A new energy scene station full space-time operation and maintenance management and control method and system

Abstract

The application provides a new energy scene station full space-time operation and maintenance management method and system, including: constructing a safe operation and maintenance protection system of a substation control center to prevent illegal intrusion and network attacks; collecting and preprocessing historical monitoring data of each substation; using 5G network slicing communication technology to transmit the collected historical monitoring data to the substation control center; using image recognition and analysis technology to realize real-time monitoring of the substation and automatic detection of abnormal behavior; analyzing equipment state data through a fault prediction model of the substation to output potential fault prediction results; automatically controlling and optimizing the substation equipment through intelligent algorithms and control strategies; and through remote operation technology, management personnel remotely carry out equipment operation and maintenance work. The application improves the automation and intelligent management level of the substation to realize remote monitoring, intelligent control and security protection, and ensures that the substation can also operate safely and efficiently in the case of unattended operation.

Description

Technical Field

[0001] This application relates to the field of substation maintenance technology, and in particular to a method and system for all-time and all-space operation and maintenance management of new energy scenario substations. Background Technology

[0002] As one of my country's strategic emerging industries, the safe and stable operation of the new energy power generation industry is directly related to national economic development and social stability. With the increasing number of power plants under operation and maintenance, traditional manual operation and maintenance methods can no longer meet the demands. Many power supply companies in China have piloted substation monitoring systems, but existing distribution transformer monitoring systems still have some problems, mainly in the following aspects: First, due to the large number and wide distribution of distribution transformers, it is difficult to select a suitable communication method, resulting in ineffective information transmission; second, the lack of effective analysis software for distribution transformers prevents the effective application of collected information. The result is that although some distribution transformer monitoring terminals are in operation, the results are not ideal, and the distribution transformers have failed to achieve operational status and economic operation analysis.

[0003] With societal development, distributed photovoltaics, energy storage, microgrids, and incremental distribution networks have experienced rapid growth. These developments have altered the traditional characteristics of distribution network operation, transforming "passive" networks into "active and controllable" networks. The number of dispatchable objects is increasing, and control requirements are becoming more stringent. The scale of distribution networks is expanding rapidly, placing increasingly higher demands on the connection system, power quality, substation operation, and maintenance. Distribution automation is currently a hot topic in distribution networks, and remote monitoring is becoming an important monitoring method. However, its application in distribution substations has not yet received sufficient attention. Real-time remote monitoring of the three-phase balance, voltage qualification rate, load rate, and load curves of distribution substations cannot be achieved by the TTU equipment currently used in a single distribution substation. Therefore, improving the automation and intelligent management level of substations to achieve remote monitoring, intelligent control, and safety protection, ensuring the safe and efficient operation of substations even without human intervention, is a key research focus for professionals in this field. Summary of the Invention

[0004] In light of this, it is necessary to provide a method and system for all-weather, all-space operation and maintenance management of new energy power plants. This system utilizes a distributed cloud platform architecture and combines artificial intelligence technology to integrate functions such as operation monitoring, intelligent early warning, power generation analysis, video surveillance, and operation and maintenance management, enabling unmanned operation and maximizing power plant revenue. It not only achieves centralized monitoring of substations and management of their operation and maintenance, but also allows for remote task distribution via the cloud platform. Maintenance personnel can receive maintenance tasks via mobile phones, and periodic tasks can be scheduled, greatly improving the efficiency of operation and maintenance work. The system stores power plant revenue data long-term according to user needs, which is of great significance for optimizing operational strategies and improving power plant power generation efficiency.

[0005] It can overcome at least one of the above defects.

[0006] Firstly, embodiments of this application provide a method for all-weather, all-space operation and maintenance management of new energy scenario stations.

[0007] The method is characterized by comprising the following operational steps:

[0008] S1: Construct a security operation and maintenance protection system for the substation control center, including physical security and network security, to prevent illegal intrusion and network attacks;

[0009] S2: Collect and preprocess historical monitoring data from each substation;

[0010] S3: Use 5G network slicing communication technology to transmit the collected historical monitoring data to the substation control center;

[0011] S4: Utilize image recognition and analysis technology to achieve real-time monitoring and automatic detection of abnormal behavior in substations;

[0012] S5: Analyze equipment status data using the substation fault prediction model and output potential fault prediction results;

[0013] S6: Automatic control and optimization of substation equipment through intelligent algorithms and control strategies;

[0014] S7: Through remote operation technology, managers can remotely operate and maintain equipment.

[0015] In one embodiment, step S1 involves constructing a security operation and maintenance protection system for the substation control center, including physical security and network security, to prevent illegal intrusion and network attacks, including:

[0016] Construct physical security defense modules and network security defense modules;

[0017] The physical security defense module integrates and analyzes the information characteristics of digital signals flowing from the substation control center with the physical characteristics of the power grid to achieve abnormal detection of the physical system's operating status.

[0018] The network security defense module detects the information characteristics of digital signals flowing into the substation control center, including identity and permissions, network traffic information system objects, and APT attack anomaly detection, thereby enabling malicious data identification.

[0019] In one embodiment, step S2: collecting and preprocessing historical monitoring data from each substation, including:

[0020] Step 1: Set a preset time period, collect the status variable data of the preset time period through the acquisition module of the substation control center, and store it in the data storage module of the substation control center. The status data includes: output of new energy substation, power grid operation parameters, and load consumption.

[0021] Step 2: Real-time acquisition of state variable data x from each monitoring point via the acquisition module of the substation control center. i ,in,

[0022] x i ={x 1i ,x 2i ,...,x pi}, i = 1, 2, ..., p, where p and i are the number of indicators; x is the sample data, and n is the number of samples;

[0023] Step 3: Using the historical data stored in the data storage module of the substation control center, predict the output value of the current system status to obtain the predicted value;

[0024] Step 4: Compare the real-time collected state variable data of each monitoring point with the predicted value of the current system state. When the difference error exceeds the threshold range, return to step 1 to resample.

[0025] When the difference error is within the set threshold range, the data stored in the data storage module of the substation control center is used as the sampling data; the sampling data includes image data and text data.

[0026] Wherein, the predicted value x i+1 The calculation formula is:

[0027]

[0028] Where, x i It is real-time data acquisition, A and B are parameter matrices of state data, u i It is a control variable;

[0029] The formula for the difference error is:

[0030]

[0031] Where L is the cost function.

[0032] In one embodiment, step S3: transmitting the collected historical monitoring data to the substation control center using 5G network slicing communication technology includes:

[0033] A network slice management model is constructed based on 5G network slice communication technology, wherein the construction of the network slice management model includes: a network slice layering module and a network slice layering management module;

[0034] The network slicing layering module divides a physical network into multiple virtual logical network slices based on different types of substation equipment. Each network slice is logically isolated from the wireless access network to the bearer network and then to the core network. Each network slice includes at least a wireless sub-slice, a bearer sub-slice, and a core network sub-slice to adapt to various services and applications and achieve end-to-end isolation.

[0035] The network slice hierarchical management module monitors network operation, reports network status, benchmarks network indicators, and updates, adjusts, and configures network slices in real time, enabling end-to-end on-demand customization to meet changing business needs.

[0036] In one embodiment, step S4: using image recognition and analysis technology to achieve real-time monitoring and automatic detection of abnormal behavior in the substation, includes:

[0037] Obtain image data from the sampled data as sample image data;

[0038] Image features are extracted from the sample image and the template image by the feature extraction layer in the convolutional neural network, respectively, to obtain the sample image feature map X and the template image feature map T.

[0039] Obtain the length N and width M of the template image feature map T to get the aspect ratio of the template image feature map;

[0040] Obtain the length W and width H of the feature map X of the sample image, set a sliding window, and extract the optimal feature map from the feature map of the sample image using the aspect ratio of the template image feature map as the objective function through a scale-adaptive feature extraction method.

[0041] Calculate feature map Similarity measure with feature map T:

[0042]

[0043] in, This represents the mean value of the pixel at (k+a,j+b) in the feature map of the optimal sample image. Let represent the mean value of the pixel at (i,j) in the template image feature map, and k,j represent the count within the pixel range; a and b represent the width and length of the sliding window, respectively, and 0≤a≤HM, 0≤b≤WN;

[0044] When S im When (a,b) is close to 1, the sample image and the template image are perfectly matched, and the automatic detection result of abnormal behavior in real time is obtained.

[0045] In one embodiment, before acquiring image data from the sampled data as sample image data, the following is performed:

[0046] Based on ground control points, a geometric correction model is used to construct the geometric relationship between the image and the ground coordinates;

[0047] The image data is calibrated.

[0048] In one embodiment, step S5: analyzing equipment status data using a substation fault prediction model and outputting potential fault prediction results, includes:

[0049] Obtain text data from the sampled data as training samples;

[0050] Train a fault prediction model using training samples;

[0051] Analyze equipment status data using fault prediction models;

[0052] Output potential fault prediction results.

[0053] In one embodiment, step S6: automatically controlling and optimizing substation equipment through intelligent algorithms and control strategies, includes:

[0054] Different control models are established based on different strategy control algorithms;

[0055] Select the target control model via the menu;

[0056] The fault prediction results are input into the target control model for fuzzy matching, enabling automatic control and optimization of substation equipment.

[0057] In one embodiment, step S7: Using remote operation technology, managers remotely perform equipment operation and maintenance work, including:

[0058] Install a remote monitoring application on the terminal device of the management personnel;

[0059] Management personnel send remote operation commands to the control terminal of the substation control center through a remote monitoring application. After the substation control center's security operation and maintenance protection system performs security authentication and authorization authentication for the remote monitoring application and management personnel, the control commands are accepted to enable click and button operations on the controlled terminal.

[0060] Secondly, embodiments of this application provide a full-time and all-space operation and maintenance management system for new energy scenario stations, applied to the full-time and all-space operation and maintenance management method for new energy scenario stations as described in the first aspect, the system comprising:

[0061] Security Protection Module: Constructs a security operation and maintenance protection system for the substation control center, including physical security and network security, to prevent unauthorized intrusion and network attacks;

[0062] Data acquisition module: Collects and preprocesses historical monitoring data from each substation;

[0063] Data transmission module: Utilizes 5G network slicing communication technology to transmit the collected historical monitoring data to the substation control center;

[0064] Image recognition module: Utilizes image recognition and analysis technology to achieve real-time monitoring of substations and automatic detection of abnormal behavior;

[0065] Fault prediction module: Analyzes equipment status data using a fault prediction model for the substation and outputs potential fault prediction results;

[0066] Control optimization module: Automatically controls and optimizes substation equipment through intelligent algorithms and control strategies;

[0067] Operation and maintenance module: Through remote operation technology, managers can remotely carry out equipment operation and maintenance work.

[0068] Thirdly, embodiments of this application provide an electronic device, including:

[0069] processor;

[0070] Memory used to store processor-executable instructions;

[0071] The processor is configured to implement the all-time and all-space operation and maintenance management method for new energy scenario stations as described in the first aspect when executing the instructions.

[0072] Fourthly, embodiments of this application provide a computer-readable storage medium including instructions that instruct a device to execute the all-time and all-space operation and maintenance management method for new energy scenario stations as described in the first aspect.

[0073] This application provides a method and system for all-weather operation and maintenance management of substations in new energy scenarios. This system improves the automation and intelligent management level of substations, enabling remote monitoring, intelligent control, and security protection, ensuring safe and efficient operation even when unattended. The specific benefits of this system include:

[0074] (1) Real-time monitoring of substations, including data acquisition, status monitoring and fault diagnosis.

[0075] (2) Conduct research on intelligent algorithms and control strategies to achieve automatic control and optimization of substation equipment.

[0076] (3) Utilize advanced communication technologies to achieve high-speed, stable, and secure data transmission between substations and control centers.

[0077] (4) Through remote operation technology, managers can remotely carry out equipment operation and maintenance work.

[0078] (5) By constructing a security protection system for substations, including physical security and network security, illegal intrusion and network attacks can be prevented.

[0079] (6) By conducting research on intelligent video surveillance, and utilizing image recognition and analysis technologies, we can achieve real-time monitoring of substations and automatic detection of abnormal behavior.

[0080] (7) By using substation fault prediction technology, potential faults can be predicted and maintenance can be carried out in advance by analyzing equipment status data. Attached Figure Description

[0081] Figure 1 A schematic diagram of the all-time and all-space operation and maintenance management method for new energy scenario stations provided in an embodiment of this application.

[0082] Figure 2 This is a schematic diagram illustrating the 5G network slicing communication technology provided in another embodiment of this application.

[0083] Figure 3 This is a schematic diagram of an image recognition technology using a sliding window, provided as another embodiment of this application.

[0084] Figure 4 A schematic diagram of a new energy scenario station all-time operation and maintenance management system module provided in an embodiment of this application.

[0085] Figure 5 This is a schematic diagram of an electronic terminal device provided in an embodiment of this application. Detailed Implementation

[0086] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them.

[0087] It should be noted that, in the embodiments of this application, "at least one" refers to one or more, and "more than one" refers to two or more. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the specification of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application.

[0088] It should be noted that in the embodiments of this application, the terms "first," "second," etc., are used only for descriptive purposes and should not be construed as indicating or implying relative importance, nor as indicating or implying order. Features specified as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application 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.

[0089] Based on the embodiments described in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0090] With the rapid development of the substation industry, the operating environment of substation facilities is becoming increasingly complex, and equipment failures and defects pose a severe challenge to the stability and safety of these facilities. In particular, critical facilities such as high-voltage transmission lines, substations, and power generation equipment are particularly vulnerable; failures in these areas can not only affect the stable operation of the substation system but may also lead to widespread power outages, and even cause casualties and property damage. Therefore, how to promptly detect potential failures and defects during equipment operation has become an urgent problem to be solved in the substation industry.

[0091] Currently, traditional methods for inspecting substation facilities largely rely on manual inspections and periodic checks. This approach is not only labor-intensive but also limited by the frequency and efficiency of manual inspections, making it difficult to detect minor defects in a timely manner. Furthermore, traditional methods lack continuous monitoring and trend prediction of equipment operating status, making it difficult to meet the increasingly complex needs of substation facility management.

[0092] In view of this, this application provides a method for all-time and all-space operation and maintenance management of new energy scenario substations, which can improve the automation and intelligent management level of substations, so as to realize remote monitoring, intelligent control and safety protection, and ensure that the substations can operate safely and efficiently even when unattended.

[0093] Figure 1 This is a schematic diagram of the all-weather, all-space operation and maintenance management method for new energy scenario stations provided in an embodiment of this application. For example... Figure 1 The method for all-weather, all-space operation and maintenance management of a new energy scenario station, as shown, includes at least the following operational steps:

[0094] S1: Construct a security operation and maintenance protection system for the substation control center, including physical security and network security, to prevent illegal intrusion and network attacks.

[0095] As is understandable, physical security refers to a series of measures to protect physical facilities and resources from unauthorized intrusion, damage, or theft. This may include measures such as installing surveillance cameras, access control systems, and safes to protect physical spaces. On the other hand, cybersecurity refers to a series of measures to protect computer systems, networks, and data from unauthorized access, damage, or theft. This includes using firewalls, encryption technologies, and strong passwords to protect network and data security.

[0096] Specifically, although physical security and cybersecurity are two distinct concepts, they are closely related. A breach of physical security can lead to cybersecurity threats, and vice versa. For example, if an unauthorized person gains physical access to a security facility, they may be able to insert malware or directly steal sensitive information. Similarly, if a network system has vulnerabilities, hackers may be able to remotely control physical devices such as surveillance cameras or security access control systems.

[0097] In this embodiment, to comprehensively consider both physical security and network security, we can adopt some practical methods. Step S1 involves constructing a security operation and maintenance protection system for the substation control center, including both physical and network security, to prevent unauthorized intrusion and network attacks, including:

[0098] Construct physical security defense modules and network security defense modules;

[0099] The physical security defense module integrates and analyzes the information characteristics of digital signals flowing from the substation control center with the physical characteristics of the power grid to achieve abnormal detection of the physical system's operating status.

[0100] The network security defense module detects the information characteristics of digital signals flowing into the substation control center, including identity and permissions, network traffic information system objects, and APT attack anomaly detection, thereby enabling malicious data identification.

[0101] S2: Collect and preprocess historical monitoring data from each substation.

[0102] In this embodiment of the application, step S2: collecting and preprocessing historical monitoring data from each substation, includes:

[0103] Step 1: Set a preset time period, collect the status variable data of the preset time period through the acquisition module of the substation control center, and store it in the data storage module of the substation control center. The status data includes: output of new energy substation, power grid operation parameters, and load consumption.

[0104] Step 2: Real-time acquisition of state variable data x from each monitoring point via the acquisition module of the substation control center. i ,in,

[0105] x i ={x 1i ,x 2i ,...,x pi}, i = 1, 2, ..., p, where p and i are the number of indicators; x is the sample data, and n is the number of samples;

[0106] Step 3: Using the historical data stored in the data storage module of the substation control center, predict the output value of the current system status to obtain the predicted value;

[0107] Step 4: Compare the real-time collected state variable data of each monitoring point with the predicted value of the current system state. When the difference error exceeds the threshold range, return to step 1 to resample.

[0108] When the difference error is within the set threshold range, the data stored in the data storage module of the substation control center is used as the sampling data; the sampling data includes image data and text data.

[0109] Wherein, the predicted value x i+1 The calculation formula is:

[0110]

[0111] Where, x i It is real-time data acquisition, A and B are parameter matrices of state data, u i It is a control variable;

[0112] The formula for the difference error is:

[0113]

[0114] Where L is the cost function.

[0115] Specifically, when the difference error exceeds the threshold range, it indicates that the data error is large and it is necessary to return to step 1 to resample the data and store it; when the difference error is within the set threshold range, it indicates that the data error is small and the data meets the processing requirements. At this time, the data stored in the data storage module of the substation control center should be obtained as the sampling data.

[0116] S3: Use 5G network slicing communication technology to transmit the collected historical monitoring data to the substation control center.

[0117] It is understandable that 5G base stations include three types of slicing mechanisms: Quality of Service (QoS) scheduling, resource reservation, and carrier isolation. QoS scheduling refers to the sharing of public radio-side resources by these slices. Scheduling is based on the priority of the 5G QoS Identifier (5QI). Resource reservation involves reserving separate resources within the cell for slices with lower security requirements but higher service awareness assurance requirements. Carrier isolation means that the hardware and software resources of a carrier on the base station are dedicated to that slice, suitable for slices with higher security requirements or larger bandwidth requirements.

[0118] In this embodiment of the application, step S3: transmitting the collected historical monitoring data to the substation control center using 5G network slicing communication technology includes:

[0119] A network slice management model is constructed based on 5G network slice communication technology, wherein the construction of the network slice management model includes: a network slice layering module and a network slice layering management module;

[0120] The network slicing layering module divides a physical network into multiple virtual logical network slices based on different types of substation equipment. Each network slice is logically isolated from the wireless access network to the bearer network and then to the core network. Each network slice includes at least a wireless sub-slice, a bearer sub-slice, and a core network sub-slice to adapt to various services and applications and achieve end-to-end isolation.

[0121] The network slice hierarchical management module monitors network operation, reports network status, benchmarks network indicators, and updates, adjusts, and configures network slices in real time, enabling end-to-end on-demand customization to meet changing business needs.

[0122] Specifically, considering the unique characteristics of slicing, network slicing in a radio access network (RAN) describes the optimized configuration of the control plane and user plane. Furthermore, two aspects need to be studied: a) Radio access type. This supports the services provided by the slice. b) Correct configuration of RAN functions. This applies to appropriate unit deployment in each slice based on requirements. The network slice management model should be appropriately adjusted for each slice based on factors such as QoS requirements, communication load, or communication type. For example, due to the network slice management model configuration, each slice uses different types of units: slice 1 uses only macro units; slice 2 uses only small units; and slice 3 uses both macro units and small units. In other scenarios, slice 1 can use both macro units and small units, while slice 3 uses only small units.

[0123] like Figure 2 As shown, 5G base stations can allocate different PRB resource shares to different slice groups. The base station scheduler schedules resources according to the allocated shares, ensuring that resource shortages in one slice group do not affect the service quality of another slice group, thus achieving a certain degree of resource isolation. The isolation effect is balanced, and the cost is moderate. The network can reserve resources for slice groups through the maximum RB resource ratio, the dedicated RB resource ratio, and the minimum RB resource ratio. RB reservation based on slice groups is as follows: Figure 2 As shown.

[0124] S4: Utilize image recognition and analysis technology to achieve real-time monitoring and automatic detection of abnormal behavior in substations.

[0125] Specifically, such as Figure 3 The diagram shown illustrates an image recognition technique utilizing a sliding window, as provided in another embodiment of this application. In this embodiment, a sliding window is used, with the aspect ratio of the template image feature map as the objective function. The optimal feature map is extracted from the feature map of the sample image using a scale-adaptive feature extraction method. For example, a template image with length N and width M is matched on a sample image of length W and width H at a given sliding search position (a, b). A sliding window is set and adaptively adjusted with step sizes a and b. The aspect ratio of the template image feature map is used as the objective function. When the two ratios are close, the optimal feature map in the feature map of the sample image is extracted.

[0126] In this embodiment of the application, step S4: using image recognition and analysis technology to achieve real-time monitoring and automatic detection of abnormal behavior in substations, includes:

[0127] The image data in the sampling data is obtained as the sample image data.

[0128] Image features are extracted from the sample image and the template image by the feature extraction layer in the convolutional neural network, respectively, to obtain the sample image feature map X and the template image feature map T.

[0129] Obtain the length N and width M of the template image feature map T to obtain the aspect ratio of the template image feature map.

[0130] Obtain the length W and width H of the feature map X of the sample image, set a sliding window, and extract the optimal feature map from the feature map of the sample image using the aspect ratio of the template image feature map as the objective function through a scale-adaptive feature extraction method.

[0131] Calculate feature map Similarity measure with feature map T:

[0132]

[0133] in, This represents the mean value of the pixel at (k+a,j+b) in the feature map of the optimal sample image. Let represent the mean value of the pixel at (i,j) in the template image feature map, and k,j represent the count within the pixel range; a and b represent the width and length of the sliding window, respectively, and 0≤a≤HM, 0≤b≤WN.

[0134] When S im When (a,b) is close to 1, the sample image and the template image are perfectly matched, and the automatic detection result of abnormal behavior in real time is obtained.

[0135] Specifically, based on image recognition and analysis technology, and fully utilizing its powerful graphics rendering engine and real-time monitoring capabilities, the platform enables integrated management of substation equipment, safety, and resources. It aims to improve inspection efficiency and safety, reduce the risk of human error and misjudgment, and drive substations towards refined management and modernization.

[0136] Specifically, in order to improve the accuracy of image detection, before acquiring the image data in the sampling data as sample image data, the following steps are performed: based on ground control points, using a geometric correction model, constructing the geometric relationship between the image and ground coordinates; and calibrating the image data.

[0137] It is understandable that image correction can include various types such as geographic coordinate correction, geometric correction, and jitter correction, and the appropriate type can be selected according to actual needs.

[0138] S5: Analyze equipment status data using the substation fault prediction model and output potential fault prediction results.

[0139] In this embodiment of the application, step S5: analyzing equipment status data through a substation fault prediction model and outputting potential fault prediction results, includes:

[0140] Obtain text data from the sampled data as training samples;

[0141] Train a fault prediction model using training samples;

[0142] Analyze equipment status data using fault prediction models;

[0143] Output potential fault prediction results.

[0144] Specifically, the model training process mainly consists of four steps: 1. Pre-training phase; 2. Supervised fine-tuning, also known as instruction fine-tuning phase; 3. Reward model training phase; 4. Reinforcement learning fine-tuning phase. The specific training steps include: 1) Data collection and preprocessing: collecting and organizing the datasets needed for training; 2) Model design and construction: designing and building a suitable model according to task requirements; 3) Model training: training the model using the training dataset, continuously adjusting the model parameters to enable the model to better fit the data; 4) Model evaluation and optimization: evaluating the trained model using the test dataset and optimizing the model based on the evaluation results; 5) Model deployment and use: deploying the trained model to real-world application scenarios and applying it.

[0145] Understandably, analyzing large amounts of text data can uncover potential trends, patterns, or correlations. By analyzing text content, we can understand people's emotional inclinations towards specific topics, products, or services, such as positive, negative, or neutral. It can also be used for entity naming recognition and document classification. This invention utilizes text features to build a fault prediction model to achieve fault prediction or classification.

[0146] S6: Automatically controls and optimizes substation equipment through intelligent algorithms and control strategies.

[0147] In this embodiment of the application, step S6: automatically controlling and optimizing substation equipment through intelligent algorithms and control strategies, includes:

[0148] Different control models are established based on different strategy control algorithms;

[0149] Select the target control model via the menu;

[0150] The fault prediction results are input into the target control model for fuzzy matching, enabling automatic control and optimization of substation equipment.

[0151] Specifically, different strategy control algorithms can establish different control models for different devices. Based on the control model, it is determined whether the current monitoring data corresponding to the control model meets the adjustment conditions of the controlled equipment in the substation. If so, the corresponding adjustment command is output to the controlled equipment, or the corresponding adjustment command is issued to the controlled equipment through the control center main station or the centralized monitoring system of the auxiliary equipment.

[0152] S7: Through remote operation technology, managers can remotely operate and maintain equipment.

[0153] In this embodiment of the application, step S7: Using remote operation technology, managers remotely perform equipment operation and maintenance work, including:

[0154] Install a remote monitoring application on the terminal device of the management personnel;

[0155] Management personnel send remote operation commands to the control terminal of the substation control center through a remote monitoring application. After the substation control center's security operation and maintenance protection system performs security authentication and authorization authentication for the remote monitoring application and management personnel, the control commands are accepted to enable click and button operations on the controlled terminal.

[0156] Specifically, when carrying out equipment operation and maintenance work, it is also possible to set up online monitoring levels, safety warnings, and maintenance work allocation, make relevant inspection or fault handling plans, arrange maintenance personnel to handle them, help maintenance personnel respond quickly, and reduce the risk of major failures caused by neglecting minor defects.

[0157] It is understood that the all-weather operation and maintenance management method for new energy substations provided in this application can achieve automatic control and optimization of substation equipment through real-time monitoring of substations, including data acquisition, status monitoring, and fault diagnosis. It conducts research on intelligent algorithms and control strategies to achieve high-speed, stable, and secure data transmission between the substation and the control center using advanced communication technologies. Remote operation technology enables managers to remotely operate and maintain equipment. A substation security protection system, including physical and network security, is constructed to prevent illegal intrusion and network attacks. Research on intelligent video surveillance is conducted, utilizing image recognition and analysis technologies to achieve real-time monitoring of the substation and automatic detection of abnormal behavior. Substation fault prediction technology analyzes equipment status data to predict potential faults and perform maintenance in advance.

[0158] Figure 4 This is a schematic diagram of a module of a new energy scenario station all-time and all-space operation and maintenance management system provided in an embodiment of this application, which is applied to... Figure 1The method for all-weather operation and maintenance management of new energy scenario stations is shown. The system modules specifically include: a security protection module 11, a data acquisition module 12, a data transmission module 13, an image recognition module 14, a fault prediction module 15, a control optimization module 16, and an operation and maintenance module 17. Its functions are similar to... Figure 1 The parts shown in the text are the same or similar, and will not be repeated here.

[0159] In this embodiment, the security protection module 11 is used to construct a security operation and maintenance protection system for the substation control center, including physical security and network security, to prevent unauthorized intrusion and network attacks. Please refer to the following for details. Figure 1 The details and their corresponding descriptions are not repeated here.

[0160] In this embodiment, the data acquisition module 12 is used to collect and preprocess historical monitoring data from each substation. Please refer to the following for details. Figure 1 The details and their corresponding descriptions are not repeated here.

[0161] In this embodiment, the data transmission module 13 is used to transmit the collected historical monitoring data to the substation control center using 5G network slicing communication technology. Please refer to the following for details. Figure 1 The details and their corresponding descriptions are not repeated here.

[0162] In this embodiment, the image recognition module 14 is used to achieve real-time monitoring and automatic detection of abnormal behavior in substations using image recognition and analysis technology. Please refer to the following for details. Figure 1 The details and their corresponding descriptions are not repeated here.

[0163] In this embodiment, the fault prediction module 15 is used to analyze equipment status data using a substation fault prediction model and output potential fault prediction results. Please refer to the following for details. Figure 1 The details and their corresponding descriptions are not repeated here.

[0164] In this embodiment, the control optimization module 16 is used to automatically control and optimize substation equipment through intelligent algorithms and control strategies.

[0165] See Figure 5 , Figure 5 This is an embodiment of the electronic terminal device 500 provided in this application. For example... Figure 5The electronic terminal device 500 shown includes at least the following components: one or more processors 501, one or more input devices 502, one or more output devices 503, and one or more memories 504. The processors 501, input devices 502, output devices 503, and memories 504 communicate with each other via a communication bus 505. The memories 504 store computer programs, including program instructions. The processors 501 execute the program instructions stored in the memories 504. Specifically, the processors 501 are configured to invoke the program instructions to perform the functions of the modules / units in the above-described device embodiments, such as... Figure 4 The functions of the module shown.

[0166] In embodiments of this application, a computer-readable storage medium includes instructions that instruct a device to perform the method as described in the first aspect. For example, the instructions instruct the device to perform... Figure 1 The steps in this paper demonstrate the all-weather, all-space operation and maintenance management method for new energy scenario stations.

[0167] It should be understood that, in this embodiment of the invention, the processor 501 may be a central processing unit (CPU), or it may 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 may be a microprocessor or any conventional processor. It should be noted that a portion of the electronic device 500 described above can also be implemented using a computer. In this case, the program for implementing the control function can be recorded on a computer-readable recording medium, and the program recorded on the recording medium can be read into the computer and executed.

[0168] Input device 502 may include a touchpad, a fingerprint sensor (for collecting the user's fingerprint information and fingerprint orientation information), a microphone, etc., and output device 503 may include a display (LCD, etc.), a speaker, etc.

[0169] It should be noted that the term "computer" as used here refers to a computer built into electronic device 500, employing hardware including an operating system and peripheral devices. Furthermore, "computer-readable recording media" refers to removable media such as floppy disks, magneto-optical disks, ROMs, and CD-ROMs, as well as storage devices such as hard drives built into the computer.

[0170] Furthermore, a "computer-readable recording medium" can include: a medium that dynamically stores a program for a short period of time, such as a communication line used when transmitting a program via a network such as the Internet or a communication line such as a telephone line; or a medium that stores a program for a fixed period of time, such as volatile memory inside a computer that serves as a server or client in this case. In addition, the aforementioned program can be a program used to implement the above-mentioned functions, or it can be a program that can implement the above-mentioned functions by combining with programs already recorded in the computer.

[0171] Furthermore, the electronic device 500 in the above embodiments can also be implemented as an assembly (device group) composed of multiple devices. Each device constituting the device group can possess some or all of the functions or functional blocks of the electronic device 500 in the above embodiments. As a device group, it is sufficient to have all the functions or functional blocks of the electronic device 500.

[0172] It is understood that the all-weather operation and maintenance management system, method, electronic equipment, and storage medium for new energy substations provided in this application can effectively improve the operation and maintenance level of substation facilities and solve the problem that traditional manual inspections cannot identify facility defects in real time and comprehensively. Through the application of this system, potential fault hazards in facilities can be quickly discovered, and corresponding repair suggestions and optimization solutions can be provided through intelligent analysis, thereby minimizing equipment downtime and maintenance costs. At the same time, the system's adaptive capability allows it to continuously learn and optimize, improving performance based on more data collected in real time, further enhancing its adaptability to different types of substation facilities and its ability to predict future defects.

[0173] Those skilled in the art should recognize that the above embodiments are only used to illustrate this application and are not intended to limit this application. Any appropriate changes and variations made to the above embodiments within the essential spirit and scope of this application fall within the scope of protection claimed in this application.

Claims

1. A method for all-weather, all-space operation and maintenance management of new energy scenario stations, characterized in that, The method includes the following steps: S1: Construct a security operation and maintenance protection system for the substation control center, including physical security and network security, to prevent illegal intrusion and network attacks; S2: Collect and preprocess historical monitoring data from each substation, including: Step 1: Set a preset time period, collect the status variable data of the preset time period through the acquisition module of the substation control center, and store it in the data storage module of the substation control center. The status data includes: output of new energy substation, power grid operation parameters, and load consumption. Step 2: Collect status variable data from each monitoring point in real time through the acquisition module of the substation control center. , It is the number of samples; Step 3: Using the historical data stored in the data storage module of the substation control center, predict the output value of the current system status to obtain the predicted value; Step 4: Compare the real-time collected state variable data of each monitoring point with the predicted value of the current system state. When the difference error exceeds the threshold range, return to step 1 to resample. When the difference error is within the set threshold range, the data stored in the data storage module of the substation control center is used as the sampling data; the sampling data includes image data and text data. Among them, the predicted value The calculation formula is: ， in, It collects data in real time. It is a parameter matrix of state data. It is a control variable; The formula for the difference error is: ， in, It is a cost function; S3: Utilizing 5G network slicing communication technology to transmit collected historical monitoring data to the substation control center, including: 5G base stations include three types of slicing mechanisms: Quality of Service (QoS) scheduling, resource reservation, and carrier isolation. QoS scheduling means that these slices share public radio resources; scheduling is based on the priority of the 5G QoS identifier. Resource reservation is to reserve separate resources for a slice within the cell, which is used for slices with lower security requirements but higher service perception assurance requirements. Carrier isolation means that the hardware and software resources of a carrier on the base station are dedicated to that slice, which is suitable for slices with higher security requirements or larger bandwidth requirements. A network slice management model is constructed based on 5G network slice communication technology, wherein the construction of the network slice management model includes: a network slice layering module and a network slice layering management module; The network slicing layering module divides a physical network into multiple virtual logical network slices based on different types of substation equipment. Each network slice is logically isolated from the wireless access network to the bearer network and then to the core network. Each network slice includes at least a wireless sub-slice, a bearer sub-slice, and a core network sub-slice to adapt to various services and applications and achieve end-to-end isolation. The network slice hierarchical management module monitors network operation, reports network operation status, benchmarks network indicators, and updates, adjusts, and configures network slices in real time to achieve end-to-end on-demand customization and meet the needs of changing business. Based on factors such as QoS requirements, communication load, or communication type, the network slice management model should be appropriately adjusted for each slice. Due to the configuration of the network slice management model, each slice uses different types of units: slice 1 uses only macro units; slice 2 uses only small units; slice 3 uses both macro units and small units, or slice 1 uses both macro units and small units, while slice 3 uses only small units. S4: Utilize image recognition and analysis technology to achieve real-time monitoring and automatic detection of abnormal behavior in substations; S5: Analyze equipment status data using the substation fault prediction model and output potential fault prediction results; S6: Automatic control and optimization of substation equipment through intelligent algorithms and control strategies; S7: Through remote operation technology, managers can remotely carry out equipment operation and maintenance work.

2. The all-weather, all-space operation and maintenance management method for new energy scenario stations according to claim 1, characterized in that, In step S1, a security operation and maintenance protection system for the substation control center is constructed, including physical security and network security, to prevent illegal intrusion and network attacks, including: Construct physical security defense modules and network security defense modules; The physical security defense module integrates and analyzes the information characteristics of digital signals flowing from the substation control center with the physical characteristics of the power grid to achieve abnormal detection of the physical system's operating status. The network security defense module detects the information characteristics of digital signals flowing into the substation control center, including identity and permissions, network traffic information system objects, and APT attack anomaly detection, thereby enabling malicious data identification.

3. The all-weather, all-space operation and maintenance management method for new energy scenario stations according to claim 1, characterized in that, Step S4: Utilizing image recognition and analysis technology to achieve real-time monitoring and automatic detection of abnormal behavior in substations, including: Obtain image data from the sampled data as sample image data; Image features are extracted from the sample image and the template image respectively by the feature extraction layer in the convolutional neural network to obtain the sample image feature map. and template image feature map ; Obtain the feature map of the template image Given the length N and width M, the aspect ratio of the template image feature map is obtained; Obtain the feature map of the sample image Given a length W and a width H, a sliding window is set. Using the aspect ratio of the template image feature map as the objective function, the optimal feature map is extracted from the feature map of the sample image using a scale-adaptive feature extraction method. ; Calculate feature map With feature map Similarity metric: ； ； ； in, Indicates the optimal sample image feature map in The average value of the pixels at that location; Indicates the feature map of the template image in The average value of the pixels at that location. Represents a count within a pixel range; Let represent the width and length of the sliding window, respectively, and , ; when When the value is close to 1, the sample image and the template image are perfectly matched, and the automatic detection results of abnormal behavior under real-time monitoring are obtained.

4. The all-weather, all-space operation and maintenance management method for new energy scenario stations according to claim 3, characterized in that, Before obtaining the image data from the sampling data as sample image data, perform the following: Based on ground control points, a geometric correction model is used to construct the geometric relationship between the image and the ground coordinates; The image data is calibrated.

5. The all-weather, all-space operation and maintenance management method for new energy scenario stations according to claim 4, characterized in that, Step S5: Analyze equipment status data using the substation fault prediction model, and output potential fault prediction results, including: Obtain text data from the sampled data as training samples; Train a fault prediction model using training samples; Analyze equipment status data using fault prediction models; Output potential fault prediction results.

6. The all-weather, all-space operation and maintenance management method for new energy scenario stations according to claim 5, characterized in that, Step S6: Automatic control and optimization of substation equipment through intelligent algorithms and control strategies, including: Different control models are established based on different strategy control algorithms; Select the target control model via the menu; The fault prediction results are input into the target control model for fuzzy matching, enabling automatic control and optimization of substation equipment.

7. The all-weather, all-space operation and maintenance management method for new energy scenario stations according to claim 6, characterized in that, Step S7: Through remote operation technology, managers remotely perform equipment operation and maintenance work, including: Install a remote monitoring application on the terminal device of the management personnel; Management personnel send remote operation commands to the control terminal of the substation control center through a remote monitoring application. After the substation control center's security operation and maintenance protection system performs security authentication and authorization authentication for the remote monitoring application and management personnel, the control commands are accepted to enable click and button operations on the controlled terminal.

8. A real-time, all-space operation and maintenance management system for new energy scenario stations, applied to the real-time, all-space operation and maintenance management method for new energy scenario stations as described in any one of claims 1 to 6, characterized in that, The system includes: Security Protection Module: Constructs a security operation and maintenance protection system for the substation control center, including physical security and network security, to prevent unauthorized intrusion and network attacks; Data acquisition module: Collects and preprocesses historical monitoring data from each substation; Data transmission module: Utilizes 5G network slicing communication technology to transmit the collected historical monitoring data to the substation control center; Image recognition module: Utilizes image recognition and analysis technology to achieve real-time monitoring of substations and automatic detection of abnormal behavior; Fault prediction module: Analyzes equipment status data using a fault prediction model for the substation and outputs potential fault prediction results; Control optimization module: Automatically controls and optimizes substation equipment through intelligent algorithms and control strategies; Operation and maintenance module: Through remote operation technology, managers can remotely carry out equipment operation and maintenance work.

Images