating to the lightness level are determined in advance, and by comparing the lightness level for each region of the image with these three thresholds, halation, thin cloud 9, thick cloud 9 or It can be separated into four empty. In this way, sky, thick cloud 9, thin cloud 9 or halation can be designated for each region of the whole sky image.

  In step S3, as shown in FIG. 4, a labeling process is performed in which the area designated as the cloud 9 among the areas adjacent to the area designated as the cloud 9 is collected as a cloud lump. FIG. 4 shows a first cloud 9 mass and a second cloud 9 mass in which the reference numerals 1 or 2 are written in the region.

In step S4, it detects the movement vector cloud 9 in detection period [Delta] T 2.
For the area designated as cloud 9, the velocity and direction of the movement vector are detected by using, for example, an optical flow method using the current image and the image before one capture period ΔT 1 .
FIG. 5 shows the motion vectors detected for the area related to the first cloud 9 mass and the second cloud 9 mass.
In step S5, the cloud 9 of each region obtained by the processing in steps S2, 3 and 4, separation information regarding sky or halation, labeling information for the region designated as cloud 9, and a movement vector for the region designated as cloud 9 Information is output to the power prediction processing unit 7.

FIG. 6 is a flowchart of a power generation output prediction routine executed by the prediction processing unit 7 according to the first embodiment. FIG. 7 is a diagram for describing registration of cloud distribution and generated power. FIG. 8 is a diagram for explaining prediction of future cloud distribution. FIG. 9 is a diagram for explaining the prediction of generated power from the future cloud distribution.
Next, the power generation output prediction routine executed by the prediction processing unit 7 according to the first embodiment will be described with reference to FIGS.
In step S11, the cloud 9, the sky or the halation separation information of each region at the detection period ΔT2, the labeling information for the region designated as the cloud 9, the movement vector information for the region designated as the cloud 9, and the power value of the power conditioner 11 Is entered.
In step S12, the distribution information of the cloud 9 and the photovoltaic power are associated with each other based on the output information from the image processing unit 6 input in the detection cycle ΔT 2 and the measurement information on the power value of the power conditioner 11, and the database 4 Register with.
When the distribution information of the cloud 9 and the photovoltaic power generation are associated and registered in the database 4, as shown in FIG. 7, the domain 31 including the region where the sun 8 is currently located, the sun 8 is currently located. The process is divided into the peripheral domain 32 and the whole sky domain 33 adjacent to the region. The area where the sun is currently located can be accurately determined from the date and time. Then, the ratio of the cloud 9 in each domain and the average density of the cloud 9, for example, the photovoltaic power generation for the four seasons of spring, summer, autumn and winter, for example, every hour, are registered as a ratio to the rating.
Note that the region determined to be halation by the image processing unit 6 is complemented using the distribution prediction information of the cloud 9 in the same region ahead of the detection cycle ΔT 2 obtained in step S 13 before one detection cycle ΔT 2. .
The ratio is registered as a frequency distribution in increments of 10%, for example.

In step S13, to predict the distribution of cloud 9 at the time of the prediction period [Delta] T 3 destination based on the distribution information of the current cloud 9 and the moving vector information. The prediction cycle ΔT 3 is the time from the current time to the time of prediction, for example, a predetermined time from 10 seconds ahead to 30 minutes ahead.
The distribution information and the moving vector information cloud 9 cloud 9, as shown in FIG. 8, to predict the distribution of clouds 9 point prediction period [Delta] T 3 destination. In addition, prediction processing is performed for each cloud 9 using the labeling information of the cloud 9. In this way, the distribution of the cloud 9 at the time point ahead of the prediction period ΔT 3 is calculated.

In step S14, it performs prediction of the photovoltaic power point in the estimated cycle [Delta] T 3 destination. That is, by searching the data of the distribution and photovoltaic power cloud 9 being registered in the distribution information and the step S12 in the prediction period [Delta] T 3 destination point cloud 9 obtained in step S13 in the database 4, FIG. 9, the photovoltaic power generation related to the distribution of the registered cloud 9 that matches the distribution of the cloud 9 at the time point ahead of the prediction cycle ΔT 3 is read out, and the value is calculated as the predicted value of the photovoltaic power generation. To do. At this time, when the distribution of the completely matching clouds 9 is not registered in the database 4, for example, a certain condition (average grayscale information of the whole sky cloud 9) is relaxed or not used. Use actual data.
Moreover, when the actual value of photovoltaic power generation varies on the same conditions, the simple average value and the weighted average value are used.

Such a solar power generation system 1 includes a 360-degree omnidirectional camera 12 that captures an image of the entire sky above the solar panel 10, and is used for the cloud 9 around the sun that immediately affects power generation and for future power generation. Since the cloud 9 that may have an impact can also be determined, for example, in a system such as a microgrid aiming at self-sufficiency of energy in a certain region, it becomes easier to balance the supply and demand. The frequency accuracy in the microgrid can be improved.
In addition, since it is not necessary to provide a driving unit to the 360-degree omnidirectional camera 12 to track the sun 8, it is possible to improve reliability at low cost.
In addition, when a secondary battery is used in the microgrid, when the secondary battery is charged by another power source, the power generation amount by the future solar power generation system can be predicted and charged without excess or deficiency. The amount can be determined, and the charge / discharge efficiency of the secondary battery can be improved.
In addition, regarding the secondary battery capacity, it is possible to reduce the margin of the plan as compared with the case where the photovoltaic power generation output is not predicted, and the installation capacity can be reduced.

In the first embodiment, when the photovoltaic power is registered, it is registered as a ratio to the rating, but the power value itself may be registered.
In the first embodiment, the image is segmented using the horizon coordinate system. However, the image may be converted into the XY coordinate system. When converting to the XY coordinate system, the altitude h of the cloud 9 is required. However, when the positions of the solar panel 10 and the 360-degree omnidirectional camera 12 are adjacent to each other, an appropriate altitude may be assumed.
Further, although the cloud 9 distribution is predicted in the horizon coordinate system, it may be converted into the XY coordinate system.

Embodiment 2. FIG.
FIG. 10 is a configuration diagram of a photovoltaic power generation system according to Embodiment 2 of the present invention.
The solar power generation system 1B according to the second embodiment of the present invention includes a second solar panel 10b and a solar power generation system 1 according to the first embodiment at a point different from the point where the solar panel 10a is installed in the solar power generation system 1 according to the first embodiment. Since the power conditioner 11b is added and the power prediction processing unit 7B and the database 4B are different from each other, and the other parts are the same, the same portions are denoted by the same reference numerals, and the description thereof is omitted.

The solar power generation system 1B according to the second embodiment is configured with 360 prediction of the solar power generation power of the solar panel 10a (abbreviated as the first panel in FIG. 11) in which the 360-degree omnidirectional camera 12 is installed. The solar power generation power of the solar panel 10b (abbreviated as the second panel in FIG. 11) where the omnidirectional camera 12 is not installed is predicted.
The prediction of the photovoltaic power generation of the solar panel 10a in which the 360-degree omnidirectional camera 12 is installed has been described in the first embodiment, and is omitted, and the 360-degree omnidirectional camera 12 is not installed. Only the prediction of the photovoltaic power generation of the solar panel 10b will be described. In addition, although FIG. 10 demonstrates as one solar panel 10b in which the 360 degree | times omnidirectional camera 12 is not installed, even if it is multiple, prediction of photovoltaic power generation can be performed similarly.

The solar panel 10b according to the second embodiment is installed at a point away from the solar panel 10a.
In the database 4B according to the second embodiment, the information described in the first embodiment regarding the all-sky image of the spot where the solar panel 10a is imaged from the 360-degree omnidirectional camera 12 and the solar panel 10a. It is registered in association with photovoltaic power. Furthermore, in the database 4B according to the second embodiment, information regarding the all-sky image at the point where the solar panel 10b converted from the all-sky image captured from the 360-degree omnidirectional camera 12 is installed, and the solar panel 10b solar power generation is associated and registered.

FIG. 11 is a flowchart relating to a power prediction processing routine executed by the power prediction processing unit 7B according to the second embodiment. FIG. 12 is a diagram for explaining the relationship between cloud altitude and cloud distribution. FIG. 13 is a diagram illustrating a transition of generated power with respect to different cloud altitudes.
Next, a power prediction processing routine executed by the power prediction processing unit 7B according to the second embodiment will be described with reference to FIGS.
In step S21, detected with a period [Delta] T 2, moving vector information and power conditioners 11a of each region cloud 9, the separation information related to empty or halation, labeling information for the area specified as a cloud 9, for the area specified as a cloud 9, The measurement information of the power value of 11b is received.
In step S22, as in the procedure in step S12, the cloud 9 of each region, separation information regarding sky or halation, labeling information for the region designated as cloud 9, and movement vector information and sunlight for the region designated as cloud 9 The photovoltaic power generation of the panel 10a is associated and registered in the database 4B.

In step S23, it is determined whether or not the time exceeds the altitude review period of the cloud 9, for example, 1 hour. If the time exceeds the altitude review period, the process proceeds to step S24, and if the time has not passed the altitude review period, step S28. Proceed to
The procedure of steps S24 to S27 is a procedure for obtaining the altitude h of the cloud 9 necessary for converting the distribution and movement vector of the cloud 9 in the horizon coordinate system to the distribution and movement vector of the cloud 9 in the XY coordinate system.
In step S24, three altitudes h, h + α, and h−α of the cloud 9 are assumed using the currently set altitude value h and a predetermined deviation α.
In step S25, the distribution of the cloud 9 in the horizon coordinate system is converted to the distribution of the cloud 9 in the XY coordinate system using the altitudes h, h + α, and h−α. That is, as shown in FIG. 12, if the altitude h of the cloud 9 is lower than the actual cloud 9, the obtained cloud 9 distribution is narrower than the actual cloud 9 distribution.
In step S26, the distribution of the cloud 9 in the XY coordinate system obtained by conversion using the altitudes h, h + α, h−α is the position of the XY coordinate system where the solar panel 10b is installed as the center of the horizon coordinate system. The cloud 9 distribution in the XY coordinate system is converted into the cloud 9 distribution in the horizon coordinate system.

  In step S27, the actual value of the photovoltaic power generation of the solar panel 10b is searched using the distribution of clouds 9 in the horizon coordinate system corresponding to the altitudes h, h + α, h−α at the point where the solar panel 10b is installed. To do. And as shown in FIG. 13, distribution of the cloud 9 of the horizon coordinate system related to the photovoltaic power generation of the solar panel 10b registered in the database 4B closest to the photovoltaic power generation of the current solar panel 10b. Is the current cloud 9 distribution, and the altitude used when the cloud 9 distribution is obtained is the actual cloud 9 altitude.

Then, the distribution and movement vector of the cloud 9 in the horizon coordinate system after the next time are replaced with the altitude h used when converting the distribution and movement vector of the cloud 9 in the XY coordinate system. Furthermore, the distribution of the cloud 9 in the horizontal coordinate system of the solar panel 10b corresponding to the obtained altitude and the photovoltaic power generation of the solar panel 10b are associated with each other and stored in the database 4B.
Further, the movement vector is converted into a movement vector on the horizon coordinate system centered on the solar panel 10b using the obtained altitude, and the process proceeds to step S29.

  In step S28, the distribution and movement vector of the cloud 9 in the horizon coordinate system are converted into the distribution and movement vector of the cloud 9 in the XY coordinate system using the altitude h. Then, the distribution and movement vector of the cloud 9 in the XY coordinate system obtained by using the altitude h is the position of the cloud 9 in the horizon coordinate system with the position of the XY coordinate system where the solar panel 10b is installed as the center of the horizon coordinate system. Convert to distribution and transfer vector.

In step S29, the distribution and distribution on the basis of the motion vector estimated cycle [Delta] T 3 destination point cloud 9 cloud 9 horizontal coordinate system when the position of the solar panel 10b is provided with the center of the horizontal coordinate system Predict. That is, the distribution of the cloud 9 at the time point ahead of the prediction period ΔT 3 is predicted from the distribution information of the cloud 9 and the movement vector information of the cloud 9. In addition, prediction processing is performed for each cloud 9 using the labeling information of the cloud 9.

In step S30, the sun of the distribution and solar panels 10b cloud 9 distribution information and solar panel 10b registered in step S27 in cloud 9 point prediction period [Delta] T 3 destination for solar panels 10b obtained in the step S29 Using the photovoltaic power data, the photovoltaic power corresponding to the stored cloud 9 distribution that matches the distribution of the cloud 9 at the time point ahead of the prediction period ΔT 3 is used as the photovoltaic power of the solar panel 10b. The predicted value of.

  Such a solar power generation system 1B uses the actual value of the solar power generation power of the two solar panels 10a and 10b installed at remote points even if there is one 360-degree omnidirectional camera 12. The altitude of the cloud 9 is estimated, and the distribution of the cloud 9 on the solar panel 10b where the 360-degree omnidirectional camera 12 is not installed can be predicted, so the sun installed at a position away from the 360-degree omnidirectional camera 12 The accuracy of prediction of the photovoltaic power generation of the optical panel 10b can be improved.

Embodiment 3 FIG.
FIG. 14 is a configuration diagram of a photovoltaic power generation system according to Embodiment 3 of the present invention.
The photovoltaic power generation system 1C according to the third embodiment of the present invention is provided by adding a 360-degree omnidirectional color camera 52 having a different characteristic from the 360-degree omnidirectional camera 12 to the photovoltaic power generation system 1 according to the first embodiment. Accordingly, the image processing unit 6C is different, and the other parts are the same. Therefore, the same parts are denoted by the same reference numerals, and the description thereof is omitted.
The 360-degree omnidirectional color camera 52 according to the third embodiment is for color image processing, and the aperture is automatically adjusted according to the weather conditions.

FIG. 15 is a flowchart relating to an image processing routine executed by the image processing unit 6C according to the third embodiment. FIG. 16 is a diagram for explaining a process of separating the sky, the cloud 9, and the halation based on the brightness of the image area and the saturation of the image area.
In the image processing unit 6C according to the third embodiment, as shown in FIG. 16, four threshold values relating to brightness are set. Of the four brightness thresholds, the first and second brightness thresholds are the same as in the first embodiment, and the fourth and fifth brightness thresholds are different. If the brightness is higher than the fourth brightness threshold, it can be determined that the cloud 9 is clearly thick, and if the brightness is lower than the fifth brightness threshold, it can be determined that the sky is clearly empty.
Further, as shown in FIG. 16, a saturation threshold for blue saturation is set in the image processing unit 6C. Since the blue saturation of the sky is high and the blue saturation of the thick cloud 9 is low, it is possible to discriminate the sky and the thick cloud 9 that are difficult to discriminate in the infrared image by using the blue saturation.

Next, an image processing routine executed by the image processing unit 6C according to the third embodiment will be described with reference to FIGS. Note that the steps S33 to S35 in FIG. 15 are the same as the steps S3 to S5 in FIG.
In step S31, capturing in period [Delta] T 1 captures infrared all-sky image taken by the 360-degree omnidirectional camera 12. Further, capturing in the period [Delta] T 2 captures color Zenten image captured by the 360-degree omnidirectional color camera 52.

  In step S32, the brightness of the captured infrared all-sky image region is compared with four threshold values relating to the brightness to separate the light into halation, thin cloud 9, thick cloud 9 or sky. Next, the thick cloud 9 and the sky are separated by blocking red and yellow from the color sky image and comparing the blue saturation with the saturation threshold for the blue saturation.

  In such a solar power generation system 1C, by using the infrared image and the color image in combination, it is possible to clearly separate the thick cloud 9 and the sky whose boundaries are uncertain from the infrared image alone. The accuracy of the separation process of sky or halation can be improved.

Embodiment 4 FIG.
FIG. 17 is a configuration diagram of a photovoltaic power generation system according to Embodiment 4 of the present invention.
A photovoltaic power generation system 1D according to the fourth embodiment of the present invention includes a third photovoltaic panel 10c, a power conditioner 11c, and a second 360-degree omnidirectional camera 12 in addition to the photovoltaic power generation system 1B according to the second embodiment. Since the power prediction processing unit 7D and the database 4D are different from each other and are the same except for this, the same parts are denoted by the same reference numerals, and the description thereof is omitted.

The solar power generation system 1D according to the fourth embodiment includes a solar panel 10a (abbreviated as first panel in FIG. 18) and 10c (third panel in FIG. 18) on which 360-degree omnidirectional cameras 12 are respectively installed. The solar power generation power of the solar panel 10b (abbreviated as the second panel) in which the 360-degree omnidirectional camera 12 is not installed is predicted.
The prediction of the photovoltaic power generation of the solar panels 10a and 10c in which the 360-degree omnidirectional camera 12 is installed has been described in the first embodiment, and is omitted, and the 360-degree omnidirectional camera 12 is installed. Only the prediction of the photovoltaic power generation of the solar panel 10b that is not performed will be described.

The 360-degree omnidirectional camera 12 to be added is installed near the additional solar panel 10c, and image data to be captured is transmitted to the image processing unit 6 in the same manner as the 360-degree omnidirectional camera 12.
In the database 4D according to the fourth embodiment, the cloud 9 of each region from the 360-degree omnidirectional camera 12 corresponding to each of the solar panels 10a and 10c, separation information regarding sky or halation, and the region designated as the cloud 9 are stored. The labeling information and the movement vector information for the area designated as the cloud 9 and the power values of the power conditioners 11a and 11c are stored in association with each other. Furthermore, in the database 4D according to the fourth embodiment, information on the all-sky image at the point where the solar panel 10b converted from the all-sky image captured from the 360-degree omnidirectional camera 12 is installed, and the solar panel 10b of photovoltaic power is stored in association with each other.

FIG. 18 is a flowchart of a power prediction processing routine executed by the power prediction processing unit 7D according to the fourth embodiment. FIG. 19 is a diagram showing a state of comparing cloud distribution maps captured at different points converted into the XY coordinate system.
Next, the generated power prediction routine executed by the power prediction processing unit 7D according to the fourth embodiment will be described with reference to FIGS.
In step S41, the cloud 9 of each region for each solar panel 10a, 10c, separation information regarding sky or halation, labeling information for the region designated as cloud 9, movement vector information and power conditioner for the region designated as cloud 9 The power values of 11a and 11c are received. The power value of the power conditioner 11b is also received.
In step S42, in the same manner as in step S12, the cloud 9 of each region obtained from the all-sky image captured by each 360-degree omnidirectional camera 12, information on the sky or halation, the region designated as the cloud 9 And the movement vector information for the area designated as the cloud 9 and the photovoltaic power generation of the solar panels 10a and 10c are associated and registered in the database 4D.
In step S43, three altitudes h, h + α, and h−α of the cloud 9 are assumed using the currently set altitude value h and a predetermined deviation α.

In step S44, the distribution of the cloud 9 in the horizon coordinate system related to one solar panel 10a is converted into the distribution of the cloud 9 in the XY coordinate system using the altitudes h, h + α, and h−α. Further, the distribution of the cloud 9 in the horizon coordinate system related to the other solar panel 10c is converted into the distribution of the cloud 9 in the XY coordinate system using the altitudes h, h + α, and h−α.
In step S45, for each altitude, the distribution of the clouds 9 in the XY coordinate system regarding the solar panel 10a and the solar panel 10c is compared using the least square method as shown in FIG. 19, and the distribution is most similar. The height used to calculate the distribution of the clouds 9 is estimated as the actual height of the clouds 9. Then, the distribution and movement vector of the cloud 9 in the horizon coordinate system after the next time are replaced with the altitude h used when converting the distribution and movement vector of the cloud 9 in the XY coordinate system.

  In step S46, the distribution of the cloud 9 in the XY coordinate system related to the solar panel 10a converted at the determined altitude is converted into the distribution of the cloud 9 with the position of the solar panel 10b in the XY coordinate system as the center of the horizon coordinate axis. The movement vector is converted into a movement vector on the horizon coordinate system centered on the solar panel 10b using the altitude obtained together with the distribution of the cloud 9 relating to the obtained solar panel 10b and the photovoltaic power generation of the solar panel 10b. The electric power is associated and stored in the database 4D, and the process proceeds to step S46.

In step S47, the predicting the distribution of the cloud 9 point prediction period [Delta] T 3 destination based on the distribution and movement vector cloud 9 according to the solar panel 10b obtained.
In step S48, the sun distribution and solar panels 10b cloud 9 distribution information and solar panel 10b registered in step S45 in cloud 9 point prediction period [Delta] T 3 destination for solar panels 10b obtained in Step S46 Using the photovoltaic power data, the photovoltaic power corresponding to the stored cloud 9 distribution that matches the distribution of the cloud 9 at the time point ahead of the prediction period ΔT 3 is used as the photovoltaic power of the solar panel 10b. The predicted value of.

  In such a photovoltaic power generation system 1D, a plurality of 360-degree omnidirectional cameras 12 having different installation positions are installed, and the distribution of clouds 9 captured by one 360-degree omnidirectional camera 12 is all 360-degree omnidirectional. By estimating the altitude of the cloud 9 so as to be similar to the distribution of the cloud 9 from the position where the azimuth camera 12 is installed, the altitude of the cloud 9 is estimated with high accuracy, so the 360-degree omnidirectional camera 12 is installed. It is possible to accurately predict the photovoltaic power generated with respect to the solar panel 10b installed in a place where it is not.

  In the fourth embodiment, one image processing unit 6, one power prediction processing unit 7D, and one database 4D are arranged. Good.

Embodiment 5 FIG.
FIG. 20 is a configuration diagram of a photovoltaic power generation system according to Embodiment 5 of the present invention. FIG. 21 is a diagram illustrating a transition of a power generation plan in the solar power generation system according to the fifth embodiment.
The photovoltaic power generation system 1E according to the fifth embodiment of the present invention is different from the photovoltaic power generation system 1 according to the first embodiment in that a supply and demand control device 14 and two adjustment power sources 15 are added. Are the same, the same reference numerals are attached to the same parts, and the description is omitted.

The supply and demand control device 14 adjusts the output of the adjustment power supply 15 so that the balance between the demand and the supply of the area in charge of power supply in the microgrid matches. At this time, if the operating characteristics (relationship between power generation output and cost) of the regulated power supply 15 are not the same, for example, in “Study on Supply and Demand Control of Microgrid” by Kojima et al. It has a function of determining an output command value for each power supply by the optimum economic load distribution control described.
In addition, the power supply is not performed, and the total value of the photovoltaic power generation and the regulated power supply 15 is controlled to be constant, or the total power of the photovoltaic power generation and the regulated power supply 15 is controlled to fluctuate more slowly than the power fluctuation of the photovoltaic power generation. You may do it.
The adjustment power supply 15 is a cogeneration system including a diesel generator or a gas engine generator whose output is adjusted by the supply and demand control device 14.

In the solar power generation system 1E according to the fifth embodiment of the present invention, power is constantly generated by solar power generation with respect to the power demand, and the remaining power is satisfied by operating the regulated power supply 15 to generate power. For example, as shown in FIGS. 21A and 21B, the solar power generation and one gas engine generator currently satisfy the demand.
As described in the first embodiment, for example, the photovoltaic power generation power is predicted for a time period up to 60 minutes ahead. For example, as shown in FIG. 21 (a), Once at the time t 1 and photovoltaic power is expected to decrease, because only one of the gas engine generator can not meet the power demand, new A power generation plan can be made by operating the second gas engine generator. Furthermore, in order to operate the second unit of the gas engine generator are the necessary warm-up operation, it is possible to design plans to start warming up from the time t 2 prior to the time point t 1.

  Such a photovoltaic power generation system 1E predicts fluctuations in the photovoltaic power generation using the 360-degree omnidirectional camera 12, and can plan in advance the start and stop of the adjustment power supply 15 necessary to compensate for the fluctuations. Therefore, compensation is performed at an appropriate timing, fluctuations in generated power can be suppressed, and power quality can be kept good.

Embodiment 6 FIG.
FIG. 22 is a configuration diagram of a photovoltaic power generation system according to Embodiment 6 of the present invention.
As shown in FIG. 22, the photovoltaic power generation system 1F according to the sixth embodiment of the present invention can receive the weather forecast 18 via the Internet 17 to the photovoltaic power generation system 1E according to the fifth embodiment. Since the other points are the same, the same parts are denoted by the same reference numerals, and the description thereof is omitted.
When the power prediction processing unit 7F according to the sixth embodiment predicts the photovoltaic power generation, the short-term prediction from 30 minutes to 60 minutes ahead predicts the distribution of the clouds 9 as described in the first embodiment. The long-term prediction up to several hours ahead is performed based on the radar image obtained from the weather forecast 18.

  Such a photovoltaic power generation system 1 </ b> F has an effect of being able to perform photovoltaic power generation output prediction for a long time by combining weather information.

It is a block diagram of the solar energy power generation system concerning Embodiment 1 of this invention. 4 is a flowchart relating to an image processing routine executed by the image processing unit according to the first embodiment. It is a figure for demonstrating the process which isolate | separates sky, a cloud, and halation based on the brightness level of the area | region of an image. It is a figure for demonstrating the labeling process which puts together the area | region isolate | separated into the clouds as a cloudy cloud. It is the image which entered the movement vector of the cloud lump. 4 is a flowchart relating to a generated power prediction routine executed by a prediction processing unit according to the first embodiment. It is a figure for demonstrating registering a cloud distribution and generated electric power. It is a figure for demonstrating predicting the future cloud distribution. It is a figure for demonstrating predicting generated electric power from the future cloud distribution. It is a block diagram of the solar energy power generation system concerning Embodiment 2 of this invention. 10 is a flowchart regarding a generated power prediction routine executed by a prediction processing unit according to the second embodiment. It is a figure for demonstrating the relationship between the altitude of a cloud, and the distribution of a cloud. It is a figure which shows the mode of the transition of the generated electric power with respect to the altitude of a different cloud. It is a block diagram of the solar energy power generation system concerning Embodiment 3 of this invention. 10 is a flowchart relating to an image processing routine executed by an image processing unit according to the third embodiment. It is a figure for demonstrating the process which isolate | separates sky, a cloud, and a halation based on the brightness and saturation of the area | region of an image. It is a block diagram of the solar energy power generation system concerning Embodiment 4 of this invention. 14 is a flowchart relating to a generated power prediction routine executed by a prediction processing unit according to the fourth embodiment. It is a figure which shows a mode that the cloud distribution map imaged in the different point converted into XY coordinate system is contrasted. It is a block diagram of the solar energy power generation system concerning Embodiment 5 of this invention. It is a figure which shows the mode of the power generation plan of the adjustment power supply performed based on the predicted value of photovoltaic power generation. It is a block diagram of the solar energy power generation system concerning Embodiment 6 of this invention.

Explanation of symbols

  1, 1B, 1C, 1D, 1E, 1F Solar power generation system, 4, 4B, 4D database, 5 systems, 6, 6C Image processing unit, 7, 7B, 7D, 7F Power prediction processing unit, 8 Sun, 9 Cloudy 10, 10a, 10b, 10c Solar panel, 11, 11a, 11b, 11c Power conditioner, 12 360 degree omnidirectional camera, 13 Infrared transmission filter, 14 Supply / demand control device, 15 Adjustment power supply, 17 Internet, 18 Weather forecast, 31 domain, 32 peripheral domain, 33 all-sky domain, 52 360 degree omnidirectional color camera.

Claims (6)

  1. A solar panel that receives sunlight to generate electricity;
    A 360-degree omnidirectional camera that captures an image of the whole sky at the point where the solar panel is installed;
    An image processing unit for detecting a cloud distribution and a cloud movement from the whole sky image;
    Predict the cloud distribution at a predetermined future time point based on the cloud distribution and cloud movement, and predict the generated power of the solar panel at the future time point based on the predicted cloud distribution A power prediction processing unit to
    A photovoltaic power generation system comprising:
  2. The 360-degree omnidirectional color camera is installed at a point where the 360-degree omnidirectional camera is installed, and captures the omnidirectional sky as a color image.
    The solar power generation system according to claim 1, wherein the image processing unit detects a cloud distribution by combining the image from the 360-degree omnidirectional camera and the color image.
  3. A second solar panel installed at a different location from the solar panel,
    The power prediction processing unit calculates a predetermined future at the point based on the cloud distribution and the cloud movement at the point where the second solar panel calculated from the sky image is installed. 3. The cloud distribution at a time point is predicted, and the generated power of the second solar panel at the future time point is predicted based on the predicted cloud distribution. Solar power system.
  4. A third solar panel installed at a different point from the solar panel and the second solar panel;
    A second 360-degree omnidirectional camera that is installed at a point where the third solar panel is installed and that captures the entire sky at the point as an image;
    With
    The power prediction processing unit estimates a cloud altitude using images from the 360-degree omnidirectional camera and the second 360-degree omnidirectional camera, and uses the altitude to estimate the altitude of the cloud from the omnidirectional image. The solar power generation system according to claim 3, wherein the distribution of clouds and the movement of clouds at a point where the solar panels of 2 are installed are calculated.
  5. A regulated power supply for supplying power to the grid connected to the solar panel;
    A supply and demand control device for controlling the adjusted power source based on an expected value of the generated power of the solar panel predicted by the power prediction processing unit;
    The photovoltaic power generation system according to claim 1 or 2, further comprising:
  6.   The solar power generation system according to claim 5, wherein the power prediction processing unit predicts the power generation of the solar panel over a long period based on weather information acquired from the outside.
JP2006000578A 2006-01-05 2006-01-05 Solar photovoltaic power generation system Granted JP2007184354A (en)

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Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005312163A (en) * 2004-04-20 2005-11-04 Canon Inc Power generation controller and power generation system

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005312163A (en) * 2004-04-20 2005-11-04 Canon Inc Power generation controller and power generation system

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