Fishing-light complementary photovoltaic module monitoring system based on power generation analysis

Abstract

The invention provides a fish-light complementary photovoltaic module monitoring system based on generating capacity analysis, and belongs to the technical field of new energy power generation intelligent monitoring. In combination with the distributed characteristics of fish-light complementary photovoltaic power generation, a fish-light complementary region is divided; accurate prediction model construction and generating capacity analysis based on multi-element generating capacity characteristic factor parameters are provided for the inherent characteristics of fishing light, and meanwhile, the monitoring accuracy of each area is further improved based on consistency abnormal condition detection and abnormal value reverse check, so that accurate monitoring of the abnormal condition of the distributed fishing light complementary photovoltaic module is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent monitoring of renewable energy power generation, and in particular to a monitoring system for a photovoltaic component of fishery-photovoltaic complementarity based on power generation analysis. Background Art

[0002] "Fish-solar complementation" is a new model that combines fish farming with photovoltaic power generation, aiming to improve resource utilization and the economic value of land per unit area. This model can promote the large-scale implementation of photovoltaic power stations and plays an important role in the promotion of photovoltaic power stations. With the continuous change of installed capacity, how to dynamically calculate all distributed fish-solar complementation photovoltaic components within the monitoring range for high-precision monitoring has become an urgent problem to be solved.

[0003] In the prior art, CN118137970A is a method and system for monitoring the operating performance of an N-type photovoltaic module. It considers the installation inclination and azimuth of the module, performs operating status diagnosis based on the operating performance results, ensures the normal operation and efficient power generation of the photovoltaic module, improves the stability of photovoltaic power generation and the reliability of the power generation system, and guides the optimization of the system integration solution. CN118570141A is a method and system for monitoring the quality of photovoltaic modules based on computer vision, constructs a three-dimensional simulation model of the photovoltaic module; identifies the component hierarchical features of the three-dimensional simulation model, uses the trained defect recognition model to identify the photovoltaic module defect coefficient of the photovoltaic module, and constructs a quality report of the photovoltaic module.

[0004] There is no targeted monitoring method proposed in the existing technology for distributed fish-photovoltaic complementary modules. At the same time, the above monitoring methods do not take the power generation factor into consideration, and thus cannot meet the requirements of high accuracy and dynamic monitoring, resulting in hidden dangers for photovoltaic power generation. Summary of the invention

[0005] The purpose of the present invention is to solve the above-mentioned problems existing in the prior art. In the first aspect, a monitoring system for a photovoltaic assembly of fishery-photovoltaic hybrid based on power generation analysis is provided, which specifically includes the following technical contents:

[0006] The fishery-photovoltaic complementary sub-region division unit obtains the output voltage u and current i of the photovoltaic power station in each sub-region, processes abnormal points, normalizes and extracts output features, and implements the distributed fishery-photovoltaic complementary photovoltaic cluster sub-region division based on unsupervised clustering, and obtains the number of sub-regions as k;

[0007] The power generation prediction unit collects power generation data according to the sub-areas divided as above, and obtains the sub-area and power generation characteristic factor parameters, which include environment-related parameters, season and location-related parameters, light parameters, and fishing light influence parameters; forms the power generation characteristic H of the current sub-area at the current time t n, and obtain n-1 continuous features {H1...H n-1};

[0008] Specifically, the above-mentioned environmental factors at least include illumination amplitude, temperature, humidity, wind direction, wind speed, visibility, PM2.5 value of environmental pollution, cloud cover and solar incidence angle; the location-related parameters include longitude and latitude parameters; the fishing light impact parameters include the current temperature value T in the water and the total area S of the sub-region waters; the following neural network power generation prediction model can be obtained through sample learning training:

[0009]

[0010] P1 represents the power generation forecast result, W j To represent the mapping relationship between the input layer photovoltaic output feature vector and the projection layer, f i represents the activation function for extracting nonlinear features of factors affecting photovoltaic output in the jth sub-region; H is the power generation feature, λ i Represents the mapping coefficient of the input influencing factor feature extraction projection layer and activation layer; σ t represents the confidence coefficient of the sample data, μ represents the bias coefficient of the prediction model, and k is the number of sub-regions obtained by dividing the fishery-photovoltaic complementary sub-region into units.

[0011] The line loss analysis unit determines the line loss of each sub-region in order to facilitate the evaluation of the impact of photovoltaic power generation on access line loss. The present invention only considers the change of variable loss, which is specifically expressed as follows:

[0012]

[0013] In the formula: O is the load power; Q is the load reactive power; u is the voltage; R is the resistance of the output line; L is the distance between the load and the fish-light complementary photovoltaic power station.

[0014] The abnormality detection and analysis unit obtains the output voltage u and output current i corresponding to each sub-area and the line loss to determine the corresponding time-series output power generation P2;

[0015] Further, abnormality detection is performed on the predicted power generation P1 and the output power generation P2 obtained according to the power generation characteristic factor parameters; specifically including:

[0016] Based on the detection of the output power generation P2 and the predicted power generation P1, it is determined whether the consistency check between the two has passed. If the consistency check between the two has not passed, a sliding calculation window is further set for the power generation P2 and the predicted power generation P1 sequence data, the average value in each window is obtained, and the deviation value diff between the two is calculated based on the average value;

[0017] Specifically, the above consistency test specifically includes: extracting a plurality of data samples from the output power generation P2 and the predicted power generation P1, calculating the test statistic z value using the sample data, and determining its probability p under the assumed distribution;

[0018] Determine the rejection region and find the critical value of the rejection region (such as the z value of the normal distribution) according to the significance level;

[0019] Consistency decision, which compares the deviation between sample data and expected value to evaluate whether the deviation is large enough to reject the null hypothesis; if the test statistic falls into the rejection region, the consistency test fails; if the test statistic is not in the rejection region, the consistency test passes.

[0020] Define the positive and negative directions of the deviation value diff, slide the calculation window and accumulate in the positive and negative directions respectively (called positive cumulative sum and negative cumulative sum). If any cumulative sum exceeds the set threshold th, the power generation characteristic factor parameters within the time series range are checked for abnormal values. If there is no abnormal value in the characteristic factor parameter monitoring, it is determined that the corresponding sub-region fishery photovoltaic module is abnormal.

[0021] Optionally, it further includes an output display module, which displays the output monitoring result information through the terminal display interface, and displays an alarm for the sub-area with abnormal monitoring results. Specifically, it is preferred to display different colors on the photovoltaic power generation monitoring interface through a web page.

[0022] Optionally, the system further includes an output display module, which displays the output monitoring result information through the terminal display interface, and displays an alarm for the sub-area where abnormal monitoring results occur. Specifically, it is preferred to display different colors on the photovoltaic power generation monitoring interface through a web page.

[0023] Compared with the prior art, the beneficial effects of the present invention are: providing a monitoring system for fish-photovoltaic complementary components based on power generation analysis, combining the distributed characteristics of fish-photovoltaic complementary power generation, dividing the fish-photovoltaic complementary areas, and proposing accurate prediction model construction and power generation analysis based on multivariate power generation characteristic factor parameters according to the inherent characteristics of fish-photovoltaic, while further improving the accuracy of monitoring in each area based on consistency detection of abnormal conditions and reverse checking based on abnormal values, thereby realizing accurate monitoring of abnormal conditions of distributed fish-photovoltaic complementary components. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a block diagram of the monitoring system for photovoltaic components for fishery-photovoltaic hybridization based on power generation analysis for this application;

[0025] Figure 2 This is a schematic diagram of abnormal monitoring of the fishery-photovoltaic complementary photovoltaic assembly for this application;

[0026] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0027] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0028] Example 1, see attached Figure 1 , provides a monitoring system for photovoltaic components of fishery-photovoltaic hybrid based on power generation analysis, which specifically includes the following technical contents:

[0029] The fishery-photovoltaic complementary sub-region division unit obtains the output voltage u and current i of the photovoltaic power station in each sub-region, processes abnormal points, normalizes and extracts output features, and implements the distributed fishery-photovoltaic complementary photovoltaic cluster sub-region division based on unsupervised clustering, and obtains the number of sub-regions as k;

[0030] The power generation prediction unit collects power generation data according to the sub-areas divided as above, and obtains the sub-area and power generation characteristic factor parameters, which include environment-related parameters, season and location-related parameters, light parameters, and fishing light influence parameters; forms the power generation characteristic H of the current sub-area at the current time t n , and obtain n-1 continuous features {H1...H n-1};

[0031] Specifically, the above-mentioned environmental factors at least include illumination amplitude, temperature, humidity, wind direction, wind speed, visibility, PM2.5 value of environmental pollution, cloud cover and solar incidence angle; the location-related parameters include longitude and latitude parameters; the fishing light impact parameters include the current temperature value T in the water and the total area S of the sub-region waters; the following neural network power generation prediction model can be obtained through sample learning training:

[0032]

[0033] P1 represents the power generation forecast result, W j To represent the mapping relationship between the input layer photovoltaic output feature vector and the projection layer, f i represents the activation function for extracting nonlinear features of factors affecting photovoltaic output in the jth sub-region; H is the power generation feature, λ i Represents the mapping coefficient of the input influencing factor feature extraction projection layer and activation layer; σ t represents the confidence coefficient of the sample data, μ represents the bias coefficient of the prediction model, and k is the number of sub-regions obtained by the fishery-photovoltaic complementary sub-region division unit. The above neural network model specifically includes an input layer, a projection layer, an activation layer, and an output layer. Sample input and training iteration are well known to those skilled in the art and will not be described in detail here.

[0034] The line loss analysis unit determines the line loss of each sub-region in order to facilitate the evaluation of the impact of photovoltaic power generation on access line loss. The present invention only considers the change of variable loss, which is specifically expressed as follows:

[0035]

[0036] In the formula: O is the load power; Q is the load reactive power; u is the voltage; R is the resistance of the output line; L is the distance between the load and the fish-light complementary photovoltaic power station.

[0037] The abnormality detection and analysis unit obtains the output voltage u and output current i corresponding to each sub-area and the line loss to determine the corresponding time-series output power generation P2;

[0038] Further, abnormality detection is performed on the predicted power generation P1 and the output power generation P2 obtained according to the power generation characteristic factor parameters; specifically including:

[0039] Based on the detection of the output power generation P2 and the predicted power generation P1, it is determined whether the consistency check between the two has passed. If the consistency check between the two has not passed, a sliding calculation window is further set for the power generation P2 and the predicted power generation P1 sequence data, the average value in each window is obtained, and the deviation value diff between the two is calculated based on the average value;

[0040] Specifically, the above consistency test specifically includes: extracting a plurality of data samples from the output power generation P2 and the predicted power generation P1, calculating the test statistic z value using the sample data, and determining its probability p under the assumed distribution;

[0041] Determine the rejection region and find the critical value of the rejection region (such as the z value of the normal distribution) according to the significance level;

[0042] Consistency decision, which compares the deviation between sample data and expected value to evaluate whether the deviation is large enough to reject the null hypothesis; if the test statistic falls into the rejection region, the consistency test fails; if the test statistic is not in the rejection region, the consistency test passes.

[0043] Define the positive and negative directions of the deviation value diff, slide the calculation window and accumulate in the positive and negative directions respectively (called positive cumulative sum and negative cumulative sum). If any cumulative sum exceeds the set threshold th, the power generation characteristic factor parameters within the time series range are checked for abnormal values. If there is no abnormal value in the characteristic factor parameter monitoring, it is determined that the corresponding sub-region fishery photovoltaic module is abnormal.

[0044] Optionally, it further includes an output display module, which displays the output monitoring result information through the terminal display interface, and displays an alarm for the sub-area with abnormal monitoring results. Specifically, it is preferred to display different colors on the photovoltaic power generation monitoring interface through a web page.

[0045] Optionally, the system further includes an output display module, which displays the output monitoring result information through the terminal display interface, and displays an alarm for the sub-area where abnormal monitoring results occur. Specifically, it is preferred to display different colors on the photovoltaic power generation monitoring interface through a web page.

[0046] See attached Figure 2 This is a schematic diagram of the monitoring anomaly of the fish-light complementary photovoltaic assembly for this application. The boxed area is displayed in different colors for the abnormal area.

[0047] In this embodiment, see the attached Figure 3 The electronic device shown further includes a bus 103 and a communication interface 104 , and the processor 101 , the communication interface 104 and the memory 102 are connected via the bus 103 .

[0048] The memory 102 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk storage. The bus 103 may be an ISA bus, a PCI bus, or an EISA bus. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0049] The communication interface 104 is used to connect to at least one user terminal and other network units through a network interface, and send the encapsulated IPv4 message or IPv4 message to the user terminal through the network interface.

[0050] The processor 101 may be an integrated circuit chip with signal processing capabilities. In the implementation process, the functional steps implemented by the above system can be completed by the hardware integrated logic circuit or software instructions in the processor 101. The above processor 101 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The disclosed systems, steps and logic block diagrams in the embodiments of the present disclosure can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The functions of the system disclosed in conjunction with the embodiments of the present disclosure can be directly embodied as a hardware decoding processor for execution, or a combination of hardware and software modules in the decoding processor for execution. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 102, and the processor 101 reads the information in the memory 102 and completes the functions of the photovoltaic module monitoring system in the above embodiment in combination with its hardware.

[0051] An embodiment of the present invention further provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the functions of the photovoltaic component monitoring system in the above-mentioned embodiment are realized.

[0052] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0053] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0054] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0055] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present invention can essentially or in other words, the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0056] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A photovoltaic module monitoring system for fishery-photovoltaic hybrid based on power generation analysis, characterized in that Include: The fishery-photovoltaic complementary sub-region division unit obtains the output voltage u and current i of the photovoltaic power station in each sub-region, processes abnormal points, normalizes and extracts output features, and implements the distributed fishery-photovoltaic complementary photovoltaic cluster sub-region division based on unsupervised clustering, and obtains the number of sub-regions as k; The power generation prediction unit collects power generation data according to the sub-areas divided as above, and obtains the sub-area and power generation characteristic factor parameters, which include environment-related parameters, season and location-related parameters, light parameters, and fishing light influence parameters; forms the power generation characteristic H of the current sub-area at the current time t n , and obtain n-1 continuous features {H1...H n-1 }; Through sample learning training, the following neural network power generation prediction model is obtained; Line loss analysis unit, which evaluates the impact of photovoltaic power generation on access line losses and determines the line loss in each sub-area; The abnormality detection and analysis unit obtains the output voltage u and output current i corresponding to each sub-area and the line loss to determine the corresponding time-series output power generation P2; further performs abnormality detection based on the predicted power generation P1 and output power generation P2 obtained according to the power generation characteristic factor parameters.

2. The monitoring system for photovoltaic components for fishery-photovoltaic hybridization based on power generation analysis according to claim 2 is characterized in that: It further includes an output display module, which displays the output monitoring result information through the terminal display interface, and displays an alarm for the sub-area with abnormal monitoring results. Specifically, it is preferred to display it in different colors on the photovoltaic power generation monitoring interface through a web page.

3. The monitoring system for photovoltaic components for fishery-photovoltaic hybridization based on power generation analysis according to claim 2 is characterized in that: An alarm is displayed for the sub-area where abnormal monitoring results occur, and preferably different colors are displayed on the photovoltaic power generation monitoring interface through a web page.

4. The monitoring system for photovoltaic components for fishery-photovoltaic hybridization based on power generation analysis according to claim 1 is characterized in that: Through sample learning training, the following neural network power generation prediction model is obtained: P1 represents the power generation forecast result, W j To represent the mapping relationship between the input layer photovoltaic output feature vector and the projection layer, f i represents the activation function for extracting nonlinear features of factors affecting photovoltaic output in the jth sub-region; H is the power generation feature, λ i Represents the mapping coefficient of the input influencing factor feature extraction projection layer and activation layer; σ t represents the confidence coefficient of the sample data, μ represents the bias coefficient of the prediction model, and k is the number of sub-regions obtained by dividing the fishery-photovoltaic complementary sub-region into units.

5. The monitoring system for photovoltaic components for fishery-photovoltaic hybridization based on power generation analysis according to claim 4 is characterized in that: The line loss analysis unit determines the line loss of each sub-area and is specifically expressed as follows: In the formula: O is the load power; Q is the load reactive power; u is the voltage; R is the resistance of the output line; L is the distance between the load and the fish-light complementary photovoltaic power station.

6. The monitoring system for photovoltaic components for fishery-photovoltaic hybridization based on power generation analysis according to claim 4 is characterized in that: In the abnormality detection and analysis unit, the output power generation P2 and the predicted power generation P1 are detected to determine whether the consistency check between the two has passed. If the consistency check between the two has not passed, a sliding calculation window is further set for the power generation P2 and the predicted power generation P1 sequence data, the average value in each window is obtained, and the deviation value diff between the two is calculated based on the average value; Define the positive and negative directions of the deviation value diff, slide the calculation window and accumulate in the positive and negative directions respectively (called positive cumulative sum and negative cumulative sum). If any cumulative sum exceeds the set threshold th, the power generation characteristic factor parameters within the time series range are checked for abnormal values. If there is no abnormal value in the characteristic factor parameter monitoring, it is determined that the corresponding sub-region fishery photovoltaic module is abnormal.

7. The monitoring system for photovoltaic components for fishery-photovoltaic hybridization based on power generation analysis according to claim 6 is characterized in that: The above consistency test specifically includes: extracting multiple data samples from the output power generation P2 and the predicted power generation P1, calculating the test statistic z value using the sample data, and determining its probability p under the assumed distribution; Determine the rejection region and find the critical value of the rejection region based on the significance level; consistency decision, which compares the deviation between the sample data and the expected value to evaluate whether the deviation is large enough to reject the null hypothesis; if the test statistic falls into the rejection region, the consistency test fails; if the test statistic is not in the rejection region, the consistency test passes.

8. An electronic device, characterized in that: The system comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor can implement the function of any one of the systems of claims 1 to 7 when executing the program.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the program can realize the function of any one of the systems in claims 1-7 when executed by a processor.

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