New energy scene station full-time air operation and maintenance management and control method and system

Abstract

The invention provides a new energy scene station full-time air operation and maintenance management and control method and system, and the method comprises the steps: constructing a safety operation and maintenance protection system of a transformer substation control center, and preventing illegal invasion and network attack; historical monitoring data of each transformer substation is collected and preprocessed; transmitting the collected historical monitoring data to a substation control center by using a 5G network slice communication technology; the real-time monitoring of the transformer substation and the automatic detection of abnormal behaviors are realized by utilizing an image recognition and analysis technology; analyzing the equipment state data through the fault prediction model of the transformer substation, and outputting a potential fault prediction result; the substation equipment is automatically controlled and optimized through an intelligent algorithm and a control strategy; and through a remote operation technology, management personnel remotely carry out equipment operation and maintenance work. According to the invention, the automatic and intelligent management level of the transformer substation is improved, so that remote monitoring, intelligent control and safety protection are realized, and the transformer substation is ensured to operate safely and efficiently under the unattended condition.

Description

Technical Field

[0001] The present application relates to the technical field of substation maintenance, and in particular to a full-time and space operation and maintenance control method and system for a new energy scenario station. Background Art

[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 the country's economic development and social stability. With the increase in the number of power stations under operation and maintenance, the traditional manual operation and maintenance methods can no longer meet the needs. Substation monitoring systems have been piloted in many domestic power supply companies, but there are still some problems with the existing distribution transformer monitoring systems, which are mainly manifested in the following aspects: On the one hand, due to the large number of distribution points and wide distribution of distribution transformers, it is difficult to choose a suitable communication method, so that information cannot be effectively transmitted; on the other hand, due to the lack of effective distribution transformer analysis software, the collected distribution transformer information cannot be effectively applied. The result is that although some distribution transformer monitoring terminals have been put into operation on site, the results are not ideal, and the distribution transformers have not been analyzed for operating status and economic operation.

[0003] With the development of society, distributed photovoltaic, energy storage, microgrid, and incremental distribution network have developed rapidly. These scenarios have changed the operating characteristics of traditional distribution network, and the "passive" network has gradually developed into an "active controllable" network. The dispatch objects are increasing, and the control requirements are getting higher and higher. The scale of the distribution network is rapidly expanding, and the requirements for access systems are getting higher and higher, and the requirements for power quality, substation operation and maintenance are also getting higher and higher. Distribution automation is a current development hotspot in the distribution network. Remote monitoring is becoming an important monitoring method, but this has not been paid attention to in the application of distribution substations. In terms of real-time remote monitoring of the three-phase balance, voltage qualification rate, load rate, load curve, etc. of the distribution substation, it is impossible for a single TTU device currently used in the distribution substation to achieve this. Therefore, how to improve the automation and intelligent management level of the substation to achieve remote monitoring, intelligent control and safety protection, and ensure that the substation can operate safely and efficiently even without supervision, is an important research content for technical personnel in this field. Summary of the invention

[0004] In view of this, it is necessary to provide a full-time and space operation and maintenance control method and system for new energy scene stations, which utilizes the system architecture of the distributed cloud platform and combines artificial intelligence technology to integrate the functions of operation monitoring, intelligent early warning, power generation analysis, video monitoring and operation and maintenance management for the monitoring and operation and maintenance system of new energy power stations, so as to realize unattended power stations and maximize the benefits of power stations. It not only realizes the centralized monitoring of substations, manages the operation and maintenance of power stations, and issues remote tasks through the cloud platform, but also allows operation and maintenance personnel to receive operation and maintenance tasks through their mobile phones. For periodic tasks, they can also be issued regularly by appointment, which greatly improves the efficiency of operation and maintenance work. The system stores power generation revenue data of power stations for a long time according to user needs, which is of great significance for optimizing operation strategies and improving power generation efficiency of power stations.

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

[0006] In a first aspect, the present application provides a full-time and space operation and maintenance management method for a new energy scene station.

[0007] Characterized in that the method comprises the following steps:

[0008] S1: Build a secure 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 pre-process the historical monitoring data of each substation;

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

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

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

[0013] S6: Automatically control and optimize substation equipment through intelligent algorithms and control strategies;

[0014] S7: Through remote operation technology, managers can remotely carry out equipment operation and maintenance work.

[0015] In one embodiment, in step S1, a security operation and maintenance protection system of the substation control center is constructed, 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] Through the physical security defense module, the information characteristics of the digital signals flowing out of the substation control center and the physical characteristics of the power grid are integrated and analyzed to realize the abnormal detection of the operating status of the physical system;

[0018] Through the network security defense module, the information characteristics of the digital signals flowing into the substation control center are detected, including identity permissions, network traffic information system objects, and anomaly detection of APT attacks to achieve malicious data identification.

[0019] In one embodiment, the step S2: collecting and preprocessing the historical monitoring data of each substation includes:

[0020] Step 1, set a preset time period, collect state variable data of the preset time period through the collection module of the substation control center, and store it in the data storage module of the substation control center, wherein the state data includes: output of new energy substation, grid operation parameters, and load consumption;

[0021] Step 2: The state variable data x of each monitoring point is collected in real time through the acquisition module of the substation control center i ,in,

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

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

[0024] Step 4: compare the state variable data collected at each monitoring point in real time with the predicted value of the current system state. When the difference error exceeds the threshold range, return to step 1 for resampling;

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

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

[0027]

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

[0029] The difference error formula is:

[0030]

[0031] Among them, L is the cost function.

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

[0033] Constructing a network slice management model according to the 5G network slice communication technology, wherein the constructed network slice management model includes: a network slice hierarchical module and a network slice hierarchical management module;

[0034] Through the network slicing layering module, a physical network is divided into multiple virtual logical network slices according to 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 operation is monitored through the network slicing hierarchical management module, the network operation status is reported, the network indicators are benchmarked, and the network slices are updated, adjusted, and configured in real time to achieve end-to-end on-demand customization to meet the needs of business changes.

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

[0037] Acquire image data from the sampling data as sample image data;

[0038] The image features in the sample image and the template image are extracted respectively through the feature extraction layer in the convolutional neural network to obtain the sample image feature map X and the template image feature map T;

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

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

[0041] Compute feature maps Similarity measure with feature map T:

[0042]

[0043] in, Represents the mean value of the pixel points at (k+a,j+b) of the optimal sample image feature map; represents the mean value of the pixel at (i, j) of the template image feature map, k, j represent the counts within the pixel range; a, 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 completely match, and the automatic detection result of abnormal behavior under real-time monitoring is obtained.

[0045] In one embodiment, before acquiring the image data in the sampling data as the sample image data, executing:

[0046] Based on the ground control points, the geometric relationship between the image and the ground coordinates is constructed using the geometric correction model;

[0047] Perform calibration processing on the image data.

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

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

[0050] Train the fault prediction model through training samples;

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

[0052] Output potential failure prediction results.

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

[0054] Establish different control models according to different strategic control algorithms;

[0055] Select the target regulation model through the menu;

[0056] The fault prediction results are input into the target control model for fuzzy matching to automatically control and optimize the substation equipment.

[0057] In one embodiment, the step S7: using remote operation technology, the management personnel remotely carry out equipment operation and maintenance work, including:

[0058] Install remote monitoring applications on managers’ terminal devices;

[0059] The manager sends remote operation commands to the control end of the substation control center through the remote monitoring application. After the security operation and maintenance protection system of the power station control center performs security authentication and authority authentication on the remote monitoring application and the manager, it accepts the control command to realize click and key operations on the controlled end.

[0060] In a second aspect, an embodiment of the present application provides a full-time and space operation and maintenance management system for a new energy scene station, which is applied to the full-time and space operation and maintenance management method for a new energy scene station as described in the first aspect, and the system includes:

[0061] Security protection module: Build a safe operation and maintenance protection system for the substation control center, including physical security and network security, to prevent illegal intrusion and network attacks;

[0062] Data acquisition module: collects and preprocesses the historical monitoring data of each substation;

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

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

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

[0066] Control optimization module: automatic control and optimization of 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] In a third aspect, an embodiment of the present application provides an electronic device, including:

[0069] processor;

[0070] a memory for storing processor-executable instructions;

[0071] Among them, the processor is configured to implement the full-time and space operation and maintenance control method of the new energy scene station as described in the first aspect when executing the instructions.

[0072] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, comprising instructions, wherein the instructions instruct a device to execute the full-time and space operation and maintenance control method of a new energy scene station as described in the first aspect.

[0073] The embodiment of the present application provides a method and system for all-time and space operation and maintenance of new energy scene stations, 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 substations can operate safely and efficiently even when unattended. The beneficial effects of the system include:

[0074] (1) Through real-time monitoring of substations, including data collection, 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) Use advanced communication technology to achieve high-speed, stable and secure data transmission between substations and control centers.

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

[0078] (5) Prevent illegal intrusion and cyber attacks by building a security protection system for the substation, including physical security and network security.

[0079] (6) Through the research on intelligent video surveillance and the use of image recognition and analysis technology, real-time monitoring of substations and automatic detection of abnormal behavior can be achieved.

[0080] (7) Through the fault prediction technology of the substation, by analyzing the equipment status data, potential failures can be predicted and maintenance can be carried out in advance. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 A schematic flow chart of a method for all-time and space operation and maintenance control of a new energy scenario station provided in one embodiment of the present application.

[0082] Figure 2 A functional diagram of 5G network slicing communication technology provided for another embodiment of the present application.

[0083] Figure 3 A schematic diagram of an image recognition technology using a sliding window provided in another embodiment of the present application.

[0084] Figure 4 A schematic diagram of the full-time and space operation and maintenance control system module of a new energy scenario station provided in one embodiment of the present application.

[0085] Figure 5 A schematic diagram of an electronic terminal device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0086] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0087] It should be noted that, in the embodiments of the present application, "at least one" refers to one or more, and "more" refers to two or more. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art in the present application. The terms used in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application.

[0088] It should be noted that, in the embodiments of the present application, words such as "first" and "second" are only used for the purpose of distinguishing descriptions, and cannot be understood as indicating or implying relative importance, nor can they be understood as indicating or implying order. Features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete way.

[0089] Based on the implementations in this application, all other implementations obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0090] With the rapid development of the substation industry, the operating environment of substation facilities has become increasingly complex, and equipment failures and defects have posed a severe challenge to the stability and safety of substation facilities. In particular, key facilities such as high-voltage transmission lines, substations, and power generation equipment, once they fail, will not only affect the stable operation of the substation system, but may also cause widespread power outages, and even cause casualties and property losses. 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] At present, traditional substation facility inspection methods mostly rely on manual inspections and regular inspections. This method 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. In addition, the disadvantage of traditional methods is the lack of continuous monitoring and trend prediction of equipment operating status, making it difficult to cope with the increasingly complex substation facility management needs.

[0092] In view of this, the present application provides a full-time and space operation and maintenance control method for a new energy scenario station, which can improve the automation and intelligent management level of the substation, so as to realize remote monitoring, intelligent control and safety protection, and ensure that the substation can operate safely and efficiently even without human supervision.

[0093] Figure 1 This is a flow chart of the full-time and space operation and maintenance control method for a new energy scene station provided by an embodiment of the present application. Figure 1 A new energy scene station full-time and space operation and maintenance control method shown includes at least the following steps:

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

[0095] It can be understood that physical security refers to a series of measures to protect physical facilities and resources from illegal intrusion, damage or theft. This may include installing surveillance cameras, access control systems, safes and other measures to protect physical spaces. On the other hand, network security refers to a series of measures to protect computer systems, networks and data from unauthorized access, damage or theft. This includes the use of firewalls, encryption technology, strong passwords, etc. to protect network and data security.

[0096] Specifically, although physical security and cybersecurity are two different concepts, there is a close relationship between them. Physical security breaches may lead to cybersecurity threats, and cybersecurity breaches may also lead to physical security issues. For example, if an unauthorized person is able to physically access a secure facility, they may be able to insert malware or directly steal sensitive information. Similarly, if a network system has a vulnerability, hackers may be able to remotely control physical devices such as surveillance cameras or security access systems.

[0097] In the embodiment of the present application, in order to comprehensively consider physical security and network security, we can take some practical methods. In step S1, a safe operation and maintenance protection system of the substation control center is constructed, including physical security and network security, to prevent illegal intrusion and network attacks, including:

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

[0099] Through the physical security defense module, the information characteristics of the digital signals flowing out of the substation control center and the physical characteristics of the power grid are integrated and analyzed to realize the abnormal detection of the operating status of the physical system;

[0100] Through the network security defense module, the information characteristics of the digital signals flowing into the substation control center are detected, including identity permissions, network traffic information system objects, and anomaly detection of APT attacks to achieve malicious data identification.

[0101] S2: Collect and pre-process the historical monitoring data of each substation.

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

[0103] Step 1, set a preset time period, collect state variable data of the preset time period through the collection module of the substation control center, and store it in the data storage module of the substation control center, wherein the state data includes: output of new energy substation, grid operation parameters, and load consumption;

[0104] Step 2: The state variable data x of each monitoring point is collected in real time through the acquisition module of the substation control center i ,in,

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

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

[0107] Step 4: compare the state variable data collected at each monitoring point in real time with the predicted value of the current system state. When the difference error exceeds the threshold range, return to step 1 for resampling;

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

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

[0110]

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

[0112] The difference error formula is:

[0113]

[0114] Among them, L is the cost function.

[0115] Specifically, when the difference error exceeds the threshold range, it means that the data error at this time 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 means that the data error at this time 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 sampled data.

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

[0117] It can be understood that 5G base stations contain three types of slicing mechanisms: Quality of Service QoS scheduling, resource reservation and carrier isolation. QoS scheduling refers to the sharing of public resources on the wireless side by this type of slices. Scheduling is performed according to the priority of the 5G QoS identifier (5G QoSIdentifier, 5QI). Resource reservation is to reserve separate resources for slices within the cell for slices with lower security requirements but high service perception requirements. Carrier isolation means that the software and hardware resources of a carrier on the base station are dedicated to the slice, which is suitable for slices with higher security requirements or larger bandwidth requirements.

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

[0119] Constructing a network slice management model according to the 5G network slice communication technology, wherein the constructed network slice management model includes: a network slice hierarchical module and a network slice hierarchical management module;

[0120] Through the network slicing layering module, a physical network is divided into multiple virtual logical network slices according to 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 operation is monitored through the network slicing hierarchical management module, the network operation status is reported, the network indicators are benchmarked, and the network slices are updated, adjusted, and configured in real time to achieve end-to-end on-demand customization to meet the needs of business changes.

[0122] Specifically, considering the particularity of slices, network slices in the wireless access network describe the optimized configuration of the control plane and the user plane. In addition, two aspects need to be studied. a. Wireless access type. It supports the services provided by the slices. b. Correct configuration of wireless access network functions. It applies to the appropriate unit deployment in each slice based on demand. Based on factors such as QoS requirements, communication load or communication type, the network slice management model should make appropriate adjustments for each slice. For example, due to the configuration of the network slice management model, each slice uses a different type of unit: slice 1 uses only macro cells; slice 2 uses only small cells; slice 3 uses macro cells and small cells. In other scenarios, slice 1 can use macro cells and small cells, while slice 3 uses only small cells.

[0123] like Figure 2 As shown in the figure, the 5G base station can allocate different PRB resource shares to different slice groups. The base station scheduler schedules resources according to the allocated shares to ensure that the resource shortage of one slice group will not affect the service quality of another slice group, thereby achieving a certain degree of resource isolation, a compromise isolation effect, and a moderate cost. The network can reserve resources occupied by slice groups through the maximum RB resource ratio, dedicated RB resource ratio, and minimum RB resource ratio. RB reservation based on slice groups is as follows Figure 2 shown.

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

[0125] Specifically, Figure 3 FIG. 1 is a schematic diagram of an image recognition technology using a sliding window provided by another embodiment of the present application. In the embodiment of the present application, a sliding window is set, and the aspect ratio of the template image feature map is used as the objective function. The optimal feature map is extracted from the feature map in the sample image by a scale-adaptive feature extraction method. For example, a template image with a length of N and a width of M is matched on a sample image with a length of W and a width of H according to a given sliding search position (a, b), a sliding window is set, and adaptive adjustment is performed with a step size of a and b. The aspect ratio of the feature map of the template image is used as the objective function. When the ratios of the two are close, the optimal feature map in the feature map of the sample image is extracted.

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

[0127] Image data in the sampling data is acquired as sample image data.

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

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

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

[0131] Compute feature maps Similarity measure with feature map T:

[0132]

[0133] in, Represents the mean value of the pixel points at (k+a,j+b) of the optimal sample image feature map; represents the mean value of the pixel point at (i, j) in the template image feature map, k, j represent the counts within the pixel range; a, 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 completely match, and the automatic detection result of abnormal behavior under real-time monitoring is obtained.

[0135] Specifically, based on image recognition and analysis technology, it makes full use of its powerful graphics rendering engine and real-time monitoring capabilities to achieve integrated management of substation equipment, safety and resources. The platform is committed to improving inspection efficiency and safety, reducing the risk of man-made accidents and misjudgments, and promoting the refined management and modernization of substations.

[0136] Specifically, in order to improve the accuracy of image detection, before obtaining 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 a geometric relationship between the image and the ground coordinates; and calibrating the image data.

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

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

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

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

[0141] Train the fault prediction model through training samples;

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

[0143] Output potential failure prediction results.

[0144] Specifically, the model training process mainly consists of four steps: 1. Pre-training stage; 2. Supervised fine-tuning, also called instruction fine-tuning stage; 3. Reward model training stage; 4. Reinforcement learning fine-tuning stage. The training steps specifically include: 1) Data collection and preprocessing: Collect and organize the data sets needed for training. 2) Model design and construction: Design and build a suitable model according to task requirements. 3) Model training: Use the training data set to train the model, and continuously adjust the model parameters so that the model can better fit the data. 4) Model evaluation and optimization: Use the test data set to evaluate the trained model, and optimize the model based on the evaluation results. 5) Model deployment and use: Deploy the trained model to the actual application scenario and apply it.

[0145] It is understandable that by analyzing a large amount of text data, potential trends, patterns or associations can be mined. By analyzing the text content, people's emotional tendencies towards specific topics, products or services, such as positive, negative or neutral, can be understood. Entity naming recognition and document classification can also be performed. The present invention uses text features to establish a fault prediction model to achieve fault prediction or classification.

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

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

[0148] Establish different control models according to different strategic control algorithms;

[0149] Select the target regulation model through the menu;

[0150] The fault prediction results are input into the target control model for fuzzy matching to automatically control and optimize the substation equipment.

[0151] Specifically, different strategy control algorithms can establish control models of different strategy control algorithms according to different devices. According to the control model, it is judged 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 instructions are output to the controlled equipment, or the corresponding adjustment instructions are issued to the controlled equipment through the control center main station or the auxiliary equipment centralized monitoring system.

[0152] S7: Through remote operation technology, managers can remotely carry out equipment operation and maintenance work.

[0153] In the embodiment of the present application, the step S7: the management personnel remotely carry out equipment operation and maintenance work through remote operation technology, including:

[0154] Install remote monitoring applications on managers’ terminal devices;

[0155] The manager sends remote operation commands to the control end of the substation control center through the remote monitoring application. After the security operation and maintenance protection system of the power station control center performs security authentication and authority authentication on the remote monitoring application and the manager, it accepts the control command to realize click and key operations on the controlled end.

[0156] Specifically, in addition to carrying out equipment operation and maintenance work, you can also set 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 ignoring small defects.

[0157] It is understandable that the full-time and space operation and maintenance control method of the new energy scenario station provided in the embodiment of the present application can be achieved through real-time monitoring of the substation, including data collection, status monitoring and fault diagnosis. Research on intelligent algorithms and control strategies is carried out to realize automatic control and optimization of substation equipment. Advanced communication technology is used to achieve high-speed, stable and secure data transmission between the substation and the control center. Through remote operation technology, managers can remotely carry out equipment operation and maintenance work. By building a security protection system for the substation, including physical security and network security, illegal intrusion and network attacks are prevented. By conducting research on intelligent video surveillance and using image recognition and analysis technology, real-time monitoring of substations and automatic detection of abnormal behaviors can be achieved. Through the fault prediction technology of the substation, by analyzing the equipment status data, potential faults are predicted and maintenance is performed in advance.

[0158] Figure 4 This is a schematic diagram of the full-time and space operation and maintenance management system module of the new energy scene station provided by the first embodiment of the present application, which is applied to Figure 1The system modules specifically include: security protection module 11, data acquisition module 12, data transmission module 13, image recognition module 14, fault prediction module 15, control optimization module 16, and operation and maintenance module 17. Its functions are similar to Figure 1 The parts shown in FIG. 1 are the same or similar and will not be described in detail here.

[0159] In the embodiment of the present application, the security protection module 11 is used to build a safe operation and maintenance protection system for the substation control center, including physical security and network security, to prevent illegal intrusion and network attacks. Figure 1 And the corresponding description thereof, this application will not repeat them here.

[0160] In the embodiment of the present application, the data acquisition module 12 is used to collect and pre-process the historical monitoring data of each substation. Figure 1 And the corresponding description thereof, this application will not repeat them here.

[0161] In the embodiment of the present application, the data transmission module 13 is used to transmit the collected historical monitoring data to the substation control center using the 5G network slicing communication technology. Figure 1 And the corresponding description thereof, this application will not repeat them here.

[0162] In the embodiment of the present application, the image recognition module 14 is used to realize real-time monitoring of the substation and automatic detection of abnormal behavior by using image recognition and analysis technology. Figure 1 And the corresponding description thereof, this application will not repeat them here.

[0163] In the embodiment of the present application, the fault prediction module 15 is used to analyze the equipment status data through the fault prediction model of the substation and output the potential fault prediction result. Figure 1 And the corresponding description thereof, this application will not repeat them here.

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

[0165] See also Figure 5 , Figure 5 This is an electronic terminal device 500 provided in an embodiment of the present application. Figure 5The electronic terminal device 500 shown includes at least the following parts: 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 memory 504 is used to store computer programs, which include program instructions. The processor 501 is used to execute the program instructions stored in the memory 504. The processor 501 is configured to call the program instructions to perform the following operations to perform the functions of the modules / units in the above-mentioned device embodiments, such as Figure 4 Functionality of the modules shown.

[0166] In an embodiment of the present application, a computer-readable storage medium includes instructions, and the instructions instruct a device to execute the method of the first aspect. For example, the instructions instruct the device to execute Figure 1 The steps in the figure show the full-time and space operation and maintenance control method of the new energy scene station.

[0167] It should be understood that in the embodiment of the present invention, the processor 501 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc. It should be noted that a part of the electronic device 500 of the above embodiment may also be implemented by a computer. In this case, the program for implementing the control function may be recorded in a computer-readable recording medium, and the program recorded in the recording medium may be read into a computer and executed.

[0168] The input device 502 may include a touch panel, a fingerprint sensor (for collecting the user's fingerprint information and fingerprint direction information), a microphone, etc., and the output device 503 may include a display (LCD, etc.), a speaker, etc.

[0169] It should be noted that the "computer" mentioned here refers to a computer built into the electronic device 500, and uses a computer including hardware such as an OS and peripheral devices. In addition, "computer-readable recording medium" refers to removable media such as a floppy disk, a magneto-optical disk, a ROM, a CD-ROM, and a storage device such as a hard disk built into the computer.

[0170] Furthermore, "computer-readable recording media" may include: media that dynamically store programs for a short period of time, such as communication lines when sending programs via networks such as the Internet or communication lines such as telephone lines; and media that store programs for a fixed period of time, such as volatile memories inside computers that serve as servers or clients in this case. In addition, the above-mentioned program may be a program for realizing a part of the above-mentioned functions, or a program that can realize the above-mentioned functions by combining with a program already recorded in a computer.

[0171] In addition, the electronic device 500 in the above-mentioned embodiment can also be implemented as a collection (device group) composed of multiple devices. Each device constituting the device group can have a part or all of the functions or functional blocks of the electronic device 500 in the above-mentioned embodiment. As a device group, it is sufficient to have all the functions or functional blocks of the electronic device 500.

[0172] It is understandable that the full-time and space operation and maintenance control system, method, electronic device and storage medium of the new energy scenario station provided by the embodiment of the present 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 hidden dangers of facility failures 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 adaptive ability of the system enables it to continuously learn and optimize, improve performance based on more data collected in real time, and further enhance 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 the present application and are not intended to be limiting of the present application. As long as they are within the spirit and scope of the present application, appropriate changes and modifications to the above embodiments are within the scope of protection claimed in the present application.

Claims

1. A full-time and space operation and maintenance control method for a new energy scene station, characterized in that: The method comprises the following steps: S1: Build a secure 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 pre-process the historical monitoring data of each substation; S 3: Use 5G network slicing communication technology to transmit the collected historical monitoring data to the substation control center; S 4: Use image recognition and analysis technology to achieve real-time monitoring of substations and automatic detection of abnormal behavior; S 5: Analyze the equipment status data through the fault prediction model of the substation and output the potential fault prediction results; S 6: Automatically control and optimize substation equipment through intelligent algorithms and control strategies; S 7: Through remote operation technology, managers can perform equipment operation and maintenance work remotely.

2. The full-time and space operation and maintenance management method of the new energy scene station according to claim 1 is 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; Through the physical security defense module, the information characteristics of the digital signals flowing out of the substation control center and the physical characteristics of the power grid are integrated and analyzed to realize the abnormal detection of the operating status of the physical system; Through the network security defense module, the information characteristics of the digital signals flowing into the substation control center are detected, including identity permissions, network traffic information system objects, and anomaly detection of APT attacks to achieve malicious data identification.

3. The full-time and space operation and maintenance management method of the new energy scene station according to claim 2 is characterized in that: The step S2: collecting and preprocessing the historical monitoring data of each substation includes: Step 1, set a preset time period, collect state variable data of the preset time period through the collection module of the substation control center, and store it in the data storage module of the substation control center, wherein the state data includes: output of new energy substation, grid operation parameters, and load consumption; Step 2: The state variable data x of each monitoring point is collected in real time through the acquisition module of the substation control center i ,in, x i ={x 1i ,x 2i ,...,x pi }, i = 1, 2, ..., p, p, i are the number of indicators; x is the sample data, n is the number of samples; Step 3, using the historical data stored in the data storage module of the substation control center to predict the output value of the current system state, to obtain a predicted value; Step 4: compare the state variable data collected at each monitoring point in real time with the predicted value of the current system state. When the difference error exceeds the threshold range, return to step 1 for resampling; When the difference error is within a set threshold range, the data stored in the data storage module of the substation control center is obtained as sampling data; the sampling data includes image data and text data; Among them, the predicted value x i+1 The calculation formula is: Among them, x i is real-time data collection, A and B are parameter matrices of state data, and u i is a control variable; The difference error formula is: Among them, L is the cost function.

4. The full-time and space operation and maintenance control method of the new energy scene station according to claim 3 is characterized in that: The step S3: using 5G network slicing communication technology to transmit the collected historical monitoring data to the substation control center, includes: Constructing a network slice management model according to 5G network slice communication technology, wherein the constructed network slice management model includes: a network slice hierarchical module and a network slice hierarchical management module; Through the network slicing layering module, a physical network is divided into multiple virtual logical network slices according to 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 operation is monitored through the network slicing hierarchical management module, the network operation status is reported, the network indicators are benchmarked, and the network slices are updated, adjusted, and configured in real time to achieve end-to-end on-demand customization to meet the needs of business changes.

5. The full-time and space operation and maintenance control method of the new energy scene station according to claim 4 is characterized in that: Step S4: using image recognition and analysis technology to achieve real-time monitoring of the substation and automatic detection of abnormal behavior, including: Acquire image data from the sampling data as sample image data; The image features in the sample image and the template image are extracted respectively through the feature extraction layer in the convolutional neural network to obtain the sample image feature map X and the template image feature map T; Obtaining the length N and width M of the template image feature map T to obtain the aspect ratio of the template image feature map; Get the length W and width H of the sample image feature map X, set a sliding window, take the aspect ratio of the template image feature map as the objective function, and extract the optimal feature map from the feature map in the sample image by using the scale-adaptive feature extraction method Compute feature maps Similarity measure with feature map T: in, Represents the mean value of the pixel points at (k+a,j+b) of the optimal sample image feature map; represents the mean value of the pixel at (i, j) of the template image feature map, k, j represent the counts within the pixel range; a, b represent the width and length of the sliding window, respectively, and 0≤a≤HM, 0≤b≤WN; When S im When (a, b) is close to 1, the sample image and the template image completely match, and the automatic detection result of abnormal behavior under real-time monitoring is obtained.

6. The full-time and space operation and maintenance control method of the new energy scene station according to claim 5 is characterized in that: Before acquiring the image data in the sampling data as the sample image data, executing: Based on the ground control points, the geometric relationship between the image and the ground coordinates is constructed using the geometric correction model; Perform calibration processing on the image data.

7. The full-time and space operation and maintenance control method of the new energy scene station according to claim 6 is characterized in that: The step S5: analyzing the equipment status data through the fault prediction model of the substation and outputting potential fault prediction results, including: Obtain text data from the sampled data as training samples; Train the fault prediction model through training samples; Analyze equipment status data through fault prediction models; Output potential failure prediction results.

8. The full-time and space operation and maintenance control method of the new energy scene station according to claim 7 is characterized in that: The step S6: automatically controlling and optimizing the substation equipment through intelligent algorithms and control strategies, including: Establish different control models according to different strategic control algorithms; Select the target regulation model through the menu; The fault prediction results are input into the target control model for fuzzy matching to automatically control and optimize the substation equipment.

9. The full-time and space operation and maintenance control method of the new energy scene station according to claim 8 is characterized in that: Step S7: Through remote operation technology, the management personnel remotely carry out equipment operation and maintenance work, including: Install remote monitoring applications on managers’ terminal devices; The manager sends remote operation commands to the control end of the substation control center through the remote monitoring application. After the security operation and maintenance protection system of the power station control center performs security authentication and authority authentication on the remote monitoring application and the manager, it accepts the control command to realize click and key operations on the controlled end.

10. A full-time and space operation and maintenance management system for a new energy scene station, applied to the full-time and space operation and maintenance management method for a new energy scene station as claimed in any one of claims 1 to 9, characterized in that: The system comprises: Security protection module: Build a safe operation and maintenance protection system for the substation control center, including physical security and network security, to prevent illegal intrusion and network attacks; Data acquisition module: collects and preprocesses the historical monitoring data of each substation; Data transmission module: uses 5G network slicing communication technology to transmit the collected historical monitoring data to the substation control center; Image recognition module: Use image recognition and analysis technology to achieve real-time monitoring of substations and automatic detection of abnormal behaviors; Fault prediction module: Analyzes equipment status data through the fault prediction model of the substation and outputs potential fault prediction results; Control optimization module: automatic control and optimization of 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