A method for self-adapting control of opening degree of electronic expansion valve in vapor compression refrigeration system

Abstract

The application provides a vapor compression refrigeration system electronic expansion valve opening degree adaptive control method, and belongs to the technical field of intelligent control of refrigeration air conditioners. The method pre-establishes a liquid supply dynamic characteristic matching model including double heavy time delay characteristics of an electronic expansion valve offline, completes state space working condition partition with evaporation pressure, condensation pressure, compressor power level and pressure difference before and after the valve as partition dimensions, and pre-optimizes and stores an optimal control rule library as a lookup table. During online operation, multi-cycle liquid supply demand prediction is performed, with minimization of the deviation between liquid supply and evaporation as the target, offline pre-optimization and online lookup table interpolation strategy are used to solve the optimal opening degree instruction, and the model parameters are adaptively corrected with physical constraints. The application compresses the time consumption of single cycle calculation to less than 5 ms, controls the liquid supply matching deviation to be within plus or minus 2%, reduces the valve action frequency by more than 62%, and can be widely applied to liquid supply optimization control of refrigeration systems of various types of full-liquid or falling film evaporators.

Description

Technical Field

[0001] This invention relates to the field of positive displacement compressor technology, and more particularly to an adaptive control method for the opening degree of an electronic expansion valve in a vapor compression refrigeration system. Background Technology

[0002] Vapor compression refrigeration systems (such as water chillers, heat pump units, and cold storage refrigeration units) are core equipment in industrial refrigeration, commercial building air conditioning, data center cooling, pharmaceutical cold chain, and food freezing. These systems account for 30%-50% of a building's total energy consumption, and their operating efficiency directly impacts the overall energy consumption level.

[0003] In flooded or falling film refrigeration systems, the opening control of the electronic expansion valve is crucial for ensuring a precise match between the evaporator's liquid supply and refrigeration demand. If the electronic expansion valve opens too wide, liquid will be carried into the evaporator outlet, increasing the risk of liquid slugging in the compressor. If the opening is too narrow, insufficient liquid level in the evaporator will lead to excessively low evaporation temperature, low suction pressure alarms, and shutdowns, while also reducing heat exchange efficiency and impacting cooling capacity and energy efficiency. Therefore, precise control of the electronic expansion valve is essential for system stability and energy efficiency.

[0004] Existing electronic expansion valve control technologies mainly include the following schemes: First, the traditional closed-loop control scheme using suction superheat as feedback signal; however, in flooded / falling film systems, the superheat of the gas inside the evaporator is close to 0℃, resulting in poor control accuracy. Second, the scheme using exhaust superheat as feedback; however, its effective control range is less than 1℃ and it easily causes compressor reliability problems. Third, the direct detection scheme based on liquid level sensors; however, the liquid level detection error is large and the cost is high. Fourth, the calculation method for valve opening; this method calculates the valve opening statically and cannot solve the system time delay problem. Fifth, the scheme based on polynomial fitting of suction and exhaust pressure difference; however, its static characteristics cannot adapt to long-term performance changes of the unit. Sixth, the zone lookup table control scheme; however, it does not consider the large time delay characteristics of the electronic expansion valve, which easily leads to abrupt changes in opening. Seventh, the general model predictive control (MPC) scheme; however, due to the strong nonlinearity and complex dynamic characteristics of the refrigeration system, its computing power requirement is too high, making it difficult to implement on industrial PLCs. None of these existing technologies can simultaneously solve the core problems of variable operating condition liquid supply matching, time delay compensation, and long-term adaptation under the computing power limitations of industrial PLCs. Faced with the limitations of computing power in industrial PLCs, the conventional approach in this field is to simplify the prediction model or reduce the control update frequency, rather than to fully realize multi-cycle liquid supply demand prediction and constrained optimization on a low-computing-power platform by combining offline working condition partitioning pre-optimization with online table lookup interpolation.

[0005] In summary, existing technologies cannot simultaneously solve the core industry challenges of precise liquid supply matching under varying operating conditions, dual large time delay compensation, mechanical wear suppression, and long-term adaptive operation while meeting the real-time computing power constraints of industrial PLCs. There is an urgent need for an adaptive control scheme for the opening of electronic expansion valves in vapor compression refrigeration systems that is suitable for industrial scenarios and balances the accuracy of liquid supply with the lifespan of equipment. Summary of the Invention

[0006] To address the persistent industry problems of oscillations in electronic expansion valve opening and significant fluctuations in evaporator liquid level caused by insufficient sensitivity of traditional superheat feedback control signals, and to resolve the phase lag issue in liquid supply control resulting from the superposition of mechanical inertia lag in the electronic expansion valve actuator and refrigerant transmission delay, this approach aims to achieve real-time and accurate matching of evaporator liquid supply and evaporation under all varying operating conditions. It overcomes the inherent accuracy bottleneck of existing solutions that only consider the single dimension of suction and discharge pressure difference, and addresses the technical limitation of not incorporating the coupling characteristics of multiple factors such as pipeline pressure drop loss, oil separation efficiency, evaporator outlet superheat, and refrigerant charge into the model. It also overcomes the computational bottleneck of general intelligent control methods, which suffer from excessive computational load and cannot be implemented in real-time on mainstream industrial PLCs, enabling real-time liquid supply control under low computational requirements. Furthermore, it balances liquid supply accuracy with equipment mechanical lifespan, significantly reducing the frequency of electronic expansion valve operation while minimizing evaporator liquid level fluctuations. Finally, it addresses performance drift issues caused by long-term unit operation, such as heat exchanger scaling, oil separation efficiency degradation, and minor refrigerant leakage, by achieving adaptive model correction. This invention provides an adaptive control method for the opening degree of an electronic expansion valve in a vapor compression refrigeration system.

[0007] The technical means employed in this invention are as follows: An adaptive control method for the opening degree of an electronic expansion valve in a vapor compression refrigeration system includes: S0. A dynamic characteristic matching model for the liquid supply regulation mechanism of the electronic expansion valve is pre-established offline. The optimal control rule base for the state space operating condition partitioning, with four rapidly changing measurable variables (evaporation pressure, condensation pressure, compressor energy level, and pressure difference across the electronic expansion valve) as the core partition dimensions, is pre-optimized. The control rule base is stored as a lookup table. The dynamic characteristic matching model for the liquid supply is then incorporated into the mechanical inertia hysteresis characteristics, refrigerant transport delay characteristics, evaporator phase change thermal inertia characteristics, strong nonlinear characteristics of the electronic expansion valve opening-flow rate, multi-factor coupling characteristics, and execution dead zone nonlinear characteristics of the electronic expansion valve actuator. A feedforward compensation amount is preset for the execution dead zone nonlinear characteristics. The mechanical inertia hysteresis time and refrigerant transport delay time are time-varying parameters calculated in real-time based on the current operating conditions. S1. At the beginning of each control cycle, collect the operating status parameters of the vapor compression refrigeration system; S2. Based on the established liquid supply dynamic characteristic matching model, multi-cycle liquid supply demand prediction is performed in the current control cycle. The time span of the liquid supply demand prediction cycle is dynamically adjusted according to the real-time calculated refrigerant transmission delay, and is not less than the sum of mechanical inertia lag time and refrigerant transmission delay time, fully covering the entire dynamic response process of the adjustment action, and calculating the balance trajectory of liquid supply and evaporation in multiple future control cycles. S3. With minimizing the deviation between evaporator liquid supply and evaporation rate as the optimization objective, and the evaporator liquid level safety range predicted by the liquid supply dynamic characteristic matching model as the constraint, a strategy combining offline operating condition partitioning pre-optimization and online table lookup interpolation is employed to solve the constrained optimization problem in real time. This yields the optimal electronic expansion valve opening adjustment command and sends it to the electronic expansion valve actuator. The offline operating condition partitioning uses only four rapidly changing measurable variables—evaporation pressure, condensation pressure, compressor energy level, and pressure difference across the electronic expansion valve—as the core partitioning dimensions; slowly changing parameters are not included in the fixed partitioning. The number of offline operating condition partitions is an integer power of 2, adapting to the PLC's binary fast addressing logic. Within the overlapping buffer of adjacent state space partitions, a convex combination interpolation method based on normalized distance is used to smoothly switch control rules, avoiding abrupt changes in the opening command. The optimization objective includes a smoothness constraint on the electronic expansion valve adjustment action, with the weight of the smoothness constraint dynamically adjusted according to the operating conditions. S4. An adaptive algorithm with hard constraints of physical upper and lower bounds is adopted. Based on the deviation between the measured operating parameters and the calculated values ​​of the liquid supply dynamic characteristic matching model, the key parameters of the liquid supply dynamic characteristic matching model are adaptively corrected at a preset period. Among them, the adaptive algorithm is used to update the slowly changing parameters of the model online, and the parameters are always kept within the preset physical upper and lower bounds during the parameter update process to prevent parameter drift. The adaptive parameter update does not change the core dimension and partition boundary of the offline working condition partition, and the slowly changing parameters never participate in the fixed partition division. S5. After the parameters of the liquid supply dynamic characteristic matching model are corrected, the next control cycle begins, and steps S1 to S4 are repeated.

[0008] Further, step S0 includes: S01. Establish an offline matching model for the liquid supply dynamic characteristics of the electronic expansion valve's liquid supply regulation mechanism. The state vector of the liquid supply dynamic characteristics matching model is:

[0009] in, Indicates evaporation pressure, unit The data is collected by an evaporation pressure sensor. Indicates condensation pressure, unit The data is collected by a condensation pressure sensor. This indicates the compressor energy level; it is dimensionless and ranges from 0.25 to 1.00. This represents the relative opening of the electronic expansion valve, dimensionless, with a value range of 0-1, determined by the actual number of steps. The total number of steps for the entire journey is obtained by normalization. This represents the pipeline pressure drop loss coefficient, which is dimensionless, with an initial value of 1.0 and a reasonable range of 0.5-1.5. This represents the oil separation efficiency decay coefficient, which is dimensionless, with an initial value of 0.85 and a reasonable range of 0.6-0.95. This represents the refrigerant charge correction factor, which is dimensionless, with an initial value of 1.0 and a reasonable range of 0.85-1.15. This represents the flow characteristic coefficient of the electronic expansion valve. It is dimensionless, with an initial value of 1.0 and a reasonable range of 0.7-1.3. It is estimated online through soft sensing. Represents the time constant of the phase change heat inertia of the evaporator, in units of The initial value is 15.0, and the reasonable range is 8-30, which is estimated online through soft measurement. S02. The dynamic characteristic matching model for the liquid supply incorporates the mechanical inertia hysteresis characteristics of the electronic expansion valve actuator, the refrigerant transport delay characteristics, the evaporator phase change thermal inertia characteristics, the strong nonlinear characteristics of the electronic expansion valve opening-flow rate, the multi-factor coupling characteristics, and the nonlinear characteristics of the execution dead zone, among which: The mechanical inertial hysteresis characteristic is a first-order inertial characteristic exhibited by the electronic expansion valve stepper motor drive system, with the transfer function being: This corresponds to a mechanical response lag time of 350ms, where, Indicates the time lag of the implementing agency; The refrigerant transport delay characteristic is calculated based on the real-time flow rate, using the following formula: ,in, Indicates the length of the liquid supply line. , This represents the cross-sectional area of ​​the pipe. This indicates the real-time volumetric flow rate of the electronic expansion valve; 200ms at full load and 500ms at 25% low load. The phase change thermal inertia characteristic of the evaporator is the phase change thermal inertia time constant of the evaporator. ; The nonlinear characteristic of the electronic expansion valve's opening-flow rate is constructed by building a flow calculation submodule using opening-flow rate calibration curves under different pressure differentials, strictly distinguishing between critical and subcritical flow conditions, wherein: The formula for calculating the flow rate under subcritical flow conditions is:

[0010] in, express The relative opening of the electronic expansion valve at any given time; This indicates the density of the liquid refrigerant; Indicates the pressure before the valve; Indicates the pressure after the valve; The formula for calculating the flow rate under critical flow conditions is:

[0011] The multi-factor coupling characteristic is that it simultaneously incorporates the pipeline pressure drop loss coefficient. Oil efficiency attenuation coefficient Refrigerant charge correction factor The coupled effect on the liquid supply characteristics; The nonlinear characteristic of the execution dead zone is generated by the static friction force of the electronic expansion valve and stepper motor. The measured control dead zone is... 3 steps, feedforward compensation amount is ,in To compensate for the opening command, This is the original opening command. This represents the current actual opening degree. For the sign function, the coefficient 3 in the compensation quantity corresponds to... The control dead zone is controlled in 3 steps; among them, the compensated opening command is subjected to 0-3812 steps of hard constraint limiting, and then... The output is limited to a rate of 50 steps / cycle and then normalized to a relative opening for flow calculation. S03, based on evaporation pressure Condensing pressure Compressor energy level Differential pressure across the electronic expansion valve Four rapidly changing measurable variables are the core dimensions of the partitioning, and each dimension is evenly divided into 4 intervals, for a total of [number] partitions. =256; Evaporator outlet superheat Exhaust superheat Current opening degree of electronic expansion valve As a state feedback variable, it is included in the calculation of the liquid supply dynamic characteristic matching model, but does not participate in the fixed zone division; pipeline pressure drop loss coefficient Oil efficiency attenuation coefficient Refrigerant charge correction factor Flow characteristic coefficient of electronic expansion valve Evaporator phase change thermal inertia time constant As a slowly changing parameter, it is updated in real time through adaptive correction and does not participate in fixed partitioning; the selection criteria for the 256 partitions are: complete table lookup and positioning within 5ms, adapting to mainstream PLCs; fully covering 25%-100% full load conditions, with fluctuations in operating conditions within the partition having less than 1% impact on the control effect; and the integer powers of 2 are adapted to the PLC's binary fast addressing logic. S04. For each partition obtained in step S03, perform offline pre-optimization, with the goal of minimizing the deviation between the evaporator liquid supply and the evaporation rate, solve for the optimal control rules corresponding to each partition, construct a pre-optimization rule base, and store the pre-optimization rule base as a lookup table.

[0012] Further, step S1 includes: S11. At the beginning of each control cycle, the operating status parameters of the vapor compression refrigeration system are collected from the sensing unit, including the evaporation pressure. Condensing pressure Compressor energy level Evaporator outlet temperature, evaporator outlet pressure, exhaust temperature, exhaust pressure, and actual opening of the electronic expansion valve. and evaporator liquid level ; S12. Perform signal preprocessing on the collected operating status parameters, including filtering and noise reduction, outlier removal, and unit conversion, to obtain the preprocessed operating status parameters. S13. Calculate the evaporator outlet superheat based on the saturation temperature corresponding to the pretreated evaporator outlet temperature and evaporation pressure. The exhaust superheat is calculated based on the saturation temperature corresponding to the pretreated exhaust temperature and condensation pressure. ; S14. Verify the validity of the preprocessed operating status parameters. If any key parameter exceeds the preset physical reasonable range or the sensor signal is abnormal, trigger the fault tolerance mechanism. S15. Assemble the valid operating state parameters into a state vector and output it to the liquid supply dynamic characteristic matching model.

[0013] Further, step S2 includes: S21. Calculate the refrigerant transmission delay time in real time based on the current real-time flow rate of the electronic expansion valve and the inner diameter of the liquid supply pipeline, and dynamically adjust the time span of the multi-cycle liquid supply demand prediction accordingly. The lower limit of the time span shall not be less than the sum of the full stroke response time of the electronic expansion valve and the refrigerant transmission delay time, and the upper limit shall not be greater than 1 / 10 of the evaporator phase change thermal inertia time constant. S22. Using the rolling time-domain prediction method, based on the current state vector, the state vector sequence and liquid supply output trajectory for multiple future control cycles are calculated iteratively through the state equation. The state equation is as follows:

[0014] in, Indicates the number of iterations. , To predict the time domain; S23. The change trend of phase change heat transfer thermal inertia of the evaporator is predicted in real time by the disturbance observer. The disturbance observer takes the evaporator outlet water temperature and compressor suction mass flow rate as inputs and the total disturbance formed by the change of evaporator thermal inertia, pipeline pressure drop and oil separation efficiency as output. S24. Compensate the total disturbance output by the disturbance observer to the liquid supply dynamic characteristic matching model, correct the liquid supply output trajectory, and obtain the compensated liquid supply and evaporation balance trajectory.

[0015] Further, step S3 includes: S31. Construct an optimization objective function with minimizing the deviation between the evaporator liquid supply and evaporation rate as the core term and the smoothness constraint of the electronic expansion valve adjustment action as an additional term. The formula is as follows:

[0016] in, This indicates the optimization of the objective function value; This indicates the real-time liquid supply mass flow rate of the electronic expansion valve; This indicates the real-time evaporation mass flow rate of the evaporator; This represents the penalty coefficient for changes in opening degree. This represents the opening acceleration penalty coefficient. This indicates the change in opening degree. Indicates the opening acceleration. ; S32. Transform the absolute value term in the objective function into a standard quadratic programming solution-friendly linear inequality constraint by introducing auxiliary slack variables: Introduction Transform into and Two linear inequality constraints; Introduction Transform into and Two linear inequality constraints; S33. Based on linear inequality constraints, the optimization objective is transformed into... ; S34. Set constraints, including hard constraints and soft constraints. The hard constraint is the safe range of the evaporator liquid level. Electronic expansion valve opening range Step and rate of change of opening Step / cycle; soft constraint is exhaust superheat ; S35. Read the current state vector through the PLC and control the partition fast addressing module to use boundary inequalities. Dimensionally determined, the current operating condition is located within two instruction cycles, belonging to the corresponding partition. Indicates the first The boundary coefficient matrix of each partition. Indicates the first The boundary threshold vector of each partition; S36. Look up the control rules in the table within the partition and use the normalized distance convex combination interpolation method in the overlapping buffer of adjacent partitions to obtain the electronic expansion valve opening command to achieve smooth switching; the interpolation time is controlled within 2.5ms. S37. The opening command of the electronic expansion valve is executed sequentially. After 50 steps / cycle rate limiting, dead zone feedforward compensation, and hard constraint limiting of opening range, the output is sent to the electronic expansion valve actuator, and the total single cycle time is controlled within 4.5ms. S38. When the liquid level of the evaporator predicted by the liquid supply dynamic characteristic matching model exceeds 80% of the safe range, the opening command is automatically triggered for limiting and feedforward correction.

[0017] Furthermore, step S36 also includes: determining the liquid level fluctuation condition and dynamically adjusting the penalty coefficient for the change in opening based on the liquid level fluctuation condition result. To achieve a smooth transition, specifically including: S361, at rated liquid level Based on this, the liquid level fluctuation condition is determined: when the condition is met for three consecutive control cycles... When the liquid level fluctuates drastically, it is determined to be a condition of violent liquid level fluctuation; when the conditions are met for 10 consecutive control cycles... When this condition is reached, it is determined to be a steady-state condition; S362. Under conditions of severe liquid level fluctuations, reduce the penalty coefficient for changes in opening degree. To improve response speed; under steady-state conditions, increase the penalty coefficient for changes in opening degree. To reduce stepper motor wear; the formula for adjusting the penalty coefficient for opening change is:

[0018] in, This represents the steady-state baseline penalty coefficient, with a value of 0.8. This represents the adjustment factor, with a value of 0.12. The value range is locked within [0.2, 0.8]. S363, A first-order inertial filter with a time constant of 5 control cycles is used to apply a penalty coefficient to the opening change. Perform a smooth switch to avoid frequent jumps in the penalty coefficient.

[0019] Further, step S4 includes: S41. Using a recursive least squares method with a forgetting factor, the key parameters of the fluid supply dynamic characteristic matching model are updated online at a preset period, including the pipeline pressure drop loss coefficient. Oil efficiency attenuation coefficient Refrigerant charge correction factor Flow characteristic coefficient of electronic expansion valve and the evaporator phase change thermal inertia time constant The preset period is 50 control periods, and the forgetting factor... The value is 0.98; S42. Set physical upper and lower bounds for each parameter to be updated, including the pipeline pressure drop loss coefficient. The value range is [0.5, 1.5], and the oil separation efficiency decay coefficient is... The value range is [0.6, 0.95], which is the refrigerant charge correction factor. The value range is [0.85, 1.15], which is the flow characteristic coefficient of the electronic expansion valve. The value range is [0.7, 1.3], which is the evaporator phase change thermal inertia time constant. The value range is [8, 30]; during the parameter update process, it is always kept within the set physical upper and lower bounds to prevent parameter drift. S43. To check whether the unit is in steady-state operation, if the load change rate is less than 2% for 10 consecutive control cycles, pause parameter updates and maintain the current parameter estimates; the formula for calculating the load change rate is:

[0020] in, Indicates the compressor energy level in the current cycle. This represents the average energy level over the first 10 control cycles; S44. Online estimation of key parameters is achieved through soft sensing. The soft sensing uses an outlet water temperature prediction model, as shown in the following formula:

[0021] in, This indicates the predicted outlet water temperature for the current cycle; This indicates the measured outlet water temperature in the previous cycle; This indicates the liquid supply mass flow rate in the previous cycle; This indicates the evaporation mass flow rate in the previous cycle; Indicates the control cycle; This indicates the specific heat capacity of chilled water at constant pressure. Indicates the density of chilled water; Indicates the chilled water circulation flow rate; Indicates the heat exchange efficiency of the evaporator; S45. Based on the outlet water temperature prediction model, a sensitivity matrix is ​​constructed. Using the residual between the measured outlet water temperature and the value calculated by the outlet water temperature prediction model as input, the flow characteristic coefficient of the electronic expansion valve is recursively estimated using the recursive least squares method. With respect to the phase change thermal inertia time constant of the evaporator The optimal parameter values ​​are found; the formula for the sensitivity matrix is ​​as follows:

[0022] S46. Within each control cycle, a progressive three-level fault tolerance protection strategy is executed synchronously, specifically including: The first level of fault tolerance is when a single point of sensor failure occurs. Through cross-verification with dual sensors, if the deviation is greater than 5% for three consecutive control cycles, the fault signal is cut off and the system is switched to a valid sensor signal to continue operation. The Level 2 fault tolerance mode switches to a fixed opening protection mode when all sensors fail or the optimization is abnormal. The fixed opening is 80% of the rated operating condition design opening, maintaining the effective output of the previous cycle and triggering an alarm. Level 3 protection is triggered when the evaporator liquid level exceeds the limit. 50mm or less When the temperature reaches 30mm, the safety shutdown protection will be triggered immediately.

[0023] Furthermore, in step S21, the prediction time domain of the multi-cycle liquid supply demand prediction... The value range is 10-30 control cycles, in the control time domain. One control cycle.

[0024] Furthermore, in step S38, the threshold for determining that the evaporator liquid level exceeds the safe range by 80% as predicted by the liquid supply dynamic characteristic matching model is below 120mm or above 280mm; when the determination is triggered, the limiting and feedforward correction of the opening command are automatically executed, wherein the limiting is to restrict the opening command within the safe opening range, and the feedforward correction is to increase or decrease the opening adjustment amount according to the direction of the liquid level deviation in order to quickly return to the safe range.

[0025] Furthermore, the method is applicable to vapor compression refrigeration systems that employ flooded or falling film evaporators, including but not limited to water chillers, water source heat pump units, air-cooled water chillers, cold storage refrigeration units, and ice storage units.

[0026] Compared with the prior art, the present invention has the following advantages: 1. The present invention provides an adaptive control method for the opening degree of an electronic expansion valve in a vapor compression refrigeration system. By combining offline working condition partition pre-optimization with online table lookup interpolation, the low computing power optimization strategy reduces the online calculation time per cycle from more than 100ms in the existing calculation method to less than 5ms. This achieves computing power adaptation to mainstream PLCs in industrial sites and enables industrial-grade real-time control without the need for high-end hardware investment.

[0027] 2. The present invention provides an adaptive control method for the opening degree of an electronic expansion valve in a vapor compression refrigeration system. By pre-establishing an offline matching model of the liquid supply dynamic characteristics, incorporating the mechanical inertia hysteresis characteristics of the electronic expansion valve actuator, the refrigerant transmission delay characteristics, and the evaporator phase change thermal inertia characteristics, and performing multi-cycle liquid supply demand prediction covering the entire cycle of dual large time delays, the method solves the problems of control oscillation and accuracy inaccuracy under varying operating conditions in large time delay liquid supply control systems. It achieves the technical effects of reducing the liquid supply adjustment response time by 55% under load step conditions, eliminating any liquid level overshoot oscillation, and controlling the liquid supply quantity matching deviation within ±2% under all operating conditions.

[0028] 3. The present invention provides an adaptive control method for the opening of the electronic expansion valve in a vapor compression refrigeration system. By incorporating the multi-factor coupling characteristics of suction and discharge pressure difference, compressor energy level, pipeline pressure drop loss coefficient, oil separation efficiency decay coefficient, evaporator outlet superheat, and refrigerant charge correction coefficient into the dynamic characteristic matching model of liquid supply, this method replaces the single-factor static formula of the prior art. It fundamentally solves the inherent accuracy bottleneck of the prior art, which is "accurate under a certain operating condition but deviates under different operating conditions". It achieves full-condition liquid supply accuracy guarantee that the liquid supply matching deviation does not exceed ±3% under extreme variable operating conditions with multi-factor coupling.

[0029] 4. The present invention provides an adaptive control method for the opening degree of an electronic expansion valve in a vapor compression refrigeration system. By adding a dynamic weight adjustment constraint on the smoothness of the electronic expansion valve adjustment action to the optimization objective, the frequency of electronic expansion valve action is reduced by more than 62% under the same control accuracy, significantly reducing stepper motor wear and achieving the effect of extending the equipment mechanical life by more than double the equipment maintenance cycle.

[0030] 5. The present invention provides an adaptive control method for the opening degree of the electronic expansion valve of a vapor compression refrigeration system. Through an adaptive correction mechanism with physical upper and lower bound hard constraints, the slowly changing parameters of the model are updated online. This method automatically tracks the slowly changing performance parameters of the chiller unit during long-term operation, maintains the model prediction accuracy, and achieves improved operational stability throughout the entire life cycle without the need for manual on-site recalibration.

[0031] In summary, by applying the technical solution of this invention, the technical shortcomings of existing technologies are resolved: insufficient computing power of industrial PLCs leading to the inability to solve complex optimization problems in real time; phase lag and oscillation in liquid supply control caused by dual large time delay systems; inaccurate liquid supply accuracy under varying operating conditions due to single-factor static models; severe wear of stepper motors caused by frequent operation of electronic expansion valves; and inability of fixed empirical parameters to adapt to long-term performance drift of the unit. This invention solves the aforementioned problems in existing technologies through a low-computing-power strategy of offline pre-optimization and online table lookup, a dedicated dynamic model incorporating dual large time delay characteristics and full-cycle liquid supply prediction, a multi-factor coupled dynamic model, dynamic weight smoothness constraints, and an adaptive correction mechanism with physical hard constraints. Therefore, the technical solution of this invention solves the problems of liquid supply mismatch under varying operating conditions, phase lag in large time delay systems, insufficient PLC computing power, severe mechanical wear of equipment, and long-term performance drift in existing technologies.

[0032] Based on the above reasons, this invention can be widely applied in fields such as industrial refrigeration, commercial building air conditioning, data center cooling, pharmaceutical cold chain, and food freezing. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is a flowchart illustrating an adaptive control method for the opening degree of an electronic expansion valve in a vapor compression refrigeration system provided by the present invention.

[0035] Figure 2 The measured step response curve of the hysteresis characteristic of the electronic expansion valve actuator is shown.

[0036] Figure 3 This is a comparison curve of the load step response of the present invention and a comparative example. Detailed Implementation

[0037] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0038] It should be noted that the terms "comprising" and "having" and any variations thereof in the specification, claims and accompanying drawings of this invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such process, method, product or device.

[0039] The application object of this embodiment is a commercial air conditioning falling film screw chiller unit, which uses R134a refrigerant, has a rated cooling capacity of 880kW, a semi-hermetic screw compressor with a rated power of 180kW, and is equipped with a certain brand of ETS-400 electronic expansion valve, which has 3812 steps from fully closed to fully open. The evaporator is a falling film evaporator and the condenser is a shell and tube condenser.

[0040] The hardware configuration is as follows: the control system uses a Siemens S7-1516 PLC, with a control cycle set to 50ms (reasonable range 10ms-100ms); a high-precision evaporation pressure sensor is also included (range 0-1.0MPa, accuracy...). 0.5%FS), condensation pressure sensor (range 0-2.5MPa, accuracy 0.5%) 0.5%FS), exhaust temperature sensor (range -40°C to 200°C, accuracy 0.5%) 0.5°C), evaporator outlet temperature sensor (range -40°C) 100°C, precision 0.5°C), electronic expansion valve opening feedback module (stepper motor driver with built-in position feedback, accuracy) Step 1), Evaporator level sensor (range 0-500mm, accuracy) 2%FS (used for verification, not control).

[0041] Based on the aforementioned application devices and hardware configurations, this invention provides an adaptive control method for the opening degree of an electronic expansion valve in a vapor compression refrigeration system, such as... Figure 1 As shown, it includes: S0. A dynamic characteristic matching model for the liquid supply regulation mechanism of the electronic expansion valve is pre-established offline. The optimal control rule base for the state space operating condition partitioning, with four rapidly changing measurable variables (evaporation pressure, condensation pressure, compressor energy level, and pressure difference across the electronic expansion valve) as the core partition dimensions, is pre-optimized. The control rule base is stored as a lookup table. The dynamic characteristic matching model for the liquid supply is then incorporated into the mechanical inertia hysteresis characteristics, refrigerant transport delay characteristics, evaporator phase change thermal inertia characteristics, strong nonlinear characteristics of the electronic expansion valve opening-flow rate, multi-factor coupling characteristics, and execution dead zone nonlinear characteristics of the electronic expansion valve actuator. A feedforward compensation amount is preset for the execution dead zone nonlinear characteristics. The mechanical inertia hysteresis time and refrigerant transport delay time are time-varying parameters calculated in real-time based on the current operating conditions. S1. At the beginning of each control cycle, collect the operating status parameters of the vapor compression refrigeration system; S2. Based on the established liquid supply dynamic characteristic matching model, multi-cycle liquid supply demand prediction is performed in the current control cycle. The time span of the liquid supply demand prediction cycle is dynamically adjusted according to the real-time calculated refrigerant transmission delay, and is not less than the sum of mechanical inertia lag time and refrigerant transmission delay time, fully covering the entire dynamic response process of the adjustment action, and calculating the balance trajectory of liquid supply and evaporation in multiple future control cycles. S3. With minimizing the deviation between evaporator liquid supply and evaporation rate as the optimization objective, and the evaporator liquid level safety range predicted by the liquid supply dynamic characteristic matching model as the constraint, a strategy combining offline operating condition partitioning pre-optimization and online table lookup interpolation is employed to solve the constrained optimization problem in real time. This yields the optimal electronic expansion valve opening adjustment command and sends it to the electronic expansion valve actuator. The offline operating condition partitioning uses only four rapidly changing measurable variables—evaporation pressure, condensation pressure, compressor energy level, and pressure difference across the electronic expansion valve—as the core partitioning dimensions; slowly changing parameters are not included in the fixed partitioning. The number of offline operating condition partitions is an integer power of 2, adapting to the PLC's binary fast addressing logic. Within the overlapping buffer of adjacent state space partitions, a convex combination interpolation method based on normalized distance is used to smoothly switch control rules, avoiding abrupt changes in the opening command. The optimization objective includes a smoothness constraint on the electronic expansion valve adjustment action, with the weight of the smoothness constraint dynamically adjusted according to the operating conditions. S4. An adaptive algorithm with hard constraints of physical upper and lower bounds is adopted. Based on the deviation between the measured operating parameters and the calculated values ​​of the liquid supply dynamic characteristic matching model, the key parameters of the liquid supply dynamic characteristic matching model are adaptively corrected at a preset period. Among them, the adaptive algorithm is used to update the slowly changing parameters of the model online, and the parameters are always kept within the preset physical upper and lower bounds during the parameter update process to prevent parameter drift. The adaptive parameter update does not change the core dimension and partition boundary of the offline working condition partition, and the slowly changing parameters never participate in the fixed partition division. S5. After the parameters of the liquid supply dynamic characteristic matching model are corrected, the next control cycle begins, and steps S1 to S4 are repeated.

[0042] In a specific implementation, as a preferred embodiment of the present invention, step S0 includes: S01. Establish an offline matching model for the liquid supply dynamic characteristics of the electronic expansion valve's liquid supply regulation mechanism. The state vector of the liquid supply dynamic characteristics matching model is:

[0043] in, Indicates evaporation pressure, unit The data is collected by an evaporation pressure sensor. Indicates condensation pressure, unit The data is collected by a condensation pressure sensor. This indicates the compressor energy level; it is dimensionless and ranges from 0.25 to 1.00. This represents the relative opening of the electronic expansion valve, dimensionless, with a value range of 0-1, determined by the actual number of steps. The total number of steps for the entire journey is obtained by normalization. This represents the pipeline pressure drop loss coefficient, which is dimensionless, with an initial value of 1.0 and a reasonable range of 0.5-1.5. This represents the oil separation efficiency decay coefficient, which is dimensionless, with an initial value of 0.85 and a reasonable range of 0.6-0.95. This represents the refrigerant charge correction factor, which is dimensionless, with an initial value of 1.0 and a reasonable range of 0.85-1.15. This represents the flow characteristic coefficient of the electronic expansion valve. It is dimensionless, with an initial value of 1.0 and a reasonable range of 0.7-1.3. It is estimated online through soft sensing. Represents the time constant of the phase change heat inertia of the evaporator, in units of The initial value is 15.0, and the reasonable range is 8-30, which is estimated online through soft measurement. In this embodiment, the general expression for the discrete state-space model is: ,in, It is a 9×9 state transition matrix. The input matrix is ​​9×1. After chiller unit operating condition identification and linearization, the state transition matrix is... The diagonal elements are the self-holding coefficients of each state variable, and the off-diagonal elements are the coupling influence coefficients between state variables. (Coefficients in the fourth row and fourth column) 0.85 corresponds to the first-order inertial hysteresis characteristic of the electronic expansion valve stepper motor drive system of 350ms, derived from the formula... The calculation yielded that, =50 To control the cycle, =350 The actuator lag time constant; the fourth row contains the coefficients. 0.15 is the influence coefficient of the electronic expansion valve opening adjustment command on the actual opening, obtained by identifying the correspondence between the measured position and flow rate of the actuator. (Ninth line) (Corresponding row) Diagonal coefficient =1.0, Input coefficient =0 indicates that the thermal inertia time constant is a slowly varying parameter and is not directly affected by the control input. Matrix and The remaining coefficients were identified by the least squares method based on the step response test data of the unit under rated operating conditions.

[0044] S02. The dynamic characteristic matching model for the liquid supply incorporates the mechanical inertia hysteresis characteristics of the electronic expansion valve actuator, the refrigerant transport delay characteristics, the evaporator phase change thermal inertia characteristics, the strong nonlinear characteristics of the electronic expansion valve opening-flow rate, the multi-factor coupling characteristics, and the nonlinear characteristics of the execution dead zone, among which: like Figure 2 As shown, the mechanical inertial hysteresis characteristic is the first-order inertial characteristic exhibited by the electronic expansion valve stepper motor drive system, and its transfer function is... This corresponds to a mechanical response lag time of 350ms, where, Indicates the time lag of the implementing agency; The refrigerant transport delay characteristic is calculated based on the real-time flow rate, using the following formula: ,in, Indicates the length of the liquid supply line. , This represents the cross-sectional area of ​​the pipe. This indicates the real-time volumetric flow rate of the electronic expansion valve; 200ms at full load and 500ms at 25% low load. The phase change thermal inertia characteristic of the evaporator is the phase change thermal inertia time constant of the evaporator. ; The nonlinear characteristic of the electronic expansion valve's opening-flow rate is constructed by building a flow calculation submodule using opening-flow rate calibration curves under different pressure differentials, strictly distinguishing between critical and subcritical flow conditions, wherein: The formula for calculating the flow rate under subcritical flow conditions is:

[0045] in, express The relative opening of the electronic expansion valve at any given time; This indicates the density of the liquid refrigerant; Indicates the pressure before the valve; Indicates the pressure after the valve; The formula for calculating the flow rate under critical flow conditions is:

[0046] The multi-factor coupling characteristic is that it simultaneously incorporates the pipeline pressure drop loss coefficient. Oil efficiency attenuation coefficient Refrigerant charge correction factor The coupled effect on the liquid supply characteristics; The nonlinear characteristic of the execution dead zone is generated by the static friction force of the electronic expansion valve and stepper motor. The measured control dead zone is... 3 steps, feedforward compensation amount is ,in To compensate for the opening command, This is the original opening command. This represents the current actual opening degree. For the sign function, the coefficient 3 in the compensation quantity corresponds to... The control dead zone is controlled in 3 steps; among them, the compensated opening command is subjected to 0-3812 steps of hard constraint limiting, and then... The output is limited to a rate of 50 steps / cycle and then normalized to a relative opening for flow calculation. S03, based on evaporation pressure Condensing pressure Compressor energy level Differential pressure across the electronic expansion valve Four rapidly changing measurable variables are the core dimensions of the partitioning, and each dimension is evenly divided into 4 intervals, for a total of [number] partitions. =256; Evaporator outlet superheat Exhaust superheat Current opening degree of electronic expansion valve As a state feedback variable, it is included in the calculation of the liquid supply dynamic characteristic matching model, but does not participate in the fixed zone division; pipeline pressure drop loss coefficient Oil efficiency attenuation coefficient Refrigerant charge correction factor Flow characteristic coefficient of electronic expansion valve Evaporator phase change thermal inertia time constant As a slowly changing parameter, it is updated in real time through adaptive correction and does not participate in fixed partitioning; the selection criteria for the 256 partitions are: complete table lookup and positioning within 5ms, adapting to mainstream PLCs; fully covering 25%-100% full load conditions, with fluctuations in operating conditions within the partition having less than 1% impact on the control effect; and the integer powers of 2 are adapted to the PLC's binary fast addressing logic. S04. For each partition obtained in step S03, perform offline pre-optimization, with the goal of minimizing the deviation between the evaporator liquid supply and the evaporation rate, solve for the optimal control rules corresponding to each partition, construct a pre-optimization rule base, and store the pre-optimization rule base as a lookup table.

[0047] In a specific implementation, as a preferred embodiment of the present invention, step S1 includes: S11. At the beginning of each 50ms control cycle, collect the operating status parameters of the vapor compression refrigeration system from the sensing unit, including evaporation pressure. Condensing pressure Compressor energy level Evaporator outlet temperature, evaporator outlet pressure, exhaust temperature, exhaust pressure, and actual opening of the electronic expansion valve. and evaporator liquid level ; S12. Perform signal preprocessing on the collected operating status parameters, including filtering and noise reduction, outlier removal, and unit conversion, to obtain the preprocessed operating status parameters. S13. Calculate the evaporator outlet superheat based on the saturation temperature corresponding to the pretreated evaporator outlet temperature and evaporation pressure. The exhaust superheat is calculated based on the saturation temperature corresponding to the pretreated exhaust temperature and condensation pressure. ; S14. Verify the validity of the preprocessed operating status parameters. If any key parameter exceeds the preset physical reasonable range or the sensor signal is abnormal, trigger the fault tolerance mechanism. S15. Assemble the valid operating state parameters into a state vector and output it to the liquid supply dynamic characteristic matching model.

[0048] In a specific implementation, as a preferred embodiment of the present invention, step S2 includes: S21. Calculate the refrigerant transmission delay time in real time based on the current real-time flow rate of the electronic expansion valve and the inner diameter of the liquid supply pipeline, and dynamically adjust the time span of the multi-cycle liquid supply demand prediction accordingly. The lower limit of the time span is not less than the sum of the full-stroke response time of the electronic expansion valve and the refrigerant transmission delay time, and the upper limit is not greater than 1 / 10 of the evaporator phase change thermal inertia time constant. In this embodiment, a liquid supply demand prediction cycle is set. =15 (corresponding to 750ms), fully covering the sum of the electronic expansion valve's full-stroke response time (350ms) and the refrigerant transfer delay under current operating conditions (200-500ms); S22. Using the rolling time-domain prediction method, based on the current state vector, the state vector sequence and liquid supply output trajectory for multiple future control cycles are calculated iteratively through the state equation. The state equation is as follows:

[0049] in, Indicates the number of iterations. , To predict the time domain; S23. The change trend of the thermal inertia of the evaporator phase change heat transfer is predicted in real time by a disturbance observer. The disturbance observer takes the evaporator outlet water temperature and the compressor suction mass flow rate as inputs, and the total disturbance formed by the change of evaporator thermal inertia, pipeline pressure drop, and oil separation efficiency as output. In this embodiment, the thermal inertia of the evaporator phase change heat transfer ( The disturbance (approximately 15s) is a slowly varying disturbance, and its changing trend is predicted in real time through a disturbance observer; the 750ms prediction period covers the rapid response process of the electronic expansion valve's mechanical dynamics and transmission delay; the slowly varying thermal inertia is tracked in real time through adaptive correction, and the combination of the two achieves dual large time delay full dynamic control.

[0050] S24. Compensate the total disturbance output by the disturbance observer to the liquid supply dynamic characteristic matching model, correct the liquid supply output trajectory, and obtain the compensated liquid supply and evaporation balance trajectory.

[0051] In a specific implementation, as a preferred embodiment of the present invention, step S3 includes: S31. Construct an optimization objective function with minimizing the deviation between the evaporator liquid supply and evaporation rate as the core term and the smoothness constraint of the electronic expansion valve adjustment action as an additional term. The formula is as follows:

[0052] in, This indicates the optimization of the objective function value; Indicates the real-time liquid supply mass flow rate of the electronic expansion valve ( ); This indicates the real-time evaporation mass flow rate of the evaporator ( () Directly take the compressor suction mass flow rate (Evaporator evaporation capacity = compressor suction capacity, which is a basic principle of mass conservation in refrigeration systems.) This represents the penalty coefficient for changes in opening degree. This represents the opening acceleration penalty coefficient. This indicates the change in opening degree. Indicates the opening acceleration. ; S32. Transform the absolute value term in the objective function into a standard quadratic programming solution-friendly linear inequality constraint by introducing auxiliary slack variables: Introduction Transform into and Two linear inequality constraints; Introduction Transform into and Two linear inequality constraints; S33. Based on linear inequality constraints, the optimization objective is transformed into... In this embodiment, the liquid supply volume is calculated in actual engineering practice. With evaporation The deviation is uniformly converted to the equivalent mass flow rate or equivalent cooling capacity for deviation calculation to ensure the physical consistency of the deviation term in the optimization objective function.

[0053] S34. Set constraints, including hard constraints and soft constraints. The hard constraint is the safe range of the evaporator liquid level. (Based on pre-calibration of evaporator structural parameters and rated operating conditions), electronic expansion valve opening range Step and rate of change of opening Step / cycle; soft constraint is exhaust superheat In this embodiment, a dimension consistency explanation is given: the control objective of this invention is to balance the refrigerant mass flow rate (supply liquid mass flow rate = evaporation mass flow rate), avoiding calculation deviations caused by mixing cooling capacity / volume flow rate; if it is necessary to convert to cooling capacity, it can be calculated through the supply liquid / evaporation enthalpy value, without affecting the core logic of the control scheme of this invention.

[0054] S35. Read the current state vector through the PLC and control the partition fast addressing module to use boundary inequalities. Dimensionally determined, the current operating condition is located within two instruction cycles, belonging to the corresponding partition. Indicates the first The boundary coefficient matrix of each partition. Indicates the first The boundary threshold vector of each partition; S36. Look up the control rules in the table within the partition and use the normalized distance convex combination interpolation method in the overlapping buffer of adjacent partitions to obtain the electronic expansion valve opening command to achieve smooth switching; the interpolation time is controlled within 2.5ms. S37. The opening command of the electronic expansion valve is executed sequentially. After 50 steps / cycle rate limiting, dead zone feedforward compensation, and hard constraint limiting of opening range, the output is sent to the electronic expansion valve actuator, and the total single cycle time is controlled within 4.5ms. S38. When the liquid level of the evaporator predicted by the liquid supply dynamic characteristic matching model exceeds 80% of the safe range, the opening command is automatically triggered for limiting and feedforward correction.

[0055] In a preferred embodiment of the present invention, step S36 further includes: determining the liquid level fluctuation condition and dynamically adjusting the penalty coefficient for the change in opening based on the liquid level fluctuation condition result. To achieve a smooth transition, specifically including: S361, at rated liquid level Based on this, the liquid level fluctuation condition is determined: when the condition is met for three consecutive control cycles... When the liquid level fluctuates drastically, it is determined to be a condition of violent liquid level fluctuation; when the conditions are met for 10 consecutive control cycles... When this condition is reached, it is determined to be a steady-state condition; S362. Under conditions of severe liquid level fluctuations, reduce the penalty coefficient for changes in opening degree. To improve response speed; under steady-state conditions, increase the penalty coefficient for changes in opening degree. To reduce stepper motor wear; the formula for adjusting the penalty coefficient for opening change is:

[0056] in, This represents the steady-state baseline penalty coefficient, with a value of 0.8. This represents the adjustment factor, with a value of 0.12. The value range is locked within [0.2, 0.8]. S363, A first-order inertial filter with a time constant of 5 control cycles is used to apply a penalty coefficient to the opening change. Perform a smooth switch to avoid frequent jumps in the penalty coefficient.

[0057] In a specific implementation, as a preferred embodiment of the present invention, step S4 includes: S41. Using a recursive least squares method with a forgetting factor, the key parameters of the fluid supply dynamic characteristic matching model are updated online at a preset period, including the pipeline pressure drop loss coefficient. Oil efficiency attenuation coefficient Refrigerant charge correction factor Flow characteristic coefficient of electronic expansion valve and the evaporator phase change thermal inertia time constant The preset period is 50 control periods, and the forgetting factor... The value is 0.98; S42. Set physical upper and lower bounds for each parameter to be updated, including the pipeline pressure drop loss coefficient. The value range is [0.5, 1.5], and the oil separation efficiency decay coefficient is... The value range is [0.6, 0.95], which is the refrigerant charge correction factor. The value range is [0.85, 1.15], which is the flow characteristic coefficient of the electronic expansion valve. The value range is [0.7, 1.3], which is the evaporator phase change thermal inertia time constant. The value range is [8, 30]; during the parameter update process, it is always kept within the set physical upper and lower bounds to prevent parameter drift. S43. To check whether the unit is in steady-state operation, if the load change rate is less than 2% for 10 consecutive control cycles, pause parameter updates and maintain the current parameter estimates; the formula for calculating the load change rate is:

[0058] in, Indicates the compressor energy level in the current cycle. This represents the average energy level over the first 10 control cycles; S44. Online estimation of key parameters is achieved through soft sensing. The soft sensing uses an outlet water temperature prediction model, as shown in the following formula:

[0059] in, This indicates the predicted outlet water temperature for the current cycle; This indicates the measured outlet water temperature in the previous cycle; This indicates the liquid supply mass flow rate in the previous cycle; This indicates the evaporation mass flow rate in the previous cycle; Indicates the control cycle (unit) ); This indicates the specific heat capacity of chilled water at constant pressure (unit: ). ); This indicates the density of chilled water (units). ); Indicates chilled water circulation flow rate (unit) ); This represents the heat exchange efficiency of the evaporator (dimensionless). S45. Based on the outlet water temperature prediction model, a sensitivity matrix is ​​constructed. Using the residual between the measured outlet water temperature and the value calculated by the outlet water temperature prediction model as input, the flow characteristic coefficient of the electronic expansion valve is recursively estimated using the recursive least squares method. With respect to the phase change thermal inertia time constant of the evaporator The optimal parameter values ​​are found; the formula for the sensitivity matrix is ​​as follows:

[0060] S46. Within each control cycle, a progressive three-level fault tolerance protection strategy is executed synchronously, specifically including: The first level of fault tolerance is when a single point of sensor failure occurs. Through cross-verification with dual sensors, if the deviation is greater than 5% for three consecutive control cycles, the fault signal is cut off and the system is switched to a valid sensor signal to continue operation. The Level 2 fault tolerance mode switches to a fixed opening protection mode when all sensors fail or the optimization is abnormal. The fixed opening is 80% of the rated operating condition design opening, maintaining the effective output of the previous cycle and triggering an alarm. Level 3 protection is triggered when the evaporator liquid level exceeds the limit. 50mm or less When the temperature reaches 30mm, the safety shutdown protection will be triggered immediately.

[0061] In a specific implementation, as a preferred embodiment of the present invention, in step S21, the prediction time domain of the multi-cycle liquid supply demand prediction... The value range is 10-30 control cycles, in the control time domain. One control cycle is defined. In this embodiment, a liquid supply demand prediction cycle is set. =15 (corresponding to 750ms), fully covering the sum of the electronic expansion valve's full-stroke response time (350ms) and the refrigerant transfer delay under current operating conditions (200-500ms).

[0062] In a specific implementation, as a preferred embodiment of the present invention, in step S38, the threshold for determining that the evaporator liquid level exceeds the safe range by 80% as predicted by the liquid supply dynamic characteristic matching model is below 120mm or above 280mm; when the determination is triggered, the limiting and feedforward correction of the opening command are automatically executed, wherein the limiting is to restrict the opening command within the safe opening range, and the feedforward correction is to add or subtract the opening adjustment amount according to the direction of the liquid level deviation to quickly return to the safe range.

[0063] In specific implementation, as a preferred embodiment of the present invention, the method is applicable to vapor compression refrigeration systems that use flooded or falling film evaporators, including but not limited to water chillers, water source heat pump units, air-cooled water chillers, cold storage refrigeration units, and ice storage units.

[0064] Example 1 (Control Effect) All test data in this embodiment are based on the standard operating conditions of GB / T 18430.1-2024, and were obtained through MATLAB / Simulink 2024a simulation verification and actual testing on a full-performance test bench for an 880kW falling film screw chiller: refrigerant R134a, rated cooling capacity 880kW, rated condensing temperature 40°C (cooling water inlet 32°C, outlet 37°C), rated evaporating temperature 5°C (chilled water inlet 12°C, outlet 7°C). All data are the average of three repeated tests. The frequency of electronic expansion valve operation is the average of 24 hours of continuous stable operation under rated conditions. The load step test is 50%. 100% load with no buffering instantaneous switching, sampling period 10ms. Test load range 25%-100% of rated load.

[0065] Under the condition of stepping from 50% load to 100% load, the liquid supply regulation response time is only 2.5s, with no liquid level overshoot; under the condition of 25% low load, the liquid supply matching deviation is controlled within ±1.8%, and the electronic expansion valve operates 42 times / hour; under the condition of 50% load, the matching deviation is ≤±1.6%, and the operating frequency is 35 times / hour; under the condition of 75% load, the matching deviation is ≤±1.5%, and the operating frequency is 30 times / hour; under the condition of full load, the electronic expansion valve operates only 22 times / hour, which is more than 62% lower than the simplified calculation method; the liquid supply matching deviation under all operating conditions is stably controlled within ±2%, and the matching deviation under rated operating conditions is ≤±1.5%, with no alarm phenomena of liquid carrying out of the outlet or low suction pressure.

[0066] Furthermore, compared with the simplified calculation method, the present invention achieves an overall energy saving rate of over 12%, compared with the applicant's prior patent application ZL201510358203.2, an overall energy saving rate of over 10%, and compared with traditional PID control, an overall energy saving rate of over 15%.

[0067] Example 2 (High-precision 2048 partitioning scheme) Based on the aforementioned adaptive control method for the opening of the electronic expansion valve in a vapor compression refrigeration system, two new partition dimensions—evaporator outlet superheat and exhaust superheat—are added, resulting in a total of 2048 partitions. This method is compatible with mid-to-high-end PLCs such as Siemens S7-1500, and the single-cycle calculation time is [not specified]. 4.8ms, fluid supply matching deviation under 25% low load condition 1.5%.

[0068] Example 3 (Combined Control Scheme for Variable Frequency Units) Based on the above-mentioned adaptive control method for the opening of the electronic expansion valve in a vapor compression refrigeration system, the control input is expanded to the opening of the electronic expansion valve and the compressor speed. The optimization target is supplemented by the speed smoothness constraint, which is adapted to the variable frequency drive falling film screw chiller unit. The valve action frequency is reduced by 65% ​​and the speed adjustment number is reduced by 40%.

[0069] Comparative example (effect verification without using the core solution of this invention) Comparative Example 1 (Traditional Simplified Method for Calculating Opening) Using the same hardware platform and operating conditions as the aforementioned adaptive control method for the electronic expansion valve opening in a vapor compression refrigeration system, the control scheme employs a simplified calculation method for the opening degree and was optimized through on-site debugging by senior engineers. 50% Under a 100% load step, the response time is approximately 6.5 seconds, indicating liquid level overshoot. 22mm; 25% Low load liquid supply deviation 8%, full-load valve actuation frequency 60 times / hour; when pipeline pressure drop increases by 15%, the deviation worsens to If the percentage is above 12%, manual recalibration is required.

[0070] Comparative Example 2 (Patent ZL201510358203.2 filed by the applicant: Expansion Valve Opening Control Scheme Based on Polynomial Fitting of Intake and Exhaust Pressure Difference) The hardware platform and operating conditions are exactly the same as the adaptive control method for the electronic expansion valve opening in the aforementioned vapor compression refrigeration system. The control scheme adopts the expansion valve opening control scheme based on polynomial fitting of the suction and discharge pressure difference, as described in the applicant's prior patent application ZL201510358203.2. 50% Under a 100% load step, the response time is approximately 6 seconds, resulting in liquid level overshoot. 18mm; 25% low-load liquid supply deviation 7%, full-load valve operation frequency 58 times / hour; when oil separator efficiency decreases by 20%, the deviation worsens to More than 10%.

[0071] Comparative Example 3 (Traditional Exhaust Superheat PID Control) Using the same hardware platform and operating conditions as the adaptive control method for the electronic expansion valve opening in the aforementioned vapor compression refrigeration system, PID control is performed with exhaust superheat as the feedback signal. Under a 50%→100% load step, the response time is approximately 12 seconds, and liquid level fluctuations... Above 35mm, two instances of air intake and liquid carryover occurred; under 25% low load, the valve opening continued to oscillate, the liquid level fluctuated significantly, and the control stability was extremely poor.

[0072] Comparative Example 4 (Online Real-Time Optimization Solution Without Offline Partition Lookup (Negative Comparative Example)) Using the same hardware platform, model, and prediction strategy as the above-mentioned adaptive control method for the opening of the electronic expansion valve in a vapor compression refrigeration system, only the core features of offline partition pre-optimization and online table lookup interpolation are removed, and the problem is solved online in real time as a quadratic programming problem.

[0073] Actual test results: Figure 3 As shown, the single-cycle calculation time on a Siemens S7-1516 PLC is approximately 120ms, far exceeding the 50ms control cycle requirement. Even with simplified optimization, the time remains no less than 80ms, failing to meet the real-time control requirements of industrial PLCs. Actual tests on Siemens S7-1214C PLC, Mitsubishi FX5U-32MT, and Q03UDV PLCs showed that the single-cycle online calculation time of this solution was 4.2ms, 3.8ms, and 3.2ms respectively, all consistently within 5ms, fully adapting to the computing power and control cycle requirements of mainstream PLCs in industrial settings.

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for adaptive control of the opening degree of an electronic expansion valve in a vapor compression refrigeration system, characterized in that, include: S0. A dynamic characteristic matching model for the liquid supply regulation mechanism of the electronic expansion valve is pre-established offline. The optimal control rule base for the state space operating condition partitioning, with four rapidly changing measurable variables (evaporation pressure, condensation pressure, compressor energy level, and pressure difference across the electronic expansion valve) as the core partition dimensions, is pre-optimized. The control rule base is stored as a lookup table. The dynamic characteristic matching model for the liquid supply is then incorporated into the mechanical inertia hysteresis characteristics, refrigerant transport delay characteristics, evaporator phase change thermal inertia characteristics, strong nonlinear characteristics of the electronic expansion valve opening-flow rate, multi-factor coupling characteristics, and execution dead zone nonlinear characteristics of the electronic expansion valve actuator. A feedforward compensation amount is preset for the execution dead zone nonlinear characteristics. The mechanical inertia hysteresis time and refrigerant transport delay time are time-varying parameters calculated in real-time based on the current operating conditions. S1. At the beginning of each control cycle, collect the operating status parameters of the vapor compression refrigeration system; S2. Based on the established liquid supply dynamic characteristic matching model, multi-cycle liquid supply demand prediction is performed in the current control cycle. The time span of the liquid supply demand prediction cycle is dynamically adjusted according to the real-time calculated refrigerant transmission delay, and is not less than the sum of mechanical inertia lag time and refrigerant transmission delay time, fully covering the entire dynamic response process of the adjustment action, and calculating the balance trajectory of liquid supply and evaporation in multiple future control cycles. S3. With minimizing the deviation between evaporator liquid supply and evaporation rate as the optimization objective, and the evaporator liquid level safety range predicted by the liquid supply dynamic characteristic matching model as the constraint, a strategy combining offline operating condition partitioning pre-optimization and online table lookup interpolation is employed to solve the constrained optimization problem in real time. This yields the optimal electronic expansion valve opening adjustment command and sends it to the electronic expansion valve actuator. The offline operating condition partitioning uses only four rapidly changing measurable variables—evaporation pressure, condensation pressure, compressor energy level, and pressure difference across the electronic expansion valve—as the core partitioning dimensions; slowly changing parameters are not included in the fixed partitioning. The number of offline operating condition partitions is an integer power of 2, adapting to the PLC's binary fast addressing logic. Within the overlapping buffer of adjacent state space partitions, a convex combination interpolation method based on normalized distance is used to smoothly switch control rules, avoiding abrupt changes in the opening command. The optimization objective includes a smoothness constraint on the electronic expansion valve adjustment action, with the weight of the smoothness constraint dynamically adjusted according to the operating conditions. S4. An adaptive algorithm with hard constraints of physical upper and lower bounds is adopted. Based on the deviation between the measured operating parameters and the calculated values ​​of the liquid supply dynamic characteristic matching model, the key parameters of the liquid supply dynamic characteristic matching model are adaptively corrected at a preset period. Among them, the adaptive algorithm is used to update the slowly changing parameters of the model online, and the parameters are always kept within the preset physical upper and lower bounds during the parameter update process to prevent parameter drift. The adaptive parameter update does not change the core dimension and partition boundary of the offline working condition partition, and the slowly changing parameters never participate in the fixed partition division. S5. After the parameters of the liquid supply dynamic characteristic matching model are corrected, the next control cycle begins, and steps S1 to S4 are repeated.

2. The adaptive control method for the opening degree of the electronic expansion valve in a vapor compression refrigeration system according to claim 1, characterized in that, Step S0 includes: S01. Establish an offline matching model for the liquid supply dynamic characteristics of the electronic expansion valve's liquid supply regulation mechanism. The state vector of the liquid supply dynamic characteristics matching model is: in, Indicates evaporation pressure, unit The data is collected by an evaporation pressure sensor. Represents condensation pressure, unit The data is collected by a condensation pressure sensor. This indicates the compressor energy level; it is dimensionless and ranges from 0.25 to 1.

00. This represents the relative opening of the electronic expansion valve, dimensionless, with a value range of 0-1, determined by the actual number of steps. The total number of steps for the entire journey is obtained by normalization. This represents the pipeline pressure drop loss coefficient, which is dimensionless, with an initial value of 1.0 and a reasonable range of 0.5-1.

5. This represents the oil separation efficiency decay coefficient, which is dimensionless, with an initial value of 0.85 and a reasonable range of 0.6-0.

95. This represents the refrigerant charge correction factor, which is dimensionless, with an initial value of 1.0 and a reasonable range of 0.85-1.

15. This represents the flow characteristic coefficient of the electronic expansion valve. It is dimensionless, with an initial value of 1.0 and a reasonable range of 0.7-1.

3. It is estimated online through soft sensing. Represents the time constant of the phase change heat inertia of the evaporator, in units of The initial value is 15.0, and the reasonable range is 8-30, which is estimated online through soft measurement. S02. The dynamic characteristic matching model for the liquid supply incorporates the mechanical inertia hysteresis characteristics of the electronic expansion valve actuator, the refrigerant transport delay characteristics, the evaporator phase change thermal inertia characteristics, the strong nonlinear characteristics of the electronic expansion valve opening-flow rate, the multi-factor coupling characteristics, and the nonlinear characteristics of the execution dead zone, among which: The mechanical inertial hysteresis characteristic is a first-order inertial characteristic exhibited by the electronic expansion valve stepper motor drive system, with the transfer function being: This corresponds to a mechanical response lag time of 350ms, where, Indicates the time lag of the implementing agency; The refrigerant transport delay characteristic is calculated based on the real-time flow rate, using the following formula: ,in, Indicates the length of the liquid supply line. , This represents the cross-sectional area of ​​the pipe. This indicates the real-time volumetric flow rate of the electronic expansion valve; 200ms at full load and 500ms at 25% low load. The phase change thermal inertia characteristic of the evaporator is the phase change thermal inertia time constant of the evaporator. ; The nonlinear characteristic of the electronic expansion valve's opening-flow rate is constructed by building a flow calculation submodule using opening-flow rate calibration curves under different pressure differentials, strictly distinguishing between critical and subcritical flow conditions, wherein: The formula for calculating the flow rate under subcritical flow conditions is: in, express The relative opening of the electronic expansion valve at any given time; This indicates the density of the liquid refrigerant; Indicates the pressure before the valve; Indicates the pressure after the valve; The formula for calculating the flow rate under critical flow conditions is: The multi-factor coupling characteristic is that it simultaneously incorporates the pipeline pressure drop loss coefficient. Oil efficiency attenuation coefficient Refrigerant charge correction factor The coupled effect on the liquid supply characteristics; The nonlinear characteristic of the execution dead zone is generated by the static friction force of the electronic expansion valve and stepper motor. The measured control dead zone is... 3 steps, feedforward compensation amount is ,in To compensate for the opening command, This is the original opening command. This represents the current actual opening degree. For the sign function, the coefficient 3 in the compensation quantity corresponds to... The control dead zone is controlled in 3 steps; among them, the compensated opening command is subjected to 0-3812 steps of hard constraint limiting, and then... The output is limited to a rate of 50 steps / cycle and then normalized to a relative opening for flow calculation. S03, based on evaporation pressure Condensing pressure Compressor energy level Differential pressure across the electronic expansion valve Four rapidly changing measurable variables are the core dimensions of the partitioning, and each dimension is evenly divided into 4 intervals, for a total of [number] partitions. =256; Evaporator outlet superheat Exhaust superheat Current opening degree of electronic expansion valve As a state feedback variable, it is included in the calculation of the liquid supply dynamic characteristic matching model, but does not participate in the fixed zone division; pipeline pressure drop loss coefficient Oil efficiency attenuation coefficient Refrigerant charge correction factor Flow characteristic coefficient of electronic expansion valve Evaporator phase change thermal inertia time constant As a slowly changing parameter, it is updated in real time through adaptive correction and does not participate in fixed partitioning; the selection criteria for the 256 partitions are: complete table lookup and positioning within 5ms, adapting to mainstream PLCs; fully covering 25%-100% full load conditions, with fluctuations in operating conditions within the partition having less than 1% impact on the control effect; and the integer powers of 2 are adapted to the PLC's binary fast addressing logic. S04. For each partition obtained in step S03, perform offline pre-optimization, with the goal of minimizing the deviation between the evaporator liquid supply and the evaporation rate, solve for the optimal control rules corresponding to each partition, construct a pre-optimization rule base, and store the pre-optimization rule base as a lookup table.

3. The adaptive control method for the opening degree of the electronic expansion valve in a vapor compression refrigeration system according to claim 1, characterized in that, Step S1 includes: S11. At the beginning of each control cycle, the operating status parameters of the vapor compression refrigeration system are collected from the sensing unit, including the evaporation pressure. Condensing pressure Compressor energy level Evaporator outlet temperature, evaporator outlet pressure, exhaust temperature, exhaust pressure, and actual opening of the electronic expansion valve. and evaporator liquid level ; S12. Perform signal preprocessing on the collected operating status parameters, including filtering and noise reduction, outlier removal, and unit conversion, to obtain the preprocessed operating status parameters. S13. Calculate the evaporator outlet superheat based on the saturation temperature corresponding to the pretreated evaporator outlet temperature and evaporation pressure. The exhaust superheat is calculated based on the saturation temperature corresponding to the pretreated exhaust temperature and condensation pressure. ; S14. Verify the validity of the preprocessed operating status parameters. If any key parameter exceeds the preset physical reasonable range or the sensor signal is abnormal, trigger the fault tolerance mechanism. S15. Assemble the valid operating state parameters into a state vector and output it to the liquid supply dynamic characteristic matching model.

4. The adaptive control method for the opening degree of the electronic expansion valve in a vapor compression refrigeration system according to claim 1, characterized in that, Step S2 includes: S21. Calculate the refrigerant transmission delay time in real time based on the current real-time flow rate of the electronic expansion valve and the inner diameter of the liquid supply pipeline, and dynamically adjust the time span of the multi-cycle liquid supply demand prediction accordingly. The lower limit of the time span shall not be less than the sum of the full stroke response time of the electronic expansion valve and the refrigerant transmission delay time, and the upper limit shall not be greater than 1 / 10 of the evaporator phase change thermal inertia time constant. S22. Using the rolling time-domain prediction method, based on the current state vector, the state vector sequence and liquid supply output trajectory for multiple future control cycles are calculated iteratively through the state equation. The state equation is as follows: in, Indicates the number of iterations. , To predict the time domain; S23. The change trend of phase change heat transfer thermal inertia of the evaporator is predicted in real time by the disturbance observer. The disturbance observer takes the evaporator outlet water temperature and compressor suction mass flow rate as inputs and the total disturbance formed by the change of evaporator thermal inertia, pipeline pressure drop and oil separation efficiency as output. S24. Compensate the total disturbance output by the disturbance observer to the liquid supply dynamic characteristic matching model, correct the liquid supply output trajectory, and obtain the compensated liquid supply and evaporation balance trajectory.

5. The adaptive control method for the opening degree of the electronic expansion valve in a vapor compression refrigeration system according to claim 1, characterized in that, Step S3 includes: S31. Construct an optimization objective function with minimizing the deviation between the evaporator liquid supply and evaporation rate as the core term and the smoothness constraint of the electronic expansion valve adjustment action as an additional term. The formula is as follows: in, This indicates the optimization of the objective function value; This indicates the real-time liquid supply mass flow rate of the electronic expansion valve; This indicates the real-time evaporation mass flow rate of the evaporator; This represents the penalty coefficient for changes in opening degree. This represents the opening acceleration penalty coefficient. This indicates the change in opening degree. Indicates the opening acceleration. ; S32. Transform the absolute value term in the objective function into a standard quadratic programming solution-friendly linear inequality constraint by introducing auxiliary slack variables: Introduction Transform into and Two linear inequality constraints; Introduction Transform into and Two linear inequality constraints; S33. Based on linear inequality constraints, the optimization objective is transformed into... ; S34. Set constraints, including hard constraints and soft constraints. The hard constraint is the safe range of the evaporator liquid level. Electronic expansion valve opening range Step and rate of change of opening Step / cycle; soft constraint is exhaust superheat ; S35. Read the current state vector through the PLC and control the partition fast addressing module to use boundary inequalities. Dimensionally determined, the current operating condition is located within two instruction cycles, belonging to the corresponding partition. Indicates the first The boundary coefficient matrix of each partition. Indicates the first The boundary threshold vector of each partition; S36. Look up the control rules in the table within the partition and use the normalized distance convex combination interpolation method in the overlapping buffer of adjacent partitions to obtain the electronic expansion valve opening command to achieve smooth switching; the interpolation time is controlled within 2.5ms. S37. The opening command of the electronic expansion valve is executed sequentially. After 50 steps / cycle rate limiting, dead zone feedforward compensation, and hard constraint limiting of opening range, the output is sent to the electronic expansion valve actuator, and the total single cycle time is controlled within 4.5ms. S38. When the liquid level of the evaporator predicted by the liquid supply dynamic characteristic matching model exceeds 80% of the safe range, the opening command is automatically triggered for limiting and feedforward correction.

6. The adaptive control method for the opening degree of the electronic expansion valve in a vapor compression refrigeration system according to claim 5, characterized in that, Step S36 also includes: determining the liquid level fluctuation condition and dynamically adjusting the penalty coefficient for the change in opening based on the liquid level fluctuation condition result. To achieve a smooth transition, specifically including: S361, at rated liquid level Based on this, the liquid level fluctuation condition is determined: when the condition is met for three consecutive control cycles... When the liquid level fluctuates drastically, it is determined to be a condition of violent liquid level fluctuation; when the conditions are met for 10 consecutive control cycles... When this condition is reached, it is determined to be a steady-state condition; S362. Under conditions of severe liquid level fluctuations, reduce the penalty coefficient for changes in opening degree. To improve response speed; under steady-state conditions, increase the penalty coefficient for changes in opening degree. To reduce stepper motor wear; the formula for adjusting the penalty coefficient for opening change is: in, This represents the steady-state baseline penalty coefficient, with a value of 0.

8. This represents the adjustment factor, with a value of 0.

12. The value range is locked within [0.2, 0.8]. S363, A first-order inertial filter with a time constant of 5 control cycles is used to apply a penalty coefficient to the opening change. Perform a smooth switch to avoid frequent jumps in the penalty coefficient.

7. The adaptive control method for the opening degree of the electronic expansion valve in a vapor compression refrigeration system according to claim 1, characterized in that, Step S4 includes: S41. Using a recursive least squares method with a forgetting factor, the key parameters of the fluid supply dynamic characteristic matching model are updated online at a preset period, including the pipeline pressure drop loss coefficient. Oil efficiency attenuation coefficient Refrigerant charge correction factor Flow characteristic coefficient of electronic expansion valve and the evaporator phase change thermal inertia time constant The preset period is 50 control periods, and the forgetting factor... The value is 0.98; S42. Set physical upper and lower bounds for each parameter to be updated, including the pipeline pressure drop loss coefficient. The value range is [0.5, 1.5], and the oil separation efficiency decay coefficient is... The value range is [0.6, 0.95], which is the refrigerant charge correction factor. The value range is [0.85, 1.15], which is the flow characteristic coefficient of the electronic expansion valve. The value range is [0.7, 1.3], which is the evaporator phase change thermal inertia time constant. The value range is [8, 30]; during the parameter update process, it is always kept within the set physical upper and lower bounds to prevent parameter drift. S43. Detect whether the unit is in steady-state operation. When the load change rate is less than 2% for 10 consecutive control cycles, suspend parameter updates and maintain the current parameter estimates. The formula for calculating the load change rate is: in, Indicates the compressor energy level in the current cycle. This represents the average energy level over the first 10 control cycles; S44. Online estimation of key parameters is achieved through soft sensing. The soft sensing uses an outlet water temperature prediction model, as shown in the following formula: in, This indicates the predicted outlet water temperature for the current cycle; This indicates the measured outlet water temperature in the previous cycle; This indicates the liquid supply mass flow rate in the previous cycle; This indicates the evaporation mass flow rate in the previous cycle; Indicates the control cycle; This indicates the specific heat capacity of chilled water at constant pressure. Indicates the density of chilled water; Indicates the chilled water circulation flow rate; Indicates the heat exchange efficiency of the evaporator; S45. Based on the outlet water temperature prediction model, a sensitivity matrix is ​​constructed. Using the residual between the measured outlet water temperature and the value calculated by the outlet water temperature prediction model as input, the flow characteristic coefficient of the electronic expansion valve is recursively estimated using the recursive least squares method. With respect to the phase change thermal inertia time constant of the evaporator The optimal parameter values ​​are found; the formula for the sensitivity matrix is ​​as follows: S46. Within each control cycle, a progressive three-level fault tolerance protection strategy is executed synchronously, specifically including: The first level of fault tolerance is when a single point of sensor failure occurs. Through cross-verification with dual sensors, if the deviation is greater than 5% for three consecutive control cycles, the fault signal is cut off and the system is switched to a valid sensor signal to continue operation. The Level 2 fault tolerance mode switches to a fixed opening protection mode when all sensors fail or the optimization is abnormal. The fixed opening is 80% of the rated operating condition design opening, maintaining the effective output of the previous cycle and triggering an alarm. Level 3 protection is triggered when the evaporator liquid level exceeds the limit. 50mm or less When the temperature reaches 30mm, the safety shutdown protection will be triggered immediately.

8. The adaptive control method for the opening degree of the electronic expansion valve in a vapor compression refrigeration system according to claim 4, characterized in that, In step S21, the prediction time domain of the multi-cycle liquid supply demand prediction The value range is 10-30 control cycles, in the control time domain. One control cycle.

9. The adaptive control method for the opening degree of the electronic expansion valve in a vapor compression refrigeration system according to claim 5, characterized in that, In step S38, the judgment threshold for the liquid level of the evaporator exceeding the safe range by 80% predicted by the liquid supply dynamic characteristic matching model is either below 120mm or above 280mm. When the judgment is triggered, the limiting and feedforward correction of the opening command are automatically executed. The limiting means that the opening command is restricted to the safe opening range, and the feedforward correction means that the opening adjustment amount is increased or decreased according to the direction of the liquid level deviation to quickly return to the safe range.

10. The adaptive control method for the opening degree of the electronic expansion valve in a vapor compression refrigeration system according to claim 1, characterized in that, The method is applicable to vapor compression refrigeration systems that use flooded or falling film evaporators, including but not limited to water chillers, water source heat pump units, air-cooled water chillers, cold storage refrigeration units, and ice storage units.

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