CN114782880A - Monitoring system for off-grid photovoltaic power generation system - Google Patents

Monitoring system for off-grid photovoltaic power generation system Download PDF

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CN114782880A
CN114782880A CN202210708919.0A CN202210708919A CN114782880A CN 114782880 A CN114782880 A CN 114782880A CN 202210708919 A CN202210708919 A CN 202210708919A CN 114782880 A CN114782880 A CN 114782880A
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gaussian distribution
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CN114782880B (en
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苏建华
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Sori New Energy Technology Nantong Co ltd
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Abstract

The invention relates to the technical field of photovoltaic power generation, in particular to a monitoring system for an off-grid photovoltaic power generation system. The system can be used in the solar industry and comprises a suspected background Gaussian distribution acquisition module, a category identification module, an initial background image acquisition module and a detection alarm module; the suspected background Gaussian distribution acquisition module is used for acquiring a video image of the photovoltaic cell panel and suspected background Gaussian distribution corresponding to each pixel point in the video image; the category identification module is used for classifying all the pixel points to obtain a plurality of categories and identifying the photovoltaic cell category and the grating category; the initial background image acquisition module is used for updating the pixel value of each pixel point to obtain an initial background image; the detection alarm module is used for correcting the initial background image again to obtain a background image, and obtaining a shading area according to the background image to perform alarm processing; the monitoring timeliness of the photovoltaic system is improved, and the shading area is more accurately detected.

Description

Monitoring system for off-grid photovoltaic power generation system
Technical Field
The invention relates to the technical field of photovoltaic power generation, in particular to a monitoring system for an off-grid photovoltaic power generation system.
Background
The off-grid photovoltaic power generation system is an independent photovoltaic power generation system, and the purpose of supplying power is achieved by directly or indirectly converting solar radiation energy into electric energy through a photoelectric effect or a photochemical effect by absorbing sunlight; the off-grid photovoltaic power generation system is widely applied to places such as remote mountainous areas, non-electricity areas, islands, communication base stations, street lamps and the like.
During use, the off-grid photovoltaic power generation system is often influenced by the environment, so that shading such as tree shadows, dust, fallen leaves and the like is formed. When the battery assembly of the off-grid photovoltaic power generation system has a local shading condition, part of batteries can be heated, and a hot spot phenomenon is generated. If the local shading exists for a long time, when the hot spot effect reaches a certain degree, welding spots on the assembly can be melted and the grid lines can be damaged, so that the whole photovoltaic cell assembly is scrapped.
At present, the local shading condition of a battery assembly of an off-grid photovoltaic power generation system is monitored and detected usually through manual inspection or an image identification method. However, the manual inspection is high in labor cost and low in efficiency, the method for recognizing the images by shooting the images is easily interfered by moving objects such as flying birds and swaying branches and leaves, and the accuracy rate of detection results is low.
Disclosure of Invention
In order to solve the above technical problem, the present invention provides a monitoring system for an off-grid photovoltaic power generation system, which includes the following modules:
the suspected background Gaussian distribution acquisition module is used for acquiring a video image of the photovoltaic cell panel within a period of time; constructing a plurality of Gaussian models of each pixel point in the video image, and performing weighted summation on different Gaussian models corresponding to each pixel point to obtain corresponding Gaussian mixture background modeling, wherein when the Gaussian mixture background modeling is larger than a background model threshold value, the Gaussian models corresponding to the Gaussian mixture background modeling are in suspected background Gaussian distribution;
the category identification module is used for acquiring the suspected background Gaussian distribution with the largest weight of each pixel point as the maximum characteristic Gaussian distribution, calculating the difference of the maximum characteristic Gaussian distribution between any two pixel points, and dividing all the pixel points into a plurality of categories based on the difference between all the pixel points; identifying the photovoltaic cell type and the grid type in the types, and acquiring a first Gaussian distribution of the photovoltaic cell type and a second Gaussian distribution of the grid type;
an initial background image obtaining module, configured to obtain, according to the first gaussian distribution and the second gaussian distribution, a probability that each of the suspected background gaussian distributions is a background, obtain an initial pixel value of each pixel point based on the probability that all of the suspected background gaussian distributions corresponding to each pixel point are the background, where the initial pixel values of all the pixel points form an initial background image;
the detection alarm module is used for judging whether each pixel point in the initial background image meets first Gaussian distribution or second Gaussian distribution, when the first Gaussian distribution and the second Gaussian distribution are not met, the background model threshold is corrected and calculated to obtain a new background image, and when the pixel points which do not meet the first Gaussian distribution and the second Gaussian distribution exist in the background image, the pixel points are pixel points in a shading area, and alarm processing is timely carried out.
Preferably, the method for calculating the difference of the maximum feature gaussian distribution between any two pixel points in the category identification module includes:
and obtaining an expected difference value of the maximum characteristic Gaussian distribution corresponding to the two pixel points and a difference value of the coverage area, and obtaining the difference of the maximum characteristic Gaussian distribution between the two pixel points based on the expected difference value and the difference value of the coverage area.
Preferably, the method for identifying the photovoltaic cell category and the grid category in the category identification module includes:
counting the number of pixel points in each category, wherein the category corresponding to the maximum number of the pixel points is the category of the photovoltaic cell;
and obtaining the remaining categories except the categories of the photovoltaic battery pieces, calculating the sum of the distances between all pixel points in each remaining category, wherein the sum result is the dispersion degree of the remaining categories, and the remaining category with the maximum dispersion degree in all the remaining categories is the grid category.
Preferably, the method for acquiring a first gaussian distribution of the photovoltaic cell category and a second gaussian distribution of the grid category in the category identification module includes:
acquiring an expected mean value and a mean value of covariance matrices corresponding to all pixel points in the photovoltaic cell category, and obtaining a first Gaussian distribution of the photovoltaic cell category based on the expected mean value and the mean value of the covariance matrices;
and obtaining an expected mean value and a mean value of covariance matrixes corresponding to all pixel points in the grid category, and obtaining a second Gaussian distribution of the grid category based on the expected mean value and the mean value of the covariance matrixes.
Preferably, the method for acquiring, by the initial background image acquisition module, the probability that each suspected background gaussian distribution is a background according to the first gaussian distribution and the second gaussian distribution includes:
acquiring a background rate of each suspected background Gaussian distribution corresponding to any pixel point, wherein the ratio of the background rate of each suspected background Gaussian distribution to the sum of the background rates of all the suspected background Gaussian distributions corresponding to the pixel points is the probability that the suspected background Gaussian distribution is a background;
the formula for obtaining the background rate of the Gaussian distribution of each suspected background is as follows:
Figure DEST_PATH_IMAGE002
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE004
is shown as
Figure DEST_PATH_IMAGE006
The first of each pixel point
Figure DEST_PATH_IMAGE008
The background rate of the suspected background Gaussian distribution;
Figure DEST_PATH_IMAGE010
is shown as
Figure 118328DEST_PATH_IMAGE006
The first of each pixel point
Figure 973152DEST_PATH_IMAGE008
A difference between the respective suspected background gaussian distribution and the first gaussian distribution;
Figure DEST_PATH_IMAGE012
is shown as
Figure 628255DEST_PATH_IMAGE006
The first of each pixel point
Figure 253491DEST_PATH_IMAGE008
A difference between the suspected background gaussian distribution and the second gaussian distribution;
Figure DEST_PATH_IMAGE014
is shown as
Figure 943229DEST_PATH_IMAGE006
The first of each pixel point
Figure 906637DEST_PATH_IMAGE008
The suspected background of Gaussian distribution and
Figure 406626DEST_PATH_IMAGE006
the first of each pixel point
Figure DEST_PATH_IMAGE016
The difference between the gaussian distributions of the individual suspected backgrounds;
Figure DEST_PATH_IMAGE018
is shown as
Figure 418576DEST_PATH_IMAGE006
The number of suspected background Gaussian distributions of the pixel points;
Figure DEST_PATH_IMAGE020
is shown as
Figure 464286DEST_PATH_IMAGE006
The first of each pixel point
Figure 395332DEST_PATH_IMAGE008
The suspected background of Gaussian distribution and
Figure 290607DEST_PATH_IMAGE006
the mean value of the difference of other suspected background Gaussian distributions of the pixel points;
Figure DEST_PATH_IMAGE022
represents a preset threshold;
Figure DEST_PATH_IMAGE024
representing an exponential function;
Figure DEST_PATH_IMAGE026
represents the minimum function.
Preferably, the method for obtaining the initial pixel value of the pixel point based on the probability that the gaussian distribution of all the suspected backgrounds corresponding to each pixel point is taken as the background includes:
and acquiring the maximum value of the probability that all the suspected background Gaussian distributions corresponding to each pixel point are backgrounds, wherein the expectation of the suspected background Gaussian distribution corresponding to the maximum value is the initial pixel value of the pixel point.
Preferably, the method for determining whether each pixel point in the initial background image satisfies a first gaussian distribution in the detection alarm module includes:
calculating a difference between a pixel value of each of the pixel points and an expectation of the first gaussian distribution, the pixel points satisfying the first gaussian distribution when the difference is not greater than a threshold; the threshold value is 2.5
Figure DEST_PATH_IMAGE028
，
Figure 710175DEST_PATH_IMAGE028
The standard deviation of the first gaussian distribution is indicated.
The invention has the following beneficial effects: the invention provides a monitoring system for an off-grid photovoltaic power generation system, which can be used in the solar industry, wherein the monitoring system detects and analyzes a shading part through Gaussian distribution of each pixel point in a video image corresponding to a photovoltaic cell panel, can eliminate shading interference caused by conditions of birds, swaying branches and leaves and the like, enables monitoring of the photovoltaic system to be more timely, enables a shading area to be more accurate through detection, and can perform alarm reminding and timely processing on workers.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a system block diagram of monitoring for an off-grid photovoltaic power generation system according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a difference of coverage areas of gaussian distributions according to an embodiment of the present invention.
Detailed Description
To further illustrate the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description, the structure, the features and the effects of a monitoring system for an off-grid photovoltaic power generation system according to the present invention will be provided with reference to the accompanying drawings and the preferred embodiments. In the following description, different "one embodiment" or "another embodiment" refers to not necessarily the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The method is suitable for analyzing whether the shading area exists on the surface of the photovoltaic cell panel in the off-grid photovoltaic power generation system, in order to solve the problem of low detection efficiency in the prior art, in the embodiment of the invention, a plurality of Gaussian models corresponding to each pixel point are obtained through video images of a photovoltaic cell panel within a period of time, suspected background Gaussian distribution corresponding to each pixel point is obtained, obtaining the initial pixel value of each pixel point according to the probability that all suspected background Gaussian distributions corresponding to each pixel point belong to the background so as to form an initial background image, analyzing and correcting each pixel point in the initial background image to obtain a final background image, analyzing whether a shading area exists on the photovoltaic cell panel or not based on the final background image, when the shading area is detected, the system can timely alarm to the staff for processing, and the analysis accuracy and the detection efficiency are effectively improved.
The following describes a specific scheme of the monitoring system for the off-grid photovoltaic power generation system provided by the invention in detail with reference to the accompanying drawings.
Referring to fig. 1, a block diagram of a system for monitoring an off-grid photovoltaic power generation system according to an embodiment of the present invention is shown, the system including the following modules:
a suspected background Gaussian distribution acquisition module 10, configured to acquire a video image of the photovoltaic cell panel within a period of time; and constructing a plurality of Gaussian models of each pixel point in the video image, carrying out weighted summation on different Gaussian models corresponding to each pixel point to obtain corresponding mixed Gaussian background modeling, and when the mixed Gaussian background modeling is larger than a background model threshold value, taking the plurality of Gaussian models corresponding to the mixed Gaussian background modeling as suspected background Gaussian distribution.
Specifically, carry on the camera on unmanned aerial vehicle, stop unmanned aerial vehicle at the place ahead of photovoltaic cell board and shoot the video image of this photovoltaic cell board in a period, the video is shot regional and is only contained the photovoltaic cell board region. In order to analyze and detect the accuracy of the photovoltaic cell panel, the shooting time of the video image is set to be 1 minute, and video image acquisition is performed once in the morning, at noon and in the afternoon of a day.
For a photovoltaic cell panel, dust, fallen leaves on the surface of the photovoltaic cell panel, tree shadows projected on the photovoltaic cell panel and the like belong to shading, and shadows caused by birds, branches, leaves and the like near the photovoltaic cell panel shake do not belong to shading; however, factors such as birds, branches, leaves and the like may exist in the video image captured by the camera to interfere with the surface image of the photovoltaic cell panel, and therefore the captured video image of the photovoltaic cell panel needs to be analyzed and identified to eliminate interference of the factors such as the birds, the branches, the leaves and the like.
Because the video image is actually formed by a plurality of frames of video images, the pixel points of the photovoltaic cell panel contained in each frame of video image are the same, but the pixel values of the pixel points may have different changes; for each pixel point in the video image, the change of the pixel point in the video image is random, so that a corresponding Gaussian model can be constructed according to the change of the pixel value of the pixel point in the whole video image, namely the corresponding Gaussian model is constructed according to the change of the pixel value of the same pixel point in continuous video images; in order to increase the reliability of data, in the embodiment of the present invention, 5 gaussian models are constructed for each position pixel, and in other embodiments, an implementer may set the models according to the situation.
Further, obtaining a mixed Gaussian background modeling of each position pixel point in the video image, wherein the mixed Gaussian background modeling is obtained by overlapping Gaussian models corresponding to the position pixel point in the video image through different weights, namely the mixed Gaussian background modeling of each position pixel point is obtained by overlapping 5 Gaussian models of the position pixel point under different weights; the weight value of each Gaussian model is correspondingly obtained when the Gaussian model is constructed; and preliminarily dividing the classes of the pixel points based on the mixed Gaussian background modeling corresponding to each pixel point.
In the embodiment of the invention, the photovoltaic cell panel and the shading part are recorded as a background, and the non-shading interference part is recorded as a foreground; when the mixed Gaussian background modeling of any pixel point is larger than the background model threshold, marking the Gaussian distribution of the pixel point as the suspected background Gaussian distribution, and acquiring the suspected background Gaussian distribution by the specific method comprises the following steps:
in the embodiment of the invention, the threshold value of a background model is set to be 0.6, the superposition of 5 Gaussian models corresponding to each pixel point is calculated, the Gaussian models with different quantities are superposed according to weights to obtain different modeling results of mixed Gaussian backgrounds, and when the weight of any 1 Gaussian model in the 5 Gaussian models is more than 0.6, the 1 Gaussian model of the pixel point is in suspected background Gaussian distribution; when the sum of the weights corresponding to the two Gaussian models is greater than 0.6, the two Gaussian models of the pixel point are in suspected background Gaussian distribution; that is, when the weight superposition of the minimum number of gaussian models in the gaussian models corresponding to each pixel point is greater than 0.6, the gaussian models are the suspected background gaussian distribution corresponding to the pixel point.
The category identification module 20 is configured to obtain a suspected background gaussian distribution with the largest weight of each pixel point as a maximum feature gaussian distribution, calculate a difference between the maximum feature gaussian distributions of any two pixel points, and divide all the pixel points into a plurality of categories based on the difference between all the pixel points; and identifying the photovoltaic cell type and the grid type in the types, and acquiring a first Gaussian distribution of the photovoltaic cell type and a second Gaussian distribution of the grid type.
When the color of the pixel point at the same position in the video image is unchanged, namely the photovoltaic cell panel is not interfered by any moving target, the pixel point is corresponding to only one suspected background Gaussian distribution; when the color of the pixel point at the same position in the video image changes, for example, the pixel point shows the color of a branch at some time, the color of a leaf at some time and the color of a photovoltaic cell panel at some time due to the shaking of the branch and the leaf, the pixel point may correspond to a plurality of suspected background Gaussian distributions; therefore, it is necessary to find an accurate background from a gaussian distribution of a plurality of suspected backgrounds corresponding to each pixel point, so as to keep the shading characteristics of dust, tree shadows, fallen leaves, and the like while eliminating the interference of non-shading objects such as birds, swaying branches and leaves, and the like.
Firstly, obtaining the number of the suspected background Gaussian distribution corresponding to any pixel point and recording the number as
Figure 967981DEST_PATH_IMAGE018
When it comes to
Figure DEST_PATH_IMAGE030
When indicates that
Figure 273192DEST_PATH_IMAGE006
The probability that the suspected background Gaussian distribution of the pixel points is taken as the background is 1; when the temperature is higher than the set temperature
Figure DEST_PATH_IMAGE032
Calculating the probability that each suspected background Gaussian distribution of the pixel point is a background; will be first
Figure 91981DEST_PATH_IMAGE006
The first of each pixel point
Figure 304788DEST_PATH_IMAGE008
The gaussian distribution of each suspected background is given as:
Figure DEST_PATH_IMAGE034
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE036
is shown as
Figure 934877DEST_PATH_IMAGE006
The first of each pixel point
Figure 706261DEST_PATH_IMAGE008
A gaussian distribution of individual suspected backgrounds;
Figure DEST_PATH_IMAGE038
denotes the first
Figure 310549DEST_PATH_IMAGE006
First pixel point
Figure 327047DEST_PATH_IMAGE008
Covariance of Gaussian distribution of individual suspected backgroundsA matrix;
Figure DEST_PATH_IMAGE040
representing a transposed symbol;
Figure DEST_PATH_IMAGE042
representing the circumferential ratio;
Figure DEST_PATH_IMAGE044
is shown as
Figure 71362DEST_PATH_IMAGE006
The first of each pixel point
Figure 13648DEST_PATH_IMAGE008
(ii) an expectation of a gaussian distribution of the suspected background;
Figure 901969DEST_PATH_IMAGE024
representing an exponential function.
It should be noted that each frame of video image in the video image is an RGB image, and each suspected background gaussian distribution is a three-dimensional gaussian distribution.
Then, the suspected background Gaussian distribution with the largest weight in all the suspected background Gaussian distributions corresponding to each pixel point is obtained and recorded as the maximum characteristic Gaussian distribution of the pixel point, the difference between the maximum characteristic Gaussian distributions between every two pixel points is calculated and used as the difference distance between every two corresponding pixel points, and all the pixel points are subjected to spectral clustering based on the difference distance between every two pixel points, so that a plurality of categories are obtained.
Preferably, in the embodiment of the present invention, all the pixel points are divided into 10 categories.
And obtaining an expected difference value of the maximum characteristic Gaussian distribution corresponding to the two pixel points and a difference value of the coverage area, and obtaining the difference of the maximum characteristic Gaussian distribution between the two pixel points based on the expected difference value and the difference value of the coverage area. Calculating the difference between the maximum characteristic Gaussian distributions between every two pixel points as follows:
Figure DEST_PATH_IMAGE046
wherein, the first and the second end of the pipe are connected with each other,
Figure DEST_PATH_IMAGE048
denotes the first
Figure 771093DEST_PATH_IMAGE006
Maximum characteristic Gaussian distribution and number of pixel points
Figure DEST_PATH_IMAGE050
The difference between the maximum characteristic gaussian distributions of the individual pixel points;
Figure DEST_PATH_IMAGE052
denotes the first
Figure 435161DEST_PATH_IMAGE006
Expectation of maximum characteristic Gaussian distribution of each pixel point;
Figure DEST_PATH_IMAGE054
denotes the first
Figure 253076DEST_PATH_IMAGE050
Expectation of maximum characteristic Gaussian distribution of each pixel point;
Figure DEST_PATH_IMAGE056
representing a normalized coefficient;
Figure DEST_PATH_IMAGE058
is shown as
Figure 23103DEST_PATH_IMAGE006
Maximum characteristic Gaussian distribution of each pixel point;
Figure DEST_PATH_IMAGE060
denotes the first
Figure 319086DEST_PATH_IMAGE050
Maximum characteristic Gaussian distribution of the pixel points;
Figure DEST_PATH_IMAGE062
represent
Figure 899978DEST_PATH_IMAGE058
And with
Figure 951110DEST_PATH_IMAGE060
A difference in coverage area;
Figure DEST_PATH_IMAGE064
an L2 paradigm representing the expected difference of the two maximum characteristic Gaussian distributions;
Figure DEST_PATH_IMAGE066
a hyperbolic tangent function is represented for normalization.
Further, take a one-dimensional gaussian distribution as an example; referring to fig. 2, a schematic diagram of the difference of coverage areas with gaussian distributions is shown, wherein the black part is the difference of coverage areas with two gaussian distributions, and when the difference of coverage areas is larger, the difference of distribution of two gaussian distributions is larger; the difference in normalized coverage areas is closer to 1 as the two gaussian distributions are farther apart. But the effect of the difference between the two largest characteristic gaussian distributions measured only in terms of the distance covering the difference is limited, so that in combination with the expected difference between the two largest characteristic gaussian distributions, the greater the expected difference, the greater the difference between the two largest characteristic gaussian distributions is indicated.
Counting the number of pixel points in each category, wherein the category corresponding to the maximum number of the pixel points is the category of the photovoltaic cell; and obtaining the remaining categories except the categories of the photovoltaic cells, calculating the sum of the distances between all pixel points in each remaining category, wherein the sum result is the dispersion degree of the remaining categories, and the remaining category with the maximum dispersion degree in all the remaining categories is the grid category. Acquiring an expected mean value and a mean value of covariance matrixes corresponding to all pixel points in the photovoltaic cell category, and obtaining a first Gaussian distribution of the photovoltaic cell category based on the expected mean value and the mean value of the covariance matrixes; and obtaining an expected mean value and a mean value of the covariance matrix corresponding to all pixel points in the grid category, and obtaining a second Gaussian distribution of the grid category based on the expected mean value and the mean value of the covariance matrix.
Specifically, because the area of the photovoltaic cell on the photovoltaic cell panel is large, and the shading is only a local area on the photovoltaic cell panel, the category corresponding to the maximum number of the pixel points in the 10 categories is identified as the category of the photovoltaic cell, the expected mean value of the maximum characteristic gaussian distribution corresponding to all the pixel points in the category of the photovoltaic cell and the mean value of the covariance matrix are obtained, and the first gaussian distribution of the category of the photovoltaic cell obtained based on the expected mean value of all the pixel points and the mean value of the covariance matrix is:
Figure DEST_PATH_IMAGE068
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE070
representing a first Gaussian distribution of the photovoltaic cell;
Figure DEST_PATH_IMAGE072
representing the mean value of covariance matrixes of all pixel points in the photovoltaic cell category;
Figure DEST_PATH_IMAGE074
representing expected mean values of all pixel points in the category of the photovoltaic cell;
Figure 112227DEST_PATH_IMAGE040
representing a transposed symbol;
Figure 769823DEST_PATH_IMAGE024
representing an exponential function.
Further, residual categories except the photovoltaic cell category are analyzed, the distance between every two pixel points in each residual category is calculated and summed, the obtained result is used for expressing the dispersion degree of all the pixel points in the residual category in the video image, and the larger the distance sum is, the wider the distribution of the pixel points in the residual category is; the grids of the photovoltaic cell panel are distributed over the whole photovoltaic cell panel area, and the distribution range is wide; the shaded or shaken object is only in a local area of the photovoltaic cell panel, and the distribution range is small; therefore, the remaining category with the largest dispersion degree among the remaining categories except the photovoltaic cell sheet category is identified as the grid category of the photovoltaic cell panel. Correspondingly, obtaining the expected mean value of the maximum characteristic gaussian distribution corresponding to all the pixel points in the grid category and the mean value of the covariance matrix, and obtaining the second gaussian distribution of the grid category of the photovoltaic cell panel according to the expected mean value of all the pixel points in the grid category and the mean value of the covariance matrix is as follows:
Figure DEST_PATH_IMAGE076
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE078
a second gaussian distribution representing a grid;
Figure DEST_PATH_IMAGE080
representing the mean of the covariance matrices of all the pixels in the grid class;
Figure DEST_PATH_IMAGE082
representing an expected mean of all pixel points in the grid category;
Figure 628058DEST_PATH_IMAGE040
representing a transposed symbol;
Figure 646829DEST_PATH_IMAGE024
representing an exponential function.
The initial background image obtaining module 30 is configured to obtain a probability that each suspected background gaussian distribution is a background according to the first gaussian distribution and the second gaussian distribution, obtain initial pixel values of the pixel points based on the probabilities that all the suspected background gaussian distributions corresponding to each pixel point are the background, and form an initial background image with the initial pixel values of all the pixel points.
The category identification module 20 obtains the first gaussian distribution of the photovoltaic cell and the second gaussian distribution of the grid, and calculates the probability that each suspected background gaussian distribution is the background based on the first gaussian distribution of the photovoltaic cell and the second gaussian distribution of the grid as follows:
Figure DEST_PATH_IMAGE084
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE086
denotes the first
Figure 278930DEST_PATH_IMAGE006
The first of each pixel point
Figure 447874DEST_PATH_IMAGE008
Probability that the suspected background Gaussian distribution is the background;
Figure 363878DEST_PATH_IMAGE004
denotes the first
Figure 491234DEST_PATH_IMAGE006
The first of each pixel point
Figure 824345DEST_PATH_IMAGE008
Background rate of each suspected background gaussian distribution;
Figure DEST_PATH_IMAGE088
denotes the first
Figure 265822DEST_PATH_IMAGE006
The first of each pixel point
Figure 505173DEST_PATH_IMAGE016
Background rate of each suspected background gaussian distribution;
Figure 301966DEST_PATH_IMAGE018
denotes the first
Figure 626768DEST_PATH_IMAGE006
The number of suspected background gaussian distributions for each pixel.
The calculation of the background rate of the gaussian distribution of the specific suspected background is:
Figure DEST_PATH_IMAGE002A
wherein the content of the first and second substances,
Figure 983187DEST_PATH_IMAGE004
is shown as
Figure 811466DEST_PATH_IMAGE006
The first of each pixel point
Figure 342942DEST_PATH_IMAGE008
Background rate of each suspected background gaussian distribution;
Figure 889461DEST_PATH_IMAGE010
is shown as
Figure 702434DEST_PATH_IMAGE006
The first of each pixel point
Figure 650798DEST_PATH_IMAGE008
The difference between the suspected background Gaussian distribution and the first Gaussian distribution of the photovoltaic cell slice;
Figure 353175DEST_PATH_IMAGE012
is shown as
Figure 386990DEST_PATH_IMAGE006
The first of each pixel point
Figure 531883DEST_PATH_IMAGE008
A difference between the gaussian distribution of each suspected background and a second gaussian distribution of the grid;
Figure 334753DEST_PATH_IMAGE014
is shown as
Figure 411294DEST_PATH_IMAGE006
The first of each pixel point
Figure 932405DEST_PATH_IMAGE008
The suspected background of Gaussian distribution and
Figure 618339DEST_PATH_IMAGE006
the first of each pixel point
Figure 275716DEST_PATH_IMAGE016
The difference between the gaussian distributions of the individual suspected backgrounds;
Figure 523158DEST_PATH_IMAGE018
is shown as
Figure 531565DEST_PATH_IMAGE006
The number of suspected background Gaussian distributions of the pixel points;
Figure 24120DEST_PATH_IMAGE020
is shown as
Figure 67162DEST_PATH_IMAGE006
The first of each pixel point
Figure 485505DEST_PATH_IMAGE008
The suspected background of Gaussian distribution and
Figure 715630DEST_PATH_IMAGE006
the mean value of the difference of other suspected background Gaussian distributions of the pixel points;
Figure 743366DEST_PATH_IMAGE022
represents a preset threshold;
Figure 375336DEST_PATH_IMAGE024
representing an exponential function;
Figure 964580DEST_PATH_IMAGE026
represents the minimum function.
When it comes to
Figure 947580DEST_PATH_IMAGE006
The first of each pixel point
Figure 799515DEST_PATH_IMAGE008
The suspected background of Gaussian distribution and
Figure 285991DEST_PATH_IMAGE006
the average value of the difference of other suspected background Gaussian distributions of the pixel points is larger than a preset threshold value
Figure 577295DEST_PATH_IMAGE022
When it comes to
Figure 483809DEST_PATH_IMAGE006
The first of each pixel point
Figure 620392DEST_PATH_IMAGE008
The suspected background Gaussian distribution may contain the characteristics of swaying leaves, branches and photovoltaic panels, namely
Figure DEST_PATH_IMAGE090
The smaller the value of (A), the
Figure 164637DEST_PATH_IMAGE006
The first of each pixel point
Figure 862728DEST_PATH_IMAGE008
The difference between the suspected background gaussian distribution and the first gaussian distribution of the photovoltaic cell or the second gaussian distribution of the grid is small,
Figure DEST_PATH_IMAGE092
the larger, the corresponding, the
Figure 695686DEST_PATH_IMAGE006
The first of each pixel point
Figure 370381DEST_PATH_IMAGE008
Background rate of Gaussian distribution of suspected backgrounds
Figure 329985DEST_PATH_IMAGE004
The larger, the
Figure 697512DEST_PATH_IMAGE006
The first of each pixel point
Figure 611241DEST_PATH_IMAGE008
Probability of background being Gaussian distribution of suspected background
Figure 89627DEST_PATH_IMAGE086
The larger.
When it comes to
Figure 431966DEST_PATH_IMAGE006
The first of each pixel point
Figure 704816DEST_PATH_IMAGE008
Gaussian distribution of suspected background and
Figure 371420DEST_PATH_IMAGE006
the average value of the difference of other suspected background Gaussian distributions of each pixel point is less than a preset threshold value
Figure 387918DEST_PATH_IMAGE022
When it comes to
Figure 322113DEST_PATH_IMAGE006
The first of each pixel point
Figure 969127DEST_PATH_IMAGE008
Individual suspected backgroundThe Gaussian distribution may include the characteristics of a swaying tree shadow and a photovoltaic cell panel, the tree shadow is a shadow part superposed on the photovoltaic cell panel, and compared with the photovoltaic cell panel, the tree shadow is darker in color and is closer to the characteristics of the photovoltaic cell panel; when the surface of the photovoltaic cell panel has the tree shadow, the tree shadow area belongs to the background, and the photovoltaic cell panel area covered by the tree shadow does not belong to the background; thus at the time of judging
Figure 123027DEST_PATH_IMAGE006
The first of each pixel point
Figure 444681DEST_PATH_IMAGE008
When the probability that the Gaussian distribution of each suspected background belongs to the background is determined, the probability is actually the first
Figure 427DEST_PATH_IMAGE006
The first of each pixel point
Figure 880658DEST_PATH_IMAGE008
Probability that each suspected background Gaussian distribution belongs to a tree shadow; when it comes to
Figure 459538DEST_PATH_IMAGE006
The first of each pixel point
Figure 581953DEST_PATH_IMAGE008
When the suspected background Gaussian distribution belongs to the tree shadow, the difference between the suspected background Gaussian distribution and the first Gaussian distribution of the photovoltaic cell and the difference between the suspected background Gaussian distribution and the second Gaussian distribution of the grid are larger, and at the moment, the difference is larger
Figure 992206DEST_PATH_IMAGE090
Is also larger, correspondingly
Figure 981021DEST_PATH_IMAGE006
The first of each pixel point
Figure 605120DEST_PATH_IMAGE008
Background rate of Gaussian distribution of individual suspected backgrounds
Figure 767111DEST_PATH_IMAGE004
The larger, the
Figure 235132DEST_PATH_IMAGE006
The first of each pixel point
Figure 722746DEST_PATH_IMAGE008
Probability of background being Gaussian distribution of suspected background
Figure 571491DEST_PATH_IMAGE086
The larger.
Preferably, the preset threshold is set in the embodiment of the invention
Figure DEST_PATH_IMAGE094
。
By analogy, obtain the first
Figure 412539DEST_PATH_IMAGE006
And the background rate of each suspected background Gaussian distribution of each pixel point is obtained, so that the probability that each suspected background Gaussian distribution belongs to the background is obtained according to the background rate.
Further, corresponding on a per pixel basis
Figure DEST_PATH_IMAGE096
Obtaining initial background image according to probability of the Gaussian distribution of the suspected background belonging to the background, and selecting the point corresponding to each pixel point
Figure 439794DEST_PATH_IMAGE096
The suspected background Gaussian distribution belongs to the maximum value of the probability of the background, and the expectation of the suspected background Gaussian distribution corresponding to the maximum value is used as the initial pixel value of the pixel point; and obtaining initial pixel values of all the pixel points to form an initial background image in the same way.
And the detection alarm module 40 is used for judging whether each pixel point in the initial background image meets the first Gaussian distribution or the second Gaussian distribution, when the first Gaussian distribution and the second Gaussian distribution are not met, correcting and calculating the threshold value of the background model to obtain a new background image, and when the pixel points which do not meet the first Gaussian distribution and the second Gaussian distribution exist in the background image, the pixel points are pixel points in a shading area, and timely carrying out alarm processing.
When the color of the photovoltaic cell panel is less, the situation that the pixel does not contain the photovoltaic cell panel when suspected background Gaussian distribution is selected may be caused; therefore, the suspected background gaussian distribution corresponding to each pixel point needs to be corrected.
Specifically, it is determined whether the pixel value of each pixel in the initial background image satisfies the first gaussian distribution of the photovoltaic cell or the second gaussian distribution of the grid, that is, the difference between the pixel value of the pixel and the expectation of the first gaussian distribution of the photovoltaic cell is calculated, and when the difference is not greater than 2.5
Figure 832729DEST_PATH_IMAGE028
If so, the pixel point meets the first Gaussian distribution of the photovoltaic cell; otherwise, the pixel point does not conform to the first Gaussian distribution of the photovoltaic cell; wherein, the first and the second end of the pipe are connected with each other,
Figure 935814DEST_PATH_IMAGE028
standard deviation of a first Gaussian distribution of a photovoltaic cell sheet, covariance matrix of the first Gaussian distribution
Figure 938143DEST_PATH_IMAGE072
The square root of the diagonal elements is taken.
And similarly, calculating the difference between the pixel value of the pixel point and the expectation of the second Gaussian distribution of the grating, and judging whether the pixel point accords with the second Gaussian distribution of the grating.
When any pixel point does not accord with the first Gaussian distribution of the photovoltaic cell piece and does not accord with the second Gaussian distribution of the grating, recording the pixel point as a central point to judge whether the pixel point in the neighborhood meets the first Gaussian distribution of the photovoltaic cell piece or the second Gaussian distribution of the grating; in the embodiment of the invention, the neighborhood pixel points of any pixel point comprise 25 pixel points; when the pixel points in 25 neighborhood pixel points of the pixel point accord with the first Gaussian distribution of the photovoltaic cell or the second Gaussian distribution of the grating, the central point is a suspected background pixel point;
obtaining all suspected background pixel points in the initial background image, correcting background model thresholds of all the suspected background pixel points, obtaining new suspected background Gaussian distribution corresponding to each suspected background pixel point based on the corrected background model threshold, further calculating the probability of belonging to the background of all the suspected background Gaussian distributions to obtain a new pixel value of each suspected background pixel point, and thus obtaining a final background image.
Preferably, in the embodiment of the present invention, the background model threshold is modified to 0.8.
And further, whether each pixel point in the background image belongs to the first Gaussian distribution of the photovoltaic cell or the second Gaussian distribution of the grid is judged, when the pixel points do not satisfy the first Gaussian distribution and do not satisfy the second Gaussian distribution, the pixel points are pixel points of the shading area, all the pixel points which do not satisfy the first Gaussian distribution and do not satisfy the second Gaussian distribution are obtained to obtain the final shading area, the alarm notification is carried out on the staff, and the staff can timely process shading.
To sum up, an embodiment of the present invention provides a monitoring system for an off-grid photovoltaic power generation system, where the monitoring system includes: a suspected background Gaussian distribution acquisition module 10, a category identification module 20, an initial background image acquisition module 30 and a detection alarm module 40; the method comprises the steps of analyzing a video image of a photovoltaic cell panel within a period of time to obtain a plurality of Gaussian models corresponding to each pixel point, further obtaining suspected background Gaussian distribution in the Gaussian models corresponding to each pixel point, calculating the probability that each suspected background Gaussian distribution belongs to a background, obtaining an initial pixel value of each pixel point according to the probability that all the suspected background Gaussian distributions corresponding to each pixel point belong to the background, forming an initial background image by the initial pixel values of all the pixel points, analyzing and correcting each pixel point in the initial background image to obtain a final background image, analyzing whether a shading area exists on the photovoltaic cell panel based on the final background image, timely alarming to a worker for processing when the shading area is detected, and effectively improving the accuracy of analysis and the efficiency of detection.
It should be noted that: the precedence order of the above embodiments of the present invention is only for description, and does not represent the merits of the embodiments. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
All the embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from other embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and should not be taken as limiting the scope of the present invention, which is intended to cover any modifications, equivalents, improvements, etc. within the spirit of the present invention.

Claims (7)

1. A monitoring system for an off-grid photovoltaic power generation system, the system comprising the following modules:
the suspected background Gaussian distribution acquisition module is used for acquiring a video image of the photovoltaic cell panel within a period of time; constructing a plurality of Gaussian models of each pixel point in the video image, weighting and summing different Gaussian models corresponding to each pixel point to obtain corresponding Gaussian mixture background modeling, wherein when the Gaussian mixture background modeling is larger than a background model threshold value, the Gaussian models corresponding to the Gaussian mixture background modeling are in suspected background Gaussian distribution;
the category identification module is used for acquiring the suspected background Gaussian distribution with the largest weight of each pixel point as the maximum characteristic Gaussian distribution, calculating the difference of the maximum characteristic Gaussian distribution between any two pixel points, and dividing all the pixel points into a plurality of categories based on the difference between all the pixel points; identifying the photovoltaic cell type and the grid type in the types, and acquiring a first Gaussian distribution of the photovoltaic cell type and a second Gaussian distribution of the grid type;
an initial background image obtaining module, configured to obtain, according to the first gaussian distribution and the second gaussian distribution, a probability that each of the suspected background gaussian distributions is a background, obtain an initial pixel value of each pixel point based on the probability that all of the suspected background gaussian distributions corresponding to each pixel point are the background, where the initial pixel values of all the pixel points form an initial background image;
the detection alarm module is used for judging whether each pixel point in the initial background image meets first Gaussian distribution or second Gaussian distribution, when the first Gaussian distribution and the second Gaussian distribution are not met, the background model threshold is corrected and calculated to obtain a new background image, and when the pixel points which do not meet the first Gaussian distribution and the second Gaussian distribution exist in the background image, the pixel points are pixel points in a shading area, and alarm processing is timely carried out.
2. The monitoring system for the off-grid photovoltaic power generation system according to claim 1, wherein the method for calculating the difference of the maximum characteristic gaussian distribution between any two pixel points in the category identification module comprises:
and obtaining an expected difference value of the maximum characteristic Gaussian distribution corresponding to the two pixel points and a difference value of the coverage area, and obtaining the difference of the maximum characteristic Gaussian distribution between the two pixel points based on the expected difference value and the difference value of the coverage area.
3. The monitoring system for the off-grid photovoltaic power generation system according to claim 1, wherein the method for identifying the photovoltaic cell category and the grid category in the category identification module comprises:
counting the number of pixel points in each category, wherein the category corresponding to the maximum number of the pixel points is the category of the photovoltaic cell;
and obtaining the residual categories except the photovoltaic cell categories, calculating the sum of the distances between all pixel points in each residual category, wherein the sum result is the dispersion degree of the residual categories, and the residual category with the maximum dispersion degree in all the residual categories is the grid category.
4. The monitoring system for the off-grid photovoltaic power generation system according to claim 1, wherein the method for obtaining the first gaussian distribution of the photovoltaic cell category and the second gaussian distribution of the grid category in the category identification module comprises:
acquiring an expected mean value and a mean value of a covariance matrix corresponding to all pixel points in the photovoltaic cell category, and obtaining a first Gaussian distribution of the photovoltaic cell category based on the expected mean value and the mean value of the covariance matrix;
and obtaining an expected mean value and a mean value of the covariance matrix corresponding to all pixel points in the grid category, and obtaining a second Gaussian distribution of the grid category based on the expected mean value and the mean value of the covariance matrix.
5. The monitoring system for the off-grid photovoltaic power generation system according to claim 1, wherein the method for obtaining the probability of each suspected background gaussian distribution as the background according to the first gaussian distribution and the second gaussian distribution in the initial background image obtaining module comprises:
acquiring a background rate of each suspected background Gaussian distribution corresponding to any pixel point, wherein the ratio of the background rate of each suspected background Gaussian distribution to the sum of the background rates of all the suspected background Gaussian distributions corresponding to the pixel points is the probability that the suspected background Gaussian distribution is the background;
the formula for obtaining the background rate of each suspected background Gaussian distribution is as follows:
Figure DEST_PATH_IMAGE001
wherein, the first and the second end of the pipe are connected with each other,
Figure 186863DEST_PATH_IMAGE002
denotes the first
Figure 793425DEST_PATH_IMAGE003
The first of each pixel point
Figure 754166DEST_PATH_IMAGE004
Background rate of each suspected background gaussian distribution;
Figure 180599DEST_PATH_IMAGE005
is shown as
Figure 386452DEST_PATH_IMAGE003
The first of each pixel point
Figure 113100DEST_PATH_IMAGE004
A difference between the respective suspected background gaussian distribution and the first gaussian distribution;
Figure 716513DEST_PATH_IMAGE006
is shown as
Figure 895822DEST_PATH_IMAGE003
The first of each pixel point
Figure 46311DEST_PATH_IMAGE004
A difference between the respective suspected background gaussian distribution and the second gaussian distribution;
Figure 860421DEST_PATH_IMAGE007
is shown as
Figure 664429DEST_PATH_IMAGE003
The first of each pixel point
Figure 65455DEST_PATH_IMAGE004
The suspected background of Gaussian distribution and
Figure 347532DEST_PATH_IMAGE003
the first of each pixel point
Figure 48771DEST_PATH_IMAGE008
The difference between the gaussian distributions of the individual suspected backgrounds;
Figure 11565DEST_PATH_IMAGE009
is shown as
Figure 165466DEST_PATH_IMAGE003
The number of suspected background Gaussian distributions of the pixel points;
Figure 985654DEST_PATH_IMAGE010
denotes the first
Figure 275821DEST_PATH_IMAGE003
The first of each pixel point
Figure 389009DEST_PATH_IMAGE004
The suspected background of Gaussian distribution and
Figure 30206DEST_PATH_IMAGE003
the mean value of the difference of other suspected background Gaussian distributions of the pixel points;
Figure 654085DEST_PATH_IMAGE011
represents a preset threshold;
Figure 798758DEST_PATH_IMAGE012
representing an exponential function;
Figure 351356DEST_PATH_IMAGE013
represents the minimum function.
6. The monitoring system for the off-grid photovoltaic power generation system according to claim 1, wherein the method for obtaining the initial pixel value of the pixel point based on the probability that all suspected background gaussian distributions corresponding to each pixel point are backgrounds comprises:
and acquiring the maximum value of the probability that all the suspected background Gaussian distributions corresponding to each pixel point are backgrounds, wherein the expectation of the suspected background Gaussian distribution corresponding to the maximum value is the initial pixel value of the pixel point.
7. The monitoring system for the off-grid photovoltaic power generation system according to claim 1, wherein the method for determining whether each pixel point in the initial background image satisfies a first gaussian distribution in the detection alarm module comprises:
calculating a difference between a pixel value of each of the pixel points and an expectation of the first gaussian distribution, the pixel points satisfying the first gaussian distribution when the difference is not greater than a threshold; the threshold value is 2.5
Figure 214270DEST_PATH_IMAGE014
，
Figure 376261DEST_PATH_IMAGE014
The standard deviation of the first gaussian distribution is indicated.
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