Optimization control method and device of compressor cluster, electronic equipment and storage medium

Abstract

The invention discloses an optimization control method and device for a compressor cluster, electronic equipment and a storage medium, and relates to the compressor control technology, the method comprises the steps that state parameters of the compressor cluster at the current moment are obtained, and the state parameters at least comprise operation parameters corresponding to all compressors and system parameters of the compressor cluster; determining constraint conditions, wherein the constraint conditions comprise a surge sub-constraint, a guide vane opening sub-constraint and a start-stop state sub-constraint; a target function is established according to the operation power, the guide vane opening degree, the real-time rotating speed, the outlet pressure, the flow and the system pipe network pressure; and the state parameters of the compressor cluster at the current moment are input into the parameter optimization model, the target function is solved under the constraint condition, optimal control parameters of each compressor are obtained, and the compressor cluster is controlled based on the optimal control parameters corresponding to the compressors at the next moment. The overall operation efficiency of the compressor cluster can be improved, and the operation cost is saved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of compressor control, and particularly relates to an optimization control method and device for a compressor cluster, an electronic device and a storage medium. BACKGROUND

[0002] In the field of compressor control, the existing technology relies on a traditional architecture of independent adjustment of a single device, and the core logic is to achieve local parameter closed-loop control through a classic PID control algorithm. Specifically, each compressor only collects single-point operating data such as its own outlet pressure and real-time gas flow, and independently completes vane opening degree adjustment, speed matching and bypass valve state switching based on the data. This single independent adjustment mode is prone to cause a local optimal trap, resulting in low overall operating efficiency of the compressor cluster.

[0003] When the system gas flow demand fluctuates, the above-mentioned traditional architecture may cause multiple units to simultaneously increase the vane opening degree to increase the gas flow, which easily causes some devices to approach the surge boundary, and thus forces the bypass valve to be opened for pressure relief, thereby increasing energy consumption costs. SUMMARY

[0004] The present application provides an optimization control method and device for a compressor cluster, an electronic device and a storage medium, which can improve the operating efficiency of the compressor cluster and reduce energy consumption costs.

[0005] In a first aspect, the present application provides an optimization control method for a compressor cluster, comprising:

[0006] obtaining state parameters of the compressor cluster at a current time, wherein the state parameters at least include operating parameters corresponding to each compressor and system parameters of the compressor cluster, the operating parameters at least include operating power, vane opening degree, real-time speed, outlet pressure and gas flow, and the system parameters at least include system pipe network pressure;

[0007] determining constraint conditions, wherein the constraint conditions include surge sub-constraints, vane opening degree sub-constraints and start-stop state sub-constraints;

[0008] establishing a target function according to the operating power, the vane opening degree, the real-time speed, the outlet pressure, the gas flow and the system pipe network pressure;

[0009] inputting the state parameters of the compressor cluster at the current time into a parameter optimization model, solving the target function under the constraint conditions, obtaining optimization control parameters of each compressor, and controlling the compressor cluster based on the optimization control parameters corresponding to each compressor at a next time;

[0010] The optimization control parameters include a guide vane opening adjustment amount, a bypass valve opening adjustment amount, a unit start-stop state, and a pipe network pressure set value.

[0011] In a second aspect, the present application provides an optimization control device for a compressor cluster, the device comprising:

[0012] a parameter acquisition module configured to acquire state parameters of the compressor cluster at a current time, the state parameters including at least operating parameters corresponding to each compressor and system parameters of the compressor cluster, the state parameters including at least operating power, guide vane opening, real-time rotating speed, outlet pressure, and gas flow, and the system parameters including at least system pipe network pressure;

[0013] a constraint determination module configured to determine constraint conditions, the constraint conditions including surge sub-constraints, guide vane opening sub-constraints, and start-stop state sub-constraints;

[0014] a function establishment module configured to establish a target function according to the operating power, the guide vane opening, the real-time rotating speed, the outlet pressure, the gas flow, and the system pipe network pressure;

[0015] an optimization control module configured to input the state parameters of the compressor cluster at the current time into a parameter optimization model, solve the target function under the constraint conditions, and obtain optimization control parameters of each compressor, so as to control the compressor cluster based on the optimization control parameters corresponding to each compressor at a next time;

[0016] The optimization control parameters include a guide vane opening adjustment amount, a bypass valve opening adjustment amount, a unit start-stop state, and a pipe network pressure set value.

[0017] In a third aspect, the present application provides an electronic device, the electronic device comprising:

[0018] at least one processor; and

[0019] a memory communicatively connected to the at least one processor; wherein

[0020] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the optimization control method for the compressor cluster according to any one of the embodiments of the present application.

[0021] In a fourth aspect, the present application provides a computer readable storage medium storing computer instructions, the computer instructions being configured to enable a processor to execute the optimization control method for the compressor cluster according to any one of the embodiments of the present application when executed by the processor.

[0022] In a fifth aspect, the present application also provides a computer program product comprising a computer program which, when executed by a processor, implements the optimization control method of the compressor cluster according to any of the embodiments of the present application.

[0023] The optimization control scheme of the compressor cluster provided by the embodiments of the present application can construct a target function through the state parameters of the compressor cluster at the current moment, and solve the target function under the constraint condition through a parameter optimization model, that is, obtain the guide vane opening adjustment amount, the bypass valve opening adjustment amount, the unit start-stop state and the pipe network pressure set value of each compressor through the overall deployment manner, so as to improve the overall operation efficiency of the compressor cluster and save the operation cost. Meanwhile, the surge constraint, the guide vane opening constraint and the start-stop state constraint are taken as the constraint conditions, so as to ensure that the optimization result is always within the safety boundary of the equipment, and the vibration, over-temperature and other faults caused by the surge can be avoided, and the equipment life can be prolonged.

[0024] It should be noted that the computer instructions can be stored on the computer readable storage medium in whole or in part. The computer readable storage medium can be packaged together with the processor of the optimization control device of the compressor cluster, or can be packaged separately from the processor of the optimization control device of the compressor cluster, and the present application does not limit this.

[0025] The description of the second aspect, the third aspect, the fourth aspect and the fifth aspect of the present application can refer to the detailed description of the first aspect; and the beneficial effects of the description of the second aspect, the fourth aspect and the fifth aspect can refer to the beneficial effect analysis of the first aspect, which will not be described here.

[0026] It should be understood that the contents described in this part are not intended to identify the key or important features of the embodiments of the present application, nor are they used to limit the scope of the present application. Other features of the present application will become apparent through the following description.

[0027] It can be understood that, before using the technical solutions disclosed in the embodiments of the present application, the type, use range and use scene of the personal information involved in the present application should be informed to the user and the authorization of the user should be obtained through appropriate means according to relevant laws and regulations. BRIEF DESCRIPTION OF DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some of the embodiments of the present application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0029] Figure 1is a flowchart of an optimization control method of a compressor cluster provided by an embodiment of the present application;

[0030] Figure 2 is a structural diagram of an optimization control device of a compressor cluster provided by an embodiment of the present application;

[0031] Figure 3 is a structural diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0032] In order to enable those skilled in the art to better understand the present application, the technical solutions in the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work should fall within the scope of protection of the present application.

[0033] It should be noted that the terms “reference”, “target” and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms “include” and “have” and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to the process, method, product or device.

[0034] The present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, but not to limit the present application. In addition, it should be noted that, for the convenience of description, only the parts related to the present application are shown in the drawings, rather than all the structures.

[0035] Figure 1 is a flowchart of an optimization control method of a compressor cluster provided by an embodiment of the present application, which can be applicable to the case of optimizing the running state of each compressor in the compressor cluster. The method can be executed by an optimization control device of the compressor cluster, which can be realized in the form of hardware and / or software and integrated in an electronic device for executing the method. Preferably, the electronic device in the embodiment of the present application can be a server, and can also be a computer device, etc.

[0036] Reference Figure 1The optimization control method of the compressor cluster of the embodiment includes but is not limited to the following steps:

[0037] In S110, a state parameter of the compressor cluster at a current time is acquired, the state parameter at least including an operating parameter corresponding to each compressor and a system parameter of the compressor cluster.

[0038] The state parameter of the compressor cluster at the current time refers to a set of operating characteristic data of the compressor cluster as a whole and each compressor at a specific time point, which can be used to reflect the local state of each compressor and the global state of the compressor cluster. In the embodiment, the state parameter is composed of the operating parameter of each compressor and the system parameter of the compressor cluster, which is used for input analysis of a subsequent optimization control model.

[0039] In the embodiment, the operating parameter at least includes operating power, guide vane opening, real-time rotating speed, outlet pressure and gas flow.

[0040] The operating power refers to the energy power consumed by the compressor in a unit of time, and the numerical value is closely related to the load of the compressor. For example, when the guide vane opening, the rotating speed or the outlet pressure increases, the operating power usually increases accordingly. The guide vane opening is used to describe the opening degree of the inlet guide vane of the compressor, and the value ranges from 0% (fully closed) to 100% (fully open). The guide vane is a key component for adjusting the inlet air volume of the compressor. By changing the blade angle and the air flow passage area, the gas flow entering the compressor is controlled, and the output load of the device is adjusted. The real-time rotating speed refers to the instantaneous rotating speed of the main shaft of the compressor. The rotating speed directly affects the gas processing capacity and compression efficiency of the compressor. That is, within the allowable rotating speed range of the device, the higher the rotating speed, the more times the impeller compresses the gas in a unit of time, and the stronger the output gas flow and pressure. The outlet pressure refers to the pressure of the gas discharged from the outlet after being compressed by the compressor. It needs to be matched with the pipe network pressure. If the outlet pressure is too high, the pipe network may be at risk of overpressure. If it is too low, it cannot meet the gas pressure demand of the downstream user. The gas flow refers to the amount of gas passing through the compressor in a unit of time. The gas flow not only reflects the actual operating load of the compressor, but also is a key basis for judging whether the device is close to the surge state. When the gas flow is lower than a certain critical value, the periodic oscillation of the gas flow in the compressor is easy to occur, which causes the surge phenomenon. Therefore, the gas flow is a key monitoring parameter for ensuring the safe operation of the device.

[0041] In the embodiment, the system parameters at least include a system pipeline pressure; the system pipeline pressure refers to an average pressure in an entire gas pipeline network connected with the compressor cluster. The system pipeline pressure affects the gas stability of downstream users and the overall energy consumption of the compressor cluster. When the gas flow is the same, the higher the system pipeline pressure, the more power the compressor needs to consume. Therefore, the system pipeline pressure is a core index for balancing the operation efficiency and process demand.

[0042] Optionally, the system parameters can further include a gas storage tank pressure, a pipeline flow, a pressure fluctuation amplitude, a temperature, a dew point, a total gas supply, a total demand, a supply-demand matching degree, and a running time of each compressor, and the specific system parameters are not limited herein.

[0043] In a preferred implementation, the embodiment obtains the running parameters corresponding to each compressor, including:

[0044] The initial parameters corresponding to each compressor in the compressor cluster at the current time are obtained; the initial parameters corresponding to each compressor are standardized respectively by using a standardization method to obtain the running parameters corresponding to each compressor.

[0045] The initial parameters refer to the original running data of the compressor at the current time without processing, and are the basis for subsequent standardization processing. The original running data are captured in real time by a sensor, for example, the instantaneous values of voltage and current directly output by a power sensor, rather than the calculated power values. The initial parameters of different magnitudes and units are converted into a uniform scale through the standardization processing, so as to eliminate the influence of the differences in dimensions and numerical ranges on subsequent analysis.

[0046] In the embodiment, the initial parameters can be standardized based on a normalization algorithm or a standardization algorithm, and the type of the algorithm selected for the standardization processing is not limited herein.

[0047] The embodiment can unify the scales of the initial parameters of different types of compressors after the standardization, so as to facilitate the horizontal comparison of the load rates and efficiencies of the devices in the cluster. In addition, the standardization processing of the running parameters can help to improve the sensitivity of the parameter optimization model to the input data and improve the solution accuracy of the model in the subsequent steps.

[0048] In S120, constraint conditions are determined, and the constraint conditions include a surge constraint, a guide vane opening constraint, and a start-stop state constraint.

[0049] The constraint conditions refer to the limiting rules set for the safety of the devices, the stability of the process, and the operation logic in the optimization control process of the compressor cluster, and are boundary conditions that must be met when the parameter optimization model is solved.

[0050] In the present embodiment, the surge constraint refers to a critical condition limit set to prevent the compressor from entering a surge state, and the core is to ensure that the equipment operating point maintains a safe distance from the surge boundary. Surge is a periodic oscillation phenomenon of gas flow that occurs in the compressor under low flow conditions, which can cause vibration to intensify, efficiency to drop sharply, and even equipment damage. In the present embodiment, the implementation of the surge constraint can be: set a safe surge critical value, that is, during real-time operation, the surge margin cannot be lower than the surge critical value, such as 10%; optionally, the surge critical value can also be adjusted synchronously according to the real-time operating conditions, such as inlet temperature, pressure changes, etc., such as it can be increased from 10% to 12% under high temperature conditions, etc.

[0051] The guide vane opening degree sub-constraint refers to the range limit of the opening degree of the compressor inlet guide vane, including the upper and lower limits of the opening degree and the adjustment rate constraint. The guide vane opening degree directly affects the intake and load, and excessive opening (such as exceeding the mechanical structure limit) may cause the actuator to be damaged, and excessive closing (such as below the minimum stable opening) may cause surge, and frequent or drastic adjustments may exacerbate valve wear. In the present embodiment, the guide vane opening degree sub-constraint can be implemented in the following way: combining the mechanical characteristics of the equipment and the stability requirements of the operation, the constraint is constructed from the opening degree range and the adjustment rate, wherein the opening degree range can include upper limit constraint and lower limit constraint, the lower limit constraint is determined according to the characteristics of the equipment, and below this value, surge is easy to be caused. For example, the upper limit constraint is not greater than 100%, and the lower limit constraint is not less than 20%, etc.; the adjustment rate can include single adjustment amount constraint and adjustment times constraint per unit time, such as the single adjustment amplitude of the guide vane opening degree is not greater than 5% to avoid flow fluctuations caused by drastic adjustment, and the adjustment times constraint per unit time: adjustment times ≤3 times within 5 minutes to reduce valve wear, etc.

[0052] The start-stop state sub-constraint refers to the restrictive rules for the start and stop operation of the compressor unit, including the start-stop interval time, the running time, the working condition matching constraint, etc. The start-stop process of the unit has mechanical impact and energy consumption peak, and frequent start-stop will shorten the service life of the equipment, and after starting, it needs a certain time to stabilize the operation, therefore, unreasonable start-stop operation needs to be avoided through constraint to ensure the continuity of the cluster operation. In the present embodiment, the start-stop state sub-constraint can be implemented in the following way: based on the service life and the continuity requirements of the operation, the time and working condition constraints of the start-stop operation are set, such as including the start-stop interval constraint and the state switching constraint. Among them, the start-stop interval constraint includes the restart interval after shutdown and the shutdown interval after startup, such as the restart interval after shutdown is the time from shutdown to restart of the unit ≥10 hours to avoid the superposition of mechanical impact in a short time; the shutdown interval after startup: the unit is continuously operated ≥30 minutes after startup to avoid energy waste caused by frequent start-stop; the state switching constraint indicates that the switching of the start-stop state of the unit, such as 0 = shutdown, 1 = running, needs to meet the above start-stop interval constraint, such as in the shutdown state, it is not allowed to switch to the running state within 10 minutes.

[0053] The setting and implementation of the above constraint conditions can ensure that the control parameters output by the parameter optimization model are within a safe and stable boundary, thereby providing a guarantee for the optimized operation of the compressor cluster.

[0054] S130, a target function is established according to the operating power, the guide vane opening, the real-time rotating speed, the outlet pressure, the gas flow and the system pipe network pressure.

[0055] The target function is used to reflect the core optimization target of the compressor cluster. The core is to superimpose the system parameters of the compressor cluster on the basis of the parameters of a single device, to form a global optimal quantitative index by weighted integration of multiple targets. In the embodiment, the target function quantifies the correlation between each parameter and the optimization target, so that the parameter optimization model can find an optimal solution with low energy consumption, high stability and small loss under the constraint condition, thereby providing a clear optimization direction for the collaborative control of the compressor cluster.

[0056] In another preferred implementation, the above-mentioned method of establishing a target function can be implemented by the following steps in the embodiment:

[0057] a) An energy consumption sub-function is established based on the operating power of each compressor, and a load balancing sub-function is established based on the variance of the operating power of each compressor.

[0058] The core of the energy consumption sub-function is to quantify the total energy consumption of the compressor cluster, and the operating power of a single compressor directly reflects its energy consumption per unit time. Therefore, by superimposing the operating power of all compressors, a sub-function with the goal of "minimizing total energy consumption" can be constructed.

[0059] The specific energy consumption sub-function can be represented by the following expression:

[0060]

[0061] where n is the number of compressors in the compressor cluster, P i represents the operating power of the i-th compressor.

[0062] The load balancing sub-function is to avoid the unbalanced state of high-load operation of some units and low-load idling of some units. The embodiment quantifies the dispersion degree of the load of each unit by the variance of the operating power. For example, the smaller the variance, the more concentrated the power distribution, and the more balanced the load.

[0063] The specific load balancing sub-function can be represented by the following expression:

[0064]

[0065] where n is the number of compressors in the compressor cluster, P To compress the average operating power of all compressors in the compressor cluster.

[0066] b) Establishing a stability sub-function based on the deviation of the system pipe network pressure and the target pressure.

[0067] The stability sub-function is used to quantify the deviation and fluctuation of the system pipe network pressure from the target pressure, ensuring that the pipe network pressure is stable within the process allowable range and avoiding damage to downstream equipment caused by sudden pressure rise / sudden pressure drop. By setting the stability sub-function, this embodiment can accurately quantify the pressure deviation and achieve smooth calibration of the pipe network pressure.

[0068] F 稳定 = ΔP = (P 管网 - P 目标 ) 2 (3)

[0069] c) Determining the surge margin corresponding to each compressor based on the guide vane opening, real-time speed, outlet pressure and gas flow rate of each compressor, and establishing a surge risk sub-function based on the surge margin corresponding to each compressor.

[0070] The surge margin is a quantitative indicator that measures the safe distance between the operating point of the compressor and the surge boundary, reflecting the strength of the anti-surge capability of the equipment. By comparing the current operating parameters with the surge critical parameters, the degree of deviation from the surge state of the equipment can be evaluated. The larger the value, the farther away from the surge boundary, the safer the operation; if the value is too small, the risk of surge should be alerted.

[0071] The surge risk sub-function is a mathematical expression that integrates the surge margins of multiple compressors into a cluster-level risk indicator, used to quantify the overall surge risk level.

[0072] The specific way to construct the surge risk sub-function can be to determine the surge margin corresponding to each compressor, and then determine the system surge margin based on the average value of the surge margin corresponding to each compressor, to establish the surge risk sub-function based on the system surge margin.

[0073] The specific surge risk sub-function can be represented by the following expression:

[0074]

[0075] In the above formula, SM i represents the surge margin corresponding to the i-th compressor.

[0076] In another preferred implementation, the above "determining the surge margin corresponding to each compressor based on the guide vane opening, real-time speed, outlet pressure and gas flow rate of each compressor" can be implemented in the following way:

[0077] An inlet temperature and an inlet pressure corresponding to the current compressor are acquired, a surge boundary database corresponding to the current compressor is acquired, in which one rotating speed interval and one guide vane opening interval correspond to one reference surge flow rate, a target reference surge flow rate is determined from the surge boundary database according to a real-time rotating speed and a guide vane opening generated by the current compressor at the current time, the target reference surge flow rate is corrected according to the inlet temperature and the inlet pressure to obtain an actual surge flow rate, and a surge margin of the current compressor at the current time is determined according to the target reference surge flow rate, the actual surge flow rate and a gas flow rate.

[0078] A temperature sensor and a pressure sensor are installed on a straight pipe section of a compressor inlet pipe to acquire an inlet temperature and an inlet pressure.

[0079] The surge boundary database adopts a three-dimensional mapping structure of rotating speed interval-guide vane opening interval-reference surge flow rate. As shown in Table 1, Table 1 is an example of a surge boundary database provided in the embodiment.

[0080] Table 1

[0081] Speed interval (r / min) Gate interval (%) reference surge flow (m 3 / h) 2800-3000 40-50 100 3000-3200 40-50 110

[0082] In the formula, the reference surge flow rate is calibration data based on an industry standard state.

[0083] A current real-time rotating speed and a guide vane opening are acquired from a compressor control system to match the surge boundary database to obtain a corresponding target reference surge flow rate, such as Q 基准 = 110 m 3 / h.

[0084] Since the reference surge flow rate is a value in a standard state, the target reference surge flow rate needs to be corrected according to the inlet temperature and the inlet pressure to obtain an actual surge flow rate in a current working condition, so as to reflect a real flow state. In the formula, the actual surge flow rate Q 实际 can be obtained through the following formula:

[0085]

[0086] In the formula, P 标准 = 101.325 kpa, and T 标准 = 20℃.

[0087] Further, a current actual gas flow rate Q 气体 is acquired through a flowmeter at a compressor outlet or an inlet pipe, and a surge margin SM of the current compressor at the current time is obtained in the following formula:

[0088]

[0089] d) establishing a target function according to the energy consumption sub-function, the stability sub-function, the surge risk sub-function and the load balance sub-function.

[0090] The target sub-function established in the embodiment is expressed by the following formula:

[0091]

[0092] wherein, P i represents the running power of the i-th compressor; represents the energy consumption sub-function; ΔP represents the stability sub-function; SurgeMargin represents the surge risk sub-function; Var(Load) represents the load balance sub-function; and α, β, γ and δ respectively represent the weight coefficients corresponding to the respective sub-functions, which are constants.

[0093] Alternatively, based on the above formula (1) to formula (4), the target sub-function established in the embodiment can also be expressed as follows:

[0094] R = - αF 能耗 - βF 稳定 + γF 喘振 - δF 负荷 (8) S140, inputting the state parameters of the compressor cluster at the current moment into the parameter optimization model, solving the target function under the constraint condition, and obtaining the optimized control parameters of each compressor, so as to control the compressor cluster based on the respective optimized control parameters of each compressor at the next moment.

[0095] The parameter optimization model is a model trained for solving the optimal control parameters of the compressor cluster. In the embodiment, the parameter optimization model can be obtained by training using a particle swarm algorithm, a genetic algorithm or a model predictive control algorithm, and the specific algorithm used is not limited herein. The core function of the parameter optimization model is to find the control parameter combination that makes the target function (such as the minimum energy consumption and the highest stability) optimal under the given constraint condition through iterative calculation.

[0096] wherein, in the process of model solving by the parameter optimization model, the constraint conditions are loaded, such as the surge margin of each compressor ≥10%, the guide vane opening adjustment amount ≤+5% / time, the pipe network pressure fluctuation ≤±0.02Mpa, the restart interval after shutdown ≥10 minutes, and the continuous operation after startup ≥30 minutes; further, the target function is loaded, wherein the weight coefficients of each sub-function in the target function are known quantities; and then the model performs iterative calculation to find the optimal solution in the form of multiple iterations.

[0097] The process of specific iteration can be: randomly generating N (such as 100) groups of candidate control parameters, such as the start-stop state of each unit and the pipe network pressure value, and each group of parameters needs to meet the constraint condition; then the target function value corresponding to each group of candidate control parameters is calculated, and the smaller the value is, the higher the fitness is; then the parameters with the top 30% fitness can be reserved, and new candidate parameters are generated based on the mean and standard deviation thereof, and repeated iteration is performed until the target function value reaches a convergence state, and the optimized control parameters of each compressor are obtained.

[0098] In the embodiment, the optimized control parameters include the guide vane opening adjustment amount, the bypass valve opening adjustment amount, the unit start-stop state and the pipe network pressure set value corresponding to each compressor. Further, the optimized control parameters obtained at the current time are used to control the corresponding compressor at the next time. In this way, dynamic optimization control of the compressor cluster can be realized, and each unit can be ensured to operate according to the optimal parameters, taking into account efficiency, safety and stability.

[0099] In this way, when the gas flow demand is low, part of the units can be shut down, and the remaining units can be adjusted to the high efficiency interval, so as to reduce the total power loss of the cluster; when the gas flow fluctuates, the guide vane and the bypass valve are adjusted cooperatively, so as to avoid energy waste caused by frequent start-stop or dramatic parameter adjustment of a single device.

[0100] In another preferred implementation, the scheme provided in the embodiment further includes the following during solving the target function based on the parameter optimization model:

[0101] The real-time surge margin of each compressor is determined respectively; and when the real-time surge margin of any compressor is lower than a safety threshold, each compressor is controlled based on a preset control strategy at the next time.

[0102] The above method of determining the real-time surge margin of each compressor is realized by the above formula (5) and formula (6), and will not be repeated here.

[0103] Further, in order to guarantee the optimized control and improve the operation safety of the compressor cluster, when it is monitored that the real-time surge margin of any compressor is lower than the safety threshold, the preset control strategy is started to control the compressor cluster.

[0104] In the embodiment, the safety threshold can be that the real-time surge margin is not greater than a first threshold, and the gas flow rate of change is not lower than a second threshold for a continuous preset time.

[0105] The first threshold is a critical safety value of the real-time surge margin, and when the surge margin is less than or equal to the value, it indicates that the compressor has entered a low safety area. In combination with the device characteristics, it can be set to 5%, 8% or 10%, and the selection of the first threshold is not limited here.

[0106] The second threshold refers to the critical value of the gas flow rate of change, reflecting the severity of the flow rate drop. It can be set to -5% / second, where the negative sign indicates a flow rate drop, i.e., the condition is triggered when the flow rate reduction per second is ≥ 5% of the current flow rate; the preset time refers to the duration for which the flow rate of change needs to continuously meet the second threshold, used to distinguish between transient fluctuations and sustained deterioration trends, and can typically be set to 2 seconds, 3 seconds, or 5 seconds. Taking 2 seconds as an example, if the condition is met for 2 consecutive seconds, it is determined to be a risk state.

[0107] Specifically, the preset control strategy can be a strategy of automatically switching back to traditional PID control when the real-time surge margin is detected to be > 5% and the flow rate of change is continuously lower than -5% of the rated flow rate per second, to ensure system safety.

[0108] The optimization control method for the compressor cluster provided in this embodiment constructs a target function through the state parameters of the compressor cluster at the current time, and solves the target function under the constraint conditions through a parameter optimization model, i.e., obtains the guide vane opening adjustment amount, the bypass valve opening adjustment amount, the unit start-stop state, and the pipe network pressure set value of each compressor in a unified deployment manner, which can improve the overall operation efficiency of the compressor cluster and save operation costs; at the same time, the surge constraint, the guide vane opening constraint, and the start-stop state constraint are used as constraint conditions to ensure that the optimization result is always within the equipment safety boundary, which can avoid vibration, over-temperature, and other faults caused by surges, and can prolong the service life of the equipment.

[0109] Figure 2 is a structural schematic diagram of the optimization control device for the compressor cluster provided in this embodiment, which is suitable for executing the optimization control method for the compressor cluster provided in this embodiment. As shown in Figure 2 the device can specifically include a parameter acquisition module 210, a constraint determination module 220, a function establishment module 230, and an optimization control module 240, wherein:

[0110] The parameter acquisition module 210 is configured to acquire the state parameters of the compressor cluster at the current time, wherein the state parameters at least include the operating parameters corresponding to each compressor respectively and the system parameters of the compressor cluster, and the state parameters at least include operating power, guide vane opening, real-time rotating speed, outlet pressure, and gas flow rate; and the system parameters at least include system pipe network pressure.

[0111] The constraint determination module 220 is configured to determine the constraint conditions, wherein the constraint conditions include the surge constraint, the guide vane opening constraint, and the start-stop state constraint.

[0112] The function establishing module 230 is configured to establish a target function according to the operating power, the guide vane opening degree, the real-time rotating speed, the outlet pressure, the gas flow and the system pipe network pressure.

[0113] The optimization control module 240 is configured to input the state parameters of the compressor cluster at the current moment into a parameter optimization model, solve the target function under the constraint condition, and obtain the optimization control parameters of each compressor, so as to control the compressor cluster based on the optimization control parameters corresponding to each compressor respectively at the next moment.

[0114] The optimization control parameters include a guide vane opening degree adjustment amount, a bypass valve opening degree adjustment amount, a unit start-stop state and a pipe network pressure set value.

[0115] The optimization control device of the compressor cluster provided in the embodiment can improve the overall operation efficiency of the compressor cluster and save operation cost by constructing a target function through the state parameters of the compressor cluster at the current moment, solving the target function under a constraint condition through a parameter optimization model, and obtaining the guide vane opening degree adjustment amount, the bypass valve opening degree adjustment amount, the unit start-stop state and the pipe network pressure set value of each compressor through overall planning and deployment. Meanwhile, the surge constraint, the guide vane opening degree constraint and the start-stop state constraint are used as constraint conditions to ensure that the optimization result is always within the safety boundary of the equipment, so that vibration, over-temperature and other failures caused by surge can be avoided, and the service life of the equipment can be prolonged.

[0116] In an embodiment, the function establishing module 230 is specifically configured to establish an energy consumption sub-function based on the operating power corresponding to each compressor respectively, establish a load balance sub-function based on the variance of the operating power corresponding to each compressor respectively, establish a stability sub-function based on the deviation of the system pipe network pressure and the target pressure, determine the corresponding surge margin based on the guide vane opening degree, the real-time rotating speed, the outlet pressure and the gas flow corresponding to each compressor respectively, establish a surge risk sub-function according to the surge margin corresponding to each compressor respectively, and establish the target function according to the energy consumption sub-function, the stability sub-function, the surge risk sub-function and the load balance sub-function.

[0117] In an embodiment, the function establishing module 230 is further configured to acquire an inlet temperature and an inlet pressure corresponding to the current compressor; acquire a surge boundary database corresponding to the current compressor, wherein one rotating speed interval and one guide vane opening interval correspond to one reference surge flow in the surge boundary database; acquire the real-time rotating speed and the guide vane opening generated by the current compressor at the current time from the surge boundary database to determine a target reference surge flow; correct the target reference surge flow according to the inlet temperature and the inlet pressure to obtain an actual surge flow; and determine a surge margin of the current compressor at the current time according to the target reference surge flow, the actual surge flow and the gas flow.

[0118] In an embodiment, the target function is expressed in the following manner:

[0119]

[0120] wherein P i represents the operating power of the i-th compressor; represents an energy consumption sub-function; ΔP represents a stability sub-function; SurgeMargin represents a surge risk sub-function; Var(Load) represents a load balance sub-function; and α, β, γ and δ respectively represent weight coefficients corresponding to the respective sub-functions, which are constants.

[0121] In an embodiment, the device further comprises a real-time margin determining module, wherein:

[0122] The real-time margin determining module is configured to determine a real-time surge margin corresponding to each compressor respectively; and when the real-time surge margin of any one of the compressors is lower than a safety threshold, control each compressor based on a preset control strategy at the next time.

[0123] In an embodiment, the safety threshold is that when the real-time surge margin is not greater than a first threshold and a gas flow change rate is not lower than a second threshold for a continuous preset time.

[0124] In an embodiment, the parameter acquiring module 210 is specifically configured to acquire initial parameters corresponding to each compressor in a compressor cluster at the current time; and perform standardization processing on the initial parameters corresponding to each compressor respectively by using a standardization method to obtain operating parameters corresponding to each compressor respectively.

[0125] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional modules is exemplified, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the above described functional modules can refer to the corresponding process in the foregoing method embodiment, and will not be repeated here.

[0126] The electronic device provided in the embodiments of the present application includes at least one processor and a memory connected with the at least one processor in communication; the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the optimization control method of the compressor cluster.

[0127] The electronic device provided in the embodiments of the present application includes at least one processor and a memory connected with the at least one processor in communication; the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the optimization control method of the compressor cluster.

[0128] The following refers to Figure 3 , Figure 3 is a structural schematic diagram of an electronic device provided in the embodiments of the present application. It shows a structural schematic diagram of a computer system 500 of the electronic device suitable for implementing the embodiments of the present application. Figure 3 The electronic device shown is only an example and should not impose any limitation on the functions and use range of the embodiments of the present application.

[0129] As Figure 3 shown, the computer system 500 includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 502 or programs loaded from a storage portion 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the system 500 are also stored. The CPU 501, the ROM 502, and the RAM 503 are connected with each other through a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0130] The following components are connected to the I / O interface 505: an input part 506 including a keyboard, a mouse, etc.; an output part 507 including a display such as a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage part 508 including a hard disk, etc.; and a communication part 509 including a network interface card such as a LAN card, a modem, etc. The communication part 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the I / O interface 505 as necessary. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is attached to the drive 510 as necessary, so that a computer program read out therefrom is installed in the storage part 508 as necessary.

[0131] In particular, according to embodiments of the present disclosure, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for executing the methods illustrated by the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network by the communication part 509, and / or installed from the removable medium 511. When the computer program is executed by the central processing unit (CPU) 501, the above-described functions defined in the system of the present disclosure are executed.

[0132] It should be noted that computer-readable medium of the present application can be computer-readable signal medium or computer-readable storage medium or any combination thereof. Computer-readable storage medium can be, for example but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination thereof. More specific examples of computer-readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, computer-readable storage medium can be any tangible medium that contains or stores a program used by an instruction execution system, apparatus or device, and can be used by or in connection with the instruction execution system, apparatus or device. In the present application, computer-readable signal medium can include a data signal that propagates in baseband or as part of a carrier wave by any means of transmission, including but not limited to wired or wireless transmission. Such a propagated signal can take any of a variety of forms, including but not limited to electro-magnetic, optical, or any suitable combination thereof. Computer-readable signal medium can also be any computer-readable medium that is not a storage medium, which can be used by or in connection with an instruction execution system, apparatus or device.

[0133] The flow diagrams and block diagrams in the drawings are illustrations of possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flow diagrams or block diagrams can represent a module, a segment, or a portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or the blocks can sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flow diagrams, and combinations thereof, can be implemented by special purpose hardware-based systems that perform the specified functions or operations, or combinations of special purpose hardware and computer instructions.

[0134] The modules and / or units described in the embodiments of the present application can be implemented in the form of software or in the form of hardware. The described modules and / or units can also be arranged in a processor, for example, a processor can be described as including a parameter acquisition module, a constraint determination module, a function establishment module and a constraint determination module. In some cases, the names of these modules do not constitute a limitation on the modules themselves.

[0135] As another aspect, the present application also provides a computer readable medium, which can be included in the device described in the above embodiments, or can exist independently without being assembled into the device. The computer readable medium carries one or more programs, which, when executed by the device, cause the device to include: acquiring state parameters of a compressor cluster at a current time, the state parameters including at least operating parameters corresponding to each compressor and system parameters of the compressor cluster, the operating parameters including at least operating power, guide vane opening, real-time speed, outlet pressure and gas flow, and the system parameters including at least system pipe network pressure;

[0136] establishing a target function according to the operating power, the guide vane opening, the real-time speed, the outlet pressure, the flow and the system pipe network pressure;

[0137] inputting the state parameters of the compressor cluster at the current time into a parameter optimization model, solving the target function under the constraint conditions to obtain optimized control parameters of each compressor, and controlling the compressor cluster based on the optimized control parameters corresponding to each compressor at the next time;

[0138] The optimized control parameters include guide vane opening adjustment, bypass valve opening adjustment, unit start-stop state and pipe network pressure set value.

[0139] According to the technical solution of the present embodiment, the target function is constructed by the state parameters of the compressor cluster at the current time, so that the target function is solved by the parameter optimization model under the constraint conditions, that is, the guide vane opening adjustment, the bypass valve opening adjustment, the unit start-stop state and the pipe network pressure set value of each compressor are obtained by overall planning and allocation, which can improve the overall operation efficiency of the compressor cluster and save operation cost. At the same time, the surge sub-constraint, the guide vane opening sub-constraint and the start-stop state sub-constraint are used as constraint conditions to ensure that the optimization result is always within the safety boundary of the device, which can avoid vibration, over-temperature and other faults caused by surge, and can prolong the service life of the device.

[0140] The foregoing detailed description has set forth various embodiments of the devices and / or processes via the use of specific terminology. However, embodiments thereof can be practiced with the exact description not being set forth but with the same essence. Therefore, embodiments cannot be limited to the specific details and / or the exact examples described. It should be appreciated that the specific order or hierarchy of steps in the processes can differ from what is described herein, depending on the implementation. Also, it is possible that one or more of the steps could be eliminated from the processes, and other steps could be added to the processes, without departing from the scope of the present application. Further, weight percentages, concentrations, and other quantities that can have been recited herein are meant to be approximations. Any numerical values are approximations only as exact control is not, in practice, necessary to achieve satisfactory results. Thus

Claims

1. An optimized control method for a compressor cluster, characterized in that, include: Obtain the status parameters of the compressor cluster at the current moment. The status parameters include at least the operating parameters of each compressor and the system parameters of the compressor cluster. The operating parameters include at least the operating power, guide vane opening, real-time speed, outlet pressure, and gas flow rate. The system parameters include at least the system pipeline pressure; Determine the constraints, which include surge constraints, guide vane opening constraints, and start / stop state constraints. An objective function is established based on the operating power, the guide vane opening, the real-time rotational speed, the outlet pressure, the gas flow rate, and the system pipeline pressure. The current state parameters of the compressor cluster are input into the parameter optimization model. The objective function is solved under the constraints to obtain the optimized control parameters of each compressor. The compressor cluster is then controlled in the next moment based on the optimized control parameters corresponding to each compressor. The optimized control parameters include guide vane opening adjustment, bypass valve opening adjustment, unit start / stop status, and pipeline pressure setpoint.

2. The optimized control method for a compressor cluster according to claim 1, characterized in that, The establishment of the objective function based on the operating power, the guide vane opening, the real-time rotational speed, the outlet pressure, the gas flow rate, and the system pipeline pressure includes: An energy consumption sub-function is established based on the operating power corresponding to each compressor, and a load balancing sub-function is established based on the variance of the operating power corresponding to each compressor. A stability sub-function is established based on the deviation between the system pipeline pressure and the target pressure; The surge margin is determined based on the guide vane opening, real-time speed, outlet pressure and gas flow rate of each compressor, and a surge risk sub-function is established based on the surge margin of each compressor. The objective function is established based on the energy consumption sub-function, the stability sub-function, the surge risk sub-function, and the load balancing sub-function.

3. The optimized control method for a compressor cluster according to claim 2, characterized in that, The determination of the corresponding surge margin based on the guide vane opening, real-time rotational speed, outlet pressure, and gas flow rate for each compressor includes: Obtain the current inlet temperature and inlet pressure of the compressor; Obtain the surge boundary database corresponding to the current compressor. In the surge boundary database, a speed range and a guide vane opening range correspond to a reference surge flow rate. The target reference surge flow rate is determined from the surge boundary database based on the real-time speed of the compressor at the current moment and the guide vane opening. The target reference surge flow rate is corrected based on the inlet temperature and the inlet pressure to obtain the actual surge flow rate; The surge margin of the current compressor at the current moment is determined based on the target reference surge flow rate, the actual surge flow rate, and the gas flow rate.

4. The optimized control method for a compressor cluster according to claim 2, characterized in that, The objective function is expressed as follows: Among them, P i This represents the operating power of the i-th compressor; ΔP represents the energy consumption subfunction; ΔP represents the stability subfunction; SurgeMargin represents the surge risk subfunction; Var(Load) represents the load balancing subfunction; α, β, γ, and δ represent the weighting coefficients corresponding to the respective subfunctions, which are constants.

5. The optimized control method for a compressor cluster according to claim 1, characterized in that, The method further includes, during the process of inputting the current state parameters of the compressor cluster into the parameter optimization model and solving the objective function under the constraints, as follows: Determine the real-time surge margin for each compressor; When the real-time surge margin of any compressor falls below the safety threshold, each compressor will be controlled in the next moment based on the preset control strategy.

6. The optimized control method for a compressor cluster according to claim 5, characterized in that, The security threshold is: When the real-time surge margin is not greater than the first threshold, and the gas flow rate change rate is not lower than the second threshold for a continuous preset time.

7. The optimized control method for a compressor cluster according to claim 1, characterized in that, Obtain the operating parameters for each compressor, including: Obtain the initial parameters for each compressor in the compressor cluster at the current moment; The initial parameters of each compressor were standardized using a standardization method to obtain the operating parameters for each compressor.

8. An optimized control device for a compressor cluster, characterized in that, include: The parameter acquisition module is used to acquire the status parameters of the compressor cluster at the current moment. The status parameters include at least the operating parameters of each compressor and the system parameters of the compressor cluster. The status parameters include at least the operating power, guide vane opening, real-time speed, outlet pressure and gas flow rate. The system parameters include at least the system pipeline pressure; The constraint determination module is used to determine the constraint conditions, which include surge constraint, guide vane opening sub-constraint, and start / stop state sub-constraint. The function establishment module is used to establish a target function based on the operating power, the guide vane opening, the real-time rotational speed, the outlet pressure, the gas flow rate, and the system pipeline pressure. The optimization control module is used to input the current state parameters of the compressor cluster into the parameter optimization model, solve the objective function under the constraints, obtain the optimized control parameters of each compressor, and control the compressor cluster based on the optimized control parameters corresponding to each compressor in the next moment. The optimized control parameters include guide vane opening adjustment, bypass valve opening adjustment, unit start / stop status, and pipeline pressure setpoint.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the optimized control method of the compressor cluster according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the optimized control method for the compressor cluster as described in any one of claims 1-7.

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