CN113065982A - System and method for predicting power of dust-accumulated photovoltaic panel based on dense residual error network - Google Patents
System and method for predicting power of dust-accumulated photovoltaic panel based on dense residual error network Download PDFInfo
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Abstract
A system and a method for predicting the power of a dust-deposition photovoltaic panel based on a dense residual error network comprise a data acquisition system and a data monitoring center, wherein data information acquired by the data acquisition system is sent to the data monitoring center through a data transmitter and a data receiver, the data monitoring center calculates the power of the photovoltaic panel, and the prediction method comprises the steps that the data acquisition system acquires electrical data and meteorological data of the photovoltaic panel; the invention discloses a photovoltaic panel gray image monitoring method, which comprises the steps of carrying out dimension reduction processing on a photovoltaic panel gray image by a data monitoring center, and finally calculating the power of a photovoltaic panel by the data monitoring center through an intensive residual error network. The method can provide important reference for a power grid dispatching plan, and has important significance for full grid-connected operation of optical volt-ampere.
Description
Technical Field
The invention relates to the technical field of solar photovoltaic power generation, in particular to a system and a method for predicting the power of a dust-accumulated photovoltaic panel based on a dense residual error network.
Background
In recent years, in order to solve the global warming problem, many countries have concentrated on energy conversion, and have been focusing on the development of renewable energy. Solar photovoltaic power generation is taken as a power generation technology with development prospect in the field of new energy, and new vitality is released under the background of transformation and upgrading of global energy structures by virtue of the industrial characteristics of wide distribution, rich reserves and mature technology. However, photovoltaic power generation has the problems of intermittency, randomness, fluctuation and the like, and challenges are brought to planning and operation of the existing power system. The traditional solution of the power grid dispatching department can only limit power by passive switching-off, and a photovoltaic power prediction system becomes more important along with the increase of the penetration rate of a power grid power supply structure of a photovoltaic power station. The method realizes accurate prediction of photovoltaic power generation, and has important significance for providing high-quality electric energy for terminal users and improving the operation reliability of the power system.
Disclosure of Invention
The invention aims to solve the problem of insufficient accuracy of the conventional power prediction and provides a system and a method for predicting the power of a gray-deposited photovoltaic panel based on a dense residual error network.
The purpose of the invention is realized by the following technical scheme:
the utility model provides an area grey photovoltaic board power prediction system based on intensive residual error network, includes data acquisition system and data monitoring center, data acquisition system includes visible light camera, meteorological detector, smart electric meter and paster temperature sensor, the visible light camera is used for shooing photovoltaic board area grey image, meteorological detector is used for detecting the meteorological data of worn-out fur, smart electric meter is used for collecting the electrical data of photovoltaic board, paster temperature sensor is used for detecting photovoltaic board temperature, and the data information that data acquisition system gathered sends to data monitoring center through data transmitter and data receiver, and data monitoring center calculates photovoltaic board power at last.
Further, the meteorological data of the photovoltaic panel comprise irradiation data, environment temperature data, atmospheric humidity data, wind speed data, wind direction data and air pressure data.
Further, the intelligent electric meter is installed in a distribution box of the photovoltaic panel, and the electrical data of the photovoltaic panel comprises output voltage data, output current data and photovoltaic panel conversion efficiency of the photovoltaic panel;
further, the voltage range of the intelligent electric meter is direct current 0-1000V, and the current range is 0-10A.
Furthermore, the data monitoring center is an upper computer.
Further, the data acquisition interval of the upper computer is 15 min.
Further, the data transmitter and the data receiver transmit the dust deposition images and the operation parameters of the photovoltaic panel to the upper computer through two data transmission channels.
A prediction method of a power prediction system of a soot photovoltaic panel based on a dense residual error network specifically comprises the following steps:
s1, collecting electrical data and meteorological data of the photovoltaic panel by the data collection system, wherein the electrical data comprise a photovoltaic panel dust deposition image, photovoltaic panel output voltage, photovoltaic panel output current and photovoltaic panel temperature; meteorological data including irradiation, ambient temperature, atmospheric humidity, wind speed, wind direction, and barometric pressure;
s2, sending the data information acquired by the data acquisition system to a data monitoring center through a data transmitter and a data receiver;
s3, the data monitoring center performs dimensionality reduction processing on the voltage board accumulated dust image, and specifically comprises the following steps:
(1) subjecting the collected photovoltaic panel dust image to perspective transformation to obtain a corrected photovoltaic panel image;
(2) enhancing the characteristic information of the dust concentration by using an Adaptive Contrast Enhancement (ACE) algorithm;
(3) transforming the histogram of the image of the photovoltaic panel, and carrying out three-class classification on the basis of the value at the highest point of the ordinate, and sorting av from high to low1，av2，av3；
(4) Calculating the average pixel value av of each equal division, wherein the calculation formula is as follows:
wherein, in the above formula; k is an equal classification number; n is the total pixel number of the picture; n is a radical ofkIs the total number of pixels in the k classes, nkThe number of pixel values in the k classes; x is the number ofiIs the ith pixel value, p, of class kiThe probability of this pixel value in the whole picture;
s4, calculating the power of the photovoltaic panel by the data monitoring center by using an intensive residual error network, wherein: the hidden layer between the input layer and the output layer of the dense residual network has N (A, B, C …) scales, and each scale has N (N)1,N2,N3…) layers of nThe output of each layer is added to the output of the first layer in the activation function, and the neuron vectorization in each scale of the hidden layer can be expressed as:
Ni＝activation(Ni-1·wi-1+b+N1)。
further, in step S1, the photovoltaic panel temperature is obtained by averaging N different position measurements of the photovoltaic back panel, and the calculation formula is:
the invention has the beneficial effects that: according to the system and the method for predicting the power of the gray-deposited photovoltaic panel based on the dense residual error network, the output power of the photovoltaic panel is predicted by using the monitoring data, and compared with the situation that only environmental and electrical parameters are considered, dimension-reduced image gray-deposited information is introduced, the dense residual error network is provided, the network depth is increased, and the problem of network degradation such as gradient disappearance is solved. The method can provide important reference for a power grid dispatching plan, and has important significance for full grid-connected operation of optical volt-ampere.
Drawings
FIG. 1 is a block diagram of a power prediction system for a soot photovoltaic panel according to the present invention;
FIG. 2 is a flow chart of the dimension reduction of the gray image information according to the present invention;
FIG. 3 is a schematic block diagram of a power prediction system for a soot photovoltaic panel according to the present invention;
FIG. 4 is a diagram of the intensive residual network architecture of the present invention;
FIG. 5 is a simplified diagram of the dense residual network structure according to the present invention.
In the figure, 1-a visible light camera, 2-a meteorological detector, 3-a smart meter, 4-a patch temperature sensor, 5-a data transmitter, 6-a data receiver and 7-a data monitoring center.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
Example 1:
the system comprises a data acquisition system and a data monitoring center 7, wherein the data acquisition system comprises a visible light camera 1, a meteorological detector 2, an intelligent ammeter 3 and a patch temperature sensor 4, the visible light camera 3 is used for shooting a dust image of the photovoltaic panel, the meteorological detector 2 is used for detecting meteorological data of a light panel, the intelligent ammeter 3 is used for collecting electrical data of the photovoltaic panel, the patch temperature sensor 4 is used for detecting the temperature of the photovoltaic panel, data information acquired by the data acquisition system is sent to the data monitoring center 7 through a data emitter 5 and a data receiver 6, and the data monitoring center 7 calculates the power of the photovoltaic panel finally.
In the above embodiment, the meteorological data of the photovoltaic panel includes irradiation data, ambient temperature data, atmospheric humidity data, wind speed data, wind direction data, and air pressure data.
In the above embodiment, the smart meter 3 is installed in a distribution box of a photovoltaic panel, and the electrical data of the photovoltaic panel includes output voltage data, output current data and conversion efficiency of the photovoltaic panel;
in the above embodiment, the voltage range of the intelligent ammeter 3 is dc 0-1000V, and the current range is 0-10A.
In the above embodiment, the data monitoring center 7 is an upper computer, a Visual Studio-based software management system is pre-installed in the upper computer 7, the upper computer 7 collects the operation data and the meteorological data of the photovoltaic panel through the software management system, the collected data interval is 15min, and then the data is processed to calculate the power of the photovoltaic panel, and meanwhile, the upper computer also provides data storage and historical data query functions.
In the above embodiment, the data transmitter 5 and the data receiver 7 transmit the photovoltaic panel dust deposition image and the operation parameters to the upper computer through two data transmission channels, so as to guarantee the quality of data transmission.
It should be noted that for the above embodiments, the data acquisition system should be arranged by following:
(1) and (3) overall coverage: all photovoltaic panels of a photovoltaic plant should be monitored and should target sufficient spatial coverage, should be evenly distributed in the monitored area, and should be balanced between a sufficient number of points and costs.
(2) Dust multi-zone focus: due to different environmental conditions, the area of a photovoltaic power plant is strongly affected by dust. The best acquisition point should be selected based on location and wind conditions.
(3) Law of rational utilization of resources and cost effectiveness: the power supply of the equipment should be considered when the existing resources are used, and the equipment is easy to deploy and maintain.
Example 2:
referring to fig. 3, a prediction method of a power prediction system of a gray photovoltaic panel based on a dense residual error network specifically includes the following steps:
s1, collecting electrical data and meteorological data of the photovoltaic panel by the data collection system, wherein the electrical data comprise a photovoltaic panel dust deposition image, photovoltaic panel output voltage, photovoltaic panel output current and photovoltaic panel temperature; meteorological data including irradiation, ambient temperature, atmospheric humidity, wind speed, wind direction, and barometric pressure;
s2, sending the data information acquired by the data acquisition system to a data monitoring center through a data transmitter and a data receiver;
s3, the data monitoring center performs a dimension reduction process on the voltage board gray image, specifically, as shown in fig. 2, the method includes the following steps:
(1) the image of the photovoltaic panel has a certain inclination due to the problem of shooting angle, the inclination angle is obtained by searching the edge slope, the collected image of the accumulated dust of the photovoltaic panel is subjected to perspective transformation to obtain a corrected image of the photovoltaic panel, and the inclination angle is adjusted to zero;
(2) enhancing the characteristic information of the dust concentration by using an Adaptive Contrast Enhancement (ACE) algorithm, specifically, dividing an image into a low-frequency part (unsharp mask) and a high-frequency part, wherein the low-frequency part is obtained by smoothing and other operations of a low-pass filter on the image; the high frequency part is obtained by subtracting the low frequency part from the original image, and then the high frequency part is enlarged (the enlargement factor is CG). Finally, adding the high-frequency part and the low-frequency part after the gain to obtain an enhanced image;
(3) transforming the histogram of the image of the photovoltaic panel, and carrying out three-class classification on the basis of the value at the highest point of the ordinate, and sorting av from high to low1，av2，av3；
(4) Calculating the average pixel value av of each equal division, wherein the calculation formula is as follows:
wherein, in the above formula; k is an equal classification number; n is the total pixel number of the picture; n is a radical ofkIs the total number of pixels in the k classes, nkThe number of pixel values in the k classes; x is the number ofiIs the ith pixel value, p, of class kiThe probability of this pixel value in the whole picture;
and S4, calculating the power of the photovoltaic panel by the data monitoring center by using an intensive residual error network.
In the above embodiment, the hidden layer between the input layer and the output layer of the dense residual network has N (a, B, C …) scales, and each scale has N (N)1,N2,N3…) layers, where the output of each layer is within the activation functionAdding the output of the first layer, specifically referring to fig. 4 and 5, the dimension of the input layer is (1 × 13), corresponding to 13 parameters in the data set; the hidden layer has 3 sizes of A (8 × 64), B (8 × 16) and C (8 × 4), which can be expressed as that the hidden layer of the scale A has 8 layers, each layer has 64 neurons, the hidden layer of the scale B has 8 layers, each layer has 16 neurons, the hidden layer of the scale C has 8 layers, and each layer has 4 neurons; the output layer is a photovoltaic panel output power parameter.
Within each scale, the output of each layer is added to the output of the first layer within the activation function. The neuron vectorization in each scale of the hidden layer is represented as:
Ni＝activation(Ni-1·wi-1+bi-1+N1)
the RELU activation function is chosen for each layer, and the neuron vector for each layer is expressed as:
A1＝re(X·w0+b)0
wherein w0The scale is (13X 64), b0Is (1 × 64), the input parameter X is transformed and activated, and then output A1
A2＝re(A1·w1+b1)
A3＝re(A2·w2+b2+A1)
A4＝re(A3·w3+b3+A1)
Likewise, a transformation is made up to the last layer of dimension a:
A8＝re(A7·w7+b+7A)
wherein w1～w7The scale is (64X 64), b1～b7Is (1 × 64);
B1＝re(A8·w8+b)8
B1and (4) for the first layer of the hidden layer B scale, obtaining the transformation formulas of all the layers according to the rule. W8The scale is (64X 16), b8Is (1X 16). Wherein w9～w15The scale is (16 is multiplied by 16)，b9～b15Is (1X 16). The same applies to the dimension C.
In the above facts, preferably, in step S1, the photovoltaic panel temperature is obtained by averaging N different position measurements of the photovoltaic back panel, and the calculation formula is:
the above-mentioned embodiments only express the specific embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention.
Claims (9)
1. The utility model provides an area grey photovoltaic board power prediction system based on intensive residual error network, its characterized in that, includes data acquisition system and data monitoring center, data acquisition system includes visible light camera, meteorological detector, smart electric meter and paster temperature sensor, the visible light camera is used for shooing photovoltaic board area grey image, meteorological detector is used for detecting the meteorological data of worn-out fur, smart electric meter is used for collecting the electric data of photovoltaic board, paster temperature sensor is used for detecting photovoltaic board temperature, and the data information that data acquisition system gathered sends to data monitoring center through data transmitter and data receiver, and data monitoring center calculates photovoltaic board power.
2. The system of claim 1, wherein the meteorological data of the photovoltaic panel comprises irradiation data, ambient temperature data, atmospheric humidity data, wind speed data, wind direction data, and barometric pressure data.
3. The system of claim 1, wherein the smart meter is installed in a power distribution box of a photovoltaic panel, and the electrical data of the photovoltaic panel comprises output voltage data, output current data and photovoltaic panel conversion efficiency of the photovoltaic panel.
4. The system of claim 3, wherein the smart meter has a voltage range of 0-1000V DC and a current range of 0-10A.
5. The system for predicting the power of the gray-deposited photovoltaic panel based on the dense residual error network as claimed in claim 1, wherein the data monitoring center is an upper computer.
6. The system for predicting the power of the soot-deposited photovoltaic panel based on the dense residual error network as claimed in claim 5, wherein the interval of the data collected by the upper computer is 15 min.
7. The system for predicting the power of the gray-deposited photovoltaic panel based on the dense residual error network as claimed in claim 6, wherein the data transmitter and the data receiver transmit the gray-deposited image and the operation parameters of the photovoltaic panel to the upper computer through two data transmission channels.
8. The prediction method of the dense residual error network-based power prediction system for the gray-covered photovoltaic panel is characterized by comprising the following steps:
s1, collecting electrical data and meteorological data of the photovoltaic panel by the data collection system, wherein the electrical data comprise a photovoltaic panel dust deposition image, photovoltaic panel output voltage, photovoltaic panel output current and photovoltaic panel temperature; meteorological data including irradiation, ambient temperature, atmospheric humidity, wind speed, wind direction, and barometric pressure;
s2, sending the data information acquired by the data acquisition system to a data monitoring center through a data transmitter and a data receiver;
s3, the data monitoring center performs dimensionality reduction processing on the voltage board accumulated dust image, and specifically comprises the following steps:
(1) subjecting the collected photovoltaic panel dust image to perspective transformation to obtain a corrected photovoltaic panel image;
(2) enhancing the characteristic information of the dust concentration by using an Adaptive Contrast Enhancement (ACE) algorithm;
(3) transforming the histogram of the image of the photovoltaic panel, and carrying out three-class classification on the basis of the value at the highest point of the ordinate, and sorting av from high to low1，av2，av3；
(4) Calculating the average pixel value av of each equal division, wherein the calculation formula is as follows:
wherein, in the above formula; k is an equal classification number; n is the total pixel number of the picture; n is a radical ofkIs the total number of pixels in the k classes, nkThe number of pixel values in the k classes; x is the number ofiIs the ith pixel value, p, of class kiThe probability of this pixel value in the whole picture;
s4, calculating the power of the photovoltaic panel by the data monitoring center by using an intensive residual error network, wherein: the hidden layer between the input layer and the output layer of the dense residual network has N (A, B, C …) scales, and each scale has N (N)1,N2,N3…) layers, where the output of each layer is added to the output of the first layer within the activation function, the neuron vectorization in each scale of the hidden layer can be expressed as:
Ni＝activation(Ni-1·wi-1+b+N1)。
9. the prediction method of the ash deposition photovoltaic panel power prediction system based on the dense residual error network as claimed in claim 8, wherein in step S1, the photovoltaic panel temperature is obtained by averaging N different position measurements of the photovoltaic back panel, and the calculation formula is:
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