A hydrogen storage tank intelligent filtering control method for a wind-solar coupling electrolytic hydrogen production system

Abstract

This invention provides an intelligent filtering control method for the hydrogen storage tank of a wind-solar coupled electrolysis hydrogen production system. The method includes: designing a filter and dynamically adjusting its cutoff frequency based on the time constant of the hydrogen storage tank, finding the optimal filter order through an optimization mechanism; determining the current operating condition based on the hydrogen charging and discharging status of the storage tank, and selecting an appropriate type from FIR filters, IIR filters, and adaptive hybrid filters according to different operating conditions; filtering the hydrogen production process, adjusting the hydrogen charging and discharging amount of the storage tank based on the difference between the target hydrogen production rate and the actual hydrogen production rate to obtain a smooth hydrogen output; evaluating the performance of the hydrogen production system based on the volatility of the output hydrogen, and readjusting the filter parameters based on the evaluation results. This invention dynamically selects the appropriate filter type according to different operating conditions, achieving a smoother hydrogen output rate, ensuring system stability, and meeting the needs of subsequent large-scale industrial production.

Description

Technical Field

[0001] This invention relates to the field of wind-solar coupled electrolysis hydrogen production technology, specifically, but not limited to, an intelligent filtering control method for hydrogen storage tanks in a wind-solar coupled electrolysis hydrogen production system. Background Technology

[0002] With the growing global demand for clean energy, wind and solar power have been widely adopted as major renewable energy sources. The intermittency and uncertainty of these energy sources pose challenges to the power grid, especially in applications requiring stable power supply, such as water electrolysis for hydrogen production. To overcome this issue, battery energy storage systems are typically used to balance the difference between power generation and load, ensuring a stable power supply. In existing technologies, most wind-solar hybrid power generation systems rely solely on a single type of battery filter to smooth the output power. However, this only partially addresses the problem of high-frequency power fluctuations affecting the normal operation of the electrolyzer. Due to battery capacity limitations, power fluctuations cannot be completely smoothed, resulting in fluctuations in the actual hydrogen production rate during the electrolysis process. Therefore, it is necessary to install hydrogen storage tanks to store excess hydrogen or replenish insufficient hydrogen in real time according to the hydrogen production rate, while simultaneously designing filters to smooth the hydrogen production rate, thereby improving the stability of hydrogen production to meet the needs of subsequent large-scale industrial production.

[0003] Traditional single filters are suitable for batteries because batteries charge and discharge relatively quickly, providing or absorbing large amounts of power in a short time, which matches the fixed cutoff frequency and fast response characteristics of filters. Unlike batteries, hydrogen storage tanks charge and discharge hydrogen relatively slowly, especially near full load or empty load, where the response speed further decreases. This is because the compression and release of hydrogen involves complex physical and chemical processes that cannot be completed as quickly as in a battery. This means that when using a single filter in a hydrogen storage tank, the filter's cutoff frequency is fixed and cannot be adjusted according to the real-time state of the system. Under various operating conditions over a long period, the filter's performance may not be optimal. In cases of rapid hydrogen charging or discharging, a filter with a fixed cutoff frequency may not respond promptly, resulting in large fluctuations in the hydrogen output rate; conversely, in cases of slow charging or discharging, a filter with a fixed cutoff frequency may over-smooth the flow, leading to a sluggish system response. In addition, traditional single filters do not have adaptive characteristics and cannot automatically adjust parameters according to the dynamic changes of the system. For example, IIR filters have good attenuation characteristics in the low frequency band, but perform poorly in the high frequency band. Therefore, single filters are difficult to cope with the complex operating conditions of hydrogen storage tanks, especially under abnormal conditions such as overcharging and over-discharging, which may lead to equipment damage or system failure.

[0004] In view of this, a new control method is needed to solve at least some of the above problems. Summary of the Invention

[0005] To address one or more problems in the existing technology, this invention proposes an intelligent filtering control method for the hydrogen storage tank in a wind-solar coupled electrolysis hydrogen production system. By introducing a filter selection mechanism based on the operating conditions of the hydrogen storage tank, the appropriate filter type is dynamically selected according to different operating conditions, thereby achieving a smoother hydrogen output rate, ensuring the stability and reliability of the system, and meeting the needs of subsequent large-scale industrial production.

[0006] The technical solution to achieve the purpose of this invention is as follows:

[0007] A method for intelligent filtering control of hydrogen storage tanks in a wind-solar coupled electrolysis hydrogen production system includes:

[0008] S1. Design the filter and dynamically adjust its cutoff frequency to obtain the optimal filter order through an optimization mechanism.

[0009] S2. Obtain the hydrogen charging and discharging status of the hydrogen storage tank, determine the current operating condition of the hydrogen storage tank based on the hydrogen charging and discharging status, and select the corresponding filter based on the operating condition.

[0010] S3. Under the limitation of the limited volume of the hydrogen storage tank, the smoothed target hydrogen production rate is obtained by using the selected filter. Based on the difference between the target hydrogen production rate and the actual hydrogen production rate, the amount of hydrogen charged and discharged in the hydrogen storage tank is adjusted. The hydrogen output operation is performed according to the adjusted amount of hydrogen charged and discharged, and the hydrogen output rate is recorded.

[0011] S4. Calculate the volatility during the hydrogen output process, evaluate the performance of the wind-solar coupled electrolysis hydrogen production system based on the volatility, and readjust the hydrogen production system parameters according to the evaluation results.

[0012] Furthermore, the intelligent filtering control method for the hydrogen storage tank of the wind-solar coupled electrolysis hydrogen production system of the present invention, S1 specifically includes:

[0013] S1-1. Combining FIR and IIR filters, an adaptive hybrid filter is designed based on the adaptive LMS algorithm as follows:

[0014] y(n)=α(n)·y FIR (n)+(1-α(n))·y IIR (n)

[0015] Where y(n) is the output signal of the adaptive hybrid filter, α(n) is the weight of the FIR filter, in the range 0 < α(n) < 1, 1-α(n) is the weight of the IIR filter, and α(n) is updated using the adaptive LMS algorithm; FIR (n) is the output signal of the FIR filter, y IIR (n) is the output signal of the IIR filter; where:

[0016] FIR filter y FIR (n) is:

[0017]

[0018] Where n represents the current sample point being processed, k is the counting variable, x(n) is the input signal, h(k) is the coefficient of the FIR filter, and N is the order of the FIR filter;

[0019] IIR filter y IIR (n) is:

[0020]

[0021] Where x(n) is the input signal, b(k) and a(k) are the forward and feedback coefficients of the IIR filter, respectively, and M and N' are the forward and feedback orders of the IIR filter, respectively.

[0022] S1-2. The cutoff frequency of the filter is dynamically adjusted according to the time constant of the hydrogen storage tank. The adjustment formula is as follows:

[0023]

[0024] Among them, f c It is the cutoff frequency of the filter, τ. h It is the time constant of the hydrogen storage tank;

[0025] S1-3. Define volatility as an evaluation metric for filter performance. Define the optimization range according to the filter type, and use the bisection method to find the filter order that minimizes volatility. The volatility calculation formula is shown below:

[0026]

[0027] Where N is the number of data points, X i It is the i-th data point. It is the average of all data points.

[0028] Furthermore, in the intelligent filtering control method for the hydrogen storage tank of the wind-solar coupled electrolysis hydrogen production system of the present invention, the update formula for the weight α(n) in S1-1 is as follows:

[0029] α(n+1)=α(n)+η·e(n)·(y FIR (n)-y IIR (n))

[0030] Where η is the learning rate for weight updates, e(n) is the error signal, representing the difference between the filter output and the desired output, and y FIR (n)-yIIR (n) is the difference between the FIR filter output and the IIR filter output, used to guide the adjustment of the weights.

[0031] Furthermore, the intelligent filtering control method for the hydrogen storage tank of the wind-solar coupled electrolysis hydrogen production system of the present invention, specifically includes the following steps in S1-3: The method employs a binary search approach for optimization.

[0032] S1-3-1. Set the left and right boundaries of the filter order, calculate the volatility of the signal after filtering, and record the filter order and volatility.

[0033] S1-3-2. Take the average value of the left and right boundaries as the boundary of the new filter order. If the calculated volatility is less than the recorded volatility, update the record and expand the left boundary; if the calculated volatility is greater than the recorded volatility, shrink the right boundary.

[0034] S1-3-3, Stop when the left boundary of the filter order is greater than the right boundary. At this time, the recorded filter order is the filter order that minimizes the volatility.

[0035] Furthermore, the intelligent filtering control method for the hydrogen storage tank of the wind-solar coupled electrolysis hydrogen production system of the present invention, S2 specifically includes:

[0036] S2-1: The real-time state formula for the hydrogen storage tank is:

[0037]

[0038] Among them, H e H r H represents the actual hydrogen production rate and the rated hydrogen production rate of the hydrogen production system, respectively. s (t) represents the current storage capacity of the hydrogen storage tank, H s (t-1) represents the amount of hydrogen stored in the storage tank at the previous moment, Δt represents the time step, i.e. the interval between two time points, and δ represents the threshold for judging the hydrogen charging and discharging rate of the storage tank.

[0039] S2-2: Determine the current operating condition of the hydrogen storage tank and select the appropriate filter:

[0040] When H e >H r At +δ, the hydrogen storage tank is in rapid hydrogen filling mode, so an FIR filter is selected;

[0041] When H r <H e ≤H r When +δ is applied, the hydrogen storage tank is in slow hydrogen filling mode, so an IIR filter is selected.

[0042] When H e =H rAt that time, the hydrogen storage tank is in a balanced state, and an IIR filter is selected;

[0043] When H e <H r When -δ is the hydrogen storage tank is in rapid hydrogen release mode, an FIR filter is selected.

[0044] When H r -δ≤H e <H r At that time, the hydrogen storage tank is in slow hydrogen release mode, so an IIR filter is selected;

[0045] When H s (t) is greater than the hydrogen storage tank volume V h If the value is less than zero, the hydrogen storage tank will experience overcharging or over-discharging conditions, and an adaptive hybrid filter will be selected.

[0046] Furthermore, in the intelligent filtering control method for the hydrogen storage tank of the wind-solar coupled electrolysis hydrogen production system of the present invention, the strategy for adjusting the hydrogen charging and discharging amount of the hydrogen storage tank in S3 is as follows:

[0047]

[0048] Among them, H charge H discharge H represents the rates of hydrogen filling and discharging from the hydrogen storage tank, respectively. target H represents the target hydrogen production rate after smoothing the hydrogen storage tank filter. e H r V represents the actual hydrogen production rate and the rated hydrogen production rate of the hydrogen production system, respectively. h For the volume of the hydrogen storage tank, H s H represents the hydrogen storage capacity of the hydrogen storage tank. s (t) represents the current storage capacity of the hydrogen storage tank, H s (t-1) represents the amount of hydrogen stored in the storage tank at the previous moment, Q max This represents the maximum hydrogen filling and discharging rate of the hydrogen storage tank.

[0049] Furthermore, in the intelligent filtering control method for the hydrogen storage tank of the wind-solar coupled electrolysis hydrogen production system of the present invention, step S4, which involves readjusting the hydrogen production system parameters based on the evaluation results, includes:

[0050] If the volatility is ≤10%, it means that the hydrogen production system meets the needs of industrial production and its parameters do not need to be adjusted.

[0051] If the volatility is greater than 10%, the parameters of the hydrogen production system are adjusted by successively expanding the filter order range of the optimization mechanism, reducing the judgment time length of the hydrogen storage tank operating condition, and adjusting the judgment threshold of the hydrogen charging and discharging rate.

[0052] Compared with the prior art, the present invention, employing the above technical solution, has the following technical effects:

[0053] 1. The intelligent filtering control method for hydrogen storage tanks in the wind-solar coupled electrolysis hydrogen production system of the present invention divides the state of the hydrogen storage tank into seven working conditions, and selects appropriate filters according to the characteristics of different working conditions, so that the hydrogen storage tank has a good filtering effect during long-term operation.

[0054] 2. The intelligent filtering control method for the hydrogen storage tank in the wind-solar coupled electrolysis hydrogen production system of the present invention designs an adaptive hybrid filter for the overcharge and over-discharge conditions of the hydrogen storage tank. When the hydrogen storage tank is rapidly charged or discharged to the point of overcharge or over-discharge, compared with the FIR filter, the hybrid filter sacrifices some filtering effect to prolong the process of the hydrogen storage tank's hydrogen storage capacity being greater than its volume or the hydrogen storage capacity being zero. This allows the filter to filter for a longer period of time under these conditions, preventing the hydrogen storage tank from quickly reaching its limit and losing its filtering effect, thus making it difficult to suppress the fluctuations in the rapid hydrogen charge and discharge rate. When the hydrogen storage tank is slowly charged or discharged to the point of overcharge or over-discharge, compared with the IIR filter, the hybrid filter sacrifices some filtering time to achieve a better overall filtering effect.

[0055] 3. The intelligent filtering control method for the hydrogen storage tank in the wind-solar coupled electrolysis hydrogen production system of the present invention, compared with the traditional filter whose cutoff frequency and order are fixed, allows the cutoff frequency of the three filters to be dynamically adjusted according to the time constant of the hydrogen storage tank, so that the filter can better adapt to different operating conditions of the hydrogen storage tank; at the same time, an optimization method is introduced to optimize the filter order based on the operating data feedback of each operating condition, so that the filter has a better filtering effect under complex and changing operating conditions. Attached Figure Description

[0056] The accompanying drawings are provided to further illustrate the invention and, together with the description, serve to explain embodiments of the invention, but do not constitute a limitation thereof. In the drawings:

[0057] Figure 1 A flowchart of the intelligent filtering control method for the hydrogen storage tank of the wind-solar coupled electrolysis hydrogen production system of the present invention is shown.

[0058] Figure 2 The graph shows a comparison between the battery-compensated power smoothed by the filter and the original power.

[0059] Figure 3 The graph shows a comparison between the smoothed hydrogen production rate and the original hydrogen production rate after applying different filters under different operating conditions of the hydrogen storage tank.

[0060] Figure 4 The present invention illustrates an intelligent filtering control method for the hydrogen storage tank of a wind-solar coupled electrolysis hydrogen production system. Detailed Implementation

[0061] To further understand the present invention, preferred embodiments of the present invention are described below in conjunction with examples. However, it should be understood that these descriptions are only for further illustrating the features and advantages of the present invention, and not for limiting the scope of the claims of the present invention.

[0062] The description in this section pertains only to typical embodiments, and the present invention is not limited to the scope of the embodiments described. Combinations of different embodiments, substitution of some technical features in different embodiments, and substitution of similar or identical prior art with some technical features in the embodiments are also within the scope of the description and protection of the present invention.

[0063] Wind and solar energy exhibit fluctuations with both long and short periods, leading to various operating conditions for hydrogen storage tanks. Traditionally, hydrogen storage tanks are configured with specific filters, such as IIR or FIR filters, to address these fluctuations. However, this approach may result in suboptimal filter performance over extended periods. To address this issue, a new approach is proposed that different filters be designed for different operating conditions of the hydrogen storage tank. An adaptive hybrid filter is designed to handle overcharge and over-discharge conditions where traditional filters are ineffective. The filter cutoff frequency is dynamically adjusted, and an optimization mechanism is used to find the optimal filter order for each operating condition, allowing the filter to better adapt to the operating conditions of the hydrogen storage tank. Within the limited volume of the hydrogen storage tank, the designed filter yields a smoothed target hydrogen production rate. Based on the difference between the target and actual hydrogen production rates, the hydrogen charging and discharging rates of the storage tank are adjusted. Hydrogen output is then performed according to the adjusted charging and discharging rates, ultimately achieving a smoother actual hydrogen production rate. If the output hydrogen fluctuation rate is too large, the hydrogen production system parameters need to be adjusted to meet subsequent industrial production needs.

[0064] A smart filtering control method for the hydrogen storage tank in a wind-solar coupled electrolysis hydrogen production system is proposed. This method introduces a filter selection mechanism based on the operating conditions of the hydrogen storage tank, dynamically selecting the appropriate filter type according to different operating conditions. This achieves a smoother hydrogen output rate, ensuring system stability and reliability, and meeting the needs of subsequent large-scale industrial production. Figure 1 As shown, the specific steps include:

[0065] (1) Design the selected type of filter, dynamically adjust the cutoff frequency of the filter according to the time constant of the hydrogen storage tank, and find a better filter order through the optimization mechanism.

[0066] The following are the types of filters designed:

[0067] 1) FIR Filter: A Finite Impulse Response (FIR) filter is a linear time-invariant system whose output depends only on the current and past input samples, and not on past outputs. Its mathematical expression is shown below:

[0068]

[0069] Among them, y FIR x(n) is the output signal of the filter, x(n) is the input signal, h(k) is the coefficient of the filter, and N is the order of the filter.

[0070] The main characteristics of an FIR filter include:

[0071] Linear phase: FIR filters can achieve linear phase through symmetrical or antisymmetric design, which means that it does not introduce phase distortion and is suitable for applications where signal integrity needs to be maintained.

[0072] Stability: FIR filters are always stable because they have no feedback loops and no poles, so they will not become unstable.

[0073] Flexibility: FIR filters can achieve various frequency responses, such as low-pass, high-pass, and band-pass, by adjusting the coefficients.

[0074] 2) IIR Filter: An Infinite Impulse Response (IIR) filter is a linear time-invariant system containing a feedback loop. Its output depends not only on the current and past input samples, but also on past output samples. The mathematical expression is shown below:

[0075]

[0076] Among them, y IIR x(n) is the output signal of the filter, x(n) is the input signal, b(k) and a(k) are the forward and feedback coefficients of the filter, respectively, and M and N' are the orders of the forward and feedback, respectively.

[0077] The main characteristics of IIR filters include:

[0078] Efficiency: IIR filters typically have higher frequency selectivity than FIR filters of the same order, can achieve the same filtering effect at lower orders, and have lower computational complexity.

[0079] Nonlinear phase: Due to the presence of feedback loops, IIR filters typically do not have a linear phase, which may introduce phase distortion.

[0080] Potential instability: The stability of an IIR filter depends on the location of its poles. If the poles are located outside the unit circle, the filter may become unstable.

[0081] 3) Adaptive hybrid filter: The adaptive hybrid filter combines the advantages of FIR and IIR filters and dynamically adjusts the weights of each filter through the adaptive LMS algorithm to cope with complex operating conditions. That is, it automatically adjusts the filter parameters according to the real-time status of the system to achieve the optimal filtering effect.

[0082] An adaptive hybrid filter employing a parallel weighted structure is used, where the FIR and IIR filters operate in parallel, and their respective outputs are summed using weighted methods to obtain the final result. This structure allows for independent optimization of the FIR and IIR filters and is suitable for handling different types of signal components.

[0083] Assume the output of the FIR filter is y FIR (n), the output of the IIR filter is y IIR (n). The final output of the adaptive hybrid filter is obtained by weighted summation:

[0084] y(n)=α(n)·y FIR (n)+(1-α(n))·y IIR (n)

[0085] Where α(n) is the weight of the FIR filter, ranging from 0 < α(n) < 1, and 1-α(n) is the weight of the IIR filter. The adaptive LMS algorithm updates the filter coefficients using the following formula:

[0086] ω[n+1]=ω[n]+μ·e[n]·x[n]

[0087] Where ω[n] is the filter coefficient vector, μ is the step size factor that controls the convergence speed of the filter, e[n] is the error signal that represents the difference between the filter output and the desired output, and x[n] is the input signal.

[0088] Therefore, the update formula for weight α(n) can be expressed as:

[0089] α(n+1)=α(n)+η·e(n)·(y FIR (n)-y IIR (n))

[0090] Where η is the learning rate for weight updates, and y FIR (n)-y IIR (n) represents the difference between the outputs of the FIR and IIR filters, used to guide the adjustment of the weights.

[0091] The main characteristics of adaptive filters include:

[0092] Adaptability: The filter coefficients can be automatically adjusted according to the real-time status of the system to adapt to different operating conditions.

[0093] Robustness: Adaptive filters can maintain good performance in uncertain environments, and are especially suitable for nonlinear or time-varying systems.

[0094] Flexibility: Adaptive filters can select different filter structures (such as FIR or IIR) according to different needs and dynamically adjust their parameters.

[0095] The following is the dynamic adjustment of the cutoff frequency and the optimization mechanism:

[0096] Dynamic cutoff frequency adjustment: Whether it's an FIR filter, IIR filter, or adaptive hybrid filter, the cutoff frequency is dynamically adjusted based on the inverse relationship between the cutoff frequency and the time constant of the hydrogen storage tank. The cutoff frequency can be calculated using the following formula:

[0097]

[0098] Among them, f c It is the cutoff frequency of the filter, τ. h It is the time constant of the hydrogen storage tank.

[0099] The above formula indicates that the smaller the system's time constant, the higher the cutoff frequency; conversely, the larger the time constant, the lower the cutoff frequency. A higher cutoff frequency means the filter can respond to high-frequency fluctuations more quickly, while a lower cutoff frequency means the filter is more inclined to smooth low-frequency fluctuations.

[0100] Optimization Mechanism: To ensure optimal filter performance, an optimization mechanism can be used to find the optimal filter order. The specific steps are as follows:

[0101] Define the optimization objective: Define an evaluation metric to assess the performance of the filter.

[0102] Common evaluation metrics include:

[0103] Volatility: Measures the degree of fluctuation in the filtered signal. The lower the volatility, the better the filtering effect.

[0104] Mean Square Error (MSE): Measures the difference between the filtered signal and the desired signal. The smaller the MSE, the better the filtering effect.

[0105] Phase distortion: For certain applications, maintaining the phase integrity of a signal is very important, so phase distortion can be considered as an evaluation metric.

[0106] In the application of hydrogen storage tanks, volatility is a commonly used evaluation indicator, and its calculation formula is as follows:

[0107]

[0108] Where N is the number of data points, X i It is the i-th data point. It is the average of all data points.

[0109] Define the optimization range: Define an appropriate optimization range based on the type of filter.

[0110] FIR filters typically start with lower orders and gradually increase to higher orders.

[0111] IIR filters: Since IIR filters can provide good frequency selectivity at relatively low orders, they usually do not need to be of very high order.

[0112] Adaptive hybrid filters: The order of an adaptive filter can be dynamically adjusted based on the real-time state of the system, thus eliminating the need for a fixed order. Instead, the filter's structure or coefficients can be dynamically adjusted based on the fluctuations in the subsequent hydrogen production rate.

[0113] Apply the binary search method: set the left and right boundaries of the filter order, and record the optimal order and minimum volatility. Each time, take the average of the left and right boundaries as the new filter order, design the filter and filter the signal.

[0114] Evaluation and Update: Calculate the volatility of the filtered signal. If it is less than the minimum recorded volatility, update the record. If the volatility is high, shrink the right boundary; otherwise, expand the left boundary.

[0115] Selecting the optimal filter order: Stop the search when the left boundary is greater than the right boundary. The optimal filter order recorded at this time is the filter order that minimizes volatility found throughout the entire search range.

[0116] (2) Determine the current operating conditions based on the hydrogen charging and discharging status of the hydrogen storage tank.

[0117] Hydrogen charging is divided into two types: fast charging and slow charging. When the battery's compensated power P... b Far exceeding the system requirement P demand At that time, the hydrogen storage tank needs to store a large amount of excess hydrogen, and the actual hydrogen production rate H e Significantly greater than the rated hydrogen production rate H r H e >H r +δ, this is the rapid hydrogen charging condition; when the battery-compensated power is slightly higher than the system demand, but the change is relatively slow, the hydrogen production rate is also relatively gradual, i.e., H r <H e ≤H r +δ, this is the slow hydrogen charging condition;

[0118] The hydrogen release operation is divided into two scenarios: rapid hydrogen release and slow hydrogen recharge. When the power after battery compensation is far lower than the system demand, resulting in a hydrogen production deficit, the hydrogen storage tank needs to quickly release the stored hydrogen energy to supplement the insufficient hydrogen supply. The actual hydrogen production rate H e Significantly less than the rated hydrogen production rate H r H e <H r -δ, this is the rapid hydrogen release condition; when the power after battery compensation is slightly lower than the system demand, but the difference is small and the hydrogen production deficit is not large, the hydrogen release rate of the hydrogen storage tank changes relatively slowly, that is, the actual hydrogen production rate H is at this time. e Significantly greater than the rated hydrogen production rate H r H r -δ≤H e <H r This is a slow hydrogen release condition;

[0119] When the battery-compensated power equals the system demand, the actual hydrogen production rate equals the rated hydrogen production rate, i.e., H0 e =H r At this point, the hydrogen production system is in a state of equilibrium;

[0120] Abnormal operating conditions are divided into overcharge and over-discharge conditions. During real-time continuous hydrogen charging, overcharging may occur, i.e., H... s (t)+(H e -H r )×Δt>V h Similarly, during continuous hydrogen release in real time, over-release may also occur, i.e., H+. s (t)-(H r -H e )×Δt<0.

[0121] The state of the hydrogen storage tank is updated in real time during hydrogen filling, discharging, and equilibrium states. Under ideal conditions without considering the volume limitation of the hydrogen storage tank, the equation of state is as follows:

[0122]

[0123] Among them, H e H r For the actual hydrogen production rate and rated hydrogen production rate of the hydrogen production system, H s (t) represents the current storage capacity of the hydrogen storage tank, H s (t-1) represents the amount of hydrogen stored in the storage tank at the previous moment, V h δ represents the volume of the hydrogen storage tank, and δ is the threshold value for determining the rate of hydrogen filling and discharging in the hydrogen storage tank.

[0124] (3) Select the appropriate filter type based on the current operating conditions.

[0125] Rapid hydrogen charging: In this operating condition, a fast response is required to fully utilize the excess electricity generated by renewable energy sources. Therefore, FIR filters with fast response characteristics can be selected because they have no feedback loop and can provide more stable performance.

[0126] Slow hydrogen charging: In this case, the hydrogen production system does not need to respond particularly quickly, so an IIR filter with better low-frequency performance can be selected. Because IIR filters have steeper attenuation characteristics in the low-frequency range, they can better handle slowly changing signals, thereby ensuring the stability and smoothness of the hydrogen production process.

[0127] Rapid hydrogen release: Under this condition, the hydrogen production rate remains constant, but the hydrogen release rate from the storage tank is significantly increased to meet the system's immediate energy demands. To ensure the system can respond quickly and remain stable, FIR filters are a good choice because they provide a fast and stable state response, ensuring timely hydrogen supply without affecting the overall stability of the system.

[0128] Slow hydrogen release: In this scenario, the hydrogen production rate remains constant, while the hydrogen release system needs to provide a stable hydrogen energy output. IIR filters can be selected; these filters excel at maintaining system stability and can provide consistent output over long periods, ensuring the smoothness and efficiency of hydrogen energy release.

[0129] Balanced operating condition: Under this condition, neither additional hydrogen charging nor discharging is required, and the system operates in a stable state. To maintain this balance, the control system should focus on maintaining steady-state accuracy and minimizing fluctuations. For this purpose, an IIR filter with smoothing characteristics can be selected. An IIR filter can effectively reduce unnecessary oscillations and improve the system's operating efficiency and reliability without affecting the overall system performance.

[0130] Overcharging: This condition can damage the equipment or affect system safety. Therefore, a hybrid filter with adaptive characteristics is used. This filter automatically adjusts its parameters based on the real-time status of the system to prevent the hydrogen storage tank from overcharging, ensuring it always operates within a safe range and guaranteeing long-term stable system operation.

[0131] Over-discharge condition: This situation can also affect the stability and safety of the system. Therefore, a hybrid filter with adaptive characteristics is used. This filter can automatically adjust its parameters according to the real-time status of the system, preventing excessive hydrogen release from the hydrogen storage tank, ensuring sufficient hydrogen reserves at all times, and guaranteeing the long-term stable operation of the system.

[0132] (4) Under the condition of limited hydrogen storage tank volume, a filter is applied to obtain the smoothed target hydrogen production rate, and the hydrogen charging and discharging amount of the hydrogen storage tank is adjusted according to the difference between the target hydrogen production rate and the actual hydrogen production rate. The hydrogen charging and discharging process of the hydrogen storage tank is based on the actual hydrogen production rate H. e (t) and target hydrogen production rate H target The decision is made by (t), and the specific strategy is shown in the following formula:

[0133]

[0134] Among them, H charge H discharge H represents the rates of hydrogen filling and discharging from the hydrogen storage tank, respectively. target Q represents the target hydrogen production rate after smoothing by the hydrogen storage tank filter. max This represents the maximum hydrogen filling and discharging rate of the hydrogen storage tank.

[0135] When H e (t)>H target When (t), the excess will be stored in the hydrogen storage tank; when H e (t) <H target At time (t), the hydrogen storage tank will release hydrogen to replenish the shortfall; when H e (t)=H target At time (t), the hydrogen storage tank neither adds nor releases hydrogen.

[0136] The hydrogen filling rate of the hydrogen storage tank must not exceed its maximum hydrogen filling rate Q. max And at this time, the hydrogen charging rate H of the hydrogen storage tank charge The amount of hydrogen that can be contained must not exceed V. h -H s Similarly, the hydrogen release rate of a hydrogen storage tank cannot exceed its maximum hydrogen release rate Q. max And the hydrogen release rate H of the hydrogen storage tank discharge It cannot exceed the storage capacity H at this time. s This means that the capacity of the hydrogen storage tank should be considered in real time during the process of charging and discharging hydrogen.

[0137] (5) Based on the adjusted hydrogen storage tank filling and discharging amount, perform the hydrogen output operation and record the hydrogen output rate during this process.

[0138] (6) The performance of the hydrogen production system is evaluated by calculating the fluctuation rate during the hydrogen output process, and the parameters of the hydrogen production system are readjusted according to the evaluation results. Generally, the fluctuation rate of the output hydrogen rate is about 10%, which can meet the needs of subsequent industrial production. If the fluctuation is too large, the parameters of the hydrogen production system can be adjusted by expanding the order range in the optimization mechanism, reducing the time length of the working condition judgment, and adjusting the hydrogen charging and discharging rate judgment threshold δ in turn to reduce the fluctuation of hydrogen production.

[0139] Example 1

[0140] Taking wind-solar coupled battery-compensated electrolysis hydrogen production as an example, this paper introduces an intelligent filtering control method for the hydrogen storage tank of an electrolysis hydrogen production system.

[0141] First, a wind-solar hybrid system needs to be constructed, combining wind power and photovoltaic power generation to achieve a more stable power output. The output power P of the wind turbine... w The relationship with wind speed v can be described by a piecewise function:

[0142]

[0143] in:

[0144] v ci This is the cut-in speed, which is usually 3 m / s.

[0145] v s It is the rated wind speed, which is usually 13 m / s.

[0146] v co This is the cut-out speed, which is usually 25 m / s.

[0147] P ws It is the rated power of a single device, usually 3MW.

[0148] Photovoltaic power generation P p Relationship with light intensity and temperature:

[0149]

[0150] in:

[0151] P ps This is the rated power of the photovoltaic module, typically 250W.

[0152] G T and G 25℃ These are the real-time radiation intensity and the reference radiation intensity (at 25°C), respectively.

[0153] α p It is the temperature coefficient of power, with a value of -0.0035.

[0154] f is the influence coefficient, with a value of 0.9.

[0155] T is the operating temperature (unit: °C).

[0156] Using wind speed, solar irradiance, and temperature data from Zhalute Banner, Inner Mongolia in 2017, the output power of wind power and photovoltaic power generation was calculated. This step involves the analysis of wind speed, solar irradiance, and temperature data, as well as the consideration of the performance parameters of wind turbines and photovoltaic modules. The two complement each other to form the wind-solar coupled power.

[0157] like Figure 2 The image shows a comparison between the battery-compensated power, smoothed by a filter, and the original power. The wind-solar coupling power fluctuates drastically at this point. To balance the difference between power generation and load and prevent equipment damage, a battery energy storage system was designed. This system adjusts its charging and discharging strategy based on the output power of the wind-solar coupling complementary system and system requirements. To smooth the compensated output power, an FIR filter was designed, which significantly improves the system's stability and efficiency in the wind-solar coupling battery compensation system, while reducing noise impact and providing a cleaner and more stable operating environment for the energy conversion process.

[0158] like Figure 3 The figure shows a comparison between the smoothed curves of different filters applied under different actual operating conditions of the hydrogen storage tank and the original curve. Due to the limitation of battery capacity, the power fluctuation cannot be completely smoothed. At this time, when the battery capacity is 3400MWh, the power fluctuation rate is 29.31%, which leads to a certain fluctuation in the actual hydrogen production rate in electrolytic hydrogen production. It is necessary to set up a hydrogen storage tank and a filter to smooth the hydrogen production rate.

[0159] The filter types are FIR filters, IIR filters, and adaptive hybrid filters. The mathematical expressions for these three types of filters are:

[0160]

[0161] Among them, y FIR (n), y IIR y(n) and y(n) are the output signals of the FIR filter, IIR filter and hybrid filter, respectively. x(n) is the input signal, h(k) is the coefficient of the FIR filter, N is the order of the FIR filter, b(k) and a(k) are the forward and feedback coefficients of the IIR filter, respectively. M and N′ are the orders of the forward and feedback, respectively. α(n) is the weight of the FIR filter in the hybrid filter, with an initial value of 0.5.

[0162] The adaptive hybrid filter uses an adaptive algorithm to update the weights, as shown in the following equation:

[0163] α(n+1)=α(n)+η·e(n)·(y FIR (n)-y IIR (n))

[0164] Where η is the learning rate for weight updates, e(n) is the error signal, representing the difference between the filter output and the desired output, and y FIR (n)-y IIR (n) represents the difference between the outputs of the FIR and IIR filters, used to guide the adjustment of the weights;

[0165] The cutoff frequencies of the three filters mentioned above are dynamically adjusted based on the inverse relationship between the cutoff frequency and the time constant of the hydrogen storage tank, as shown in the following formula:

[0166]

[0167] Among them, f c It is the cutoff frequency of the filter, τ. h It is the time constant of the hydrogen storage tank;

[0168] Volatility is defined as an evaluation metric to assess the performance of the filter. The optimization range is defined according to the filter type, and a bisection method is applied for optimization. The volatility calculation formula is shown below:

[0169]

[0170] Where N is the number of data points, X i It is the i-th data point. It is the average of all data points.

[0171] Hydrogen storage tank operating condition classification, filter selection, and real-time update formula for hydrogen storage tank status:

[0172]

[0173] Among them, H e The actual hydrogen production rate of the hydrogen production system is expressed in t / h, H. s (t) represents the current storage capacity of the hydrogen storage tank, H s (t-1) represents the amount of hydrogen stored in the storage tank at the previous moment, and δ is the threshold for judging the hydrogen charging and discharging rate of the storage tank, which is generally taken as 0.05 in this case.

[0174] Similarly, the operating conditions of the hydrogen storage tank are divided into seven cases: rapid hydrogen charging, slow hydrogen charging, equilibrium state, rapid hydrogen discharging, slow hydrogen discharging, overcharging, and over-discharging. Among them, when H e >3.45 is the rapid hydrogen charging condition, so an FIR filter is selected; when 3.4... <H e When H is ≤3.45, it is a slow hydrogen charging condition, and an IIR filter is selected; when H e When H = 3.4, the hydrogen production system is in equilibrium, and an IIR filter is selected at this time; when H e When H < 3.35, it is a rapid hydrogen release condition, and an FIR filter should be selected; when 3.35 ≤ H eWhen the value is less than 3.4, it is a slow hydrogen release condition, and an IIR filter is selected; otherwise, during this time Δt, H s (t) is greater than the hydrogen storage tank volume V h If the value is less than zero, the hydrogen storage tank will experience overcharging and over-discharging. In this case, a hybrid filter with adaptive characteristics will be used.

[0175] The actual hydrogen filling and discharging strategy for the hydrogen storage tank is as follows:

[0176]

[0177] Among them, H charge H discharge H represents the rates of hydrogen filling and discharging from the hydrogen storage tank, respectively. target The target hydrogen production rate after smoothing by the hydrogen storage tank filter.

[0178] At this time, the wind-solar coupled battery-compensated electrolysis hydrogen production system, through the action of the battery and the hydrogen storage tank, produces hydrogen in a tank with a volume of 9000m³. 3 In this scenario, the fluctuation rate of hydrogen production is only about 13%, achieving a smoother hydrogen output rate and ensuring the stability and reliability of the electrolysis hydrogen production system. However, due to the selection of conditions, the hydrogen storage tank may not be available under certain operating conditions. A comparison of the original hydrogen production rate and the smoothed hydrogen production rate of a wind-solar coupled battery-compensated electrolysis hydrogen production system over a year is shown below. Figure 4 As shown.

[0179] The description and application of the present invention herein are illustrative and not intended to limit the scope of the invention to the embodiments described above. The effects or advantages described in the specification may not be apparent in actual experimental cases due to uncertainties in specific conditions or other factors, and such descriptions are not intended to limit the scope of the invention. Variations and modifications to the embodiments disclosed herein are possible, and various substitutions and equivalents of the components in the embodiments are well known to those skilled in the art. It should be understood by those skilled in the art that the invention can be implemented in other forms, structures, arrangements, proportions, and with other components, materials, and parts without departing from the spirit or essential characteristics of the invention. Other variations and modifications can be made to the embodiments disclosed herein without departing from the scope and spirit of the invention.

Claims

1. A method for intelligent filtering control of hydrogen storage tanks in a wind-solar coupled electrolysis hydrogen production system, characterized in that, include: S1. Design the filter and dynamically adjust its cutoff frequency to obtain the optimal filter order through an optimization mechanism. Specifically, it includes: S1-1. Combining FIR and IIR filters, an adaptive hybrid filter is designed based on the adaptive LMS algorithm as follows: ， in, It is the output signal of the adaptive hybrid filter. These are the weights of the FIR filter, ranging from... , These are the weights of the IIR filter, and Update using the adaptive LMS algorithm; This is the output signal of the FIR filter. The output signal of the IIR filter; where: FIR filter for: ， Where n represents the currently processed sample point, and k is the counting variable. It is the input signal. These are the coefficients of the FIR filter. It is the order of the FIR filter; IIR filter for: ， in, It is the input signal. and These are the forward and feedback coefficients of the IIR filter, respectively. and These are the forward order and feedback order of the IIR filter, respectively. S1-2. The cutoff frequency of the filter is dynamically adjusted according to the time constant of the hydrogen storage tank. The adjustment formula is as follows: ， in, It is the cutoff frequency of the filter. It is the time constant of the hydrogen storage tank; S1-3. Define volatility as an evaluation metric for filter performance. Define the optimization range according to the filter type, and use the bisection method to find the filter order that minimizes volatility. The volatility calculation formula is shown below: ， in, It is the number of data points. It is the first Data points, It is the average of all data points; S2. Obtain the hydrogen charging and discharging status of the hydrogen storage tank, determine the current operating condition of the hydrogen storage tank based on the hydrogen charging and discharging status, and select the corresponding filter based on the operating condition. S3. Under the limitation of the limited volume of the hydrogen storage tank, the smoothed target hydrogen production rate is obtained by using the selected filter. Based on the difference between the target hydrogen production rate and the actual hydrogen production rate, the amount of hydrogen charged and discharged in the hydrogen storage tank is adjusted. The hydrogen output operation is performed according to the adjusted amount of hydrogen charged and discharged, and the hydrogen output rate is recorded. S4. Calculate the volatility during the hydrogen output process, evaluate the performance of the wind-solar coupled electrolysis hydrogen production system based on the volatility, and readjust the hydrogen production system parameters according to the evaluation results.

2. The intelligent filtering control method for the hydrogen storage tank of the wind-solar coupled electrolysis hydrogen production system according to claim 1, characterized in that, The weights described in S1-1 The update formula is: ， in, It is the learning rate of weight updates. The error signal represents the difference between the filter output and the desired output. It is the difference between the output of the FIR filter and the output of the IIR filter, used to guide the adjustment of the weights.

3. The intelligent filtering control method for the hydrogen storage tank of the wind-solar coupled electrolysis hydrogen production system according to claim 1, characterized in that, The optimization method used in S1-3 specifically includes: S1-3-1. Set the left and right boundaries of the filter order, calculate the volatility of the signal after filtering, and record the filter order and volatility. S1-3-2. Take the average value of the left and right boundaries as the boundary of the new filter order. If the calculated volatility is less than the recorded volatility, update the record and expand the left boundary; if the calculated volatility is greater than the recorded volatility, shrink the right boundary. S1-3-3, Stop when the left boundary of the filter order is greater than the right boundary. At this time, the recorded filter order is the filter order that minimizes the volatility.

4. The intelligent filtering control method for the hydrogen storage tank of the wind-solar coupled electrolysis hydrogen production system according to claim 1, characterized in that, S2 specifically includes: S2-1: The real-time state formula for the hydrogen storage tank is: ， in, , These are the actual hydrogen production rate and the rated hydrogen production rate of the hydrogen production system, respectively. This represents the current storage capacity of the hydrogen storage tank. The value represents the amount of hydrogen stored in the storage tank at the previous moment, and Δt represents the time step, i.e., the interval between two time points. A threshold for determining the hydrogen charging and discharging rate of a hydrogen storage tank; S2-2: Determine the current operating condition of the hydrogen storage tank and select the appropriate filter: when When the hydrogen storage tank is in rapid hydrogen filling mode, an FIR filter is selected; when At this time, the hydrogen storage tank is in slow hydrogen charging mode, so an IIR filter is selected; when At that time, the hydrogen storage tank is in a balanced state, and an IIR filter is selected; when When the hydrogen storage tank is in a rapid hydrogen release condition, an FIR filter is selected; when At that time, the hydrogen storage tank is in slow hydrogen release mode, so an IIR filter is selected; when Larger than the hydrogen storage tank volume If the value is less than zero, the hydrogen storage tank will experience overcharging or over-discharging conditions, and an adaptive hybrid filter will be selected.

5. The intelligent filtering control method for the hydrogen storage tank of the wind-solar coupled electrolysis hydrogen production system according to claim 1, characterized in that, The strategy for adjusting the hydrogen filling and discharging of the hydrogen storage tank in S3 is as follows: ， in, , These represent the rates of hydrogen filling and discharging from the hydrogen storage tank, respectively. The target hydrogen production rate after smoothing by the hydrogen storage tank filter. This represents the actual hydrogen production rate of the hydrogen production system. For the volume of the hydrogen storage tank, The amount of hydrogen stored in the hydrogen storage tank. This represents the current storage capacity of the hydrogen storage tank. This represents the amount of hydrogen stored in the storage tank at the previous moment. This represents the maximum hydrogen filling and discharging rate of the hydrogen storage tank.

6. The intelligent filtering control method for the hydrogen storage tank of the wind-solar coupled electrolysis hydrogen production system according to claim 1, characterized in that, The parameters of the hydrogen production system in S4, based on the evaluation results, are readjusted as follows: If the volatility is ≤10%, it means that the hydrogen production system meets the needs of industrial production and its parameters do not need to be adjusted. If the volatility is greater than 10%, the parameters of the hydrogen production system are adjusted by successively expanding the filter order range of the optimization mechanism, reducing the judgment time length of the hydrogen storage tank operating condition, and adjusting the judgment threshold of the hydrogen charging and discharging rate.

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