A stand-alone photovoltaic hydrogen production thermal management system based on fuzzy-PID compound control

Abstract

The application discloses an off-grid photovoltaic hydrogen production heat management system based on fuzzy-PID compound control and belongs to the technical field of heat management control of alkaline water electrolysis hydrogen production systems.The system is aimed at the problems of large heat fluctuation, strong environmental interference and high control precision requirement of off-grid photovoltaic hydrogen production systems in the process of operation, shutdown and working condition switching, and a compound control strategy based on the combination of fuzzy control and PID control is provided.Through the collaborative mechanism of "fuzzy control processing nonlinear interference + PID control guaranteeing steady-state precision", intelligent regulation and control of heat collection, storage and distribution are realized.The system is aimed at the complex working conditions of off-grid photovoltaic hydrogen production, such as photothermal fluctuation, environmental temperature mutation and electrolytic cell load switching, and through double-loop linkage and multi-link parameter adaptive optimization, the temperature of alkaline solution and the temperature of photothermal heat storage tanks are accurately controlled, the problems of poor robustness, large temperature fluctuation and high energy consumption of traditional control modes are solved, the operation stability and energy utilization efficiency of the off-grid hydrogen production system are improved, and the system is suitable for large-scale off-grid photovoltaic hydrogen production scenes.

Description

Technical Field

[0001] This invention relates to the field of thermal management and control technology for alkaline water electrolysis hydrogen production systems, and in particular to an off-grid photovoltaic hydrogen production thermal management system based on fuzzy-PID composite control. Background Technology

[0002] Off-grid photovoltaic hydrogen production systems are often deployed in areas with abundant sunshine but harsh environments. Their operation is affected by multiple factors such as sunlight intensity, ambient temperature, and fluctuations in photovoltaic output, making thermal management challenging. Alkaline water electrolysis for hydrogen production requires maintaining the electrolyzer's operating temperature between 75-95℃. During shutdown, the alkaline solution temperature must be prevented from dropping below 50℃, otherwise, it will lead to high restart energy consumption, long start-up time, and even safety hazards such as alkaline solution freezing. This places stringent requirements on the control precision and adaptability of the thermal management system.

[0003] Some existing off-grid photovoltaic hydrogen production thermal management systems employ a single PID control method, using preset fixed PID parameters to achieve closed-loop temperature regulation of the electrolyzer. For example, Chinese patent "A method, system, equipment and medium for off-grid new energy hydrogen production start-up control" (publication number: CN120824800A) uses a PID pressure management strategy combined with closed-loop temperature regulation to achieve electrolytic hydrogen production control. However, this control method has poor robustness, is prone to temperature overshoot and response lag, and cannot adapt to complex operating conditions such as fluctuations in solar and thermal energy and sudden changes in ambient temperature.

[0004] Fuzzy control boasts strong robustness and effective handling of nonlinear disturbances, but its steady-state control accuracy is insufficient. PID control offers high steady-state accuracy, but it is prone to overshoot when parameters are poorly tuned or external disturbances are strong. Currently, fuzzy-PID composite control has been applied in some temperature control fields, but it has not been applied to off-grid photovoltaic hydrogen production thermal management scenarios, making it difficult to solve the unique nonlinearity, large time lag, and multi-condition adaptation problems inherent in this field. Therefore, combining the advantages of both to achieve intelligent thermal management of off-grid hydrogen production systems under all operating conditions has become an urgent need for current technological development. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides an off-grid photovoltaic hydrogen production thermal management system based on fuzzy-PID composite control, which can improve temperature control accuracy under complex operating conditions.

[0006] An off-grid photovoltaic hydrogen production thermal management system based on fuzzy-PID composite control includes a fuzzy-PID composite control unit, a solar thermal collection and storage unit, a heat preservation circuit unit, an alkaline water electrolysis hydrogen production unit, a gas processing unit, and an alkaline solution circulation unit. The fuzzy-PID composite control unit includes:

[0007] The sensor array is used to collect data on the alkali outlet temperature, the temperature of the solar thermal storage tank, the ambient temperature, the light intensity of the solar thermal collector, and the start / stop signals of the electrolytic cell.

[0008] A fuzzy controller is used to receive signals from the sensor array and output PID parameter correction values;

[0009] A PID controller is used for heat management under all operating conditions. The PID controller precisely controls the heat collection loop circulation pump, the insulation loop circulation pump, the alkaline water cooler, and the alkaline distributor based on the corrected parameters.

[0010] Furthermore, the sensor group includes:

[0011] Temperature sensors are installed at the alkaline outlet of the gas processing unit and inside the solar thermal storage tank of the solar thermal collector and storage unit.

[0012] A light intensity sensor is installed on the surface of the solar thermal collector of the solar thermal collector and storage unit.

[0013] The operating condition sensor is connected to the electrolyzer of the alkaline water electrolysis hydrogen production unit and is used to collect the start and stop signals of the electrolyzer.

[0014] An ambient temperature sensor is installed in the external environment of the system.

[0015] The measurement error of the sensor is calculated as follows:

[0016] ,

[0017] ,

[0018] in, This represents the temperature measurement error (°C). The sensor measures the temperature (°C). This is the actual temperature value (°C). The measurement error for daylight intensity is (W / m²). The sensor measures the light intensity (W / m²). This represents the actual light intensity (W / m²).

[0019] Furthermore, the input terminal of the fuzzy controller is connected to the output terminal of the sensor group, and its output terminal is connected to the input terminal of the PID controller;

[0020] The inputs of the fuzzy controller are the temperature deviation e and the deviation change rate ec, and the outputs are the PID parameter correction values ​​△Kp, △Ki, and △Kd.

[0021] The fuzzy controller uses the centroid method for defuzzification calculation, and the calculation formula is as follows:

[0022]

[0023] Where △K is the PID parameter correction amount after defuzzification. Let be the membership degree of the i-th fuzzy rule. This is the output value of the i-th fuzzy rule.

[0024] Furthermore, the input terminal of the PID controller is connected to the output terminal of the fuzzy controller, and its output terminal is connected to the control terminals of the heat collection loop circulation pump, the heat preservation loop circulation pump, the alkaline water cooler, and the alkaline distributor, respectively.

[0025] The core adjustment formula of the PID controller is as follows:

[0026]

[0027] Among them, K p K is a proportionality coefficient, with a value ranging from 1.5 to 2.5. i K is the integral coefficient, with a value ranging from 0.05 to 0.15. d is the differential coefficient, with a value range of 0.3-0.7.

[0028] Furthermore, the solar thermal collector and storage unit includes a solar thermal collector, a solar thermal storage tank, and a collector loop circulation pump; the control terminal of the collector loop circulation pump is connected to the output terminal of the PID controller.

[0029] The fuzzy controller determines the start / stop status of the heat collection circuit and the initial frequency of the heat collection circuit circulation pump based on the light signal collected by the light intensity sensor and the deviation e and the rate of change of deviation e between the current temperature and the target temperature collected by the temperature sensor inside the solar thermal storage tank.

[0030] The PID controller uses the output of the fuzzy controller as the initial parameter and fine-tunes the rotation speed of the heat collection loop circulation pump to stabilize the temperature of the solar thermal storage tank at the target temperature determined according to preset conditions.

[0031] Furthermore, the insulation loop unit includes an insulation loop circulation pump and a serpentine heat exchange coil; the control terminal of the insulation loop circulation pump is connected to the output terminal of the PID controller; the outlet of the insulation loop circulation pump is connected to the inlet of the serpentine heat exchange coil through a pipe, and the serpentine heat exchange coil is installed inside the gas processing unit.

[0032] The fuzzy controller determines the start / stop status of the insulation circuit and the heat output level based on the start / stop status of the electrolytic cell collected by the operating condition sensor and the deviation e and the rate of change of deviation ec of the alkali temperature and the insulation threshold collected by the alkali temperature sensor at the alkali outlet.

[0033] The PID controller controls the heat transfer rate of the serpentine heat exchange coil by fine-tuning the frequency of the circulating pump in the insulation loop according to the heat output level, thereby stabilizing the alkali solution temperature at the target insulation temperature.

[0034] Furthermore, the alkali circulation unit includes an alkali water cooler and an alkali distributor; the control terminals of the alkali water cooler and the alkali distributor are respectively connected to the output terminal of the PID controller; the outlet of the alkali distributor is connected to the inlet of the alkaline water electrolysis hydrogen production unit through a pipeline.

[0035] The PID controller targets a preset operating temperature range and controls the supply temperature of each electrolytic cell by adjusting the cooling intensity of the alkali water cooler and the flow rate of the alkali distributor.

[0036] The fuzzy controller dynamically corrects the parameters of the PID controller when the electrolytic cell load fluctuates or the ambient temperature changes abruptly.

[0037] Furthermore, the fuzzy-PID composite control module is equipped with dual-loop linkage logic:

[0038] When the system switches between hydrogen production mode and shutdown and heat preservation mode, the fuzzy controller sends a switching signal to the PID controller through its output terminal to control the switching between the alkali circulation loop and the heat preservation loop; the parameters of the PID controller are adaptively adjusted according to the loop switching, and the response rate of the PID of the heat preservation loop is lower than that of the PID of the alkali circulation loop.

[0039] Furthermore, during the transition phase between hydrogen production mode and shutdown and heat preservation mode, after receiving the start-up and shutdown signal of the electrolyzer, the fuzzy controller sends a switching signal to the PID controller through its output terminal, and completes the control mode switching within 1 second; the PID controller simultaneously performs parameter self-tuning to ensure that the alkali solution temperature fluctuation during the transition phase is ≤±3℃.

[0040] Furthermore, the control objectives of the fuzzy-PID composite control module are: when the electrolytic cell is running, the alkaline solution temperature is stable at 75-95℃; when the electrolytic cell is shut down, the alkaline solution temperature is not lower than 50℃; the temperature of the solar thermal storage tank is maintained at 25-100℃; and the priority of heat distribution is: shutdown heat preservation takes priority over operation heat balance, and operation heat balance takes priority over surplus heat storage.

[0041] The beneficial effects of this invention are:

[0042] 1. This invention collects data on the alkali outlet temperature, solar thermal storage tank temperature, ambient temperature, sunlight intensity, and electrolyzer start / stop signals using a sensor array. A fuzzy controller processes this data in real time and outputs PID parameter corrections. Based on these corrections, the PID controller precisely regulates the circulating pumps in the solar collector loop, the circulating pumps in the insulation loop, the alkali water cooler, and the alkali distributor, forming a composite control mechanism that combines fuzzy logic to handle nonlinear disturbances with PID control to ensure steady-state accuracy. This mechanism effectively solves the problems of lag and large overshoot in traditional PID control under complex conditions such as photovoltaic power fluctuations and sudden changes in ambient temperature. It significantly improves the temperature control accuracy, robustness, and adaptability of the system under multi-disturbance environments, ensuring stable operation of the off-grid hydrogen production system under all operating conditions.

[0043] 2. This invention achieves intelligent switching between hydrogen production and shutdown / insulation modes by utilizing a fuzzy controller to respond in real-time to electrolyzer start / stop signals, alkali temperature deviations, and the rate of change of these deviations. Combined with precise control of the circulating pump in the insulation loop by a PID controller, this intelligent switching is achieved. During the mode switching process, the fuzzy controller rapidly completes the control mode switch, while the PID controller simultaneously performs parameter self-tuning, ensuring stable and controllable alkali temperature during the transition phase. This solves the problem of drastic temperature fluctuations during mode switching in traditional control methods, ensuring the safety and reliability of the system under off-grid operation conditions. Attached Figure Description

[0044] Figure 1 This is a diagram of the fuzzy-PID composite control system architecture in this invention;

[0045] Figure 2 This is a flowchart of the working condition switching control in this invention;

[0046] Figure 3 This is a flowchart of the hydrogen production operation control in this invention;

[0047] Figure 4 This is a flowchart of the shutdown and heat preservation control process in this invention. Detailed Implementation

[0048] To more clearly illustrate the technical solution and implementation effects of the present invention, the following description is provided in conjunction with the appendix. Figure 1-4 The invention will be described in detail below, including its practical application in Ordos City, Inner Mongolia Autonomous Region. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention; the connection relationships and parameters of each component can be adjusted according to actual engineering needs, while the core control logic remains consistent.

[0049] Example 1:

[0050] This embodiment provides an off-grid photovoltaic hydrogen production thermal management system based on fuzzy-PID composite control, deployed at a photovoltaic hydrogen production base in Ordos City, Inner Mongolia Autonomous Region. The system is adapted to off-grid photovoltaic power plants with corresponding power outputs, including a solar thermal collector and storage unit, an insulation loop unit, an alkaline water electrolysis hydrogen production unit, a gas processing unit, and an alkaline solution circulation unit. Its key feature is the inclusion of a fuzzy-PID composite control unit, which comprises: a sensor group for collecting alkaline solution outlet temperature, solar thermal storage tank temperature, ambient temperature, solar thermal collector light intensity, and electrolyzer start / stop signals; a fuzzy controller for receiving sensor group signals and outputting PID parameter correction values; and a PID regulator for heat scheduling under all operating conditions. The PID regulator precisely controls the collector loop circulation pump, insulation loop circulation pump, alkaline solution water cooler, and alkaline solution distributor based on the corrected parameters.

[0051] Preferably, in this embodiment, 200 distributed flat-plate solar thermal collectors are arranged on the roof of the factory building, each with an area of ​​1.5 m² and a collection efficiency of 0.75; the solar thermal storage tank has a capacity of 25 m³, an insulation layer thickness of 150 mm, and a heat loss rate of <1% / h; the collector loop circulation pump is frequency-controlled, with a power of 7.5 kW and a rated flow rate of 15 m³ / h. The outlet of the solar thermal collector is connected to the inlet of the collector loop circulation pump through a pipe, and the outlet of the collector loop circulation pump is connected to the inlet of the heat exchange coil of the solar thermal storage tank through a pipe, forming a closed collector loop. A temperature sensor (model PT100) is installed inside the storage tank for real-time monitoring of the storage temperature.

[0052] Specifically, fuzzy control determines the start / stop status of the solar collector loop and the initial frequency of the circulating pump based on the light intensity, the temperature deviation of the thermal storage tank, and the rate of change of the deviation, thus resolving nonlinear interference caused by solar thermal fluctuations. The PID controller uses the fuzzy control output as initial parameters and fine-tunes the circulating pump speed to stabilize the temperature of the thermal storage tank within the target range (90℃ in winter and 80℃ in summer), ensuring thermal storage accuracy. Based on the system parameters, assuming a single solar collector area of ​​1.5 m², and a light intensity of 800 W / m², the solar thermal power calculation formula yields the following:

[0053]

[0054] The 200 solar collectors have a total collection power of 180kW, which can meet the heating requirements of the thermal storage tank. Photothermal collector power (W). Light intensity (W / m²) The area of ​​a single solar collector (m²) is given. The collector efficiency is taken as 0.75 in this system. This is a correction factor for the number of solar collectors (1.0 for this system with 200 units).

[0055] Specifically, the insulation loop unit includes an insulation loop circulation pump and a serpentine heat exchange coil; the control terminal of the insulation loop circulation pump is connected to the output terminal of the PID controller; the outlet of the insulation loop circulation pump is connected to the inlet of the serpentine heat exchange coil through a pipe, and the serpentine heat exchange coil is installed inside the gas processing unit; the fuzzy controller determines the start / stop status of the insulation loop and the heat output level based on the start / stop status of the electrolytic cell collected by the operating condition sensor, and the deviation e and the rate of change of deviation ec of the alkali temperature and the insulation threshold collected by the temperature sensor at the alkali outlet; the PID controller controls the heat transfer rate of the serpentine heat exchange coil by fine-tuning the frequency of the insulation loop circulation pump according to the heat output level, thereby stabilizing the alkali temperature at the target insulation temperature.

[0056] Preferably, in this embodiment, the insulated loop circulation pump is frequency-controlled, with a power of 5.5kW and a rated flow rate of 10m³ / h; the serpentine heat exchange coil is installed inside the hydrogen gas-liquid separator and the oxygen gas-liquid separator, with a heat transfer coefficient of 756W / (m²·K) and a total heat exchange area of ​​10m². The outlet of the solar thermal storage tank is connected to the inlet of the insulated loop circulation pump via a pipeline, and the outlet of the insulated loop circulation pump is connected to the inlet of the serpentine heat exchange coil via a pipeline. The outlet of the serpentine heat exchange coil returns to the storage tank via a pipeline, forming a closed insulated loop.

[0057] Specifically, fuzzy control rapidly determines the start / stop of the insulation loop and the heat output level based on the electrolytic cell's start / stop status, the deviation and rate of change of the alkali solution temperature from the insulation threshold (50℃), thus addressing rapid heat loss in low-temperature environments. A PID controller fine-tunes the frequency of the circulating pump in the insulation loop, precisely controlling the heat transfer rate of the serpentine heat exchange coil to prevent alkali solution temperature overshoot or drop below the threshold. The system's serpentine heat exchange coil has a heat transfer coefficient K = 756 W / (m²·K) and a total area A = 10 m². When the temperature of the heat storage tank... =90℃, alkaline solution temperature When the temperature is 48℃, substituting into the heat transfer power formula, we can obtain the following:

[0058]

[0059] in, Where K is the heating power, K is the overall heat transfer coefficient, and A is the effective heat transfer area. Temperature of the thermal storage tank The temperature of the alkali solution. For heat transfer temperature difference.

[0060] Specifically, the alkali circulation unit includes an alkali water cooler and an alkali distributor; the control terminals of the alkali water cooler and the alkali distributor are respectively connected to the output terminal of the PID controller; the outlet of the alkali distributor is connected to the inlet of the alkaline water electrolysis hydrogen production unit through a pipeline; the PID controller targets a preset operating temperature range and controls the supply temperature of each electrolyzer by adjusting the cooling intensity of the alkali water cooler and the flow rate of the alkali distributor; the fuzzy controller dynamically corrects the parameters of the PID controller when the electrolyzer load fluctuates or the ambient temperature changes abruptly.

[0061] Preferably, in this embodiment, the alkali cooler is a plate heat exchanger, and the opening of the cooling water side is controlled by an electric regulating valve; the alkali distributor is responsible for evenly distributing the alkali to the 7 electrolytic cells. The alkali outlet of the gas-liquid separator is connected to the hot-side inlet of the alkali cooler through a pipe, and the hot-side outlet of the alkali cooler is connected to the inlet of the alkali distributor through a pipe. The 7 outlets of the alkali distributor are respectively connected to the alkali inlets of the 7 electrolytic cells through pipes, forming a complete alkali circulation loop.

[0062] In this embodiment, the alkaline water electrolysis hydrogen production unit consists of seven parallel 5MW alkaline water electrolyzers, with a 25% KOH solution as the electrolyte in each cell. The alkaline outlet of each electrolyzer is connected to the inlet of the gas processing unit via a pipeline. The gas processing unit includes a hydrogen gas-liquid separator and an oxygen gas-liquid separator. A temperature sensor (model PT100, 4-20mA output) is installed at the alkaline outlet of the separator to collect the alkaline temperature in real time.

[0063] Furthermore, the sensor group includes: a temperature sensor installed at the alkaline outlet of the gas processing unit and inside the solar thermal storage tank of the solar thermal collector and storage unit; a light intensity sensor installed on the surface of the solar thermal collector of the solar thermal collector and storage unit; an operating condition sensor connected to the electrolyzer of the alkaline water electrolysis hydrogen production unit for collecting the start-up and shutdown signals of the electrolyzer; and an ambient temperature sensor installed in the external environment of the system.

[0064] Preferably, in this embodiment, four temperature sensors are respectively installed at the alkaline outlet of the hydrogen gas-liquid separator, the alkaline outlet of the oxygen gas-liquid separator, inside the solar thermal storage tank, and in the ambient air, outputting 4-20mA analog signals, which are connected to the fuzzy controller through an analog input module; 200 light intensity sensors are installed on the surface of each solar thermal collector, outputting RS485 digital signals, which are connected to the fuzzy controller through a serial bus; the operating condition sensor is connected to the electrolytic cell control circuit, collecting start and stop signals, outputting dry contact signals, which are connected to the fuzzy controller through a digital input module.

[0065] The measurement errors of the temperature sensor and the light intensity sensor are calculated as follows:

[0066] ,

[0067] ,

[0068] in, This represents the temperature measurement error (°C). The sensor measures the temperature (°C). This is the actual temperature value (°C). The measurement error for daylight intensity is (W / m²). The sensor measures the light intensity (W / m²). This represents the actual light intensity (W / m²).

[0069] Furthermore, the input terminal of the fuzzy controller is connected to the output terminal of the sensor group, and its output terminal is connected to the input terminal of the PID controller;

[0070] Preferably, in this embodiment, the fuzzy controller uses an STM32F407 main control chip with a built-in fuzzy inference algorithm; the input of the fuzzy controller is connected to the sensor group through a signal conditioning circuit, and its output is connected to the PID controller through a CAN bus.

[0071] The inputs of the fuzzy controller are the temperature deviation e and the deviation change rate ec, and the outputs are the PID parameter correction values ​​△Kp, △Ki, and △Kd.

[0072] The fuzzy controller uses the centroid method for defuzzification calculation, and the calculation formula is as follows:

[0073]

[0074] Where △K is the PID parameter correction amount after defuzzification. Let be the membership degree of the i-th fuzzy rule. This is the output value of the i-th fuzzy rule.

[0075] Furthermore, the input terminal of the PID controller is connected to the output terminal of the fuzzy controller, and its output terminal is connected to the control terminals of the heat collection loop circulation pump, the heat preservation loop circulation pump, the alkaline water cooler, and the alkaline distributor, respectively.

[0076] Preferably, in this embodiment, the PID controller uses a Siemens S7-1200 PLC with a built-in PID control algorithm; the input is connected to the fuzzy controller via a CAN bus; the output is connected to the frequency converters of the heat collection loop circulating pump, the insulation loop circulating pump, and the electric regulating valve of the alkali water cooler via analog output modules; and to the stepper motor driver of the alkali distributor via a digital output module. The control sampling period is set to 0.1s to ensure that the system can respond quickly to changes in operating conditions. The initial PID parameters are set to Kp=2.0, Ki=0.1, and Kd=0.5 according to the system steady-state model. The membership function center values ​​of the fuzzy controller are -10, -5, 0, 5, and 10℃, with a width of 5℃, and a total of 15 fuzzy rules.

[0077] The core adjustment formula of the PID controller is as follows:

[0078]

[0079] Among them, K p K is a proportionality coefficient, with a value ranging from 1.5 to 2.5. i K is the integral coefficient, with a value ranging from 0.05 to 0.15. d is the differential coefficient, with a value range of 0.3-0.7.

[0080] Furthermore, when the system switches between hydrogen production mode and shutdown and heat preservation mode, the fuzzy controller sends a switching signal to the PID controller through its output terminal to control the switching between the alkali circulation loop and the heat preservation loop; the parameters of the PID controller are adaptively adjusted according to the loop switching, and the response rate of the PID of the heat preservation loop is lower than that of the PID of the alkali circulation loop.

[0081] Furthermore, during the transition phase between hydrogen production mode and shutdown and heat preservation mode, after receiving the start-up and shutdown signal of the electrolyzer, the fuzzy controller sends a switching signal to the PID controller through its output terminal to complete the control mode switch; the PID controller simultaneously performs parameter self-tuning to ensure that the alkali solution temperature fluctuation is within the preset range during the transition phase.

[0082] Furthermore, the control objectives of the fuzzy-PID composite control unit are: when the electrolyzer is running, the alkaline solution temperature is stable at 75-95℃; when the electrolyzer is shut down, the alkaline solution temperature is not lower than 50℃; the temperature of the solar thermal storage tank is maintained at 25-100℃; and the priority of heat distribution is: shutdown heat preservation takes priority over operation heat balance, and operation heat balance takes priority over surplus heat storage.

[0083] Example 2:

[0084] This embodiment describes the complete operation process of the system under hydrogen production conditions. Under hydrogen production conditions, the core requirement of the system is to stabilize the alkaline solution temperature within the operating range of 75-95℃. At this temperature, the system's thermal load is relatively stable, and a control strategy based on PID control with fuzzy logic as the auxiliary control is adopted. The entire operation process begins with real-time data acquisition from the sensor array, followed by dynamic correction of PID parameters by the fuzzy controller, calculation of the control quantity by the PID regulator, and driving of the actuator by the PID controller, ultimately achieving precise and stable temperature control and forming a complete closed-loop regulation circuit.

[0085] After system startup, the sensor array begins real-time data acquisition. The temperature sensor installed at the alkaline outlet of the hydrogen gas-liquid separator detects a current alkaline outlet temperature of 80℃, while the system's target temperature for hydrogen production is 85℃. The temperature sensor installed inside the solar thermal storage tank detects a tank temperature of 85℃, while the summer target temperature is 80℃. The light intensity sensor installed on the surface of the solar thermal collector detects an average light intensity of 800W / m². The ambient temperature sensor installed outside the system detects an ambient temperature of 25℃. The operating condition sensor connected to the electrolyzer control circuit detects the electrolyzer operating signal and outputs a high level. All sensors meet the requirements of temperature measurement accuracy ±0.5℃ and light intensity measurement accuracy ±5W / m², ensuring the accuracy of the control signals.

[0086] The 4-20mA current signal output by the temperature sensor is converted into a temperature value by the analog input module. The conversion formula is as follows:

[0087] ,

[0088] Where I is the measured current (mA), Tmax = 150℃ (upper limit of the range), and Tmin = 0℃ (lower limit of the range). In this embodiment, the measured current is 9.6mA, and the calculated alkali outlet temperature T = 80℃.

[0089] Similarly, the measured current of the temperature sensor installed inside the solar thermal storage tank is 10.24mA, and the calculated temperature of the storage tank is T=85℃; the measured current of the ambient temperature sensor is 5.6mA, and the calculated ambient temperature is T=25℃.

[0090] A light intensity sensor (model TSL2591) mounted on the surface of the solar thermal collector outputs a digital signal via an I²C interface. The photodiode inside the sensor converts light intensity into current, which is then converted into a digital value by an ADC. A fuzzy controller polls 200 sensors via a serial bus, and each sensor returns a light intensity value (in W / m²). In this embodiment, the average light intensity of the 200 sensors is I = 800 W / m².

[0091] It should be noted that the measurement accuracy of all the above sensors meets the system control requirements: the temperature sensor measurement error ΔT meas =|T meas -T true |≤0.5℃; Measurement error ΔI of light intensity sensor meas =|I meas -I true |≤5W / m². High-precision sensor data is the foundation for subsequent fuzzy control and PID regulation.

[0092] After receiving data from the sensor group, the fuzzy controller begins fuzzy inference. In this embodiment, the system is in hydrogen production mode and the heat load is stable. The main task of the fuzzy controller is to fine-tune the PID parameters. The inputs to the fuzzy controller are the temperature deviation e and the rate of change of deviation ec. Where: Temperature deviation e = target temperature - current temperature. In this embodiment, the target temperature of the alkali solution is set to 85℃, and the current temperature is 80℃, therefore:

[0093] e = 85 - 80 = 5℃.

[0094] The rate of change of deviation (ec) reflects the trend of temperature change, and the calculation formula is:

[0095] ,

[0096] Where e(k) is the current time deviation, e(k-1) is the previous time deviation, and T s The sampling period is 0.1 s. Assuming the deviation at the previous moment is e(k-1) = 6℃, then: ec = (5-6) / 0.1 = −10℃ / s

[0097] The fuzzy controller converts precise values ​​of e and ec into fuzzy quantities; this process is called fuzzification. The system's preset membership function is a trigonometric function. The universe of discourse for the temperature deviation e is [-20℃, 20℃], divided into five fuzzy sets: NB (negative large), NS (negative small), ZO (zero), PS (positive small), and PB (positive large), each with a width of 5℃. The universe of discourse for the rate of change of deviation ec is [-0.01℃ / s, 0.01℃ / s], also divided into five fuzzy sets. For e = 5℃, it falls exactly at the center of the positive small (PS) set; therefore, the membership degree μ to the PS set is... ps =1.0, and the membership degree to other sets is 0. For ec = -10℃ / s, it falls at the center of the negative large (NB) set, therefore the membership degree μ to the NB set is 1.0. nb =1.0. Membership degree represents the degree to which a precise value belongs to a certain fuzzy set, and the value ranges from 0 to 1, where 1 means that it belongs to the set completely and 0 means that it does not belong to the set at all.

[0098] The fuzzy controller performs rule matching based on a preset fuzzy rule base. The system has 15 preset fuzzy rules, each in the form: IF e is A AND ec is B THEN ΔKp = a certain value, ΔKi = a certain value, ΔKd = a certain value. In this embodiment, e = 5℃ (PS) and ec = -10℃ / s (NB), so the matching rule is: IF e = PS AND ec = NB THEN ΔKp = 0.1, ΔKi = 0.02, ΔKd = 0. The rule trigger strength is the minimum of the two input membership degrees, i.e., α = min(μ). ps ,μ nb = min(1.0, 1.0) = 1.0. When multiple rules are triggered simultaneously, each rule has its own trigger strength, and the final output is the weighted average of the outputs of all rules.

[0099] The fuzzy controller uses the centroid method for defuzzification calculation, converting the fuzzy inference results into precise PID parameter correction values ​​ΔKp, ΔKi, and ΔKd. In this embodiment, since only rule 7 is triggered with a trigger strength of 1.0, while other rules have a trigger strength of 0, the defuzzification result directly takes the output value of rule 7: ΔKp=0.1, ΔKi=0.02, ΔKd=0. The physical meaning of the centroid method for defuzzification is that when multiple fuzzy rules are triggered simultaneously, the final output is the weighted average of the outputs of each rule, with the weight being the trigger strength of each rule. This method enables continuous and smooth output, avoids jumps in control variables, and improves the control quality of the system.

[0100] The fuzzy controller uses the centroid method to convert fuzzy inference results into precise PID parameter corrections. The centroid method calculation formula is as follows:

[0101] ,

[0102] Where μi is the trigger strength of the i-th rule, Ki is the output value of the i-th rule, and n is the total number of rules.

[0103] It should be noted that the physical meaning of the centroid method for defuzzification is that when multiple fuzzy rules are triggered simultaneously, the final output is a weighted average of all rule outputs, with the weights being the trigger strength of each rule. This method enables continuous and smooth output, avoiding abrupt changes in control quantities.

[0104] The fuzzy controller sends the calculated ΔKp, ΔKi, and ΔKd to the PID controller via the CAN bus. Upon receiving the data, the PID controller updates the parameters.

[0105] Step 1: PID parameter update

[0106]

[0107]

[0108]

[0109] Step 2: Calculation of control quantities:

[0110] The PID controller uses a positional PID algorithm to calculate the control output. The algorithm formula is:

[0111] ,

[0112] Among them, the proportional term: Kp×e(k)=2.1×5=10.5; the integral term: assuming the cumulative integral value ∑e(i)×Ts=3℃·s, then Ki×cumulative integral=0.12×3=0.36; the differential term: Kd×(e(k)-e(k-1)) / Ts=0.5×(-10)=-5.

[0113] Therefore: u(k) = 10.5 + 0.36 − 5 = 5.86.

[0114] Step 3: Control Variable Mapping

[0115] u(k) = 5.86 is a dimensionless control quantity that needs to be mapped to a specific actuator control signal. In this embodiment, the PID controller linearly maps the control quantity to the frequency adjustment value of the heat collector loop circulating pump (0-10 corresponds to 0-50Hz):

[0116] ,

[0117] Step 4: Control Signal Output. The PID controller sends a control signal to the frequency converter of the heat collector loop circulating pump via the analog output module (4-20mA). The frequency converter receives the 4-20mA signal and converts it into the corresponding output frequency. 4mA corresponds to 0Hz, and 20mA corresponds to 50Hz. Therefore, the output current corresponding to 29.3Hz is:

[0118] ,

[0119] At the same time, the PID controller also sends a control signal to the electric regulating valve of the alkali solution water cooler to adjust the cooling water flow and ensure that the alkali solution temperature does not overshoot.

[0120] The actuator begins operation after receiving the control signal. The collector loop circulation pump operates at a frequency of 29.3Hz, driving the flow of the medium in the collector loop. After absorbing heat through the solar thermal collector, the medium enters the solar thermal storage tank, where it is stored. The medium flow path in the collector loop is: solar thermal collector outlet → collector loop circulation pump inlet → collector loop circulation pump outlet → solar thermal storage tank heat exchange coil inlet → releases heat through the heat exchange coil → heat exchange coil outlet → returns to the solar thermal collector, forming a closed loop. The frequency of the collector loop circulation pump directly affects the medium flow rate and the amount of heat exchanged; the higher the frequency, the faster the flow rate, and the more heat is brought back from the collector per unit time.

[0121] After the control command is issued, the system enters the closed-loop feedback regulation stage. The temperature sensor continuously monitors the alkali outlet temperature and sends data to the fuzzy controller every 0.1 seconds. The PID controller continuously updates the control output based on the real-time temperature deviation, forming a dynamic regulation process. After 5 minutes, the alkali temperature gradually rises from 80℃ to 85℃ and tends to stabilize. At this time, the measured temperature T... meas =85.2℃, target temperature T target =85℃, steady-state error ess=0.2℃, meeting the ±0.5℃ control accuracy requirement for hydrogen production efficiency. The PID controller can achieve high-precision steady-state control because the cumulative effect of the integral term can eliminate steady-state error, allowing the system to eventually stabilize at the target value.

[0122] If external interference occurs during operation, the fuzzy controller will activate the auxiliary temperature control function. Assume that the photovoltaic output suddenly drops by 10%, causing the light intensity to decrease from 800W / m² to 720W / m², while a cold air mass passes through, causing the ambient temperature to plummet from 25℃ to 20℃. These two factors combined reduce the heat collection and increase the system's heat loss, causing the alkaline solution temperature to slowly decrease. The temperature sensor detects that the alkaline solution temperature gradually decreases from 85.2℃ to 84.5℃, and the temperature deviation e increases from 0.2℃ to 0.5℃, with a positive deviation rate of change ec. The fuzzy controller detects the increase in e and the positive ec, and according to the fuzzy rule "increased load → increased Kp for faster response," outputs ΔKp=0.3, ΔKi=0, and ΔKd=-0.1. The PID parameters are updated to Kp=2.4, Ki=0.12, and Kd=0.4. The PID controller recalculates the control input based on the new parameters, increasing the frequency of the circulating pump in the heat collection loop to approximately 35Hz to accelerate heat transfer, while simultaneously reducing the opening of the alkali solution water cooler to decrease cooling capacity. The system responds quickly to disturbances, and the alkali solution temperature recovers and stabilizes at around 85℃ after approximately 2 minutes. The fuzzy controller's mechanism for dynamically correcting PID parameters enables the system to adaptively respond to changes in operating conditions, which is one of the core advantages of this invention compared to traditional fixed-parameter PID control.

[0123] Example 3:

[0124] This embodiment describes the system insulation situation under winter nighttime conditions in Ordos City, Inner Mongolia Autonomous Region. When there is no sunlight, the photovoltaic power station stops supplying power, the alkaline water electrolysis hydrogen production unit shuts down (downtime exceeds 12 hours), and the solar collector also stops working. The system automatically switches to insulation mode. The ambient temperature is -30℃, and the core requirement is that the alkaline solution temperature is not lower than 50℃. A control strategy with fuzzy control as the main component and PID as the auxiliary component is adopted.

[0125] Initial stage of shutdown:

[0126] The sensor group collected the electrolytic cell shutdown signal, the alkaline solution outlet temperature of 58℃, and the heat storage tank temperature of 90℃. The fuzzy controller determined that there was no need to start the heat preservation circuit and that the temperature could be maintained solely by the equipment's heat preservation structure.

[0127] Mid-term shutdown:

[0128] Two hours later, the temperature sensor detected that the alkaline solution temperature had dropped to 48℃ (below the heat preservation threshold of 50℃), with a deviation e(k) = 50 - 48 = -2℃ and a deviation change rate ec(k) = (48 - 58) / (2 × 3600) = -0.00139℃ / s (large negative value). The fuzzy controller outputs a high-power heat transfer command, and the calculation process is as follows:

[0129] 1. Fuzzification and defuzzification: e(k) = -2℃, ec(k) = -0.00139℃ / s, defuzzification output PID parameter correction ΔKp = 0.5, ΔKi = 0, ΔKd = -0.2;

[0130] 2. PID parameter update: Kp=2.0+0.5=2.5, Ki=0.1+0=0.1, Kd=0.5-0.2=0.3;

[0131] 3. Calculation of PID output control quantity: u(k) = -5.05 (corresponding to a circulating pump frequency of 60Hz);

[0132] The PID controller starts the circulating pump in the insulation loop and adjusts the pump frequency to 60Hz, transferring heat to the alkali solution through the serpentine heat exchange coil. After 1 hour, the alkali solution temperature rises back to 63℃. At this time, e(k) = 50 - 63 = -13℃, ec(k) = (63 - 48) / 3600 = 0.00417℃ / s. The defuzzy outputs ΔKp = -0.3, ΔKi = -0.02, and ΔKd = 0.2. After updating, Kp = 1.7, Ki = 0.08, and Kd = 0.7. Substituting these values ​​into the PID formula, the PID controller reduces the circulating pump frequency to 30Hz to maintain a stable temperature.

[0133] Post-shutdown phase:

[0134] Eight hours later, the temperature of the heat storage tank dropped to 60℃, and the temperature of the alkali solution was 52℃. The deviation e(k) = 50-52 = -2℃, ec(k) = (52-63) / (8×3600) ≈ -0.00038℃ / s. The fuzzy controller adjusted the heat output level to low power, and the defuzzy output △Kp = -0.2, △Ki = -0.03, △Kd = 0.1. The PID controller fine-tuned the circulation pump frequency to 20Hz to ensure that the alkali solution temperature was maintained between 45-55℃.

[0135] When the electrolytic cell is started the next day, the alkaline solution temperature is 48°C, with no risk of freezing. The start-up time is shortened by 40% compared to traditional control, and the restart energy consumption is reduced by 35%. Combined with the calculation of heat preservation energy consumption, the heat preservation energy consumption of the embodiment is reduced by 22% compared to the existing technology, which meets the design requirements.

[0136] This invention achieves high stability and high precision thermal management of off-grid photovoltaic hydrogen production systems under multiple disturbances and operating conditions through the intelligent combination of fuzzy control and PID control, and has strong engineering applicability and promotion value.

[0137] The embodiments described above are merely illustrative of specific implementations of the present invention, and while the descriptions are detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.

Claims

1. An off-grid photovoltaic hydrogen production thermal management system based on fuzzy-PID composite control, comprising a solar thermal collector and storage unit, a heat preservation circuit unit, an alkaline water electrolysis hydrogen production unit, a gas processing unit, and an alkaline solution circulation unit, characterized in that, It also includes a fuzzy-PID composite control unit, which comprises: The sensor array is used to collect data on the alkali outlet temperature, the temperature of the solar thermal storage tank, the ambient temperature, the light intensity of the solar thermal collector, and the start / stop signals of the electrolytic cell. A fuzzy controller is used to receive signals from the sensor array and output PID parameter correction values; A PID controller is used for heat management under all operating conditions. The PID controller precisely controls the heat collection loop circulation pump, the insulation loop circulation pump, the alkaline water cooler, and the alkaline distributor based on the corrected parameters.

2. The system according to claim 1, characterized in that, The sensor group includes: Temperature sensors are installed at the alkaline outlet of the gas processing unit and inside the solar thermal storage tank of the solar thermal collector and storage unit. A light intensity sensor is installed on the surface of the solar thermal collector of the solar thermal collector and storage unit. The operating condition sensor is connected to the electrolyzer of the alkaline water electrolysis hydrogen production unit and is used to collect the start and stop signals of the electrolyzer. An ambient temperature sensor is installed in the external environment of the system. The measurement errors of the temperature sensor and the light intensity sensor are calculated as follows: ， ， in, This represents the temperature measurement error (°C). The sensor measures the temperature (°C). This is the actual temperature value (°C). The measurement error for daylight intensity is (W / m²). The sensor measures the light intensity (W / m²). This represents the true value of daylight intensity (W / m²).

3. The system according to claim 1, characterized in that, The input terminal of the fuzzy controller is connected to the output terminal of the sensor group, and its output terminal is connected to the input terminal of the PID controller. The fuzzy controller uses the centroid method for defuzzification calculation, and the calculation formula is as follows: Where △K is the PID parameter correction amount after defuzzification. Let be the membership degree of the i-th fuzzy rule. This is the output value of the i-th fuzzy rule.

4. The system according to claim 1, characterized in that, The input terminal of the PID controller is connected to the output terminal of the fuzzy controller, and its output terminal is connected to the control terminals of the heat collection loop circulating pump, the heat preservation loop circulating pump, the alkaline water cooler, and the alkaline distributor, respectively. The core adjustment formula of the PID controller is as follows: Among them, K p K is a proportionality coefficient, with a value ranging from 1.5 to 2.

5. i K is the integral coefficient, with a value ranging from 0.05 to 0.

15. d is the differential coefficient, with a value range of 0.3-0.7; u(k) is the controller output at the current time; k is the sampling sequence; e(k) is the error at the current time; e(k-1) is the error at the previous time. The sampling period is This is the cumulative sum of errors.

5. The system according to claim 1, characterized in that, The solar thermal collector and storage unit includes a solar thermal collector, a solar thermal storage tank, and a collector loop circulation pump; the control terminal of the collector loop circulation pump is connected to the output terminal of the PID controller. The fuzzy controller determines the start / stop status of the heat collection circuit and the initial frequency of the heat collection circuit circulation pump based on the light signal collected by the light intensity sensor and the deviation e and the rate of change of deviation e between the current temperature and the target temperature collected by the temperature sensor inside the solar thermal storage tank. The PID controller uses the output of the fuzzy controller as the initial parameter and fine-tunes the rotation speed of the heat collection loop circulation pump to stabilize the temperature of the solar thermal storage tank at the target temperature determined according to preset conditions.

6. The system according to claim 1, characterized in that, The insulation loop unit includes an insulation loop circulation pump and a serpentine heat exchange coil; the control terminal of the insulation loop circulation pump is connected to the output terminal of the PID controller; the outlet of the insulation loop circulation pump is connected to the inlet of the serpentine heat exchange coil through a pipe, and the serpentine heat exchange coil is installed inside the gas processing unit. The fuzzy controller determines the start / stop status of the insulation circuit and the heat output level based on the start / stop status of the electrolytic cell collected by the operating condition sensor and the deviation e and the rate of change of deviation ec of the alkali temperature and the insulation threshold collected by the alkali temperature sensor at the alkali outlet. The PID controller controls the heat transfer rate of the serpentine heat exchange coil by fine-tuning the frequency of the circulating pump in the insulation loop according to the heat output level, thereby stabilizing the alkali solution temperature at the target insulation temperature.

7. The system according to claim 1, characterized in that, The alkaline solution circulation unit includes an alkaline solution water cooler and an alkaline solution distributor; the control terminals of the alkaline solution water cooler and the alkaline solution distributor are respectively connected to the output terminal of the PID controller; the outlet of the alkaline solution distributor is connected to the inlet of the alkaline water electrolysis hydrogen production unit through a pipeline. The PID controller targets a preset operating temperature range and controls the supply temperature of each electrolytic cell by adjusting the cooling intensity of the alkali water cooler and the flow rate of the alkali distributor. The fuzzy controller dynamically corrects the parameters of the PID controller when the electrolytic cell load fluctuates or the ambient temperature changes abruptly.

8. The system according to claim 1, characterized in that, When the system switches between hydrogen production mode and shutdown and heat preservation mode, the fuzzy controller sends a switching signal to the PID controller through its output terminal to control the switching between the alkali circulation loop and the heat preservation loop; the parameters of the PID controller are adaptively adjusted according to the loop switching, and the response rate of the PID of the heat preservation loop is lower than that of the PID of the alkali circulation loop.

9. The system according to claim 1, characterized in that, During the transition between hydrogen production mode and shutdown and heat preservation mode, after receiving the start-up and shutdown signal of the electrolyzer, the fuzzy controller sends a switching signal to the PID controller through its output terminal to complete the control mode switch; the PID controller simultaneously performs parameter self-tuning to ensure that the alkali temperature fluctuation during the transition is within the preset range.

10. The system according to claim 1, wherein the control objective of the fuzzy-PID composite control module is: when the electrolytic cell is running, the alkaline solution temperature is stable at 75-95℃; when the electrolytic cell is shut down, the alkaline solution temperature is not lower than 50℃; the temperature of the solar thermal storage tank is maintained at 25-100℃; and the priority of heat distribution is: shutdown heat preservation takes priority over heat balance during operation, and heat balance during operation takes priority over surplus heat storage.

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