A distributed automation control method and system for oilfield power grid

Abstract

The present invention belongs to the technical field of oilfield power grids and discloses a distributed automation control method and system for oilfield power grids. The method comprises: obtaining operating data of each distributed node in the oilfield power grid and extracting operating characteristic data; using a factor analysis algorithm to analyze the operating characteristic data and identify key factors affecting the automated dispatching performance of the oilfield power grid; constructing an oilfield power grid dispatching state prediction model based on the key factors, using the oilfield power grid dispatching state prediction model to dynamically predict the operating state of each distributed node at future moments, and optimizing the automated dispatching control strategy. The present invention can effectively improve the reliability and stability of the oilfield power grid, reduce power outage time and energy loss, and optimize the operating efficiency of the power grid, ensuring the continuity and safety of oilfield production.

Description

Technical Field

[0001] The present invention relates to the technical field of oilfield power grids, and in particular to a distributed automation control method and system for oilfield power grids. Background Art

[0002] As the core infrastructure of oilfield production, the oilfield power grid bears the heavy responsibility of ensuring the normal operation of the oilfield. As oilfields continue to expand, traditional centralized control models are no longer able to meet the needs of efficient and flexible management. Therefore, distributed automation control methods for oilfield power grids have emerged. Leveraging advanced information and communication technologies, intelligent management of the grid is achieved through real-time monitoring and data collection of distributed nodes in the oilfield distribution network. This distributed control system enables localized control and optimized scheduling of various parts of the grid, effectively improving the reliability and cost-effectiveness of the grid and reducing energy losses and outages. Furthermore, the system can rapidly respond to grid faults, improving the automation level and management efficiency of the oilfield power grid, and ensuring the safe and stable operation of the oilfield.

[0003] Existing technologies lack the ability to perceive and dynamically predict the operating status of distributed nodes in real time, making it difficult to respond to load changes and fault conditions in a timely manner. This leads to low scheduling efficiency, high energy consumption, and easily causes power supply instability, limiting the improvement of the intelligence level of the power grid.

[0004] Therefore, how to provide a distributed automation control method and system for oilfield power grid is a problem that needs to be solved urgently. Summary of the Invention

[0005] The embodiments of the present invention provide a distributed automation control method and system for an oilfield power grid to address the problems in the prior art of lacking the ability to perceive and dynamically predict the operating status of distributed nodes in real time, making it difficult to respond to load changes and fault conditions in a timely manner, resulting in low scheduling efficiency, high energy consumption, and easily causing power supply instability, thereby limiting the improvement of the intelligence level of the power grid.

[0006] To provide a basic understanding of some aspects of the disclosed embodiments, the following is a brief summary. This summary is not intended to be an extensive review, identify key or critical elements, or delineate the scope of these embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the detailed description that follows.

[0007] According to a first aspect of an embodiment of the present invention, a distributed automation control method for an oilfield power grid is provided.

[0008] In one embodiment, a distributed automation control method for an oilfield power grid includes:

[0009] Obtain the operating data of each distributed node in the oilfield power grid and extract the operating characteristic data;

[0010] Utilize factor analysis algorithms to analyze operational characteristic data and identify key factors that affect the automated dispatching performance of oilfield power grids;

[0011] Based on key factors, an oilfield power grid dispatching status prediction model is constructed. The oilfield power grid dispatching status prediction model is used to dynamically predict the operating status of each distributed node in the future and optimize the automated dispatching control strategy.

[0012] In one embodiment, obtaining the operating data of each distributed node in the oilfield power grid and extracting the operating characteristic data includes:

[0013] Collect operating data from each distributed node of the oilfield power grid and calculate key indicators related to node performance based on frequency domain characteristics analysis;

[0014] Use signal filtering methods to pre-process the operating data of each distributed node and extract effective operating status signals;

[0015] Use demodulation algorithms to analyze the operating status signal, extract its time-frequency characteristics, and construct an operating spectrum diagram;

[0016] Combined with the peak characteristics and other key indicators in the operation spectrum diagram, the operation characteristic data of each distributed node is extracted.

[0017] In one embodiment, a factor analysis algorithm is used to analyze the operational characteristic data to identify key factors affecting the automated dispatching performance of the oilfield power grid, including:

[0018] Initialize the operating feature set according to the operating feature data and calculate the fitness value of each operating feature;

[0019] According to the fitness ascending order, the optimal running feature is selected to simulate binary crossover and optimize the running feature vector;

[0020] Evaluate the correlation between various operating characteristics and dispatch performance, and preliminarily screen out the key factors affecting the automated dispatch performance of the oilfield power grid;

[0021] Optimize feature selection through mutation operations and feature optimization mechanisms, and update the running feature set;

[0022] The optimal operating characteristics of each iteration are retained, and the final solution that meets the maximum number of iterations is output as the key factor affecting the performance of oilfield power grid automation scheduling.

[0023] In one embodiment, based on key factors, an oilfield power grid dispatching state prediction model is constructed, and the oilfield power grid dispatching state prediction model is used to dynamically predict the operating state of each distributed node at a future time, and optimize the automated dispatching control strategy, including:

[0024] Collect multi-condition scheduling data, obtain key node status signals, and construct a multi-dimensional original feature sample set;

[0025] Use arithmetic optimization algorithms to determine the optimal decomposition parameters and extract key modal features of node operation status;

[0026] Reconstruct important state feature sequences based on correlation, generate high-quality samples, and divide them into training sets and test sets;

[0027] Build an oilfield power grid dispatch status prediction model, input a training set, and adjust parameters to improve prediction accuracy;

[0028] The performance of the oilfield power grid dispatching state prediction model was verified through the test set to evaluate its ability to predict changes in the oilfield power grid dispatching state;

[0029] The constructed oilfield power grid dispatching status prediction model is used to dynamically predict the operating status of each distributed node in the future, and the automated dispatching strategy is optimized based on the prediction results.

[0030] According to a second aspect of an embodiment of the present invention, a distributed automation control system for an oilfield power grid is provided.

[0031] In one embodiment, the oilfield power grid distributed automation control system includes:

[0032] The data acquisition module is used to obtain the operating data of each distributed node in the oil field power grid and extract the operating characteristic data;

[0033] The factor analysis module is used to analyze the operation characteristic data using the factor analysis algorithm to identify the key factors affecting the performance of oilfield power grid automation scheduling;

[0034] The state prediction and optimization control module is used to build an oilfield power grid dispatching state prediction model based on key factors, use the oilfield power grid dispatching state prediction model to dynamically predict the operating state of each distributed node in the future, and optimize the automated dispatching control strategy.

[0035] According to a third aspect of an embodiment of the present invention, a computer device is provided.

[0036] In some embodiments, the computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0037] According to a fourth aspect of embodiments of the present invention, a computer-readable storage medium is provided.

[0038] In one embodiment, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0039] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0040] 1. The present invention improves the intelligence and accuracy of power grid dispatching by acquiring the operating data of each distributed node in real time and identifying key influencing factors using a factor analysis algorithm. By constructing a dispatching state prediction model, it can realize dynamic prediction of future operating states and timely adjust the automated dispatching strategy, thereby effectively improving the reliability and stability of the oilfield power grid, reducing power outage time and energy loss, and optimizing the operating efficiency of the power grid, ensuring the continuity and safety of oilfield production.

[0041] 2. The present invention accurately identifies key operating characteristics that affect dispatching performance through factor analysis and feature optimization mechanisms, improves the accuracy of feature selection and global optimization capabilities, and enhances the adaptability and prediction accuracy of the oilfield power grid dispatching status prediction model to complex power grid operating conditions, thereby effectively optimizing the automated dispatching control strategy of the oilfield power grid.

[0042] 3. The present invention optimizes the decomposition parameters through an arithmetic optimization algorithm, accurately extracts the key modal features of the node operating status, effectively improves the accuracy of the oilfield power grid scheduling status prediction, and dynamically predicts the future operating status of each node and optimizes the scheduling strategy based on the prediction results, thereby improving the scheduling efficiency, reliability and stability of the oilfield power grid, ensuring the intelligent and efficient operation of the oilfield power grid.

[0043] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0045] Figure 1 This is a flow chart showing a method for distributed automation control of an oilfield power grid according to an exemplary embodiment;

[0046] Figure 2 This is a principle block diagram of an oilfield power grid distributed automation control system according to an exemplary embodiment;

[0047] Figure 3The figure is a schematic diagram showing the structure of a computer device according to an exemplary embodiment. DETAILED DESCRIPTION

[0048] The following description and accompanying drawings sufficiently illustrate the specific embodiments herein to enable those skilled in the art to practice them. Portions and features of some embodiments may be included in or substituted for portions and features of other embodiments. The scope of the embodiments herein includes the entire scope of the claims, including all available equivalents thereof. Herein, the terms "first," "second," and the like are used solely to distinguish one element from another and do not require or imply any actual relationship or order between these elements. In practice, the first element can also be referred to as the second element, and vice versa. Furthermore, the terms "comprise," "comprising," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a structure, device, or apparatus comprising a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such structure, device, or apparatus. Without further limitation, an element defined by the phrase "comprising a..." does not preclude the presence of other identical elements in the structure, device, or apparatus comprising the element. The various embodiments herein are described in a progressive manner, with each embodiment focusing on its differences from the other embodiments. Similar or identical parts between the various embodiments can be referenced to each other.

[0049] The terms "longitudinal", "transverse", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like used herein to indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are intended only to facilitate the description of this document and simplify the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on the present invention. In the description herein, unless otherwise specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, they can be mechanical or electrical connections, or they can be internal connections between two elements, they can be directly connected, or they can be indirectly connected through an intermediate medium. For those of ordinary skill in the art, the specific meanings of the above terms can be understood according to the specific circumstances.

[0050] As used herein, unless otherwise specified, the term "plurality" means two or more.

[0051] In this document, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0052] In this article, the term "and / or" is used to describe the association relationship between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or, A and B.

[0053] It should be understood that, although the various steps in the flowchart are shown in sequence as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps may be performed in other orders. Moreover, at least a portion of the steps in the figure may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily performed at the same time, but may be performed at different times. The execution order of these sub-steps or stages is not necessarily to be performed in sequence, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0054] Each module in the device or system of the present application can be implemented in whole or in part by software, hardware, or a combination thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software so that the processor can call and execute the operations corresponding to the above modules.

[0055] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0056] Figure 1 An embodiment of the oilfield power grid distributed automation control method of the present invention is shown.

[0057] In this optional embodiment, the oilfield power grid distributed automation control method includes:

[0058] Step S101, obtaining the operating data of each distributed node in the oilfield power grid and extracting the operating characteristic data;

[0059] Specifically, the operating data of each distributed node in the oilfield power grid can be obtained through sensors and monitoring equipment or wireless communication and remote transmission.

[0060] Specifically, operating data includes current, voltage, power, frequency, load, temperature and humidity data, switch status, equipment health status, etc.

[0061] Specifically, the operating characteristic data includes the node's load fluctuation characteristics, voltage and current stability characteristics, power factor characteristics, frequency fluctuation characteristics, load forecast characteristics, environmental impact characteristics, etc.

[0062] Step S102: Analyze the operation characteristic data using a factor analysis algorithm to identify key factors that affect the performance of oilfield power grid automation scheduling;

[0063] Specifically, the key factors include grid load characteristics, voltage stability, power factor, synchronization of current and voltage, access to distributed energy resources, grid frequency, environmental and meteorological factors, etc.

[0064] Step S103: construct an oilfield power grid dispatching state prediction model based on key factors, use the oilfield power grid dispatching state prediction model to dynamically predict the operating state of each distributed node at a future moment, and optimize the automated dispatching control strategy.

[0065] In this optional embodiment, when obtaining the operating data of each distributed node in the oilfield power grid and extracting the operating characteristic data, the operating data of each distributed node in the oilfield power grid can be collected, and key indicators related to node performance can be calculated based on frequency domain characteristics analysis; the operating data of each distributed node can be preprocessed using a signal filtering method to extract an effective operating status signal; the operating status signal can be analyzed using a demodulation algorithm to extract its time-frequency characteristics and construct an operating spectrum diagram; and the operating characteristic data of each distributed node can be extracted by combining the peak characteristics and other key indicators in the operating spectrum diagram.

[0066] Specifically, first, the voltage, current and other operating data of each distributed node are continuously collected through intelligent terminals (sensors and monitoring equipment or wireless communication and remote transmission, etc.) deployed at the oil field site. Then, frequency domain feature analysis technology is used to identify indicators closely related to node performance, such as load fluctuations and power quality. To improve data validity, the signal is first filtered to remove noise, and then the time-frequency characteristics of the signal are mined through a demodulation algorithm to generate a spectrum diagram. Finally, based on the peak phenomenon and performance indicators in the spectrum diagram, the characteristic data representing the actual operating status of the node is accurately extracted, thereby improving the expression ability of the characteristic data and facilitating subsequent scheduling prediction and optimization analysis.

[0067] In this optional embodiment, when using a demodulation algorithm to analyze the operating status signal, extract its time-frequency characteristics, and construct an operating spectrum diagram, the short-time Fourier transform method can be used to perform preliminary time-frequency analysis on the operating signal to determine the key frequency components and instantaneous frequency information; the phase function of each signal component is calculated based on the instantaneous frequency, and the analytical signal is demodulated to separate the frequency components to obtain a demodulated signal with a clear modulation structure; the demodulated signal is Hilbert transformed to extract the envelope and frequency characteristics to construct a complete operating spectrum diagram.

[0068] Specifically, the demodulation algorithm is an iterative generalized demodulation algorithm, which uses the calculation of instantaneous frequency and inverse modulation processing to gradually extract the time-frequency characteristics of the signal, thereby achieving efficient demodulation and analysis of complex signals.

[0069] In this optional embodiment, when using a factor analysis algorithm to analyze the operating characteristic data and identify the key factors affecting the automated scheduling performance of the oilfield power grid, the operating characteristic set can be initialized based on the operating characteristic data, and the fitness value of each operating characteristic can be calculated; the optimal operating characteristics can be selected for simulated binary crossover according to the fitness in ascending order, and the operating characteristic vector can be optimized; the degree of correlation between each operating characteristic and the scheduling performance is evaluated, and the key factors affecting the automated scheduling performance of the oilfield power grid are preliminarily screened out; the feature selection is optimized through mutation operations and feature optimization mechanisms, and the operating characteristic set is updated; the optimal operating characteristics of each round of iteration are retained, and the final solution that meets the maximum number of iterations is output as the key factor affecting the automated scheduling performance of the oilfield power grid.

[0070] Specifically, the factor analysis algorithm is a transit search algorithm, an intelligent optimization algorithm inspired by planetary transits that searches for optimal solutions by simulating planetary trajectories. In this paper, the algorithm is used to initialize an operational feature set, calculate fitness, perform crossover and mutation operations, and iteratively optimize the operational feature vectors to identify key factors influencing oilfield power grid dispatch performance.

[0071] Specifically, the feature set is first initialized based on the collected operational characteristic data. The fitness value of each feature is calculated and sorted in ascending order. Next, the operational characteristic with the best fitness is selected for simulated binary crossover to optimize the feature vector. By evaluating the correlation between the characteristics and dispatch performance, key factors are screened, and the feature set is further optimized through mutation operations. The optimal feature is retained after each iteration until the maximum number of iterations is reached. Finally, the key factors affecting power grid dispatch performance are output, thereby accurately screening key features, optimizing dispatch performance, and improving the efficiency of oilfield power grid automation control.

[0072] In this optional embodiment, when evaluating the degree of correlation between each operating characteristic and the dispatching performance and preliminarily screening out the key factors affecting the oilfield power grid automation dispatching performance, the effect of each operating characteristic on the performance can be evaluated and the optimal operating characteristic can be selected based on the degree of correlation between the selected operating characteristic and the dispatching performance; verify whether the selected optimal operating characteristic meets the constraint conditions of the power grid dispatching performance. If not, fall back and select the suboptimal operating characteristic; continuously adjust the operating characteristic set to enhance the correlation between each operating characteristic and the dispatching performance until a local optimal operating characteristic combination is found, which is the best operating characteristic combination that meets the dispatching requirements; by introducing random factors, fine-tune the existing optimal operating characteristic set to enhance its impact on the power grid dispatching performance, continue to optimize the operating characteristic combination, and find the global optimal operating characteristic combination as the preliminarily screened key factors affecting the oilfield power grid automation dispatching performance.

[0073] Specifically, in the present invention, a greedy algorithm is used to gradually evaluate the impact of various operating characteristics on scheduling performance, give priority to the optimal characteristics, and combine the fallback and fine-tuning mechanism to optimize the optimal feature combination that meets the scheduling requirements.

[0074] In this optional embodiment, when optimizing feature selection through mutation operation and feature optimization mechanism and updating the running feature set, the relevant parameters and the maximum number of iterations of the feature optimization mechanism can be initialized; the initial feature set is generated by using the modified chaotic mapping method, and the running features are randomly selected and combined; according to the preset evaluation criteria, the fitness of each running feature combination is calculated, and the adjustment coefficient of the feature selection is adjusted through the learning automaton mechanism to optimize the feature selection process; the running feature set is updated by using Levy mutation, and the refraction opposition solution of each running feature combination is calculated according to the refraction opposition learning mechanism, and the running feature combination with the best fitness is retained; if the maximum number of iterations is reached, the iterative optimization process ends, and the optimal running feature of each iteration is output.

[0075] Specifically, the feature optimization mechanism is the Grasshopper Optimization Algorithm (GA), an intelligent optimization algorithm that simulates the foraging behavior of grasshopper colonies, finding the optimal solution through information sharing and collective collaboration between individuals. In this invention, the GA is used to initialize a feature set and optimize operational features through random selection, combination, and mutation operations. A learning automaton is used to adjust the selection coefficient, combined with a refracted adversarial learning mechanism, to ultimately select the optimal feature combination to improve grid dispatch performance.

[0076] In this optional embodiment, the formula for calculating the refraction opposition solution for each operating feature combination according to the refraction opposition learning mechanism is:

[0077] ;

[0078] Where, M a Indicates the a The refracted opposite solution of the combination of operating characteristics; W a Indicates the a The current solution for a combination of operating characteristics; W r represents the reference solution; δ represents the refraction factor; Represents the Euclidean distance between the current solution and the reference solution; θ a Indicates the a The angle factor between a running characteristic combination and the reference solution.

[0079] In this optional embodiment, when constructing an oilfield power grid dispatching state prediction model based on key factors, using the oilfield power grid dispatching state prediction model to dynamically predict the operating state of each distributed node in the future, and optimizing the automated dispatching control strategy, multi-condition dispatching data can be collected, key node state signals can be obtained, and a multi-dimensional original feature sample set can be constructed; the arithmetic optimization algorithm can be used to determine the optimal decomposition parameters, and the key modal features of the node operating state can be extracted; the important state feature sequence can be reconstructed according to the correlation, and high-quality samples can be generated and divided into a training set and a test set; the oilfield power grid dispatching state prediction model can be constructed, and the training set can be input, and the parameters can be adjusted to improve the prediction accuracy; the performance of the oilfield power grid dispatching state prediction model can be verified through the test set, and its ability to predict changes in the oilfield power grid dispatching state can be evaluated; the constructed oilfield power grid dispatching state prediction model can be used to dynamically predict the operating state of each distributed node in the future, and the automated dispatching strategy can be optimized based on the prediction results.

[0080] Specifically, we first collect scheduling data under multiple working conditions, obtain the operating status signals of key nodes, construct the original feature sample set, determine the decomposition parameters through the arithmetic optimization algorithm, extract the key modal features, and reconstruct the high-correlation state feature sequence. Then, we divide the samples into training set and test set, build an oilfield power grid scheduling state prediction model, and use the training set to train the model, optimize the parameters to improve the accuracy. After verifying the model performance through the test set, we use it to predict the future state of each distributed node, and dynamically optimize the scheduling strategy accordingly, so as to achieve high-precision state prediction and improve the scheduling response efficiency and power grid operation stability.

[0081] In this optional embodiment, when using the arithmetic optimization algorithm to determine the optimal decomposition parameters and extract the key modal features of the node operating status, the initial parameters and acceleration function of the arithmetic optimization algorithm can be set, and the search step and range can be dynamically adjusted according to the number of iterations; in the global search stage, the parameter search space is expanded and the feasible area of ​​the decomposition parameters is roughly located; the multiplication or division strategy is determined according to the random number, and the position of the decomposition parameters is dynamically updated; the optimization probability is used to adjust the exploration and development ratio, balance the decomposition accuracy and search range, and based on the optimization results, the node signal is decomposed and the key modal features of the node operating status are extracted.

[0082] Specifically, an arithmetic optimization algorithm is an intelligent optimization method based on arithmetic operations (addition, subtraction, multiplication, and division). It adjusts operator combinations to globally search for the optimal solution. In this paper, the arithmetic optimization algorithm is used to dynamically adjust decomposition parameters. It optimizes the parameter search process through acceleration functions and iterative mechanisms, improving the accuracy of node signal decomposition and thereby extracting key modal features of the operating state.

[0083] In this optional embodiment, the multiplication or division strategy is determined according to the random number, and the formula for dynamically updating the position of the decomposition parameter is:

[0084] ;

[0085] Where, C ( t +1) indicates the t +1 iteration, the position after the decomposition parameters are updated; C b ( t ) indicates the t At the iteration, the position of the decomposition parameters after update; MOP Represents the mathematical optimizer probability, controlling the exploration accuracy of the decomposition; UB represents the upper bound of the decomposition parameter; LB represents the lower bound of the decomposition parameter; φ Represents the search control parameter, which controls the search step size; r 2 represents a random number, r 2 belongs to (0, 1), which determines whether to use the addition (multiplication exploration) or subtraction (division exploration) strategy.

[0086] Figure 2 An embodiment of the oilfield power grid distributed automation control system of the present invention is shown.

[0087] In this optional embodiment, the oilfield power grid distributed automation control system includes:

[0088] The data acquisition module 201 is used to obtain the operating data of each distributed node in the oil field power grid and extract the operating characteristic data;

[0089] The factor analysis module 202 is used to analyze the operation characteristic data using a factor analysis algorithm to identify key factors that affect the performance of the oilfield power grid automation dispatch;

[0090] The state prediction and optimization control module 203 is used to construct an oilfield power grid dispatching state prediction model based on key factors, use the oilfield power grid dispatching state prediction model to dynamically predict the operating state of each distributed node at future moments, and optimize the automated dispatching control strategy.

[0091] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3As shown. The computer device includes a processor, a memory, and a network interface connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the steps of the above-mentioned method embodiment are implemented.

[0092] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0093] In addition, the present invention also provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiment when executing the computer program.

[0094] In addition, the present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0095] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware using a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes in the above-described method embodiments. Any reference to memory, storage, database, or other media used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0096] The present invention is not limited to the structures described above and shown in the drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A distributed automation control method for an oilfield power grid, characterized in that: include: Obtain the operating data of each distributed node in the oilfield power grid and extract the operating characteristic data; Initialize the operating feature set according to the operating feature data and calculate the fitness value of each operating feature; According to the fitness ascending order, the optimal running feature is selected to simulate binary crossover and optimize the running feature vector; Evaluate the correlation between various operating characteristics and dispatch performance, and preliminarily screen out the key factors affecting the automated dispatch performance of the oilfield power grid; Initialize the relevant parameters and maximum number of iterations of the feature optimization mechanism; use the modified chaotic mapping method to generate the initial feature set, and randomly select and combine the operating features; calculate the fitness of each operating feature combination according to the preset evaluation criteria, and adjust the adjustment coefficient of feature selection through the learning automaton mechanism to optimize the feature selection process; update the operating feature set using Lévy mutation, calculate the refraction opposition solution of each operating feature combination based on the refraction opposition learning mechanism, and retain the operating feature combination with the best fitness; If the maximum number of iterations is reached, the iterative optimization process ends and the optimal operating characteristics of each iteration are output; The optimal operating characteristics of each iteration are retained, and the final solution that satisfies the maximum number of iterations is output as the key factor affecting the performance of oilfield power grid automation scheduling; Based on key factors, an oilfield power grid dispatching state prediction model is constructed. This model is used to dynamically predict the operating state of each distributed node in the future and optimize the automated dispatching control strategy. The formula for calculating the refraction opposition solution for each running feature combination according to the refraction opposition learning mechanism is: ； Where, M a Indicates the a The refracted opposite solution of the combination of operating characteristics; W a Indicates the a The current solution for a combination of operating characteristics; W r represents the reference solution; δ represents the refraction factor; Represents the Euclidean distance between the current solution and the reference solution; θ a Indicates the a The angle factor between a running characteristic combination and the reference solution.

2. The oilfield power grid distributed automation control method according to claim 1, characterized in that: The obtaining of the operating data of each distributed node in the oilfield power grid and extracting the operating characteristic data includes: Collect operating data from each distributed node of the oilfield power grid and calculate key indicators related to node performance based on frequency domain characteristics analysis; Use signal filtering methods to pre-process the operating data of each distributed node and extract effective operating status signals; Use demodulation algorithms to analyze the operating status signal, extract its time-frequency characteristics, and construct an operating spectrum diagram; Combined with the peak characteristics and other key indicators in the operation spectrum diagram, the operation characteristic data of each distributed node is extracted.

3. The oilfield power grid distributed automation control method according to claim 2, characterized in that: The use of a demodulation algorithm to analyze the operating status signal, extract its time-frequency characteristics, and construct an operating spectrum diagram includes: Use the short-time Fourier transform method to conduct preliminary time-frequency analysis on the operating signal to determine the key frequency components and instantaneous frequency information; The phase function of each signal component is calculated according to the instantaneous frequency, and the analytical signal is demodulated to separate the frequency components and obtain a demodulated signal with a clear modulation structure; Perform Hilbert transform on the demodulated signal, extract the envelope and frequency characteristics, and construct a complete operating spectrum diagram.

4. The oilfield power grid distributed automation control method according to claim 1, characterized in that: By evaluating the correlation between various operating characteristics and dispatching performance, the key factors affecting the automation dispatching performance of oilfield power grids are initially screened out, including: According to the correlation degree between the selected operating characteristics and the scheduling performance, the effect of each operating characteristic on the performance is evaluated and the optimal operating characteristic is selected; Verify whether the selected optimal operating characteristics meet the constraints of the grid dispatch performance. If not, fall back and select the suboptimal operating characteristics; Continuously adjust the operating feature set to enhance the correlation between each operating feature and scheduling performance until the local optimal operating feature combination is found, which is the best operating feature combination that meets scheduling requirements; By introducing random factors, the existing optimal operating feature set is fine-tuned to enhance its impact on the grid dispatching performance. The operating feature combination is further optimized to find the global optimal operating feature combination as the preliminary screening of the key factors affecting the automated dispatching performance of the oilfield grid.

5. The oilfield power grid distributed automation control method according to claim 1, characterized in that: The oilfield power grid dispatching state prediction model is constructed based on key factors, and the oilfield power grid dispatching state prediction model is used to dynamically predict the operating state of each distributed node at future moments, and the automated dispatching control strategy is optimized, including: Collect multi-condition scheduling data, obtain key node status signals, and construct a multi-dimensional original feature sample set; Use arithmetic optimization algorithms to determine the optimal decomposition parameters and extract key modal features of node operation status; Reconstruct important state feature sequences based on correlation, generate high-quality samples, and divide them into training sets and test sets; Build an oilfield power grid dispatch status prediction model, input a training set, and adjust parameters to improve prediction accuracy; The performance of the oilfield power grid dispatching state prediction model was verified through the test set to evaluate its ability to predict changes in the oilfield power grid dispatching state; The constructed oilfield power grid dispatching state prediction model is used to dynamically predict the operating state of each distributed node in the future, and the automated dispatching strategy is optimized based on the prediction results.

6. The oilfield power grid distributed automation control method according to claim 5, characterized in that: The method of using an arithmetic optimization algorithm to determine the optimal decomposition parameters and extract key modal features of the node operation state includes: Set the initial parameters and acceleration function of the arithmetic optimization algorithm, and dynamically adjust the search step size and range based on the number of iterations; In the global search phase, the parameter search space is expanded and the feasible region of the decomposition parameters is roughly located; Determine the multiplication or division strategy based on the random number and dynamically update the position of the decomposition parameters; The optimization probability is used to adjust the exploration and development ratio, balance the decomposition accuracy and search range, and decompose the node signal according to the optimization results to extract the key modal features of the node operation status.

7. The oilfield power grid distributed automation control method according to claim 6, characterized in that: The formula for dynamically updating the position of the decomposition parameter by determining the multiplication or division strategy based on the random number is: ； Where, C ( t +1) indicates the t +1 iteration, the position after the decomposition parameters are updated; C b ( t ) indicates the t At the iteration, the position of the decomposition parameters after update; MOP represents the mathematical optimizer probability; UB represents the upper bound of the decomposition parameter; LB represents the lower bound of the decomposition parameter; φ Represents search control parameters; r 2 represents a random number.

8. An oilfield power grid distributed automation control system, characterized in that: include: The data acquisition module is used to obtain the operating data of each distributed node in the oil field power grid and extract the operating characteristic data; The factor analysis module is used to initialize the operating feature set based on the operating feature data and calculate the fitness value of each operating feature. It then sorts the features in ascending order of fitness, selects the optimal operating feature, simulates binary crossover, and optimizes the operating feature vector. It also evaluates the correlation between each operating feature and dispatch performance, and preliminarily screens the key factors that affect the automated dispatch performance of the oilfield power grid. Initialize the relevant parameters and maximum number of iterations of the feature optimization mechanism; use the modified chaotic mapping method to generate the initial feature set, and randomly select and combine the operating features; calculate the fitness of each operating feature combination according to the preset evaluation criteria, and adjust the adjustment coefficient of feature selection through the learning automaton mechanism to optimize the feature selection process; update the operating feature set using Lévy mutation, calculate the refraction opposition solution of each operating feature combination based on the refraction opposition learning mechanism, and retain the operating feature combination with the best fitness; If the maximum number of iterations is reached, the iterative optimization process ends and the optimal operating characteristics of each iteration are output; the optimal operating characteristics of each iteration are retained, and the final solution that meets the maximum number of iterations is output as the key factor affecting the performance of oilfield power grid automation scheduling; The formula for calculating the refraction opposition solution for each running feature combination according to the refraction opposition learning mechanism is: ； Where, M a Indicates the a The refracted opposite solution of the combination of operating characteristics; W a Indicates the a The current solution for a combination of operating characteristics; W r represents the reference solution; δ represents the refraction factor; Represents the Euclidean distance between the current solution and the reference solution; θ a Indicates the a The angle factor between the running characteristic combination and the reference solution; The state prediction and optimization control module is used to build an oilfield power grid dispatching state prediction model based on key factors, use the oilfield power grid dispatching state prediction model to dynamically predict the operating state of each distributed node in the future, and optimize the automated dispatching control strategy.

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