A capacity optimization method for new energy water electrolysis hydrogen production project

Abstract

The present invention discloses a capacity optimization method for a new energy water electrolysis hydrogen production project, which relates to the technical field of new energy water electrolysis hydrogen production, and includes the following steps: obtaining various operating parameter information during the operation of a PID control system in real time through sensors and data acquisition systems. The present invention monitors the key operating parameters of the PID control system in real time, and the system can quickly identify abnormal conditions of the electrolyzer, such as when the current density increases abnormally, it will automatically switch to a safe mode and issue an early warning. This effectively reduces the risk of hydrogen accumulation, ensures equipment safety, and protects on-site personnel. By analyzing and controlling output fluctuations and frequency response distortion information, the system can intelligently judge the operating status, accurately control the equipment, ensure that hydrogen production is close to the expected value, improve energy conversion efficiency, reduce production costs, promote the application of hydrogen energy in the field of renewable energy, and promote green transformation.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy water electrolysis hydrogen production, and in particular to a new energy water electrolysis hydrogen production project capacity optimization method. Background Art

[0002] Capacity optimization of new energy water electrolysis hydrogen production projects refers to the optimization of various components in the water electrolysis hydrogen production process through systematic methods and technical means to improve the efficiency and economy of hydrogen production. This process covers multiple aspects such as the selection and design of electrolyzers, the adjustment of power supply systems, the dynamic adjustment of process parameters, and equipment maintenance strategies. In terms of electrolyzers, selecting efficient electrolyzer types (such as PEM or alkaline electrolyzers) and optimizing their structures can increase hydrogen production; in terms of power supply systems, using intelligent power management and energy storage systems, reasonably configuring the volatility of renewable energy, and ensuring the stable operation of electrolyzers. At the same time, through real-time monitoring and feedback control, parameters such as temperature, pressure, and current density in the electrolysis process are dynamically adjusted to improve energy conversion efficiency and reduce production costs.

[0003] In this process, PID control, as an important feedback control algorithm, can effectively improve the response speed and stability of the system. By implementing PID control, the system can monitor the operating status of the electrolyzer in real time and make fine adjustments to the temperature, pressure and current density to ensure that the electrolysis process is always in the best working state. The proportional, integral and differential parts of PID control work together to quickly eliminate errors, predict future changes and make corresponding adjustments, optimize hydrogen production efficiency, and reduce the unit cost of hydrogen. In addition, establishing a data monitoring and fault prediction mechanism can reduce equipment downtime and maintenance costs. Through the above comprehensive measures, the capacity optimization of the new energy water electrolysis hydrogen production project can not only achieve the high efficiency and economy of hydrogen production, but also promote the widespread application of hydrogen energy and green transformation.

[0004] The prior art has the following deficiencies:

[0005] During the process of producing hydrogen by electrolysis of water, if the PID control system is abnormal and not detected in time, it may cause the current density of the electrolyzer to increase abnormally, causing the hydrogen production to exceed the design range. Excessive hydrogen accumulates in the equipment and may form a dangerous concentration. Once hydrogen is mixed with air and reaches a certain ratio, it will form an explosive gas. If it encounters a fire source or high temperature, it may cause a violent explosion, resulting in damage to equipment, destruction of production facilities, and even serious personal injury and life risk.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0007] The purpose of the present invention is to provide a new energy electrolysis water hydrogen production project capacity optimization method, by real-time monitoring of the key operating parameters of the PID control system, can quickly identify the abnormal state of the electrolyzer, especially when the current density is abnormally increased, the system will immediately switch to the safe mode and send out an early warning signal. This mechanism effectively reduces the risk of hydrogen accumulation in the equipment, ensures the safety of equipment and production facilities, prevents the explosion hazard caused by the mixing of hydrogen and air to the greatest extent, protects the life safety of on-site personnel, and reduces potential equipment damage and economic losses. In addition, by analyzing the control output fluctuation and frequency response distortion information, the generated operating state coefficient enables the system to intelligently judge the operating state, thereby accurately regulating the equipment during stable operation to ensure that the hydrogen production is close to the expected value. This optimization not only improves energy conversion efficiency and production costs, but also promotes the widespread application of hydrogen in the field of renewable energy, and promotes green transformation to solve the problems in the above-mentioned background technology.

[0008] In order to achieve the above object, the present invention provides the following technical solution: comprising the following steps:

[0009] Obtain various operating parameter information of the PID control system in real time through sensors and data acquisition systems;

[0010] Perform data analysis on the acquired operating parameters, and use machine learning models to comprehensively evaluate the analyzed data information to determine the operating status of the PID control system;

[0011] If the evaluation results show that the PID control system is out of the safe operating range and is in an abnormal operating state, the system should immediately switch to the safe mode and issue an early warning signal for the abnormal operating state;

[0012] After receiving the early warning signal, the system takes maintenance measures and performs real-time analysis on the maintained PID control system to ensure that the PID control system is in a stable operating state during operation;

[0013] If the evaluation results show that the PID control system is operating stably, a controlled variable output plan is formulated, and the equipment is regulated according to the original plan so that its output matches the target value.

[0014] Preferably, the specific steps of obtaining various operating parameter information during the operation of the PID control system in real time are as follows:

[0015] Clarify the key parameter information that needs to be monitored in the PID control system, and select the corresponding sensor type based on the determined monitoring parameters;

[0016] Install sensors at selected monitoring locations, build a data acquisition system, and start real-time monitoring and recording of operating parameters;

[0017] During the operation of the data acquisition system, the changes of various operating parameters are observed in real time through the monitoring software;

[0018] After the data collection process is completed, the collected real-time data needs to be archived and stored.

[0019] Preferably, data analysis is performed on the acquired operating parameters, and the acquired operating parameters include control output fluctuation information and frequency response distortion information. The control output fluctuation information and the frequency response distortion information are placed under a detection window. After analyzing and processing the control output fluctuation information and the frequency response distortion information, a control output fluctuation index and a frequency response distortion index are generated. A machine learning model is used to evaluate the analyzed control output fluctuation index and frequency response distortion index, and an operating status coefficient is generated. The operating status of the PID control system is intelligently judged through the operating status coefficient.

[0020] Preferably, the operating state coefficient generated by the PID control system under the detection window is compared and analyzed with a preset operating state coefficient reference threshold to determine whether the operation process of the PID control system is in a stable operating state. The results of the comparison and analysis are as follows:

[0021] If the operating state coefficient is greater than or equal to the preset operating state coefficient reference threshold, it is judged that the PID control system is unstable and in an abnormal operating state;

[0022] If the operating state coefficient is less than a preset operating state coefficient reference threshold, it is determined that the PID control system is in a stable operating state.

[0023] Preferably, when the PID control system is unstable, in an abnormal operating state, and triggers an alarm, a number of subsequently generated operating state coefficients are comprehensively analyzed with a preset operating state coefficient reference threshold to generate an evaluation coefficient ππ, based on the formula: In the formula, O S参考 Indicates the reference threshold of the operating status coefficient, represents the operating status coefficient that is greater than or equal to the operating status coefficient reference threshold, a represents the number of the operating status coefficient that is greater than or equal to the operating status coefficient reference threshold, a=1, 2, 3..., q, q is a positive integer.

[0024] Preferably, the generated evaluation coefficient is compared and analyzed with a preset evaluation coefficient reference threshold, and the comparison and analysis results are as follows:

[0025] If the evaluation coefficient is greater than or equal to the preset evaluation coefficient reference threshold, a system stable operation signal is generated, indicating that the PID control system is in a stable operation state, and the corresponding parameters can be adjusted through the PID control system;

[0026] If the evaluation coefficient is less than the preset evaluation coefficient reference threshold, a system operation instability signal is generated, indicating that the PID control system is operating abnormally and is in an unstable state. The corresponding parameters cannot be adjusted through the PID control system, and an early warning prompt is issued to inform relevant personnel.

[0027] Preferably, after performing abnormal analysis on the control output fluctuation generated during the operation of the PID control system, the steps of generating the control output fluctuation index are as follows:

[0028] Under the detection window, the deviation between the actual control output and the target setting value at each time point is obtained. The calculation expression of the deviation is as follows: e(t) = r(t) - u(t), where e(t) represents the deviation between the actual control output and the target setting value at time t, r(t) represents the setting value at time t, and u(t) represents the actual control output value at time t;

[0029] Calculate the fluctuation range of the deviation and use the standard deviation to measure the size of the fluctuation range. The calculation expression is as follows: In the formula, σ e represents the fluctuation range of the deviation, N represents the total number of time points, Represents the mean deviation within the detection window, and the calculation expression is:

[0030] The frequency characteristics of the control output are analyzed by fast Fourier transform to obtain the fluctuation frequency. The expression of fast Fourier transform is: In the formula, f w Indicates the fluctuation frequency of the control output, It represents the loss change rate of the control output;

[0031] Fluctuation energy is a measure of the intensity of the control output fluctuation. The cumulative energy of the fluctuation energy is calculated using integration. The calculation expression is as follows: In the formula, E w represents the cumulative energy of the wave energy;

[0032] The fluctuation range of the deviation is σ e , control the output fluctuation frequency f w And the cumulative energy of the wave energy E w Generate the control output fluctuation index, the calculation expression is as follows: In the formula, I cw represents the control output fluctuation index, α is the fluctuation amplitude of the deviation σe The weight coefficient, β is the fluctuation frequency f of the control output w The weight coefficient, γ is the cumulative energy E of the wave energy w The weight coefficient of .

[0033] Preferably, after performing abnormal analysis on the frequency response distortion generated during the operation of the PID control system, the steps of generating the frequency response distortion index are as follows:

[0034] In the detection window, the input signal and the output signal are obtained, and they are calibrated as Q(s) and Y(s) respectively using Laplace transform. The system transfer function is generated based on the dynamic relationship between the input signal and the output signal. The calculation expression of the system transfer function is as follows: Where G(s) represents the system transfer function, Y(s) is the Laplace transform of the output signal, Q(s) is the Laplace transform of the input signal, s is a complex frequency domain variable, s = σ + jw, where σ is the attenuation factor, j is the imaginary unit, and w is the frequency angular velocity;

[0035] In the time domain, the dynamic response of the system includes the actual response and the ideal response. The dynamic gain distortion is calculated by the difference between the actual response and the ideal response of the system. The calculation expression is as follows: Where ΔG(t) is the dynamic gain distortion, y ideal (t) represents the ideal response;

[0036] The phase distortion is obtained by the difference between the peak time of the actual response signal and the peak time of the ideal response signal. The calculation expression is as follows: Δφ(t) = t peak -t ideal , where Δφ(t) is the phase distortion, t peak is the peak time of the actual response signal, t ideal is the peak time of the ideal response signal;

[0037] Taking into account the dynamic gain distortion and phase distortion at different time points, the frequency response distortion is described in combination with the dynamic characteristics of the system. The frequency response distortion calculation expression is as follows: D(t) = w G |ΔG(t)|+w φ |Δφ(t)|, where D(t) is the frequency response distortion, and w G is the dynamic gain distortion weight factor, w φ is the phase distortion weight factor;

[0038] The frequency response distortion in the comprehensive response process is used to obtain the frequency response distortion index. The calculation expression is as follows: In the formula, E Drepresents the frequency response distortion index, t0 is the initial time of the system response, t f is the end time of the system response.

[0039] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0040] The present invention can quickly identify whether the electrolyzer is in an abnormal operating state by real-time monitoring of key operating parameter information in the PID control system. In particular, when the current density increases abnormally, the system will immediately switch to a safe mode and send out an early warning signal. This timely response mechanism significantly reduces the risk of hydrogen accumulation in the equipment to form a dangerous concentration, ensuring the safety of the equipment and production facilities. Through effective abnormal state monitoring and early warning, the explosion hazard caused by the mixing of hydrogen and air can be prevented to the greatest extent, the life safety of on-site personnel can be protected, and the economic losses caused by potential equipment damage and production stagnation can be reduced.

[0041] The present invention analyzes the control output fluctuation information and frequency response distortion information, and generates an operating state coefficient that enables the system to intelligently judge the operating state. This judgment mechanism ensures that when the PID control system is in a stable operating state, the equipment can be accurately regulated according to the formulated controlled variable output plan, so that the hydrogen production is close to the expected target value. Through continuous monitoring and adjustment, the energy conversion efficiency in the production process is improved, the production cost is reduced, and the overall production efficiency is optimized. This optimization not only increases the output of hydrogen, but also promotes the widespread application of hydrogen energy in the field of renewable energy, and contributes to the realization of green transformation. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0043] Figure 1 The present invention is a method flow chart of a method for capacity optimization of a new energy water electrolysis hydrogen production project. DETAILED DESCRIPTION

[0044] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0045] The present invention provides Figure 1A capacity optimization method for a new energy water electrolysis hydrogen production project is shown, comprising the following steps:

[0046] Obtain various operating parameter information of the PID control system in real time through sensors and data acquisition systems;

[0047] The specific steps to obtain the various operating parameter information of the PID control system in real time are as follows:

[0048] Clarify the key parameter information that needs to be monitored in the PID control system, and select the corresponding sensor type based on the determined monitoring parameters;

[0049] Parameters usually include current density, temperature, pressure, flow rate, etc. According to the specific application scenario, select indicators directly related to system performance and safety to ensure that data collection can fully reflect the operating status of the system; for example, in the process of hydrogen production by electrolysis of water, the current density and temperature of the electrolyzer are important monitoring indicators because they directly affect the production and safety of hydrogen;

[0050] The sensor needs to have high accuracy and stability to ensure the reliability of the collected data; for example, thermocouples or thermistors can be selected to monitor temperature, and current sensors can be selected to monitor current density; the selection of sensors should also consider the conditions of the working environment (such as temperature, pressure, and chemical corrosion) to ensure its effectiveness and durability in practical applications;

[0051] Install sensors at selected monitoring locations, build a data acquisition system, and start real-time monitoring and recording of operating parameters;

[0052] Install the sensor at the selected monitoring location, ensuring good contact between the sensor and the system under test and complying with the manufacturer's installation instructions. This step is important because the choice of sensor location and installation method will directly affect the accuracy of the collected data. For example, the temperature sensor needs to be as close as possible to the heating component of the electrolyzer, while the current sensor should be installed in the power output circuit to ensure real-time reflection of the actual operating status.

[0053] Build a data acquisition system that receives signals from sensors and converts them into digital data. Data acquisition systems usually include signal conditioning equipment, analog-to-digital converters (ADCs), and data processing units. Signal conditioning ensures that the sensor output signal is within an acceptable range, while the ADC converts the analog signal into a digital signal for subsequent data analysis and processing. Ensure that the data acquisition system has sufficient sampling frequency to capture rapidly changing signals;

[0054] Start real-time monitoring and recording of operating parameters. The data acquisition system will continuously obtain data from sensors and record them in a database or data storage platform. Through the data monitoring software, various parameters can be visualized in real time and alarm mechanisms can be set to detect abnormal conditions in a timely manner. This process ensures that the monitoring of the PID control system is always in an active state, which helps to take quick action when problems arise;

[0055] During the operation of the data acquisition system, the changes of various operating parameters are observed in real time through the monitoring software;

[0056] During the operation of the data acquisition system, the monitoring software acts as a bridge between the user and the real-time data. Through a graphical interface, the software displays the changing trends of various operating parameters, such as current density, temperature and pressure, which are collected and transmitted to the system in real time through sensors. Users can set thresholds. Once the parameters exceed the preset range, the system will automatically issue an alarm to prompt the operator to take corresponding measures. In addition, the monitoring software usually provides data visualization functions, such as curve graphs and bar graphs, to facilitate intuitive analysis of data changes. Through real-time monitoring, operators can promptly identify abnormal situations, such as a sudden increase in current or abnormal temperature fluctuations, so as to respond quickly and prevent potential equipment damage or safety hazards. At the same time, the monitoring software also supports data recording and historical playback, which is convenient for subsequent analysis and performance evaluation, helping to optimize PID control strategies and ensure that the system operates in the best state.

[0057] After the data collection process is completed, the collected real-time data needs to be archived and stored;

[0058] After the data collection process is completed, the collected real-time data needs to be archived and stored for subsequent analysis and reporting; this data can not only be used to optimize the PID control algorithm, but also provide important information for equipment maintenance, fault diagnosis and safety assessment; establish a database and analysis mechanism so that historical data can be quickly queried and analyzed in the future, thereby improving the overall efficiency and safety of the system;

[0059] Perform data analysis on the acquired operating parameters, and use machine learning models to comprehensively evaluate the analyzed data information to determine the operating status of the PID control system;

[0060] Perform data analysis on the acquired operating parameters, which include control output fluctuation information and frequency response distortion information. The control output fluctuation information and frequency response distortion information are placed under a detection window. After analyzing and processing the control output fluctuation information and frequency response distortion information, a control output fluctuation index and a frequency response distortion index are generated. A machine learning model is used to evaluate the analyzed control output fluctuation index and frequency response distortion index, and an operating state coefficient is generated. The operating state coefficient is used to intelligently judge the operating state of the PID control system.

[0061] Control output fluctuation refers to the fluctuation of the actual output signal (such as temperature, pressure, etc.) relative to the set target value during the operation of the control system;

[0062] The control output fluctuation of PID control system usually indicates that the system is in an unstable operating state; the control output fluctuation means that the output signal of the controller changes frequently and drastically, instead of gradually approaching the set value within the stable range; this phenomenon usually stems from improper setting of PID parameters, for example, the proportional gain is too high, making the system overly sensitive to errors, resulting in frequent overshoot or undershoot; in addition, the differential term excessively amplifies the noise or instantaneous changes in the system, which can also cause the controller output to fluctuate violently, especially in the case of external interference or slow system response; these fluctuations will not only affect the control accuracy, but also increase the workload of the actuator, increase wear, and may cause equipment to operate inefficiently or even fail;

[0063] Control output fluctuations not only affect the stability of the system, but may also cause system oscillations. This oscillation is often manifested as the system frequently fluctuating back and forth on both sides of the set value when adjusting the error, and cannot stabilize within the target range. Long-term oscillations indicate that the system has not yet converged to a stable state, and there is a problem of over- or under-adjustment of the controller in the feedback loop. This poses a risk to the reliability of the entire system, especially in sensitive industrial processes such as water electrolysis to produce hydrogen, where fluctuations may cause abnormalities in important parameters such as temperature and pressure, thereby affecting system safety and hydrogen production. Therefore, control output fluctuations are one of the signals of unstable operation, and it is necessary to adjust PID parameters, such as appropriately reducing the proportional gain or adjusting the integral and differential coefficients to eliminate oscillations and ensure smooth operation of the system.

[0064] After performing an abnormal analysis on the control output fluctuation generated during the operation of the PID control system, the steps to generate the control output fluctuation index are as follows:

[0065] In the detection window, the deviation between the actual control output and the target setting value at each time point is obtained. The deviation is the key to system regulation and reflects the tracking ability of the system within the window. The calculation expression of the deviation is as follows: e(t) = r(t) - u(t), where e(t) represents the deviation between the actual control output and the target setting value at time t, r(t) represents the setting value at time t, and u(t) represents the actual control output value at time t;

[0066] It should be noted that obtaining the deviation between the actual control output and the target set value at each time point is a key step in the performance analysis of the PID control system; specifically, by monitoring the signals of the sensor and the control system, the output value and the set value can be collected in real time, and then the deviation can be calculated by comparison; the process of obtaining these data is described in detail below;

[0067] 1. Get the target setting value

[0068] The target set value is the output value that the control system expects to achieve, which is usually determined by external input or preset reference value; this set value may change dynamically according to process requirements, or remain constant for a certain period of time; in actual operation, the steps to obtain the set value are as follows:

[0069] Source of set value: The set value usually comes from operator input, host computer control or program preset of the automation system; for example, in the water electrolysis hydrogen production system, the target pressure or flow rate of hydrogen is the set value of the system;

[0070] Reading set values ​​in real time: The target set values ​​can be collected in real time through the host computer system or PLC (Programmable Logic Controller); these data are usually stored in the internal registers of the controller and can be read through communication protocols (such as Modbus, Profibus, etc.);

[0071] Dynamic adjustment: In some cases, the set value will be adjusted dynamically according to external signals or conditions; for example, in the process of hydrogen production by electrolysis of water, the target power of the electrolyzer may be adjusted in real time according to the fluctuation of grid load or wind power and photovoltaic power generation;

[0072] 2. Get the actual control output

[0073] The actual control output is the signal that the PID controller outputs to the actuator through regulation. In the system of hydrogen production by electrolysis of water, the actual control output may be the voltage or current applied by the controller to the electrolyzer, or the opening of the control flow valve. The process of obtaining the actual control output is as follows:

[0074] Sensor acquisition: The actual control output is determined by the signals fed back by field sensors and actuators; for example, the electrolyzer current sensor, pressure sensor or flow meter will feed back the real-time output signal to the control system;

[0075] Data acquisition system: These feedback signals are acquired in real time through data acquisition modules (such as A / D converters, PLC control systems, etc.); the signals collected by sensors are usually continuous analog signals, which need to be sampled and converted into digital signals through the data acquisition system for easy storage and processing;

[0076] Communication interface: Data is exchanged between the control system and the actuator through the communication interface (such as I / O interface, field bus, etc.) to ensure real-time acquisition of control output at each time point;

[0077] 3. Deviation represents the gap between the actual system output and the set target, which is the basis for the PID control system to adjust; the specific steps for calculating the deviation are as follows:

[0078] Real-time calculation: After each sampling period, the system will calculate the deviation at the current moment in real time and store it in the controller's memory or database; through continuous deviation calculation, a deviation curve changing over time can be drawn;

[0079] Storage and analysis: The calculated deviation value can be stored by the monitoring system for subsequent analysis and processing; for example, the fluctuation trend can be analyzed through historical data, or the threshold can be used to determine whether the system is in normal working condition;

[0080] 4. Example

[0081] In the application scenario of hydrogen production by water electrolysis, it is assumed that the goal of system control is to maintain a certain hydrogen flow rate. The hydrogen production rate is controlled by adjusting the current output of the electrolyzer. The actual output of the hydrogen flow rate is obtained in real time through the flow meter sensor, and then compared with the target set value to calculate the current deviation. If the deviation is too large, the system will adjust the current of the electrolyzer through the PID control algorithm to make the flow rate approach the target value.

[0082] 5. Summary

[0083] The actual output signal is collected by the sensor and the target setting value is obtained from the controller or host computer system, and then the deviation between them is calculated in real time; this deviation is used as the input of the PID control system to guide the controller's adjustment process of the system; accurately obtaining and calculating these parameters is the core to ensure the stable and efficient operation of the system;

[0084] Calculate the fluctuation range of the deviation and use the standard deviation to measure the size of the fluctuation range. The standard deviation reflects the degree of dispersion of the deviation within the detection window. The calculation expression is as follows: In the formula, σ e represents the fluctuation range of the deviation, N represents the total number of time points, Represents the mean deviation within the detection window, and the calculation expression is:

[0085] It should be noted that there are several reasons for using standard deviation to measure the size of fluctuations, and there are also other ways to do this; the following is a detailed explanation of the advantages of standard deviation and some alternative methods;

[0086] Reasons for using standard deviation

[0087] 1. Reflect the degree of discreteness:

[0088] The standard deviation can effectively reflect the dispersion and volatility of data. It provides a quantitative measure of the variation of the entire data set by calculating the difference between each data point and the mean.

[0089] 2. Sensitivity to outliers:

[0090] The standard deviation is sensitive to extreme values ​​(outliers), which makes it very useful in detecting abnormal fluctuations in the control output;

[0091] 3. Mathematical properties:

[0092] The calculation of standard deviation is relatively simple and has good mathematical properties, which can be used in many statistical and control theories; it is applicable in many situations and is easy to understand and interpret;

[0093] 4. Conform to normal distribution:

[0094] In many practical applications, the deviation data of the control system is approximately normally distributed, and the standard deviation can well describe the characteristics of this distribution;

[0095] In addition to standard deviation, there are several other ways to measure volatility:

[0096] 1. Variance:

[0097] Variance is the square of the standard deviation and can provide the same information as the standard deviation, but because its unit is the square of the original data unit, it is relatively unintuitive to interpret;

[0098] 2. Absolute Deviation:

[0099] Absolute deviation is the average of the absolute differences between the data points and the mean; it is not affected by extreme values ​​and is suitable for combating noisy data sets;

[0100] 3. Scope:

[0101] Calculating the difference between the maximum and minimum values ​​in a data set gives a simple measure of volatility, but this method is very sensitive to outliers and can be misleading;

[0102] 4. Root Mean Square (RMS):

[0103] The root mean square value is the average of the squared data and then the square root, which can reflect the overall fluctuation of the data, especially in the analysis of periodic signals.

[0104] 5. Coefficient of variation (CV):

[0105] The coefficient of variation is the ratio of the standard deviation to the mean and is often used to compare the volatility of different data sets, especially when the means differ widely;

[0106] 6. Autocorrelation and spectral analysis:

[0107] For time series data, the volatility can be detected using autocorrelation function or spectrum analysis, which is suitable for analyzing periodic and trend changes;

[0108] Summarize

[0109] Although standard deviation is widely used and effective in measuring fluctuations, other methods can also be used according to specific application scenarios and data characteristics. When choosing a measurement method, the distribution of the data, the impact of noise, and specific analysis requirements should be considered;

[0110] The frequency characteristics of the control output are analyzed by fast Fourier transform to obtain the fluctuation frequency, which reflects the number of oscillations of the output signal per unit time. The expression of fast Fourier transform is: In the formula, f w Indicates the fluctuation frequency of the control output, It represents the loss change rate of the control output;

[0111] It should be noted that frequency characteristics refer to the response characteristics of a control system under different frequency inputs, which reflects the system's sensitivity and response capabilities to signals of different frequencies. In the field of control theory and signal processing, frequency characteristics are usually used to describe how a system processes periodic signals, especially in analyzing the stability and performance of the system.

[0112] 1. Definition and Importance

[0113] Definition: Frequency characteristics describe the relationship between the output and input of a system, especially how the system output changes when the input signal frequency changes; it can be represented by a frequency response function or transfer function, which is usually a complex function that represents the gain and phase shift of the system;

[0114] Importance: Frequency characteristics are crucial for analyzing and designing control systems because they reveal the behavior of the system at different frequencies and help engineers understand the stability, dynamic response, and adaptability of the system. Frequency characteristics can be used to evaluate the performance of the system at high or low frequencies and guide parameter adjustments to optimize system performance.

[0115] Fluctuation energy is a measure of the intensity of the control output fluctuation. The cumulative energy of the fluctuation energy is calculated using integration. The calculation expression is as follows: In the formula, E w represents the cumulative energy of the wave energy;

[0116] The fluctuation range of the deviation is σ e , control the output fluctuation frequency f w And the cumulative energy of the wave energy E w Generate the control output fluctuation index, the calculation expression is as follows: In the formula, I cw represents the control output fluctuation index, α is the fluctuation amplitude of the deviation σ e The weight coefficient, β is the fluctuation frequency f of the control output w The weight coefficient, γ is the cumulative energy E of the wave energy w The weight coefficient of

[0117] Under the detection window, it can be seen from the calculation expression of the control output fluctuation index that the larger the control output fluctuation index performance value generated during the operation of the PID control system, the higher the probability of instability in the operation of the PID control system; this is because the control output fluctuation index reflects the fluctuation amplitude and frequency of the system when responding to changes; when the fluctuation index is large, it means that there is significant fluctuation in the output of the control system during the adjustment process, which may cause the system to be unable to quickly and accurately track the set point, thereby increasing the risk of overshoot, oscillation and even system instability; on the contrary, when the control output fluctuation index performance value is smaller, it indicates that the control output is relatively stable, the system can maintain a stable response, better track the set point, and reduce the possibility of instability;

[0118] Frequency response distortion refers to the response characteristics of the control system to signals of different frequencies, reflecting the performance of the system when facing rapidly changing inputs;

[0119] When a PID control system has frequency response distortion, it usually indicates that the system is in an unstable operating state. This distortion means that when responding to input signals of different frequencies, the system fails to control in the expected manner, resulting in a change in the relationship between the output signal and the input signal. Specifically, frequency response distortion may be manifested in inconsistent gain, excessive phase delay, or harmonic distortion. This means that the system responds too strongly or insufficiently to the input signal at certain frequencies, resulting in unsatisfactory control effects and may even cause oscillations. When the distortion is too large, the system cannot accurately track changes in the set point, especially in the face of rapidly changing external disturbances, which manifests as lags or errors in the output, resulting in reduced system stability and, in turn, more serious control problems.

[0120] In addition, frequency response distortion may be caused by improper parameter settings of the PID controller; for example, if the proportional gain is set too high, the system may over-respond to high-frequency interference, thereby causing oscillation; and insufficient differential gain may cause the system to be slow to respond to rapidly changing signals and unable to correct errors in a timely manner; these problems not only affect the dynamic performance of the system, but may also cause the system to become unstable during long-term operation, or even lead to loss of control; therefore, in order to ensure the stability of the PID control system, it is necessary to monitor the frequency response regularly and adjust the control parameters based on feedback to optimize the dynamic characteristics and response capabilities of the system; by improving the frequency response of the system, its robustness and reliability in actual operation can be improved, thereby achieving a more efficient control effect;

[0121] After performing an abnormal analysis on the frequency response distortion generated during the operation of the PID control system, the steps to generate the frequency response distortion index are as follows:

[0122] In the detection window, the input signal and the output signal are obtained, and they are calibrated as Q(s) and Y(s) respectively using Laplace transform. The system transfer function is generated based on the dynamic relationship between the input signal and the output signal. The calculation expression of the system transfer function is as follows: Where G(s) represents the system transfer function, Y(s) is the Laplace transform of the output signal, Q(s) is the Laplace transform of the input signal, s is a complex frequency domain variable, s = σ + jw, where σ is the attenuation factor, j is the imaginary unit, and w is the frequency angular velocity;

[0123] It should be noted that the input signal and output signal acquisition method

[0124] Signal definition:

[0125] Input signal: usually the set value or control quantity of the system, such as the target temperature, speed or other physical quantity in the control system; it can be generated by sensors, user input or automation system;

[0126] Output signal: It is the actual state of the system after responding to the input signal, such as actual temperature, speed or position. Output signals are usually monitored in real time through sensors.

[0127] Collection method:

[0128] Sensor: A device that converts a physical quantity into an electrical signal, such as a temperature sensor, pressure sensor, or other device; these electrical signals can then be digitized and recorded as input or output signals;

[0129] Data acquisition system: Use data acquisition hardware (such as acquisition cards) to convert analog signals into digital signals for subsequent processing and analysis; the data acquisition system can regularly collect input and output signals;

[0130] Computer software: Using real-time monitoring software or data analysis software, the signal can be visualized, showing the relationship between input and output, and helping with subsequent control and optimization;

[0131] The Laplace transform is a mathematical tool used to convert a function (signal) in the time domain into a representation in the frequency domain;

[0132] Why use Laplace transform

[0133] Simplify differential equations: The behavior of many dynamic systems can be described by differential equations; Laplace transform converts these differential equations into algebraic equations, making the solution process simpler and more intuitive;

[0134] Frequency domain analysis: In the frequency domain, the dynamic characteristics of the system (such as stability, gain, and phase response) are easier to analyze, especially for complex systems; this can help design and optimize controllers (such as PID controllers);

[0135] Handling initial conditions: Laplace transform can effectively handle the initial conditions of the system without having to consider them separately during analysis;

[0136] System response: Through Laplace transform, the system response to various input signals (such as step signals, impulse signals or sinusoidal signals) can be easily analyzed;

[0137] Other transformation methods

[0138] In addition to the Laplace transform, there are other commonly used transform methods that can be used for signal analysis and system modeling:

[0139] Fourier transform: Converts time domain signals into frequency domain signals, especially suitable for periodic signals or infinite time signal analysis; suitable for analyzing the spectrum and frequency components of signals, but not suitable for processing initial conditions and unstable signals;

[0140] Z transform: mainly used for discrete time signals, suitable for digital signal processing and control systems; Z transform makes the analysis of discrete systems simple by converting signals into complex domain;

[0141] Wavelet transform: used for time domain and frequency domain analysis, can provide time and frequency information at the same time, suitable for processing non-stationary signals, such as image and audio signal analysis;

[0142] Hilbert transform: provides phase and envelope information of the signal, and is often used to calculate instantaneous frequency and instantaneous amplitude in signal analysis;

[0143] In the time domain, the dynamic response of the system includes the actual response and the ideal response. The dynamic gain distortion is calculated by the difference between the actual response and the ideal response of the system. The calculation expression is as follows: Where ΔG(t) is the dynamic gain distortion, y ideal (t) represents the ideal response;

[0144] It should be noted that actual response and ideal response are commonly used concepts in control theory to describe the difference between the actual performance of a system under the action of an input signal and the expected performance;

[0145] 1. Actual response

[0146] The actual response refers to the real reaction of the system to the input signal under real conditions; it reflects the real behavior of the system under the influence of various uncertain factors (such as friction, noise, environmental changes, etc.); the actual response is usually measured by sensors, which is affected by various real conditions and may deviate from the theoretical ideal performance;

[0147] Features: The actual response is affected by many factors such as system hardware, environmental conditions, modeling errors, noise, interference, etc., and may experience overshoot, delay, oscillation or other non-ideal phenomena;

[0148] For example, suppose in a temperature control system, you want to increase the temperature of a certain environment from 25°C to 50°C. The actual response is the temperature curve that the system actually reaches after a period of time. This curve may have overshoot or delay, rather than being accurate to 50°C.

[0149] 2. Ideal response

[0150] The ideal response is the perfect response of the system under the action of a given input signal, also known as the target response. It is based on the mathematical model and design expectations of the system and is the optimal result obtained without considering any external interference or uncertain factors.

[0151] Features: Ideal response is usually the target or standard response when designing a system. It reflects the expected behavior of the system under perfect conditions. Ideal response has no delay, overshoot, noise, etc., and is carried out completely in accordance with the design parameters.

[0152] Example: In the temperature control system above, the ideal response is that the system increases from 25°C to 50°C without error at the designed rate without any overshoot or oscillation;

[0153] Differences between actual and ideal responses

[0154] Time response difference: The actual response usually has a response delay or a change in the time constant, which causes the actual output signal to fluctuate, delay or oscillate before reaching a stable state; while the ideal response is to reach the target value instantly and accurately;

[0155] Oscillation and overshoot: The actual response may produce overshoot (exceeding the target value and then returning to the target value) or oscillation in some systems, while the ideal response is usually a smooth curve without overshoot;

[0156] Steady-state error: The actual response may still have a certain deviation from the ideal value after reaching steady state, which is the steady-state error; the ideal response is the ideal output without any deviation;

[0157] Summarize

[0158] The actual response is the real output of the system under real conditions, which may contain interference, noise, delay and other undesirable factors;

[0159] Ideal response is the intended goal when the system is designed, perfect performance without any interference;

[0160] The ideal response is the goal of system design, while the actual response is the real behavior of the system. One of the tasks of the control system is to make the actual response as close to the ideal response as possible.

[0161] The phase distortion is obtained by the difference between the peak time of the actual response signal and the peak time of the ideal response signal. The calculation expression is as follows: Δφ(t) = t peak -t ideal , where Δφ(t) is the phase distortion, t peak is the peak time of the actual response signal, t ideal is the peak time of the ideal response signal;

[0162] It should be noted that

[0163] Phase offset refers to the phase difference between the periodic variation of a signal and a reference signal (usually an ideal sine wave or square wave) in a periodic signal or waveform. Phase offset is usually expressed in degrees or radians, reflecting the time delay or advance of the signal. The presence of phase offset may cause the waveform of the signal to align differently, thus affecting the overall performance of the system, especially in the field of control systems and signal processing.

[0164] The physical meaning of phase shift is that it affects the position of the signal on the time axis. When a system responds to an input signal, ideally, the output signal should be synchronized with the input signal. However, due to system characteristics, delays or other external factors, the output signal may shift in time relative to the input signal. This shift not only affects the overall shape of the signal, but may also cause system performance degradation, such as causing resonance or increasing steady-state errors.

[0165] The measurement and analysis of phase offset is important in many applications, especially in control theory, signal processing, and communication systems. By understanding the phase offset, engineers can optimize system response and adjust controller parameters to ensure the system achieves optimal performance when handling dynamic inputs.

[0166] The peak time of the actual response signal refers to the time required for the output signal to reach its maximum value for the first time during the actual operation of the system. This time is usually affected by various factors, including the nonlinear characteristics of the system, external interference, noise, and the dynamic response capability of the equipment. The peak time of the actual response is often longer than the theoretical expected time, and may be accompanied by overshoot or oscillation.

[0167] The peak time of the actual response signal is of great significance in system evaluation because it reflects the rapidity and responsiveness of the system. By measuring this time, engineers can determine whether the system can quickly and effectively follow the changes in the input signal. This is particularly critical in the design and optimization of control systems because fast response is an important performance indicator for many applications (such as automatic control, real-time monitoring, etc.).

[0168] In practical applications, obtaining the peak time of the actual response signal usually requires data acquisition and analysis technology; monitoring the curve of the output signal changing over time can clearly identify the time when the peak occurs, thereby providing data support for system improvement;

[0169] The peak time of the ideal response signal refers to the time required for the system output signal to reach its maximum value under theoretical conditions; this is defined based on the mathematical model of the system and design expectations, assuming that the system operates without any interference and nonlinear effects; the ideal response peak time is usually what the designer expects and is a reflection of the ideal system performance;

[0170] The peak time of the ideal response reflects the optimal dynamic characteristics considered when designing the system. In many control systems, the peak time of the ideal response signal is one of the key parameters used to measure system performance. It represents the rapidity of the system in following the changes in the input signal and is usually one of the system design goals, especially in applications that pursue high accuracy and high efficiency.

[0171] In actual operation, the peak time of the ideal response can be used as a performance benchmark to help engineers evaluate the difference between the actual response and the ideal response. By comparing these two time parameters, engineers can identify deficiencies in system performance and further optimize the control strategy to minimize the deviation between the actual response and the ideal response.

[0172] Taking into account the dynamic gain distortion and phase distortion at different time points, the frequency response distortion is described in combination with the dynamic characteristics of the system. The frequency response distortion calculation expression is as follows: D(t) = w G |ΔG(t)|+w φ |Δφ(t)|, where D(t) is the frequency response distortion, and w G is the dynamic gain distortion weight factor, w φ is the phase distortion weight factor;

[0173] The frequency response distortion in the comprehensive response process is used to obtain the frequency response distortion index. The calculation expression is as follows: In the formula, E D represents the frequency response distortion index, t0 is the initial time of the system response, t f is the end time of the system response;

[0174] In the monitoring window, the frequency response distortion index calculation expression shows that the larger the frequency response distortion index performance value generated during the operation of the PID control system, the worse the system's operating stability is usually. This is because the frequency response distortion index reflects the system's sensitivity and response characteristics to changes in the input signal frequency. When this index is high, it means that the system's gain and phase characteristics at different frequencies have large fluctuations, which may cause the system to be unable to respond in time when facing rapidly changing inputs, thereby increasing the risk of system instability. This instability may manifest as oscillation, overshoot or long-term steady-state error of the output signal, and in severe cases may even cause the system to lose control. Therefore, a high value of the frequency response distortion index not only indicates potential instability, but also prompts engineers to further analyze and adjust the system to ensure its stable operation. On the contrary, when the frequency response distortion index performance value is smaller, it indicates that the system's response to different frequency inputs is more consistent, and the gain and phase remain within a reasonable range, which usually means that the system is more stable and can effectively track changes in the input signal.

[0175] The machine learning model is not specifically limited here, and can achieve the control output fluctuation index I cw and frequency response distortion index E D Perform comprehensive analysis to generate operating status coefficients Status In order to realize the technical solution of the present invention, the present invention provides a specific implementation method;

[0176] Operation status coefficient O S The resulting calculation formula is:

[0177]

[0178] , where S1 and S2 are the control output fluctuation index I cw and frequency response distortion index E D The preset proportional coefficient, and S1 and S2 are both greater than 0;

[0179] It can be seen from the calculation expression of the operating state coefficient that, under the detection window, the larger the control output fluctuation index performance value generated by the abnormal analysis of the control output fluctuation information, the larger the frequency response distortion index performance value generated by the abnormal analysis of the frequency response distortion information, and the larger the generated operating state coefficient performance value, the greater the probability of the PID control system being unstable and abnormal. On the contrary, it means that the PID control system is in a stable operating state.

[0180] The operating state coefficient generated by the PID control system under the detection window is compared and analyzed with the preset operating state coefficient reference threshold to determine whether the operation process of the PID control system is in a stable operating state. The results of the comparison analysis are as follows:

[0181] If the operating state coefficient is greater than or equal to the preset operating state coefficient reference threshold, it is judged that the PID control system is unstable and in an abnormal operating state;

[0182] If the operating state coefficient is less than the preset operating state coefficient reference threshold, it is judged that the PID control system is in a stable operating state;

[0183] If the evaluation results show that the PID control system is out of the safe operating range and is in an abnormal operating state, the system should immediately switch to safe mode and issue an early warning signal for the abnormal operating state;

[0184] After receiving the early warning signal, the system takes maintenance measures and performs real-time analysis on the maintained PID control system to ensure that the PID control system is in a stable operating state during operation;

[0185] Stabilization measures to control output fluctuations

[0186] After receiving the early warning signal, the system can take maintenance measures to optimize the control algorithm in response to control output fluctuations. First, consider adjusting the parameters of the PID controller, especially the proportional (P), integral (I) and differential (D) parameters, to achieve a smoother control output. By optimizing these parameters, the system's resistance to external disturbances and changes can be improved, and unnecessary output fluctuations can be reduced. In addition, the introduction of gain scheduling technology, that is, dynamically adjusting control parameters according to the system's operating status, can also effectively cope with output fluctuations under different working conditions, ensuring that the system can maintain good stability under various conditions.

[0187] Stabilization measures for frequency response distortion

[0188] After receiving the warning signal, the system can take a variety of frequency domain analysis and compensation measures for frequency response distortion. First, perform frequency response analysis to identify the distortion characteristics of the system at a specific frequency, and design filters accordingly to compensate for these distortions. For example, use a phase compensator or gain compensator to improve the system's performance in the frequency domain, thereby improving the system's stability. At the same time, monitor the frequency response distortion index in real time. If it is found to exceed the set threshold, adjust the control strategy or implement the adaptive control algorithm in time to ensure that the system can quickly adapt to frequency changes and maintain a good stable state. These measures will help reduce frequency response distortion and improve the reliability and performance of the system in a dynamic environment.

[0189] When the PID control system is unstable, in an abnormal operating state, and triggers an alarm, a number of subsequently generated operating state coefficients are comprehensively analyzed with the pre-set operating state coefficient reference threshold to generate an evaluation coefficient ππ, based on the following formula: In the formula, O S参考 Indicates the reference threshold of the operating status coefficient, represents the operating state coefficient that is greater than or equal to the operating state coefficient reference threshold, a represents the number of the operating state coefficient that is greater than or equal to the operating state coefficient reference threshold, a=1, 2, 3, ..., q, q is a positive integer;

[0190] The generated evaluation coefficient is compared and analyzed with the preset evaluation coefficient reference threshold. The comparison and analysis results are as follows:

[0191] If the evaluation coefficient is greater than or equal to the preset evaluation coefficient reference threshold, a system stable operation signal is generated, indicating that the PID control system is in a stable operation state, and the corresponding parameters can be adjusted through the PID control system;

[0192] If the evaluation coefficient is less than the preset evaluation coefficient reference threshold, a system operation instability signal is generated, indicating that the PID control system is operating abnormally and is in an unstable state. The corresponding parameters cannot be adjusted through the PID control system, and an early warning prompt is issued to inform relevant personnel.

[0193] If the evaluation results show that the PID control system is operating stably, a controlled variable output plan is formulated, and the equipment is regulated according to the original plan so that its output matches the target value;

[0194] If the evaluation results show that the PID control system is running stably and generating stable operation signals, an output plan for the controlled variable can be formulated to ensure that the output of the equipment can approach the expected target value; the specific steps are as follows:

[0195] First of all, evaluating the stability of the PID control system is a prerequisite for confirming that the system can effectively control the controlled variables (such as temperature, pressure, flow, etc.). Some performance indicators may be used in the evaluation process, such as overshoot, steady-state error, and response time. If the evaluation results show stable operation, it means that the system's feedback control can quickly and accurately follow the set target value, which lays the foundation for the subsequent regulation.

[0196] Next, developing an output plan for the controlled variable involves specifying the target value and the corresponding control strategy. For example, if the target value is a certain temperature, the output plan may include adjusting the power of the heater, changing the flow rate of the fluid, or optimizing the operating parameters of the equipment. Based on the evaluated stable state, the control strategy may be based on the existing PID parameters, or make small adjustments when necessary to avoid introducing additional fluctuations.

[0197] Finally, regulating the equipment according to the original plan means converting the evaluation results into specific operation steps. The operator will continuously monitor the output variables according to the established plan and make real-time adjustments to ensure that they are within an acceptable range. This process can not only improve production efficiency, but also ensure the safety and stability of the equipment, thereby achieving better production results. In this way, the PID control system can effectively maintain the output near the desired target and achieve optimized operation of the system.

[0198] The present invention can quickly identify whether the electrolyzer is in an abnormal operating state by real-time monitoring of key operating parameter information in the PID control system. In particular, when the current density increases abnormally, the system will immediately switch to a safe mode and send out an early warning signal. This timely response mechanism significantly reduces the risk of hydrogen accumulation in the equipment to form a dangerous concentration, ensuring the safety of the equipment and production facilities. Through effective abnormal state monitoring and early warning, the explosion hazard caused by the mixing of hydrogen and air can be prevented to the greatest extent, the life safety of on-site personnel can be protected, and the economic losses caused by potential equipment damage and production stagnation can be reduced.

[0199] The present invention analyzes the control output fluctuation information and frequency response distortion information, and generates an operating state coefficient that enables the system to intelligently judge the operating state. This judgment mechanism ensures that when the PID control system is in a stable operating state, the equipment can be accurately regulated according to the formulated controlled variable output plan, so that the hydrogen production is close to the expected target value. Through continuous monitoring and adjustment, the energy conversion efficiency in the production process is improved, the production cost is reduced, and the overall production efficiency is optimized. This optimization not only increases the output of hydrogen, but also promotes the widespread application of hydrogen energy in the field of renewable energy, and contributes to the realization of green transformation.

[0200] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A capacity optimization method for a new energy water electrolysis hydrogen production project, characterized in that: The following steps are involved: Obtain various operating parameter information of the PID control system in real time through sensors and data acquisition systems; Perform data analysis on the acquired operating parameters, and use machine learning models to comprehensively evaluate the analyzed data information to determine the operating status of the PID control system; If the evaluation results show that the PID control system is out of the safe operating range and is in an abnormal operating state, the system should immediately switch to safe mode and issue an early warning signal for the abnormal operating state; After receiving the early warning signal, the system takes maintenance measures and performs real-time analysis on the maintained PID control system to ensure that the PID control system is in a stable operating state during operation; If the evaluation results show that the PID control system is operating stably, a controlled variable output plan is formulated, and the equipment is regulated according to the original plan so that its output matches the target value; Perform data analysis on the acquired operating parameters, which include control output fluctuation information and frequency response distortion information. The control output fluctuation information and frequency response distortion information are placed under a detection window. After analyzing and processing the control output fluctuation information and frequency response distortion information, a control output fluctuation index and a frequency response distortion index are generated. A machine learning model is used to evaluate the analyzed control output fluctuation index and frequency response distortion index, and an operating state coefficient is generated. The operating state coefficient is used to intelligently judge the operating state of the PID control system. After performing an abnormal analysis on the control output fluctuation generated during the operation of the PID control system, the steps to generate the control output fluctuation index are as follows: Under the detection window, the deviation between the actual control output and the target setting value at each time point is obtained. The calculation expression of the deviation is as follows: e(t) = r(t) - u(t), where e(t) represents the deviation between the actual control output and the target setting value at time t, r(t) represents the setting value at time t, and u(t) represents the actual control output value at time t; Calculate the fluctuation range of the deviation and use the standard deviation to measure the size of the fluctuation range. The calculation expression is as follows: In the formula, σ e represents the fluctuation range of the deviation, N represents the total number of time points, Represents the mean deviation within the detection window, and the calculation expression is: The frequency characteristics of the control output are analyzed by fast Fourier transform to obtain the fluctuation frequency. The expression of fast Fourier transform is: In the formula, f w Indicates the fluctuation frequency of the control output, It represents the loss change rate of the control output; Fluctuation energy is a measure of the intensity of the control output fluctuation. The cumulative energy of the fluctuation energy is calculated using integration. The calculation expression is as follows: In the formula, E w represents the cumulative energy of the wave energy; The fluctuation range of the deviation σ e , control the output fluctuation frequency f w And the cumulative energy of the wave energy E w Generate the control output fluctuation index, the calculation expression is as follows: In the formula, I cw represents the control output fluctuation index, α is the fluctuation amplitude of the deviation σ e The weight coefficient, β is the fluctuation frequency f of the control output w The weight coefficient, γ is the cumulative energy E of the wave energy w The weight coefficient of .

2. The capacity optimization method of a new energy water electrolysis hydrogen production project according to claim 1 is characterized in that: The specific steps to obtain the various operating parameter information of the PID control system in real time are as follows: Clarify the key parameter information that needs to be monitored in the PID control system, and select the corresponding sensor type based on the determined monitoring parameters; Install sensors at selected monitoring locations, build a data acquisition system, and start real-time monitoring and recording of operating parameters; During the operation of the data acquisition system, the changes of various operating parameters are observed in real time through the monitoring software; After the data collection process is completed, the collected real-time data needs to be archived and stored.

3. The capacity optimization method of a new energy water electrolysis hydrogen production project according to claim 1 is characterized in that: The operating state coefficient generated by the PID control system under the detection window is compared and analyzed with the preset operating state coefficient reference threshold to determine whether the operation process of the PID control system is in a stable operating state. The results of the comparison analysis are as follows: If the operating state coefficient is greater than or equal to the preset operating state coefficient reference threshold, it is judged that the PID control system is unstable and in an abnormal operating state; If the operating state coefficient is less than a preset operating state coefficient reference threshold, it is determined that the PID control system is in a stable operating state.

4. The capacity optimization method for a new energy water electrolysis hydrogen production project according to claim 1 is characterized in that: When the PID control system is unstable, in an abnormal operating state, and triggers an alarm, a number of subsequently generated operating state coefficients are comprehensively analyzed with the pre-set operating state coefficient reference threshold to generate an evaluation coefficient ππ, based on the following formula: In the formula, O S参考 Indicates the reference threshold of the operating status coefficient, represents the operating status coefficient that is greater than or equal to the operating status coefficient reference threshold, a represents the number of the operating status coefficient that is greater than or equal to the operating status coefficient reference threshold, a=1, 2, 3..., q, q is a positive integer.

5. The capacity optimization method for a new energy water electrolysis hydrogen production project according to claim 4 is characterized in that: The generated evaluation coefficient is compared and analyzed with the preset evaluation coefficient reference threshold. The comparison and analysis results are as follows: If the evaluation coefficient is greater than or equal to the preset evaluation coefficient reference threshold, a system stable operation signal is generated, indicating that the PID control system is in a stable operation state, and the corresponding parameters can be adjusted through the PID control system; If the evaluation coefficient is less than the preset evaluation coefficient reference threshold, a system operation instability signal is generated, indicating that the PID control system is operating abnormally and is in an unstable state. The corresponding parameters cannot be adjusted through the PID control system, and an early warning prompt is issued to inform relevant personnel.

6. The capacity optimization method for a new energy water electrolysis hydrogen production project according to claim 1 is characterized in that: After performing an abnormal analysis on the frequency response distortion generated during the operation of the PID control system, the steps to generate the frequency response distortion index are as follows: In the detection window, the input signal and the output signal are obtained, and they are calibrated as Q(s) and Y(s) respectively using Laplace transform. The system transfer function is generated based on the dynamic relationship between the input signal and the output signal. The calculation expression of the system transfer function is as follows: Where G(s) represents the system transfer function, Y(s) is the Laplace transform of the output signal, Q(s) is the Laplace transform of the input signal, s is a complex frequency domain variable, s = σ + jw, where σ is the attenuation factor, j is the imaginary unit, and w is the frequency angular velocity; In the time domain, the dynamic response of the system includes the actual response and the ideal response. The dynamic gain distortion is calculated by the difference between the actual response and the ideal response of the system. The calculation expression is as follows: Where ΔG(t) is the dynamic gain distortion, y ideal (t) represents the ideal response; The phase distortion is obtained by the difference between the peak time of the actual response signal and the peak time of the ideal response signal. The calculation expression is as follows: Δφ(t) = t peak -t ideal , where Δφ(t) is the phase distortion, t peak is the peak time of the actual response signal, t ideal is the peak time of the ideal response signal; Taking into account the dynamic gain distortion and phase distortion at different time points, the frequency response distortion is described in combination with the dynamic characteristics of the system. The frequency response distortion calculation expression is as follows: D(t) = w G |ΔG(t)|+w φ |Δφ(t)|, where D(t) is the frequency response distortion, and w G is the dynamic gain distortion weight factor, w φ is the phase distortion weight factor; The frequency response distortion in the comprehensive response process is used to obtain the frequency response distortion index. The calculation expression is as follows: In the formula, E D represents the frequency response distortion index, t0 is the initial time of the system response, t f is the end time of the system response.

Images