Method for Monitoring Hydrogen Power Supply Ripple in Water Electrolysis Hydrogen Production Systems

Abstract

This application relates to the field of power supply ripple monitoring technology, specifically to a method for monitoring the ripple of a hydrogen production power supply in an electrolytic water hydrogen production system. This method addresses the technical problem that the calculated ripple coefficient in existing technologies fails to accurately reflect the actual ripple situation, leading to insufficient overall monitoring accuracy. The method includes: acquiring the output voltage signal and input voltage signal of the hydrogen production power supply during the monitoring period; extracting the DC component and multiple AC mode components from the output voltage signal; identifying the ripple mode component from the multiple AC mode components based on the periodic characteristics of each AC mode component and the time-frequency similarity between each AC mode component and the corresponding frequency signal components in the AC component of the input voltage signal; determining the ripple coefficient of the hydrogen production power supply based on the DC component and the ripple mode component; and generating the ripple monitoring result of the hydrogen production power supply based on the ripple coefficient.

Description

Technical Field

[0001] This application relates to the field of power supply ripple monitoring technology, specifically to a method for monitoring power supply ripple in a hydrogen production system using water electrolysis. Background Technology

[0002] The hydrogen production power supply in a water electrolysis hydrogen production system converts unstable renewable energy into low-voltage DC power suitable for the electrolyzer. The quality of the output power directly determines the hydrogen production efficiency and the reliability of the system operation. Due to the switching action of the power devices inside the power supply and fluctuations in the front-end energy input, AC ripples of various frequencies are superimposed on the DC voltage output by the hydrogen production power supply. These ripple components cause additional energy loss and affect electrolysis efficiency. Therefore, effective monitoring of the power supply output ripple is currently necessary.

[0003] Currently, a preset filter is typically used to filter the acquired output voltage signal, and then the ripple coefficient is calculated from the DC and AC components of the filtered signal to complete the ripple monitoring of the hydrogen production power supply. However, when faced with the complex operating environment of the hydrogen production power supply or fluctuations in the input signal, the existing ripple monitoring method is prone to losing some ripple information due to the filtering process. This results in the calculated ripple coefficient failing to accurately reflect the actual ripple situation, leading to insufficient overall monitoring accuracy. Summary of the Invention

[0004] To address the technical problem that the calculated ripple coefficient in existing technologies fails to accurately reflect the actual ripple situation, resulting in insufficient overall monitoring accuracy, this application aims to provide a method for monitoring the ripple of hydrogen production power sources in water electrolysis hydrogen production systems. The specific technical solution adopted is as follows: Acquire the output voltage signal and input voltage signal of the hydrogen production power source during the monitoring period; The DC component and multiple AC mode components are extracted from the output voltage signal; the multiple AC mode components are used to characterize the signal components of different frequencies in the AC component of the output voltage signal. Based on the periodicity of each AC mode component and the time-frequency similarity between each AC mode component and the signal components of the corresponding frequency in the AC component of the input voltage signal, the ripple mode component is identified from multiple AC mode components. The ripple coefficient of the hydrogen production power source is determined based on the DC component and the ripple mode component; the ripple coefficient is used to characterize the relative magnitude of the ripple component in the DC component. Based on the ripple coefficient, the ripple monitoring results of the hydrogen production power source are generated.

[0005] In one possible implementation, extracting the DC component and multiple AC mode components from the output voltage signal includes: performing a frequency domain transformation on the output voltage signal to obtain the DC component and AC component of the output voltage signal; and decomposing the AC component based on a signal decomposition algorithm to obtain multiple AC mode components.

[0006] In one possible implementation, based on the periodicity characteristics of each AC mode component and the time-frequency similarity between each AC mode component and the corresponding frequency signal component in the AC component of the input voltage signal, ripple mode components are identified from multiple AC mode components. This includes: determining periodic characteristic values ​​based on the autocorrelation characteristics of each AC mode component; the periodic characteristic values ​​are used to characterize the periodicity of the AC mode components; determining reference mode components that match the frequency of each AC mode component from the AC components of the input voltage signal, and calculating time-frequency characteristic values ​​between each AC mode component and the corresponding reference mode component; the time-frequency characteristic values ​​are used to characterize the consistency in the time and frequency domain distributions; determining the ripple confidence of each AC mode component based on the periodic characteristic value and the time-frequency characteristic value; and selecting ripple mode components from multiple AC mode components based on the ripple confidence of each AC mode component.

[0007] In one possible implementation, the periodic characteristic value is determined based on the autocorrelation characteristics of each AC mode component, including: calculating the autocorrelation function of the target AC mode component to obtain the autocorrelation sequence of the target AC mode component; the target AC mode component is one of multiple AC mode components; extracting the maximum peak value other than zero time delay from the autocorrelation sequence; and determining the amplitude of the maximum peak value as the periodic characteristic value of the target AC mode component.

[0008] In one possible implementation, determining a reference mode component that is frequency-matched to each AC mode component from the AC components of the input voltage signal includes: decomposing the input voltage signal to obtain multiple reference mode components and determining the center frequency of each reference mode component; calculating the difference between the center frequency of the target AC mode component and the center frequency of each reference mode component; and determining the reference mode component with the smallest difference as the reference mode component that is frequency-matched to the target AC mode component.

[0009] In one possible implementation, calculating the time-frequency domain feature value between each AC mode component and its corresponding reference mode component includes: extracting multiple time-domain indices of the target AC mode component to determine a first time-domain feature vector; extracting multiple time-domain indices of the target reference mode component corresponding to the target AC mode component to determine a second time-domain feature vector; extracting multiple frequency-domain indices of the target AC mode component to determine a first frequency-domain feature vector; extracting multiple frequency-domain indices of the target reference mode component to determine a second frequency-domain feature vector; calculating a first similarity between the first time-domain feature vector and the second time-domain feature vector; calculating a second similarity between the first frequency-domain feature vector and the second frequency-domain feature vector; and determining the time-frequency domain feature value based on the first similarity and the second similarity; wherein the time-frequency domain feature value is positively correlated with the first similarity and the second similarity.

[0010] In one possible implementation, based on the ripple confidence of each AC mode component, ripple mode components are selected from multiple AC mode components, including: inputting the ripple confidence of each AC mode component into the maximum inter-class variance algorithm to obtain a confidence threshold for distinguishing ripple from noise; and selecting AC mode components with ripple confidence greater than the confidence threshold as ripple mode components.

[0011] In one possible implementation, the ripple coefficient of the hydrogen production power source is determined based on the DC component and the ripple mode component, including: reconstructing the selected ripple mode component to obtain the reconstructed AC component; calculating the first effective voltage value of the DC component and the second effective voltage value of the reconstructed AC component; and determining the ripple coefficient based on the ratio of the second effective voltage value to the first effective voltage value.

[0012] In one possible implementation, acquiring the output voltage signal and input voltage signal of the hydrogen production power source during the monitoring period includes: synchronously acquiring the output voltage signal and input voltage signal of the hydrogen production power source at a preset sampling frequency during the monitoring period; wherein the preset sampling frequency is determined according to the preset operating frequency band of the hydrogen production power source, and the monitoring period is determined according to the preset monitoring time interval.

[0013] In one possible implementation, generating ripple monitoring results for the hydrogen production power source based on the ripple coefficient includes: comparing the ripple coefficient with a preset ripple coefficient threshold; and generating monitoring results indicating abnormal ripple in the hydrogen production power source when the ripple coefficient is greater than the ripple coefficient threshold.

[0014] This application offers the following advantages: It extracts the DC component and multiple AC mode components from the output voltage signal, decomposing the complex voltage signal into targeted analysis units. This clarifies the core object of ripple analysis and avoids mutual interference between ripples of different frequencies by separating signal components of different frequencies. Subsequently, based on the periodic characteristics and time-frequency similarity of the AC mode components, the ripple mode components are identified. Through dual-dimensional feature judgment, ripple and noise are effectively distinguished, ensuring that the selected ripple mode components closely match the actual ripple characteristics and reducing misjudgments and omissions in ripple identification. Finally, the ripple coefficient is determined based on the DC component and the ripple mode components, ensuring that the obtained ripple coefficient accurately reflects the actual proportion of ripple in the DC component. This significantly improves the overall accuracy and reliability of ripple monitoring in hydrogen production power supplies, providing a scientific basis for operational intervention in water electrolysis hydrogen production systems and ensuring the efficient and stable operation of the system. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart illustrating a method for monitoring the ripple of a hydrogen production power supply in a water electrolysis hydrogen production system, as provided in one embodiment of this application. Figure 1 ; Figure 2 A flowchart illustrating a method for monitoring the ripple of a hydrogen production power supply in a water electrolysis hydrogen production system, as provided in one embodiment of this application. Figure 2 ; Figure 3 A flowchart illustrating a method for monitoring the ripple of a hydrogen production power supply in a water electrolysis hydrogen production system, as provided in one embodiment of this application. Figure 3 ; Figure 4 A flowchart illustrating a method for monitoring the ripple of a hydrogen production power supply in a water electrolysis hydrogen production system, as provided in one embodiment of this application. Figure 4 . Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a hydrogen production power supply ripple monitoring method for a water electrolysis hydrogen production system proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0019] Unless otherwise specified, the normalization functions mentioned in this application all employ maximum-minimum value normalization. The maximum and minimum values ​​are preset empirical extreme values ​​derived from a large amount of historical experimental data. If the calculated result exceeds the [0,1] interval, a truncation function is used to limit it to the [0,1] range (i.e., if the result is less than 0, it is taken as 0; if it is greater than 1, it is taken as 1) to eliminate the influence of outliers on the evaluation indicators.

[0020] The following description, in conjunction with the accompanying drawings, details the specific scheme of a hydrogen production power supply ripple monitoring method for a water electrolysis hydrogen production system provided in this application.

[0021] Please see Figure 1 This document illustrates a flowchart of a method for monitoring the ripple of a hydrogen production power supply in a water electrolysis hydrogen production system, as provided in one embodiment of this application. Figure 1 As shown, the method includes the following steps: Step 101: Obtain the output voltage signal and input voltage signal of the hydrogen production power source during the monitoring period.

[0022] As one possible implementation, this step can be achieved by simultaneously acquiring the output voltage signal and input voltage signal of the hydrogen production power source at a preset sampling frequency during the monitoring period. The preset sampling frequency is determined based on the preset operating frequency band of the hydrogen production power source, and the monitoring period is determined based on a preset monitoring time interval.

[0023] Specifically, this application incorporates an intelligent sensing system within the water electrolysis hydrogen production system to monitor the ripple of the hydrogen production power source. This intelligent sensing system processes the acquired output voltage signal from the hydrogen production power source, aiming to retain the high-frequency ripple components in the AC component of the voltage signal while effectively filtering out noise components. This ensures that the calculated ripple coefficient more accurately reflects the true AC ripple in the DC component of the hydrogen production power source output, thereby improving the reliability of the ripple monitoring results. The input of the hydrogen production power source is connected to a wind power or photovoltaic power generation and energy storage system, while its output is connected to the water electrolysis hydrogen production system. The intelligent sensing system includes a data acquisition unit, a data processing unit, and a ripple monitoring unit.

[0024] In the data acquisition unit, when the hydrogen production power supply in the water electrolysis hydrogen production system is turned on to produce hydrogen, a voltage sensor is used to collect the output voltage signal and input voltage signal at the output and input terminals of the hydrogen production power supply at a preset sampling frequency, and the collected output and input voltage signals are transmitted to the data processing unit. As an example, the preset sampling frequency is 40MHz.

[0025] In the data processing unit, taking any monitoring time t (excluding the initial two monitoring times) in the ripple monitoring unit as an example, the output voltage signal A and the input voltage signal B collected during the time period corresponding to monitoring time t and its previous monitoring time t-1 are obtained from the database of the data processing unit. In this application, the time interval between two adjacent monitoring times is set to 1 minute, but it can also be flexibly set according to the specific implementation process.

[0026] Step 102: Extract the DC component and multiple AC mode components from the output voltage signal.

[0027] Among them, multiple AC mode components are used to characterize the signal components of different frequencies in the AC component of the output voltage signal.

[0028] As one possible implementation, this step separates the output voltage signal into DC and AC components through frequency domain transformation to isolate the ripple-related components to be analyzed. Following this, the AC components are adaptively decomposed using a signal decomposition algorithm to obtain multiple AC mode components. Each AC mode component characterizes a specific frequency range of signal components within the AC component, thus providing a structured frequency domain representation basis for subsequently distinguishing ripple from noise.

[0029] As an example, the signal decomposition algorithm described above is a variational mode decomposition algorithm.

[0030] Optionally, the signal components of different frequencies refer to several basic signal units obtained after decomposing the original complex AC signal (i.e., the AC component of the output voltage signal). The energy of each basic signal unit is mainly concentrated in a specific, relatively narrow frequency band, representing the fluctuation characteristics of the original signal within that frequency band.

[0031] Step 103: Based on the periodic characteristics of each AC mode component and the time-frequency domain similarity between each AC mode component and the corresponding frequency signal component in the AC component of the input voltage signal, identify the ripple mode component from multiple AC mode components.

[0032] Optionally, ripple mode components refer to the mode components identified from multiple AC mode components of the output voltage signal, used to characterize the true ripple component. The identification of ripple mode components is based on the fact that these components simultaneously possess significant self-periodic variation characteristics and a high degree of consistency with the time-domain and frequency-domain distribution of components of the same frequency in the input voltage signal. The identified ripple mode components will be used to subsequently reconstruct the clean AC components to calculate the accurate ripple coefficient.

[0033] As one possible implementation, this step first evaluates whether each AC mode component exhibits a significant periodic variation pattern, in order to initially distinguish periodic ripple components from random noise. Then, using the AC components of the input voltage signal as a reference, the overall consistency of the time and frequency domain distributions of each AC mode component with its frequency-matched reference component is calculated. AC mode components with significant periodicity and high time-frequency similarity are then identified as ripple mode components.

[0034] Step 104: Determine the ripple coefficient of the hydrogen production power source based on the DC component and the ripple mode component.

[0035] The ripple coefficient is used to characterize the relative magnitude of the ripple component in the DC component.

[0036] Optionally, the discrete ripple mode components are synthesized into a complete reconstructed AC component to characterize the clean ripple signal after noise filtering. Afterward, the RMS voltage values ​​of the DC component and the reconstructed AC component are calculated separately. The ratio of these two RMS values ​​is determined as the ripple coefficient used to quantify the relative magnitude of the ripple component in the DC component.

[0037] Step 105: Generate the ripple monitoring results of the hydrogen production power source based on the ripple coefficient.

[0038] Optionally, the ripple coefficient can be compared with a preset ripple coefficient threshold; when the ripple coefficient is greater than the ripple coefficient threshold, a monitoring result indicating abnormal ripple of the hydrogen production power source can be generated.

[0039] The ripple coefficient threshold can be set according to the tolerance standard of the electrolyzer to power ripple in the water electrolysis hydrogen production system, or the technical specifications of the hydrogen production power supply equipment. It is typically set between 0.5 and 0.8, and can be adjusted according to actual conditions. As an example, the ripple coefficient threshold is set to 3%. Specifically, the calculated ripple coefficient δ is output as the ripple monitoring result of the hydrogen production power supply at monitoring time t. If the ripple coefficient δ is greater than the preset ripple coefficient threshold (e.g., 3%), an alarm is triggered to prompt staff to perform operational checks or maintenance on the hydrogen production power supply in the water electrolysis hydrogen production system.

[0040] This application extracts the DC component and multiple AC mode components from the output voltage signal, decomposing the complex voltage signal into targeted analysis units. This clarifies the core object of ripple analysis and avoids mutual interference between ripples of different frequencies by separating signal components of different frequencies. Subsequently, ripple mode components are identified based on the periodic characteristics and time-frequency similarity of the AC mode components. Through dual-dimensional feature judgment, ripple is effectively distinguished from noise, ensuring that the selected ripple mode components closely match the actual ripple characteristics and reducing misjudgments and omissions in ripple identification. Finally, ripple coefficients are determined based on the DC component and ripple mode components, ensuring that the obtained ripple coefficients accurately reflect the actual proportion of ripple in the DC component. This significantly improves the overall accuracy and reliability of ripple monitoring in hydrogen production power supplies, providing a scientific basis for operational intervention in water electrolysis hydrogen production systems and ensuring the efficient and stable operation of the system.

[0041] like Figure 2 As shown, in one possible implementation, the process of extracting the DC component and multiple AC mode components from the output voltage signal in step 102 above can be specifically implemented through the following steps: Step 201: Perform frequency domain transformation on the output voltage signal to obtain the DC component and AC component of the output voltage signal.

[0042] Optionally, Fourier transform is used to extract the DC and AC components of the output voltage signal A, which are denoted as DC component A1 and AC component A2 at monitoring time t, respectively. The DC component A1 is then transmitted to the ripple monitoring unit for subsequent ripple coefficient calculation.

[0043] Step 202: Decompose the AC components based on the signal decomposition algorithm to obtain multiple AC mode components.

[0044] Specifically, a signal decomposition algorithm is used to decompose the AC component A2 to separate the AC signal components of different frequencies. In this application, the signal decomposition algorithm is preferably a variational mode decomposition algorithm, which can decompose the AC component A2 into multiple mode components. Each mode component is used to characterize the signal components within a specific frequency range of the AC component, and the center frequency of each mode component can be obtained by calculation to reflect the main concentrated frequency band of the signal energy of that component. For example, the number of mode components is set to 20 in this application, and the specific number can be determined based on actual needs.

[0045] It should be noted that, in addition to variational mode decomposition algorithms, this application may also use empirical mode decomposition algorithms, ensemble empirical mode decomposition algorithms, etc., to achieve signal decomposition, and this application does not limit the specific implementation of these algorithms. The specific implementation process of these algorithms can be found in existing technologies, and this application will not elaborate on it further.

[0046] Based on the above technical solutions, this application can accurately separate the DC and AC components of the output voltage signal through frequency domain transformation, ensuring the relevance of subsequent analysis. The signal decomposition algorithm can effectively split the signal components of different frequencies in the AC component into multiple AC mode components, avoiding mutual interference between ripple components of different frequencies. This lays a solid foundation for accurate identification of ripple mode components, ensuring that the subsequent distinction between ripple and noise is more targeted, and improving the rationality and reliability of the entire ripple monitoring process.

[0047] like Figure 3 As shown, in one possible implementation, step 103 above, based on the periodicity characteristics of each AC mode component and the time-frequency similarity between each AC mode component and the signal components of the corresponding frequency in the AC component of the input voltage signal, can be specifically implemented through the following steps: Step 301: Determine the periodic characteristic value based on the autocorrelation characteristics of each AC mode component.

[0048] Among them, the periodic eigenvalue is used to characterize the degree of periodicity of the AC mode components.

[0049] One possible implementation is as follows: Calculate the autocorrelation function of the target AC mode component to obtain its autocorrelation sequence. The target AC mode component is one of multiple AC mode components. Extract the maximum peak value (excluding zero-delay) from the autocorrelation sequence. Determine the amplitude of the maximum peak value as the periodic eigenvalue of the target AC mode component.

[0050] Optionally, taking any mode component 'a' in AC component A2 as an example, the autocorrelation sequence of this mode component is calculated using the autocorrelation function to obtain its autocorrelation coefficient distribution under different time delays. The amplitude corresponding to the maximum peak value in the autocorrelation sequence other than zero time delay is denoted as the periodic characteristic value of mode component 'a'. This periodic characteristic value is used to evaluate the significance of the periodicity of the signal component corresponding to this mode component; the larger the periodic characteristic value, the more obvious its periodic variation characteristics, and the more likely the component is to belong to the ripple component.

[0051] It should be noted that the calculation process of the above autocorrelation function can refer to the existing technology, and this application will not elaborate on it.

[0052] Step 302: Determine the reference mode component that matches the frequency of each AC mode component from the AC components of the input voltage signal, and calculate the time-frequency domain characteristic value between each AC mode component and the corresponding reference mode component.

[0053] Among them, the time-frequency domain eigenvalues ​​are used to characterize the degree of consistency in the distribution in the time domain and the frequency domain.

[0054] As one possible implementation, the process of determining the reference mode components in this step includes: decomposing the input voltage signal to obtain multiple reference mode components and determining the center frequency of each reference mode component; calculating the difference between the center frequency of the target AC mode component and the center frequency of each reference mode component; and determining the reference mode component with the smallest difference as the reference mode component whose frequency matches that of the target AC mode component.

[0055] The process of determining time-frequency domain feature values ​​in this step includes: extracting multiple time-domain indices of the target AC mode component to determine a first time-domain feature vector; extracting multiple time-domain indices of the target reference mode component corresponding to the target AC mode component to determine a second time-domain feature vector; extracting multiple frequency-domain indices of the target AC mode component to determine a first frequency-domain feature vector; extracting multiple frequency-domain indices of the target reference mode component to determine a second frequency-domain feature vector; calculating a first similarity between the first and second time-domain feature vectors; and calculating a second similarity between the first and second frequency-domain feature vectors. Based on the first and second similarities, the time-frequency domain feature values ​​are determined. The time-frequency domain feature values ​​are positively correlated with both the first and second similarities.

[0056] Optionally, due to natural fluctuations such as wind and solar energy causing instability in the front-end power supply frequency, this instability is transmitted to the output voltage via the hydrogen production power supply, forming ripple associated with input voltage fluctuations. Therefore, the ripple component in the AC component of the output voltage should have consistent time and frequency domain distribution characteristics with the signal component of the same frequency in the AC component of the input voltage; while noise components do not possess such characteristics. Therefore, this application uses the same method as processing the output voltage signal A to decompose the AC component B2 of the input voltage signal B, obtaining multiple reference mode components, and acquiring the center frequency of each reference mode component. From these reference mode components, the one with the smallest difference from the center frequency of mode component a is selected and denoted as reference mode component b, representing the component in the input signal that has the same frequency as mode component a. The difference in center frequencies can be measured by calculating the absolute value of the difference between the two. Based on this process, a reference mode component that matches the frequency of each AC mode component is determined. The reference mode component that matches the frequency of the AC mode component is also called the reference mode component corresponding to the AC mode component.

[0057] Multiple time-domain indices (such as mean, standard deviation, and waveform factor) of mode component a are calculated to form the time-domain feature vector of mode component a, which characterizes its signal time-domain distribution characteristics. Multiple frequency-domain indices (such as frequency mean and frequency standard deviation) of mode component a are calculated to form the frequency-domain feature vector of mode component a, which characterizes its signal frequency-domain distribution characteristics. Based on a similar approach, the time-domain and frequency-domain feature vectors corresponding to the reference mode component b are calculated. The calculation process for the signal time-domain and frequency-domain indices can refer to existing technologies, and will not be elaborated upon in this application.

[0058] The first similarity d1 between the time-domain feature vectors of modal component a and the reference modal component b, and the second similarity d2 between their frequency-domain feature vectors are calculated respectively. The larger the first similarity d1 and the second similarity d2, the higher the consistency between the two in the time and frequency domains. The mean of d1 and d2 is denoted as the time-frequency domain feature value of modal component a; the larger this value, the more consistent the time-frequency domain distribution characteristics of modal component a are with the intermediate frequency components of the input signal, and the more likely this component is to belong to the ripple component. As an example, this application can calculate the similarity measure based on cosine similarity; the specific process can refer to existing technologies, and this application does not limit it in this regard.

[0059] Step 303: Determine the ripple confidence level of each AC mode component based on the periodic characteristic value and time-frequency domain characteristic value of each AC mode component.

[0060] As one possible implementation, the Min-Max normalization method is used to normalize the periodic eigenvalues ​​and time-frequency domain eigenvalues ​​of all modal components of AC component A2. For modal component a, its ripple confidence is calculated based on its normalized periodic eigenvalues ​​and time-frequency domain eigenvalues. The ripple confidence is used to comprehensively evaluate the probability that the component belongs to ripple; the larger the normalized eigenvalue, the higher the ripple confidence is generally. Optionally, the ripple confidence can be calculated as the mean of the two normalized eigenvalues, or a weighted sum of the two normalized eigenvalues, etc., and this application does not limit this.

[0061] Step 304: Based on the ripple confidence of each AC mode component, filter out the ripple mode components from multiple AC mode components.

[0062] One possible implementation is as follows: input the ripple confidence score of each AC mode component into the maximum inter-class variance algorithm to obtain a confidence threshold for distinguishing ripple from noise. AC mode components with ripple confidence scores greater than the confidence threshold are then selected as ripple mode components.

[0063] Specifically, the ripple confidence scores corresponding to all modal components of AC component A2 are used as input and processed using the Otsu's inter-class variance algorithm to obtain the corresponding ripple confidence thresholds. Afterward, modal components with ripple confidence scores greater than this threshold are selected from all modal components and designated as ripple modal components. These selected modal components represent the ripple components contained in the AC component A2 of the output voltage signal A.

[0064] Based on the above technical solution, this application quantifies the periodicity of AC mode components by periodic feature values ​​and combines time-frequency domain feature values ​​to reflect the distribution consistency of frequency components corresponding to the input voltage. The ripple confidence constructed in this dual-dimensional way can comprehensively and accurately characterize the ripple properties of AC mode components, effectively solving the problem of difficulty in accurately distinguishing ripple from noise in traditional methods. This ensures that the ripple mode components selected from multiple AC mode components are more in line with the actual ripple characteristics, greatly reducing the misjudgment rate of ripple identification and providing a core guarantee for the accurate calculation of subsequent ripple coefficients.

[0065] like Figure 4 As shown, in one possible implementation, the process of determining the ripple coefficient of the hydrogen production power source based on the DC component and the ripple mode component in step 104 specifically includes: Step 401: Reconstruct the selected ripple mode components to obtain the reconstructed AC components.

[0066] Optionally, the modal components characterizing all selected ripple components are reconstructed, and the reconstructed signal is denoted as the AC component A3 at monitoring time t. This AC component A3 characterizes the pure AC component obtained after noise filtering of the original AC component A2 of the output voltage signal A collected during the time period corresponding to monitoring time t and its previous monitoring time t-1. Subsequently, the AC component A3 is transmitted to the ripple monitoring unit to calculate the ripple coefficient of the hydrogen production power supply at monitoring time t. The above signal reconstruction process can refer to relevant technologies, and will not be elaborated here.

[0067] Step 402: Calculate the first effective voltage value of the DC component and the second effective voltage value of the reconstructed AC component.

[0068] Step 403: Determine the ripple coefficient based on the ratio of the effective value of the second voltage to the effective value of the first voltage.

[0069] Optionally, the ripple coefficient is calculated based on the DC component obtained in the aforementioned steps and the reconstructed AC component, and ripple monitoring of the hydrogen production power supply is completed accordingly. Specifically, in the ripple monitoring unit, the ripple coefficient δ of the hydrogen production power supply at monitoring time t is calculated based on the DC component A1 and the AC component A3 at that time. For example, δ is equal to the ratio of the effective voltage value V2 of the AC component A3 to the effective voltage value V1 of the DC component A1, i.e. It should be noted that under normal operating conditions, the effective value V1 of the DC component A1 output voltage is greater than zero. If the calculated V1=0, it indicates that the hydrogen power supply is not operating normally, and this parameter is not included in the calculation of the ripple factor.

[0070] Based on the above technical solution, this application can integrate all effective ripple components to form a complete and pure AC component by reconstructing the selected ripple mode components. The ripple coefficient is calculated based on the ratio of the effective voltage values ​​of the DC and AC components, which fully conforms to the physical definition of the ripple coefficient. This avoids coefficient distortion caused by missing ripple components or unreasonable calculation logic, so that the final ripple coefficient can truly and accurately reflect the relative magnitude of the ripple in the DC component of the hydrogen production power supply output, providing a reliable quantitative basis for ripple monitoring results.

[0071] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0072] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for monitoring the ripple of a hydrogen production power supply in a water electrolysis hydrogen production system, characterized in that, The method includes: Acquire the output voltage signal and input voltage signal of the hydrogen production power source during the monitoring period; A DC component and multiple AC mode components are extracted from the output voltage signal; the multiple AC mode components are used to characterize the signal components of different frequencies in the AC component of the output voltage signal; Based on the periodicity of each AC mode component and the time-frequency similarity between each AC mode component and the signal component of the corresponding frequency in the AC component of the input voltage signal, the ripple mode component is identified from the plurality of AC mode components. The ripple coefficient of the hydrogen production power source is determined based on the DC component and the ripple mode component; the ripple coefficient is used to characterize the relative magnitude of the ripple component in the DC component. Based on the ripple coefficient, the ripple monitoring results of the hydrogen production power source are generated.

2. The method for monitoring the ripple of hydrogen production power supply in a water electrolysis hydrogen production system according to claim 1, characterized in that, Extracting the DC component and multiple AC mode components from the output voltage signal includes: The output voltage signal is frequency domain transformed to obtain the DC component and AC component of the output voltage signal; The AC components are decomposed based on a signal decomposition algorithm to obtain the multiple AC mode components.

3. The method for monitoring the ripple of the hydrogen production power supply in a water electrolysis hydrogen production system according to claim 1, characterized in that, Based on the periodicity of each AC mode component and the time-frequency similarity between each AC mode component and the corresponding frequency signal component of the AC component of the input voltage signal, ripple mode components are identified from the plurality of AC mode components, including: Based on the autocorrelation characteristics of each AC mode component, a periodic characteristic value is determined; the periodic characteristic value is used to characterize the degree of periodicity of the AC mode component. A reference mode component that matches the frequency of each AC mode component is determined from the AC component of the input voltage signal, and a time-frequency domain feature value is calculated between each AC mode component and the corresponding reference mode component; the time-frequency domain feature value is used to characterize the degree of consistency in the time domain and frequency domain distribution. The ripple confidence level of each AC mode component is determined based on the periodic characteristic value and time-frequency domain characteristic value of each AC mode component. Based on the ripple confidence of each of the AC mode components, ripple mode components are selected from the plurality of AC mode components.

4. The method for monitoring the ripple of hydrogen production power supply in a water electrolysis hydrogen production system according to claim 3, characterized in that, Based on the autocorrelation characteristics of each of the AC mode components, the periodic characteristic values ​​are determined, including: Calculate the autocorrelation function of the target AC mode component to obtain the autocorrelation sequence of the target AC mode component; the target AC mode component is one of the plurality of AC mode components. Extract the maximum peak value (excluding zero) from the autocorrelation sequence; The amplitude of the maximum peak value is determined as the periodic characteristic value of the target AC mode component.

5. The method for monitoring the ripple of the hydrogen production power supply in a water electrolysis hydrogen production system according to claim 3, characterized in that, Determining a reference mode component that matches the frequency of each AC mode component from the AC components of the input voltage signal includes: The input voltage signal is decomposed to obtain multiple reference mode components, and the center frequency of each reference mode component is determined. Calculate the difference between the center frequency of the target AC modal component and the center frequency of each of the reference modal components; The reference mode component with the smallest difference is determined as the reference mode component whose frequency matches that of the target AC mode component.

6. The method for monitoring the ripple of the hydrogen production power supply in a water electrolysis hydrogen production system according to claim 3, characterized in that, Calculating the time-frequency domain eigenvalues ​​between each of the AC mode components and the corresponding reference mode component includes: Extract multiple time-domain indices of the target communication modal component to determine a first time-domain feature vector; and extract multiple time-domain indices of the target reference modal component corresponding to the target communication modal component to determine a second time-domain feature vector; Extract multiple frequency domain indices of the target AC mode component to determine a first frequency domain feature vector; and extract multiple frequency domain indices of the target reference mode component to determine a second frequency domain feature vector; Calculate the first similarity between the first time-domain feature vector and the second time-domain feature vector; and calculate the second similarity between the first frequency-domain feature vector and the second frequency-domain feature vector; The time-frequency domain feature value is determined based on the first similarity and the second similarity; wherein the time-frequency domain feature value is positively correlated with the first similarity and the second similarity.

7. The method for monitoring the ripple of the hydrogen production power supply in a water electrolysis hydrogen production system according to claim 3, characterized in that, Based on the ripple confidence level of each of the AC mode components, ripple mode components are selected from the plurality of AC mode components, including: The ripple confidence score of each AC mode component is input into the maximum inter-class variance algorithm to obtain the confidence threshold used to distinguish ripple from noise. AC mode components with ripple confidence scores greater than the confidence threshold are selected as ripple mode components.

8. The method for monitoring the ripple of hydrogen production power supply in a water electrolysis hydrogen production system according to claim 1, characterized in that, The ripple coefficient of the hydrogen production power source is determined based on the DC component and the ripple mode component, including: The selected ripple mode components are reconstructed to obtain the reconstructed AC components. Calculate the first effective voltage value of the DC component and the second effective voltage value of the reconstructed AC component; The ripple coefficient is determined based on the ratio of the second effective voltage value to the first effective voltage value.

9. The method for monitoring the ripple of hydrogen production power supply in a water electrolysis hydrogen production system according to claim 1, characterized in that, Acquire the output voltage signal and input voltage signal of the hydrogen production power source during the monitoring period, including: During the monitoring period, the output voltage signal and input voltage signal of the hydrogen production power source are synchronously collected at a preset sampling frequency; wherein, the preset sampling frequency is determined according to the preset operating frequency band of the hydrogen production power source, and the monitoring period is determined according to the preset monitoring time interval.

10. The method for monitoring the ripple of the hydrogen production power supply in a water electrolysis hydrogen production system according to claim 1, characterized in that, Based on the ripple coefficient, the ripple monitoring results of the hydrogen production power source are generated, including: The ripple coefficient is compared with a preset ripple coefficient threshold. When the ripple coefficient is greater than the ripple coefficient threshold, a monitoring result indicating abnormal ripple of the hydrogen production power source is generated.

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