Method and system for controlling oil delivery pump of shale oil booster station through self-adaptive fuzzy PID (Proportion Integration Differentiation)

Abstract

The invention discloses a method and a system for controlling an oil delivery pump of a shale oil booster station through self-adaptive fuzzy PID (Proportion Integration Differentiation), and the method comprises the steps: obtaining the fuzzy quantity of deviation and the fuzzy quantity of the deviation change rate based on the deviation and the deviation change rate of an actual liquid level value and a set liquid level value of a buffer tank; and fuzzy control constraint conditions, the fuzzy quantity of the deviation and the fuzzy quantity of the deviation change rate are combined to obtain fuzzy values, namely proportional gain, integral gain and differential gain, and fuzzy judgment is carried out on the proportional gain, the integral gain and the differential gain so as to convert a reasoning result from the fuzzy quantity into an accurate quantity capable of being used for actual control. The control of the oil delivery pump can be realized by combining a corresponding method based on the actually controlled accurate quantity, so that a controlled object has good dynamic and static performance. Therefore, the transportation process control of the shale oil booster station adopts a mode of combining process control and PID control added to the transportation pump, so that the stability, liquid feeding, adjustment and transportation of the crude oil are realized, and the problems in the prior art are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of the combination of fuzzy control theory and classical control theory, and relates to a method and system for adaptively fuzzy PID controlling an oil transfer pump in a shale oil boosting station. Background Technique

[0002] Traditional control methods are control methods based on accurate data models of controlled objects, lacking flexibility and adaptability, and are suitable for solving relatively simple control problems such as linearity and time-invariance. In production practice, complex control problems such as complexity, non-linearity, time-variance, uncertainty, and incompleteness can be solved by combining the experience of skilled operators and control theory. Fuzzy control is based on artificial experience, based on fuzzy set theory, fuzzy language variables, and fuzzy inference, and combined with traditional control theory to simulate the thinking mode of people, and is a control method implemented for objects that are difficult to establish a mathematical model, and its structure is easy, and its robustness and adaptability are good.

[0003] In the process of shale oil production, the boosting point (transfer station) is an essential production link. In the digital construction of the boosting point (transfer station), it is a very urgent problem to achieve stable crude oil inlet and stable oil transfer through the station library system. The station library system of the boosting point (transfer station) adopts a combination of process control (high start and low stop) and frequency PID regulation of the oil transfer pump (according to the liquid level of the buffer tank) to achieve the purpose of oil inlet and oil transfer. The frequency PID of the oil transfer pump is adjusted by manually setting PID parameters (proportional, integral, differential). This requires operators to have good professional qualities, and often the PID parameters need to be adjusted again after the on-site working conditions change. Manually adjusting the frequency PID parameters of the oil transfer pump has low work efficiency, high labor intensity, and affects the regulation performance. Summary of the Invention

[0004] The purpose of the present invention is to solve the problem of poor regulation performance of the control loop caused by manually tuning PID parameters in the prior art, and to provide a method and system for adaptively fuzzy PID controlling an oil transfer pump in a shale oil boosting station.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A method for adaptively fuzzy PID controlling an oil transfer pump in a shale oil boosting station proposed by the present invention includes the following steps:

[0007] Obtain fuzzy control constraint conditions, the deviation between the actual liquid level value and the set liquid level value of the buffer tank, and the deviation change rate;

[0008] Based on the deviation between the actual liquid level value and the set liquid level value of the buffer tank and the deviation change rate, obtain the fuzzy quantity of the deviation and the fuzzy quantity of the deviation change rate;

[0009] Obtain a fuzzy value based on the fuzzy control constraint conditions, the fuzzy quantity of the deviation, and the fuzzy quantity of the deviation change rate, and perform a fuzzy judgment on the fuzzy value to achieve the control of the oil transfer pump.

[0010] Preferably, obtain the deviation between the actual liquid level value of the buffer tank and the set liquid level value e as follows:

[0011] e(k) = r(k) - y(k)

[0012] Wherein, y(k) is the actual liquid level value of the buffer tank at time k, and r(k) is the set liquid level value of the buffer tank at time k.

[0013] Preferably, obtain the deviation change rate ec of the actual liquid level value of the buffer tank and the set liquid level value as follows:

[0014] ec(k) = e(k) - e(k - 1)

[0015] Wherein, e(k) is the deviation between the actual liquid level value of the buffer tank and the set liquid level value at time k, and e(k - 1) is the deviation between the actual liquid level value of the buffer tank and the set liquid level value at time k - 1.

[0016] Preferably, adopt a triangular membership function, and convert the deviation between the actual liquid level value of the buffer tank and the set liquid level value into a corresponding fuzzy quantity E through numerical judgment, and convert the deviation change rate into a fuzzy quantity EC through numerical judgment.

[0017] Preferably, perform a fuzzy judgment on the fuzzy value by using the maximum membership degree method, the centroid method or the weighted average method.

[0018] Preferably, after performing a fuzzy judgment on the fuzzy value, adopt the following method for control:

[0019]

[0020] Wherein, K p is the proportional gain; K i is the integral gain; K d is the differential gain; e is the deviation between the actual liquid level value of the buffer tank and the set liquid level value, e(k) is the deviation between the actual liquid level value of the buffer tank and the set liquid level value at time k, e(k - 1) is the deviation between the actual liquid level value of the buffer tank and the set liquid level value at time k - 1, and T is the sampling period.

[0021] Preferably, obtain the fuzzy control constraint conditions according to the fuzzy control rule table.

[0022] A system for adaptively fuzzy PID controlling an oil transfer pump in a shale oil booster station proposed by the present invention includes:

[0023] An initial condition acquisition module, which is used to acquire fuzzy control constraint conditions, the deviation between the actual liquid level value and the set liquid level value of the buffer tank, and the deviation change rate.

[0024] A fuzzy quantity acquisition module, which is used to acquire the fuzzy quantity of the deviation and the fuzzy quantity of the deviation change rate based on the deviation between the actual liquid level value and the set liquid level value of the buffer tank and the deviation change rate.

[0025] A fuzzy numerical judgment module, which is used to acquire a fuzzy numerical value based on the fuzzy control constraint conditions, the fuzzy quantity of the deviation, and the fuzzy quantity of the deviation change rate, and perform a fuzzy judgment on the fuzzy numerical value to achieve the control of the oil transfer pump.

[0026] A computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the method for adaptively fuzzy PID controlling the oil transfer pump of the shale oil boosting station are realized.

[0027] A computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for adaptively fuzzy PID controlling the oil transfer pump of the shale oil boosting station are realized.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] For the method for adaptively fuzzy PID controlling the oil transfer pump of the shale oil boosting station proposed by the present invention, when on-site technicians manually tune the PID parameters of the control loop, phenomena such as oscillation, overshoot, and lag are likely to occur. The present invention acquires the fuzzy quantity of the deviation and the fuzzy quantity of the deviation change rate based on the deviation between the actual liquid level value and the set liquid level value of the buffer tank and the deviation change rate; then, in combination with the fuzzy control constraint conditions, the fuzzy quantity of the deviation, and the fuzzy quantity of the deviation change rate, a fuzzy numerical value is obtained, that is, the proportional gain, integral gain, and derivative gain. Fuzzy judgment is performed on the proportional gain, integral gain, and derivative gain to convert the inference result from a fuzzy quantity into an exact quantity that can be used for actual control. Based on the exact quantity of actual control and the corresponding method, the control of the oil transfer pump can be realized, so that the controlled object has good dynamic and static performance. Therefore, the external transportation process control of the shale oil boosting station of the present invention adopts a combination of process control and adding PID control to the external transportation pump to achieve the smooth, liquid inlet, regulation, and external transportation of crude oil, and solves the problem of poor regulation performance of the control loop caused by manually tuning the PID parameters.

[0030] A system for controlling the oil transfer pump in a shale oil booster station using adaptive fuzzy PID control proposed by the present invention realizes the control of the oil transfer pump by dividing the system into an initial condition acquisition module, a fuzzy quantity acquisition module, and a fuzzy numerical judgment module. The modular idea is adopted to make each module independent of each other, facilitating the unified management of each module. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required in the embodiments. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0032] Figure 1 It is a flowchart of the method for controlling the oil transfer pump in a shale oil booster station using adaptive fuzzy PID control of the present invention.

[0033] Figure 2 It is a detailed flowchart of controlling the oil transfer pump in a shale oil booster station of the present invention.

[0034] Figure 3 It is the result of the first fuzzy control rule of the present invention.

[0035] Figure 4 It is the result of the second fuzzy control rule of the present invention.

[0036] Figure 5 It is the result of the third fuzzy control rule of the present invention.

[0037] Figure 6 It is a system diagram of the system for controlling the oil transfer pump in a shale oil booster station using adaptive fuzzy PID control of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0038] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0039] Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed present invention, but merely represents the selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0040] It should be noted that similar reference numerals and letters denote similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0041] In the description of the embodiments of the present invention, it should be noted that if terms such as "upper", "lower", "horizontal", "inner", etc. are used to indicate the orientation or positional relationship, it is based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the present invention is usually placed during use. This is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the present invention. In addition, terms such as "first", "second", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0042] In addition, if the term "horizontal" appears, it does not mean that the component is required to be absolutely horizontal, but it can be slightly inclined. For example, "horizontal" only means that its direction is more horizontal relative to "vertical", and it does not mean that the structure must be completely horizontal, but it can be slightly inclined.

[0043] In the description of the embodiments of the present invention, it should also be noted that unless otherwise clearly specified and limited, if terms such as "set", "installed", "connected", "connected" are used, they should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0044] The following further describes the present invention in detail with reference to the drawings:

[0045] A method for adaptively controlling the oil pump in a shale oil booster station by using a fuzzy PID proposed by the present invention, as Figure 1 shown, includes the following steps:

[0046] S1. Obtain the fuzzy control constraint conditions, the deviation between the actual liquid level value and the set liquid level value of the buffer tank, and the deviation change rate;

[0047] The deviation e between the actual liquid level value and the set liquid level value of the buffer tank is obtained as follows:

[0048] e(k) = r(k) - y(k)

[0049] where y(k) is the actual liquid level value of the buffer tank at time k, and r(k) is the set liquid level value of the buffer tank at time k.

[0050] Obtain the deviation change rate ec of the actual liquid level value and the set liquid level value of the buffer tank as follows:

[0051] ec(k) = e(k) - e(k - 1)

[0052] Wherein, e(k) is the deviation between the actual liquid level value and the set liquid level value of the buffer tank at time k, and e(k - 1) is the deviation between the actual liquid level value and the set liquid level value of the buffer tank at time k - 1.

[0053] According to the fuzzy control rule table, obtain the fuzzy control constraint conditions.

[0054] S2. Based on the deviation and the deviation change rate between the actual liquid level value and the set liquid level value of the buffer tank, obtain the fuzzy quantity of the deviation and the fuzzy quantity of the deviation change rate;

[0055] Adopt the triangular membership function, and convert the deviation between the actual liquid level value and the set liquid level value of the buffer tank into the corresponding fuzzy quantity E through numerical judgment, and convert the deviation change rate into the fuzzy quantity EC through numerical judgment.

[0056] S3. Based on the fuzzy control constraint conditions, the fuzzy quantity of the deviation and the fuzzy quantity of the deviation change rate, obtain the fuzzy value, and perform fuzzy judgment on the fuzzy value to realize the control of the oil transfer pump.

[0057] Adopt the maximum membership degree method, the centroid method or the weighted average method to perform fuzzy judgment on the fuzzy value.

[0058] After performing fuzzy judgment on the fuzzy value, the following method is adopted for control:

[0059]

[0060] Wherein, K p is the proportional gain; K i is the integral gain; K d is the differential gain; e is the actual liquid level value and the set liquid level value of the buffer tank, e(k) is the actual liquid level value and the set liquid level value of the buffer tank at time k, e(k - 1) is the actual liquid level value and the set liquid level value of the buffer tank at time k - 1, and T is the sampling period.

[0061] As Figure 2 shown is the detailed process of this method, and the following is a detailed description of this method:

[0062] First, obtain the accurate parameters of the input quantity

[0063] The actual liquid level value of the buffer tank is the input parameter y, the set liquid level value of the buffer tank is a constant r, the difference between the set liquid level value and the actual liquid level value is the deviation e(k) = r(k) - y(k), and the change rate of the current deviation and the previous deviation is ec(k) = e(k) - e(k - 1).

[0064] Second, precise parameter fuzzification

[0065] Using the triangular membership function, the deviation e and the deviation change rate ec are converted into corresponding fuzzy quantities E and EC through numerical judgment. E and EC take values from the fuzzy set. The fuzzy set is set as: {Negative Big (NB), Negative Small (NS), Zero (Z), Positive Small (PS), Positive Big (PB)}.

[0066] Third, fuzzy inference

[0067] Fuzzy inference is carried out according to the summarized fuzzy control rule table (expert experience). The fuzzy control planning table is shown in the attached figure. According to the fuzzy control rule table, 25 fuzzy control rules are formed, such as Figures 3 to 5 shown below.

[0068] If (E is NB) and (EC is NB) then (KP is PB) (KI is NB) (KD is PS).

[0069] If (E is NB) and (EC is NS) then (KP is PS) (KI is NS) (KD is NB).

[0070] If (E is NB) and (EC is Z) then (KP is PS) (KI is NS) (KD is NB).

[0071] If (E is NB) and (EC is PS) then (KP is PS) (KI is NS) (KD is NB).

[0072] If (E is NB) and (EC is PB) then (KP is Z) (KI is Z) (KD is PS).

[0073] If (E is NS) and (EC is NB) then (KP is PS) (KI is NB) (KD is Z).

[0074] If (E is NS) and (EC is NS) then (KP is PS) (KI is NS) (KD is NS).

[0075] If (E is NS) and (EC is Z) then (KP is PS) (KI is NS) (KD is NS).

[0076] If (E is NS) and (EC is PS), then (KP is Z), (KI is Z), (KD is NS).

[0077] If (E is NS) and (EC is PB), then (KP is NS), (KI is PS), (KD is Z).

[0078] If (E is Z) and (EC is NB), then (KP is PS), (KI is NS), (KD is Z).

[0079] If (E is Z) and (EC is NS), then (KP is PS), (KI is NS), (KD is NS).

[0080] If (E is Z) and (EC is Z), then (KP is Z), (KI is Z), (KD is NS).

[0081] If (E is Z) and (EC is PS), then (KP is NS), (KI is PS), (KD is NS).

[0082] If (E is Z) and (EC is PB), then (KP is NS), (KI is PS), (KD is NS).

[0083] If (E is PS) and (EC is NB), then (KP is PS), (KI is NS), (KD is Z).

[0084] If (E is PS) and (EC is NS), then (KP is Z), (KI is Z), (KD is Z).

[0085] If (E is PS) and (EC is Z), then (KP is NS), (KI is PS), (KD is Z).

[0086] If (E is PS) and (EC is PS), then (KP is NS), (KI is PS), (KD is Z).

[0087] If (E is PS) and (EC is PB), then (KP is NS), (KI is PB), (KD is Z).

[0088] If (E is PB) and (EC is NB) then (KP is Z) (KI is Z) (KD is Z).

[0089] If (E is PB) and (EC is NS) then (KP is NS) (KI is PS) (KD is PS).

[0090] If (E is PB) and (EC is Z) then (KP is NS) (KI is PS) (KD is PS).

[0091] If (E is PB) and (EC is PS) then (KP is NS) (KI is PS) (KD is PS).

[0092] If (E is PB) and (EC is PB) then (KP is NB) (KI is PB) (KD is PB).

[0093] Among them, the fuzzy rules are composed of a series of fuzzy conditional statements, that is, composed of many fuzzy implication relations. These conditional statements are the starting point of reasoning and the basis for obtaining correct conclusions. Each fuzzy conditional statement gives a fuzzy implication relation, that is, a control rule. If there are n rules, the n fuzzy implication relations (i = 1, 2,..., n) expressed by them are subjected to union operation to form the overall fuzzy implication relation of the system, and finally the proportional gain K p , integral gain K i and differential gain K d of fuzzy values.

[0094] Fourth, fuzzy determination: Convert the inference result from a fuzzy quantity into an exact quantity that can be used for actual control.

[0095] Through fuzzy inference, a series of fuzzy expressions are obtained, and defuzzification operations need to be performed to obtain exact data. There are three commonly used defuzzification methods, and different methods are selected according to specific requirements. The maximum membership degree method is simple to calculate and is suitable for occasions with low control requirements; the centroid method has smoother output but greater calculation difficulty; the weighted average method is the most widely used in industry.

[0096] The adaptive fuzzy PID control is as follows:

[0097]

[0098] Among them, K p is the proportional gain; K i is the integral gain; K dis the differential gain; e is the deviation between the actual liquid level value and the set liquid level value of the buffer tank, e(k) is the deviation between the actual liquid level value and the set liquid level value of the buffer tank at time k, e(k - 1) is the deviation between the actual liquid level value and the set liquid level value of the buffer tank at time k - 1, and T is the sampling period.

[0099] Fuzzy self-tuning of PID parameters is to find the three parameters k p , k i , k d and the fuzzy relationship between e and ec. During operation, by continuously detecting e and ec, the three parameters are modified online according to the fuzzy control principle to meet the requirements of control parameters for different e and ec, so that the controlled object has good dynamic and static performance.

[0100] The external transportation process control of the shale oil booster station adopts a combination of process control (starting high and stopping low) and adding PID control to the external transportation pump to achieve the stable, liquid inlet, regulation, and external transportation of crude oil. Among them, the PID control of the external transportation pump adopts adaptive fuzzy PID tuning control. It achieves the purpose of controlling the change of the three PID parameters (proportional, integral, differential) through the change of the deviation value of the buffer tank liquid level. During the online operation process, the fuzzy control system completes the online automatic correction of the PID parameters (kp, ki, kd) through the result processing, look-up table, and operation of the fuzzy logic rules.

[0101] A system for an adaptive fuzzy PID controlled oil pump in a shale oil booster station proposed by the present invention, as Figure 6 shown, includes an initial condition acquisition module, a fuzzy quantity acquisition module, and a fuzzy numerical judgment module;

[0102] The initial condition acquisition module is used to acquire the fuzzy control constraint conditions, the deviation between the actual liquid level value and the set liquid level value of the buffer tank, and the deviation change rate;

[0103] The fuzzy quantity acquisition module is used to acquire the fuzzy quantity of the deviation and the fuzzy quantity of the deviation change rate based on the deviation between the actual liquid level value and the set liquid level value of the buffer tank and the deviation change rate;

[0104] The fuzzy numerical judgment module is used to acquire fuzzy numerical values based on the fuzzy control constraint conditions, the fuzzy quantity of the deviation, and the fuzzy quantity of the deviation change rate, and perform fuzzy judgment on the fuzzy numerical values to achieve the control of the oil pump.

[0105] The terminal device provided by the embodiment of the present invention includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned method embodiments are implemented. Or, when the processor executes the computer program, the functions of each module / unit in the above-mentioned device embodiments are implemented.

[0106] The computer program can be divided into one or more modules / units, and the one or more modules / units are stored in the memory and executed by the processor to implement the present invention.

[0107] The terminal device can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The terminal device may include, but is not limited to, a processor and a memory.

[0108] The processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0109] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the terminal device by running or executing the computer program and / or module stored in the memory, and by invoking the data stored in the memory.

[0110] If the modules / units integrated in the terminal device are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above-mentioned embodiment methods of the present invention, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various method embodiments can be realized. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate forms, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0111] A method for adaptively fuzzy PID controlling the oil transfer pump in a shale oil boosting station proposed by the present invention. In the external oil transfer process of the boosting station, the adaptive fuzzy PID control method is adopted in the variable frequency regulation of the external oil transfer pump. The fuzzy tuning of PID parameters replaces the manual experience tuning of PID parameters. During operation, the PID parameters are adaptively and fuzzily tuned. The variable frequency of the external oil transfer pump is smooth and stable, the liquid inlet and outlet of the upstream and downstream are stable, and the effect is remarkable, resulting in obvious economic and social benefits. After the implementation of the project, the personnel structure is optimized, the labor cost is reduced, the operation and maintenance cost is lowered, the labor efficiency is improved, the labor intensity is reduced, and the expected goals of reducing staff and increasing efficiency, and saving energy and reducing consumption are achieved. Through the monitoring and automatic evaluation of the control loop performance, the automatic control rate of the loop reaches 100%. After the implementation of the project, 4 operation and maintenance personnel are optimized. Calculated according to the average annual cost of front-line employees in the oilfield company, the annual personnel expenditure cost is reduced by a large amount.

[0112] Through implementation, the annual crude oil output has increased by 0.1%. Calculated based on an annual crude oil output of 1 million tons, the annual increase in production is 0.1 million tons. By promoting the construction and technical application of the adaptive fuzzy PID control of the control loop, the automatic control rate and stability rate of the control loop are improved, and the operation reliability and safety of the device are improved through the optimization of process parameters, the energy consumption of the device is reduced, the cost is reduced and the efficiency is increased, the management level and maintenance production efficiency are improved, and the on-site management is transformed from decentralized management to centralized control.

[0113] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for adaptively controlling a pipeline pump in a shale oil booster station using fuzzy PID, characterized in that, It includes the following steps: Obtain the fuzzy control constraint conditions, the deviation between the actual liquid level value and the set liquid level value of the buffer tank, and the deviation change rate; Based on the deviation between the actual liquid level value and the set liquid level value of the buffer tank and the deviation change rate, obtain the fuzzy quantity of the deviation and the fuzzy quantity of the deviation change rate; Based on the fuzzy control constraint conditions, the fuzzy quantity of the deviation, and the fuzzy quantity of the deviation change rate, obtain a fuzzy value, and perform a fuzzy judgment on the fuzzy value to achieve the control of the oil transfer pump.

2. The method for adaptively fuzzy PID controlling the oil transfer pump of a shale oil boosting station according to claim 1, wherein Obtain the deviation between the actual liquid level value and the set liquid level value e of the buffer tank as follows: e(k) = r(k) - y(k) where y(k) is the actual liquid level value of the buffer tank at time k, and r(k) is the set liquid level value of the buffer tank at time k.

3. The method for adaptively fuzzy PID controlling the oil transfer pump of a shale oil boosting station according to claim 1, wherein Obtain the deviation change rate ec between the actual liquid level value and the set liquid level value of the buffer tank as follows: ec(k) = e(k) - e(k - 1) where e(k) is the deviation between the actual liquid level value and the set liquid level value of the buffer tank at time k, and e(k - 1) is the deviation between the actual liquid level value and the set liquid level value of the buffer tank at time k - 1.

4. The method for adaptively fuzzy PID controlling the oil transfer pump of a shale oil booster station according to claim 1, wherein Adopt a triangular membership function, and convert the deviation between the actual liquid level value and the set liquid level value of the buffer tank into a corresponding fuzzy quantity E through numerical judgment, and convert the deviation change rate into a fuzzy quantity EC through numerical judgment.

5. The method for adaptively fuzzy PID controlling the oil transfer pump of a shale oil booster station according to claim 1, wherein, Adopt the maximum membership degree method, the centroid method, or the weighted average method to perform a fuzzy judgment on the fuzzy value.

6. The method for adaptively fuzzy PID controlling the oil transfer pump of a shale oil booster station according to claim 1, wherein, After performing a fuzzy judgment on the fuzzy value, adopt the following method for control: Among them, K p is the proportional gain; K i is the integral gain; K d is the derivative gain; e is the deviation between the actual liquid level value and the set liquid level value of the buffer tank, e(k) is the deviation between the actual liquid level value and the set liquid level value of the buffer tank at time k, e(k - 1) is the deviation between the actual liquid level value and the set liquid level value of the buffer tank at time k - 1, and T is the sampling period.

7. The method for adaptively fuzzy PID controlling a fuel transfer pump in a shale oil boosting station according to claim 1, wherein According to the fuzzy control rule table, obtain the fuzzy control constraint conditions.

8. A system for adaptively controlling a pipeline pump in a shale oil boosting station using fuzzy PID control, characterized in that, It includes: An initial condition acquisition module, which is used to obtain the fuzzy control constraint conditions, the deviation between the actual liquid level value and the set liquid level value of the buffer tank, and the deviation change rate; A fuzzy quantity acquisition module, which is used to obtain the fuzzy quantity of the deviation and the fuzzy quantity of the deviation change rate based on the deviation between the actual liquid level value and the set liquid level value of the buffer tank and the deviation change rate; A fuzzy value judgment module, which is used to obtain a fuzzy value based on the fuzzy control constraint conditions, the fuzzy quantity of the deviation, and the fuzzy quantity of the deviation change rate, and perform a fuzzy judgment on the fuzzy value to achieve the control of the oil transfer pump.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method for adaptively fuzzy PID controlling the oil transfer pump of the shale oil booster station as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for adaptively fuzzy PID controlling the oil transfer pump of the shale oil booster station as described in any one of claims 1 to 7.

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