Power grid regulation and control method and system based on multi-source information data

Abstract

The invention provides a power grid regulation and control method and system based on multi-source information data, belongs to the technical field of computer data processing, and is used for realizing rapid prediction and accurate regulation and control of power grid abnormity while reducing data processing power consumption in a closed-loop data processing mode of rapidly issuing a strategy by using the same link. According to the method, the feature recognition link is constructed and serves as a core to drive weighted collection and strategy issuing, deep optimization of a power grid data processing flow is achieved, key information areas in a power grid are intelligently recognized, limited calculation and communication resources are intensively used for processing high-value data, blind processing of massive redundant information is avoided, and the method is suitable for large-scale popularization and application. On the premise that the monitoring precision of a key area is not sacrificed, the overall data processing load and operation power consumption of the system are remarkably reduced, a complex diagnosis process is preposed, a regulation and control strategy is generated, rapid distribution of instructions can be carried out through a preset feature recognition link, and the time delay from event recognition to control execution is remarkably shortened.

Description

Technical Field

[0001] This invention relates to the field of computer data processing technology, and in particular to a power grid control method and system based on multi-source information data. Background Technology

[0002] Power dispatching is an effective management tool used to ensure the safe and stable operation of the power grid, reliable power supply to external systems, and the orderly conduct of various power production activities. Its specific work relies on the real-time collection and processing of massive amounts of operational data from the power grid, which forms the basis for dispatching decisions. Through the analysis of this data, the dispatching center can assess the operational status of the power grid and take corresponding measures.

[0003] Power grid control typically relies on a large-scale deployment of monitoring equipment. This equipment continuously collects state parameters from various parts of the power grid at a fixed frequency and aggregates the data to the data server in the dispatch center. The computer system in the dispatch center stores and performs preliminary processing on this massive amount of data. By setting fixed thresholds, the system monitors the power grid status. If a parameter exceeds the preset range, the system will trigger an alarm. Dispatchers will then make judgments based on procedures or experience and manually or semi-automatically issue dispatch instructions to the corresponding execution.

[0004] Existing technical solutions have obvious technical defects in practical applications. The indiscriminate full data collection method generates huge data redundancy, which puts a heavy burden on the storage, transmission and real-time processing of computer systems and causes unnecessary energy consumption. The system lacks the ability to intelligently identify the value of data, making it difficult to quickly locate the root cause of the problem in massive amounts of data and meet the needs of modern power grids for rapid response. Summary of the Invention

[0005] This invention provides a power grid control method and system based on multi-source information data, which utilizes a closed-loop data processing approach that rapidly distributes strategies via the same link. This approach can reduce data processing power consumption while enabling rapid prediction and precise control of power grid anomalies.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: Firstly, a power grid control method based on multi-source information data is provided. This method is applied to a power grid control system based on multi-source information data. The system includes M grid-connected distribution objects, N first control nodes, and control terminals for forming the power grid. The method includes: The control terminal acquires a set of status parameter information of M grid-connected power distribution objects acquired by N first control nodes, wherein the number of N first control nodes is less than the number of M grid-connected power distribution objects; Based on the set of state parameter information, the control terminal establishes a first identification feature of N first control nodes containing the area where the grid-connected power distribution object is located. The first identification feature represents the state parameter information that changes after the control of the N first control nodes. The control terminal identifies key nodes of N first control nodes to obtain i second control nodes. The i second control nodes represent the key control nodes with the most associated vectors for M grid-connected distribution objects. The number of i second control nodes is less than the number of first control nodes. The control terminal establishes a feature recognition link between the i second control nodes and N first control nodes. The control terminal sequentially identifies the second identification features of the i second control nodes and N first control nodes of the feature identification link according to the virtual model of grid energy storage collaborative control. When the identified second identification feature exceeds the training threshold and an abnormal event is expected, a collaborative control strategy is generated. According to the collaborative control strategy, the control terminal first issues the control signal to the i second control nodes, and then issues it to the N first control nodes to execute the control.

[0007] Optionally, the method further includes a step of preprocessing the state parameter information set to obtain a preprocessed state parameter information set, specifically including: The state parameter information set is subjected to data deduplication to obtain a deduplicated state parameter information set; A data value assessment model is used to analyze the set of state parameter information after weight reduction, and a criticality value reflecting the importance of each feature information is obtained. Based on the assigned criticality, the collection priority of each feature information is determined, and the collection priority constitutes part of the preprocessed state parameter information set.

[0008] Optionally, the control terminal establishes a feature recognition link between the i second control nodes and the N first control nodes: Based on the criticality of the importance of each feature information reflected by the first identification feature associated with i second control nodes and N first control nodes, feature association tracing is performed to analyze the influence path of the first identification feature in the power grid topology; Based on the results of the feature association tracing, a feature association path containing node and connection relationships is established. The feature association paths are integrated to form the feature recognition link, which is used to match and associate scheduling strategies when interference items are detected.

[0009] Optionally, the control terminal sequentially identifies the second identification features of the i second control nodes and N first control nodes of the feature identification link according to the grid energy storage collaborative control virtual model, specifically including: Based on the criticality level, assign corresponding acquisition weights to i second control nodes and N first control nodes; Based on the acquisition weights, high-frequency acquisition is performed on the feature information of high-weight regions, and low-frequency acquisition is performed on the feature information of low-weight regions. The information obtained from the high-frequency acquisition and the low-frequency acquisition is integrated to form the second identification feature.

[0010] Optionally, the step of training and generating the virtual model for coordinated control of the power grid and energy storage includes: The first and second identification features are used as data to divide the data into a training dataset and a test dataset; The model parameters are trained using the training dataset by a long short-term memory network that processes time-series data and complex nonlinear relationships. The power grid state parameters in the training dataset are used as inputs, and the power grid operation results or known optimal control strategies are used as labels or target outputs. The model is iteratively optimized using gradient descent to adjust its internal parameters, minimizing the error between the predicted output and the true label, and producing a pre-trained model. The input data from the test dataset is fed into the trained model, and the generated prediction results are compared with the real labels in the test dataset to obtain a virtual model for grid-storage collaborative control.

[0011] Optionally, when analyzing the occurrence of abnormal events, the specific measures to generate a collaborative control strategy include: The virtual model for coordinated control of power grid and energy storage monitors the second identified feature in real time and predicts the changing trend of the feature information. When the predicted trend of change deviates from the safe operating boundary, the deviation is identified as the abnormal event; By combining the abnormal events with the associated paths of the features, the grid energy storage collaborative control virtual model generates the collaborative control strategy containing multiple coordination instructions.

[0012] Optionally, the step of using the feature recognition link to distribute the collaborative control strategy to the area to be regulated includes: Based on the abnormal event targeted by the collaborative control strategy, the strategy delivery path is determined from the feature recognition link; The collaborative control strategy is transmitted along the strategy distribution path to one or more corresponding regional nodes among the i second control nodes and N first control nodes; The regional nodes execute the coordination instructions in the collaborative control strategy to complete power grid regulation.

[0013] Optionally, it also includes: After the coordinated control strategy is executed at the regional node, data reflecting the control effect of the new power grid state are collected. The collaborative control strategy and the corresponding regulation effect data are fed back as a set of empirical samples to the grid energy storage collaborative control virtual model. The virtual model for coordinated control of power grid and energy storage learns from the experience samples and updates its internal model parameters.

[0014] Optionally, the updated internal model parameters can be used to optimize and adjust the model parameters; Based on the results of the model parameter optimization and adjustment, update the feature recognition rules used to evaluate data importance; Based on the updated feature recognition rules, the feature association path in the feature recognition link is dynamically adjusted.

[0015] Secondly, a power grid control system based on multi-source information data is provided, including: The parameter acquisition module is used to acquire a set of state parameter information from multiple sides of the power grid; The preprocessing module is used to preprocess the state parameter information set to obtain a preprocessed state parameter information set. The feature recognition module is used to establish a feature recognition link containing electrical logic relationships between power grid areas based on the preprocessed set of state parameter information. The data acquisition module is used to perform weighted acquisition based on the feature identification link to obtain the acquired data; The model training module is used to train and generate a virtual model for coordinated control of power grid and energy storage based on the collected data. The strategy generation module is used to generate a collaborative control strategy when the virtual model for collaborative control of the power grid and energy storage identifies characteristic information indicating that an abnormal event will occur. The strategy distribution module is used to distribute the collaborative control strategy to the area to be regulated using the feature recognition link to execute the regulation.

[0016] Thirdly, an electronic device is provided, comprising: a processor and a memory; the memory is used to store a computer program, which, when executed by the processor, causes the electronic device to perform the power grid control method and system based on multi-source information data as described in the first aspect.

[0017] In one possible design, the electronic device described in the third aspect may further include a transceiver. This transceiver may be a transceiver circuit or an interface circuit. The transceiver can be used for communication between the electronic device described in the third aspect and other electronic devices.

[0018] In the embodiments of the present invention, the electronic device described in the third aspect may be a terminal, or a chip (system) or other component or assembly disposed in the terminal, or a system containing the terminal.

[0019] Fourthly, a computer-readable storage medium is provided, comprising: a computer program or instructions; when the computer program or instructions are executed on a computer, the computer causes the computer to perform the power grid control method based on multi-source information data as described in the first aspect.

[0020] In summary, the above methods and systems have the following technical effects: This invention achieves deep optimization of the power grid data processing flow by constructing a feature recognition link and using it as the core to drive weighted data acquisition and strategy distribution. It intelligently identifies key information areas in the power grid, concentrates limited computing and communication resources on processing high-value data, avoids blindly processing massive amounts of redundant information, and significantly reduces the overall data processing load and power consumption of the system without sacrificing the monitoring accuracy of key areas. By bringing the complex diagnostic process forward and generating control strategies, instructions can be quickly distributed through the preset feature recognition link, significantly shortening the time delay from event recognition to control execution. Attached Figure Description

[0021] Figure 1 A flowchart illustrating the power grid control method based on multi-source information data provided in this embodiment of the invention. Figure 2 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0022] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0023] In this embodiment of the invention, "instruction" can include direct and indirect instructions, as well as explicit and implicit instructions. The information indicated by a certain piece of information is called the information to be instructed. In specific implementation, there are many ways to instruct the information to be instructed, such as, but not limited to, directly instructing the information to be instructed, such as the information to be instructed itself or its index. It can also indirectly instruct the information to be instructed by instructing other information, where there is a correlation between the other information and the information to be instructed. It can also instruct only a part of the information to be instructed, while the other parts are known or pre-agreed upon. For example, the instruction of specific information can be achieved by using a pre-agreed (e.g., protocol-defined) arrangement of various pieces of information, thereby reducing instruction overhead to some extent. Simultaneously, common parts of various pieces of information can be identified and uniformly indicated to reduce the instruction overhead caused by individually indicating the same information.

[0024] Furthermore, the specific indication method can also be any existing indication method, such as, but not limited to, the above-mentioned indication methods and their various combinations. Specific details of various indication methods can be found in existing technologies, and will not be elaborated upon here. As described above, for example, when multiple pieces of information of the same type need to be indicated, the indication methods for different pieces of information may differ. In specific implementation, the required indication method can be selected according to specific needs. This embodiment of the invention does not limit the selected indication method; therefore, the indication methods involved in this embodiment of the invention should be understood to cover various methods that enable the party to be indicated to obtain the information to be indicated.

[0025] It should be understood that the information to be indicated can be sent as a whole or divided into multiple sub-information messages sent separately, and the sending period and / or timing of these sub-information messages can be the same or different. The specific sending method is not limited in this embodiment of the invention. The sending period and / or timing of these sub-information messages can be predefined, for example, according to a protocol, or configured by the sending device by sending configuration information to the receiving device.

[0026] "Predefined" or "pre-configured" can be achieved by pre-saving corresponding codes, tables, or other means that can be used to indicate relevant information in the device. This embodiment of the invention does not limit the specific implementation method. "Saving" can refer to saving in one or more memories. These memories can be separate installations or integrated into the encoder, decoder, processor, or electronic device. Alternatively, some memories can be separately installed, while others are integrated into the decoder, processor, or electronic device. The type of memory can be any form of storage medium, and this embodiment of the invention does not limit this.

[0027] In the embodiments of this invention, "protocol" may refer to a protocol family in the field of communication, a standard protocol with a similar protocol family frame structure, or a related protocol applied to future power grid control methods and systems based on multi-source information data. The embodiments of this invention do not specifically limit this.

[0028] In this embodiment of the invention, descriptions such as "when," "under the circumstances," "if," and "if" all refer to the device making corresponding processing under certain objective circumstances, and are not limited to a specific time. They do not require the device to make a judgment action during implementation, nor do they imply any other limitations.

[0029] In the description of the embodiments of the present invention, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship. For example, A / B can represent A or B. "And / or" in the embodiments of the present invention is merely a description of the relationship between the related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural. Furthermore, in the description of the embodiments of the present invention, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple. Additionally, to facilitate a clear description of the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first," "second," etc., do not limit the quantity or order of execution, and that "first," "second," etc., are not necessarily different. Furthermore, in the embodiments of this invention, words such as "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or description. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this invention should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner for ease of understanding.

[0030] The network architecture and business scenarios described in the embodiments of this invention are for the purpose of more clearly illustrating the technical solutions of the embodiments of this invention, and do not constitute a limitation on the technical solutions provided by the embodiments of this invention. As those skilled in the art will know, with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided by the embodiments of this invention are also applicable to similar technical problems.

[0031] Figure 1 This is a flowchart illustrating the method provided in an embodiment of the present invention. The power grid control method based on multi-source information data is applied to a power grid control system based on multi-source information data. The system includes M grid-connected distribution objects, N first control nodes, and control terminals for forming the power grid. The method includes: S101. The control terminal obtains a set of status parameter information of M grid-connected power distribution objects obtained by N first control nodes, wherein the number of N first control nodes is less than the number of M grid-connected power distribution objects. S201. Based on the set of state parameter information, the control terminal establishes a first identification feature of N first control nodes containing the area where the grid-connected power distribution object is located. The first identification feature represents the state parameter information that changes after the control of the N first control nodes. S301. The control terminal identifies the key nodes of N first control nodes to obtain i second control nodes. The i second control nodes represent the key control nodes with the most associated vectors for M grid-connected power distribution objects. The number of the i second control nodes is less than the number of first control nodes. The control terminal establishes a feature recognition link between the i second control nodes and N first control nodes. S601. The control terminal sequentially identifies the second identification features of the i second control nodes and N first control nodes of the feature identification link according to the virtual model of grid energy storage collaborative control. When the identified second identification feature exceeds the training threshold and an abnormal event is analyzed to occur, a collaborative control strategy is generated. S501. According to the collaborative control strategy, the control terminal first sends the data to the i second control nodes, and then sends it to the N first control nodes to execute the control.

[0032] Optionally, the method further includes a step of preprocessing the state parameter information set to obtain a preprocessed state parameter information set, specifically including: The state parameter information set is subjected to data deduplication to obtain a deduplicated state parameter information set; A data value assessment model is used to analyze the set of state parameter information after weight reduction, and a criticality value reflecting the importance of each feature information is obtained. Based on the assigned criticality, the collection priority of each feature information is determined, and the collection priority constitutes part of the preprocessed state parameter information set.

[0033] Optionally, the control terminal establishes a feature recognition link between the i second control nodes and the N first control nodes: Based on the criticality of the importance of each feature information reflected by the first identification feature associated with i second control nodes and N first control nodes, feature association tracing is performed to analyze the influence path of the first identification feature in the power grid topology; Based on the results of the feature association tracing, a feature association path containing node and connection relationships is established. The feature association paths are integrated to form the feature recognition link, which is used to match and associate scheduling strategies when interference items are detected.

[0034] Optionally, the control terminal sequentially identifies the second identification features of the i second control nodes and N first control nodes of the feature identification link according to the grid energy storage collaborative control virtual model, specifically including: Based on the criticality level, assign corresponding acquisition weights to i second control nodes and N first control nodes; Based on the acquisition weights, high-frequency acquisition is performed on the feature information of high-weight regions, and low-frequency acquisition is performed on the feature information of low-weight regions. The information obtained from the high-frequency acquisition and the low-frequency acquisition is integrated to form the second identification feature.

[0035] Optionally, the step of training and generating the virtual model for coordinated control of the power grid and energy storage includes: The first and second identification features are used as data to divide the data into a training dataset and a test dataset; The model parameters are trained using the training dataset by a long short-term memory network that processes time-series data and complex nonlinear relationships. The power grid state parameters in the training dataset are used as inputs, and the power grid operation results or known optimal control strategies are used as labels or target outputs. The model is iteratively optimized using gradient descent to adjust its internal parameters, minimizing the error between the predicted output and the true label, and producing a pre-trained model. The input data from the test dataset is fed into the trained model, and the generated prediction results are compared with the real labels in the test dataset to obtain a virtual model for grid-storage collaborative control.

[0036] Optionally, when analyzing the occurrence of abnormal events, the specific measures to generate a collaborative control strategy include: The virtual model for coordinated control of power grid and energy storage monitors the second identified feature in real time and predicts the changing trend of the feature information. When the predicted trend of change deviates from the safe operating boundary, the deviation is identified as the abnormal event; By combining the abnormal events with the associated paths of the features, the grid energy storage collaborative control virtual model generates the collaborative control strategy containing multiple coordination instructions.

[0037] Optionally, the step of using the feature recognition link to distribute the collaborative control strategy to the area to be regulated includes: Based on the abnormal event targeted by the collaborative control strategy, the strategy delivery path is determined from the feature recognition link; The collaborative control strategy is transmitted along the strategy distribution path to one or more corresponding regional nodes among the i second control nodes and N first control nodes; The regional nodes execute the coordination instructions in the collaborative control strategy to complete power grid regulation.

[0038] Optionally, it also includes: After the coordinated control strategy is executed at the regional node, data reflecting the control effect of the new power grid state are collected. The collaborative control strategy and the corresponding regulation effect data are fed back as a set of empirical samples to the grid energy storage collaborative control virtual model. The virtual model for coordinated control of power grid and energy storage learns from the experience samples and updates its internal model parameters.

[0039] Optionally, the updated internal model parameters can be used to optimize and adjust the model parameters; Based on the results of the model parameter optimization and adjustment, update the feature recognition rules used to evaluate data importance; Based on the updated feature recognition rules, the feature association path in the feature recognition link is dynamically adjusted.

[0040] A power grid control system based on multi-source information data includes: The parameter acquisition module is used to acquire a set of state parameter information from multiple sides of the power grid; The preprocessing module is used to preprocess the state parameter information set to obtain a preprocessed state parameter information set. The feature recognition module is used to establish a feature recognition link containing electrical logic relationships between power grid areas based on the preprocessed set of state parameter information. The data acquisition module is used to perform weighted acquisition based on the feature identification link to obtain the acquired data; The model training module is used to train and generate a virtual model for coordinated control of power grid and energy storage based on the collected data. The strategy generation module is used to generate a collaborative control strategy when the virtual model for collaborative control of the power grid and energy storage identifies characteristic information indicating that an abnormal event will occur. The strategy distribution module is used to distribute the collaborative control strategy to the area to be regulated using the feature recognition link to execute the regulation.

[0041] Figure 2 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Exemplarily, the electronic device may be a network device, or a chip (system) or other component or assembly that can be disposed in a network device. Figure 2As shown, electronic device 600 may include processor 601. Optionally, electronic device 600 may also include memory 602 and / or transceiver 603. The processor 601 is coupled to memory 602 and transceiver 603, for example, via a communication bus.

[0042] The following is combined Figure 2 A detailed description of each component of the electronic device 600 is provided below: The processor 601 is the control center of the electronic device 600. It can be a single processor or a collective term for multiple processing elements. For example, the processor 601 can be one or more central processing units (CPUs), application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0043] Optionally, the processor 601 can perform various functions of the electronic device 600 by running or executing software programs stored in the memory 602 and calling data stored in the memory 602, such as performing the aforementioned functions. Figure 2 The power grid control method and system based on multi-source information data are shown.

[0044] In a specific implementation, as one example, processor 601 may include one or more CPUs, for example... Figure 2 CPU0 and CPU1 are shown in the diagram.

[0045] In a specific implementation, as one example, the electronic device 600 may also include multiple processors. Each of these processors may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). Here, a processor may refer to one or more devices, circuits, and / or processing cores used for processing data (e.g., computer program instructions).

[0046] The memory 602 is used to store the software program that executes the solution of the present invention, and is controlled by the processor 601 to execute it. The specific implementation method can be referred to the above method embodiment, and will not be repeated here.

[0047] Optionally, the memory 602 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 602 may be integrated with the processor 601 or may exist independently and be accessed through the interface circuit of the electronic device 600. Figure 2 (Not shown in the image) is coupled to processor 601, but this embodiment of the invention does not specifically limit this.

[0048] Transceiver 603 is used for communication with other electronic devices. For example, if electronic device 600 is a terminal, transceiver 603 can be used to communicate with a network device or with another terminal device. As another example, if electronic device 600 is a network device, transceiver 603 can be used to communicate with a terminal or with another network device.

[0049] Alternatively, transceiver 603 may include a receiver and a transmitter. Figure 2 (Not shown separately). The receiver is used to implement the receiving function, and the transmitter is used to implement the transmitting function.

[0050] Optionally, the transceiver 603 can be integrated with the processor 601, or it can exist independently and be connected via the interface circuit of the electronic device 600. Figure 2 (Not shown in the image) is coupled to processor 601, and this embodiment of the invention does not specifically limit this.

[0051] Understandable, Figure 2 The structure of the electronic device 600 shown does not constitute a limitation on the electronic device. Actual electronic devices may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0052] Furthermore, the technical effects of the electronic device 600 can be referred to the technical effects of the power grid control method and system based on multi-source information data described in the above method embodiments, and will not be repeated here.

[0053] It should be understood that the processor in the embodiments of the present invention can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0054] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0055] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0056] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0057] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0058] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0059] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0060] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0061] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0062] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0063] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0064] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0065] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A power grid control method based on multi-source information data, characterized in that, The method is applied to a power grid control system based on multi-source information data. The system includes M grid-connected distribution objects, N first control nodes, and control terminals for forming the power grid. The method includes: The control terminal obtains a set of status parameter information of M grid-connected distribution objects obtained by N first control nodes, wherein the number of N first control nodes is less than the number of M grid-connected distribution objects; Based on the set of state parameter information, the control terminal establishes a first identification feature of N first control nodes containing the area where the grid-connected power distribution object is located. The first identification feature represents the state parameter information that changes after the control of the N first control nodes. The control terminal identifies key nodes of N first control nodes to obtain i second control nodes. The i second control nodes represent the key control nodes with the most associated vectors for M grid-connected distribution objects. The number of i second control nodes is less than the number of first control nodes. The control terminal establishes a feature recognition link between the i second control nodes and N first control nodes. The control terminal sequentially identifies the second identification features of the i second control nodes and N first control nodes of the feature identification link according to the virtual model of grid energy storage collaborative control. When the identified second identification feature exceeds the training threshold and an abnormal event is expected, a collaborative control strategy is generated. According to the collaborative control strategy, the control terminal first issues the control signal to the i second control nodes, and then issues it to the N first control nodes to execute the control.

2. The power grid control method based on multi-source information data according to claim 1, characterized in that, The method also includes a step of preprocessing the state parameter information set to obtain a preprocessed state parameter information set, specifically including: The state parameter information set is subjected to data deduplication to obtain a deduplicated state parameter information set; A data value assessment model is used to analyze the set of state parameter information after weight reduction, and a criticality value reflecting the importance of each feature information is obtained. Based on the assigned criticality, the collection priority of each feature information is determined, and the collection priority constitutes part of the preprocessed state parameter information set.

3. The power grid control method based on multi-source information data according to claim 2, characterized in that, The control terminal establishes a feature recognition link between i second control nodes and N first control nodes: Based on the criticality of the importance of each feature information reflected by the first identification feature associated with i second control nodes and N first control nodes, feature association tracing is performed to analyze the influence path of the first identification feature in the power grid topology; Based on the results of the feature association tracing, a feature association path containing node and connection relationships is established. The feature association paths are integrated to form the feature recognition link, which is used to match and associate scheduling strategies when interference items are detected.

4. The power grid control method based on multi-source information data according to claim 2, characterized in that, The control terminal sequentially identifies the second identification features of the i second control nodes and N first control nodes of the feature identification link according to the virtual model of grid-energy storage collaborative control. Specifically, this includes: Based on the criticality level, assign corresponding acquisition weights to i second control nodes and N first control nodes; Based on the acquisition weights, high-frequency acquisition is performed on the feature information of high-weight regions, and low-frequency acquisition is performed on the feature information of low-weight regions. The information obtained from the high-frequency acquisition and the low-frequency acquisition is integrated to form the second identification feature.

5. The power grid control method based on multi-source information data according to claim 2, characterized in that, The steps for training and generating the virtual model for coordinated control of the power grid and energy storage include: The first and second identification features are used as data to divide the data into a training dataset and a test dataset; The model parameters are trained using the training dataset by a long short-term memory network that processes time-series data and complex nonlinear relationships. The power grid state parameters in the training dataset are used as inputs, and the power grid operation results or known optimal control strategies are used as labels or target outputs. The model is iteratively optimized using gradient descent to adjust its internal parameters, minimizing the error between the predicted output and the true label, and producing a pre-trained model. The input data from the test dataset is fed into the trained model, and the generated prediction results are compared with the real labels in the test dataset to obtain a virtual model for grid-storage collaborative control.

6. The power grid control method based on multi-source information data according to claim 5, characterized in that, When analyzing the occurrence of abnormal events, the specific measures taken to generate collaborative control strategies include: The virtual model for coordinated control of power grid and energy storage monitors the second identified feature in real time and predicts the changing trend of the feature information. When the predicted trend of change deviates from the safe operating boundary, the deviation is identified as the abnormal event; By combining the abnormal events with the associated paths of the features, the grid energy storage collaborative control virtual model generates the collaborative control strategy containing multiple coordination instructions.

7. The power grid control method based on multi-source information data according to claim 6, characterized in that, The step of using the feature recognition link to distribute the collaborative control strategy to the area to be regulated includes: Based on the abnormal event targeted by the collaborative control strategy, the strategy delivery path is determined from the feature recognition link; The collaborative control strategy is transmitted along the strategy distribution path to one or more corresponding regional nodes among the i second control nodes and N first control nodes; The regional nodes execute the coordination instructions in the collaborative control strategy to complete power grid regulation.

8. The power grid control method based on multi-source information data according to claim 7, characterized in that, Also includes: After the coordinated control strategy is executed at the regional node, data reflecting the control effect of the new power grid state are collected. The collaborative control strategy and the corresponding regulation effect data are fed back as a set of empirical samples to the grid energy storage collaborative control virtual model. The virtual model for coordinated control of power grid and energy storage learns from the experience samples and updates its internal model parameters.

9. The power grid control method based on multi-source information data according to claim 8, characterized in that, The updated internal model parameters are used to optimize and adjust the model parameters. Based on the results of the model parameter optimization and adjustment, update the feature recognition rules used to evaluate data importance; Based on the updated feature recognition rules, the feature association path in the feature recognition link is dynamically adjusted.

10. A power grid control system based on multi-source information data, applied to the power grid control method based on multi-source information data as described in any one of claims 1-9, characterized in that, include: The parameter acquisition module is used to acquire a set of state parameter information from multiple sides of the power grid; The preprocessing module is used to preprocess the state parameter information set to obtain a preprocessed state parameter information set. The feature recognition module is used to establish a feature recognition link containing electrical logic relationships between power grid areas based on the preprocessed set of state parameter information. The data acquisition module is used to perform weighted acquisition based on the feature identification link to obtain the acquired data; The model training module is used to train and generate a virtual model for coordinated control of power grid and energy storage based on the collected data. The strategy generation module is used to generate a collaborative control strategy when the virtual model for collaborative control of the power grid and energy storage identifies characteristic information indicating that an abnormal event will occur. The strategy distribution module is used to distribute the collaborative control strategy to the area to be regulated using the feature recognition link to execute the regulation.

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