Fishery-solar complementary photovoltaic module monitoring system based on power generation analysis

Abstract

The present invention provides a monitoring system for fishery-photovoltaic complementary modules based on power generation analysis, belonging to the technical field of intelligent monitoring of new energy power generation. The present invention combines the distributed characteristics of fishery-photovoltaic complementary power generation, divides the fishery-photovoltaic complementary areas into regions, and proposes accurate prediction model construction and power generation analysis based on multivariate power generation characteristic factor parameters based on the inherent characteristics of fishery-photovoltaic. At the same time, based on consistency detection of abnormal situations and based on abnormal value backchecking, the accuracy of monitoring in each area is further improved, thereby realizing accurate monitoring of abnormal situations of distributed fishery-photovoltaic complementary modules.

Description

Technical Field

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

[0002] "Fish-solar hybridization" 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 widespread implementation of photovoltaic power plants and play a significant role in their promotion. With the continuous fluctuation of installed capacity, the problem of how to dynamically calculate and accurately monitor all distributed fish-solar hybrid photovoltaic modules within the monitoring range has become a pressing issue.

[0003] In the prior art, CN118137970A describes a method and system for monitoring the operating performance of N-type photovoltaic modules. This method considers the module's installation inclination and azimuth angles and performs operating status diagnosis based on the operating performance results, ensuring the normal operation and efficient power generation of the photovoltaic modules, improving the stability of photovoltaic power generation and the reliability of the power generation system, while also guiding the optimization of system integration solutions. CN118570141A describes a method and system for monitoring the quality of photovoltaic modules based on computer vision. This method constructs a three-dimensional simulation model of the photovoltaic module, identifies the component-level features of the three-dimensional simulation model, uses a trained defect recognition model to identify the photovoltaic module defect coefficient, and constructs a quality report for 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 therefore cannot meet the requirements of high accuracy and dynamic monitoring, causing hidden dangers in photovoltaic power generation. Summary of the Invention

[0005] The present invention aims to solve the above-mentioned problems existing in the prior art. In a first aspect, a monitoring system for a photovoltaic module with fishery-solar hybrid technology 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 outliers, normalizes them, and extracts output features. It then divides the distributed fishery-photovoltaic complementary photovoltaic cluster into sub-regions 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 impact parameters; and 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 include at least the illumination amplitude, temperature, humidity, wind direction, wind speed, visibility, PM2.5 value, cloud cover and solar incidence angle. The location-related parameters include longitude and latitude parameters. The fishing light impact parameters include the current water temperature T and the total area of ​​the sub-region water S. Through sample learning training, the following neural network power generation prediction model is obtained:

[0009]

[0010] P1 represents the power generation forecast result, W j To represent the mapping relationship between the photovoltaic output feature vector of the input layer 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] Where: 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-solar 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 output power generation P2 obtained based on the power generation characteristic factor parameters; specifically, the following steps are included:

[0016] Based on the output power generation P2 and the predicted power generation P1, a test is performed to determine whether the consistency check between the two is passed. If the consistency check between the two fails, 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 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;

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

[0019] 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.

[0020] Define the deviation value diff in positive and negative directions, slide the calculation window, and accumulate it in the positive and negative directions (called the 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 outliers. If the characteristic factor parameters are monitored without outliers, it is determined that the corresponding sub-region's fishery-solar hybrid photovoltaic system has an abnormality.

[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 where abnormal monitoring results occur. 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 existing technology, the beneficial effects of the present invention are: providing a fish-photovoltaic complementary module monitoring system based on power generation analysis, combining the distributed characteristics of fish-photovoltaic complementary power generation, dividing the fish-photovoltaic complementary area into regions, and proposing accurate prediction model construction and power generation analysis based on multi-element power generation characteristic factor parameters based on 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 modules. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0025] Figure 2 This is a schematic diagram of abnormal monitoring of the fish-light 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 with reference to the accompanying drawings:

[0028] Example 1, see attached Figure 1 , provides a monitoring system for photovoltaic modules for fishery-solar hybridization 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 outliers, normalizes them, and extracts output features. It then divides the distributed fishery-photovoltaic complementary photovoltaic cluster into sub-regions 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 impact parameters; and 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 include at least the illumination amplitude, temperature, humidity, wind direction, wind speed, visibility, PM2.5 value, cloud cover and solar incidence angle. The location-related parameters include longitude and latitude parameters. The fishing light impact parameters include the current water temperature T and the total area of ​​the sub-region water S. Through sample learning training, the following neural network power generation prediction model is obtained:

[0032]

[0033] P1 represents the power generation forecast result, W j To represent the mapping relationship between the photovoltaic output feature vector of the input layer 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 prediction model bias coefficient, and k represents the number of subregions obtained by the fishery-photovoltaic complementary subregion division unit. The above-mentioned neural network model specifically includes an input layer, a projection layer, an activation layer, and an output layer. Sample input and training iterations are well known to those skilled in the art and are not 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] Where: 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-solar 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 output power generation P2 obtained based on the power generation characteristic factor parameters; specifically, the following steps are included:

[0039] Based on the output power generation P2 and the predicted power generation P1, a test is performed to determine whether the consistency check between the two is passed. If the consistency check between the two fails, 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 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;

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

[0042] 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.

[0043] Define the deviation value diff in positive and negative directions, slide the calculation window, and accumulate it in the positive and negative directions (called the 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 outliers. If the characteristic factor parameters are monitored without outliers, it is determined that the corresponding sub-region's fishery-solar hybrid photovoltaic system has an abnormality.

[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 where abnormal monitoring results occur. 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 abnormal monitoring of the fish-photovoltaic complementary photovoltaic module in 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 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. During implementation, 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-mentioned 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 various systems, steps, and logic block diagrams disclosed 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 can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory 102. Processor 101 reads information from memory 102 and, in conjunction with its hardware, implements the functions of the photovoltaic module monitoring system described in the aforementioned embodiment.

[0051] An embodiment of the present invention further provides a storage medium having a computer program stored thereon. When the computer program is executed by a processor, the functions of the photovoltaic module monitoring system in the aforementioned embodiment are realized.

[0052] In the several embodiments provided in this 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 interface, 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 separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[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, or 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, server, or network device, etc.) to execute all or part of the steps of the method 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 scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein with equivalents. Such modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A monitoring system for photovoltaic modules based on power generation analysis for fishery-solar hybridization, characterized by 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 outliers, normalizes them, and extracts output features. It then divides the distributed fishery-photovoltaic complementary photovoltaic cluster into sub-regions 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 impact parameters; and 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: ； represents the power generation forecast result, To represent the mapping relationship between the photovoltaic output feature vector of the input layer and the projection layer, represents the activation function for extracting nonlinear features of factors affecting photovoltaic output in the i-th sub-region; H is the power generation feature, Represents the mapping coefficients of the input influencing factor feature extraction projection layer and activation layer; 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; 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 anomaly detection and analysis unit obtains the output voltage u and current i corresponding to each sub-area and the line loss to determine the corresponding time-series output power generation P2; further performs anomaly detection based on the predicted power generation P1 and output power generation P2 obtained based on the power generation characteristic factor parameters.

2. The fishery-photovoltaic hybrid module monitoring system based on power generation analysis according to claim 1 is characterized in that: It further includes an output display module, which displays the output monitoring result information through the terminal display interface, displays an alarm for the sub-area with abnormal monitoring results, and displays different colors on the photovoltaic power generation monitoring interface through the web page.

3. The fishery-photovoltaic hybrid module monitoring system based on power generation analysis according to claim 2 is characterized in that: The line loss analysis unit determines the line loss of each sub-area and is specifically expressed as follows: ； Where: 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-solar complementary photovoltaic power station.

4. The fishery-photovoltaic hybrid module monitoring system based on power generation analysis according to claim 3 is characterized by: In the anomaly detection and analysis unit, the output power generation P2 and the predicted power generation P1 are tested 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, and the average value in each window is obtained. 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 them in the positive and negative directions respectively; if any cumulative sum exceeds the set threshold th, then check the outliers of the characteristic factor parameters of the power generation within the time series range. If there are no outliers in the characteristic factor parameter monitoring, it is determined that the fishery-photovoltaic hybrid photovoltaic module in the corresponding sub-region has an abnormality.

5. The fishery-photovoltaic hybrid module monitoring system based on power generation analysis according to claim 4 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.

6. 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 functions of any one of the systems of claims 1 to 5 when executing the program.

7. 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 1-5 when executed by a processor.

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