Intelligent filtering control method for hydrogen storage tank of wind-solar coupled electrolytic hydrogen production system

Abstract

The invention provides an intelligent filtering control method for a hydrogen storage tank of a wind-solar coupled electrolytic hydrogen production system, which comprises the following steps: designing a filter, dynamically adjusting the cut-off frequency of the filter according to the time constant of the hydrogen storage tank, and finding the optimal filter order through an optimization mechanism; the current working condition is judged according to the hydrogen charging and discharging state of the hydrogen storage tank, and a proper type is selected from an FIR filter, an IIR filter and a self-adaptive hybrid filter according to different working conditions; the hydrogen production process is filtered, and the hydrogen charging and discharging amount of a hydrogen storage tank is adjusted according to the difference between the target hydrogen production rate and the actual hydrogen production rate, so that smooth hydrogen output is obtained; and evaluating the performance of the hydrogen production system according to the fluctuation ratio in the hydrogen output process, and readjusting the parameters of the filter according to the evaluation result. According to the invention, appropriate filter types are dynamically selected according to different working conditions, so that a relatively smooth hydrogen output rate is realized, the stability of the system is ensured, and the requirements of subsequent large-scale industrial production are met.

Description

Technical Field

[0001] The present invention relates to the technical field of wind-solar coupled electrolytic hydrogen production, and specifically but not limited to a method for intelligent filtering control of a hydrogen storage tank in a wind-solar coupled electrolytic hydrogen production system. Background Art

[0002] With the growing global demand for clean energy, wind energy and solar energy, as the main renewable energy sources, have been widely used. The intermittency and uncertainty of these energy sources pose challenges to the power grid, especially in applications that require stable power supply, such as the electrolytic water hydrogen production process. To overcome this problem, a battery energy storage system is usually adopted to balance the difference between power generation and load and ensure a stable power supply. In the prior art, most wind-solar hybrid power generation systems only rely on a single type of battery filter to smooth the output power, but this can only partially solve the problem that the normal operation of the electrolyzer is affected by high-frequency power fluctuations. Due to the battery capacity limitation, the power fluctuations cannot be completely smoothed, which results in certain fluctuations in the actual hydrogen production rate during the electrolytic hydrogen production process. Therefore, it is necessary to set up a hydrogen storage tank to store excess hydrogen or supplement the deficit hydrogen in real time according to the hydrogen production rate, and at the same time design a filter to smooth the hydrogen production rate, so as to improve the stability of the hydrogen production amount to meet the needs of subsequent large-scale industrial production.

[0003] The reason why the traditional single filter can adapt to the battery is that the charge and discharge process of the battery is relatively fast and can provide or absorb a large amount of power in a short time, which matches the fixed cut-off frequency and fast response characteristics of the filter. Different from the battery, the hydrogen charging and discharging process of the hydrogen storage tank is relatively slow, especially when it is close to full load or no load, its response speed will be further reduced, because the compression and release of hydrogen involve complex physical and chemical processes and cannot be completed as quickly as the battery. This means that when a single filter is used for the hydrogen storage tank, the cut-off frequency of the filter is fixed and cannot be adjusted according to the real-time state of the system. Under different working conditions for a long time, the performance of the filter may not reach the optimum. In the case of rapid hydrogen charging or rapid hydrogen discharging of the hydrogen storage tank, the filter with a fixed cut-off frequency may not be able to respond in time, resulting in large fluctuations in the hydrogen output rate; while in the case of slow hydrogen charging or slow hydrogen discharging, the filter with a fixed cut-off frequency may over-smooth, resulting in slow system response. In addition, the traditional single filter does not have an adaptive characteristic and cannot automatically adjust parameters according to the dynamic changes of the system. For example, the IIR filter has good attenuation characteristics in the low-frequency band but poor performance in the high-frequency band. Therefore, it is difficult for a single filter to cope with the complex working condition changes of the hydrogen storage tank, especially in abnormal situations such as overcharging and over-discharging, which may cause 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] In view of one or more problems in the prior art, the present invention proposes an intelligent filtering control method for a hydrogen storage tank in a wind-solar coupled electrolytic hydrogen production system. By introducing a filter selection mechanism based on the operating conditions of the hydrogen storage tank, a suitable filter type is dynamically selected according to different operating conditions, so as to achieve a relatively smooth hydrogen output rate, ensure the stability and reliability of the system, and meet the needs of subsequent large-scale industrial production.

[0006] The technical solution for achieving the purpose of the present invention is as follows:

[0007] An intelligent filtering control method for a hydrogen storage tank in a wind-solar coupled electrolytic hydrogen production system, comprising:

[0008] S1. Design a filter, dynamically adjust the cut-off frequency of the filter, and obtain the optimal filter order through an optimization mechanism;

[0009] S2. Obtain the hydrogen charging and discharging state of the hydrogen storage tank, judge the current operating conditions of the hydrogen storage tank based on the hydrogen charging and discharging state, and select the corresponding filter based on the operating conditions;

[0010] S3. Under the limitation of the limited volume of the hydrogen storage tank, use the selected filter to obtain a smoothed target hydrogen production rate, based on the difference between the target hydrogen production rate and the actual hydrogen production rate, adjust the hydrogen charging and discharging amount of the hydrogen storage tank, perform hydrogen output operation according to the adjusted hydrogen charging and discharging amount, and record the hydrogen output rate;

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

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

[0013] S1-1. Combine the FIR filter and the IIR filter, and design an adaptive hybrid filter based on the adaptive LMS algorithm as:

[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, the range is 0 < α(n) < 1, 1-α(n) is the weight of the IIR filter, and α(n) is updated using the adaptive LMS algorithm; y 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 currently processed sample point, k is a 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 coefficient and feedback coefficient of the IIR filter respectively, and M and N' are the forward order and feedback order of the IIR filter respectively;

[0022] S1-2. Dynamically adjust the cut-off frequency of the filter according to the time constant of the hydrogen storage tank, and the adjustment formula is:

[0023]

[0024] where f c is the cut-off frequency of the filter, and τ h is the time constant of the hydrogen storage tank;

[0025] S1-3. Define the volatility as an evaluation index for evaluating the performance of the filter, define the optimization range according to the filter type, and use the dichotomy method for optimization to obtain the filter order that minimizes the volatility; where the volatility calculation formula is as shown in the following formula:

[0026]

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

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

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

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

[0031] Furthermore, for the intelligent filtering control method of the hydrogen storage tank in the wind-solar coupled electrolytic hydrogen production system of the present invention, the specific steps of using the dichotomy method for optimization in S1-3 are as follows:

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

[0033] S1-3-2: Take the average value of the left and right boundaries as the new boundary of the 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, for the intelligent filtering control method of the hydrogen storage tank in the wind-solar coupled electrolytic hydrogen production system of the present invention, S2 specifically includes:

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

[0037]

[0038] where H e , H r are respectively the actual hydrogen production rate and the rated hydrogen production rate of the hydrogen production system, H s (t) is the current storage volume of the hydrogen storage tank, H s (t - 1) is the storage volume of the hydrogen storage tank at the previous moment, △t is the time step, that is, the interval between two time points, and δ is the threshold for judging the hydrogen charging and discharging speed of the hydrogen storage tank;

[0039] S2-2: Judge the current working condition of the hydrogen storage tank and select a filter:

[0040] When H e > H r + δ, the hydrogen storage tank is in the fast hydrogen charging working condition, and an FIR filter is selected;

[0041] When H r < H e ≤ H r + δ, the hydrogen storage tank is in the slow hydrogen charging working condition, and an IIR filter is selected;

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

[0043] When H e <H r -δ, the hydrogen storage tank is in the fast hydrogen release condition, and an FIR filter is selected;

[0044] When H r -δ ≤ H e <H r When, the hydrogen storage tank is in the slow hydrogen release condition, and an IIR filter is selected;

[0045] When H s (t) is greater than the volume V of the hydrogen storage tank h Or less than zero, then overcharging or over-discharging conditions occur in the hydrogen storage tank, and an adaptive hybrid filter is selected.

[0046] Furthermore, in the intelligent filtering control method of the hydrogen storage tank of the wind-solar coupled electrolytic 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:

[0047]

[0048] Among them, H charge 、H discharge Are the hydrogen charging and discharging rates of the hydrogen storage tank respectively, and H target Is the target hydrogen production rate after smoothing by the hydrogen storage tank filter, and H e 、H r Are the actual hydrogen production rate and the rated hydrogen production rate of the hydrogen production system respectively, V h Is the volume of the hydrogen storage tank, H s Is the hydrogen storage amount of the hydrogen storage tank, H s (t) is the current storage amount of the hydrogen storage tank, and H s (t - 1) is the storage amount of the hydrogen storage tank at the previous moment, and Q max Is the maximum hydrogen charging and discharging rate of the hydrogen storage tank.

[0049] Furthermore, in the intelligent filtering control method of the hydrogen storage tank of the wind-solar coupled electrolytic hydrogen production system of the present invention, re-adjusting the parameters of the hydrogen production system according to the evaluation results in S4 includes:

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

[0051] If the volatility > 10%, the parameters of the hydrogen production system are debugged by successively expanding the filter order range of the optimization mechanism, shortening the judgment time length of the hydrogen storage tank condition, and adjusting the hydrogen charging and discharging speed judgment threshold.

[0052] The present invention adopts the above technical solutions and has the following technical effects compared with the prior art:

[0053] 1. The intelligent filtering control method for the hydrogen storage tank of the wind-solar coupled electrolytic hydrogen production system of the present invention divides the state of the hydrogen storage tank into seven working conditions, and selects a suitable filter 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 of the wind-solar coupled electrolytic hydrogen production system of the present invention designs an adaptive hybrid filter for the overcharging and over-discharging conditions of the hydrogen storage tank. When the hydrogen storage tank quickly charges and discharges hydrogen to reach overcharging or over-discharging, compared with the FIR filter, the hybrid filter will sacrifice some filtering effect to extend the process in which the hydrogen storage volume in the hydrogen storage tank is greater than the volume or the hydrogen storage volume is zero, so that the filter can perform filtering for a longer time in this working condition, and will not let the hydrogen storage tank quickly reach the limit value and lose the filtering effect, thereby making it difficult to suppress the fluctuation of the rapid hydrogen charging and discharging rate; when the hydrogen storage tank slowly charges and discharges hydrogen to reach overcharging or over-discharging, compared with the IIR filter, the hybrid filter will sacrifice some filtering time to achieve a better overall filtering effect.

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

[0056] The drawings are used to provide a further understanding of the present invention, and are used together with the description to explain the embodiments of the present invention, and do not constitute a limitation to the present invention. In the drawings:

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

[0058] Figure 2 The comparison diagram of the battery compensation smoothed power and the original power through the filter is shown.

[0059] Figure 3 The comparison diagram of the smoothed hydrogen production rate and the original hydrogen production rate after filtering with different filters for different working conditions of the hydrogen storage tank is shown.

[0060] Figure 4 The intelligent filtering control method for the hydrogen storage tank of the wind-solar coupled electrolytic hydrogen production system of the present invention is shown. Detailed Embodiments

[0061] To further understand the present invention, the preferred embodiments of the present invention will be described below in conjunction with embodiments. However, it should be understood that these descriptions are only for further explaining the features and advantages of the present invention, rather than limiting the claims of the present invention.

[0062] The description of this part only focuses on typical embodiments, and the present invention is not limited to the scope described in the embodiments. Combinations of different embodiments, mutual replacement of some technical features in different embodiments, and mutual replacement of the same or similar prior art means and some technical features in the embodiments are also within the scope of description and protection of the present invention.

[0063] Wind energy and light energy have fluctuations with both long and short periods, resulting in the problem of multiple working condition switches for the hydrogen storage tank. Traditionally, the hydrogen storage tank is set to a certain type of filter, such as IIR or FIR, to solve the fluctuation problem. However, this may cause the performance of the hydrogen storage tank filter to not reach the optimal level in the long term. To address the above problems, different filters are designed for different working conditions of the hydrogen storage tank, and an adaptive hybrid filter is designed for the overcharge and over-discharge working conditions that are difficult to act on by traditional filters. The cut-off frequency of the filter is dynamically adjusted, and the optimal filter order in the filter corresponding to each working condition is found through an optimization mechanism, so that the filter can better adapt to the working conditions of the hydrogen storage tank. Under the limitation of the limited volume of the hydrogen storage tank, the smoothed target hydrogen production rate is obtained by using the designed filter. Based on the difference between the target hydrogen production rate and the actual hydrogen production rate, the hydrogen charging and discharging amount of the hydrogen storage tank is adjusted, and the hydrogen output operation is performed according to the adjusted hydrogen charging and discharging amount, and finally a relatively smooth actual hydrogen production rate is achieved. If the hydrogen output volatility is too large, the parameters of the hydrogen production system need to be adjusted to meet the needs of subsequent industrial production.

[0064] An intelligent filtering control method for the hydrogen storage tank of a wind-solar coupled electrolytic hydrogen production system, by introducing a filter selection mechanism based on the working conditions of the hydrogen storage tank, dynamically selects a suitable filter type according to different working conditions, so as to achieve a relatively smooth hydrogen output rate, ensure the stability and reliability of the system, and meet the needs of subsequent large-scale industrial production. As Figure 1 shown, the specific steps are as follows:

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

[0066] The following are the designed filter types:

[0067] 1) FIR filter: The finite impulse response (FIR) filter is a linear time-invariant system, and its output depends only on the current and past input samples, rather than on the past output. The mathematical expression is shown as follows:

[0068]

[0069] Among them, y FIR (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 the FIR filter include:

[0071] Linear phase: The FIR filter can achieve linear phase through symmetric or anti-symmetric design, which means it does not introduce phase distortion and is suitable for applications where signal integrity needs to be maintained.

[0072] Stability: The FIR filter is always stable because it has no feedback loop and no poles, so it will not become unstable.

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

[0074] 2) IIR filter: The infinite impulse response (IIR) filter is a linear time-invariant system that contains a feedback loop. Its output depends not only on the current and past input samples but also on the past output samples. The mathematical expression is shown as follows:

[0075]

[0076] Among them, y IIR (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 the IIR filter include:

[0078] High efficiency: The IIR filter usually has higher frequency selectivity than the FIR filter of the same order, can achieve the same filtering effect at a lower order, and has a lower computational complexity.

[0079] Nonlinear phase: Due to the existence of a feedback loop, the IIR filter usually does not have a linear phase and may introduce phase distortion.

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

[0081] 3) Hybrid filter with adaptive characteristics: 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 condition changes, that is, it automatically adjusts the filter parameters according to the real-time state of the system to achieve the optimal filtering effect.

[0082] The adaptive hybrid filter adopts a parallel weighted structure, where the FIR and IIR filters work in parallel, and their respective outputs are summed with weights to obtain the final result. This structure allows the FIR and IIR filters to be optimized independently and is suitable for processing different types of signal components.

[0083] Assume that the output of the FIR filter is y FIR (n), and 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, with a range of 0 < α(n) < 1, and 1 - α(n) is the weight of the IIR filter. The adaptive LMS algorithm updates the filter coefficients through the following formula:

[0086] ω[n + 1] = ω[n] + μ·e[n]·x[n]

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

[0088] Therefore, the update formula for the 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 update, and y FIR (n) - y IIR (n) is the difference between the outputs of the FIR and IIR filters, which is used to guide the adjustment of the weight.

[0091] The main characteristics of the adaptive filter include:

[0092] Self - adaptability: The coefficients of the filter can be automatically adjusted according to the real - time state of the system to adapt to different operating condition changes.

[0093] Robustness: The adaptive filter can maintain good performance in an uncertain environment and is particularly suitable for non-linear or time-varying systems.

[0094] Flexibility: The adaptive filter can select different filter structures (such as FIR or IIR) according to different requirements and dynamically adjust its parameters.

[0095] The following are the dynamic adjustment of the cut-off frequency and the optimization mechanism:

[0096] Dynamic adjustment of the cut-off frequency: Whether it is an FIR filter, an IIR filter or an adaptive hybrid filter, the cut-off frequency is dynamically adjusted based on the inverse relationship between the cut-off frequency and the time constant of the hydrogen storage tank. The following formula can be used to calculate the cut-off frequency:

[0097]

[0098] where f c is the cut-off frequency of the filter, and τ h is the time constant of the hydrogen storage tank.

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

[0100] Optimization mechanism: To ensure the optimal performance of the filter, the optimal filter order can be found through an optimization mechanism. The specific steps are as follows:

[0101] Define the optimization objective: Define an evaluation index to evaluate the performance of the filter.

[0102] Common evaluation indexes include:

[0103] Volatility: Measures the degree of fluctuation of the filtered signal. The smaller 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 some applications, it is very important to maintain the phase integrity of the signal. Therefore, phase distortion can be considered as an evaluation index.

[0106] In the application of the hydrogen storage tank, volatility is a commonly used evaluation index, and its calculation formula is:

[0107]

[0108] where N is the number of data points, and X i is the i-th data point, and is the average value of all data points.

[0109] Define the optimization range: Define a suitable optimization range according to the type of filter.

[0110] FIR filter: Usually start from a lower order and gradually increase to a higher order.

[0111] IIR filter: Since the IIR filter can provide good frequency selectivity at a lower order, very high orders are usually not required.

[0112] Adaptive hybrid filter: The order of the adaptive filter can be dynamically adjusted according to the real-time state of the system, so a fixed order is not required. Instead, the structure or coefficients of the filter can be dynamically adjusted according to the volatility of the subsequent hydrogen production rate.

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

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

[0115] Select the optimal filter order: Stop the search when the left boundary is greater than the right boundary. At this time, the recorded optimal filter order is the filter order that minimizes the volatility within the entire search range.

[0116] (2) Determine the current working condition according to the hydrogen charging and discharging state of the hydrogen storage tank.

[0117] The hydrogen charging working condition is divided into two cases: rapid hydrogen charging and slow hydrogen charging. When the power P after battery compensation b is much higher than the system demand P demand , the hydrogen storage tank needs to store a large amount of excess hydrogen, and the actual hydrogen production rate H e is significantly greater than the rated hydrogen production rate H r , that is, H e >H r +δ. At this time, it is the rapid hydrogen charging working condition; when the power after battery compensation is slightly higher than the system demand but changes relatively slowly, the hydrogen production rate is also relatively gentle accordingly, that is, H r <H e ≤H r +δ. At this time, it is the slow hydrogen charging working condition;

[0118] The hydrogen release condition is divided into two cases: rapid hydrogen release and slow hydrogen charging. When the power after battery compensation is much 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 volume. The actual hydrogen production rate H e is significantly less than the rated hydrogen production rate H r , that is, H e < H r -δ. At this time, it is the rapid hydrogen release condition; when the power after battery compensation is slightly lower than the system demand, but the gap 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 e is significantly greater than the rated hydrogen production rate H r , that is, H r -δ ≤ H e < H r . At this time, it is the slow hydrogen release condition;

[0119] When the power after battery compensation is equal to the system demand, the actual hydrogen production rate and the rated hydrogen production rate are equal, that is, H e = H r . At this time, the hydrogen production system is in a balanced state;

[0120] The abnormal condition is divided into overcharging condition and over-discharging condition. During the real-time continuous hydrogen charging process, overcharging may occur, that is, H s (t) + (H e - H r ) × Δt > V h ; similarly, during the real-time continuous hydrogen release process, over-discharging may also occur, that is, H s (t) - (H r - H e ) × Δt < 0.

[0121] The state of the hydrogen storage tank is updated in real time during hydrogen charging, hydrogen discharging, and the balanced state. In the ideal case without considering the volume limitation of the hydrogen storage tank, the state equation is shown as follows:

[0122]

[0123] Among them, H e , H r are the actual hydrogen production rate and the rated hydrogen production rate of the hydrogen production system, H s (t) is the current storage volume of the hydrogen storage tank, H s (t - 1) is the storage volume of the hydrogen storage tank at the previous moment, V h is the volume of the hydrogen storage tank, and δ is the threshold for judging the hydrogen charging and discharging speed of the hydrogen storage tank.

[0124] (3) Select a suitable filter type according to the current working condition.

[0125] Fast hydrogen charging: In this operating condition, a quick response is required to make full use of the excess power generated by renewable energy. 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 response of the hydrogen production system does not need to be particularly rapid, and IIR filters with good low-frequency performance can be selected. Since IIR filters have a steeper attenuation characteristic in the low-frequency band and can better handle slowly changing signals, the stability and smoothness of the hydrogen production process can be ensured.

[0127] Fast hydrogen discharging: In this operating condition, the hydrogen production rate remains constant, but the hydrogen discharging rate of the hydrogen storage tank increases significantly to meet the immediate energy demand of the system. To ensure that the system can respond quickly and maintain stability, FIR filters are a good choice because they can provide a fast and stable state response, ensuring the timely supply of hydrogen energy without affecting the overall stability of the system.

[0128] Slow hydrogen discharging: In this case, the hydrogen production rate still remains constant, while the hydrogen discharging system needs to provide a smooth hydrogen energy output. IIR filters can be selected, and these filters perform excellently in maintaining the stability of the system and can provide a consistent output over a long time, ensuring the smoothness and efficiency of hydrogen energy release.

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

[0130] Overcharging operating condition: This situation may cause damage to the equipment or affect the 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 state of the system, prevent the hydrogen storage tank from being overcharged, ensure that it always operates within a safe operating range, and guarantee the long-term stable operation of the system.

[0131] Overdischarging operating condition: This situation may also affect the stability and safety of the system. Therefore, a hybrid filter with adaptive characteristics is also used. This filter can automatically adjust its parameters according to the real-time state of the system, avoid the hydrogen storage tank from being overdischarged, ensure that it always has sufficient hydrogen energy reserves, and guarantee the long-term stable operation of the system.

[0132] (4) Under the condition of the limited volume of the hydrogen storage tank, the filtered target hydrogen production rate is obtained by applying a filter, and according to the difference between the target hydrogen production rate and the actual hydrogen production rate, the hydrogen charging and discharging amount of the hydrogen storage tank is adjusted. The hydrogen charging and discharging process of the hydrogen storage tank is determined according to the actual hydrogen production rate H e (t) and the target hydrogen production rate H target (t), and the specific strategy is shown as follows:

[0133]

[0134] Among them, H charge and H discharge are the hydrogen charging and discharging rates of the hydrogen storage tank respectively, H target is the target hydrogen production rate smoothed by the filter of the hydrogen storage tank, Q max is the maximum hydrogen charging and discharging rate of the hydrogen storage tank.

[0135] When H e (t) > H target (t), the excess part will be stored in the hydrogen storage tank; when H e (t) < H target (t), the hydrogen storage tank will release hydrogen to supplement the insufficient part; when H e (t) = H target (t), the hydrogen storage tank neither charges nor discharges hydrogen.

[0136] Among them, the hydrogen charging rate of the hydrogen storage tank cannot exceed its maximum hydrogen charging rate Q max , and at this time, the hydrogen charging rate H charge of the hydrogen storage tank cannot exceed the hydrogen capacity V h - H s . Similarly, the hydrogen discharging rate of the hydrogen storage tank cannot exceed its maximum hydrogen discharging rate Q max , and the hydrogen discharging rate H discharge of the hydrogen storage tank cannot exceed the storage amount H s at this time, that is, the capacity problem of the hydrogen storage tank should be considered in real time during the real-time hydrogen charging and discharging process.

[0137] (5) According to the adjusted hydrogen charging and discharging amount of the hydrogen storage tank, perform the hydrogen output operation and record the hydrogen output rate during this process.

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

[0139] Example 1

[0140] Taking the hydrogen production by electrolysis compensated with a wind-solar coupled battery as an example, an intelligent filtering control method for the hydrogen storage tank of an electrolytic hydrogen production system will be introduced.

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

[0142]

[0143] Where:

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

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

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

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

[0148] The photovoltaic power generation power P p And the relationship with light intensity and temperature:

[0149]

[0150] Where:

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

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

[0153] α p 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 working temperature (unit: °C).

[0156] Using the wind speed, light intensity, and temperature data of Zhalute Banner, Inner Mongolia in 2017, the output powers of wind power generation and photovoltaic power generation were calculated. This step involved the analysis of wind speed, light intensity, 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] As Figure 2 shown in the comparison between the battery-compensated smoothed power and the original power through the filter, the fluctuation of the wind-solar coupled power is very intense at this time. In order to balance the difference between power generation and load and prevent equipment damage, a battery energy storage system was designed. This system adjusts the charge-discharge strategy according to the output power of the wind-solar coupled complementary system and the system demand. In order to smooth the output power after compensation, a FIR filter was designed, which can significantly improve the stability and efficiency of the system in the wind-solar coupled battery compensation system, reduce the noise impact at the same time, and provide a cleaner and more stable working environment for the energy conversion process.

[0158] As Figure 3 shown in the comparison between the filtering and smoothing curves of different filters applied under different actual working conditions of the hydrogen storage tank and the original curve, due to the limitation of the battery capacity, the power fluctuation cannot be completely smoothed. At this time, when the battery capacity is 3400 MWh, the power volatility is 29.31%, which results in a certain fluctuation in the actual hydrogen production rate in electrolytic hydrogen production. It is still necessary to set up a hydrogen storage tank and a filter to smooth the hydrogen production rate.

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

[0160]

[0161] Among them, y FIR (n), y IIR (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, and α(n) is the weight of the FIR filter in the hybrid filter, and the initial value is selected as 0.5;

[0162] In the described adaptive hybrid filter, an adaptive algorithm is used to update the weight, as shown in the following formula:

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

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

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

[0166]

[0167] Among them, f c is the cut-off frequency of the filter, and τ h is the time constant of the hydrogen storage tank;

[0168] Define the volatility as an evaluation index to evaluate the performance of the filter, define the optimization range according to the filter type, and apply the bisection method for optimization. The volatility calculation formula is as shown in the following formula:

[0169]

[0170] Among them, N is the number of data points, X i is the i-th data point, is the average value of all data points.

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

[0172]

[0173] Among them, H e is the actual hydrogen production rate of the hydrogen production system, with the unit of t / h, H s (t) is the current storage volume of the hydrogen storage tank, H s (t - 1) is the storage volume of the hydrogen storage tank at the previous moment, and δ is the threshold for judging the hydrogen charging and discharging speed of the hydrogen storage tank. Generally, it is taken as 0.05 in this case.

[0174] Similarly, the conditions of the hydrogen storage tank are divided into seven situations: rapid hydrogen charging, slow hydrogen charging, balanced state, rapid hydrogen discharging, slow hydrogen discharging, overcharging, and over-discharging. Among them, when H e > 3.45, it is the rapid hydrogen charging condition, and the FIR filter is selected; when 3.4 < H e ≤ 3.45, it is the slow hydrogen charging condition, and the IIR filter is selected; when H e = 3.4, the hydrogen production system is in a balanced state, and the IIR filter is selected at this time; when H e < 3.35, it is the rapid hydrogen discharging condition, and the FIR filter is selected; when 3.35 ≤ H eWhen <3.4, it is in the slow hydrogen release condition, and an IIR filter is selected; otherwise, within this time period Δt, if H s (t) is greater than the volume V of the hydrogen storage tank h or less than zero, overcharging or over-discharging of the hydrogen storage tank occurs, and a hybrid filter with adaptive characteristics is used.

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

[0176]

[0177] Among them, H charge and H discharge are the hydrogen charging and discharging rates of the hydrogen storage tank respectively, and H target is the target hydrogen production rate smoothed by the filter of the hydrogen storage tank.

[0178] At this time, under the action of the battery and the hydrogen storage tank, the wind-solar coupled battery compensated electrolytic hydrogen production system has a hydrogen production rate volatility of only about 13% in the case where the volume of the hydrogen storage tank is 9000m 3 , which realizes a smoother hydrogen output rate and ensures the stability and reliability of the electrolytic hydrogen production system. However, due to the reason of condition selection, there may be no certain working conditions for the hydrogen storage tank. The comparison between the original hydrogen production rate and the system-smoothed hydrogen production rate of the wind-solar coupled battery compensated electrolytic hydrogen production system in a year is as Figure 4 shown.

[0179] The description and application of the present invention herein are illustrative and not intended to limit the scope of the present invention to the above embodiments. The related descriptions of the effects or advantages in the specification may not be reflected in the actual experimental examples due to the uncertainty of specific condition parameters or other factors. The related descriptions of the effects or advantages are not used to limit the scope of the invention. The deformations and changes of the disclosed embodiments herein are possible, and the substitutions and equivalent various components of the embodiments are well-known to those of ordinary skill in the art. Those skilled in the art should clearly understand that the present 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 present invention. Other deformations and changes can be made to the disclosed embodiments herein without departing from the scope and spirit of the present invention.

Claims

1. A method for intelligent filtering control of a hydrogen storage tank in a wind-solar coupled electrolytic hydrogen production system, characterized in that, Including: S1. Design a filter and dynamically adjust the cut-off frequency of the filter, and obtain the optimal filter order through an optimization mechanism; S2. Obtain the hydrogen charging and discharging state of the hydrogen storage tank, judge the current working condition of the hydrogen storage tank based on the hydrogen charging and discharging state, and select a corresponding filter based on the working condition; S3. Under the limitation of the limited volume of the hydrogen storage tank, use the selected filter to obtain the smoothed target hydrogen production rate, based on the difference between the target hydrogen production rate and the actual hydrogen production rate, and adjust the hydrogen charging and discharging amount of the hydrogen storage tank, perform the hydrogen output operation according to the adjusted hydrogen charging and discharging amount, and record the hydrogen output rate; S4. Calculate the volatility during the hydrogen output process, evaluate the performance of the wind-solar coupled electrolytic 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 electrolytic hydrogen production system according to claim 1, characterized in that S1 specifically includes: S1-1. Combine the FIR filter and the IIR filter, and design an adaptive hybrid filter based on the adaptive LMS algorithm as: y(n) = α(n)·y FIR (n)+(1 - α(n))·y IIR (n) Among them, y(n) is the output signal of the adaptive hybrid filter, α(n) is the weight of the FIR filter with a range of 0 < α(n) < 1, 1 - α(n) is the weight of the IIR filter, and α(n) is updated using the adaptive LMS algorithm; y FIR (n) is the output signal of the FIR filter, y IIR (n) is the output signal of the IIR filter; where: FIR filter y FIR (n) is: where n represents the currently processed sample point, k is a 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; IIR filter y IIR (n) is as follows: where x(n) is the input signal, b(k) and a(k) are the forward coefficient and the feedback coefficient of the IIR filter respectively, and M and N' are the forward order and the feedback order of the IIR filter respectively; S1-2. Dynamically adjust the cut-off frequency of the filter according to the time constant of the hydrogen storage tank, and the adjustment formula is: where f c is the cut-off frequency of the filter, and τ h is the time constant of the hydrogen storage tank; S1-3. Define the volatility as an evaluation index for evaluating the performance of the filter, define the optimization range according to the filter type, and use the dichotomy method for optimization to obtain the filter order that minimizes the volatility; among them, the volatility calculation formula is shown as follows: where N is the number of data points, X i is the i-th data point, is the average value of all data points.

3. The intelligent filtering control method for the hydrogen storage tank of the wind-solar coupled electrolytic hydrogen production system according to claim 2, wherein The update formula of the weight α(n) in S1-1 is: α(n + 1) = α(n) + η·e(n)·(y FIR (n) - y IIR (n)) where η is the learning rate for weight update, e(n) is the error signal representing the difference between the filter output and the desired output, y FIR (n) - y IIR (n) is the difference between the FIR filter output and the IIR filter output, which is used to guide the adjustment of weights.

4. The intelligent filtering control method for the hydrogen storage tank of the wind-solar coupled electrolytic hydrogen production system according to claim 2, characterized in that The specific steps of using the dichotomy method for optimization in S1-3 include: S1-3-1. Set the left and right boundaries of the filter order, calculate the volatility of the signal after the filter filters, and record the filter order and the volatility; S1-3-2. Take the average value of the left and right boundaries as the new boundary of the 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.

5. The intelligent filtering control method for the hydrogen storage tank of the wind-solar coupled electrolytic hydrogen production system according to claim 1, characterized in that, S2 specifically includes: S2-1: The real-time state formula of the hydrogen storage tank is: Among them, H e and H r are the actual hydrogen production rate and the rated hydrogen production rate of the hydrogen production system respectively, H s (t) is the current storage volume of the hydrogen storage tank, H s (t - 1) is the storage volume of the hydrogen storage tank at the previous moment, △t is the time step, that is, the interval between two time points, and δ is the threshold for judging the hydrogen charging and discharging speed of the hydrogen storage tank; S2-2: Judge the current working condition of the hydrogen storage tank and select a filter: When H e > H r + δ, the hydrogen storage tank is in the rapid hydrogen filling condition, and the FIR filter is selected; When H r <H e ≤ H r + δ, the hydrogen storage tank is in the slow hydrogen filling condition, and an IIR filter is selected; When H e = H r is satisfied, the hydrogen storage tank is in a balanced state and an IIR filter is selected; When H e <H r - When it is, the hydrogen storage tank is in the rapid hydrogen release condition, and an FIR filter is selected; When H r -δ ≤ H e < H r , the hydrogen storage tank is in the slow hydrogen release condition, and an IIR filter is selected; When H s (t) is greater than the hydrogen storage tank volume V h or less than zero, the hydrogen storage tank will experience overcharge or over-discharge conditions, and an adaptive hybrid filter is selected.

6. The intelligent filtering control method for the hydrogen storage tank of the wind-solar coupled electrolytic hydrogen production system according to claim 1, wherein The strategy for adjusting the hydrogen charging and discharging amount of the hydrogen storage tank in S3 is: Among them, H charge and H discharge are the hydrogen charging and discharging rates of the hydrogen storage tank respectively, H target is the target hydrogen production rate smoothed by the hydrogen storage tank filter, H e and H r are the actual hydrogen production rate and the rated hydrogen production rate of the hydrogen production system respectively, V h is the volume of the hydrogen storage tank, H s is the hydrogen storage capacity of the hydrogen storage tank, H s (t) is the current storage amount of the hydrogen storage tank, H s (t - 1) is the storage amount of the hydrogen storage tank at the previous moment, Q max is the maximum hydrogen charging and discharging rate of the hydrogen storage tank.

7. The intelligent filtering control method for the hydrogen storage tank of the wind-solar coupled electrolytic hydrogen production system according to claim 1, characterized in that Re-adjusting the hydrogen production system parameters according to the evaluation results in S4 includes: If the volatility ≤ 10%, it means that the hydrogen production system meets the industrial production requirements and its parameters do not need to be adjusted; If the volatility > 10%, the hydrogen production system parameters are debugged by successively expanding the filter order range of the optimization mechanism, shortening the judgment time length of the hydrogen storage tank working condition, and adjusting the hydrogen charging and discharging speed judgment threshold.

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