Photovoltaic module resonance monitoring and adjustment method and system

Abstract

The present invention is applicable to the field of photovoltaic monitoring technology, and provides a photovoltaic component resonance monitoring and adjustment method and system, the method comprising the following steps: collecting vibration data and wind information of photovoltaic components based on sensors, the wind information including wind speed, wind direction and time, and the vibration data including a timestamp; analyzing the vibration data through Fourier transform, determining the resonance time period, retrieving the wind speed and wind direction corresponding to the resonance time period, analyzing the retrieved wind speed and wind direction, determining the wind speed and wind direction information of the photovoltaic component generating resonance, optimizing the wind speed and wind direction information based on the vibration data and wind information; generating an adjustment suggestion scheme based on the optimized wind speed and wind direction information and the proportion of the resonance time period every day. The present invention can optimize the wind speed and wind direction information, avoid accidental factors in the wind speed and wind direction information, be more accurate, generate targeted adjustment suggestion schemes, and provide scientific basis and decision support for the operation and maintenance of photovoltaic power stations.

Description

Technical Field

[0001] The present invention relates to the field of photovoltaic monitoring technology, and in particular to a photovoltaic component resonance monitoring and adjustment method and system. Background Art

[0002] As the core component of the photovoltaic power generation system, the operating stability and efficiency of photovoltaic modules directly affect the power generation and economic benefits of the entire system. However, in the actual operating environment, photovoltaic modules are often affected by natural environments such as wind, rain, and snow, among which wind load is one of the main factors that cause photovoltaic modules to vibrate or even resonate.

[0003] The resonance phenomenon of photovoltaic modules will not only cause fatigue damage to the module structure and reduce the service life, but may also affect the power generation efficiency of photovoltaic cells and increase maintenance costs. In particular, when the wind speed and wind direction reach certain conditions, photovoltaic modules are prone to resonance. This resonance phenomenon is often difficult to predict and difficult to directly control through conventional means.

[0004] In the prior art, the vibration monitoring of photovoltaic modules mainly relies on simple sensor detection. These methods can only monitor the existence of vibration, but cannot deeply analyze the cause and characteristics of the vibration, and cannot give suggestions to avoid the occurrence of resonance. Therefore, it is necessary to provide a photovoltaic module resonance monitoring and adjustment method and system to solve the above problems. Summary of the invention

[0005] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a photovoltaic module resonance monitoring and adjustment method and system to solve the problems existing in the above-mentioned background technology.

[0006] The present invention is implemented as follows: a photovoltaic module resonance monitoring and adjustment method, the method comprising the following steps:

[0007] Collecting vibration data and wind information of the photovoltaic module based on the sensor, wherein the wind information includes wind speed, wind direction and time, and the vibration data includes a timestamp;

[0008] Analyze the vibration data through Fourier transform, determine the resonance time period, and retrieve the wind speed and wind direction corresponding to the resonance time period;

[0009] Analyze the retrieved wind speed and direction to determine the wind speed and direction information that causes the photovoltaic components to resonate, and optimize the wind speed and direction information based on vibration data and wind force information;

[0010] Generate adjustment suggestions based on the optimized wind speed and direction information and the proportion of the daily resonance time period.

[0011] Another object of the present invention is to provide a photovoltaic assembly resonance monitoring and adjustment system, the system is applied to the photovoltaic assembly resonance monitoring and adjustment method, the system comprises:

[0012] A vibration wind power collection module, which is used to collect vibration data and wind power information of photovoltaic modules based on sensors, wherein the wind power information includes wind speed, wind direction and time, and the vibration data includes a timestamp;

[0013] A vibration data analysis module, used to analyze the vibration data through Fourier transform, determine the resonance time period, and retrieve the wind speed and wind direction corresponding to the resonance time period;

[0014] The wind speed and direction optimization module is used to analyze the retrieved wind speed and direction, determine the wind speed and direction information that generates resonance for the photovoltaic components, and optimize the wind speed and direction information based on vibration data and wind force information;

[0015] The adjustment suggestion scheme module is used to generate adjustment suggestion schemes based on the optimized wind speed and direction information and the proportion of the resonance time period every day.

[0016] Compared with the prior art, the present invention has the following beneficial effects:

[0017] The present invention collects vibration data and wind information of photovoltaic modules, analyzes the vibration data through Fourier transform, determines the resonance time period, and retrieves the corresponding wind speed and wind direction data in the time period. Further, the retrieved wind speed and wind direction data are analyzed to determine the specific wind speed and wind direction conditions that cause the photovoltaic module to resonate. On this basis, the wind speed and wind direction information is optimized based on the vibration data and wind information to avoid accidental factors in the wind speed and wind direction information. Finally, a targeted adjustment suggestion plan is generated to provide a scientific basis and decision-making support for the operation and maintenance of the photovoltaic power station. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 The present invention is a flow chart of a photovoltaic module resonance monitoring and adjustment method.

[0019] Figure 2 The present invention is a flow chart for analyzing vibration data in a photovoltaic module resonance monitoring and adjustment method.

[0020] Figure 3 The present invention is a flow chart of determining a resonance time period according to a timestamp in a photovoltaic component resonance monitoring and adjustment method.

[0021] Figure 4 The present invention is a flow chart for optimizing wind speed and direction information in a photovoltaic module resonance monitoring and adjustment method.

[0022] Figure 5A flow chart for generating an adjustment suggestion scheme in a photovoltaic module resonance monitoring and adjustment method.

[0023] Figure 6 The figure is a schematic diagram of the structure of a photovoltaic module resonance monitoring and adjustment system. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0025] The specific implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0026] like Figure 1 As shown, an embodiment of the present invention provides a photovoltaic assembly resonance monitoring and adjustment method, the method comprising the following steps:

[0027] S100, collecting vibration data and wind information of the photovoltaic assembly based on a sensor, wherein the wind information includes wind speed, wind direction and time, and the vibration data includes a timestamp;

[0028] S200, analyzing the vibration data by Fourier transform, determining a resonance time period, and retrieving a wind speed and a wind direction corresponding to the resonance time period;

[0029] S300, analyzing the retrieved wind speed and wind direction to determine the wind speed and wind direction information at which the photovoltaic assembly generates resonance, and optimizing the wind speed and wind direction information based on the vibration data and wind force information;

[0030] S400: Generate an adjustment suggestion plan based on the optimized wind speed and direction information and the proportion of the resonance time period every day.

[0031] It should be noted that the resonance phenomenon of photovoltaic modules will not only cause fatigue damage to the module structure and reduce the service life, but may also affect the power generation efficiency of photovoltaic cells and increase maintenance costs. In particular, when the wind speed and wind direction reach specific conditions, photovoltaic modules are prone to resonance. This resonance phenomenon is often difficult to predict and difficult to directly control through conventional means. In the prior art, vibration monitoring of photovoltaic modules mainly relies on simple sensor detection. These methods can often only monitor the existence of vibration, but cannot deeply analyze the causes and characteristics of the vibration, and cannot give suggestions to avoid the occurrence of resonance. The embodiments of the present invention are intended to solve the above problems.

[0032] In the embodiment of the present invention, it is necessary to install a vibration sensor on the photovoltaic module, collect the vibration data of the photovoltaic module through the vibration sensor, and also need to install meteorological monitoring equipment such as anemometer and wind vane to obtain wind information, the wind information includes wind speed, wind direction and time, and the vibration data includes a timestamp. Then, the vibration data will be analyzed by Fourier transform to determine the resonance time period, and the wind speed and wind direction corresponding to the resonance time period will be retrieved. It is easy to understand that these wind speeds and wind directions are likely to cause the photovoltaic module to resonate, and it is necessary to conduct an in-depth analysis of the retrieved wind speed and wind direction to determine the wind speed and wind direction information that the photovoltaic module resonates, optimize the wind speed and wind direction information based on the vibration data and wind information, and perform further verification to avoid accidental factors in the wind speed and wind direction information. Finally, an adjustment suggestion plan will be generated based on the optimized wind speed and wind direction information and the proportion of the resonance time period every day. The content of the adjustment suggestion plan includes but is not limited to adjusting the angle direction of the photovoltaic module and strengthening the structure. The reinforcement measures include adding dampers, replacing more solid bracket materials, adjusting the bracket structure, etc.

[0033] like Figure 2 As shown, as a preferred embodiment of the present invention, the step of analyzing the vibration data by Fourier transform to determine the resonance time period specifically includes:

[0034] S201, dividing the vibration data into a plurality of data segments of equal length, applying a window function to each data segment to reduce spectrum leakage during Fourier transform analysis, wherein the window function is a Hamming window or a Hanning window;

[0035] S202, performing a fast Fourier transform on each windowed data segment to convert the time domain signal into a frequency domain signal;

[0036] S203, identifying the resonance frequency, when the frequency amplitude in the spectrum exceeds the amplitude threshold, confirming that resonance occurs, and determining the resonance time period according to the timestamp.

[0037] In an embodiment of the present invention, in order to determine the resonance time period, it is first necessary to perform denoising on the collected vibration data. A filtering method (such as low-pass filtering, band-pass filtering) can be used to remove high-frequency noise and low-frequency drift, and then the processed vibration data is divided into a number of data segments of equal length (called windows). A window function is applied to each data segment to reduce spectrum leakage during Fourier transform analysis. The window function is a Hamming window or a Hanning window. The selection of the window function should be based on signal characteristics and analysis accuracy requirements. A fast Fourier transform is performed on each windowed data segment to convert the time domain signal into a frequency domain signal. Finally, spectrum analysis and resonance frequency identification can be performed.

[0038] As a preferred embodiment of the present invention, the step of applying a Hamming window to each data segment specifically includes the following sub-steps:

[0039] Calculate the standard deviation of the input signal segment, and adaptively adjust the length of the window function according to the standard deviation of the input signal segment;

[0040] Selecting adjustment parameters, the adjustment parameters include baseline amplitude parameters, cosine modulation parameters, sine modulation parameters and nonlinear modulation parameters;

[0041] Create a time series index, where the time series index is used to represent a sequence of integers from zero to the window function length minus one;

[0042] For each time index in the time series index, the baseline amplitude parameter is used as the baseline component;

[0043] Based on the time index and the cosine modulation parameter, a first modulation component is obtained by calculating the cosine function;

[0044] Based on the time index and the sinusoidal modulation parameter, a second modulation component is obtained by sinusoidal function calculation;

[0045] Based on the time index and the nonlinear modulation parameter, the nonlinear modulation part is obtained by calculating the sine square function;

[0046] Combine the baseline component, the first modulation component, the second modulation component and the nonlinear modulation part to obtain a combined modulation result;

[0047] Apply the combined modulation result to the standard Hamming window formula to obtain the final value of the window function.

[0048] In an embodiment of the present invention, by dynamically adjusting the length and type of the window function according to the characteristics of the signal (such as the standard deviation), the local characteristics of the signal can be adapted, and the window function can be adaptively adjusted according to the severity of the signal change, so as to effectively capture the detailed changes of the signal. In the basic modulation part, cosine components and sine components are introduced. The cosine component provides the basic shape of the window function, so that it smoothly transitions to the boundary of the window; and the sine component can be used for spectrum adjustment to make fine adjustments to the window function according to specific spectrum requirements, improve spectrum leakage, and enhance the resolution of specific frequencies. In addition, in order to ensure better flexibility, nonlinear functions are used to introduce additional modulation components to increase the flexibility of the window function, so that it can better adapt to specific signal characteristics, improve the resolution of certain frequency regions, and allow real-time adjustment of the window function when processing dynamic signals, so as to better follow the changes in the signal.

[0049] As a preferred embodiment of the present invention, the step of performing fast Fourier transform on each windowed data segment to convert the time domain signal into a frequency domain signal specifically includes:

[0050] Performing zero padding on the tail of each windowed data segment to extend the data segment length to the required number of Fourier transform points to obtain a zero-padded data segment;

[0051] Each zero-filled data segment is divided into multiple overlapping segments by using a window overlapping method;

[0052] Performing a fast Fourier transform on each overlapping segment to convert the time domain signal into a frequency domain signal, and collecting the Fourier transform results of each data segment;

[0053] The Fourier transform results of all data segments are averaged to obtain the frequency domain signal.

[0054] In an embodiment of the present invention, zeros are added to the end of a data segment to increase the length of the data segment and improve the frequency resolution in the frequency domain. The data segment is divided into multiple segments through overlapping windows, and multiple FFTs are performed to capture time-frequency changes and increase the resolution in the frequency domain, making it easier to distinguish similar frequency components in the frequency domain. Multi-resolution analysis captures the time-varying characteristics of the signal and provides a more comprehensive understanding of the signal characteristics. Finally, the Fourier transform results under multiple overlapping windows are averaged to reduce the impact of noise, provide a more stable and reliable spectrum representation, and enhance the reliability of subsequent signal analysis.

[0055] like Figure 3 As shown, as a preferred embodiment of the present invention, the step of identifying the resonant frequency, confirming that resonance occurs when the frequency amplitude in the spectrum exceeds the amplitude threshold, and determining the resonance time period according to the timestamp specifically includes:

[0056] S2031, plotting the frequency domain signal into a spectrum diagram, where the horizontal axis is the frequency and the vertical axis is the amplitude of the corresponding frequency;

[0057] S2032, determining a frequency point whose amplitude exceeds an amplitude threshold based on the frequency spectrum, wherein the frequency point is identified as a resonant frequency;

[0058] S2033, recording the timestamp corresponding to the resonance frequency, and summarizing to determine the resonance time period.

[0059] In an embodiment of the present invention, the frequency domain signal is plotted as a spectrum diagram, the horizontal axis of the spectrum diagram is the frequency, and the vertical axis is the amplitude of the corresponding frequency; in addition. It is necessary to set the amplitude threshold in advance. Specifically, according to the background noise level of the vibration data and the system characteristics, a reasonable amplitude threshold is set. This threshold should be higher than the average amplitude of the background noise, but not too high to ignore the real resonance signal. In the spectrum diagram, find the frequency points whose amplitude exceeds the set threshold. These frequency points may be resonance frequencies. It should be noted that sometimes due to spectrum leakage and noise interference, multiple close peaks may appear. At this time, it is necessary to combine other information (such as continuity in time, frequency stability, etc.) for judgment. Finally, the timestamp corresponding to the resonance frequency is recorded, and the resonance time period can be determined by summarizing it.

[0060] As a preferred embodiment of the present invention, the process of obtaining the amplitude threshold specifically includes the following steps:

[0061] Divide the entire spectrum vibration signal data in the spectrum into a number of spectrum vibration signal data segments of equal length, and calculate the mean and standard deviation of each spectrum vibration signal data segment;

[0062] Using the mean values ​​of all spectrum vibration signal data segments, the average value is calculated to obtain the global mean value of the spectrum vibration signal data;

[0063] Using the standard deviation of all spectrum vibration signal data segments, calculate their standard deviation to obtain the global standard deviation of the spectrum vibration signal data;

[0064] A sensitivity factor is set, a global mean value of the spectrum vibration signal data and the sensitivity factor are multiplied to obtain a first intermediate product, a global standard deviation of the spectrum vibration signal data and the sensitivity factor are multiplied to obtain a second intermediate product, and the first intermediate product and the second intermediate product are added to obtain a preliminary threshold value;

[0065] A confidence level is set, and a corresponding z score is calculated using a normal distribution table according to the set confidence level, and the corresponding z score is used to adjust the preliminary threshold to obtain an adjusted threshold;

[0066] In the entire spectrum vibration signal data, the maximum signal value is searched and confirmed, the adjusted threshold value is compared with the maximum signal value, and the smaller of the two is taken as the amplitude threshold value.

[0067] In the embodiment of the present invention, by segmenting the spectrum vibration signal data and calculating the mean and standard deviation of each spectrum vibration signal data segment, the characteristics of the background noise can be more accurately reflected, and the threshold is adjusted using a statistical confidence level, thereby increasing the reliability of the result. By applying the z-score, it is ensured that the threshold still has a high accuracy even in the presence of random noise. By considering the maximum value in the spectrum vibration signal, the rationality of the threshold is guaranteed, and the real resonance signal is avoided from being ignored due to an overly high setting.

[0068] like Figure 4 As shown, as a preferred embodiment of the present invention, the step of optimizing the wind speed and direction information based on the vibration data and the wind force information specifically includes:

[0069] S301, performing correlation analysis on the vibration amplitude in the vibration data and the wind speed and wind direction based on the Pearson correlation coefficient;

[0070] Among them, the correlation ,in, represents the Pearson correlation coefficient, Wind speed or wind direction, Vibration amplitude, and represents their respective average values, represents the number of samples;

[0071] S302, determining a strongly correlated wind speed and a strongly correlated wind direction angle according to the correlation calculation result, when the Pearson correlation coefficient is greater than the correlation threshold, the corresponding wind speed is a strongly correlated wind speed, and the corresponding wind direction angle is a strongly correlated wind direction angle;

[0072] S303: Optimize the wind speed and wind direction information based on the strongly correlated wind speed and the strongly correlated wind direction angle, and delete part of the wind speed and wind direction information that does not belong to the strongly correlated wind speed or the strongly correlated wind direction angle.

[0073] In the embodiment of the present invention, in order to optimize the wind speed and wind direction information, a correlation analysis is first required. The embodiment of the present invention performs a correlation analysis between the vibration amplitude in the vibration data and the wind speed and wind direction based on the Pearson correlation coefficient. Then, the strongly correlated wind speed and the strongly correlated wind direction angle are determined according to the correlation calculation results, and then the wind speed and direction information is optimized according to the strongly correlated wind speed and the strongly correlated wind direction angle, and some information in the wind speed and direction information that does not belong to the strongly correlated wind speed or the strongly correlated wind direction angle is deleted. In this way, the wind speed and direction information is more concise and has more reference value.

[0074] like Figure 5As shown, as a preferred embodiment of the present invention, the step of generating an adjustment suggestion scheme based on the optimized wind speed and direction information and the proportion of the resonance time period of each day specifically includes:

[0075] S401, determining photovoltaic module angle adjustment information according to the optimized wind speed and direction information;

[0076] S402: Determine a reinforcement and shock absorption adjustment level according to the proportion of the resonance time period every day, and each reinforcement and shock absorption adjustment level corresponds to a reinforcement and shock absorption scheme.

[0077] In the embodiment of the present invention, the photovoltaic module angle adjustment information is determined based on the optimized wind speed and direction information, and the recommended adjustment angle avoids all wind direction angles in the wind speed and direction information. In addition, the reinforcement and shock absorption adjustment level is determined based on the proportion of the resonance time period every day. The longer the proportion, the higher the corresponding reinforcement and shock absorption adjustment level. Each reinforcement and shock absorption adjustment level corresponds to a reinforcement and shock absorption scheme. The embodiment of the present invention requires setting several levels of reinforcement and shock absorption schemes in advance.

[0078] like Figure 6 As shown, an embodiment of the present invention further provides a photovoltaic assembly resonance monitoring and adjustment system, the system is applied to the photovoltaic assembly resonance monitoring and adjustment method, the system comprises:

[0079] A vibration wind power collection module 100 is used to collect vibration data and wind power information of the photovoltaic module based on sensors, wherein the wind power information includes wind speed, wind direction and time, and the vibration data includes a timestamp;

[0080] A vibration data analysis module 200 is used to analyze the vibration data through Fourier transform, determine the resonance time period, and retrieve the wind speed and wind direction corresponding to the resonance time period;

[0081] The wind speed and wind direction optimization module 300 is used to analyze the retrieved wind speed and wind direction, determine the wind speed and wind direction information at which the photovoltaic module generates resonance, and optimize the wind speed and wind direction information based on the vibration data and wind force information;

[0082] The adjustment suggestion module 400 is used to generate an adjustment suggestion based on the optimized wind speed and direction information and the proportion of the resonance time period every day.

[0083] As a preferred embodiment of the present invention, the vibration data analysis module 200 includes:

[0084] A vibration data segmentation unit is used to segment the vibration data into a number of data segments of equal length, and to apply a window function to each data segment to reduce spectrum leakage during Fourier transform analysis. The window function is a Hamming window or a Hanning window.

[0085] A frequency domain signal acquisition unit, used for performing a fast Fourier transform on each windowed data segment to convert the time domain signal into a frequency domain signal;

[0086] The resonance time period determination unit is used to identify the resonance frequency. When the frequency amplitude in the spectrum exceeds the amplitude threshold, it is confirmed that resonance occurs, and the resonance time period is determined according to the timestamp.

[0087] As a preferred embodiment of the present invention, the resonance time period determination unit includes:

[0088] The spectrum plotting subunit is used to plot the frequency domain signal into a spectrum plot, where the horizontal axis is the frequency and the vertical axis is the amplitude of the corresponding frequency;

[0089] a resonance frequency determination subunit, configured to determine, based on the frequency spectrum, a frequency point whose amplitude exceeds an amplitude threshold, wherein the frequency point is identified as a resonance frequency;

[0090] The timestamp aggregation subunit is used to record the timestamps corresponding to the resonance frequencies and aggregate them to determine the resonance time period.

[0091] As a preferred embodiment of the present invention, the wind speed and wind direction optimization module 300 includes:

[0092] A correlation calculation unit, used for performing correlation analysis between the vibration amplitude in the vibration data and the wind speed and wind direction based on the Pearson correlation coefficient;

[0093] The strongly correlated information determination unit is used to determine the strongly correlated wind speed and the strongly correlated wind direction angle according to the correlation calculation result. When it is greater than the correlation threshold, the corresponding wind speed is a strongly correlated wind speed, and the corresponding wind direction angle is a strongly correlated wind direction angle;

[0094] The wind speed and wind direction optimization unit is used to optimize the wind speed and wind direction information based on the strongly correlated wind speed and the strongly correlated wind direction angle, and delete part of the wind speed and wind direction information that does not belong to the strongly correlated wind speed or the strongly correlated wind direction angle.

[0095] As a preferred embodiment of the present invention, the adjustment suggestion module 400 includes:

[0096] An angle adjustment information unit, used to determine the photovoltaic module angle adjustment information according to the optimized wind speed and direction information;

[0097] The reinforcement and shock absorption adjustment unit is used to determine the reinforcement and shock absorption adjustment level according to the proportion of the resonance time period every day, and each reinforcement and shock absorption adjustment level corresponds to a reinforcement and shock absorption plan.

[0098] The above only describes in detail the preferred embodiments of the present invention, which is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0099] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0100] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0101] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the disclosure in the specification and examples. This application is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

Claims

1. A photovoltaic module resonance monitoring and adjustment method, characterized in that: The method comprises the following steps: Collecting vibration data and wind information of the photovoltaic module based on the sensor, wherein the wind information includes wind speed, wind direction and time, and the vibration data includes a timestamp; Analyze the vibration data through Fourier transform, determine the resonance time period, and retrieve the wind speed and wind direction corresponding to the resonance time period; Analyze the retrieved wind speed and direction to determine the wind speed and direction information that causes the photovoltaic components to resonate, and optimize the wind speed and direction information based on vibration data and wind force information; Generate adjustment suggestions based on the optimized wind speed and direction information and the proportion of the daily resonance time period; The step of analyzing the vibration data by Fourier transform to determine the resonance time period specifically includes: Divide the vibration data into several data segments of equal length, and apply a window function to each data segment to reduce spectrum leakage during Fourier transform analysis. The window function is a Hamming window or a Hanning window. Perform fast Fourier transform on each windowed data segment to convert the time domain signal into the frequency domain signal; Identify the resonant frequency. When the frequency amplitude in the spectrum exceeds the amplitude threshold, it is confirmed that resonance has occurred. The resonance time period is determined based on the timestamp. The step of applying the Hamming window to each data segment specifically includes the following sub-steps: Calculate the standard deviation of the input signal segment, and adaptively adjust the length of the window function according to the standard deviation of the input signal segment; Selecting adjustment parameters, the adjustment parameters include baseline amplitude parameters, cosine modulation parameters, sine modulation parameters and nonlinear modulation parameters; Create a time series index, where the time series index is used to represent a sequence of integers from zero to the window function length minus one; For each time index in the time series index, the baseline amplitude parameter is used as the baseline component; Based on the time index and the cosine modulation parameter, a first modulation component is obtained by calculating the cosine function; Based on the time index and the sinusoidal modulation parameter, a second modulation component is obtained by sinusoidal function calculation; Based on the time index and the nonlinear modulation parameter, the nonlinear modulation part is obtained by calculating the sine square function; Combine the baseline component, the first modulation component, the second modulation component and the nonlinear modulation part to obtain a combined modulation result; Apply the combined modulation result to the standard Hamming window formula to obtain the final value of the window function.

2. The photovoltaic assembly resonance monitoring and adjustment method according to claim 1, characterized in that: The step of performing a fast Fourier transform on each windowed data segment to convert the time domain signal into a frequency domain signal specifically comprises the following sub-steps: Performing zero padding on the tail of each windowed data segment to extend the data segment length to the required number of Fourier transform points to obtain a zero-padded data segment; Each zero-filled data segment is divided into multiple overlapping segments by using a window overlapping method; Performing a fast Fourier transform on each overlapping segment to convert the time domain signal into a frequency domain signal, and collecting the Fourier transform results of each data segment; The Fourier transform results of all data segments are averaged to obtain the frequency domain signal.

3. The photovoltaic assembly resonance monitoring and adjustment method according to claim 2, characterized in that: The step of identifying the resonant frequency, confirming that resonance occurs when the frequency amplitude in the spectrum exceeds the amplitude threshold, and determining the resonance time period according to the timestamp specifically includes: The frequency domain signal is plotted as a spectrum diagram, with the horizontal axis being the frequency and the vertical axis being the amplitude of the corresponding frequency; Determining a frequency point where the amplitude exceeds an amplitude threshold based on the frequency spectrum, wherein the frequency point is identified as a resonant frequency; The timestamps corresponding to the resonant frequencies are recorded and summarized to determine the resonant time period.

4. The photovoltaic assembly resonance monitoring and adjustment method according to claim 3, characterized in that: The process of determining the amplitude threshold specifically includes the following steps: Divide the entire spectrum vibration signal data in the spectrum into a number of spectrum vibration signal data segments of equal length, and calculate the mean and standard deviation of each spectrum vibration signal data segment; Using the mean values ​​of all spectrum vibration signal data segments, the average value is calculated to obtain the global mean value of the spectrum vibration signal data; Using the standard deviation of all spectrum vibration signal data segments, calculate their standard deviation to obtain the global standard deviation of the spectrum vibration signal data; A sensitivity factor is set, a global mean value of the spectrum vibration signal data and the sensitivity factor are multiplied to obtain a first intermediate product, a global standard deviation of the spectrum vibration signal data and the sensitivity factor are multiplied to obtain a second intermediate product, and the first intermediate product and the second intermediate product are added to obtain a preliminary threshold value; A confidence level is set, and a corresponding z score is calculated using a normal distribution table according to the set confidence level, and the corresponding z score is used to adjust the preliminary threshold to obtain an adjusted threshold; In the entire spectrum vibration signal data, the maximum signal value is searched and confirmed, the adjusted threshold value is compared with the maximum signal value, and the smaller of the two is taken as the amplitude threshold value.

5. The photovoltaic assembly resonance monitoring and adjustment method according to claim 4, characterized in that: The step of optimizing the wind speed and direction information based on the vibration data and the wind force information specifically includes: The vibration amplitude in the vibration data is correlated with wind speed and wind direction based on the Pearson correlation coefficient. ,in, represents the Pearson correlation coefficient, Indicates wind speed or wind direction. represents the vibration amplitude, and represents their respective average values, represents the number of samples; According to the correlation calculation results, the strongly correlated wind speed and strongly correlated wind direction angle are determined. When it is greater than the correlation threshold, the corresponding wind speed is a strongly correlated wind speed, and the corresponding wind direction angle is a strongly correlated wind direction angle; The wind speed and direction information is optimized based on the strongly correlated wind speed and the strongly correlated wind direction angle, and part of the wind speed and direction information that does not belong to the strongly correlated wind speed or the strongly correlated wind direction angle is deleted.

6. The photovoltaic assembly resonance monitoring and adjustment method according to claim 5, characterized in that: The step of generating an adjustment suggestion plan based on the optimized wind speed and direction information and the proportion of the resonance time period every day specifically includes: Determine the photovoltaic module angle adjustment information according to the optimized wind speed and direction information; The reinforcement and shock absorption adjustment level is determined according to the proportion of the resonance time period every day, and each reinforcement and shock absorption adjustment level corresponds to a reinforcement and shock absorption plan.

7. A photovoltaic assembly resonance monitoring and adjustment system, the system being applied to the photovoltaic assembly resonance monitoring and adjustment method according to any one of claims 1 to 6, characterized in that: The system comprises: A vibration wind power collection module, which is used to collect vibration data and wind power information of photovoltaic modules based on sensors, wherein the wind power information includes wind speed, wind direction and time, and the vibration data includes a timestamp; A vibration data analysis module, used to analyze the vibration data through Fourier transform, determine the resonance time period, and retrieve the wind speed and wind direction corresponding to the resonance time period; The wind speed and direction optimization module is used to analyze the retrieved wind speed and direction, determine the wind speed and direction information that generates resonance for the photovoltaic components, and optimize the wind speed and direction information based on vibration data and wind force information; The adjustment suggestion scheme module is used to generate adjustment suggestion schemes based on the optimized wind speed and direction information and the proportion of the resonance time period every day.

8. The photovoltaic assembly resonance monitoring and adjustment system according to claim 7, characterized in that: The vibration data analysis module comprises: A vibration data segmentation unit is used to segment the vibration data into a number of data segments of equal length, and to apply a window function to each data segment to reduce spectrum leakage during Fourier transform analysis. The window function is a Hamming window or a Hanning window. A frequency domain signal acquisition unit, used for performing a fast Fourier transform on each windowed data segment to convert the time domain signal into a frequency domain signal; The resonance time period determination unit is used to identify the resonance frequency. When the frequency amplitude in the spectrum exceeds the amplitude threshold, it is confirmed that resonance occurs, and the resonance time period is determined according to the timestamp.

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