A cloud edge joint control system for intelligent variable speed driving of a pumping unit and a control method thereof

Abstract

The application provides a cloud edge joint control system for intelligent variable-speed driving of a pumping unit and a control method thereof, and belongs to the technical field of pumping unit monitoring. According to the indicator diagram obtained through electrical parameter inversion, the application performs working condition diagnosis, automatically judges the continuous pumping mode and the intermittent pumping mode according to the working condition diagnosis result and the environmental temperature, automatically adjusts the pumping frequency in the continuous pumping mode, automatically starts and stops in the intermittent pumping mode, realizes the balance between oil well supply and discharge with low energy consumption, and the whole working mode is completely self-adaptive to the change of the oil well working condition, thereby improving the oil well management efficiency and reducing the production energy consumption.

Description

Technical Field

[0001] This invention relates to the field of oil pumping unit monitoring technology, and in particular to a cloud-edge joint control system for intelligent variable speed drive of oil pumping units. Background Technology

[0002] With the continuous development of technology, the trend of intelligentization is gradually expanding to various industries, and oil pumping units also need to develop towards intelligence to meet the current needs of smart oilfield construction. Currently, most oil pumping unit control systems use programmable logic controllers (PLCs) and human-machine interfaces (HMIs) to achieve simple control of the oil pumping unit speed.

[0003] Although this type of oil pumping unit control system has a simple structure and is easy to operate, its automatic adjustment capability is poor. When the parameters of the oil pumping process change, it cannot accurately and timely adjust the oil pumping process, which will affect the working condition of the entire oil pumping unit. In addition, it lacks a real-time detection and feedback system, and manual detection is required, which is time-consuming, labor-intensive and inefficient.

[0004] Chinese patent application CN107703758B discloses an adaptive variable speed drive control system and method for an oil pumping unit. The remote control unit provides two adaptive variable speed drive control modes for manual selection. One mode calculates the efficiency of the oil pump by comparing real-time calculated pump efficiency data with a standard data model for the current stroke rate, and then adjusts the stroke rate data accordingly. The other mode offers the highest economic benefit by using a bubble sorting method to calculate the maximum value of the oil pump displacement Q, oil pumping unit power consumption W, current oil price r1, and current electricity price r2, thus determining the stroke rate adjustment data. Both modes transmit stroke rate adjustment commands to the variable speed drive module via the remote control unit, completing the automatic intelligent adjustment of the oil pumping unit's stroke rate. After a certain period, the current real-time dynamometer data is recalculated and compared, and the stroke rate is adjusted again. This process is repeated until the oil pumping unit's stroke rate meets the requirements or reaches its optimal level. The first adaptive variable speed drive control mode works as follows: Step 1: The remote control unit collects 72 load and angular displacement data points for each of the downstroke strokes using angular displacement and load sensors within a sampling interval, respectively, and establishes a data analysis database; Step 2: Extract the collected data from the database, calculate the maximum load of the upstroke stroke, the minimum load of the downstroke stroke, the slope of the upstroke loading line, and the slope of the downstroke unloading line, and derive the ideal indicator diagram; Step 3: Calculate the efficiency of the pumping unit using the geometric area method; the efficiency of the pumping unit is the area of ​​the measured indicator diagram divided by the area of ​​the ideal indicator diagram. Because the angular displacement is consistent vertically, the angular displacement is considered during the calculation.

[0005]

[0006] The data is shifted and the formula is as follows; Step 4: Adaptively adjust the running stroke.

[0007] The existing technology has at least the following shortcomings:

[0008] 1. Without considering the ambient temperature factor, the pumping unit speed control cannot achieve optimal performance.

[0009] 2. The control mode is singular and lacks adaptability.

[0010] 3. The calculation model for the conversion between "electric dynamometer diagram and dynamometer diagram" has a deviation, and the conversion result cannot reach the optimal level.

[0011] 4. The control method that does not combine remote monitoring terminal self-learning control with on-site direct control cannot meet the requirements of equipment intelligence and automation. Summary of the Invention

[0012] To address the problems existing in the prior art, this invention performs operating condition diagnosis based on the dynamometer diagram derived from electrical parameters. Based on the operating condition diagnosis results and ambient temperature, it automatically determines the continuous pumping and intermittent pumping operating modes. In the continuous pumping mode, it automatically adjusts the pumping frequency, and in the intermittent pumping mode, it automatically starts and stops, achieving a low-energy-consumption oil well supply and drainage balance. The entire operating mode is fully adaptive to changes in oil well operating conditions, improving oil well management efficiency and reducing production energy consumption.

[0013] This invention provides a cloud-edge control system for intelligent variable speed drive of an oil pumping unit, including a PLC controller, a detection module, a communication module, a variable speed control module, an energy feedback module, and an operation control module;

[0014] The speed control module and the energy feedback module are directly connected to the motor input line, and both the speed control module and the energy feedback module are connected to the PLC controller; the communication module is connected to the PLC controller, and the communication module is also connected to a remote monitoring terminal via a wireless network;

[0015] The remote monitoring terminal performs operational condition diagnosis based on the principles of maintaining consistent production, increasing pump efficiency, and minimizing unit power consumption. If pump shutdown is required, the pumping unit is automatically stopped and an alarm is triggered. If pump shutdown is not required after diagnosis, the operating parameters are optimized to obtain the optimal control parameter set for the pumping unit. Pump shutdown is required if efficiency is below 15% or the indicator diagram is abnormal; otherwise, it is not necessary. Unit power consumption refers to the minimum power consumption per unit of produced liquid, and the obtained operational condition includes unit power consumption.

[0016] The detection module collects the working parameters of the oil pumping unit related to the dynamometer diagram and transmits them to the PLC controller. The PLC controller uses the received working parameters to calculate the real-time efficiency of the oil pumping unit and to perform real-time inversion calculation of the suspension point dynamometer diagram.

[0017] The operation control module is connected to the PLC controller. The operation control module acquires mode switching signals and automatic or manual switching signals, generates constant speed or variable speed mode control logic, and controls the pumping unit motor according to the logic.

[0018] The speed control module sets the pumping unit to either constant speed mode or adaptive speed control mode, and controls the pumping unit according to the optimal control parameter set calculated by the remote monitoring terminal. In the event of a failure of the speed control module, the system automatically switches to constant speed mode.

[0019] The energy feedback module is connected to the motor input line and feeds back the electrical energy generated by the pumping unit motor during the downstroke to the power grid;

[0020] Preferably, the detection module includes an electrical parameter acquisition module and an angular displacement sensor;

[0021] The electrical parameter acquisition module acquires electrical parameters and connects them to the PLC controller;

[0022] The angular displacement sensor is installed at the middle position of the walking beam of the pumping unit; the angular displacement sensor is connected to the PLC controller via a signal line.

[0023] Preferably, the detection module detects active power, power factor, suspension point displacement, and actual liquid output, and transmits them to the PLC controller. The PLC controller uses the active power to calculate the actual load on the suspension point, and combines it with the actual liquid output to calculate the real-time efficiency of the pumping unit, and performs real-time inversion calculation of the suspension point dynamometer diagram.

[0024] Preferably, the PLC controller uses active power to calculate the actual load at the suspension point using the following formula:

[0025]

[0026] In the formula,

[0027] W represents the motor's output torque;

[0028] η1, η2, and η3 represent the efficiency of the pulley, the efficiency of the gearbox, and the efficiency between the crank and the suspension point, respectively.

[0029] i1 and i2 are the pulley transmission ratio and the gearbox transmission ratio, respectively;

[0030] W c For the weight of the rear arm of the traveling beam;

[0031] ε is the structural characteristic coefficient of the oil pumping unit;

[0032] R is the crank radius;

[0033] A is the length of the front arm of the walking beam, B is the dynamic load coefficient, and C is the length of the rear arm of the walking beam.

[0034] θ2 is the included angle of crank rotation;

[0035] θ3 is the rotation angle of the connecting rod;

[0036] θ4 is the angle between the rear arm C of the walking beam and the horizontal plane at the initial position;

[0037] θ c This represents the change in the walking beam angle corresponding to the displacement.

[0038] Preferably, based on the calculated actual load at the suspension point and the actual liquid output, the real-time efficiency of the pumping unit is calculated using the following formula:

[0039]

[0040] In the formula:

[0041] η is the real-time efficiency of the oil pumping unit;

[0042] M i The actual load at the calculated i-th suspension point;

[0043] A is the length of the forearm of the walking beam;

[0044] θ ci This represents the change in the walking beam angle corresponding to the displacement of the i-th suspension point;

[0045] n represents the number of suspension points.

[0046] Preferably, the communication module is a 4G communication module, which communicates with the remote monitoring terminal through a 4G wireless network.

[0047] Preferably, the motor is a switched reluctance motor.

[0048] Preferably, the detection module, the speed control module, the energy feedback module, and the communication module are connected to the PLC controller via a 485 communication interface.

[0049] Preferably, the remote monitoring terminal performs operating condition diagnosis through a self-learning model.

[0050] Preferably, the system also includes a system protection module, which is connected to the three-phase 380V power supply line and the ground wire, and is also connected to the three-phase input line at the motor input terminal. The operation control module is connected to the PLC controller. The connection of the three-phase 380V power supply line and the ground wire in the system protection module improves the safety of the control circuit when working in the field. It is also connected to the three-phase input line at the motor input terminal to provide comprehensive motor protection.

[0051] This invention provides a control method for a cloud-edge interconnected control system for any of the above-mentioned pumping units with intelligent variable speed drive, comprising the following steps:

[0052] Step 1: Activate the timed acquisition function of the dynamometer card data of the detection module;

[0053] Step 2: Set the acquisition frequency and acquire dynamometer data;

[0054] Step 3: The PLC controller calculates the actual dynamometer data based on the collected dynamometer data;

[0055] Step 4: Input the actual dynamometer data into the remote monitoring terminal to judge the operating condition of the pumping unit and identify faults. When the pumping unit has a fault, stop the pump and sound an alarm; when the pumping unit has no fault, calculate the current operating status parameters of the pumping unit based on the actual dynamometer data, and obtain the optimal control parameter set of the pumping unit based on the operating status parameters; the operating status parameters include two cases: constant speed and adaptive speed.

[0056] Step 5: Input the optimal control parameter set of the pumping unit into the PLC controller. The PLC controller transmits the optimal control parameter set of the pumping unit to the speed control module. The speed control module controls the pumping unit according to the optimal control parameter set of the pumping unit.

[0057] Preferably, calculating the current operating parameters of the pumping unit based on the actual dynamometer diagram data specifically includes the following steps:

[0058] (1) Put the dynamometer diagram data of different pumping units and the dynamometer diagram data of the same pumping unit at different time periods into the same folder and perform normalization, binarization and thinning processing.

[0059] (2) Create data labels and classify them according to the working condition type corresponding to the dynamometer diagram. For the classified dynamometer diagram dataset, randomly select samples accounting for the total dataset (n) as the training set, and the remaining (1-n) as the validation set (0<n<1).

[0060] (3) Build an AlexNet-BN convolutional neural network model and import dynamometer data to train and validate the network model. First, import the training set (n) to train the network model, and then import the validation set (1-n) to verify the accuracy of the trained network model, so as to realize the automatic identification, classification and diagnosis of the dynamometer of the pumping unit.

[0061] (4) The actual electrical dynamometer diagram information is input into the network model after training. The background computer performs self-learning to simulate the method of manually judging the dynamometer diagram and generate working status parameters in real time.

[0062] Preferably, the optimal control parameter set for the pumping unit obtained based on the operating state parameters specifically includes:

[0063] If the current operation is in constant speed mode, determine whether the efficiency of the pumping unit is lower than the preset threshold. If so, generate the optimal control array for intermittent pumping and write the value of the optimal control array into the variable speed control module to perform intermittent pumping control. If not, generate the optimal control array for constant speed mode and write the value of the optimal control array into the variable speed control module to complete the adjustment of the operating conditions of the pumping unit motor.

[0064] If the current mode is adaptive transmission mode, the system determines whether it is summer or winter operation mode based on the comparison between the detected temperature and the set temperature threshold.

[0065] If it is in summer operation mode, determine whether the efficiency of the pumping unit is lower than the preset threshold. If so, generate the optimal control array for intermittent pumping, write the value of the optimal control array into the speed control module, and perform intermittent pumping control. If not, calculate the optimal speed value, generate the optimal control array for adaptive speed mode, and then write the value of the optimal control array into the speed control module to complete the adjustment of the operating conditions of the pumping unit motor.

[0066] In winter operation mode, the system checks if the pump efficiency is below a preset threshold. If so, it calculates the ultra-low speed operating value without shutting down and generates an optimal control array for ultra-low speed. If not, it calculates the optimal speed value, generates an optimal control array for adaptive speed control mode, and then writes the ultra-low speed optimal control array value or the adaptive speed control mode optimal control array value into the speed control module to complete the adjustment of the pumping unit motor's operating conditions. Ultra-low speed refers to a speed of 10% to 20% of the rated speed.

[0067] Preferably, the detection module collects the useful power, displacement, and motor speed of the motor.

[0068] Preferably, the corresponding motor torque value is obtained based on the motor's active power and motor speed using the following formula:

[0069]

[0070] In the formula,

[0071] W represents the motor's output torque;

[0072] P is the active power of the motor;

[0073] m is the motor speed.

[0074] Preferably, the actual indicator diagram displacement and load data are calculated based on the displacement and the useful power of the motor using the following formula:

[0075]

[0076] In the formula,

[0077] η1, η2, and η3 are the efficiency of the pulley, the efficiency of the gearbox, and the efficiency between the crank and the suspension point, respectively.

[0078] i1 and i2 are the pulley transmission ratio and the gearbox transmission ratio, respectively;

[0079] W c For the weight of the rear arm of the traveling beam;

[0080] ε is the structural characteristic coefficient of the oil pumping unit;

[0081] R is the crank radius;

[0082] A is the length of the forearm of the walking beam;

[0083] B is the dynamic load factor;

[0084] C is the length of the rear arm of the walking beam;

[0085] θ2 is the included angle of crank rotation;

[0086] θ3 is the rotation angle of the connecting rod;

[0087] θ4 is the angle between the rear arm C of the walking beam and the horizontal plane at the initial position;

[0088] θ c This represents the change in the walking beam angle corresponding to the displacement.

[0089] Preferably, the actual indicator diagram data of a complete up and down stroke cycle is extracted and transmitted to a remote monitoring terminal. The remote monitoring terminal uses a self-learning model built based on the convolutional neural network AlexNet-BN to judge the operating condition of the pumping unit and identify faults.

[0090] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0091] This invention uses a PLC and variable speed control module as its core, combining remote monitoring terminal self-learning control with direct on-site control to achieve intelligent adjustment of the pumping unit, meeting the requirements of equipment intelligence and automation. Real-time acquisition of operating characteristic parameters, including active power, motor speed, suspension point displacement, and actual liquid output, is achieved through a detection module circuit, enabling online monitoring of the equipment's operating status. The system can display and set equipment operating parameters in real time via a display device. Real-time acquisition of flow angle and motor active power data is fed into an "electrical dynamometer diagram-dynamometer diagram" conversion calculation model to calculate the actual dynamometer diagram of the pumping unit. This diagram is then fed into a self-learning model based on the AlexNet-BN convolutional neural network for intelligent calculation and fault identification. Remote data transmission and monitoring are achieved through a 4G module, allowing for the setting of variable speed / conventional switching modes and automatic / manual switching modes. This provides multiple operating mode options for the control circuit suitable for intelligent adjustment of beam pumping units, ultimately achieving intelligent control across all operating conditions. Continuous operation, intermittent pumping operation, and ultra-low speed operation require no manual adjustment, and complete intelligent diagnostics are implemented. In addition, remote control of the oil pumping unit can be achieved by remotely sending and receiving data. Attached Figure Description

[0092] Figure 1 This is a schematic diagram of a cloud-edge control system for intelligent variable speed drive of an oil pumping unit according to an embodiment of the present invention;

[0093] Figure 2 This is a flowchart of the control method of a cloud-edge joint control system for intelligent variable speed drive of an oil pumping unit according to an embodiment of the present invention.

[0094] Figure 3 This is a flowchart of the optimal control parameter set calculation according to an embodiment of the present invention. Detailed Implementation

[0095] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0096] This invention provides a cloud-edge control system for intelligent variable speed drive of an oil pumping unit, including a PLC controller, a detection module, a communication module, a variable speed control module, an energy feedback module, and an operation control module;

[0097] The speed control module and the energy feedback module are directly connected to the motor input line, and both the speed control module and the energy feedback module are connected to the PLC controller; the communication module is connected to the PLC controller, and the communication module is also connected to a remote monitoring terminal via a wireless network;

[0098] The remote monitoring terminal performs operating condition diagnosis based on the principles of maintaining production volume, increasing pump efficiency, and minimizing liquid consumption per unit. If pump shutdown is required, the pumping unit will automatically stop and an alarm will be triggered. If pump shutdown is not required after diagnosis, the operating parameters will be optimized to obtain the optimal control parameter set for the pumping unit.

[0099] The detection module collects the working parameters of the oil pumping unit related to the dynamometer diagram and transmits them to the PLC controller. The PLC controller uses the received working parameters to calculate the real-time efficiency of the oil pumping unit and to perform real-time inversion calculation of the suspension point dynamometer diagram.

[0100] The operation control module is connected to the PLC controller. The operation control module acquires mode switching signals and automatic or manual switching signals, generates constant speed or variable speed mode control logic, and controls the pumping unit motor according to the logic.

[0101] The speed control module sets the pumping unit to either constant speed mode or adaptive speed control mode, and controls the pumping unit according to the optimal control parameter set calculated by the remote monitoring terminal. In the event of a failure of the speed control module, the system automatically switches to constant speed mode.

[0102] The energy feedback module is connected to the motor input line and feeds back the electrical energy generated by the pumping unit motor during the downstroke to the power grid;

[0103] According to a specific embodiment of the present invention, the detection module includes an electrical parameter acquisition module and an angular displacement sensor;

[0104] The electrical parameter acquisition module acquires electrical parameters and connects them to the PLC controller;

[0105] The angular displacement sensor is installed at the middle position of the walking beam of the pumping unit; the angular displacement sensor is connected to the PLC controller via a signal line.

[0106] According to a specific embodiment of the present invention, the detection module detects active power, power factor, suspension point displacement and actual liquid output, and transmits them to the PLC controller. The PLC controller uses the active power to calculate the actual load on the suspension point, and combines it with the actual liquid output to calculate the real-time efficiency of the pumping unit, and performs real-time inversion calculation of the suspension point dynamometer diagram.

[0107] According to a specific embodiment of the present invention, the PLC controller uses active power to calculate the actual load at the suspension point using the following formula:

[0108]

[0109] In the formula,

[0110] W represents the motor's output torque;

[0111] η1, η2, and η3 represent the efficiency of the pulley, the efficiency of the gearbox, and the efficiency between the crank and the suspension point, respectively.

[0112] i1 and i2 are the pulley transmission ratio and the gearbox transmission ratio, respectively;

[0113] W c For the weight of the rear arm of the traveling beam;

[0114] ε is the structural characteristic coefficient of the oil pumping unit;

[0115] R is the crank radius;

[0116] A is the length of the front arm of the walking beam, B is the dynamic load coefficient, and C is the length of the rear arm of the walking beam.

[0117] θ2 is the included angle of crank rotation;

[0118] θ3 is the rotation angle of the connecting rod;

[0119] θ4 is the angle between the rear arm C of the walking beam and the horizontal plane at the initial position;

[0120] θ c This represents the change in the walking beam angle corresponding to the displacement.

[0121] According to a specific embodiment of the present invention, the real-time efficiency of the pumping unit is calculated based on the calculated actual load at the suspension point and the actual liquid output using the following formula:

[0122]

[0123] In the formula:

[0124] η is the real-time efficiency of the oil pumping unit;

[0125] M i The actual load at the calculated i-th suspension point;

[0126] A is the length of the forearm of the walking beam;

[0127] θ ci This represents the change in the walking beam angle corresponding to the displacement of the i-th suspension point;

[0128] n represents the number of suspension points.

[0129] According to a specific embodiment of the present invention, the communication module is a 4G communication module, and the communication module communicates with the remote monitoring terminal through a 4G wireless network.

[0130] According to one specific embodiment of the present invention, the motor is a switched reluctance motor.

[0131] According to a specific embodiment of the present invention, the detection module, the speed control module, the energy feedback module and the communication module are connected to the PLC controller through a 485 communication interface.

[0132] According to one specific embodiment of the present invention, the remote monitoring terminal performs operating condition diagnosis through a self-learning model.

[0133] According to a specific embodiment of the present invention, a system protection module is further included. This system protection module is connected to a three-phase 380V power supply line and a ground wire, and is also connected to the three-phase input line at the motor input terminal. The operation control module is connected to a PLC controller. The connection of the three-phase 380V power supply line and ground wire in the system protection module improves the safety of the control circuit during field operation. It is also connected to the three-phase input line at the motor input terminal to provide comprehensive motor protection.

[0134] This invention provides a control method for a cloud-edge interconnected control system for any of the above-mentioned pumping units with intelligent variable speed drive, comprising the following steps:

[0135] Step 1: Activate the timed acquisition function of the dynamometer card data of the detection module;

[0136] Step 2: Set the acquisition frequency and acquire dynamometer data;

[0137] Step 3: The PLC controller calculates the actual dynamometer data based on the collected dynamometer data;

[0138] Step 4: Input the actual dynamometer data into the remote monitoring terminal to judge the operating condition of the pumping unit and identify faults. When the pumping unit has a fault, stop the pump and sound an alarm; when the pumping unit has no fault, calculate the current operating status parameters of the pumping unit based on the actual dynamometer data, and obtain the optimal control parameter set of the pumping unit based on the operating status parameters; the operating status parameters include two cases: constant speed and adaptive speed.

[0139] Step 5: Input the optimal control parameter set of the pumping unit into the PLC controller. The PLC controller transmits the optimal control parameter set of the pumping unit to the speed control module. The speed control module controls the pumping unit according to the optimal control parameter set of the pumping unit.

[0140] According to a specific embodiment of the present invention, calculating the current operating status parameters of the pumping unit based on actual dynamometer data specifically includes the following steps:

[0141] (1) Put the dynamometer diagram data of different pumping units and the dynamometer diagram data of the same pumping unit at different time periods into the same folder and perform normalization, binarization and thinning processing.

[0142] (2) Create data labels and classify them according to the working condition type corresponding to the dynamometer diagram. For the classified dynamometer diagram dataset, randomly select samples accounting for the total dataset (n) as the training set, and the remaining (1-n) as the validation set (0<n<1).

[0143] (3) Build an AlexNet-BN convolutional neural network model and import dynamometer data to train and validate the network model. First, import the training set (n) to train the network model, and then import the validation set (1-n) to verify the accuracy of the trained network model, so as to realize the automatic identification, classification and diagnosis of the dynamometer of the pumping unit.

[0144] (4) The actual electrical dynamometer diagram information is input into the network model after training. The background computer performs self-learning to simulate the method of manually judging the dynamometer diagram and generate working status parameters in real time.

[0145] According to a specific embodiment of the present invention, obtaining the optimal control parameter set for the pumping unit based on the operating state parameters specifically includes:

[0146] If the current operation is in constant speed mode, determine whether the efficiency of the pumping unit is lower than the preset threshold. If so, generate the optimal control array for intermittent pumping and write the value of the optimal control array into the variable speed control module to perform intermittent pumping control. If not, generate the optimal control array for constant speed mode and write the value of the optimal control array into the variable speed control module to complete the adjustment of the operating conditions of the pumping unit motor.

[0147] If the current mode is adaptive transmission mode, the system determines whether it is summer or winter operation mode based on the comparison between the detected temperature and the set temperature threshold.

[0148] If it is in summer operation mode, determine whether the efficiency of the pumping unit is lower than the preset threshold. If so, generate the optimal control array for intermittent pumping, write the value of the optimal control array into the speed control module, and perform intermittent pumping control. If not, calculate the optimal speed value, generate the optimal control array for adaptive speed mode, and then write the value of the optimal control array into the speed control module to complete the adjustment of the operating conditions of the pumping unit motor.

[0149] In winter operation mode, the system checks if the pump efficiency is below a preset threshold. If so, it calculates the ultra-low speed operating value without shutting down and generates an optimal control array for ultra-low speed. If not, it calculates the optimal speed value, generates an optimal control array for adaptive speed control mode, and then writes the ultra-low speed optimal control array value or the adaptive speed control mode optimal control array value into the speed control module to complete the adjustment of the pumping unit motor's operating conditions. Ultra-low speed refers to a speed of 10% to 20% of the rated speed.

[0150] According to one specific embodiment of the present invention, the detection module collects the useful power, displacement and speed of the motor.

[0151] According to a specific embodiment of the present invention, the corresponding motor torque value is obtained based on the motor's active power and motor speed using the following formula:

[0152]

[0153] In the formula,

[0154] W represents the motor's output torque;

[0155] P is the active power of the motor;

[0156] m is the motor speed.

[0157] According to a specific embodiment of the present invention, the actual indicator diagram displacement and load data are calculated based on the displacement and the useful power of the motor using the following formula:

[0158]

[0159] In the formula,

[0160] η1, η2, and η3 are the efficiency of the pulley, the efficiency of the gearbox, and the efficiency between the crank and the suspension point, respectively.

[0161] i1 and i2 are the pulley transmission ratio and the gearbox transmission ratio, respectively;

[0162] W c For the weight of the rear arm of the traveling beam;

[0163] ε is the structural characteristic coefficient of the oil pumping unit;

[0164] R is the crank radius;

[0165] A is the length of the forearm of the walking beam;

[0166] B is the dynamic load factor;

[0167] C is the length of the rear arm of the walking beam;

[0168] θ2 is the included angle of crank rotation;

[0169] θ3 is the rotation angle of the connecting rod;

[0170] θ4 is the angle between the rear arm C of the walking beam and the horizontal plane at the initial position;

[0171] θ c This represents the change in the walking beam angle corresponding to the displacement.

[0172] According to a specific embodiment of the present invention, actual indicator diagram data of a complete up and down stroke cycle is extracted and transmitted to a remote monitoring terminal. The remote monitoring terminal performs pumping unit condition judgment and fault identification based on a self-learning model constructed by the convolutional neural network AlexNet-BN.

[0173] Example 1

[0174] According to a specific embodiment of the present invention, the cloud-edge joint control system for intelligent variable speed drive of oil pumping unit of the present invention will be described in detail below.

[0175] This invention provides a cloud-edge control system for intelligent variable speed drive of an oil pumping unit, including a PLC controller, a detection module, a communication module, a variable speed control module, an energy feedback module, a system protection module, and an operation control module;

[0176] The speed control module and the energy feedback module are directly connected to the motor input line, and both the speed control module and the energy feedback module are connected to the PLC controller; the communication module is connected to the PLC controller, and the communication module is also connected to a remote monitoring terminal via a wireless network;

[0177] The remote monitoring terminal performs operating condition diagnosis based on the principles of maintaining production volume, increasing pump efficiency, and minimizing liquid consumption per unit. If pump shutdown is required, the pumping unit will automatically stop and an alarm will be triggered. If pump shutdown is not required after diagnosis, the operating parameters will be optimized to obtain the optimal control parameter set for the pumping unit.

[0178] The detection module collects the working parameters of the oil pumping unit related to the dynamometer diagram and transmits them to the PLC controller. The PLC controller uses the received working parameters to calculate the real-time efficiency of the oil pumping unit and to perform real-time inversion calculation of the suspension point dynamometer diagram.

[0179] The operation control module is connected to the PLC controller. The operation control module acquires mode switching signals and automatic or manual switching signals, generates constant speed or variable speed mode control logic, and controls the pumping unit motor according to the logic.

[0180] The speed control module sets the pumping unit to either constant speed mode or adaptive speed control mode, and controls the pumping unit according to the optimal control parameter set calculated by the remote monitoring terminal. In the event of a failure of the speed control module, the system automatically switches to constant speed mode.

[0181] The energy feedback module is connected to the motor input line and feeds back the electrical energy generated by the pumping unit motor during the downstroke to the power grid;

[0182] The system protection module is connected to the three-phase 380V power supply line and the ground wire, and is also connected to the three-phase input line at the motor input terminal. The operation control module is connected to the PLC controller. The three-phase 380V power supply line and the ground wire in the system protection module improve the safety of the control circuit when working in the field. It is also connected to the three-phase input line at the motor input terminal to provide comprehensive motor protection.

[0183] The detection module includes an electrical parameter acquisition module and an angular displacement sensor.

[0184] The electrical parameter acquisition module acquires electrical parameters and connects them to the PLC controller;

[0185] The angular displacement sensor is installed at the middle position of the walking beam of the pumping unit; the angular displacement sensor is connected to the PLC controller via a signal line.

[0186] The detection module detects active power, power factor, suspension point displacement, and actual liquid output, and transmits the data to the PLC controller. The PLC controller uses the active power to calculate the actual load on the suspension point, and combines this with the actual liquid output to calculate the real-time efficiency of the pumping unit, and then performs a real-time inversion calculation of the suspension point dynamometer diagram.

[0187] The PLC controller uses active power to calculate the actual load at the suspension point using the following formula:

[0188]

[0189] In the formula,

[0190] W represents the motor's output torque;

[0191] η1, η2, and η3 represent the efficiency of the pulley, the efficiency of the gearbox, and the efficiency between the crank and the suspension point, respectively.

[0192] i1 and i2 are the pulley transmission ratio and the gearbox transmission ratio, respectively;

[0193] W c For the weight of the rear arm of the traveling beam;

[0194] ε is the structural characteristic coefficient of the oil pumping unit;

[0195] R is the crank radius;

[0196] A is the length of the front arm of the walking beam, B is the dynamic load coefficient, and C is the length of the rear arm of the walking beam.

[0197] θ2 is the included angle of crank rotation;

[0198] θ3 is the rotation angle of the connecting rod;

[0199] θ4 is the angle between the rear arm C of the walking beam and the horizontal plane at the initial position;

[0200] θ c This represents the change in the walking beam angle corresponding to the displacement.

[0201] The real-time efficiency of the pumping unit is calculated based on the calculated actual load at the suspension point and the actual liquid output using the following formula:

[0202]

[0203] In the formula:

[0204] η is the real-time efficiency of the oil pumping unit;

[0205] M i The actual load at the calculated i-th suspension point;

[0206] A is the length of the forearm of the walking beam;

[0207] θ ci This represents the change in the walking beam angle corresponding to the displacement of the i-th suspension point;

[0208] n represents the number of suspension points.

[0209] The communication module is a 4G communication module, which communicates with the remote monitoring terminal through a 4G wireless network.

[0210] The motor is a switched reluctance motor.

[0211] The detection module, the speed control module, the energy feedback module, and the communication module are connected to the PLC controller via a 485 communication interface.

[0212] The remote monitoring terminal performs operational condition diagnosis through a self-learning model.

[0213] Example 2

[0214] According to a specific embodiment of the present invention, the control method of the cloud-edge joint control system for intelligent variable speed drive of oil pumping unit of the present invention will be described in detail below.

[0215] This invention provides a control method for a cloud-edge interconnected control system for any of the above-mentioned pumping units with intelligent variable speed drive, comprising the following steps:

[0216] Step 1: Activate the timed acquisition function of the dynamometer card data of the detection module;

[0217] Step 2: Set the acquisition frequency and acquire dynamometer data;

[0218] Step 3: The PLC controller calculates the actual dynamometer data based on the collected dynamometer data;

[0219] Step 4: Input the actual dynamometer data into the remote monitoring terminal to judge the operating condition of the pumping unit and identify faults. When the pumping unit has a fault, stop the pump and sound an alarm; when the pumping unit has no fault, calculate the current operating status parameters of the pumping unit based on the actual dynamometer data, and obtain the optimal control parameter set of the pumping unit based on the operating status parameters; the operating status parameters include two cases: constant speed and adaptive speed.

[0220] Step 5: Input the optimal control parameter set of the pumping unit into the PLC controller. The PLC controller transmits the optimal control parameter set of the pumping unit to the speed control module. The speed control module controls the pumping unit according to the optimal control parameter set of the pumping unit.

[0221] The specific steps for calculating the current operating parameters of the pumping unit based on actual dynamometer data include the following:

[0222] (1) Put the dynamometer diagram data of different pumping units and the dynamometer diagram data of the same pumping unit at different time periods into the same folder and perform normalization, binarization and thinning processing.

[0223] (2) Create data labels and classify them according to the working condition type corresponding to the dynamometer diagram. For the classified dynamometer diagram dataset, randomly select samples accounting for the total dataset (n) as the training set, and the remaining (1-n) as the validation set (0<n<1).

[0224] (3) Build an AlexNet-BN convolutional neural network model and import dynamometer data to train and validate the network model. First, import the training set (n) to train the network model, and then import the validation set (1-n) to verify the accuracy of the trained network model, so as to realize the automatic identification, classification and diagnosis of the dynamometer of the pumping unit.

[0225] (4) The actual electrical dynamometer diagram information is input into the network model after training. The background computer performs self-learning to simulate the method of manually judging the dynamometer diagram and generate working status parameters in real time.

[0226] The optimal control parameter set for the pumping unit obtained based on the operating state parameters specifically includes:

[0227] If the current operation is in constant speed mode, determine whether the efficiency of the pumping unit is lower than the preset threshold. If so, generate the optimal control array for intermittent pumping and write the value of the optimal control array into the variable speed control module to perform intermittent pumping control. If not, generate the optimal control array for constant speed mode and write the value of the optimal control array into the variable speed control module to complete the adjustment of the operating conditions of the pumping unit motor.

[0228] If the current mode is adaptive transmission mode, the system determines whether it is summer or winter operation mode based on the comparison between the detected temperature and the set temperature threshold.

[0229] If it is in summer operation mode, determine whether the efficiency of the pumping unit is lower than the preset threshold. If so, generate the optimal control array for intermittent pumping, write the value of the optimal control array into the speed control module, and perform intermittent pumping control. If not, calculate the optimal speed value, generate the optimal control array for adaptive speed mode, and then write the value of the optimal control array into the speed control module to complete the adjustment of the operating conditions of the pumping unit motor.

[0230] In winter operation mode, the system checks if the pump efficiency is below a preset threshold. If so, it calculates the ultra-low speed operating value without shutting down and generates an optimal control array for ultra-low speed. If not, it calculates the optimal speed value, generates an optimal control array for adaptive speed control mode, and then writes the ultra-low speed optimal control array value or the adaptive speed control mode optimal control array value into the speed control module to complete the adjustment of the pumping unit motor's operating conditions. Ultra-low speed refers to a speed of 10% to 20% of the rated speed.

[0231] The detection module collects the motor's useful power, displacement, and motor speed.

[0232] The motor torque value is obtained using the following formula based on the motor's active power and speed:

[0233]

[0234] In the formula,

[0235] W represents the motor's output torque;

[0236] P is the active power of the motor;

[0237] m is the motor speed.

[0238] The actual indicator diagram displacement and load data are calculated using the following formula based on the displacement and the useful power of the motor:

[0239]

[0240] In the formula,

[0241] η1, η2, and η3 are the efficiency of the pulley, the efficiency of the gearbox, and the efficiency between the crank and the suspension point, respectively.

[0242] i1 and i2 are the pulley transmission ratio and the gearbox transmission ratio, respectively;

[0243] W c For the weight of the rear arm of the traveling beam;

[0244] ε is the structural characteristic coefficient of the oil pumping unit;

[0245] R is the crank radius;

[0246] A is the length of the forearm of the walking beam;

[0247] B is the dynamic load factor;

[0248] C is the length of the rear arm of the walking beam;

[0249] θ2 is the included angle of crank rotation;

[0250] θ3 is the rotation angle of the connecting rod;

[0251] θ4 is the angle between the rear arm C of the walking beam and the horizontal plane at the initial position;

[0252] θ c This represents the change in the walking beam angle corresponding to the displacement.

[0253] In this process, actual indicator diagram data for a complete up and down stroke cycle is extracted and transmitted to a remote monitoring terminal. The remote monitoring terminal uses a self-learning model built based on the convolutional neural network AlexNet-BN to determine the operating condition of the pumping unit and identify faults.

[0254] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of protection of the present invention.

Claims

1. A cloud-edge control system for intelligent variable speed drive of an oil pumping unit, characterized in that, It includes a PLC controller, a detection module, a communication module, a speed control module, an energy feedback module, and an operation control module; The speed control module and the energy feedback module are directly connected to the motor input line, and both the speed control module and the energy feedback module are connected to the PLC controller; the communication module is connected to the PLC controller, and the communication module is also connected to a remote monitoring terminal via a wireless network; The remote monitoring terminal performs operating condition diagnosis based on the principles of maintaining production volume, increasing pump efficiency, and minimizing liquid consumption per unit. If pump shutdown is required, the pumping unit will automatically stop and an alarm will be triggered. If pump shutdown is not required after diagnosis, the operating parameters will be optimized to obtain the optimal control parameter set for the pumping unit. The detection module collects the working parameters of the oil pumping unit related to the dynamometer diagram and transmits them to the PLC controller. The PLC controller uses the received working parameters to calculate the real-time efficiency of the oil pumping unit and to perform real-time inversion calculation of the suspension point dynamometer diagram. The detection module includes an electrical parameter acquisition module and an angular displacement sensor; The electrical parameter acquisition module acquires electrical parameters and connects them to the PLC controller; The angular displacement sensor is installed at the middle position of the walking beam of the pumping unit; the angular displacement sensor is connected to the PLC controller via a signal line; The detection module detects active power, power factor, suspension point displacement and actual liquid output, and transmits them to the PLC controller. The PLC controller uses the active power to calculate the actual load on the suspension point, and combines it with the actual liquid output to calculate the real-time efficiency of the pumping unit, and performs real-time inversion calculation of the suspension point dynamometer diagram. The PLC controller uses active power to calculate the actual load at the suspension point using the following formula: In the formula, W represents the motor's output torque; η 1 η 2 η 3 represents the efficiency of the pulley, the efficiency of the gearbox, and the efficiency between the crank and the suspension point, respectively. i 1, i 2 represents the pulley transmission ratio and the gearbox transmission ratio, respectively; W c For the weight of the rear arm of the traveling beam; ε For the structural characteristic coefficients of the oil pumping unit; R is the crank radius; A is the length of the front arm of the walking beam, B is the dynamic load coefficient, and C is the length of the rear arm of the walking beam. θ 2 represents the crank rotation angle; θ 3 is the included angle of rotation of the connecting rod; θ 4 represents the angle between the rear arm C of the walking beam and the horizontal plane at the initial position; θ c This represents the change in the walking beam angle corresponding to the displacement; The operation control module is connected to the PLC controller. The operation control module acquires mode switching signals and automatic or manual switching signals, generates constant speed or variable speed mode control logic, and controls the pumping unit motor according to the logic. The speed control module sets the pumping unit to either constant speed mode or adaptive speed control mode, and controls the pumping unit according to the optimal control parameter set calculated by the remote monitoring terminal. In the event of a failure of the speed control module, the system automatically switches to constant speed mode. The energy feedback module is connected to the motor input line and feeds back the electrical energy generated by the pumping unit motor during the downstroke to the power grid.

2. The cloud-edge control system for intelligent variable speed drive of an oil pumping unit according to claim 1, characterized in that, Based on the calculated actual load at the suspension point and the actual liquid output, the real-time efficiency of the pumping unit is calculated using the following formula: In the formula: η For the real-time efficiency of the oil pumping unit; M i The actual load at the calculated i-th suspension point; A is the length of the forearm of the walking beam; θ ci This represents the change in the walking beam angle corresponding to the displacement of the i-th suspension point; n This represents the number of suspension points.

3. The cloud-edge control system for intelligent variable speed drive of an oil pumping unit according to claim 1, characterized in that, The communication module is a 4G communication module, which communicates with the remote monitoring terminal through a 4G wireless network.

4. The cloud-edge control system for intelligent variable speed drive of an oil pumping unit according to claim 1, characterized in that, The motor is a switched reluctance motor.

5. The cloud-edge control system for intelligent variable speed drive of an oil pumping unit according to claim 1, characterized in that, The detection module, the speed control module, the energy feedback module, and the communication module are connected to the PLC controller via a 485 communication interface.

6. The cloud-edge control system for intelligent variable speed drive of an oil pumping unit according to claim 1, characterized in that, The remote monitoring terminal performs operational condition diagnosis through a self-learning model.

7. The cloud-edge control system for intelligent variable speed drive of an oil pumping unit according to any one of claims 1-6, characterized in that, It also includes a system protection module, which is connected to the three-phase 380V power supply line and the ground wire, and is connected to the three-phase input line at the motor input terminal.

8. A control method for a cloud-edge interconnected control system for intelligent variable speed drive of an oil pumping unit as described in any one of claims 1-7, characterized in that, Includes the following steps: Step 1: Activate the timed acquisition function of the dynamometer card data of the detection module; Step 2: Set the acquisition frequency and acquire dynamometer data; Step 3: The PLC controller calculates the actual dynamometer data based on the collected dynamometer data; Step 4: Input the actual dynamometer data into the remote monitoring terminal to judge the operating condition of the pumping unit and identify faults. When the pumping unit has a fault, stop the pump and sound an alarm; when the pumping unit has no fault, calculate the current operating status parameters of the pumping unit based on the actual dynamometer data, and obtain the optimal control parameter set of the pumping unit based on the operating status parameters; the operating status parameters include two cases: constant speed and adaptive speed. Step 5: Input the optimal control parameter set of the pumping unit into the PLC controller. The PLC controller transmits the optimal control parameter set of the pumping unit to the speed control module. The speed control module controls the pumping unit according to the optimal control parameter set of the pumping unit.

9. The control method of the cloud-edge joint control system for intelligent variable speed drive of oil pumping unit according to claim 8, characterized in that, The optimal control parameter set for the pumping unit, obtained based on the operating status parameters, specifically includes: If the current operation is in constant speed mode, determine whether the efficiency of the pumping unit is lower than the preset threshold. If so, generate the optimal control array for intermittent pumping, write the value of the optimal control array into the variable speed control module, and perform intermittent pumping control. If not, generate the optimal control array for constant speed mode, and then write the value of the optimal control array into the variable speed control module to complete the adjustment of the operating conditions of the pumping unit motor. If the current mode is adaptive transmission mode, the system determines whether it is summer or winter operation mode based on the comparison between the detected temperature and the set temperature threshold. If it is in summer operation mode, determine whether the efficiency of the pumping unit is lower than the preset threshold. If so, generate the optimal control array for intermittent pumping, write the value of the optimal control array into the speed control module, and perform intermittent pumping control. If not, calculate the optimal speed value, generate the optimal control array for adaptive speed mode, and then write the value of the optimal control array into the speed control module to complete the adjustment of the operating conditions of the pumping unit motor. If it is winter operation mode, determine whether the efficiency of the oil pump is lower than the preset threshold. If so, calculate the ultra-low speed operation value without stopping and generate the ultra-low speed optimal control array. If not, calculate the optimal speed value, generate the adaptive speed mode optimal control array, and then write the ultra-low speed optimal control array value or the adaptive speed mode optimal control array value into the speed control module to complete the adjustment of the operating conditions of the oil pump motor.

10. The control method of the cloud-edge joint control system for intelligent variable speed drive of oil pumping unit according to claim 8, characterized in that, The detection module collects the useful power, displacement, and speed of the motor.

11. The control method of the cloud-edge joint control system for intelligent variable speed drive of oil pumping unit according to claim 10, characterized in that, The corresponding motor torque value is obtained using the following formula based on the motor's active power and motor speed: In the formula, W represents the motor's output torque; P is the active power of the motor; m is the motor speed.

12. The control method of the cloud-edge joint control system for intelligent variable speed drive of oil pumping unit according to claim 11, characterized in that, The actual indicator diagram displacement and load data are calculated using the following formula based on the displacement and the useful power of the motor: In the formula, η 1. η 2. η 3 represents the efficiency of the pulley, the efficiency of the gearbox, and the efficiency between the crank and the suspension point, respectively. i 1, i 2 represents the pulley transmission ratio and the gearbox transmission ratio, respectively; W c For the weight of the rear arm of the traveling beam; ε For the structural characteristic coefficients of the oil pumping unit; R is the crank radius; A is the length of the forearm of the walking beam; B is the dynamic load factor; C is the length of the rear arm of the walking beam; θ 2 represents the crank rotation angle; θ 3 is the included angle of rotation of the connecting rod; θ 4 represents the angle between the rear arm C of the walking beam and the horizontal plane at the initial position; θ c This represents the change in the walking beam angle corresponding to the displacement.

13. The control method of the cloud-edge joint control system for intelligent variable speed drive of oil pumping unit according to claim 12, characterized in that, The actual indicator diagram data of a complete up and down stroke cycle is extracted and transmitted to a remote monitoring terminal. The remote monitoring terminal uses a self-learning model built based on the convolutional neural network AlexNet-BN to judge the operating condition of the pumping unit and identify faults.

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