Pumping unit intelligent variable speed driving cloud-side joint control system and control method thereof

Abstract

The invention provides a cloud-side joint control system for intelligent variable speed driving of an oil pumping unit and a control method thereof, and belongs to the technical field of oil pumping unit monitoring. Working condition diagnosis is carried out according to an indicator diagram of electrical parameter inversion, the continuous pumping and intermittent pumping working modes are automatically judged according to the working condition diagnosis result and the environment temperature, the stroke frequency is automatically adjusted in the continuous pumping mode, the intermittent pumping mode is automatically started and stopped, low-energy-consumption oil well supply and discharge balance is achieved, the whole working mode completely adapts to oil well working condition changes, and the working efficiency is improved. The oil well management efficiency is improved, and the production energy consumption is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of oil pump monitoring, and in particular to a cloud-edge joint control system for an intelligent variable speed drive of an oil pump. Background Art

[0002] With the continuous development of science and technology, the trend of intelligence is gradually expanding to various industries, and pumping units also need to develop in the direction of intelligence to meet the current needs of smart oilfield construction. Currently, most pumping unit control systems use programmable logic control modules (PLC) and human-machine interface (HMI) to achieve simple control of pumping unit speed.

[0003] Although this type of oil pump 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, the oil pumping process cannot be adjusted accurately and timely, which will affect the working condition of the entire oil pump. In addition, there is a lack of a real-time detection feedback system, and manual detection is required, which is time-consuming, labor-intensive, and inefficient.

[0004] In Chinese patent application document CN107703758B, an adaptive variable speed drive control system and control method for an oil pump are disclosed. A remote control unit provides two adaptive variable speed drive control modes for manual selection. One mode calculates the efficiency of the oil pump, uses the real-time calculated pump efficiency data to compare with the standard data model under the current stroke frequency, obtains the efficiency of the oil pump, and then adjusts the stroke frequency data; the other mode has the highest economic benefit. Through the linkage calculation of the oil pump displacement Q, the oil pump power consumption W, the current oil price r1, and the current electricity price r2, the bubble sorting method is used to obtain the maximum value and determine the stroke frequency adjustment data. The stroke frequency data of the two modes are issued to the variable speed drive module through the remote control unit to complete the automatic intelligent adjustment of the stroke frequency of the oil pump. After a certain period of time, the current real-time power diagram data is calculated again, and the data is compared, and the stroke frequency is adjusted again, and this is repeated until the stroke frequency of the oil pump meets the requirements or reaches the optimal level. The first adaptive variable speed drive control mode has the following working principle: Step 1: The remote control unit collects 72 load and angular displacement data points of the upstroke and 72 load and angular displacement data points of the downstroke respectively through the angular displacement sensor and the load sensor within the sampling interval, and establishes a data analysis library; Step 2: Extract the collected data in the database, calculate the maximum load of the upstroke, the minimum load of the downstroke, the slope of the upstroke loading line and the slope of the downstroke unloading line, and derive the ideal dynamometer diagram; Step 3: Calculate the efficiency of the oil pump using the geometric area method; The efficiency of the oil pump is the measured dynamometer diagram area / ideal dynamometer diagram area. Since the angular displacement is consistent up and down, the angular displacement is calculated.

[0005]

[0006] The shift data is eliminated and the formula is as follows; Step 4: Adaptively adjust the running times.

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

[0008] 1. The ambient temperature factor is not taken into consideration, and the speed control of the pump cannot be optimized.

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

[0010] 3. There is a deviation in the calculation model of the "electrical dynamometer diagram-dynamometer diagram" conversion, and the conversion result cannot reach the optimal level.

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

[0012] In order to solve the problems existing in the prior art, the present invention performs operating condition diagnosis based on the dynamometer diagram of electrical parameter inversion, and automatically determines the continuous pumping and intermittent pumping working modes according to the operating condition diagnosis results and the ambient temperature. The continuous pumping mode automatically adjusts the stroke frequency, and the intermittent pumping mode automatically starts and stops, thereby achieving low-energy consumption oil well supply and drainage balance. The entire working mode is fully adaptive to changes in oil well operating conditions, thereby improving oil well management efficiency and reducing production energy consumption.

[0013] The present invention provides a cloud-edge joint control system for intelligent variable speed drive of an oil pumping unit, comprising 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 the remote monitoring terminal through a wireless network;

[0015] The remote monitoring terminal performs working condition diagnosis based on the principle of no reduction in production, increased efficiency of the oil pump, and minimum unit consumption of liquid production. If the pump needs to be stopped, the oil pump will be automatically stopped and an alarm will be output; if the pump does not need to be stopped after diagnosis, the operating parameters will be optimized to obtain the optimal control parameter group for the oil pump; if the efficiency is lower than 15% or the indicator diagram is abnormal, the pump needs to be stopped, otherwise, it does not need to be stopped. Unit consumption of liquid production refers to the lowest power consumption per unit of liquid production, and the working condition obtained includes unit consumption of liquid production.

[0016] The detection module collects the working parameters of the pumping unit related to the electric 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 pumping unit and inversely calculate the hanging point dynamometer diagram in real time.

[0017] The operation control module is connected to the PLC controller, and the operation control module obtains a mode switching signal, an automatic or manual switching signal, generates a fixed speed or variable speed mode control logic, and controls the pumping unit motor according to the logic;

[0018] The speed control module sets the oil pumping unit to two operating modes: a fixed speed mode and an adaptive speed mode, and controls the oil pumping unit according to the optimal control parameter group of the oil pumping unit calculated by the remote monitoring terminal. In the event of a failure of the speed control module, the system automatically switches to the fixed speed mode;

[0019] The energy feedback module is connected to the motor input line to feed back the electric energy generated by the pumping unit motor in 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 is connected to the PLC controller;

[0022] The angular displacement sensor is installed in the middle position of the walking beam of the pumping unit; the angular displacement sensor is connected to the PLC controller through 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 of the suspension point, combines the actual liquid output, calculates the real-time efficiency of the oil pump, and inverts and calculates the suspension point indicator diagram in real time.

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

[0025]

[0026] In the formula,

[0027] W is the motor output torque;

[0028] η1, η2, η3 are the pulley efficiency, reduction box efficiency, and efficiency from the crank to the suspension point, respectively;

[0029] i1 and i2 are the pulley transmission ratio and the reduction box transmission ratio respectively;

[0030] W c The rear arm of the traveling beam is heavy;

[0031] ε is the structural characteristic coefficient of the 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 crank rotation angle;

[0035] θ3 is the connecting rod rotation angle;

[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 is the change in the beam angle corresponding to the displacement.

[0038] Preferably, the real-time efficiency of the pumping unit is calculated according to the calculated actual load of the suspension point and the actual liquid discharge through the following formula:

[0039]

[0040] Where:

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

[0042] M i is the calculated actual load of the ith suspension point;

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

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

[0045] n is the number of suspension points.

[0046] Preferably, the communication module is a 4G communication module, and the communication module communicates with the remote monitoring terminal via 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, it also includes a system protection module, which is connected to the three-phase 380V power line and the ground line, and is connected to the three-phase input line at the motor input end; the operation control module is connected to the PLC controller; the system protection module is connected to the three-phase 380V power line and the ground line to improve the safety of the control circuit working in the field. It is also connected to the three-phase input line at the motor input end to provide comprehensive protection for the motor.

[0051] The present invention provides a control method for a cloud-edge joint control system of any of the above-mentioned intelligent variable speed drives of oil pumping units, comprising the following steps:

[0052] Step 1, starting the function of regularly collecting electrical dynamometer data of the detection module;

[0053] Step 2: Set the acquisition frequency to collect the electrical dynamometer data;

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

[0055] Step 4: Input the actual electrical dynamometer data into the remote monitoring terminal to judge the working condition of the oil pump and identify the fault. When the oil pump has a fault, stop the pump and alarm. When the oil pump has no fault, calculate the current working state parameters of the oil pump according to the actual electrical dynamometer data, and obtain the optimal control parameter group of the oil pump according to the working state parameters. The working state parameters include two conditions: fixed speed and adaptive speed.

[0056] Step five: input the optimal control parameter group of the oil pumping unit into the PLC controller, and the PLC controller transmits the optimal control parameter group of the oil pumping unit to the speed control module, and the speed control module controls the oil pumping unit according to the optimal control parameter group of the oil pumping unit.

[0057] Preferably, calculating the current working state parameters of the oil pumping unit according to the actual electrical dynamometer data specifically includes the following steps:

[0058] (1) Put the images drawn from the dynamometer data of different pumping units and the dynamometer data of the same pumping unit at different time periods into the same folder for normalization, binarization and refinement processing;

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

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

[0061] (4) The actual electrical dynamometer information is input into the trained network model, and 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, obtaining the optimal control parameter group of the oil pumping unit according to the working state parameters specifically includes:

[0063] If the current operation is in the fixed speed mode, determine whether the oil pump efficiency is lower than the preset threshold value. 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 the fixed speed mode, write the value of the optimal control array into the variable speed control module, and complete the adjustment of the operating condition of the oil pumping unit motor;

[0064] If the current mode is the adaptive speed change mode, the summer operation mode or the winter operation mode is determined according to the comparison result between the detected temperature and the set temperature threshold;

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

[0066] If it is winter operation mode, determine whether the oil pump efficiency is lower than the preset threshold. If so, calculate the non-stop ultra-low speed operation speed value and generate the ultra-low speed optimal control array; if not, calculate the optimal speed value and generate the adaptive speed change mode optimal control array, then write the ultra-low speed optimal control array value or the adaptive speed change mode optimal control array value into the speed control module to complete the adjustment of the oil pump motor 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 speed of the motor.

[0068] Preferably, the corresponding motor torque value is obtained according to the motor active power and the motor speed using the following formula:

[0069]

[0070] In the formula,

[0071] W is the motor output torque;

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

[0073] m is the motor speed.

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

[0075]

[0076] In the formula,

[0077] η1, η2, and η3 are the efficiency of the pulley, the efficiency of the reduction box, and the efficiency between the crank and the suspension point, respectively;

[0078] i1 and i2 are the pulley transmission ratio and the reduction box transmission ratio respectively;

[0079] W c The rear arm of the traveling beam is heavy;

[0080] ε is the structural characteristic coefficient of the 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 coefficient;

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

[0085] θ2 is the crank rotation angle;

[0086] θ3 is the connecting rod rotation angle;

[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 is the change in the beam angle corresponding to the displacement.

[0089] Preferably, actual dynamometer data of a complete up and down stroke cycle is extracted and transmitted to a remote monitoring terminal, which uses a self-learning model constructed based on the convolutional neural network AlexNet-BN to determine the operating condition of the oil pump and identify faults.

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

[0091] The present invention takes PLC and speed control module as the core, and adopts a control mode combining remote monitoring terminal self-learning control with on-site direct control to realize intelligent adjustment of the pumping unit and meet the requirements of equipment intelligence and automation. The detection module circuit realizes real-time collection of working characteristic parameters, including active power, motor speed, suspension point displacement, and actual liquid output, and monitors the equipment operation status online, and can display and set equipment working parameters in real time through the display device; the real-time collected wandering angle and motor active power data are sent to the "electrical dynamometer-dynamometer" conversion calculation model to calculate the actual dynamometer diagram of the pumping unit, and sent to the self-learning model constructed based on the convolutional neural network AlexNet-BN for intelligent calculation and fault identification; and the equipment is remotely transmitted and monitored through the 4G module, and the speed / conventional switching mode and automatic / manual switching mode are set, providing a variety of working mode options for the control circuit suitable for intelligent adjustment of the walking beam pumping unit, and finally realizing intelligent control of all working conditions, continuous operation, intermittent pumping operation, and ultra-low speed operation without manual adjustment, and complete intelligent diagnosis. In addition, the purpose of remotely controlling the oil pump can be achieved by sending and receiving data remotely. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0094] Figure 3 It is a flow chart of calculating the optimal control parameter group according to an embodiment of the present invention. DETAILED DESCRIPTION

[0095] The specific implementation modes of the present invention are described in detail below in conjunction with the accompanying drawings.

[0096] The present invention provides a cloud-edge joint control system for intelligent variable speed drive of an oil pumping unit, comprising 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 the remote monitoring terminal through a wireless network;

[0098] The remote monitoring terminal performs working condition diagnosis based on the principle of no reduction in production, increased efficiency of the oil pump, and minimum unit consumption of produced liquid. If the pump needs to be stopped, the oil pump is automatically stopped and an alarm is output; if the pump does not need to be stopped after diagnosis, the operating parameters are optimized to obtain the optimal control parameter group of the oil pump;

[0099] The detection module collects the working parameters of the pumping unit related to the electric 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 pumping unit and inversely calculate the hanging point dynamometer diagram in real time.

[0100] The operation control module is connected to the PLC controller, and the operation control module obtains a mode switching signal, an automatic or manual switching signal, generates a fixed speed or variable speed mode control logic, and controls the pumping unit motor according to the logic;

[0101] The speed control module sets the oil pumping unit to two operating modes: a fixed speed mode and an adaptive speed mode, and controls the oil pumping unit according to the optimal control parameter group of the oil pumping unit calculated by the remote monitoring terminal. In the event of a failure of the speed control module, the system automatically switches to the fixed speed mode;

[0102] The energy feedback module is connected to the motor input line to feed back the electric energy generated by the pumping unit motor in 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 is connected to the PLC controller;

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

[0106] According to a specific implementation 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 of the suspension point, combines the actual liquid output, calculates the real-time efficiency of the oil pump, and inverts and calculates the suspension point indicator diagram in real time.

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

[0108]

[0109] In the formula,

[0110] W is the motor output torque;

[0111] η1, η2, η3 are the pulley efficiency, reduction box efficiency, and efficiency from the crank to the suspension point, respectively;

[0112] i1 and i2 are the pulley transmission ratio and the reduction box transmission ratio respectively;

[0113] W c The rear arm of the traveling beam is heavy;

[0114] ε is the structural characteristic coefficient of the 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 crank rotation angle;

[0118] θ3 is the connecting rod rotation angle;

[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 is the change in the beam angle corresponding to the displacement.

[0121] According to a specific embodiment of the present invention, the real-time efficiency of the oil pumping unit is calculated according to the calculated actual load of the suspension point and the actual liquid discharge volume by the following formula:

[0122]

[0123] Where:

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

[0125] M i is the calculated actual load of the ith suspension point;

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

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

[0128] n is the number of suspension points.

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

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

[0131] According to a specific implementation 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 via a 485 communication interface.

[0132] According to a 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, it also includes a system protection module, which is connected to the three-phase 380V power line and the ground line, and is connected to the three-phase input line at the motor input end; the operation control module is connected to the PLC controller; the system protection module is connected to the three-phase 380V power line and the ground line to improve the safety of the control circuit working in the field. It is also connected to the three-phase input line at the motor input end to provide comprehensive protection for the motor.

[0134] The present invention provides a control method for a cloud-edge joint control system of any of the above-mentioned intelligent variable speed drives of oil pumping units, comprising the following steps:

[0135] Step 1, starting the function of regularly collecting electrical dynamometer data of the detection module;

[0136] Step 2: Set the acquisition frequency to collect the electrical dynamometer data;

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

[0138] Step 4: Input the actual electrical dynamometer data into the remote monitoring terminal to judge the working condition of the oil pump and identify the fault. When the oil pump has a fault, stop the pump and alarm. When the oil pump has no fault, calculate the current working state parameters of the oil pump according to the actual electrical dynamometer data, and obtain the optimal control parameter group of the oil pump according to the working state parameters. The working state parameters include two conditions: fixed speed and adaptive speed.

[0139] Step five: input the optimal control parameter group of the oil pumping unit into the PLC controller, and the PLC controller transmits the optimal control parameter group of the oil pumping unit to the speed control module, and the speed control module controls the oil pumping unit according to the optimal control parameter group of the oil pumping unit.

[0140] According to a specific embodiment of the present invention, calculating the current working state parameters of the oil pumping unit according to the actual electrical dynamometer data specifically includes the following steps:

[0141] (1) Put the images drawn from the dynamometer data of different pumping units and the dynamometer data of the same pumping unit at different time periods into the same folder for normalization, binarization and refinement processing;

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

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

[0144] (4) The actual electrical dynamometer information is input into the trained network model, and 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 group of the oil pumping unit according to the working state parameters specifically includes:

[0146] If the current operation is in the fixed speed mode, determine whether the oil pump efficiency is lower than the preset threshold value. 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 the fixed speed mode, write the value of the optimal control array into the variable speed control module, and complete the adjustment of the operating condition of the oil pumping unit motor;

[0147] If the current mode is the adaptive speed change mode, the summer operation mode or the winter operation mode is determined according to the comparison result between the detected temperature and the set temperature threshold;

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

[0149] If it is winter operation mode, determine whether the oil pump efficiency is lower than the preset threshold. If so, calculate the non-stop ultra-low speed operation speed value and generate the ultra-low speed optimal control array; if not, calculate the optimal speed value and generate the adaptive speed change mode optimal control array, then write the ultra-low speed optimal control array value or the adaptive speed change mode optimal control array value into the speed control module to complete the adjustment of the oil pump motor operating conditions. Ultra-low speed refers to a speed of 10% to 20% of the rated speed.

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

[0151] According to a specific implementation of the present invention, the corresponding motor torque value is obtained by using the following formula according to the motor active power and the motor speed:

[0152]

[0153] In the formula,

[0154] W is the motor 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 dynamometer displacement and load data are calculated according to 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 reduction box, and the efficiency between the crank and the suspension point, respectively;

[0161] i1 and i2 are the pulley transmission ratio and the reduction box transmission ratio respectively;

[0162] W c The rear arm of the traveling beam is heavy;

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

[0164] R 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 crank rotation angle;

[0169] θ3 is the connecting rod rotation angle;

[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 is the change in the beam angle corresponding to the displacement.

[0172] According to a specific implementation scheme of the present invention, actual dynamometer 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 constructed based on the convolutional neural network AlexNet-BN to judge the operating condition of the oil pump and identify faults.

[0173] Example 1

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

[0175] The present invention provides a cloud-edge joint control system for intelligent variable speed drive of an oil pumping unit, comprising 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 the remote monitoring terminal through a wireless network;

[0177] The remote monitoring terminal performs working condition diagnosis based on the principle of no reduction in production, increased efficiency of the oil pump, and minimum unit consumption of produced liquid. If the pump needs to be stopped, the oil pump is automatically stopped and an alarm is output; if the pump does not need to be stopped after diagnosis, the operating parameters are optimized to obtain the optimal control parameter group of the oil pump;

[0178] The detection module collects the working parameters of the pumping unit related to the electric 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 pumping unit and inversely calculate the hanging point dynamometer diagram in real time.

[0179] The operation control module is connected to the PLC controller, and the operation control module obtains a mode switching signal, an automatic or manual switching signal, generates a fixed speed or variable speed mode control logic, and controls the pumping unit motor according to the logic;

[0180] The speed control module sets the oil pumping unit to two operating modes: a fixed speed mode and an adaptive speed mode, and controls the oil pumping unit according to the optimal control parameter group of the oil pumping unit calculated by the remote monitoring terminal. In the event of a failure of the speed control module, the system automatically switches to the fixed speed mode;

[0181] The energy feedback module is connected to the motor input line to feed back the electric energy generated by the pumping unit motor in the downstroke to the power grid;

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

[0183] Wherein, the detection module includes an electrical parameter acquisition module and an angular displacement sensor;

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

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

[0186] Among them, 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 of the suspension point, combines the actual liquid output, calculates the real-time efficiency of the oil pump, and inverts and calculates the suspension point indicator diagram in real time.

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

[0188]

[0189] In the formula,

[0190] W is the motor output torque;

[0191] η1, η2, η3 are the pulley efficiency, reduction box efficiency, and efficiency from the crank to the suspension point, respectively;

[0192] i1 and i2 are the pulley transmission ratio and the reduction box transmission ratio respectively;

[0193] W c The rear arm of the traveling beam is heavy;

[0194] ε is the structural characteristic coefficient of the 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 crank rotation angle;

[0198] θ3 is the connecting rod rotation angle;

[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 is the change in the beam angle corresponding to the displacement.

[0201] Among them, according to the calculated actual load of the suspension point and the actual liquid output, the real-time efficiency of the pumping unit is calculated by the following formula:

[0202]

[0203] Where:

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

[0205] M i is the calculated actual load of the ith suspension point;

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

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

[0208] n is the number of suspension points.

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

[0210] Wherein, the motor is a switched reluctance motor.

[0211] Among them, 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.

[0212] Wherein, the remote monitoring terminal performs working condition diagnosis through a self-learning model.

[0213] Example 2

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

[0215] The present invention provides a control method for a cloud-edge joint control system of any of the above-mentioned intelligent variable speed drives of oil pumping units, comprising the following steps:

[0216] Step 1, starting the function of regularly collecting electrical dynamometer data of the detection module;

[0217] Step 2: Set the acquisition frequency to collect the electrical dynamometer data;

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

[0219] Step 4: Input the actual electrical dynamometer data into the remote monitoring terminal to judge the working condition of the oil pump and identify the fault. When the oil pump has a fault, stop the pump and alarm. When the oil pump has no fault, calculate the current working state parameters of the oil pump according to the actual electrical dynamometer data, and obtain the optimal control parameter group of the oil pump according to the working state parameters. The working state parameters include two conditions: fixed speed and adaptive speed.

[0220] Step five: input the optimal control parameter group of the oil pumping unit into the PLC controller, and the PLC controller transmits the optimal control parameter group of the oil pumping unit to the speed control module, and the speed control module controls the oil pumping unit according to the optimal control parameter group of the oil pumping unit.

[0221] The calculation of the current working state parameters of the oil pumping unit according to the actual electrical dynamometer data specifically includes the following steps:

[0222] (1) Put the images drawn from the dynamometer data of different pumping units and the dynamometer data of the same pumping unit at different time periods into the same folder for normalization, binarization and refinement processing;

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

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

[0225] (4) The actual electrical dynamometer information is input into the trained network model, and 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] Among them, the optimal control parameter group of the oil pumping unit obtained according to the working state parameters specifically includes:

[0227] If the current operation is in the fixed speed mode, determine whether the oil pump efficiency is lower than the preset threshold value. 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 the fixed speed mode, write the value of the optimal control array into the variable speed control module, and complete the adjustment of the operating condition of the oil pumping unit motor;

[0228] If the current mode is the adaptive speed change mode, the summer operation mode or the winter operation mode is determined according to the comparison result between the detected temperature and the set temperature threshold;

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

[0230] If it is winter operation mode, determine whether the oil pump efficiency is lower than the preset threshold. If so, calculate the non-stop ultra-low speed operation speed value and generate the ultra-low speed optimal control array; if not, calculate the optimal speed value and generate the adaptive speed change mode optimal control array, then write the ultra-low speed optimal control array value or the adaptive speed change mode optimal control array value into the speed control module to complete the adjustment of the oil pump motor operating conditions. Ultra-low speed refers to a speed of 10% to 20% of the rated speed.

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

[0232] Among them, the corresponding motor torque value is obtained by using the following formula according to the motor active power and motor speed:

[0233]

[0234] In the formula,

[0235] W is the motor output torque;

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

[0237] m is the motor speed.

[0238] Among them, the actual dynamometer displacement and load data are calculated using the following formula based on the displacement and motor useful power:

[0239]

[0240] In the formula,

[0241] η1, η2, and η3 are the efficiency of the pulley, the efficiency of the reduction box, and the efficiency between the crank and the suspension point, respectively;

[0242] i1 and i2 are the pulley transmission ratio and the reduction box transmission ratio respectively;

[0243] W c The rear arm of the traveling beam is heavy;

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

[0245] R crank radius;

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

[0247] B is the dynamic load coefficient;

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

[0249] θ2 is the crank rotation angle;

[0250] θ3 is the connecting rod rotation angle;

[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 is the change in the beam angle corresponding to the displacement.

[0253] The actual dynamometer data of a complete up and down stroke cycle is extracted and transmitted to the 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.

[0254] The above description is only a preferred embodiment of the present invention and is 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 are included in the protection scope of the present invention.

Claims

1. A cloud-edge joint control system for intelligent variable speed drive of oil pumping units, 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 the remote monitoring terminal through a wireless network; The remote monitoring terminal performs working condition diagnosis based on the principle of no reduction in production, increased efficiency of the oil pump, and minimum unit consumption of produced liquid. If the pump needs to be stopped, the oil pump is automatically stopped and an alarm is output; if the pump does not need to be stopped after diagnosis, the operating parameters are optimized to obtain the optimal control parameter group of the oil pump; The detection module collects the working parameters of the pumping unit related to the electric 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 pumping unit and inversely calculate the hanging point dynamometer diagram in real time. The operation control module is connected to the PLC controller, and the operation control module obtains a mode switching signal, an automatic or manual switching signal, generates a fixed speed or variable speed mode control logic, and controls the pumping unit motor according to the logic; The speed control module sets the oil pumping unit to two operating modes: a fixed speed mode and an adaptive speed mode, and controls the oil pumping unit according to the optimal control parameter group of the oil pumping unit calculated by the remote monitoring terminal. In the event of a failure of the speed control module, the system automatically switches to the fixed speed mode; The energy feedback module is connected to the motor input line to feed back the electric energy generated by the pumping unit motor in the downstroke to the power grid.

2. The cloud-edge joint control system for intelligent variable speed drive of oil pumping units according to claim 1 is characterized in that: The detection module includes an electrical parameter acquisition module and an angular displacement sensor; The electrical parameter acquisition module acquires electrical parameters and is connected to the PLC controller; The angular displacement sensor is installed in the middle position of the walking beam of the pumping unit; the angular displacement sensor is connected to the PLC controller through a signal line.

3. The cloud-edge joint control system for intelligent variable speed drive of oil pumping units according to claim 2 is characterized in that: 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 of the suspension point, combines the actual liquid output, calculates the real-time efficiency of the oil pump, and performs real-time inversion calculation of the suspension point indicator diagram.

4. The cloud-edge joint control system for intelligent variable speed drive of oil pumping units according to claim 3 is characterized in that: The PLC controller uses the active power to calculate the actual load of the suspension point through the following formula: In the formula, W is the motor output torque; η1, η2, η3 are the pulley efficiency, reduction box efficiency, and efficiency from the crank to the suspension point, respectively; i1 and i2 are the pulley transmission ratio and the reduction box transmission ratio respectively; W c The rear arm of the traveling beam is heavy; ε is the structural characteristic coefficient of the 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; θ2 is the crank rotation angle; θ3 is the connecting rod rotation angle; θ4 is the angle between the rear arm C of the walking beam and the horizontal plane at the initial position; θ c is the change in the beam angle corresponding to the displacement.

5. The cloud-edge joint control system for intelligent variable speed drive of oil pumping units according to claim 4 is characterized in that: According to the calculated actual load of the suspension point and the actual liquid output, the real-time efficiency of the pumping unit is calculated by the following formula: Where: η is the real-time efficiency of the pumping unit; M i is the calculated actual load of the ith suspension point; A is the length of the forearm of the walking beam; θ ci is the change in the beam angle corresponding to the displacement of the i-th suspension point; n is the number of suspension points.

6. The cloud-edge joint control system for intelligent variable speed drive of oil pumping units according to claim 1 is characterized in that: The communication module is a 4G communication module, and the communication module communicates with the remote monitoring terminal via a 4G wireless network.

7. The cloud-edge joint control system for intelligent variable speed drive of oil pumping units according to claim 1 is characterized in that: The motor is a switched reluctance motor.

8. The cloud-edge joint control system for intelligent variable speed drive of oil pumping units according to claim 1 is 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.

9. The cloud-edge joint control system for intelligent variable speed drive of oil pumping units according to claim 1 is characterized in that: The remote monitoring terminal performs working condition diagnosis through a self-learning model.

10. The cloud-edge joint control system for intelligent variable speed drive of oil pumping units according to any one of claims 1 to 9, characterized in that: It also includes a system protection module, which is connected to the three-phase 380V power line and the ground line, and is connected to the three-phase input line at the motor input end.

11. A control method for a cloud-edge joint control system for an intelligent variable speed drive of an oil pumping unit according to any one of claims 1 to 10, characterized in that: The steps include: Step 1, starting the function of regularly collecting electrical dynamometer data of the detection module; Step 2: Set the acquisition frequency to collect the electrical dynamometer data; Step 3, the PLC controller calculates actual electrical dynamometer data based on the collected electrical dynamometer data; Step 4: Input the actual electrical dynamometer data into the remote monitoring terminal to judge the working condition of the oil pump and identify the fault. When the oil pump has a fault, stop the pump and alarm. When the oil pump has no fault, calculate the current working state parameters of the oil pump according to the actual electrical dynamometer data, and obtain the optimal control parameter group of the oil pump according to the working state parameters. The working state parameters include two conditions: fixed speed and adaptive speed. Step five: input the optimal control parameter group of the oil pumping unit into the PLC controller, and the PLC controller transmits the optimal control parameter group of the oil pumping unit to the speed control module, and the speed control module controls the oil pumping unit according to the optimal control parameter group of the oil pumping unit.

12. The control method of the cloud-edge joint control system of the intelligent variable speed drive of the oil pumping unit according to claim 11 is characterized in that: The optimal control parameter group of the pumping unit obtained according to the working state parameters specifically includes: If the current operation is in the fixed speed mode, determine whether the oil pump efficiency is lower than the preset threshold value. 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 the fixed 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 condition of the oil pumping unit motor; If the current mode is the adaptive speed change mode, the summer operation mode or the winter operation mode is determined according to the comparison result between the detected temperature and the set temperature threshold; If it is the summer operation mode, determine whether the oil pump efficiency is lower than the preset threshold value. If so, generate the intermittent pumping optimal control array, write the optimal control array value into the speed control module, and perform intermittent pumping control; if not, calculate the optimal speed value, generate the adaptive speed mode optimal control array, and then write the optimal control array value into the speed control module to complete the adjustment of the oil pumping unit motor operating conditions; If it is winter operation mode, determine whether the oil pump efficiency is lower than the preset threshold. If so, calculate the ultra-low speed operation speed value without stopping and generate an ultra-low speed optimal control array. If not, calculate the optimal speed value, generate an adaptive speed change mode optimal control array, and then write the ultra-low speed optimal control array value or the adaptive speed change mode optimal control array value into the speed change control module to complete the adjustment of the oil pump motor operating condition.

13. The control method of the cloud-edge joint control system of the intelligent variable speed drive of the oil pumping unit according to claim 11 is characterized in that: The detection module collects the motor's useful power, displacement and motor speed.

14. The control method of the cloud-edge joint control system of the intelligent variable speed drive of the oil pumping unit according to claim 13 is characterized in that: According to the motor active power and motor speed, the corresponding motor torque value is obtained using the following formula: In the formula, W is the motor output torque; P is the active power of the motor; m is the motor speed.

15. The control method of the cloud-edge joint control system of the intelligent variable speed drive of the oil pumping unit according to claim 14 is characterized in that: The actual dynamometer displacement and load data are calculated using the following formula based on the displacement and motor useful power: In the formula, η1, η2, and η3 are the efficiency of the pulley, the efficiency of the reduction box, and the efficiency between the crank and the suspension point, respectively; i1 and i2 are the pulley transmission ratio and the reduction box transmission ratio respectively; W c The rear arm of the traveling beam is heavy; ε is the structural characteristic coefficient of the pumping unit; R is the crank radius; A is the length of the forearm of the walking beam; B is the dynamic load coefficient; C is the length of the rear arm of the walking beam; θ2 is the crank rotation angle; θ3 is the connecting rod rotation angle; θ4 is the angle between the rear arm C of the walking beam and the horizontal plane at the initial position; θ c is the change in the beam angle corresponding to the displacement.

16. The control method of the cloud-edge joint control system of the intelligent variable speed drive of the oil pumping unit according to claim 15 is characterized in that: The actual dynamometer data of a complete up and down stroke cycle is extracted and transmitted to the 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.

Images