A frequency and voltage flexible intelligent coordination optimization method for beam pumping system

Abstract

The present application relates to a frequency and voltage flexible intelligent coordination optimization method based on electrical performance diagram of beam pumping system, which relies on deep learning, and takes the time-varying frequency and time-varying voltage in the cycle as optimization variables for the periodic fluctuation load of electrical performance diagram of pumping system, so that the pumping system can predict and determine the optimal frequency and optimal voltage in real time. By collecting tens of thousands of fluctuation load and image features of electrical performance diagram of beam pumping system, and through deep learning of image features, a frequency and voltage flexible intelligent coordination optimization model based on electrical performance diagram is established. When inputting any fluctuation load characteristics, this optimization model can predict and determine the optimal frequency and optimal voltage in real time according to the characteristics, so as to realize the pumping system of "high load low frequency high voltage, low load high frequency low voltage", "peak load shifting", reduce load fluctuation, and achieve the purpose of energy saving and consumption reduction.

Description

A flexible and intelligent coordination optimization method for frequency and voltage in a beam pumping system Technical Field

[0001] This invention relates to the field of oil and gas extraction technology, and more particularly to a flexible intelligent coordination optimization method for frequency and voltage of a beam pumping system based on an electrical dynamometer diagram. Background Technology

[0002] With the deepening of oil and gas extraction and the production of crude oil, the formation supply capacity is insufficient to support the self-flowing of oil wells. To improve oil extraction efficiency, artificial lift equipment has been increasingly widely used. Currently, artificial lift equipment mainly includes pumping units, electric submersible pumps, screw pumps, and high-pressure gas lift technologies. Beam pumping units, due to their simple structure, reliable performance, and low price, occupy the majority of the market share. A beam pumping unit mainly consists of a pump, sucker rod, polished rod, four-bar linkage, gearbox, belt, and electric motor. The electric motor drives the crank to rotate through the speed reduction and torque amplification effect of the belt and gearbox. The four-bar linkage converts the rotational motion into the linear motion of the rod. Based on the direction of rod movement, the pumping unit's operation is divided into upstroke and downstroke. During the upstroke, the fluid located in the tubing and above the traveling valve is lifted, and when the stationary valve opens, the fluid in the casing is drawn into the pump barrel. During the downstroke, when the traveling valve opens, the fluid in the pump barrel is discharged, and the fluid load above the traveling valve is transferred and acts on the stationary valve, unloading the sucker rod string. This "up-suction, down-discharge" pumping method, combined with the motion characteristics of the crank-rocker mechanism and the combined effect of the counterweight, results in a "double-hump" bidirectional fluctuation in the ground load of the beam pumping system. This periodic load fluctuation not only affects the balance and reliability of the mechanical system, but also increases the required rated power of the electric motor to meet the peak torque demand. This causes the motor to operate in the low-load area most of the time, resulting in low motor power utilization and significant energy waste. In particular, the negative torque generated during localized periods drives the motor to generate electricity, reducing energy transmission efficiency and polluting the power grid, causing unstable power supply and energy waste.

[0003] Among the existing flexible frequency conversion voltage regulation solutions, such as the method for flexible synchronous coordination optimization of frequency and voltage in a beam pumping system (patent application number 202211241660.X), although it can achieve flexible synchronous coordination optimization of frequency and voltage in a beam pumping system, the series of algorithms used are time-consuming and have some shortcomings when applied to real-time operating beam pumping systems. Summary of the Invention

[0004] In view of this, the present invention provides a flexible intelligent coordination optimization method for frequency and voltage of a beam pumping system based on dynamometer diagrams. By deeply analyzing the time-varying correspondence between dynamometer diagrams and flexible frequency conversion voltage regulation, and combining deep learning to establish a flexible intelligent coordination optimization model for frequency and voltage based on dynamometer diagrams, the method realizes online real-time intelligent coordination flexible frequency conversion voltage regulation of oil wells, thereby improving energy-saving effect and solving the problem that the existing technology relies on dynamic models and optimization algorithms, which consumes a lot of time and thus reduces energy-saving effect.

[0005] The technical means employed in this invention are as follows:

[0006] This invention provides a flexible intelligent coordination optimization method for frequency and voltage in a beam pumping system based on an electrical dynamometer diagram, comprising the following steps:

[0007] An electromechanical coupling dynamic model of a flexible variable frequency voltage regulating beam pumping system was established, and the electrical power diagram under constant frequency and constant voltage conditions was obtained based on the electromechanical coupling dynamic simulation model of the system.

[0008] Based on the electromechanical coupling dynamics model of the system, the dynamic characteristics, energy-saving mechanism and coupling relationship between flexible frequency conversion and flexible voltage regulation of the system are analyzed. A frequency and voltage flexible synchronization coordination optimization model is designed, and the optimal flexible frequency and optimal flexible voltage are determined based on the frequency and voltage flexible synchronization coordination optimization model.

[0009] The electrical power diagram is used as a characteristic parameter of the oil pumping system, and the data classification standard is determined according to the balance state of the beam pumping unit well.

[0010] Based on the aforementioned data classification criteria, a BP neural network is used to intelligently classify the input electrical power diagram.

[0011] Using the power diagram as the source domain dataset and the optimal flexible frequency and optimal flexible voltage as the target domain dataset, a neural network is used to establish and train a frequency and voltage flexibility intelligent coordination optimization model based on the power diagram. The model takes the motor input power, the first derivative of the motor input power, and the second derivative of the motor input power, which can fully reflect the change shape of the power diagram curve, as inputs, and the optimized flexible voltage and optimized flexible frequency as outputs.

[0012] When given any fluctuating load and electrical diagram characteristics, the optimal frequency and voltage of the beam pumping system are predicted and determined in real time based on the frequency and voltage flexible intelligent coordination optimization model of the electrical diagram.

[0013] Furthermore, the training of the frequency and voltage flexible intelligent coordination optimization model based on the power diagram includes:

[0014] The optimal flexible frequency and optimal flexible voltage are combined with the electrical power diagram to form a training data pair;

[0015] Based on the training data, the model is optimized through multiple iterations to obtain the optimal neuron weight coefficients.

[0016] The optimizations include:

[0017] Calculate the error between the optimized flexible frequency and optimized flexible voltage output by the model and the optimal flexible frequency and optimal flexible voltage;

[0018] The error signal is backpropagated from the last layer to the next layer to obtain the error learning signal for each layer, and then the weights of the neurons in each layer are corrected based on the error learning signal.

[0019] Furthermore, an electromechanical coupling dynamic model of the flexible variable frequency pressure regulating beam pumping system is established, including:

[0020] Based on the transient equivalent circuit of the induction motor, and considering the influence of time-varying frequency, time-varying voltage and time-varying electromagnetic parameters on the mechanical characteristics of the motor, a dynamic simulation model of a flexible variable frequency voltage regulating induction motor is established.

[0021] Considering the influence of time-varying friction of each transmission pair on the torsional vibration of each component of the ground transmission system, a multibody dynamics simulation model of the ground system is established.

[0022] Taking into account the mutual coupling effects of motor rotation, surface system transmission, downhole rod string longitudinal vibration, pump reciprocating motion, and oil flow during flexible frequency conversion and pressure regulation drive, an electromechanical coupling dynamic simulation model of the flexible frequency conversion and pressure regulation beam pumping system is established.

[0023] Furthermore, a frequency and voltage flexible synchronization coordination optimization model is designed, including:

[0024] Based on the cyclic working characteristics of the oil pumping system, combined with nonlinear function transformation, the coupling relationship between flexible frequency conversion and flexible voltage regulation is analyzed, and the sensitive factors affecting the frequency and voltage curve morphology are analyzed.

[0025] Meanwhile, taking the flexible frequency and flexible voltage within the cycle as optimization variables and the minimum input power of the motor as the optimization objective, and comprehensively considering various nonlinear constraints, a flexible synchronous coordination optimization model for the frequency and voltage of the beam pumping system is established.

[0026] Furthermore, the BP neural network includes: an input layer, a hidden layer, and an output layer; the BP neural network is used to intelligently classify the input electrical power diagram, including:

[0027] The electrical power diagram characteristic parameters are input from the input layer. After the signal is processed by each hidden layer, it is finally output from the output layer in three categories: overbalanced, balanced, and underbalanced.

[0028] Furthermore, it also includes: simulating test frequency and voltage, predicting the effect, and further refining the frequency and voltage flexible intelligent coordination optimization model based on the power diagram.

[0029] In another aspect, the present invention also provides a computer-readable storage medium comprising a stored program, wherein, when the program is executed, the above-described method for flexible intelligent coordination and optimization of frequency and voltage in a beam pumping system based on an electrical dynamometer is performed.

[0030] In another aspect, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the above-described method for flexible intelligent coordination and optimization of frequency and voltage of a beam pumping system based on an electrical dynamometer diagram through the computer program.

[0031] Compared with the prior art, the present invention has the following advantages:

[0032] This invention utilizes deep learning to address the periodic fluctuations in the electrical power diagram of an oil pumping system. By using time-varying frequency and voltage within the period as optimization variables, the system can predict and determine the optimal frequency and voltage in real time. Through collecting tens of thousands of image features of fluctuating loads and electrical power diagrams from beam pumping systems, and using deep learning on these features, optimal neuron weights are trained. When any fluctuating load feature is input, this optimization system can predict and determine the optimal frequency and voltage in real time based on those features, achieving "high load, low frequency, high voltage; low load, high frequency, low voltage" for the oil pumping system. This "peak shaving and valley filling" of the load torque achieves the goal of reducing load fluctuations and saving energy in the oil pumping system in real time. Attached Figure Description

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

[0034] Figure 1 is a roadmap for the research on flexible intelligent coordination and optimization of frequency and voltage based on power diagrams according to the present invention. Detailed Implementation

[0035] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0036] With the development of technologies such as data mining, artificial intelligence, and signal processing, intelligent oil well condition diagnosis based on deep learning has gradually become the mainstream method in the field of oil well fault diagnosis. If the optimal coordination and variation law of frequency and voltage can be determined in real time based on deep learning, the technical bottleneck of relying on time-consuming optimization algorithms can be overcome.

[0037] Because dynamometers are exposed to the elements year-round, they are prone to aging, have short lifespans, poor real-time performance, require periodic calibration, and only reflect downhole operating conditions. In contrast, dynamometer data acquisition equipment (the motor input power curve over a complete cycle) is typically housed in a control cabinet, offering convenient data acquisition, stable data, and real-time reflection of operating conditions from downhole to the surface. Therefore, using dynamometers instead of dynamometers for intelligent operating condition diagnosis of characteristic parameters of the pumping system within a cycle is gradually gaining widespread application. If the optimal coordination and variation law of frequency and voltage can be determined in real time based on the changes in the dynamometer pattern, the technical bottleneck of time-consuming optimization relying on dynamic models can be overcome.

[0038] The method of this invention relies on deep learning and targets the periodic fluctuation load of the power diagram of the oil pumping system. It uses the time-varying frequency and voltage within the period as optimization variables, enabling the oil pumping system to predict and determine the optimal frequency and voltage in real time. This achieves the goal of reducing the load fluctuation of the oil pumping system and saving energy and reducing consumption in real time, and solves the problem that the existing technology relies on dynamic models and optimization algorithms, which are time-consuming and thus reduce the energy-saving effect.

[0039] As shown in Figure 1, this invention provides a flexible intelligent coordination optimization method for frequency and voltage in a beam pumping system based on an electrical power diagram, comprising the following steps:

[0040] S1. Establish an electromechanical coupling dynamic model of a flexible variable frequency voltage regulating beam pumping system; and obtain the electrical power diagram under constant frequency and constant voltage conditions based on the electromechanical coupling dynamic simulation model of the system.

[0041] The specific process of establishing the electromechanical coupling dynamic model of the flexible variable frequency pressure regulating beam pumping system includes:

[0042] S11. Based on the transient equivalent circuit of the induction motor, considering the influence of time-varying frequency, time-varying voltage and time-varying electromagnetic parameters on the mechanical characteristics of the motor, a dynamic simulation model of a flexible variable frequency voltage regulation induction motor is established.

[0043] S12. Considering the influence of time-varying friction of each transmission pair on the torsional vibration of each component of the ground transmission system, establish a multibody dynamics simulation model of the ground system.

[0044] S13. Taking into account the mutual coupling effects of motor rotation, surface system transmission, downhole rod string longitudinal vibration, oil pump reciprocating motion and oil flow during flexible frequency conversion and pressure regulation drive, an electromechanical coupling dynamic simulation model of flexible frequency conversion and pressure regulation beam pumping system is established.

[0045] S2. By establishing a system electromechanical coupling dynamic model, analyze the system's dynamic characteristics, energy-saving mechanism, and the coupling relationship between flexible frequency conversion and flexible voltage regulation, and design a frequency and voltage flexible synchronization coordination optimization model; and based on this frequency and voltage flexible synchronization coordination optimization model, determine the optimal flexible frequency and optimal flexible voltage.

[0046] The process of designing a frequency and voltage flexible synchronization coordination optimization model includes:

[0047] S21. Based on the cyclic working characteristics of the oil pumping system, combined with the nonlinear transformation of the function, the sensitive factors affecting the frequency and voltage curve shape are analyzed according to the coupling relationship between flexible frequency conversion and flexible voltage regulation.

[0048] S22. Simultaneously, taking the flexible frequency and flexible voltage within the cycle as optimization variables and the minimum input power of the motor as the optimization objective, and comprehensively considering various nonlinear constraints, a flexible synchronous coordination optimization model for the frequency and voltage of the beam pumping system is established.

[0049] S23. Select the power curve as the characteristic parameter of the pumping system, and determine the data classification standard based on the balance state of the beam pumping well, that is, the peak value ratio of the double-hump curve of the motor input power curve within the cycle.

[0050] Among them, the balance state of a beam pumping unit well includes underbalanced state, balanced state and overbalanced state.

[0051] The data classification criteria are as follows: Power P corresponding to the upstroke and downstroke of the pumping unit is tested separately. 上 and P 下 The power balance factor (PBF) is evaluated by the ratio of the latter to the former. It represents the ratio of the output power of the pumping unit during the upstroke and downstroke. The standard considers the pumping unit to be unbalanced when PBF < 0.5. When the power balance factor PBF > 1, the pumping unit is in an overbalanced state; otherwise, it is considered to be in an underbalanced state.

[0052] S4. Using the electrical power diagram output by the system electromechanical coupling model under constant frequency and constant voltage conditions and the optimal flexible frequency and optimal flexible voltage output by the flexible synchronous coordination optimization model as the source domain dataset and the target domain dataset respectively, a BP neural network is used to intelligently classify the input electrical power diagram based on the data classification standard.

[0053] A backpropagation (BP) neural network consists of an input layer, hidden layers, and an output layer. The BP neural network intelligently classifies input samples, specifically including:

[0054] The characteristic parameters of the electrical power diagram are input from the input layer. After the signal is processed through each hidden layer, it is finally output from the output layer as three categories: overbalanced, balanced, and underbalanced. Classification facilitates data processing, and what is needed is balanced data. The unbalanced data is classified, their characteristics are identified, and based on these characteristics, the unbalanced data is optimized to be balanced.

[0055] S5. Based on signal processing knowledge, the classified data is preliminarily regularized. The motor input power, the first derivative of the motor input power, and the second derivative of the motor input power, which can fully reflect the changing shape of the power graph curve, are selected as the network input parameters. A frequency and voltage flexible intelligent coordination optimization model based on the power graph is established and trained. This model is based on a neural network and outputs optimized flexible frequency and optimized flexible voltage. It can establish the mapping relationship between the power graph and the optimized flexible frequency and optimized flexible voltage.

[0056] Regularization helps improve generalization performance and prevents overfitting of classification data.

[0057] Among them, neural networks are those that can effectively process time series data, such as Long Short-Term Memory Networks (LSTM), Recurrent Neural Networks (RNN), etc.

[0058] The model training process is as follows: Using the electrical power diagram as the source domain dataset and the optimal flexible frequency and optimal flexible voltage as the target domain dataset, the optimal flexible frequency and optimal flexible voltage output from the dynamic simulation model of the flexible variable frequency and voltage regulating induction motor are combined with the electrical power diagram under constant frequency and constant voltage conditions obtained from the electromechanical coupling dynamic simulation model of the system to form a training data pair. Based on this training data pair, the model is repeatedly optimized to obtain the optimal neuron weight coefficients. More specifically, for the error between the actual output (optimized flexible frequency and optimized flexible voltage) and the expected output (optimized flexible frequency and optimal flexible voltage) of the model, the error signal is backpropagated layer by layer from the last layer to obtain the error learning signal of each layer. Then, the weights of the neurons in each layer are corrected based on the error learning signal. The above process is repeated continuously, with the weights constantly adjusted during this process. This process continues until the network output error is reduced to below a pre-set threshold.

[0059] The data assimilation algorithm for optimizing neuron weights in network efficiency screening is as follows:

[0060] The neural network is trained on multiple datasets to select and optimize neuron weight coefficients. Test data can also update neuron weight coefficients, meaning that neuron weight coefficients are optimized in each working process.

[0061] S6. When inputting arbitrary fluctuating load and electrical power diagram characteristics, based on the frequency and voltage flexible intelligent coordination optimization model of the electrical power diagram, the optimal frequency and optimal voltage of the beam pumping system are predicted and determined in real time.

[0062] The oil pumping system achieves "high load, low frequency, high pressure; low load, high frequency, low pressure," which "shaving off peaks and filling valleys" of load torque, reduces load fluctuations, and achieves the goal of energy saving and consumption reduction.

[0063] In another embodiment, the method further includes: performing simulation tests on frequency and voltage, predicting the results, and further refining the frequency and voltage flexible intelligent coordination optimization model based on the power diagram.

[0064] The method described in the above embodiments relies on deep learning. For the periodic fluctuations in the electrical power diagram of the pumping system, it uses time-varying frequency and voltage within the period as optimization variables, enabling the pumping system to predict and determine the optimal frequency and voltage in real time. By collecting tens of thousands of image features of fluctuating loads and electrical power diagrams of beam pumping systems, and through deep learning of these image features, the optimal neuron weight optimization coefficients are trained. When any fluctuating load feature is input, this optimization system can predict and determine the optimal frequency and voltage in real time based on its characteristics, achieving "high load, low frequency, high voltage; low load, high frequency, low voltage" for the pumping system, "peak shaving and valley filling" of the load torque, and achieving the goal of reducing load fluctuations and saving energy in the pumping system in real time.

[0065] In another embodiment, the present invention also provides a computer-readable storage medium comprising a stored program, wherein, when the program is executed, the above-described method for flexible intelligent coordination and optimization of frequency and voltage in a beam pumping system based on an electrical dynamometer is performed.

[0066] In another embodiment, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the above-described method for flexible intelligent coordination and optimization of frequency and voltage in a beam pumping system based on an electrical dynamometer diagram through the computer program.

[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A flexible intelligent coordination optimization method for frequency and voltage in a beam pumping system based on dynamometer diagrams, characterized in that, The steps include: establishing an electromechanical coupling dynamic model of a flexible variable frequency voltage regulating beam pumping system, and obtaining the electrical power diagram under constant frequency and constant voltage conditions based on the electromechanical coupling dynamic simulation model of the system; Based on the electromechanical coupling dynamics model of the system, the dynamic characteristics, energy-saving mechanism, and coupling relationship between flexible frequency conversion and flexible voltage regulation of the system are analyzed. A frequency and voltage flexible synchronization coordination optimization model is designed, and the optimal flexible frequency and optimal flexible voltage are determined based on the frequency and voltage flexible synchronization coordination optimization model. The power diagram is used as a characteristic parameter of the pumping system, and the data classification standard is determined according to the balance state of the beam pumping well. Based on the data classification standard, a BP neural network is used to intelligently classify the input power diagram. Using the power graph as the source domain dataset and the optimal flexible frequency and optimal flexible voltage as the target domain dataset, a neural network is used to establish and train a frequency and voltage flexibility intelligent coordination optimization model based on the power graph. This model takes the motor input power, the first derivative of the motor input power, and the second derivative of the motor input power—which fully reflect the changing shape of the power graph curve—as input, and the optimized flexible voltage and optimized flexible frequency as output. When any fluctuating load and power graph characteristics are input, the frequency and voltage flexibility intelligent coordination optimization model based on the power graph predicts and determines the optimal frequency and optimal voltage of the beam pumping system in real time. The training of the frequency and voltage flexible intelligent coordination optimization model based on the power diagram includes: forming a training data pair with the optimal flexible frequency and optimal flexible voltage and the power diagram; performing multiple iterations of optimization on the frequency and voltage flexible intelligent coordination optimization model based on the power diagram based on the training data pair to obtain the optimal neuron weight coefficients; the optimization includes: calculating the error between the optimized flexible frequency and optimized flexible voltage output by the frequency and voltage flexible intelligent coordination optimization model based on the power diagram and the optimal flexible frequency and optimal flexible voltage; backpropagating the error signal from the last layer layer by layer to obtain the error learning signal of each layer, and then correcting the weights of the neurons in each layer according to the error learning signal.

2. The method for flexible intelligent coordination and optimization of frequency and voltage in a beam pumping system based on dynamometer diagrams as described in claim 1, characterized in that, An electromechanical coupling dynamic model of a flexible variable frequency and voltage regulating beam pumping system is established, including: a dynamic simulation model of a flexible variable frequency and voltage regulating induction motor based on the transient equivalent circuit of an induction motor, considering the influence of time-varying frequency, time-varying voltage, and time-varying electromagnetic parameters on the mechanical characteristics of the motor; a multi-body dynamic simulation model of the surface system considering the influence of time-varying friction of each transmission pair on the torsional vibration of each component of the surface transmission system; and a comprehensive electromechanical coupling dynamic simulation model of the flexible variable frequency and voltage regulating beam pumping system, considering the mutual coupling effects of motor rotation, surface system transmission, longitudinal vibration of the downhole rod string, reciprocating motion of the pumping pump, and oil flow during flexible variable frequency and voltage regulating drive.

3. The method for flexible intelligent coordination and optimization of frequency and voltage in a beam pumping system based on an electrical power diagram, as described in claim 1, is characterized in that... Design a flexible synchronous coordination optimization model for frequency and voltage, including: based on the cyclic operating characteristics of the pumping system, combined with nonlinear function transformation, analyzing the coupling relationship between flexible frequency conversion and flexible voltage regulation, and considering the sensitive factors affecting the shape of the frequency and voltage curves; simultaneously, taking the flexible frequency and flexible voltage within the cycle as optimization variables, the minimum input power of the motor as the optimization objective, and comprehensively considering various nonlinear constraints, to establish a flexible synchronous coordination optimization model for frequency and voltage of the beam pumping system.

4. The method for flexible intelligent coordination and optimization of frequency and voltage in a beam pumping system based on dynamometer diagrams according to claim 1, characterized in that, The BP neural network includes an input layer, a hidden layer, and an output layer. The BP neural network is used to intelligently classify the input power diagram, including: inputting the power diagram feature parameters from the input layer, processing the signal through each hidden layer, and finally outputting three classifications from the output layer: overbalanced, balanced, and underbalanced.

5. The method for flexible intelligent coordination and optimization of frequency and voltage in a beam pumping system based on dynamometer diagrams according to claim 1, characterized in that, Also includes: Simulation tests were conducted on frequency and voltage to predict performance and further refine the frequency and voltage flexible intelligent coordination optimization model based on the power diagram.

6. A computer-readable storage medium, characterized in that, The storage medium includes a stored program, wherein, when the program is executed, it performs the frequency and voltage flexible intelligent coordination optimization method for a beam pumping system based on an electrical dynamometer, as described in any one of claims 1 to 5.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the frequency and voltage flexible intelligent coordination optimization method for a beam pumping system based on an electrical diagram, as described in any one of claims 1 to 5, through the computer program.

Images