Distributed photovoltaic data acquisition control method

Abstract

The invention provides a distributed photovoltaic data acquisition control method. The method comprises the following steps: acquiring current power generation data of each photovoltaic power generation device in a photovoltaic power generation device cluster in a current time period, and sending the current power generation data to a photovoltaic data management platform to form a current power generation data set; the photovoltaic data management platform performs power generation according to the current power generation data of any photovoltaic power generation device in the power generation data set; determining an expected data acquisition frequency of a next time period according to the historical power generation data and a current data acquisition frequency of the current time period, and sending the expected data acquisition frequency corresponding to the next time period to each photovoltaic power generation device in the photovoltaic power generation device cluster, therefore, the power generation data can be dynamically adjusted according to the real-time operation condition of the equipment, and when the state of the equipment is changed, the system can quickly respond and adjust the data acquisition frequency to adapt to a new condition.

Description

Technical Field

[0001] The present application relates to data processing technology, and in particular to a distributed photovoltaic data acquisition and control method. Background Art

[0002] In the operation of photovoltaic power generation systems, data collection is a crucial link. By real-time monitoring and collecting power generation data of photovoltaic power generation equipment, we can understand the operating status of the equipment, power generation efficiency, and the impact of external environmental factors (such as light intensity, temperature, etc.) on power generation, thereby providing strong support for the system's optimized operation, fault warning and maintenance.

[0003] However, traditional photovoltaic power generation data collection methods often use a fixed data collection frequency, which cannot be dynamically adjusted according to the real-time operating conditions of the equipment and changes in external environmental factors. This fixed-frequency data collection method has many shortcomings. On the one hand, when the equipment state is stable and the external environmental factors fluctuate slightly, too high a data collection frequency will lead to the generation of a large amount of redundant data, wasting storage resources and transmission bandwidth; on the other hand, when the equipment state changes or the external environmental factors fluctuate greatly, too low a data collection frequency may not be able to capture key information in time, resulting in data loss or inaccurate analysis. Summary of the invention

[0004] The present application provides a distributed photovoltaic data acquisition control method for dynamic adjustment according to the real-time operating status of the equipment. When the equipment status changes or external environmental factors fluctuate, the system can respond quickly and adjust the data acquisition frequency to adapt to the new situation.

[0005] In a first aspect, the present application provides a distributed photovoltaic data acquisition control method, which is applied to a distributed photovoltaic management system, wherein the distributed photovoltaic management system includes a distributed photovoltaic power generation equipment cluster and a photovoltaic data management platform, wherein each photovoltaic power generation equipment in the photovoltaic power generation equipment cluster is communicatively connected to the photovoltaic data management platform; the method includes:

[0006] Acquire current power generation data of each photovoltaic power generation device in the photovoltaic power generation device cluster in the current period, and send the current power generation data to the photovoltaic data management platform to form a current power generation data set, wherein the current power generation data includes current power generation power and current power generation power phase angle;

[0007] The photovoltaic data management platform determines the expected data collection frequency for the next period according to the current power generation data, historical power generation data of any photovoltaic power generation equipment in the power generation data set and the current data collection frequency in the current period;

[0008] The photovoltaic data management platform sends the expected data collection frequency corresponding to the next time period to each photovoltaic power generation device in the photovoltaic power generation device cluster, so that each photovoltaic power generation device collects power generation data according to the expected data collection frequency in the next time period.

[0009] In the above scheme, the real-time acquisition and transmission of the current power generation data of each device in the photovoltaic power generation equipment cluster in the current period is first realized. These power generation data, including the current power generation and the current power generation phase angle, are key indicators for evaluating the operating status and power generation efficiency of photovoltaic equipment. By sending these data to the photovoltaic data management platform in real time, the current power generation data set can be quickly formed, providing a solid foundation for subsequent data analysis and frequency adjustment. After obtaining the current power generation data set, the photovoltaic data management platform will intelligently determine the expected data collection frequency for the next period based on the current power generation data, historical power generation data and current data collection frequency of any photovoltaic power generation equipment. The core of this strategy is that it can comprehensively consider the real-time operating status, historical data and current collection efficiency of the equipment, so as to formulate a more reasonable and efficient data collection plan. By sending the expected data collection frequency corresponding to the next period to each device in the photovoltaic power generation equipment cluster, it can ensure that each device collects power generation data according to this frequency in the next period. This approach can not only avoid the waste of resources caused by frequent collection, but also effectively reduce the cost of data collection and transmission while ensuring data quality. It is worth mentioning that the expected data collection frequency determined by the above scheme is not fixed, but can be dynamically adjusted according to the real-time operating conditions of the equipment. This means that when the equipment status changes or external environmental factors (such as light intensity, temperature, etc.) fluctuate, the system can respond quickly and adjust the data collection frequency to adapt to the new situation.

[0010] Optionally, the photovoltaic data management platform determines the expected data collection frequency for the next time period according to the current power generation data, historical power generation data of any photovoltaic power generation equipment in the power generation data set and the current data collection frequency in the current time period, including:

[0011] The photovoltaic data management platform determines the expected data collection frequency according to the current power generation and the current power generation phase angle in the current power generation data, the historical power generation and the historical power generation phase angle in the historical power generation data, and the current data collection frequency.

[0012] In the above scheme, when determining the expected data collection frequency for the next period, the photovoltaic data management platform will comprehensively consider the current power generation and the current power generation phase angle in the current power generation data, the historical power generation and the historical power generation phase angle in the historical power generation data, and the current data collection frequency. This approach realizes the comprehensive monitoring of the operating status of photovoltaic power generation equipment, not only considering the real-time data, but also incorporating historical data, so as to more accurately reflect the actual operating trends and characteristics of the equipment. This comprehensive consideration method provides a solid foundation for formulating reasonable data collection strategies and ensures the accuracy of the decision-making process. By introducing real-time data such as current power generation and current power generation phase angle, as well as the comparison of historical data, the photovoltaic data management platform can realize dynamic adjustment of the data collection frequency. When the operating status of the equipment is stable or it is predicted that it will change, the system can adjust the data collection frequency accordingly to meet different monitoring needs. For example, during periods of drastic changes in light intensity, the power generation may fluctuate greatly, and the data collection frequency needs to be increased to capture more detailed information. On the contrary, during periods of stable light and gentle changes in power generation, the collection frequency can be appropriately reduced to save resources. This dynamic adjustment mechanism not only avoids unnecessary waste of resources, but also ensures the timely acquisition of key data and optimizes the overall utilization efficiency of resources. Among them, the phase angle information reflects the waveform characteristics of the current, which is of great significance for analyzing the operating status of photovoltaic power generation equipment and the stability of the power grid. By utilizing the phase angle information, the system can have a deeper understanding of the correlation between power generation data, so as to formulate a more scientific and reasonable data collection strategy. Combining historical power generation data with current power generation data not only helps to capture the long-term operating trend of the equipment, but also reflects the current status of the equipment in real time. This combination enables the photovoltaic data management platform to more accurately predict the power generation situation in the future, so as to adjust the data collection frequency in advance and ensure the integrity and timeliness of the data. This is of great significance for subsequent data analysis, fault warning and operation and maintenance decision-making.

[0013] Optionally, before determining the expected data collection times for the next time period according to the current power generation data, the historical power generation data and the current data collection frequency, the method further includes:

[0014] The photovoltaic data management platform determines a data collection adjustment coefficient based on the current light intensity, current operating temperature, current photovoltaic operating voltage and current photovoltaic operating current in the current power generation data, wherein the data collection adjustment coefficient is used to determine the expected data collection frequency.

[0015] In the above scheme, when determining the data collection adjustment coefficient, the photovoltaic data management platform will comprehensively consider the current light intensity, current operating temperature, current photovoltaic operating voltage and current photovoltaic operating current in the current power generation data. These environmental parameters are key factors affecting the efficiency of photovoltaic power generation, and their real-time changes can directly reflect the operating status and external environmental conditions of photovoltaic equipment. By comprehensively monitoring these environmental parameters, the management platform can more accurately assess the complexity and variability of the current data collection environment. The data collection adjustment coefficient determined on this basis can more accurately reflect the actual data collection needs, thereby improving the accuracy and reliability of data collection. As an important parameter for determining the expected data collection frequency, the introduction of the data collection adjustment coefficient provides a scientific basis for the dynamic optimization of the frequency. The management platform can flexibly adjust the data collection frequency according to the changes in environmental parameters and the size of the data collection adjustment coefficient to adapt to the data collection needs under different environments and conditions. By dynamically adjusting the data collection frequency, the management platform can effectively reduce the cost of data transmission, storage and processing while ensuring the quality of data collection. This not only helps to improve resource utilization efficiency, but also prolongs the service life of photovoltaic equipment and reduces operation and maintenance costs.

[0016] Optionally, before determining the data acquisition adjustment coefficient according to the current light intensity, the current operating temperature, the current photovoltaic operating voltage and the current photovoltaic operating current in the current power generation data, the method further includes:

[0017] The photovoltaic data management platform determines the target operating parameters based on the current data collection frequency and the current power generation data subset of any photovoltaic power generation equipment in the current power generation data set in the current time period, and the target operating parameter K is any one of the current light intensity, the current operating temperature, the current photovoltaic operating voltage and the current photovoltaic operating current.

[0018] In the above scheme, the current data collection frequency and the current power generation data subset of any photovoltaic power generation equipment in the current power generation data set in the current period are comprehensively considered. This comprehensive consideration of multi-dimensional data subsets can more comprehensively reflect the real-time operating status and external environmental conditions of the equipment, and provide data support for the accurate selection of target working parameters. By selecting the target working parameters (such as any one of the current light intensity, current operating temperature, current photovoltaic working voltage or current photovoltaic working current), the management platform can more specifically monitor and analyze the impact of the parameter on the data collection frequency. This targeted enhancement helps to improve the efficiency and accuracy of data collection and reduce unnecessary waste of resources. Based on the accurately calculated data collection adjustment coefficient, the management platform can formulate a more optimized frequency adjustment strategy. This strategy can dynamically adjust the data collection frequency according to the actual operating conditions and external environmental conditions of different photovoltaic power generation equipment to meet the data collection needs in different scenarios.

[0019] Optionally, after sending the current power generation data to the photovoltaic data management platform to form a current power generation data set, the method further includes:

[0020] If the photovoltaic data management platform determines that there are abnormal parameters in the first current power generation data corresponding to the first photovoltaic power generation device in the photovoltaic power generation device cluster, it determines that the first expected data collection frequency of the first photovoltaic power generation device in the next time period is the preset maximum data collection frequency.

[0021] In the above scheme, after receiving the current power generation data, the photovoltaic data management platform will immediately monitor and analyze it. Once it is found that there are abnormal parameters in the current power generation data of a certain device (such as the first photovoltaic power generation device) in the photovoltaic power generation equipment cluster, the platform can immediately identify and respond. By quickly locating the abnormal device, the management platform can prevent the potential safety hazards from further expanding and avoid power generation losses or even safety accidents caused by equipment failure. After identifying the abnormal parameters, the management platform will immediately adjust the data collection frequency of the corresponding device (such as the first photovoltaic power generation device) in the next period to the preset maximum data collection frequency. This dynamic adjustment mechanism can ensure that when the device is abnormal, the platform can obtain more intensive data samples to more accurately analyze the cause of the fault. By increasing the data collection frequency, the management platform can more quickly capture the changing trend of the equipment status and provide more accurate fault location and repair suggestions for operation and maintenance personnel. This not only shortens the fault response time, but also improves the efficiency and accuracy of fault handling. At the preset maximum data collection frequency, the management platform can obtain a large number of real-time data samples. These data samples contain rich equipment operation status information and provide sufficient data support for fault diagnosis. Based on intensively collected data samples, the management platform can use advanced algorithms and models (such as machine learning, deep learning, etc.) to analyze and predict the status of equipment more accurately. This not only improves the accuracy of fault diagnosis, but also provides a more scientific basis for decision-making for operation and maintenance personnel. By dynamically adjusting the frequency of data collection, the management platform can allocate limited resources (such as computing resources, storage resources, etc.) more reasonably. When the equipment is operating normally, a lower data collection frequency is used to reduce resource consumption; when the equipment is abnormal, the data collection frequency is increased to obtain more data to support fault diagnosis.

[0022] Optionally, the obtaining current power generation data of each photovoltaic power generation device in the photovoltaic power generation device cluster in the current period, and sending the current power generation data to the photovoltaic data management platform, includes:

[0023] Acquire power generation data of each photovoltaic power generation device in the photovoltaic power generation device cluster at each time in the current period to form a time domain power generation data sequence;

[0024] Performing a discrete Fourier transform on the time-domain power generation data sequence to generate a frequency-domain power generation data sequence corresponding to the time-domain power generation data sequence, and performing normalization processing on each frequency-domain power generation data in the frequency-domain power generation data sequence using the maximum frequency in the frequency-domain power generation data sequence to generate a frequency-domain normalized data sequence;

[0025] Extracting a frequency domain feature data sequence from the frequency domain normalized data sequence according to a preset acquisition frequency feature range;

[0026] Performing a first wavelet decomposition on the frequency domain characteristic data sequence using a preset first wavelet basis function to determine a first characteristic parameter matrix, and performing a second wavelet decomposition on the frequency domain characteristic data sequence using a preset second wavelet basis function to determine a second characteristic parameter matrix;

[0027] Generate a target characteristic parameter matrix according to the first characteristic parameter matrix and the second characteristic parameter matrix, wherein each element in the target characteristic parameter matrix includes a characteristic real part and a characteristic imaginary part, the characteristic real part is determined according to the corresponding parameter in the first characteristic parameter matrix, and the characteristic imaginary part is determined according to the corresponding parameter in the second characteristic parameter matrix;

[0028] The target characteristic parameter matrix is ​​entropy encoded by using Huffman coding to determine the compressed power generation data corresponding to the current power generation data, and the compressed power generation data is sent to the photovoltaic data management platform.

[0029] In the above scheme, by acquiring the power generation data of the photovoltaic power generation equipment at each moment, a time domain power generation data sequence is formed, and a discrete Fourier transform is performed to convert it into a frequency domain power generation data sequence. This step converts the data from the time domain to the frequency domain, which is convenient for analyzing the periodicity, volatility and other characteristics of the power generation data. The maximum frequency in the frequency domain power generation data sequence is used to normalize each frequency domain power generation data to generate a frequency domain normalized data sequence. Normalization processing helps to eliminate the dimensional differences between the data and improve the accuracy and efficiency of subsequent data processing. The frequency domain feature data sequence is extracted from the frequency domain normalized data sequence according to the preset acquisition frequency feature range. This step can screen out the frequency domain features that have an important impact on the adjustment of the data acquisition frequency, reduce the data dimension, and reduce the calculation amount of subsequent data processing. Through frequency domain feature extraction, the management platform can focus more on the analysis of key data and improve the efficiency and accuracy of data processing. The frequency domain feature data sequence is subjected to the first wavelet decomposition and the second wavelet decomposition using the preset first wavelet basis function and the second wavelet basis function, and the first feature parameter matrix and the second feature parameter matrix are determined respectively. This step can fully explore the hidden features in the data and enhance the feature expression ability. By combining the first characteristic parameter matrix and the second characteristic parameter matrix to generate the target characteristic parameter matrix, the management platform can obtain more comprehensive and accurate data characteristic information to provide support for subsequent data analysis and decision-making. Finally, the target characteristic parameter matrix is ​​entropy encoded using Huffman coding to generate compressed power generation data. Huffman coding is a lossless compression algorithm that can encode data according to its probability distribution characteristics, effectively reduce redundant information in the data, and reduce data transmission costs. Through Huffman coding compression, the management platform can transmit a large amount of power generation data to the photovoltaic data management platform in a smaller amount of data, improve data transmission efficiency, and reduce network bandwidth occupancy and transmission delay.

[0030] Optionally, before the photovoltaic data management platform determines the expected data collection frequency for the next time period according to the current power generation data, historical power generation data of any photovoltaic power generation equipment in the power generation data set and the current data collection frequency in the current time period, it also includes:

[0031] Performing inverse Huffman coding on the compressed power generation data to obtain the target characteristic parameter matrix;

[0032] Performing a first wavelet inverse transform based on the preset first wavelet basis function on the real part of each element in the target feature parameter matrix, and performing a second wavelet inverse transform based on the preset second wavelet basis function on the imaginary part of each element in the target feature parameter matrix, so as to generate an updated frequency domain feature data sequence;

[0033] The updated frequency domain characteristic data sequence is subjected to an inverse discrete Fourier transform to generate an updated time domain characteristic data sequence, the time domain power generation data sequence includes the updated time domain characteristic data sequence, and the power generation data set includes the updated time domain characteristic data sequence.

[0034] In the above scheme, the inverse Huffman coding of compressed power generation data is the first step of data decoding. Inverse Huffman coding can efficiently restore compressed data to the target characteristic parameter matrix according to the probability distribution table used in encoding. This process ensures the losslessness of data during compression and decompression, and retains all characteristic information of the original data. Inverse Huffman coding is efficient and fast, and can quickly restore compressed data to the original data format, providing a solid foundation for subsequent data reconstruction and analysis. Then, the real and imaginary parts of each element in the target characteristic parameter matrix are respectively subjected to the wavelet inverse transform based on the preset first wavelet basis function and the second wavelet basis function, which is the key step of data reconstruction. This step can accurately convert the frequency domain characteristic data sequence back to the time domain while retaining the detailed characteristics and frequency information of the data. Through the inverse transformation of multi-wavelet basis functions, the management platform can accurately reconstruct the frequency domain characteristic data sequence, providing accurate data support for the subsequent generation of time domain characteristic data sequence. This process ensures the integrity and accuracy of the data and improves the reliability of data analysis. Next, the inverse discrete Fourier transform is performed on the updated frequency domain feature data sequence, which is the final step of data decoding and reconstruction. This step can convert the frequency domain data back to the time domain to generate an updated time domain feature data sequence. The updated time domain feature data sequence contains all the feature information of the original time domain power generation data sequence, and may contain new feature information generated by the adjustment of the data acquisition frequency. Through the inverse discrete Fourier transform, the management platform can completely restore the time domain feature data sequence to ensure the continuity and consistency of the data. This process provides comprehensive data support for subsequent data analysis and decision-making. Through the steps of inverse Huffman coding, inverse transform of multi-wavelet basis function and inverse discrete Fourier transform, the management platform can accurately restore all the feature information of the original data, providing a reliable data basis for subsequent data analysis and decision-making. Data decoding and reconstruction technology is efficient and fast, and can quickly restore compressed data to the original data format and accurately reconstruct it. This process not only improves the efficiency of data processing, but also reduces the complexity and cost of data processing. By decoding and reconstructing the compressed power generation data, the management platform can obtain more comprehensive and accurate data feature information, providing strong data support for determining the expected data collection frequency for the next period. This process helps to optimize the data collection frequency, improve the efficiency and accuracy of data collection, and thus improve the performance and reliability of the entire photovoltaic data collection control system.

[0035] In a second aspect, the present application provides a distributed photovoltaic management system, including: a distributed photovoltaic power generation equipment cluster and a photovoltaic data management platform, wherein each photovoltaic power generation equipment in the photovoltaic power generation equipment cluster is communicatively connected to the photovoltaic data management platform;

[0036] Acquire current power generation data of each photovoltaic power generation device in the photovoltaic power generation device cluster in the current period, and send the current power generation data to the photovoltaic data management platform to form a current power generation data set, wherein the current power generation data includes current power generation power and current power generation power phase angle;

[0037] The photovoltaic data management platform determines the expected data collection frequency for the next period according to the current power generation data, historical power generation data of any photovoltaic power generation equipment in the power generation data set and the current data collection frequency in the current period;

[0038] The photovoltaic data management platform sends the expected data collection frequency corresponding to the next time period to each photovoltaic power generation device in the photovoltaic power generation device cluster, so that each photovoltaic power generation device collects power generation data according to the expected data collection frequency in the next time period.

[0039] Optionally, the photovoltaic data management platform determines the expected data collection frequency for the next time period according to the current power generation data, historical power generation data of any photovoltaic power generation equipment in the power generation data set and the current data collection frequency in the current time period, including:

[0040] The photovoltaic data management platform determines the expected data collection frequency according to the current power generation and the current power generation phase angle in the current power generation data, the historical power generation and the historical power generation phase angle in the historical power generation data, and the current data collection frequency.

[0041] Optionally, before determining the expected data collection times for the next time period according to the current power generation data, the historical power generation data and the current data collection frequency, the method further includes:

[0042] The photovoltaic data management platform determines a data collection adjustment coefficient based on the current light intensity, current operating temperature, current photovoltaic operating voltage and current photovoltaic operating current in the current power generation data, wherein the data collection adjustment coefficient is used to determine the expected data collection frequency.

[0043] Optionally, before determining the data acquisition adjustment coefficient according to the current light intensity, the current operating temperature, the current photovoltaic operating voltage and the current photovoltaic operating current in the current power generation data, the method further includes:

[0044] The photovoltaic data management platform determines the target operating parameters based on the current data collection frequency and the current power generation data subset of any photovoltaic power generation equipment in the current power generation data set in the current time period, and the target operating parameter K is any one of the current light intensity, the current operating temperature, the current photovoltaic operating voltage and the current photovoltaic operating current.

[0045] Optionally, after sending the current power generation data to the photovoltaic data management platform to form a current power generation data set, the method further includes:

[0046] If the photovoltaic data management platform determines that there are abnormal parameters in the first current power generation data corresponding to the first photovoltaic power generation device in the photovoltaic power generation device cluster, it determines that the first expected data collection frequency of the first photovoltaic power generation device in the next time period is the preset maximum data collection frequency.

[0047] Optionally, the obtaining current power generation data of each photovoltaic power generation device in the photovoltaic power generation device cluster in the current period, and sending the current power generation data to the photovoltaic data management platform, includes:

[0048] Acquire power generation data of each photovoltaic power generation device in the photovoltaic power generation device cluster at each time in the current period to form a time domain power generation data sequence;

[0049] Performing a discrete Fourier transform on the time-domain power generation data sequence to generate a frequency-domain power generation data sequence corresponding to the time-domain power generation data sequence, and performing normalization processing on each frequency-domain power generation data in the frequency-domain power generation data sequence using the maximum frequency in the frequency-domain power generation data sequence to generate a frequency-domain normalized data sequence;

[0050] Extracting a frequency domain feature data sequence from the frequency domain normalized data sequence according to a preset acquisition frequency feature range;

[0051] Performing a first wavelet decomposition on the frequency domain characteristic data sequence using a preset first wavelet basis function to determine a first characteristic parameter matrix, and performing a second wavelet decomposition on the frequency domain characteristic data sequence using a preset second wavelet basis function to determine a second characteristic parameter matrix;

[0052] Generate a target characteristic parameter matrix according to the first characteristic parameter matrix and the second characteristic parameter matrix, wherein each element in the target characteristic parameter matrix includes a characteristic real part and a characteristic imaginary part, the characteristic real part is determined according to the corresponding parameter in the first characteristic parameter matrix, and the characteristic imaginary part is determined according to the corresponding parameter in the second characteristic parameter matrix;

[0053] The target characteristic parameter matrix is ​​entropy encoded by using Huffman coding to determine the compressed power generation data corresponding to the current power generation data, and the compressed power generation data is sent to the photovoltaic data management platform.

[0054] Optionally, before the photovoltaic data management platform determines the expected data collection frequency for the next time period according to the current power generation data, historical power generation data of any photovoltaic power generation equipment in the power generation data set and the current data collection frequency in the current time period, it also includes:

[0055] Performing inverse Huffman coding on the compressed power generation data to obtain the target characteristic parameter matrix;

[0056] Performing a first wavelet inverse transform based on the preset first wavelet basis function on the real part of each element in the target feature parameter matrix, and performing a second wavelet inverse transform based on the preset second wavelet basis function on the imaginary part of each element in the target feature parameter matrix, so as to generate an updated frequency domain feature data sequence;

[0057] The updated frequency domain characteristic data sequence is subjected to an inverse discrete Fourier transform to generate an updated time domain characteristic data sequence, the time domain power generation data sequence includes the updated time domain characteristic data sequence, and the power generation data set includes the updated time domain characteristic data sequence.

[0058] In a third aspect, the present application provides an electronic device, including:

[0059] processor; and,

[0060] A memory, configured to store executable instructions of the processor;

[0061] The processor is configured to perform any possible method described in the first aspect by executing the executable instructions.

[0062] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement any possible method described in the first aspect.

[0063] The distributed photovoltaic data acquisition control method provided in the present application obtains the current power generation data of each photovoltaic power generation device in the photovoltaic power generation device cluster in the current time period, and sends the current power generation data to the photovoltaic data management platform to form a current power generation data set. Then, the photovoltaic data management platform determines the expected data acquisition frequency of the next time period according to the current power generation data, historical power generation data and current data acquisition frequency of any photovoltaic power generation device in the power generation data set, and sends the expected data acquisition frequency corresponding to the next time period to each photovoltaic power generation device in the photovoltaic power generation device cluster, so that each photovoltaic power generation device collects power generation data according to the expected data acquisition frequency in the next time period, so that it can be dynamically adjusted according to the real-time operating status of the equipment. When the equipment state changes or external environmental factors (such as light intensity, temperature, etc.) fluctuate, the system can respond quickly and adjust the data acquisition frequency to adapt to the new situation. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0065] Figure 1 is a flow chart of a distributed photovoltaic data acquisition control method according to an exemplary embodiment of the present application;

[0066] Figure 2 is a flow chart of a distributed photovoltaic data acquisition control method according to another exemplary embodiment of the present application;

[0067] Figure 3 is a structural schematic diagram of a distributed photovoltaic management system according to an exemplary embodiment of the present application;

[0068] Figure 4 It is a schematic diagram of the structure of an electronic device according to an exemplary embodiment of the present application.

[0069] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0070] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0071] In the operation of photovoltaic power generation systems, data collection is a vital link. Traditional photovoltaic power generation data collection methods often use a fixed data collection frequency, which cannot be dynamically adjusted according to the real-time operating conditions of the equipment and changes in external environmental factors (such as light intensity, temperature, etc.). This fixed-frequency data collection method has many shortcomings: on the one hand, when the equipment state is stable and the external environmental factors fluctuate slightly, too high a data collection frequency will lead to the generation of a large amount of redundant data, wasting storage resources and transmission bandwidth; on the other hand, when the equipment state changes or the external environmental factors fluctuate greatly, too low a data collection frequency may not be able to capture key information in time, resulting in data loss or inaccurate analysis.

[0072] In order to solve the above problems, the embodiments provided in this application aim to propose a distributed photovoltaic data acquisition control method, which can dynamically adjust the data acquisition frequency according to the real-time operating conditions of each device in the photovoltaic power generation equipment cluster and the changes in external environmental factors. Specifically, the present invention implements dynamic data acquisition control through the following steps:

[0073] Data acquisition and transmission: Obtain the current power generation data (including current power generation power and phase angle) of each device in the photovoltaic power generation equipment cluster in the current period, and send these data to the photovoltaic data management platform to form a current power generation data set.

[0074] Determination of expected data collection frequency: The photovoltaic data management platform determines the expected data collection frequency for the next period based on the current power generation data, historical power generation data and current data collection frequency of any photovoltaic power generation equipment in the power generation data set.

[0075] Frequency instruction sending and execution: The photovoltaic data management platform sends the expected data collection frequency instruction corresponding to the next time period to each device in the photovoltaic power generation equipment cluster, and each device performs the corresponding data collection operation in the next time period according to the received instruction.

[0076] In order to achieve the above core innovations, the present invention adopts the following specific implementation methods:

[0077] Data preprocessing and compression: After obtaining the original power generation data of the photovoltaic power generation equipment, preprocessing is first performed, including forming a time domain power generation data sequence, performing discrete Fourier transform to generate a frequency domain power generation data sequence, normalization processing, feature extraction, etc. Then, Huffman coding is used to entropy encode the extracted feature parameter matrix to generate compressed power generation data to reduce the amount of data transmission.

[0078] Abnormal detection and high-frequency data collection: After receiving the compressed power generation data, the photovoltaic data management platform first performs abnormal detection. If abnormal parameters are detected in the power generation data of a certain device, the data collection frequency of the device in the next period will be immediately adjusted to the preset maximum data collection frequency to more accurately analyze the cause of the fault.

[0079] Inverse transformation and data reconstruction: Before performing anomaly detection on compressed power generation data or determining the expected data acquisition frequency, inverse Huffman coding is first performed to obtain the original characteristic parameter matrix. Then, the characteristic parameter matrix is ​​subjected to inverse wavelet transform and inverse discrete Fourier transform to generate an updated time domain characteristic data sequence, providing accurate data support for subsequent data analysis.

[0080] Dynamic frequency adjustment algorithm: The photovoltaic data management platform uses a specific algorithm model to determine the expected data collection frequency for the next period based on the current power generation data, historical power generation data, and the current data collection frequency. The algorithm model will comprehensively consider multiple factors, such as weather changes, light intensity, equipment operating status, etc., to develop a more reasonable and efficient data collection plan.

[0081] The distributed photovoltaic data acquisition control method provided in the embodiment of the present application can be applied to a distributed photovoltaic management system. The system includes a distributed photovoltaic power generation equipment cluster and a photovoltaic data management platform. The photovoltaic power generation equipment cluster is composed of a plurality of photovoltaic power generation equipment distributed in different locations, and each device is connected to the photovoltaic data management platform. The photovoltaic data management platform is responsible for collecting, storing, processing and analyzing power generation data from the photovoltaic power generation equipment cluster, and formulating optimization strategies based on these data, such as adjusting the data acquisition frequency.

[0082] Figure 1 FIG. 1 is a flow chart of a distributed photovoltaic data acquisition control method according to an exemplary embodiment of the present application. Figure 1 As shown, the distributed photovoltaic data acquisition control method provided in this embodiment includes:

[0083] S101, obtaining current power generation data of each photovoltaic power generation device in a photovoltaic power generation device cluster in a current period.

[0084] In this step, the current power generation data of each photovoltaic power generation device in the photovoltaic power generation device cluster in the current period can be obtained, and the current power generation data can be sent to the photovoltaic data management platform to form a current power generation data set. The current power generation data includes the current power generation power and the current power generation power phase angle.

[0085] Specifically, each device in the photovoltaic power generation equipment cluster collects power generation data, including current power generation and current power generation phase angle, at its current data collection frequency during the current period. Then, these devices send the collected current power generation data to the photovoltaic data management platform.

[0086] For example, photovoltaic power generation equipment A collects power generation and phase angle data at a frequency of 10 times per second in the current period, and sends these data to the photovoltaic data management platform through a wireless network. After receiving these data, the management platform stores them in the database to form a current power generation data set.

[0087] S102. The photovoltaic data management platform determines the expected data collection frequency for the next period according to the current power generation data, historical power generation data and the current data collection frequency in the current period of any photovoltaic power generation device in the power generation data set.

[0088] Specifically, the photovoltaic data management platform determines the expected data collection frequency for the next time period through a specific algorithm model based on the current power generation data, historical power generation data and current data collection frequency in the current time period of any photovoltaic power generation equipment in the power generation data set.

[0089] The management platform first extracts the historical power generation data of the photovoltaic power generation equipment from the historical database, including the power generation power, phase angle and corresponding data collection frequency in the past period. Then, combined with the power generation data and collection frequency of the current period, machine learning algorithms (such as decision trees, neural networks, etc.) are used for analysis and prediction. The algorithm model will consider a variety of factors, such as weather changes, light intensity, equipment operating status, etc., to determine the optimal data collection frequency for the next period. For example, if it is predicted that the light intensity will increase significantly in the next period, the management platform may increase the data collection frequency to more accurately monitor changes in power generation.

[0090] S103: The photovoltaic data management platform sends the expected data collection frequency corresponding to the next time period to each photovoltaic power generation device in the photovoltaic power generation device cluster.

[0091] In this step, the photovoltaic data management platform sends the expected data collection frequency corresponding to the next time period to each photovoltaic power generation device in the photovoltaic power generation device cluster, so that each photovoltaic power generation device collects power generation data according to the expected data collection frequency in the next time period.

[0092] Specifically, after determining the expected data collection frequency for the next time period, the photovoltaic data management platform sends the expected data collection frequency to each photovoltaic power generation device in the photovoltaic power generation device cluster. The management platform sends an instruction containing the expected data collection frequency to each photovoltaic power generation device through a communication network (such as Ethernet, wireless network, etc.). After receiving the instruction, the photovoltaic power generation device will collect power generation data according to the expected data collection frequency in the next time period. For example, after photovoltaic power generation device A receives the instruction sent by the management platform that the expected data collection frequency is 5 times per second, it will collect power generation data according to this frequency in the next time period.

[0093] In this embodiment, the current power generation data of each photovoltaic power generation device in the photovoltaic power generation device cluster in the current time period is obtained, and the current power generation data is sent to the photovoltaic data management platform to form a current power generation data set. Then, the photovoltaic data management platform determines the expected data collection frequency of the next time period according to the current power generation data, historical power generation data and current data collection frequency of the current time period of any photovoltaic power generation device in the power generation data set, and sends the expected data collection frequency corresponding to the next time period to each photovoltaic power generation device in the photovoltaic power generation device cluster, so that each photovoltaic power generation device collects power generation data according to the expected data collection frequency in the next time period, so that dynamic adjustment can be made according to the real-time operating status of the equipment. When the equipment status changes or external environmental factors (such as light intensity, temperature, etc.) fluctuate, the system can respond quickly and adjust the data collection frequency to adapt to the new situation.

[0094] Figure 2 FIG. 1 is a flow chart of a distributed photovoltaic data acquisition control method according to another exemplary embodiment of the present application. Figure 2 As shown, the distributed photovoltaic data acquisition control method provided in this embodiment includes:

[0095] S201, obtaining current power generation data of each photovoltaic power generation device in a photovoltaic power generation device cluster in a current period.

[0096] In this step, the current power generation data of each photovoltaic power generation device in the photovoltaic power generation device cluster in the current period can be obtained, and the current power generation data can be sent to the photovoltaic data management platform to form a current power generation data set. The current power generation data includes the current power generation power and the current power generation power phase angle.

[0097] Specifically, each device in the photovoltaic power generation equipment cluster collects power generation data, including current power generation and current power generation phase angle, at its current data collection frequency during the current period. Then, these devices send the collected current power generation data to the photovoltaic data management platform.

[0098] In a possible implementation, power generation data of each photovoltaic power generation device in the photovoltaic power generation device cluster at each time in the current period is obtained to form a time domain power generation data sequence;

[0099] Performing discrete Fourier transform on the time-domain power generation data sequence to generate a frequency-domain power generation data sequence corresponding to the time-domain power generation data sequence, and performing normalization processing on each frequency-domain power generation data in the frequency-domain power generation data sequence using the maximum frequency in the frequency-domain power generation data sequence to generate a frequency-domain normalized data sequence;

[0100] Extracting a frequency domain feature data sequence from the frequency domain normalized data sequence according to a preset acquisition frequency feature range;

[0101] Performing a first wavelet decomposition on the frequency domain characteristic data sequence using the Daubechies wavelet basis function to determine a first characteristic parameter matrix, and performing a second wavelet decomposition on the frequency domain characteristic data sequence using the Symlet wavelet basis function to determine a second characteristic parameter matrix;

[0102] Generate a target characteristic parameter matrix according to the first characteristic parameter matrix and the second characteristic parameter matrix, wherein each element in the target characteristic parameter matrix includes a characteristic real part and a characteristic imaginary part, the characteristic real part is determined according to the corresponding parameter in the first characteristic parameter matrix, and the characteristic imaginary part is determined according to the corresponding parameter in the second characteristic parameter matrix;

[0103] Huffman coding is used to entropy encode the target characteristic parameter matrix to determine the compressed power generation data corresponding to the current power generation data, and the compressed power generation data is sent to the photovoltaic data management platform.

[0104] In the above scheme, the time domain signal is converted into the frequency domain signal by performing discrete Fourier transform on the time domain power generation data sequence, revealing the periodic changes and potential frequency components in the data. Discrete Fourier transform can accurately map the signal characteristics in the time domain to the frequency domain, making it possible to analyze the different frequency components in the signal. The data is normalized using the maximum frequency in the frequency domain power generation data sequence to ensure the unified dimension of the data, which is convenient for subsequent feature extraction and comparative analysis. Normalization eliminates the dimensional differences between the data, improves the consistency and comparability of the data, and facilitates subsequent data processing and analysis. The frequency domain feature data sequence is extracted from the frequency domain normalized data sequence according to the preset acquisition frequency feature range, and the key feature information closely related to the data acquisition frequency is effectively screened out. Feature extraction is performed within the preset range, reducing redundant information and improving the efficiency and accuracy of data processing. The frequency domain feature data sequence is decomposed by the first wavelet using the Daubech i es wavelet basis function to determine the first feature parameter matrix; at the same time, the second wavelet decomposition is performed by the Symlet wavelet basis function to determine the second feature parameter matrix. The two wavelet basis functions have different characteristics and can capture different detail features in the data. Multi-wavelet basis function decomposition can analyze the data from different angles and extract more comprehensive and accurate feature information. The target feature parameter matrix is ​​generated according to the first feature parameter matrix and the second feature parameter matrix, in which each element contains the feature real part and the feature imaginary part. This step effectively integrates the feature information obtained by decomposing different wavelet basis functions to form a more comprehensive feature representation. The generation of the target feature parameter matrix improves the expressiveness and analysis efficiency of the data, and provides a basis for subsequent data encoding and transmission. Then, the target feature parameter matrix is ​​entropy encoded using Huffman coding to achieve efficient data compression. Huffman coding is a lossless data compression algorithm that can generate the optimal coding table according to the probability distribution characteristics of the data, thereby minimizing the redundancy of the data. Huffman coding not only reduces the storage space of the data, but also reduces the bandwidth requirements for data transmission, and improves the efficiency and reliability of data transmission. The compressed power generation data is sent to the photovoltaic data management platform, which significantly reduces the burden of data transmission. At the same time, since Huffman coding is lossless, the management platform can completely restore the original target feature parameter matrix after receiving the data. The transmission of compressed data improves the response speed and real-time performance of the entire data acquisition and control system, enabling the management platform to obtain and analyze the power generation data of photovoltaic power generation equipment more timely.

[0105] It is worth noting that the first wavelet coefficient matrix W can be determined by using formula 1 according to the frequency domain feature data sequence D and the first mother wavelet function ψ1 of the first wavelet transform tree: 1 , where Formula 1 is:

[0106]

[0107] Among them, W i 1 is the first wavelet coefficient matrix W 1 The i-th element in, n is the number of characteristic parameters in the frequency domain characteristic data sequence D, d i is the i-th characteristic parameter in the target characteristic parameter matrix D, h(k) is the first preset filter coefficient, is the Daubech i es wavelet basis function, and M is the length of the first preset filter.

[0108] Then, using formula 2, the second wavelet coefficient matrix W is determined according to the frequency domain feature data sequence D and the second mother wavelet function ψ2 of the second wavelet transform tree: 2 , where Formula 2 is:

[0109]

[0110] in, is the second wavelet coefficient matrix W 2 The i-th element in , g(k) is the second preset filter coefficient, δ is the Sym l et wavelet basis function, L is the length of the second preset filter, and θ is the preset phase angle.

[0111] Next, using formula 3, and according to the first wavelet coefficient matrix W 1 And the second wavelet coefficient matrix W 2 Determine the target characteristic wavelet coefficient matrix W, where Formula 3 is:

[0112]

[0113] Among them, W i is the i-th element in the target feature wavelet coefficient matrix W.

[0114] In the above scheme, the Daubechies wavelet basis function has good orthogonality and compact support, so that the extracted wavelet coefficients can more accurately reflect the key information in the target feature parameter matrix. At the same time, the introduction of the first preset filter coefficient further enhances the accuracy and robustness of feature extraction.

[0115] Formula 2 defines how to determine the second wavelet coefficient matrix based on the target feature parameter matrix and the second mother wavelet function of the second wavelet transform tree. Unlike Formula 1, the second mother wavelet function is constructed by the Symlet wavelet basis function and the second preset filter coefficient, and a preset phase angle is introduced. Thus, by introducing the preset phase angle, the second wavelet coefficient matrix can capture the phase information in the target feature parameter matrix. Compared with the Daubechies wavelet basis function, the Symlet wavelet basis function has better symmetry, which makes the extracted wavelet coefficients smoother in the frequency domain. At the same time, the introduction of the second preset filter coefficient further enhances the diversity of features and provides more dimensional information for subsequent data analysis. The parameters in Formula 2 (such as the preset phase angle and the second preset filter coefficient) can be adjusted according to actual needs to adapt to different application scenarios and data characteristics. Formula 3 defines how to determine the target feature wavelet coefficient matrix based on the first wavelet coefficient matrix and the second wavelet coefficient matrix. By adding the first wavelet coefficient matrix to the second wavelet coefficient matrix, the target feature wavelet coefficient matrix integrates the feature information from different wavelet basis functions and filters. The imaginary part in formula 3 makes the target characteristic wavelet coefficient matrix expressed in complex form. This representation can capture the phase and amplitude information in the signal, which facilitates subsequent frequency domain analysis and processing. By fusing wavelet coefficient information from different scales and phases, the target characteristic wavelet coefficient matrix can more accurately reflect the key information in the target characteristic parameter matrix.

[0116] Optionally, after sending the current power generation data to the photovoltaic data management platform to form a current power generation data set, if the photovoltaic data management platform determines that there are abnormal parameters in the first current power generation data corresponding to the first photovoltaic power generation device in the photovoltaic power generation device cluster, the first expected data collection frequency of the first photovoltaic power generation device in the next time period is determined to be the preset maximum data collection frequency. It is worth noting that at this time, the first expected data collection frequency of the first photovoltaic power generation device in the next time period is directly determined to be the preset maximum data collection frequency, without the need to perform subsequent specific calculation steps of the expected data collection frequency.

[0117] In the above scheme, after receiving the current power generation data, the photovoltaic data management platform will immediately monitor and analyze it. Once it is found that there are abnormal parameters in the current power generation data of a certain device (such as the first photovoltaic power generation device) in the photovoltaic power generation equipment cluster, the platform can immediately identify and respond. By quickly locating the abnormal device, the management platform can prevent the potential safety hazards from further expanding and avoid power generation losses or even safety accidents caused by equipment failure. After identifying the abnormal parameters, the management platform will immediately adjust the data collection frequency of the corresponding device (such as the first photovoltaic power generation device) in the next period to the preset maximum data collection frequency. This dynamic adjustment mechanism can ensure that when the device is abnormal, the platform can obtain more intensive data samples to more accurately analyze the cause of the fault. By increasing the data collection frequency, the management platform can more quickly capture the changing trend of the equipment status and provide more accurate fault location and repair suggestions for operation and maintenance personnel. This not only shortens the fault response time, but also improves the efficiency and accuracy of fault handling. At the preset maximum data collection frequency, the management platform can obtain a large number of real-time data samples. These data samples contain rich equipment operation status information and provide sufficient data support for fault diagnosis. Based on intensively collected data samples, the management platform can use advanced algorithms and models (such as machine learning, deep learning, etc.) to analyze and predict the equipment status more accurately. This not only improves the accuracy of fault diagnosis, but also provides a more scientific basis for decision-making for operation and maintenance personnel. By dynamically adjusting the frequency of data collection, the management platform can allocate limited resources (such as computing resources, storage resources, etc.) more reasonably. When the equipment is operating normally, a lower data collection frequency is used to reduce resource consumption; when the equipment is abnormal, the data collection frequency is increased to obtain more data to support fault diagnosis. By optimizing resource allocation, the management platform can significantly reduce the operation and maintenance costs of photovoltaic power stations. This not only improves the economic benefits of photovoltaic power stations, but also provides strong support for the sustainable development of the photovoltaic industry.

[0118] S202, performing inverse Huffman coding processing on the compressed power generation data to obtain a target characteristic parameter matrix.

[0119] In the data collection process, in order to save storage space and transmission bandwidth, power generation data is usually compressed. Inverse Huffman coding is a commonly used lossless data compression algorithm that can restore compressed data to original data. In this step, the photovoltaic data management platform first performs inverse Huffman coding on the compressed power generation data to obtain the original, uncompressed power generation data sequence.

[0120] Specifically, the photovoltaic data management platform can read the compressed power generation data file in the storage. According to the pre-defined Huffman coding table, the compressed data is decoded. During the decoding process, the original sequence of power generation data is gradually restored according to the bit length of each code and the corresponding symbol value. After the decoding is completed, the original power generation data sequence is obtained, which contains the detailed power generation data of the photovoltaic power generation equipment at each time point.

[0121] Specifically, after obtaining the original power generation data sequence, the photovoltaic data management platform needs to further extract key characteristic parameters for subsequent data analysis and processing. By converting the original power generation data sequence into a characteristic parameter matrix, feature extraction and analysis can be performed more conveniently.

[0122] The photovoltaic data management platform processes the original power generation data sequence according to the predefined feature extraction algorithm. The feature extraction algorithm may include calculating key indicators such as average power generation, peak power generation, and power generation fluctuation rate. These feature parameters are arranged in chronological order to form a two-dimensional target feature parameter matrix. The rows of the matrix represent different time points, and the columns represent different feature parameters.

[0123] S203, performing inverse wavelet transform on the real part of each element in the target feature parameter matrix to generate an updated frequency domain feature data sequence.

[0124] In this step, the real part of each element in the target feature parameter matrix is ​​subjected to the first wavelet inverse transform based on the Daubechies wavelet basis function, and the imaginary part of each element in the target feature parameter matrix is ​​subjected to the second wavelet inverse transform based on the Symlet wavelet basis function to generate an updated frequency domain feature data sequence.

[0125] Specifically, for each element in the target characteristic parameter matrix (i.e., the characteristic parameter value at each time point), the photovoltaic data management platform first performs the first wavelet inverse transform based on the Daubechies wavelet basis function to process its real part. Select a suitable Daubechies wavelet basis function (such as db1, db2, etc.) and perform an inverse transform on the real part according to the wavelet transform algorithm. During the inverse transform process, the characteristic data sequence of the real part in the frequency domain is obtained by gradually restoring the wavelet coefficients.

[0126] Next, the imaginary part of each element in the target feature parameter matrix is ​​subjected to a second wavelet inverse transform based on the Symlet wavelet basis function. An appropriate Symlet wavelet basis function (such as sym2, sym3, etc.) is selected, and the imaginary part is inversely transformed according to the wavelet transform algorithm. Similarly, by gradually restoring the wavelet coefficients, the feature data sequence of the imaginary part in the frequency domain is obtained. The frequency domain feature data sequences of the real part and the imaginary part are merged to form an updated frequency domain feature data sequence.

[0127] S204: Perform inverse discrete Fourier transform on the updated frequency domain feature data sequence to generate an updated time domain feature data sequence.

[0128] In this step, the updated frequency domain characteristic data sequence is subjected to an inverse discrete Fourier transform to generate an updated time domain characteristic data sequence, the time domain power generation data sequence includes the updated time domain characteristic data sequence, and the power generation data set includes the updated time domain characteristic data sequence.

[0129] Specifically, the photovoltaic data management platform performs an inverse discrete Fourier transform on the updated frequency domain feature data sequence to generate an updated time domain feature data sequence. The photovoltaic data management platform reads the updated frequency domain feature data sequence. The frequency domain data is processed according to the inverse discrete Fourier transform algorithm. The updated time domain feature data sequence is obtained by calculating the time domain signal corresponding to each frequency component and gradually accumulating it. The updated time domain feature data sequence contains the updated power generation data characteristics of the photovoltaic power generation equipment at each time point, and these characteristics will be used for subsequent data analysis and processing.

[0130] Then, the PV data management platform merges the updated time-domain feature data sequence with the original time-domain power generation data sequence. During the merging process, the updated data is inserted into the corresponding position of the original data sequence according to the timestamp or index. After the updated time-domain power generation data sequence is formed, it is added to the power generation data set. The power generation data set now contains the updated time-domain power generation data sequence, which will be used for subsequent data analysis, model training, and determination of the expected data collection frequency.

[0131] S205. The photovoltaic data management platform determines the expected data collection frequency for the next period according to the current power generation data, historical power generation data and the current data collection frequency in the current period of any photovoltaic power generation device in the power generation data set.

[0132] Specifically, the photovoltaic data management platform determines the expected data collection frequency for the next time period through a specific algorithm model based on the current power generation data, historical power generation data and current data collection frequency in the current time period of any photovoltaic power generation equipment in the power generation data set.

[0133] The management platform first extracts the historical power generation data of the photovoltaic power generation equipment from the historical database, including the power generation power, phase angle and corresponding data collection frequency in the past period. Then, combined with the power generation data and collection frequency of the current period, machine learning algorithms (such as decision trees, neural networks, etc.) are used for analysis and prediction. The algorithm model will consider a variety of factors, such as weather changes, light intensity, equipment operating status, etc., to determine the optimal data collection frequency for the next period. For example, if it is predicted that the light intensity will increase significantly in the next period, the management platform may increase the data collection frequency to more accurately monitor changes in power generation.

[0134] In a possible implementation, the photovoltaic data management platform may use Formula 4 and calculate the current power generation data, historical power generation data, and current data collection frequency F. t Determine the expected data collection frequency F t+1 , where Formula 4 is:

[0135] F t+1 =F t (1+α·(P t -P t-1 )·sin(θ t -θ t-1 )·e -β·ΔT )

[0136] Among them, P t is the current power generation, P t-1 is the historical power generation in the previous period, θ t is the current power generation phase angle, θ t-1 is the historical power generation phase angle of the previous period, ΔT is the preset monitoring period, α is the data collection adjustment coefficient, and β is the preset time difference influence coefficient.

[0137] In the above scheme, Formula 4 can dynamically adjust the data collection frequency according to the real-time changes in power generation. When the power generation changes greatly, by increasing the data collection frequency, the fluctuation of power generation can be captured more accurately, providing more accurate data support for subsequent data analysis and decision-making. Compared with a fixed data collection frequency, dynamic adaptability improves the flexibility and efficiency of data collection, especially when the light intensity changes drastically or the equipment operation status is unstable, and can more effectively monitor the changes in power generation.

[0138] By introducing the change of the phase angle of the generated power, Formula 4 can take into account the phase characteristics of the generated power change. Phase sensitivity allows the adjustment of the data acquisition frequency to be based not only on the change in the magnitude of the power, but also on the trend and direction of the power change, thereby improving the accuracy and pertinence of data acquisition. Phase sensitivity helps to predict the change trend of the generated power in advance, providing strong support for the optimized operation and fault prevention of the photovoltaic system.

[0139] The influence of time difference on the adjustment of data collection frequency is considered. As the time difference increases, the influence of historical power generation data on the current data collection frequency gradually weakens, making the adjustment of data collection frequency more in line with the requirements of real-time and accuracy. The time difference impact mechanism ensures that the adjustment of data collection frequency can take into account the importance of historical data and real-time data, avoiding the lag caused by over-reliance on historical data.

[0140] In addition, the data collection adjustment coefficient in Formula 4 is an adjustable parameter. By adjusting these parameters, the adjustment strategy of the data collection frequency can be optimized according to different photovoltaic system characteristics and operating environments. The parameter adjustability improves the applicability and flexibility of Formula 4, so that the adjustment strategy of the data collection frequency can better adapt to different photovoltaic system requirements and operating environments.

[0141] Specifically, the photovoltaic data management platform can use Formula 5 and determine the data collection adjustment coefficient α according to the current light intensity G, the current operating temperature T, the current photovoltaic operating voltage V, and the current photovoltaic operating current I in the current power generation data, where Formula 5 is:

[0142]

[0143] Among them, α0 is the preset calibration data acquisition adjustment coefficient, G0 is the preset calibration light intensity, T0 is the preset calibration working temperature, V0 is the preset calibration photovoltaic working voltage, I0 is the preset calibration photovoltaic working current, a, b, c, d are the first preset weight coefficient, the second preset weight coefficient, the third preset weight coefficient and the fourth preset weight coefficient respectively.

[0144] In the above scheme, Formula 5 dynamically calculates the data collection adjustment coefficient by comprehensively considering multiple key parameters in the current power generation data, thereby achieving fine adjustment of the data collection frequency. This method can more accurately reflect the actual operating status of the photovoltaic system and improve the pertinence and effectiveness of data collection.

[0145] Specifically, Formula 5 takes into account multiple parameters such as light intensity, operating temperature, photovoltaic operating voltage and photovoltaic operating current, which fully reflects the operating environment and working status of the photovoltaic system. By comprehensively considering the changes in these parameters, the power generation potential and stability of the photovoltaic system can be more accurately judged, providing a more scientific basis for adjusting the data acquisition frequency. Compared with the single parameter adjustment method, the comprehensive consideration of multiple parameters improves the accuracy and reliability of the data acquisition adjustment coefficient, making the adjustment of the data acquisition frequency more in line with the actual operation of the photovoltaic system.

[0146] In addition, by introducing weight coefficients, Formula 5 allows for flexible adjustment of the degree of influence of different parameters. This adjustment can be customized according to the specific characteristics and operating environment of the photovoltaic system to meet the data collection needs in different scenarios. The flexibility of weight coefficient adjustment improves the applicability and versatility of Formula 2, making the calculation of data collection adjustment coefficients more in line with actual needs, and also provides more possibilities for the optimized operation of photovoltaic systems.

[0147] In addition, based on the data collection adjustment coefficient calculated by Formula 5, the photovoltaic data management platform can dynamically adjust the data collection frequency. When the photovoltaic system is in an efficient power generation state, by increasing the data collection frequency, the fluctuation of power generation can be captured more accurately; when the photovoltaic system power generation efficiency is low or in a stable state, the data collection frequency can be appropriately reduced to save resources and costs. The dynamic adjustment of the data collection frequency improves the efficiency and pertinence of data collection, allowing the photovoltaic data management platform to obtain and analyze key data more timely, providing strong support for the optimized operation and fault prevention of the photovoltaic system.

[0148] Formula 5 helps improve the stability and reliability of the photovoltaic system by comprehensively considering multiple key parameters in the current power generation data and dynamically adjusting the data collection frequency. Reasonable adjustment of the data collection frequency can ensure that the photovoltaic system can obtain accurate and reliable data support under various operating conditions, thereby avoiding system performance degradation or resource waste caused by insufficient or excessive data collection. Improving system stability and reliability helps reduce the operation and maintenance costs and difficulty of the photovoltaic system, and also provides a strong guarantee for the long-term stable operation of the photovoltaic system.

[0149] In addition, before determining the data acquisition adjustment coefficient α according to the current light intensity G, the current operating temperature T, the current photovoltaic operating voltage V, and the current photovoltaic operating current I in the current power generation data, the following may also be included:

[0150] The photovoltaic data management platform uses formula 6 and according to the current data collection frequency F tAnd the current power generation data set of any photovoltaic power generation equipment in the current period of time to determine the target operating parameter K, where Formula 6 is:

[0151]

[0152] Among them, K i is the i-th operating parameter in the current power generation data subset, wherein the target operating parameter K is any one of the current light intensity G, the current operating temperature T, the current photovoltaic operating voltage V, and the current photovoltaic operating current I.

[0153] In the above scheme, Formula 6 calculates the target working parameters by averaging all the working parameters collected in the current period. This method can effectively reflect the average working status of the photovoltaic power generation equipment in the current period, and provide a more accurate and reliable basis for the calculation of the subsequent data collection adjustment coefficient. By averaging the working parameters in the current period through Formula 6, the influence of individual abnormal values ​​on the results can be eliminated, and the accuracy and reliability of the data can be improved. This improvement in accuracy provides a more solid foundation for the calculation of the subsequent data collection adjustment coefficient. Compared with directly using the working parameter value at a single moment, the average calculation can more comprehensively reflect the overall working status of the photovoltaic power generation equipment in the current period, avoiding misjudgment or deviation caused by individual abnormal values.

[0154] By adjusting the data collection frequency, the density and quality of data collection can be flexibly controlled. When more accurate data support is required, the data collection frequency can be appropriately increased to obtain more detailed and accurate working parameter data. The target working parameter in Formula 6 can be any one of the current light intensity, current working temperature, current photovoltaic working voltage, or current photovoltaic working current. This flexibility allows the photovoltaic data management platform to select the most critical working parameters for calculation and analysis according to actual needs. The flexibility of target working parameter selection improves the adaptability and practicality of the photovoltaic data management platform, enabling it to better meet the data collection and analysis needs of different photovoltaic systems.

[0155] S206. The photovoltaic data management platform sends the expected data collection frequency corresponding to the next time period to each photovoltaic power generation device in the photovoltaic power generation device cluster.

[0156] In this step, the photovoltaic data management platform sends the expected data collection frequency corresponding to the next time period to each photovoltaic power generation device in the photovoltaic power generation device cluster, so that each photovoltaic power generation device collects power generation data according to the expected data collection frequency in the next time period.

[0157] Specifically, after determining the expected data collection frequency for the next time period, the photovoltaic data management platform sends the expected data collection frequency to each photovoltaic power generation device in the photovoltaic power generation device cluster. The management platform sends an instruction containing the expected data collection frequency to each photovoltaic power generation device through a communication network (such as Ethernet, wireless network, etc.). After receiving the instruction, the photovoltaic power generation device will collect power generation data according to the expected data collection frequency in the next time period. For example, after photovoltaic power generation device A receives the instruction sent by the management platform that the expected data collection frequency is 5 times per second, it will collect power generation data according to this frequency in the next time period.

[0158] Figure 3 FIG. 1 is a schematic diagram of a distributed photovoltaic management system according to an exemplary embodiment of the present application. Figure 3 As shown, the distributed photovoltaic management system 300 provided in this embodiment includes: a distributed photovoltaic power generation equipment cluster 310 and a photovoltaic data management platform 320, and each photovoltaic power generation equipment in the photovoltaic power generation equipment cluster 310 is communicatively connected with the photovoltaic data management platform 320;

[0159] Acquire current power generation data of each photovoltaic power generation device in the photovoltaic power generation device cluster 310 in the current period, and send the current power generation data to the photovoltaic data management platform 320 to form a current power generation data set, wherein the current power generation data includes current power generation and current power generation phase angle;

[0160] The photovoltaic data management platform 320 determines the expected data collection frequency for the next period according to the current power generation data, historical power generation data and the current data collection frequency in the current period of any photovoltaic power generation equipment in the power generation data set;

[0161] The photovoltaic data management platform 320 sends the expected data collection frequency corresponding to the next time period to each photovoltaic power generation device in the photovoltaic power generation device cluster 310, so that each photovoltaic power generation device collects power generation data according to the expected data collection frequency in the next time period.

[0162] Optionally, the photovoltaic data management platform 320 determines the expected data collection frequency for the next period according to the current power generation data, historical power generation data and the current data collection frequency in the current period of any photovoltaic power generation equipment in the power generation data set, including:

[0163] The photovoltaic data management platform 320 determines the expected data collection frequency according to the current power generation and the current power generation phase angle in the current power generation data, the historical power generation and the historical power generation phase angle in the historical power generation data, and the current data collection frequency.

[0164] Optionally, before determining the expected data collection times for the next time period according to the current power generation data, the historical power generation data and the current data collection frequency, the method further includes:

[0165] The photovoltaic data management platform 320 determines a data collection adjustment coefficient according to the current light intensity, the current operating temperature, the current photovoltaic operating voltage and the current photovoltaic operating current in the current power generation data, wherein the data collection adjustment coefficient is used to determine the expected data collection frequency.

[0166] Optionally, before determining the data acquisition adjustment coefficient according to the current light intensity, the current operating temperature, the current photovoltaic operating voltage and the current photovoltaic operating current in the current power generation data, the method further includes:

[0167] The photovoltaic data management platform 320 determines the target operating parameter based on the current data collection frequency and the current power generation data subset of any photovoltaic power generation equipment in the current power generation data set in the current time period, and the target operating parameter K is any one of the current light intensity, the current operating temperature, the current photovoltaic operating voltage and the current photovoltaic operating current.

[0168] Optionally, after sending the current power generation data to the photovoltaic data management platform 320 to form a current power generation data set, the method further includes:

[0169] If the photovoltaic data management platform 320 determines that there are abnormal parameters in the first current power generation data corresponding to the first photovoltaic power generation device in the photovoltaic power generation device cluster 310, it determines that the first expected data collection frequency of the first photovoltaic power generation device in the next time period is the preset maximum data collection frequency.

[0170] Optionally, the obtaining of current power generation data of each photovoltaic power generation device in the photovoltaic power generation device cluster 310 in a current period, and sending the current power generation data to the photovoltaic data management platform 320, includes:

[0171] Acquire power generation data of each photovoltaic power generation device in the photovoltaic power generation device cluster 310 at each time in the current period to form a time domain power generation data sequence;

[0172] Performing a discrete Fourier transform on the time-domain power generation data sequence to generate a frequency-domain power generation data sequence corresponding to the time-domain power generation data sequence, and performing normalization processing on each frequency-domain power generation data in the frequency-domain power generation data sequence using the maximum frequency in the frequency-domain power generation data sequence to generate a frequency-domain normalized data sequence;

[0173] Extracting a frequency domain feature data sequence from the frequency domain normalized data sequence according to a preset acquisition frequency feature range;

[0174] Performing a first wavelet decomposition on the frequency domain characteristic data sequence using a preset first wavelet basis function to determine a first characteristic parameter matrix, and performing a second wavelet decomposition on the frequency domain characteristic data sequence using a preset second wavelet basis function to determine a second characteristic parameter matrix;

[0175] Generate a target characteristic parameter matrix according to the first characteristic parameter matrix and the second characteristic parameter matrix, wherein each element in the target characteristic parameter matrix includes a characteristic real part and a characteristic imaginary part, the characteristic real part is determined according to the corresponding parameter in the first characteristic parameter matrix, and the characteristic imaginary part is determined according to the corresponding parameter in the second characteristic parameter matrix;

[0176] The target characteristic parameter matrix is ​​entropy encoded using Huffman coding to determine the compressed power generation data corresponding to the current power generation data, and the compressed power generation data is sent to the photovoltaic data management platform 320 .

[0177] Optionally, before the photovoltaic data management platform 320 determines the expected data collection frequency for the next time period according to the current power generation data, historical power generation data of any photovoltaic power generation equipment in the power generation data set and the current data collection frequency in the current time period, it also includes:

[0178] Performing inverse Huffman coding on the compressed power generation data to obtain the target characteristic parameter matrix;

[0179] Performing a first wavelet inverse transform based on the preset first wavelet basis function on the real part of each element in the target feature parameter matrix, and performing a second wavelet inverse transform based on the preset second wavelet basis function on the imaginary part of each element in the target feature parameter matrix, so as to generate an updated frequency domain feature data sequence;

[0180] The updated frequency domain characteristic data sequence is subjected to an inverse discrete Fourier transform to generate an updated time domain characteristic data sequence, the time domain power generation data sequence includes the updated time domain characteristic data sequence, and the power generation data set includes the updated time domain characteristic data sequence.

[0181] Figure 4 is a schematic diagram of the structure of an electronic device according to an exemplary embodiment of the present application. Figure 4 As shown, an electronic device 400 provided in this embodiment includes: a processor 401 and a memory 402; wherein:

[0182] The memory 402 is used to store computer programs, and the memory may also be a flash memory.

[0183] The processor 401 is used to execute the execution instructions stored in the memory to implement each step in the above method. For details, please refer to the relevant description in the above method embodiment.

[0184] Optionally, the memory 402 may be independent or integrated with the processor 401 .

[0185] When the memory 402 is a device independent of the processor 401, the electronic device 400 may further include:

[0186] The bus 403 is used to connect the memory 402 and the processor 401 .

[0187] This embodiment further provides a readable storage medium, in which a computer program is stored. When at least one processor of an electronic device executes the computer program, the electronic device executes the methods provided in the above-mentioned various implementation modes.

[0188] This embodiment also provides a program product, which includes a computer program stored in a readable storage medium. At least one processor of the electronic device can read the computer program from the readable storage medium, and at least one processor executes the computer program so that the electronic device implements the methods provided in the above various embodiments.

[0189] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the claims.

[0190] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A distributed photovoltaic data acquisition control method, characterized in that: Applied to a distributed photovoltaic management system, the distributed photovoltaic management system includes a distributed photovoltaic power generation equipment cluster and a photovoltaic data management platform, each photovoltaic power generation equipment in the photovoltaic power generation equipment cluster is communicatively connected with the photovoltaic data management platform; the method includes: Acquire current power generation data of each photovoltaic power generation device in the photovoltaic power generation device cluster in the current period, and send the current power generation data to the photovoltaic data management platform to form a current power generation data set, wherein the current power generation data includes current power generation power and current power generation power phase angle; The photovoltaic data management platform determines the expected data collection frequency for the next period according to the current power generation data, historical power generation data of any photovoltaic power generation equipment in the power generation data set and the current data collection frequency in the current period; The photovoltaic data management platform sends the expected data collection frequency corresponding to the next time period to each photovoltaic power generation device in the photovoltaic power generation device cluster, so that each photovoltaic power generation device collects power generation data according to the expected data collection frequency in the next time period.

2. The distributed photovoltaic data acquisition control method according to claim 1 is characterized in that: The photovoltaic data management platform determines the expected data collection frequency for the next time period according to the current power generation data, historical power generation data and the current data collection frequency in the current time period of any photovoltaic power generation equipment in the power generation data set, including: The photovoltaic data management platform determines the expected data collection frequency according to the current power generation and the current power generation phase angle in the current power generation data, the historical power generation and the historical power generation phase angle in the historical power generation data, and the current data collection frequency.

3. The distributed photovoltaic data acquisition control method according to claim 2 is characterized in that: Before determining the expected data collection times for the next period according to the current power generation data, the historical power generation data and the current data collection frequency, the method further includes: The photovoltaic data management platform determines a data collection adjustment coefficient based on the current light intensity, current operating temperature, current photovoltaic operating voltage and current photovoltaic operating current in the current power generation data, wherein the data collection adjustment coefficient is used to determine the expected data collection frequency.

4. The distributed photovoltaic data acquisition control method according to claim 3 is characterized in that: Before determining the data acquisition adjustment coefficient according to the current light intensity, the current operating temperature, the current photovoltaic operating voltage and the current photovoltaic operating current in the current power generation data, the method further includes: The photovoltaic data management platform determines the target operating parameters based on the current data collection frequency and the current power generation data subset of any photovoltaic power generation equipment in the current power generation data set in the current time period, and the target operating parameter K is any one of the current light intensity, the current operating temperature, the current photovoltaic operating voltage and the current photovoltaic operating current.

5. The distributed photovoltaic data acquisition control method according to any one of claims 1 to 4, characterized in that: After sending the current power generation data to the photovoltaic data management platform to form a current power generation data set, the method further includes: If the photovoltaic data management platform determines that there are abnormal parameters in the first current power generation data corresponding to the first photovoltaic power generation device in the photovoltaic power generation device cluster, it determines that the first expected data collection frequency of the first photovoltaic power generation device in the next time period is the preset maximum data collection frequency.

6. The distributed photovoltaic data acquisition control method according to any one of claims 1 to 4, characterized in that: The obtaining of current power generation data of each photovoltaic power generation device in the photovoltaic power generation device cluster in the current period, and sending the current power generation data to the photovoltaic data management platform, includes: Acquire power generation data of each photovoltaic power generation device in the photovoltaic power generation device cluster at each time in the current period to form a time domain power generation data sequence; Performing a discrete Fourier transform on the time-domain power generation data sequence to generate a frequency-domain power generation data sequence corresponding to the time-domain power generation data sequence, and performing normalization processing on each frequency-domain power generation data in the frequency-domain power generation data sequence using the maximum frequency in the frequency-domain power generation data sequence to generate a frequency-domain normalized data sequence; Extracting a frequency domain feature data sequence from the frequency domain normalized data sequence according to a preset acquisition frequency feature range; Performing a first wavelet decomposition on the frequency domain characteristic data sequence using a preset first wavelet basis function to determine a first characteristic parameter matrix, and performing a second wavelet decomposition on the frequency domain characteristic data sequence using a preset second wavelet basis function to determine a second characteristic parameter matrix; Generate a target characteristic parameter matrix according to the first characteristic parameter matrix and the second characteristic parameter matrix, wherein each element in the target characteristic parameter matrix includes a characteristic real part and a characteristic imaginary part, the characteristic real part is determined according to the corresponding parameter in the first characteristic parameter matrix, and the characteristic imaginary part is determined according to the corresponding parameter in the second characteristic parameter matrix; The target characteristic parameter matrix is ​​entropy encoded by using Huffman coding to determine the compressed power generation data corresponding to the current power generation data, and the compressed power generation data is sent to the photovoltaic data management platform.

7. The distributed photovoltaic data acquisition and control method according to claim 6, characterized in that: Before the photovoltaic data management platform determines the expected data collection frequency for the next period according to the current power generation data, historical power generation data of any photovoltaic power generation equipment in the power generation data set and the current data collection frequency in the current period, it also includes: Performing inverse Huffman coding on the compressed power generation data to obtain the target characteristic parameter matrix; Performing a first wavelet inverse transform based on the preset first wavelet basis function on the real part of each element in the target feature parameter matrix, and performing a second wavelet inverse transform based on the preset second wavelet basis function on the imaginary part of each element in the target feature parameter matrix, so as to generate an updated frequency domain feature data sequence; The updated frequency domain characteristic data sequence is subjected to an inverse discrete Fourier transform to generate an updated time domain characteristic data sequence, the time domain power generation data sequence includes the updated time domain characteristic data sequence, and the power generation data set includes the updated time domain characteristic data sequence.

8. A distributed photovoltaic management system, characterized in that: include: A distributed photovoltaic power generation equipment cluster and a photovoltaic data management platform, wherein each photovoltaic power generation equipment in the photovoltaic power generation equipment cluster is communicatively connected with the photovoltaic data management platform; Acquire current power generation data of each photovoltaic power generation device in the photovoltaic power generation device cluster in the current period, and send the current power generation data to the photovoltaic data management platform to form a current power generation data set, wherein the current power generation data includes current power generation power and current power generation power phase angle; The photovoltaic data management platform determines the expected data collection frequency for the next period according to the current power generation data, historical power generation data of any photovoltaic power generation equipment in the power generation data set and the current data collection frequency in the current period; The photovoltaic data management platform sends the expected data collection frequency corresponding to the next time period to each photovoltaic power generation device in the photovoltaic power generation device cluster, so that each photovoltaic power generation device collects power generation data according to the expected data collection frequency in the next time period.

9. An electronic device, characterized in that: include: processor; as well as, A memory, configured to store executable instructions of the processor; The processor is configured to perform the method of any one of claims 1 to 7 by executing the executable instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.

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