Hydrogen production power supply ripple monitoring method for water electrolysis hydrogen production system

Abstract

The invention relates to the technical field of power supply ripple monitoring, in particular to a hydrogen production power supply ripple monitoring method for a water electrolysis hydrogen production system, and aims to solve the technical problem that the overall monitoring accuracy is insufficient due to the fact that a ripple coefficient calculated in the prior art cannot truly reflect the actual ripple condition. The method comprises the following steps: acquiring an output voltage signal and an input voltage signal of a hydrogen production power supply in a monitoring time period; extracting a direct current component and a plurality of alternating current mode components from the output voltage signal; identifying a ripple modal component from the plurality of AC modal components based on the periodic characteristics of each AC modal component and the time-frequency domain similarity between each AC modal component and the signal component of the corresponding frequency in the AC component of the input voltage signal; determining a ripple coefficient of the hydrogen production power supply according to the direct current component and the ripple modal component; and generating a ripple monitoring result of the hydrogen production power supply according to the ripple coefficient.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power supply ripple monitoring, in particular to a hydrogen production power supply ripple monitoring method for a water electrolysis hydrogen production system. BACKGROUND

[0002] The hydrogen production power supply in the water electrolysis hydrogen production system is used to convert unstable renewable energy power into low-voltage direct current suitable for electrolysis cells. The quality of the output power of the hydrogen production power supply directly determines the hydrogen production efficiency and the reliability of the system operation. Due to the switching action of the internal power device of the power supply and the fluctuation of the front-end energy input, the direct current voltage output by the hydrogen production power supply will superimpose various frequency AC ripples. These ripple components will cause additional energy loss and affect the electrolysis efficiency. Therefore, it is necessary to effectively monitor the output ripple of the power supply.

[0003] Currently, a preset filter is usually used to filter the collected output voltage signal, and then the ripple coefficient is calculated through the direct and alternating components of the filtered signal to complete the ripple monitoring of the hydrogen production power supply. However, the existing ripple monitoring method is prone to loss of part of the ripple information due to the filtering process when facing complex working environment or input signal fluctuation of the hydrogen production power supply, so that the calculated ripple coefficient cannot truly reflect the actual ripple situation, resulting in insufficient overall monitoring accuracy. SUMMARY

[0004] In order to solve the technical problem that the calculated ripple coefficient in the prior art cannot truly reflect the actual ripple situation, resulting in insufficient overall monitoring accuracy, the purpose of the present application is to provide a hydrogen production power supply ripple monitoring method for a water electrolysis hydrogen production system, and the technical solution adopted is as follows: Obtain the output voltage signal and the input voltage signal of the hydrogen production power supply within a monitoring period; Extract a direct current component and a plurality of alternating modal components from the output voltage signal; the plurality of alternating modal components are used to represent signal components of different frequencies in the alternating component of the output voltage signal; Based on the periodicity of each alternating modal component and the time-frequency domain similarity between each alternating modal component and the signal component of the corresponding frequency in the alternating component of the input voltage signal, identify a ripple modal component from the plurality of alternating modal components; Determine the ripple coefficient of the hydrogen production power supply according to the direct current component and the ripple modal component; the ripple coefficient is used to represent the relative size of the ripple component in the direct current voltage; Generate a ripple monitoring result of the hydrogen production power supply according to the ripple coefficient.

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

[0006] In a possible implementation, identifying the ripple modal component from the plurality of AC modal components based on the periodicity feature of each AC modal component and the time-frequency domain similarity between each AC modal component and the signal component of the corresponding frequency in the AC component of the input voltage signal comprises: determining a periodicity feature value according to the autocorrelation characteristic of each AC modal component; the periodicity feature value is used to represent the periodicity degree of the AC modal component; determining a reference modal component matching the frequency of each AC modal component from the AC component of the input voltage signal, and calculating a time-frequency domain feature value between each AC modal component and the corresponding reference modal component; the time-frequency domain feature value is used to represent the consistency degree in the time domain and the frequency domain distribution; determining a ripple confidence of each AC modal component according to the periodicity feature value and the time-frequency domain feature value of each AC modal component; and screening the ripple modal component from the plurality of AC modal components based on the ripple confidence of each AC modal component.

[0007] In a possible implementation, determining the periodicity feature value according to the autocorrelation characteristic of each AC modal component comprises: calculating the autocorrelation function of the target AC modal component to obtain an autocorrelation sequence of the target AC modal component; the target AC modal component is one of the plurality of AC modal components; extracting the maximum peak value except for the zero time lag from the autocorrelation sequence; and determining the amplitude of the maximum peak value as the periodicity feature value of the target AC modal component.

[0008] In a possible implementation, determining the reference modal component matching the frequency of each AC modal component from the AC component of the input voltage signal comprises: decomposing the input voltage signal to obtain a plurality of reference modal components, and determining the center frequency of each reference modal component; calculating the difference between the center frequency of the target AC modal component and the center frequency of each reference modal component; and determining the reference modal component with the minimum difference as the reference modal component matching the frequency of the target AC modal component.

[0009] In a possible implementation, the time-frequency domain feature value between each alternating current modal component and the corresponding reference modal component is calculated, including: extracting a plurality of time domain indexes of the target alternating current modal component to determine a first time domain feature vector; and extracting a plurality of time domain indexes of the target reference modal component corresponding to the target alternating current modal component to determine a second time domain feature vector; extracting a plurality of frequency domain indexes of the target alternating current modal component to determine a first frequency domain feature vector; and extracting a plurality of frequency domain indexes of the target reference modal 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; and calculating a second similarity between the first frequency domain feature vector and the second frequency domain feature vector; determining the time-frequency domain feature value according to 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 a possible implementation, the ripple modal component is screened from the plurality of alternating current modal components based on the ripple confidence of each alternating current modal component, including: inputting the ripple confidence of each alternating current modal component into an inter-class variance algorithm to obtain a confidence threshold for distinguishing the ripple from the noise; and screening the alternating current modal component with the ripple confidence greater than the confidence threshold as the ripple modal component.

[0011] In a possible implementation, the ripple coefficient of the hydrogen production power supply is determined according to the direct current component and the ripple modal component, including: reconstructing the screened ripple modal component to obtain a reconstructed alternating current component; calculating a first voltage effective value of the direct current component and a second voltage effective value of the reconstructed alternating current component; and determining the ripple coefficient according to the ratio of the second voltage effective value to the first voltage effective value.

[0012] In a possible implementation, the output voltage signal and the input voltage signal of the hydrogen production power supply in a monitoring period are acquired, including: synchronously collecting the output voltage signal and the input voltage signal of the hydrogen production power supply at a preset sampling frequency in the monitoring period; wherein the preset sampling frequency is determined according to a preset working frequency band of the hydrogen production power supply, and the monitoring period is determined according to a preset monitoring time interval.

[0013] In a possible implementation, the ripple monitoring result of the hydrogen production power supply is generated according to the ripple coefficient, including: comparing the ripple coefficient with a preset ripple coefficient threshold; and generating a monitoring result indicating that the ripple of the hydrogen production power supply is abnormal when the ripple coefficient is greater than the ripple coefficient threshold.

[0014] The application has the following beneficial effects: the application extracts the direct current component and a plurality of alternating current modal components from the output voltage signal, disassembles the complex voltage signal into targeted analysis units, clearly identifies the core object of ripple analysis, and avoids mutual interference of ripples of different frequencies by separating signal components of different frequencies. Subsequently, the ripple modal components are identified based on the periodic characteristics of the alternating current modal components and the similarity in the time-frequency domain, the ripple and noise are effectively distinguished through double-dimensional feature judgment, the selected ripple modal components are ensured to match the actual ripple characteristics, and the misjudgment and omission of ripple identification are reduced. Finally, the ripple coefficient is determined according to the direct current component and the ripple modal component, so that the obtained ripple coefficient can truly reflect the actual proportion of the ripple in the direct current voltage, significantly improves the overall accuracy and reliability of the hydrogen production power ripple monitoring, provides a scientific basis for the operation intervention of the water electrolysis hydrogen production system, and ensures the efficient and stable operation of the system. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0016] Figure 1 A flowchart of a hydrogen production power ripple monitoring method for a water electrolysis hydrogen production system provided by an embodiment of the present application Figure 1 ; Figure 2 A flowchart of a hydrogen production power ripple monitoring method for a water electrolysis hydrogen production system provided by an embodiment of the present application Figure 2 ; Figure 3 A flowchart of a hydrogen production power ripple monitoring method for a water electrolysis hydrogen production system provided by an embodiment of the present application Figure 3 ; Figure 4 A flowchart of a hydrogen production power ripple monitoring method for a water electrolysis hydrogen production system provided by an embodiment of the present application Figure 4 . DETAILED DESCRIPTION

[0017] To further clarify the technical means and effects taken by the present application to achieve the predetermined object of the application, the following describes in detail the specific implementation, structure, features and effects of a hydrogen production power ripple monitoring method for a water electrolysis hydrogen production system according to the present application, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the 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 belongs.

[0019] The normalization function mentioned in the present application adopts maximum-minimum value normalization unless otherwise specified. The maximum and minimum values are preset empirical extreme values based on a large amount of historical experimental data. If the calculation result exceeds the interval [0, 1], it is limited within the interval [0, 1] through a truncation function (i.e. if the result is less than 0, take 0, and if the result is greater than 1, take 1) to eliminate the influence of abnormal values on the evaluation index.

[0020] The specific scheme of the hydrogen production power ripple monitoring method for a water electrolysis hydrogen production system provided by the present application is described in detail below in combination with the accompanying drawings.

[0021] Please refer to Figure 1 , which shows the method flowchart of the hydrogen production power ripple monitoring method for a water electrolysis hydrogen production system provided by an embodiment of the present application, as shown in Figure 1 , the method comprises the following steps: Step 101, obtaining the output voltage signal and the input voltage signal of the hydrogen production power source within the monitoring period.

[0022] As a possible implementation, this step can be implemented by synchronously collecting the output voltage signal and the input voltage signal of the hydrogen production power source at a preset sampling frequency within the monitoring period. The preset sampling frequency is determined according to the preset working frequency band of the hydrogen production power source, and the monitoring period is determined according to the preset monitoring time interval.

[0023] Specifically, the application sets an intelligent sensing system in the water electrolysis hydrogen production system, which is used for ripple monitoring of the hydrogen production power supply. The intelligent sensing system processes the collected hydrogen production power supply output voltage signal, aiming to retain the high-frequency ripple component in the alternating current component of the voltage signal and effectively filter out the noise component, so that the ripple coefficient calculated finally can more accurately reflect the true situation of the alternating current ripple in the direct current voltage output by the hydrogen production power supply, thereby improving the reliability of the ripple monitoring result. The input end of the hydrogen production power supply is connected to a wind power generation or photovoltaic power generation and storage system, and the output end 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 for hydrogen production, the output voltage signal and the input voltage signal of the output end and the input end of the hydrogen production power supply are collected by the voltage sensor 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 one monitoring time t (not including 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 in the time period corresponding to the monitoring time t and the previous monitoring time t-1 are obtained from the database of the data processing unit. In the present application, the time interval between the adjacent two monitoring times is set to 1min, which can also be flexibly set according to the situation in the specific implementation process.

[0026] Step 102, extracting a direct current component and a plurality of alternating current modal components from the output voltage signal.

[0027] Among them, the plurality of alternating current modal components are used to represent the signal components of different frequencies in the alternating current component of the output voltage signal.

[0028] As a possible implementation manner, the output voltage signal is separated into a direct current component and an alternating current component by frequency domain transformation in this step, so as to isolate the ripple-related components to be analyzed. After that, the alternating current component is adaptively decomposed based on a signal decomposition algorithm to obtain a plurality of alternating current modal components, each of which is used to represent the signal components of a specific frequency range in the alternating current component, thereby providing a structured frequency domain representation basis for subsequent differentiation between ripple and noise.

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

[0030] Optionally, the signal components of different frequencies refer to a plurality of basic signal units obtained by signal decomposition on the original complex alternating current signal (i.e. the alternating current component of the output voltage signal). The energy of each basic signal unit is mainly concentrated in a specific and relatively narrow frequency band, representing the fluctuation characteristics in the frequency band range of the original signal.

[0031] Step 103, identifying the ripple modal component from the plurality of alternating current modal components based on the periodic characteristics of each alternating current modal component and the time-frequency domain similarity between each alternating current modal component and the signal component of the corresponding frequency in the alternating current component of the input voltage signal.

[0032] Optionally, the ripple modal component refers to the modal component identified from the plurality of alternating current modal components of the output voltage signal for characterizing the true ripple component. The identification of the ripple modal component is based on the fact that such component has both significant self-periodic variation characteristics and high consistency in time and frequency domain distribution with the same frequency component in the input voltage signal. The identified ripple modal component will be used for subsequent reconstruction of the pure alternating current component to calculate the accurate ripple coefficient.

[0033] As a possible implementation, in this step, firstly, for each alternating current modal component, it is evaluated whether its own signal presents significant periodic variation rule to preliminarily distinguish the ripple component with periodic characteristics from random noise. Thereafter, taking the alternating current component of the input voltage signal as a reference, the overall consistency in time and frequency domain distribution between each alternating current modal component and the reference component matching its frequency is calculated to identify the alternating current modal component with significant periodicity and high time-frequency domain similarity as the ripple modal component.

[0034] Step 104, determining the ripple coefficient of the hydrogen production power supply according to the direct current component and the ripple modal component.

[0035] The ripple coefficient is used to represent the relative size of the ripple component in the direct current voltage.

[0036] Optionally, the discrete ripple modal components are integrated into a complete reconstructed alternating current component to represent the pure ripple signal filtered from the noise. Thereafter, the voltage effective value of the direct current component and the voltage effective value of the reconstructed alternating current component are calculated respectively. The ratio of the effective values of the two is determined as the ripple coefficient for quantitatively representing the relative size of the ripple component in the direct current voltage.

[0037] Step 105, generating the ripple monitoring result of the hydrogen production power supply according to the ripple coefficient.

[0038] Optionally, the ripple coefficient is compared with a preset ripple coefficient threshold value; when the ripple coefficient is greater than the ripple coefficient threshold value, a monitoring result indicating the ripple abnormality of the hydrogen production power supply is 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 voltage. 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 one modal component a in the alternating component A2 as an example, the autocorrelation function is used to calculate the autocorrelation sequence of the modal component, to obtain the autocorrelation coefficient distribution of the modal component under different time lags. The amplitude corresponding to the maximum peak value in the autocorrelation sequence except for the zero time lag is recorded as the periodic characteristic value of the modal component a. The periodic characteristic value is used to evaluate the periodicity significance of the signal component corresponding to the modal component; the greater the periodic characteristic value, the more obvious the periodic change characteristics of the component, and the more likely the component belongs to the ripple component.

[0051] It should be noted that the calculation process of the autocorrelation function can refer to the prior art, and the present application will not be repeated here.

[0052] Step 302, determining a reference modal component matching the frequency of each alternating modal component from the alternating components of the input voltage signal, and calculating the time-frequency domain characteristic value between each alternating modal component and the corresponding reference modal component.

[0053] The time-frequency domain characteristic value is used to represent the consistency degree in the time domain and frequency domain distribution.

[0054] As a possible implementation, the process of determining the reference modal component in this step includes: decomposing the input voltage signal to obtain a plurality of reference modal components, and determining the center frequency of each reference modal component. The difference between the center frequency of the target alternating modal component and the center frequency of each reference modal component is calculated. The reference modal component with the smallest difference is determined as the reference modal component matching the frequency of the target alternating modal component.

[0055] The process of determining the time-frequency domain characteristic value in this step includes: extracting a plurality of time domain indicators of the target alternating modal component to determine a first time domain feature vector; and extracting a plurality of time domain indicators of the target reference modal component corresponding to the target alternating modal component to determine a second time domain feature vector. Extracting a plurality of frequency domain indicators of the target alternating modal component to determine a first frequency domain feature vector; and extracting a plurality of frequency domain indicators of the target reference modal component to determine a second frequency domain feature vector. The first similarity between the first time domain feature vector and the second time domain feature vector is calculated; and the second similarity between the first frequency domain feature vector and the second frequency domain feature vector is calculated. The time-frequency domain characteristic value is determined according to the first similarity and the second similarity. The time-frequency domain characteristic value is positively correlated with the first similarity and the second similarity.

[0056] Optionally, due to the natural fluctuations of wind energy, solar energy and the like, the front-end power supply frequency is unstable, and this instability is transmitted to the output voltage of the hydrogen production power supply, forming a ripple associated with the input voltage fluctuation. Therefore, the ripple component in the output voltage alternating current component should have the same time domain and frequency domain distribution characteristics as the signal component of the same frequency in the input voltage alternating current component; while the noise component does not have such characteristics. Therefore, the present application uses the same method as processing the output voltage signal A to decompose the alternating current component B2 of the input voltage signal B, obtain a plurality of reference modal components, and obtain the center frequency of each reference modal component. From these reference modal components, the one with the smallest difference in center frequency from modal component a is selected and denoted as reference modal component b to represent the component of the same frequency as modal component a in the input signal. The center frequency difference can be measured by calculating the absolute value of the difference between the two. Based on this process, the reference modal component that matches the frequency of each alternating current modal component is determined. The reference modal component that matches the frequency of the alternating current modal component is also referred to as the reference modal component corresponding to the alternating current modal component.

[0057] A plurality of time domain indicators (such as mean, standard deviation, waveform factor) of modal component a are calculated to form a time domain feature vector of modal component a to represent its signal time domain distribution characteristics. A plurality of frequency domain indicators (such as frequency mean, frequency standard deviation) of modal component a are calculated to form a frequency domain feature vector of modal component a to represent its signal frequency domain distribution characteristics. In a similar manner, the time domain feature vector and the frequency domain feature vector corresponding to the reference modal component b are calculated. The calculation process of the signal time domain and frequency domain indicators can refer to the prior art, and will not be repeated here.

[0058] The first similarity d1 between the time domain feature vectors of modal component a and reference modal component b, and the second similarity d2 between the frequency domain feature vectors of the two are calculated respectively. The greater the first similarity d1 and the second similarity d2, the higher the consistency of the two in time domain and frequency domain. The mean of d1 and d2 is denoted as the time-frequency domain feature value of modal component a; the greater the value, the more consistent the time-frequency domain distribution characteristics of modal component a and the intermediate frequency component in the input signal, and the more likely the component belongs to the ripple component. As an example, the present application can calculate the similarity measure based on the cosine similarity, and the specific process can refer to the prior art, which will not be limited here.

[0059] Step 303, determining the ripple confidence of each alternating current modal component according to the period characteristic value and the time-frequency domain feature value of each alternating current modal component.

[0060] As a possible implementation manner, the period characteristic value and the time-frequency domain characteristic value of all modal components of the alternating component A2 are normalized respectively by using a Min-Max normalization method. For the modal component a, the ripple confidence is calculated according to the normalized period characteristic value and the normalized time-frequency domain characteristic value. The ripple confidence is used to comprehensively evaluate the possibility that the component belongs to the ripple; the larger the normalized characteristic value is, the higher the ripple confidence is generally. Alternatively, the ripple confidence can be calculated as the mean value of the two normalized characteristic values, or the weighted sum value of the two normalized characteristic values, and the present application does not limit this.

[0061] Step 304: screening the ripple modal component from the plurality of alternating modal components based on the ripple confidence of each alternating modal component.

[0062] As a possible implementation manner, this step can be specifically implemented as follows: inputting the ripple confidence of each alternating modal component into the maximum inter-class variance algorithm to obtain a confidence threshold for distinguishing the ripple from the noise. The alternating modal component with the ripple confidence greater than the confidence threshold is screened as the ripple modal component.

[0063] Specifically, the ripple confidences corresponding to all modal components of the alternating component A2 are taken as inputs, and the maximum inter-class variance algorithm is used to process the inputs to obtain the corresponding ripple confidence threshold. Thereafter, the modal component with the ripple confidence greater than the threshold is screened from all modal components as the ripple modal component. These screened modal components represent the ripple component contained in the alternating component A2 of the output voltage signal A.

[0064] Based on the above technical solutions, the period characteristic value is used to quantify the periodicity of the alternating modal component, and the time-frequency domain characteristic value is used to reflect the distribution consistency of the alternating modal component with the frequency component corresponding to the input voltage. The ripple confidence constructed in the dual dimensions can comprehensively and accurately depict the ripple attribute of the alternating modal component, effectively solve the problem that the ripple and the noise are difficult to accurately distinguish in the traditional method, ensure that the ripple modal component screened from the plurality of alternating modal components is more consistent with the actual ripple feature, greatly reduce the misjudgment rate of the ripple recognition, and provide a core guarantee for the accurate calculation of the ripple coefficient.

[0065] As shown in FIG. 4, Figure 4 In a possible implementation manner, the process of determining the ripple coefficient of the hydrogen production power supply according to the direct current component and the ripple modal component in step 104 specifically includes: Step 401: reconstructing the screened ripple modal component to obtain a reconstructed alternating component.

[0066] Optionally, all modal components representing the ripple component are filtered out and signal reconstruction is performed, and the reconstructed signal is denoted as an alternating current component A3 at the monitoring time t. The alternating current component A3 is used to represent a pure alternating current component obtained by filtering the original alternating current component A2 of the output voltage signal A collected in the time period corresponding to the monitoring time t and the previous monitoring time t-1. Subsequently, the alternating current component A3 is transmitted to the ripple monitoring unit for calculating the ripple coefficient of the hydrogen production power supply at the monitoring time t. The signal reconstruction process can refer to related technologies, which will not be described herein.

[0067] Step 402, calculating a first voltage effective value of the direct current component and a second voltage effective value of the reconstructed alternating current component.

[0068] Step 403, determining the ripple coefficient according to the ratio of the second voltage effective value to the first voltage effective value.

[0069] Optionally, the ripple coefficient is calculated based on the direct current component and the reconstructed alternating current component obtained in the foregoing steps, and the 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 the monitoring time t is calculated according to the direct current component A1 and the alternating current component A3 at the monitoring time t. For example, δ is equal to the ratio of the voltage effective value V2 of the alternating current component A3 to the voltage effective value V1 of the direct current component A1, that is, It should be noted that, under the condition that the hydrogen production power supply is working normally, the voltage effective value V1 of the output direct current component A1 is greater than zero. If V1=0 is calculated, it indicates that the hydrogen production power supply fails to work normally at this time, and the parameters at this time do not participate in the calculation of the ripple coefficient.

[0070] Based on the above technical solutions, the signal reconstruction is performed on the filtered-out ripple modal components, all effective ripple components are integrated to form a complete and pure alternating current component, the ripple coefficient is calculated based on the ratio of the voltage effective values of the direct and alternating current components, which completely conforms to the physical definition of the ripple coefficient, avoids the distortion of the coefficient caused by the missing of the ripple component or the unreasonable calculation logic, and makes the finally obtained ripple coefficient truly and accurately reflect the relative size of the ripple in the output direct current voltage of the hydrogen production power supply, thereby providing a reliable quantitative basis for the ripple monitoring result.

[0071] It should be noted that the above-mentioned sequence of the embodiments is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0072] The various embodiments described in this specification are presented by way of example, and each embodiment is not inherently more important than any other embodiment.

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; 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 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 supply 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 voltage. 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 the 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 except for zero time delay 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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