Control method, device and system of electrolytic hydrogen production equipment

Abstract

The invention provides a control method, device and system for electrolytic hydrogen production equipment, an operation stage is divided into an intervention control stage and an automatic control stage, and the electrolytic hydrogen production equipment is controlled to reach an optimal working state through manual intervention in the intervention control stage; and learning controllable parameters under the condition that the electrolytic hydrogen production equipment reaches the optimal working state through a sequential network model, inputting an association relationship between electric energy and state parameters, and entering an automatic control stage under the condition that the prediction accuracy reaches a preset requirement. And then predicting state parameters when the electrolytic hydrogen production equipment reaches the optimal working state at the next moment by adopting the sequential network model, and controlling the automatic controller to adjust each controllable parameter of the electrolytic hydrogen production equipment by taking the prediction result as a control target. According to the scheme, the sequential network model learns the incidence relation between the controllable parameters and the state parameters under the condition that the input electric energy serves as interference, and therefore the fluctuation of the input electric energy of the electrolytic hydrogen production equipment can be adapted.

Description

Technical Field

[0001] The present application relates to the field of wind and solar energy new energy technology, and in particular to a control method, device and system for electrolytic hydrogen production equipment. Background Art

[0002] Wind power generation and photovoltaic power generation are clean new energy sources that can generate electricity sustainably.

[0003] The main principle of the wind-solar hydrogen production system is to use the electricity from wind power and photovoltaic power generation to convert water into hydrogen through an electrolyzer. However, the volatility of wind energy and light energy will directly affect the stability of electricity, and the instability of electricity will affect the efficiency of the electrolysis process and the output of hydrogen. When dealing with this change, the existing technology usually adopts a single control strategy, resulting in insufficient adaptability of the system in the face of changes in electricity, and the inability to efficiently utilize the volatility of photovoltaic and wind power generation, resulting in low energy efficiency of the hydrogen production system. Summary of the invention

[0004] This specification provides a control method, device and system for hydrogen production equipment to improve the adaptability of hydrogen production equipment to changes in electric energy, effectively utilize the volatility of photovoltaic power generation and wind power generation, and improve the energy efficiency of the hydrogen production system.

[0005] The first aspect of the present specification provides a control method for an electrolytic hydrogen production device, comprising: in an intervention control stage, recording various controllable parameters and input electric energy of the electrolytic hydrogen production device, and collecting state parameters of the electrolytic hydrogen production device; in the intervention control stage, controlling the electrolytic hydrogen production device to achieve an optimal working state through manual intervention; learning the correlation between the controllable parameters, input electric energy and state parameters of the electrolytic hydrogen production device through a timing network model; entering an automatic control stage when the prediction accuracy of the timing network model reaches a preset requirement; in the automatic control stage, predicting various state parameters of each electrolytic hydrogen production device through the timing network model; using the prediction result of the timing network model as a control target, controlling the automatic controller to adjust various controllable parameters of the electrolytic hydrogen production device.

[0006] In some embodiments, the status parameters of the electrolytic hydrogen production equipment include the set temperature of the electrolyte in the electrolytic cell or the heating power of the heater in the electrolytic cell, the set temperature of the electrolyte replenisher in the circulation pipeline or the cooling power of the cooler on the circulation pipeline, and the valve opening of the alkali box connected to the electrolytic cell to replenish the electrolyte.

[0007] In some embodiments, the correlation between the controllable parameters, input electric energy and state parameters of the electrolytic hydrogen production equipment is learned through a timing network model, including: in the intervention control stage, marking the optimal state parameter group of the electrolytic hydrogen production equipment; screening out the optimal state parameter group of at least N consecutive acquisition moments; determining training data based on the optimal state parameter group and the values ​​of the controllable parameters and input electric energy at the corresponding acquisition moments, thereby obtaining a first training data set; N is an integer greater than 2; and using the first training data set to train the timing network model.

[0008] In some embodiments, after training the timing network model, it also includes: screening out the best state parameters of at least M consecutive acquisition moments to form a first verification data set, the intersection of the first verification data set and the first training data set is empty; M is an integer greater than 2; and evaluating the prediction accuracy of the timing network model through the first verification data set.

[0009] In some embodiments, the method also includes: in the automatic control stage, the best state parameter group of the first automatic sub-stage is used as training data to form a second training data set, and the best state parameter group of the second automatic sub-stage is used as verification data to form a second verification data set; the second automatic sub-stage is the operation sub-stage after the first automatic sub-stage; the timing network model is updated through the second training data set; the timing network model is verified through the second verification data set; if the prediction accuracy of the timing network model meets the preset requirements, the automatic control stage is continued; if the prediction accuracy of the timing network model does not meet the preset requirements, the automatic control stage is exited.

[0010] In some embodiments, during the automatic control stage, when the observed parameters of the actuators, raw materials, and products of the hydrogen production equipment exceed the optimal value range, an alarm message is generated.

[0011] In some embodiments, after the alarm information is generated, the method further includes: sending the alarm information to a user terminal; and receiving a target value of a target controllable parameter sent by the user terminal.

[0012] In some embodiments, the automatic controller comprises a PID controller or a LADRC controller.

[0013] In a second aspect, the present specification provides a control device for an electrolytic hydrogen production equipment, comprising: an acquisition unit, used to record various controllable parameters and input electric energy of the electrolytic hydrogen production equipment and collect state parameters of the electrolytic hydrogen production equipment in an intervention control stage; in the intervention control stage, the electrolytic hydrogen production equipment is controlled by manual intervention to achieve an optimal working state; a training unit, used to learn the correlation between the controllable parameters, input electric energy and state parameters of the electrolytic hydrogen production equipment through a timing network model; a switching unit, used to enter the automatic control stage when the prediction accuracy of the timing network model reaches a preset requirement; a prediction unit, used to predict various state parameters of each electrolytic hydrogen production equipment through the timing network model in the automatic control stage; a control unit, used to control the automatic controller to adjust the various controllable parameters of the electrolytic hydrogen production equipment using the prediction result of the timing network model as a control target.

[0014] The third aspect of this specification provides a control system for an electrolytic hydrogen production equipment, including: an electrolytic hydrogen production equipment, which is used to implement hydrogen preparation using electrolytic hydrogen production technology; an automatic controller, which is used to adjust various controllable parameters of the electrolytic hydrogen production equipment to adjust the working state of the electrolytic hydrogen production equipment; and a control device of the electrolytic hydrogen production equipment, which is used to execute the control method of the electrolytic hydrogen production equipment described in any one of the first aspects.

[0015] The control method, device and system of the hydrogen production equipment by electrolysis provided in this specification divide the operation stages of the hydrogen production equipment by electrolysis into an intervention control stage and an automatic control stage. In the intervention control stage, the hydrogen production equipment by electrolysis is controlled to reach an optimal working state through manual intervention. The controllable parameters of the hydrogen production equipment by electrolysis are learned through a timing network model when the hydrogen production equipment reaches an optimal working state, and the relationship between the input electric energy and the state parameters. When the prediction accuracy of the timing network model reaches the preset requirements, the user can choose to enter the automatic control stage. In the automatic control stage, the timing network model is used to predict the state parameters of the hydrogen production equipment by electrolysis when it reaches an optimal working state at the next moment. The prediction result is used as the control target to control the automatic controller to adjust the controllable parameters of the hydrogen production equipment by electrolysis. This solution allows users to ensure that the electrolytic hydrogen production equipment is in the best working state without intervening in the operation process of the electrolytic hydrogen production equipment during the automatic control stage; and the timing network model of this solution learns the relationship between the controllable parameters of the electrolytic hydrogen production equipment, "input power" and the state parameters, and the input power of the electrolytic hydrogen production equipment is volatile, that is, the timing network model learns the relationship between the controllable parameters and the state parameters when there is input power as interference, so that controlling the hydrogen production equipment according to the prediction results of the timing network model can not only make the hydrogen production equipment reach the best working state in the automatic control stage, but also adapt to the volatility of the input power of the electrolytic hydrogen production equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some implementation methods recorded in this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0017] Figure 1 This is a schematic diagram of the use scenario of the electrolytic hydrogen production equipment;

[0018] Figure 2 A schematic diagram of an electrolytic hydrogen production device using alkaline electrolysis technology;

[0019] Figure 3 A flow chart of a control method for an electrolytic hydrogen production device provided in this specification;

[0020] Figure 4 A schematic diagram of a method for learning the correlation between the controllable parameters, input electric energy and state parameters of an electrolytic hydrogen production device through a time series network model;

[0021] Figure 5 A schematic diagram of another method for learning the correlation between the controllable parameters, input electric energy and state parameters of an electrolytic hydrogen production device through a time series network model;

[0022] Figure 6 Another schematic flow chart of a control method for an electrolytic hydrogen production device provided in this specification;

[0023] Figure 7 This is the structure diagram of the LADRC controller;

[0024] Figure 8 A block diagram of the control device of the electrolytic hydrogen production equipment provided in this specification;

[0025] Fig. 9 This is a schematic diagram of the structure of the electronic device provided in this manual. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0027] In order to improve the adaptability of electrolytic hydrogen production equipment to changes in electric energy, effectively utilize the volatility of photovoltaic power generation and wind power generation, and improve the energy efficiency of the hydrogen production system, this specification provides a control method for electrolytic hydrogen production equipment.

[0028] Electrolysis hydrogen production equipment is a general term for devices used to produce hydrogen. It can convert hydrogen-containing compounds or raw materials such as water into hydrogen through various chemical or physical methods. Figure 1 This is a schematic diagram of the use scenario of electrolytic hydrogen production equipment. The electricity generated by wind power generation and photovoltaic power generation is mainly used to supply the load, and the remaining electricity used by the load is supplied to the electrolytic hydrogen production equipment.

[0029] Current hydrogen production technologies include traditional alkaline electrolyzers, proton exchange membrane (PEM) electrolyzers and solid oxide electrolytic cells (SOEC).

[0030] Proton exchange membrane (PEM) electrolyzer uses proton exchange membrane as electrolyte, has high energy efficiency, can operate at high current density, has fast startup and response speed, and high hydrogen purity. However, the high cost of proton exchange membrane and catalyst (such as precious metals such as platinum) limits its large-scale application.

[0031] Solid oxide electrolyzers have high energy efficiency, especially when using high-temperature waste heat or high-temperature thermal energy from renewable energy. However, they need to operate at high temperatures, which places high demands on the high-temperature resistance of materials and the thermal management of equipment.

[0032] Traditional alkaline electrolyzers are mainly composed of an anode, a cathode and an electrolyte (such as sodium hydroxide or potassium hydroxide). The biggest advantage of alkaline electrolysis technology is that the anode and cathode electrodes do not contain precious metals, so the cost of the electrolyzer is relatively low.

[0033] Alkaline electrolysis technology decomposes water into hydrogen and oxygen by passing an electric current through an electrolyte. At the anode (positive electrode), water molecules are oxidized, releasing oxygen, electrons and hydrogen ions. The reaction formula is:

[0034]

[0035] At the cathode (negative electrode), hydrogen ions accept electrons and are reduced to generate hydrogen. The reaction formula is:

[0036]

[0037] Through these two processes, the electrolyzer achieves the electrolysis of water to generate hydrogen and oxygen. The concentration of the electrolyte has a direct impact on the reaction rate and efficiency. Alkaline electrolyte has good conductivity and can effectively reduce the resistance during the electrolysis process and increase the production of hydrogen.

[0038] New energy sources generated by wind power and solar power provide the necessary DC current for the electrolyzer to drive the electrolysis reaction. The stability of the power supply and the precise control of the output voltage directly affect the efficiency of the electrolysis reaction and the output of hydrogen.

[0039] Temperature and pressure also have a significant impact on the efficiency of the electrolysis reaction. By adjusting the operating temperature and pressure of the electrolyzer, the reaction conditions can be optimized and the hydrogen production efficiency can be improved.

[0040] In order to ensure that the generated hydrogen and oxygen can be effectively separated, the electrolyzer is usually equipped with a gas separation device to prevent the mixing of hydrogen and oxygen and ensure the safety and purity of the product.

[0041] Figure 2 This is a schematic diagram of an electrolytic hydrogen production device using alkaline electrolysis technology. The two electrodes in the alkaline electrolyzer form the hydrogen side and the oxygen side respectively. In order to separate the hydrogen and oxygen produced by electrolysis, a hydrogen separator, an oxygen separator, a scrubber, and a purification system are set up respectively. Oxygen can be directly discharged into the outdoor air.

[0042] During the process of hydrogen production by electrolysis, the temperature in the electrolyzer will gradually rise due to the thermal effect of the electrolysis reaction itself, the ohmic heat generated when each conductive component is energized, the friction heat generated by gas diffusion and convection, and other factors. The optimal operating temperature of hydrogen production equipment by electrolysis is usually 70-90°C. Exceeding this temperature will lead to reduced electrolysis efficiency, accelerated material aging, reduced hydrogen quality, and even increased risks of hydrogen explosion. Therefore, it is necessary to cool the electrolyte in the electrolyzer. Usually, a circulation pipeline is set for the electrolyte, and the electrolyte in the circulation pipeline is cooled by a cooler.

[0043] The electrolyte in the electrolytic cell can be recycled through a circulation pipeline, a cooler and a circulation pump are arranged in the circulation pipeline, and the cooler is controlled by a first temperature controller to cool the electrolyte in the circulation pipeline to a set temperature. The electrolyte is supplied to the electrolytic cell through an alkali tank.

[0044] In order to improve the efficiency of hydrogen production by electrolysis, the heater in the electrolyzer can be controlled by the second temperature controller to heat the electrolyte in the electrolyzer to an optimal working temperature.

[0045] The core part of the electrolyzer, the diaphragm, can prevent hydrogen and oxygen molecules from passing through the diaphragm, but allows electrolyte ions to pass through, and is resistant to corrosion from high-concentration alkali solutions. However, if the wind and solar power fluctuate too much, causing the input power of the electrolytic hydrogen production equipment to suddenly increase, or if the load drops suddenly, the liquid level will drop. If the liquid level difference is large, it will cause certain damage to the diaphragm, so it is necessary to control the liquid level of the electrolyzer.

[0046] The control method of the electrolytic hydrogen production equipment provided in this specification can be used for Figure 2 The hydrogen production equipment shown.

[0047] like Figure 3 As shown, the control method of the electrolytic hydrogen production equipment provided in this specification includes the following S10 to S50.

[0048] S10: In the intervention control stage, various controllable parameters and input electric energy of the electrolytic hydrogen production equipment are recorded, and state parameters of the electrolytic hydrogen production equipment are collected; in the intervention control stage, the electrolytic hydrogen production equipment is controlled by manual intervention to achieve an optimal working state.

[0049] Controllable parameters refer to the parameters that can be adjusted by the user in the electrolytic hydrogen production equipment. By adjusting these controllable parameters, the electrolytic hydrogen production equipment can adapt to the changes in electric energy and achieve the best working state.

[0050] The controllable parameters may include the set temperature of the first temperature controller and the set temperature of the second temperature controller. Specifically, the controllable parameters may include the set temperature of the electrolyte in the electrolytic cell or the heating power of the heater in the electrolytic cell, the set temperature of the electrolyte replenisher in the circulation pipeline or the cooling power of the cooler on the circulation pipeline, and the valve opening of the alkali tank connected to the electrolytic cell to replenish the electrolyte.

[0051] Input electric energy refers to the electric energy generated by wind power generation and photovoltaic power generation that is supplied to the load and then allocated to the hydrogen production equipment for use.

[0052] The state parameters refer to the state parameters that can reflect the working state of the hydrogen production equipment. The state parameters may include the measured temperature of the electrolyte in the electrolyzer, the measured pressure of the electrolyzer, the measured liquid level of the electrolyzer, etc.

[0053] The manual intervention stage means that the user can monitor the status parameters of the electrolytic hydrogen production equipment in real time and manually adjust the controllable parameters at any time. During the manual intervention stage, the electrolytic hydrogen production equipment can achieve the best working state.

[0054] The optimal working state means that the observed parameters of the actuators (such as electrodes, electrolyzers, heaters, coolers, heating pumps, circulating pumps, exhaust devices, etc.), raw materials (such as electrolytes), and products (such as hydrogen and oxygen) of the hydrogen production equipment are within the optimal value range. The data within the optimal value range enables the hydrogen production equipment to have higher energy efficiency without hidden dangers of failure and lower losses.

[0055] S20: Learn the relationship between the controllable parameters, input power and state parameters of the electrolytic hydrogen production equipment through the timing network model.

[0056] The time series network model can adopt deep learning time series analysis models such as long short-term memory network LSTM, hidden Markov model, dynamic Bayesian network and other time series network models based on probabilistic graph models.

[0057] The controllable parameters of the electrolytic hydrogen production equipment at the current collection time and the input electric energy at the current collection time are used as the input data of the time series network model, and the state parameters at the next collection time are used as the output data of the time series network model, so that the time series network model can predict the change of the state parameters of the hydrogen production equipment according to the controllable parameters and input electric energy of the electrolytic hydrogen production equipment. In other words, the controllable parameters at the current collection time are used as the processing actions taken under the state parameters at the current collection time, and the state parameters at the next collection time are used as the processing results of the processing actions taken at the current collection time.

[0058] In some embodiments, the current acquisition time of the controllable parameters and input power may be different from the current acquisition time of the state parameters. For example, t1 and t2 are two consecutive moments, the controllable parameters and input power at the current acquisition moment are acquired at t1, and the state parameters at the current moment are acquired at t2.

[0059] In some embodiments, Figure 4 As shown, S20 includes the following S21, S22, S23 and S24.

[0060] S21: In the intervention control stage, the optimal state parameter group of the electrolytic hydrogen production equipment is marked.

[0061] The state parameter group refers to the set of state parameters (i.e., observation parameters) of each actuator, raw material, and product collected at the same collection time and the corresponding input power. In the optimal state parameter group, each state parameter (i.e., observation parameter) is within the optimal value range.

[0062] The optimal value range of each observation parameter can be preset, and the value of each state parameter can be compared with its most recent value range to determine whether a group of state parameters collected at the same collection time is the optimal state parameter group.

[0063] The values ​​of each state parameter collected at each collection moment can also be presented to the user, and the user can judge whether each state parameter group is the optimal state parameter group based on his own experience and the preset optimal value range. In other words, in the intervention control stage, the user can participate in adjusting the controllable parameters and select the optimal state parameter group.

[0064] S22: Filter out the best state parameter group of at least N consecutive acquisition moments.

[0065] S23: Determine training data according to the optimal state parameter group and the controllable parameters and input electric energy values ​​at the corresponding acquisition time, so as to obtain a first training data set; N is an integer greater than 3.

[0066] For example, t1, t2, and t3 are three consecutive acquisition moments, respectively. If the state parameter groups corresponding to the acquisition moments t1, t2, and t3 are all optimal state parameter groups, then the state parameter groups corresponding to the acquisition moments t1, t2, and t3 constitute one training data in the first training data set. If the state parameter group at the acquisition moment t4 after the acquisition moment t3 is also the optimal state parameter group, then the state parameter groups corresponding to the acquisition moments t1, t2, t3, and t4 constitute one training data in the first training data set. In other words, the number of acquisition moments corresponding to one training data is not fixed, and can be 3, 4, or more, but at least 2.

[0067] S24: Use the first training data set to train the temporal network model.

[0068] Since the number of collection moments corresponding to a piece of training data is not fixed, it is possible to specifically determine how to use training data with different numbers of collection moments when training the temporal network model.

[0069] The specific method of training the temporal network model through training data is prior art and will not be described in detail in this specification.

[0070] S30: When the prediction accuracy of the temporal network model reaches a preset requirement, the automatic control stage is entered.

[0071] In some embodiments, after training the temporal network model, such as Figure 5 As shown, the following S25 and S26 may also be included.

[0072] S25: Filter out the best state parameters of at least M consecutive acquisition moments to form a first verification data set, where the intersection of the first verification data set and the first training data set is empty; M is an integer greater than 2.

[0073] S26: Evaluate the prediction accuracy of the temporal network model using the validation data set.

[0074] The first training data set and the first validation data set may be selected from the same data set.

[0075] In some embodiments, the manual intervention stage includes a first intervention sub-stage and a second intervention sub-stage. The first intervention sub-stage is a data collection stage, and the first set of collected data is used to filter data to form a first training data set. The second intervention sub-stage is a temporal network model trial operation stage, and the state parameter group of this stage constitutes the second set.

[0076] The best state parameter group can be selected from the first set to form the first verification data set, the best state parameter group can be selected from the second set to form the first verification data set, or the best state parameter group can be selected from the first set and the second set respectively to form the first verification data set.

[0077] Typically, state data at multiple acquisition times are obtained in one data acquisition phase, and the best state data group is selected from these state data to form the best data set. A part of the best data group in the best data set is used as training data, and another part of the best data group is used as verification data. In this way, the first training data set and the first verification data set are derived from the same operation phase of the hydrogen production equipment, and it is difficult to verify the generalization of the time series network model in other operation phases.

[0078] The method provided in this specification for selecting the best state data from the second set to form verification data, the first verification data set and the first training data set are derived from different operating stages of the hydrogen production equipment, which can more effectively verify the generalization of the timing network model in other operating stages.

[0079] In some embodiments, Figure 6 As shown, the control method of the electrolytic hydrogen production equipment also includes the following S61, S62, S63 and S64.

[0080] S61: In the automatic control stage, the optimal state parameter group of the first automatic sub-stage is used as training data to form a second training data set, and the optimal state parameter group of the second automatic sub-stage is used as verification data to form a second verification data set; the second automatic sub-stage is the operating sub-stage after the first automatic sub-stage.

[0081] S62: Update the temporal network model using the second training data set.

[0082] S63: Verify the temporal network model using the second verification data set.

[0083] S64: When the prediction accuracy of the temporal network model reaches the preset requirement, continue the automatic control stage; when the prediction accuracy of the temporal network model does not reach the preset requirement, exit the automatic control stage.

[0084] Through the above S61 to S64, the timing network model can be updated in time according to the operating data in the automatic control stage, and the generalization of the timing network model can be verified, so that the timing network model can adapt to the changes in the hydrogen production process by electrolysis, and more accurately predict the state parameters of the hydrogen production equipment by electrolysis, thereby more accurately determining the control target and preparing for the automatic controller to control the hydrogen production equipment by electrolysis.

[0085] S40: In the automatic control stage, the state parameters of each hydrogen production equipment are predicted by the timing network model.

[0086] S50: Using the prediction result of the timing network model as a control target, controlling the automatic controller to adjust various controllable parameters of the electrolytic hydrogen production equipment.

[0087] The control method of the hydrogen production equipment by electrolysis provided in this specification divides the operation stages of the hydrogen production equipment by electrolysis into an intervention control stage and an automatic control stage. In the intervention control stage, the hydrogen production equipment by electrolysis is controlled to reach an optimal working state through manual intervention. The controllable parameters of the hydrogen production equipment by electrolysis are learned through a timing network model when the hydrogen production equipment reaches an optimal working state, and the relationship between the input electric energy and the state parameters. When the prediction accuracy of the timing network model reaches the preset requirements, the user can choose to enter the automatic control stage. In the automatic control stage, the timing network model is used to predict the state parameters of the hydrogen production equipment by electrolysis when it reaches an optimal working state at the next moment. The prediction result is used as the control target to control the automatic controller to adjust the controllable parameters of the hydrogen production equipment by electrolysis. This solution allows users to ensure that the electrolytic hydrogen production equipment is in the best working state without intervening in the operation process of the electrolytic hydrogen production equipment during the automatic control stage; and the timing network model of this solution learns the relationship between the controllable parameters of the electrolytic hydrogen production equipment, "input power" and the state parameters, and the input power of the electrolytic hydrogen production equipment is volatile, that is, the timing network model learns the relationship between the controllable parameters and the state parameters when there is input power as interference, so that controlling the hydrogen production equipment according to the prediction results of the timing network model can not only make the hydrogen production equipment reach the best working state in the automatic control stage, but also adapt to the volatility of the input power of the electrolytic hydrogen production equipment.

[0088] In some embodiments, in order to ensure the operational safety of the electrolytic hydrogen production equipment without human intervention during the automatic control stage, during the automatic control stage, an alarm message is generated when the observed parameters of the actuators, raw materials, and products of the hydrogen production equipment exceed the optimal value range.

[0089] After the alarm information is generated, the alarm information can be sent to the user terminal; the target value of the target controllable parameter sent by the user terminal is received. The user can issue an instruction to adjust the target controllable parameter through the user terminal, and the instruction includes the target value of the target controllable parameter. Through this setting, the user can timely obtain information that the electrolytic hydrogen production device is not in the best working state during the automatic control stage, and conveniently intervene in the automatic control stage of the electrolytic hydrogen production equipment through the user terminal.

[0090] It should be noted that manual intervention in the operation of the electrolytic hydrogen production equipment through the user terminal in the automatic control stage is different from manual intervention in the operation of the electrolytic hydrogen production equipment in the intervention control stage. The difference is that the former only intervenes through the user terminal when an alarm message is received, and the user does not need to take care of the operating status of the electrolytic hydrogen production equipment in real time, while the latter requires the user to take care of and adjust the operating status of the electrolytic hydrogen production equipment at all times. Compared with the latter, manual intervention in the automatic control stage can reduce the user's workload while ensuring that the electrolytic hydrogen production equipment is in the best working condition.

[0091] The automatic controller mentioned above may be a PID controller or a LADRC controller.

[0092] The LADRC (Linear Active Disturbance Rejection Control) controller includes: Linear Extended State Observer (LESO), which is used to estimate the uncertainty and external disturbances in the system in real time; the object model P, which is used to describe the dynamic characteristics of the system; and the linear state error feedback control (LSEF) module, which is used to quickly compensate for disturbances. The electrolytic hydrogen production equipment can be converted into the following state space expression:

[0093]

[0094] Where x(t) is the state vector, y(t) is the output vector, and u(t) is the input vector. , and there is

[0095]

[0096] And the controller expression is

[0097]

[0098] Where z(t) is the control variable, L o is the observer gain. At this time, the observer gain is set to

[0099]

[0100] Assume that the generalized perturbation is bounded, when When the process gradually stabilizes, (p is an integer) will approximate the system output y(t) and its derivatives of various orders, and It will also approach So you can use Perform anti-disturbance control.

[0101] Through the state and total disturbance obtained by the observer, the following control law can be designed to compensate for the total disturbance

[0102]

[0103] In the above formula It is the reference signal for tracking, which can be the set value of each state parameter.

[0104]

[0105] is a generalized reference signal, which includes the reference signal and its derivatives of various orders, and the state feedback controller gain is:

[0106]

[0107] In summary, the state space expression of the linear active disturbance rejection controller is:

[0108]

[0109] The LADRC controller structure diagram mainly consists of the controlled object, the controller (such as Figure 7 Ko in Fig. 1) and an extended state observer (as shown in ESO), which can be expressed as Figure 7 form.

[0110] The output signal y(t) of each state parameter is subtracted from the set reference signal r(t), and then the error signal e(t) is directly input into the LADRC controller (i.e. Ko) for control, so that real-time feedback and compensation of disturbances, including disturbance deviations caused by all factors including input power, slot, environment, etc., can be achieved, so that the output is as equal to the set value as possible.

[0111] The parameters of the LADRC control algorithm can be determined by an adaptive adjustment mechanism and user intervention. Specifically, in the adaptive adjustment mechanism, the parameters of the LADRC control algorithm can be adjusted in real time through the adaptive mechanism. The system will dynamically adjust the controllable parameters according to the state parameters monitored in real time to adapt to changes in the external environment and internal disturbances. For example, when an abnormal increase in the internal pressure of the electrolyzer is detected, the LADRC controller will automatically increase the opening degree of the pressure relief valve to maintain system stability.

[0112] The user intervention method can be specifically to set a user intervention interface, through which the user can view the status parameters in real time through a mobile terminal such as a mobile phone, and manually adjust the parameters of the LADRC algorithm as needed. For example, the user can adjust the sensitivity of the pressure control or the response speed of the temperature control according to experience or a preset optimization goal.

[0113] This specification provides a control device for an electrolytic hydrogen production device, which can be used to implement the control method of the electrolytic hydrogen production device. Figure 8 As shown, the device includes an acquisition unit 10, a training unit 20, a switching unit 30, a prediction unit 40 and a control unit 50.

[0114] The acquisition unit 10 is used to record the controllable parameters and input electric energy of the electrolytic hydrogen production equipment and collect the state parameters of the electrolytic hydrogen production equipment during the intervention control stage; during the intervention control stage, the electrolytic hydrogen production equipment is controlled by manual intervention to achieve the best working state.

[0115] The training unit 20 is used to learn the correlation between the controllable parameters, input electric energy and state parameters of the electrolytic hydrogen production equipment through a time series network model.

[0116] The switching unit 30 is used to enter the automatic control stage when the prediction accuracy of the temporal network model reaches a preset requirement.

[0117] The prediction unit 40 is used to predict the state parameters of each electrolytic hydrogen production equipment through the timing network model during the automatic control stage.

[0118] The control unit 50 is used to control the automatic controller to adjust various controllable parameters of the electrolytic hydrogen production equipment by taking the prediction result of the timing network model as the control target.

[0119] The description and functions of the above-mentioned devices can be understood by referring to the control method of the electrolytic hydrogen production equipment, and will not be repeated here.

[0120] The embodiment of the present invention further provides an electronic device, such as Fig. 9 As shown, the electronic device may include a processor 901 and a memory 902, wherein the processor 901 and the memory 902 may be connected via a bus or other means. Fig. 9 The example of connecting through bus is taken in the following.

[0121] The processor 901 may be a central processing unit (CPU). The processor 901 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above chips.

[0122] The memory 902 is a non-transitory computer-readable storage medium that can be used to store non-transitory software programs, non-transitory computer executable programs and modules, such as program instructions / modules corresponding to the control method of the electrolytic hydrogen production device in the embodiment of the present invention (for example, Figure 8 The processor 901 executes various functional applications and data processing of the processor by running the non-transient software programs, instructions and modules stored in the memory 902, that is, the control method of the electrolytic hydrogen production equipment in the above method embodiment is realized.

[0123] The memory 902 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required by at least one function; the data storage area may store data created by the processor 901, etc. In addition, the memory 902 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 902 may optionally include a memory remotely arranged relative to the processor 901, and these remote memories may be connected to the processor 901 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0124] The one or more modules are stored in the memory 902, and when executed by the processor 901, the following is performed: Figure 4 and Figure 5 The control method of the electrolytic hydrogen production equipment in the illustrated embodiment.

[0125] The specific details of the above electronic device can be understood by referring to the corresponding descriptions and effects in the method embodiment, and will not be repeated here.

[0126] The present specification also provides a computer storage medium, wherein the computer storage medium stores computer program instructions, and when the computer program instructions are executed, the steps of the control method of the above-mentioned electrolytic hydrogen production equipment are implemented.

[0127] The present specification also provides a computer program product, comprising a computer program, which implements the steps of the control method of the above-mentioned electrolytic hydrogen production equipment when executed by a processor.

[0128] Those skilled in the art can understand that the implementation of all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above-mentioned types of memory.

[0129] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0130] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions.

[0131] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0132] It can be known from the description of the above implementation methods that those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be essentially or partly contributed to the prior art in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute certain parts of the methods of each implementation method of the present application.

[0133] The present application can be used in many general or special computer system environments or configurations, such as personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc.

[0134] The present application may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0135] Although the present application has been described through embodiments, those skilled in the art will appreciate that there are many modifications and variations to the present application without departing from the spirit of the present application, and it is intended that the appended claims include these modifications and variations without departing from the spirit of the present application.

Claims

1. A control method for electrolytic hydrogen production equipment, characterized in that: include: In the intervention control stage, the controllable parameters and input electric energy of the electrolytic hydrogen production equipment are recorded, and the state parameters of the electrolytic hydrogen production equipment are collected; In the intervention control stage, the electrolytic hydrogen production equipment is controlled by manual intervention to achieve the best working state; The relationship between the controllable parameters, input power and state parameters of the electrolytic hydrogen production equipment is learned through the time series network model; When the prediction accuracy of the time series network model reaches the preset requirement, the automatic control stage is entered; In the automatic control stage, the state parameters of each electrolytic hydrogen production device are predicted by the timing network model; The prediction result of the timing network model is used as a control target to control the automatic controller to adjust various controllable parameters of the electrolytic hydrogen production equipment.

2. The method according to claim 1, characterized in that The status parameters of the electrolytic hydrogen production equipment include the set temperature of the electrolyte in the electrolytic cell or the heating power of the heater in the electrolytic cell, the set temperature of the electrolyte replenisher in the circulation pipeline or the cooling power of the cooler on the circulation pipeline, and the valve opening of the alkali tank connected to the electrolytic cell to replenish the electrolyte.

3. The method according to claim 1, characterized in that The relationship between the controllable parameters, input power and state parameters of the electrolytic hydrogen production equipment is learned through the time series network model, including: In the intervention control stage, the optimal state parameter group of the electrolytic hydrogen production equipment is marked; Filter out the best state parameter group for at least N consecutive acquisition moments; Determine the training data according to the optimal state parameter group and the controllable parameters and input electric energy values ​​at the corresponding acquisition time, thereby obtaining a first training data set; N is an integer greater than 2; The first training data set is used to train the temporal network model.

4. The method according to claim 3, characterized in that After training the temporal network model, the method further includes: Filter out the best state parameters of at least M consecutive acquisition moments to form a first validation data set, where the intersection of the first validation data set and the first training data set is empty; M is an integer greater than 2; The prediction accuracy of the temporal network model is evaluated using the first validation data set.

5. The method according to claim 1, characterized in that The method further comprises: In the automatic control stage, the optimal state parameter group of the first automatic sub-stage is used as training data to form a second training data set, and the optimal state parameter group of the second automatic sub-stage is used as verification data to form a second verification data set; the second automatic sub-stage is the operation sub-stage after the first automatic sub-stage; Updating the temporal network model using the second training data set; Verifying the temporal network model using the second verification data set; When the prediction accuracy of the timing network model reaches the preset requirement, the automatic control stage continues; when the prediction accuracy of the timing network model does not reach the preset requirement, the automatic control stage is exited.

6. The method according to claim 1, characterized in that In the automatic control stage, when the observed parameters of the actuators, raw materials and products of the hydrogen production equipment exceed the optimal value range, an alarm message is generated.

7. The method according to claim 1, characterized in that After the alarm information is generated, it also includes: Sending the alarm information to a user terminal; Receive a target value of a target controllable parameter sent by a user terminal.

8. The method according to claim 1, characterized in that The automatic controller includes a PID controller or a LADRC controller.

9. A control device for electrolytic hydrogen production equipment, characterized in that: include: An acquisition unit is used to record various controllable parameters and input electric energy of the electrolytic hydrogen production equipment and collect state parameters of the electrolytic hydrogen production equipment during the intervention control stage; In the intervention control stage, the electrolytic hydrogen production equipment is controlled by manual intervention to achieve the best working state; A training unit, used for learning the correlation between the controllable parameters, input electric energy and state parameters of the electrolytic hydrogen production equipment through a time series network model; A switching unit, used to enter the automatic control stage when the prediction accuracy of the time series network model reaches a preset requirement; A prediction unit, used for predicting various state parameters of various electrolytic hydrogen production equipment through the timing network model during the automatic control stage; The control unit is used to control the automatic controller to adjust various controllable parameters of the electrolytic hydrogen production equipment by taking the prediction result of the timing network model as the control target.

10. A control system for an electrolytic hydrogen production device, characterized in that: include: Electrolytic hydrogen production equipment, used to prepare hydrogen using electrolytic hydrogen production technology; An automatic controller, used to adjust various controllable parameters of the electrolytic hydrogen production equipment to adjust the working state of the electrolytic hydrogen production equipment; A control device for hydrogen production equipment by electrolysis, used to execute the control method for hydrogen production equipment by electrolysis according to any one of claims 1 to 8.

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