Solar power generation efficiency estimation method

Abstract

A method for estimating solar power generation efficiency includes the following steps: analyzing multiple images obtained at multiple time points in a time series to obtain the grayscale average value of each image and the change amount corresponding to each time point. Based on the grayscale average value and the change amount, a stable time zone is obtained from the time point. Then, a solar energy evaluation index corresponding to the stable time zone is calculated.

Description

Technical Field

[0001] The present invention relates to a method for evaluating power generation efficiency, and in particular to a method for estimating solar power generation efficiency. Background Art

[0002] Solar power generation is an emerging renewable energy source. Solar technology is widely characterized as a passive or active way to capture, convert and distribute sunlight. Solar power generation often has the characteristic of unstable sunshine, which makes it difficult to accurately evaluate the power generation efficiency of solar power plants. The traditional method is to install a sunshine meter to calculate the sunshine amount A during a specific period, and then compare it with the power generation B obtained from the electricity meter during a specific period to obtain the power generation efficiency (power generation / sunshine amount).

[0003] However, if the operating environment of the solar power plant is bad and the signal interference is serious, it is inevitable that the sunshine meter circuit will be damaged, resulting in insufficient data collection and inaccurate data calculation. In addition, sunshine meters are expensive, and due to long-term use, sunshine meters are prone to numerical deviation, requiring maintenance and calibration (calibration every six months, and cannot be performed on site), and the maintenance cost is extremely high. Therefore, most solar power plants do not install sunshine meters. Even if some solar power plants installed sunshine meters in the early stage, the accuracy will gradually decrease due to lack of maintenance and calibration, and the data obtained will eventually become unreliable. Summary of the invention

[0004] The invention provides a solar power generation efficiency estimation method, which can utilize wide-area image data to estimate the solar power generation efficiency.

[0005] The solar power generation efficiency estimation method of the present invention includes: analyzing multiple images obtained at multiple time points in a time series to obtain the grayscale average value of each image and the change amount corresponding to each time point; based on the grayscale average value and the change amount, obtaining a stable time zone from the time point; and calculating the solar energy evaluation index corresponding to the stable time zone.

[0006] In one embodiment of the present invention, the step of obtaining a stable time zone from the time point based on the grayscale average value and the variation includes: judging whether the degree of change of each image corresponding to each time point is large or small based on the grayscale average value and the variation of each image; if the degree of change corresponding to the t-th time point and the degree of change corresponding to the t+1-th time point are both judged to be small, judging the t-th time point to be a stable state; if the degree of change corresponding to at least one of the t-th time point and the t+1-th time point is judged to be large, judging the t-th time point to be an unstable state; and combining the time points judged to be stable into a stable time zone.

[0007] In one embodiment of the present invention, the step of judging whether the degree of change of each image corresponding to each time point is large or small includes: judging whether the grayscale average value of the image corresponding to the t-th time point is less than a first threshold value; if the grayscale average value of the image corresponding to the t-th time point is not less than the first threshold value, judging that the degree of change corresponding to the t-th time point is large; if the grayscale average value of the image corresponding to the t-th time point is less than the first threshold value, judging whether the change amount corresponding to the t-th time point is less than a second threshold value; if the change amount corresponding to the t-th time point is not less than the second threshold value, judging that the degree of change corresponding to the t-th time point is large; and if the change amount corresponding to the t-th time point is less than the second threshold value, judging that the degree of change corresponding to the t-th time point is small.

[0008] In one embodiment of the present invention, the step of analyzing the images obtained at the time points in the time series includes: summing up the grayscale values ​​of a plurality of pixels of each image and averaging them to obtain a grayscale average value.

[0009] In one embodiment of the present invention, the step of analyzing the image obtained at the time point in the time series includes: subtracting multiple pixels of the image corresponding to the t-th time point from multiple pixels of the image corresponding to the t-1-th time point and taking the absolute value to obtain the difference corresponding to each pixel; summing up the pixel differences and taking the average to obtain the change corresponding to the t-th time point.

[0010] In an embodiment of the present invention, the solar power generation efficiency estimation method further includes: extracting a plurality of target areas corresponding to the target position from a plurality of meteorological images included in the time series respectively to serve as the image.

[0011] In an embodiment of the present invention, the category of the meteorological image is one of a radar echo image, a satellite cloud image, and an all-sky imaging image.

[0012] In one embodiment of the present invention, the step of calculating the solar energy evaluation index corresponding to the stable time zone includes: calculating the total sunshine amount by adding up the multiple sunshine amount estimates included in the stable time zone; calculating the total power generation by adding up the multiple power generation included in the stable time zone; and calculating the solar energy evaluation index based on the total sunshine amount, the total power generation and the installation capacity of the solar module, wherein the solar energy evaluation index = (total power generation / installation capacity) / (total sunshine amount / 1000).

[0013] Based on the above, the present disclosure utilizes wide-area image data to simultaneously analyze the power generation efficiency of a large number of solar power plants, which is helpful for power plant operators to perform operation and maintenance work and for power plant owners to perform wide-area assessments of the health of power plant assets. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 is a block diagram of an electronic device according to an embodiment of the present invention.

[0015] Figure 2 is a flow chart of a solar power generation efficiency estimation method according to an embodiment of the present invention.

[0016] Figure 3 is a schematic diagram of a weather image according to an embodiment of the present invention.

[0017] Figure 4 is a diagram showing examples of multiple images according to an embodiment of the present invention.

[0018] Figure 5 FIG. 4 is a schematic diagram of cloud cover variation according to an embodiment of the present invention.

[0019] Figure 6 is a flow chart of a method for determining a degree of change at a time point t according to an embodiment of the present invention.

[0020] Figure 7 is a graph of a comparative example according to an embodiment of the present invention.

[0021] The reference numerals are described as follows:

[0022] 100: Electronic devices

[0023] 110: Processor

[0024] 120: Storage

[0025] 121: Image analysis module

[0026] 123: Sunshine Estimation Module

[0027] 310: Weather images

[0028] 311: Target area

[0029] 410: Time Series

[0030] 411-415, 510-530: Image

[0031] S205-S215: Steps of the solar power generation efficiency estimation method

[0032] S605-S620: Steps of the method for determining the degree of change at the time point t DETAILED DESCRIPTION

[0033] Figure 1 is a block diagram of an electronic device according to an embodiment of the present invention. Figure 1The electronic device 100 includes a processor 110 and a storage 120. The processor 110 is coupled to the storage 120. The processor 110 is, for example, a central processing unit (CPU), a physical processing unit (PPU), a programmable microprocessor (Microprocessor), an embedded control chip, a digital signal processor (DSP), an application specific integrated circuit (ASIC) or other similar devices.

[0034] The storage 120 is, for example, any type of fixed or removable random access memory (RAM), read-only memory (ROM), flash memory, hard disk or other similar devices or a combination of these devices. The storage 120 includes an image analysis module 121 and a sunshine estimation module 123. The image analysis module 121 and the sunshine estimation module 123 are composed of one or more program code segments. After the program code segments are installed, the processor 110 will execute the following solar power generation efficiency estimation method.

[0035] Figure 2 is a flow chart of a method for estimating solar power generation efficiency according to an embodiment of the present invention. Figure 1 and Figure 2 In step S205, the image analysis module 121 analyzes multiple images obtained at multiple time points in the time series to obtain the grayscale average value of each image and the change amount corresponding to each time point. The images are images corresponding to the same target position.

[0036] In this embodiment, the image analysis module 121 is used to extract multiple target areas corresponding to the target position from the multiple meteorological images included in the time series as images for subsequent analysis. Here, the category of the meteorological image is one of a radar echo image, a satellite cloud image, and an all-sky imaging image. In this embodiment, a target area corresponding to a target position can be extracted from the meteorological image for analysis. In other embodiments, multiple target areas corresponding to different target positions can also be extracted from one meteorological image.

[0037] Figure 3 is a schematic diagram of a weather image according to an embodiment of the present invention. Figure 3The weather image 310 at one point in time is shown. The image analysis module 121 extracts a target area 311 corresponding to the location in the weather image 310 according to the location where the solar module is actually installed.

[0038] Figure 4 is a diagram of multiple images according to an embodiment of the present invention. Figure 4 The image analysis module 121 extracts images 411-415 at multiple time points (only time points 1-5 are shown here) of the time series 410. Next, the image analysis module 121 sums up the grayscale values ​​of multiple pixels of the images 411-415 one by one and averages them to obtain a grayscale average value.

[0039] The image f at time point t is calculated by the following formula t The average value of the pixel value (x,y) gm t , where the image size is M×N.

[0040]

[0041] The image analysis module 121 subtracts the images corresponding to the two previous and next time points to obtain the change amount corresponding to the t-th time point. The change amount refers to the change amount of cloud cover between the two adjacent time points. That is, multiple pixels of the image corresponding to the t-th time point are subtracted from multiple pixels of the image corresponding to the t-1-th time point and the absolute value is taken to obtain the difference corresponding to each pixel. The difference values ​​of the pixels are summed up and averaged to obtain the change amount corresponding to the t-th time point.

[0042] Figure 5 FIG. 4 is a schematic diagram of cloud cover variation according to an embodiment of the present invention. Figure 5 The image 510 at time point t-1 and the image 520 at time point t are shown. Image 530 can be obtained by subtracting image 520 from image 510. t-1 (x,y))) and the image 520(f t (x,y))), subtract the corresponding positions, add the absolute values ​​and average them to get the cloud change V at time point t t , the formula is as follows, where the image size is M×N.

[0043]

[0044] In step S210, the image analysis module 121 obtains a stable time zone from the time point based on the grayscale average value and the variation. In this embodiment, the degree of change between the time point t and the time point t-1 and the degree of change between the time point t and the time point t+1 are determined to determine whether the time point t is in a stable state. The time point t is determined to be in a stable state only when the degree of change between the time point t and the two time points before and after it is small.

[0045] Specifically, the image analysis module 121 determines whether the degree of change of each image corresponding to each time point is large or small based on the grayscale average value and the variation of each image. If the degree of change corresponding to the t-th time point and the degree of change corresponding to the t+1-th time point are both determined to be small, the t-th time point is determined to be in a stable state. If the degree of change corresponding to at least one of the t-th time point and the t+1-th time point is determined to be large, the t-th time point is determined to be in an unstable state. Finally, the time points determined to be in a stable state are combined into a stable time zone.

[0046] Figure 6 is a flow chart of a method for determining the degree of change at a time point t according to an embodiment of the present invention. Figure 6 In step S605, the image analysis module 121 determines the grayscale average gm of the image corresponding to the t-th time point. t Is it less than the first threshold value? If the grayscale average gm of the image corresponding to the tth time point t is not less than the first threshold value, as shown in step S610, the image analysis module 121 determines that the change degree corresponding to the t-th time point is large. That is, when the grayscale average gm at time point t is t If it is not less than the first threshold value, it means that the cloud cover rate is high, and the degree of change corresponding to the time point t is directly judged to be large.

[0047] If the grayscale average value of the image corresponding to the t-th time point is less than the first threshold value, indicating that the cloud cover rate is low, then in step S615, the image analysis module 121 determines the change amount V corresponding to the t-th time point t Is it less than the second threshold value? That is, determine the change in cloud cover V between the image corresponding to the t-th time point and the image corresponding to the t-1-th time point. t Is it less than the second threshold value? If the change V corresponding to the tth time point t is not less than the second threshold value, as shown in step S610, the image analysis module 121 determines that the change degree corresponding to the t-th time point is large. t is less than the second threshold value, as shown in step S620, it is determined that the degree of change corresponding to the t-th time point is small.

[0048] Table 1 below is an embodiment, which is used to represent the stable state of multiple time points. In Table 1, time point t1 is the first time point in the time series, and it is impossible to compare the change in cloud cover with the previous time point. Therefore, the image analysis module 121 does not determine its stable state at the first time point t1. The corresponding change degree of time points t2 to t7 can be obtained through the above steps S605 to S620.

[0049] Table 1

[0050]

[0051] Afterwards, each time point is judged whether it is in a stable state according to its degree of change and the degree of change of the next time point. For time point t2, the degree of change of both time point t2 and time point t3 is small, so the image analysis module 121 determines that time point t2 is in a stable state, which means that the degree of change of the cloud amount between time point t2 and time point t1 is small, and the degree of change of the cloud amount between time point t2 and time point t3 is also small. By analogy, it is further judged whether time points t3 to t7 are in a stable state.

[0052] Afterwards, in step S215, the sunshine estimation module 123 calculates the solar energy evaluation index corresponding to the stable time zone. Specifically, the sunshine estimation module 123 calculates the total sunshine amount after adding up the multiple sunshine amount estimation values ​​included in the stable time zone, and calculates the total power generation amount after adding up the multiple power generation amounts included in the stable time zone. Afterwards, the sunshine estimation module 123 calculates the solar energy evaluation index based on the total sunshine amount, the total power generation amount and the device capacity of the solar module, where the solar energy evaluation index = (total power generation amount / device capacity) / (total sunshine amount / 1000). Here, the solar energy evaluation index is the performance ratio (Performance Ratio, PR).

[0053] After obtaining the stable time zone, the image corresponding to the stable time zone, the longitude and latitude corresponding to the target position, the setting data of the solar modules located in the target area (such as module inclination angle, direction angle and other parameters), and time information (season, month, date, etc.) are input into the sunshine estimation module 123 to obtain the estimated sunshine amount at each time point in the specified time series.

[0054] Furthermore, the device capacity of the corresponding solar module is obtained according to the target position. The device capacity represents the maximum power output of the solar module (unit: peak kilowatt (1 kWp)).

[0055] Table 2 illustrates a time series corresponding to the sunshine amount and the power generation amount. In this embodiment, the installation capacity of the solar module at the target location is 498 peak kW, and the stable time zone includes time points t2-t4, t7, and t8.

[0056] Table 2

[0057]

[0058] Take Table 2 as an example,

[0059] Solar energy evaluation index = ((282+326+323+183+181) / 498) /

[0060] ((690+756+758+399+404) / 1000)=86.48%.

[0061] Performance ratio evaluation can help understand the overall quality of a solar power plant, including the operating efficiency and reliability of solar modules. Users can determine the actual potential of solar modules based on performance ratio evaluation to obtain relevant information and maximize energy / financial benefits. Evaluating the photovoltaic performance ratio allows you to compare the energy output of a power station with other sites or monitor the operation of a power station over a longer period of time.

[0062] Figure 7 is a graph of a comparative example according to an embodiment of the present invention. Figure 7 In the figure, taking the evaluation index (PR) of solar power plants as an example, the standard deviation of PR in the stable time zone (solid line) is 1.707, which is 0.758 lower than the standard deviation of PR 2.465 in the whole time period (dashed line). This means that this method is not affected by the unstable PR caused by unstable sunshine, and is more representative for evaluating the power generation efficiency of solar power plants.

[0063] In summary, the above embodiments provide a low-cost and highly versatile solar power generation efficiency estimation method, which analyzes wide-area image data to distinguish between stable and unstable time periods of sunshine. Based on this, the solar power plant efficiency is calculated to obtain a relatively stable value, thereby evaluating the state of solar power generation.

Claims

1. A method for estimating solar power generation efficiency, comprising: Analyzing a plurality of images obtained at a plurality of time points in a time series to obtain a grayscale average value of each of the images and a variation corresponding to each of the time points; Based on the grayscale average value and the variation, a stable time zone is obtained from the time point; and Calculate a solar energy evaluation index corresponding to the stable time zone, The step of analyzing the images obtained at the time points in the time series comprises: Subtracting a plurality of pixels of the image corresponding to the t-th time point from a plurality of pixels of the image corresponding to the t-1-th time point and taking absolute values ​​thereof, thereby obtaining a difference corresponding to each of the pixels; The pixel differences are summed up and averaged to obtain the change corresponding to the t-th time point. The step of determining whether the degree of change of each of the images corresponding to each of the time points is large or small comprises: Determine whether the grayscale average value of the image corresponding to the t-th time point is less than a first threshold value; If the grayscale average value of the image corresponding to the t-th time point is not less than the first threshold value, it is determined that the degree of change corresponding to the t-th time point is large; If the grayscale average value of the image corresponding to the t-th time point is less than the first threshold value, determining whether the change amount corresponding to the t-th time point is less than a second threshold value; If the change amount corresponding to the t-th time point is not less than the second threshold value, the change degree corresponding to the t-th time point is determined to be large; and If the change amount corresponding to the t-th time point is less than the second threshold value, it is determined that the change degree corresponding to the t-th time point is small.

2. The solar power generation efficiency estimation method as claimed in claim 1, wherein the step of obtaining the stable time zone from the time point based on the grayscale average value and the variation comprises: If the degree of change corresponding to the t-th time point and the degree of change corresponding to the t+1-th time point are both determined to be small, the t-th time point is determined to be a stable state; If the degree of change corresponding to at least one of the t-th time point and the t+1-th time point is determined to be large, the t-th time point is determined to be in an unstable state; and The time points determined to be in the stable state are combined into the stable time zone.

3. The solar power generation efficiency estimation method according to claim 1, wherein the step of analyzing the image obtained at the time point in the time series comprises: The grayscale values ​​of the multiple pixels of each of the images are summed up and averaged to obtain the grayscale average value.

4. The solar power generation efficiency estimation method according to claim 1, further comprising: A plurality of target areas corresponding to a target position are respectively extracted from a plurality of meteorological images included in the time series to serve as the image. 5 . The solar power generation efficiency estimation method as claimed in claim 4 , wherein the type of the meteorological image is one of a radar echo image, a satellite cloud image and an all-sky imaging image.

6. The solar power generation efficiency estimation method as claimed in claim 1, wherein the step of calculating the solar energy evaluation index corresponding to the stable time zone comprises: Calculate the total sunshine amount in one day by adding up the multiple sunshine amount estimation values ​​included in the stable time zone; Calculating a total power generation amount obtained by adding up a plurality of power generation amounts included in the stable time zone; as well as The solar energy evaluation index is calculated based on the total sunshine amount, the total power generation amount and an installation capacity of a solar module, wherein the solar energy evaluation index=(the total power generation amount / the installation capacity) / (the total sunshine amount / 1000).

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