Control method and device of power transmission system, equipment and storage medium

Abstract

The invention relates to a control method, device and equipment for a power transmission system and a storage medium, and the method comprises the steps: obtaining sensor data, external environment data and historical operation and maintenance data in the power transmission system, inputting the sensor data and the external environment data into a monitoring model, and carrying out the monitoring of a working state, thereby obtaining a monitoring result, and when the monitoring result shows that the power transmission system is abnormal, inputting the historical operation and maintenance data into the resource scheduling model for resource scheduling. According to the method, due to the fact that the working state of the power transmission system is analyzed in combination with the real-time monitoring data, the historical operation and maintenance data and other multi-dimensional data, data fusion and efficient processing are achieved, the accuracy and integrity of the data are improved to a certain degree, and powerful data support is provided for subsequent decision support.

Description

Technical Field

[0001] The present application relates to the technical field of power system monitoring, and in particular to a control method, device, equipment and storage medium for a power transmission system. Background Art

[0002] In modern power system operation, efficient and reliable operation of the transmission network is crucial to ensure stable power supply. However, traditional transmission management and monitoring methods face several major technical challenges:

[0003] Data island problem: Different power transmission and monitoring equipment often operate independently, with insufficient data sharing and integration capabilities, resulting in inefficient data utilization.

[0004] Insufficient prediction accuracy: There is a lack of effective data analysis tools to predict potential problems in the transmission system, such as load anomalies and equipment failures, making it difficult to achieve early warning and prevention.

[0005] Insufficient support for optimization decisions: In the operation and maintenance decision-making of the transmission system, there is a lack of support based on big data analysis, resulting in low optimization of operation and maintenance efficiency and resource allocation.

[0006] Insufficient safety risk management: Faced with complex external environments and internal equipment conditions, existing technologies make it difficult to comprehensively assess and manage safety risks in the power transmission process.

[0007] Through the above analysis, the problems and defects of the prior art are as follows:

[0008] Existing technologies have problems such as data silos, insufficient prediction accuracy, insufficient support for optimization decisions, and insufficient security risk management. Summary of the invention

[0009] Based on this, it is necessary to provide a control method, device, equipment and storage medium for a power transmission system that can solve the problems of the prior art in response to the above technical problems.

[0010] In a first aspect, the present application provides a control method for a power transmission system, the method comprising:

[0011] Obtain sensor data, external environment data, and historical operation and maintenance data in the power transmission system;

[0012] Input the sensor data and external environment data into the monitoring model to monitor the working status and obtain the monitoring results;

[0013] When the monitoring results indicate that there are abnormalities in the transmission system, the historical operation and maintenance data is input into the resource scheduling model for resource scheduling.

[0014] In one embodiment, the method further comprises:

[0015] Input the monitoring results into the risk assessment model to conduct risk assessment and obtain the assessment results;

[0016] When the monitoring results indicate that there is an abnormality in the transmission system, the historical operation and maintenance data is input into the resource scheduling model for resource scheduling, including:

[0017] When the evaluation result indicates that there is an abnormality in the transmission system and the risk value corresponding to the abnormality is greater than the preset risk threshold, the historical operation and maintenance data and the risk value corresponding to the abnormality are input into the resource scheduling model for resource scheduling.

[0018] In one embodiment, the method further comprises:

[0019] When the evaluation result indicates that there is an abnormality in the transmission system and the risk value corresponding to the abnormality is greater than the preset risk threshold, the dispatch strategy selection options are displayed;

[0020] In response to the user's selection operation based on the scheduling policy selection item, resource scheduling is performed according to the scheduling policy indicated by the selection operation, and the step of inputting historical operation and maintenance data into the resource scheduling model for resource scheduling is not performed.

[0021] In one embodiment, the method further comprises any of the following:

[0022] Display monitoring results;

[0023] When the monitoring results indicate that there is an abnormality in the power transmission system, an alarm message is output.

[0024] In one embodiment, the method further comprises:

[0025] Preprocessing sensor data, external environment data and historical operation and maintenance data in the power transmission system to obtain preprocessed sensor data, external environment data and historical operation and maintenance data; the preprocessing includes at least one of cleaning processing, normalization processing, missing value filling processing and outlier removal processing;

[0026] Input sensor data and external environment data into the monitoring model to monitor the working status and obtain monitoring results, including:

[0027] The preprocessed sensor data, the preprocessed external environment data and the preprocessed historical operation and maintenance data are input into the monitoring model for monitoring to obtain the monitoring results.

[0028] In one embodiment, the method further comprises:

[0029] Obtain sensor sample data, external environment sample data and historical operation and maintenance sample data in the power transmission system;

[0030] The initial monitoring model is trained based on sensor sample data, external environment sample data and historical operation and maintenance sample data to obtain a monitoring model.

[0031] In a second aspect, the present application further provides a control device for a power transmission system, the device comprising:

[0032] An acquisition module is used to acquire sensor data, external environment data, and historical operation and maintenance data in the power transmission system;

[0033] The monitoring module is used to input the sensor data and the external environment data into the monitoring model to monitor the working status and obtain the monitoring results;

[0034] The scheduling module is used to input historical operation and maintenance data into the resource scheduling model for resource scheduling when the monitoring results indicate that there is an abnormality in the transmission system.

[0035] In a third aspect, the present application further provides a computer device, the computer device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the following steps when executing the computer program:

[0036] Obtain sensor data, external environment data, and historical operation and maintenance data in the power transmission system;

[0037] Input the sensor data and external environment data into the monitoring model to monitor the working status and obtain the monitoring results;

[0038] When the monitoring results indicate that there are abnormalities in the transmission system, the historical operation and maintenance data is input into the resource scheduling model for resource scheduling.

[0039] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0040] Obtain sensor data, external environment data, and historical operation and maintenance data in the power transmission system;

[0041] Input the sensor data and external environment data into the monitoring model to monitor the working status and obtain the monitoring results;

[0042] When the monitoring results indicate that there are abnormalities in the transmission system, the historical operation and maintenance data is input into the resource scheduling model for resource scheduling.

[0043] In a fifth aspect, the present application further provides a computer program product, the computer program product comprising a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0044] Obtain sensor data, external environment data, and historical operation and maintenance data in the power transmission system;

[0045] Input the sensor data and external environment data into the monitoring model to monitor the working status and obtain the monitoring results;

[0046] When the monitoring results indicate that there are abnormalities in the transmission system, the historical operation and maintenance data is input into the resource scheduling model for resource scheduling.

[0047] The control method, device, equipment and storage medium of the above-mentioned power transmission system obtains sensor data, external environment data and historical operation and maintenance data in the power transmission system, and then inputs the sensor data and external environment data into the monitoring model to monitor the working status, obtains the monitoring results, and when the monitoring results indicate that there is an abnormality in the power transmission system, inputs the historical operation and maintenance data into the resource scheduling model for resource scheduling. In the above-mentioned method, since the method combines multi-dimensional data such as real-time monitoring data and historical operation and maintenance data to analyze the working status of the power transmission system, it realizes data fusion and efficient processing, so it improves the accuracy and completeness of the data to a certain extent, and also provides strong data support for subsequent decision support. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is an internal structure diagram of a computer device in one embodiment;

[0049] Figure 2 is a flow chart of a control method for a power transmission system in one embodiment;

[0050] Figure 3 is a flow chart of a control method for a power transmission system in another embodiment;

[0051] Figure 4 is a flow chart of a control method for a power transmission system in another embodiment;

[0052] Figure 5 is a flow chart of a control method for a power transmission system in another embodiment;

[0053] Figure 6 is a flow chart of a control method for a power transmission system in another embodiment;

[0054] Figure 7 is a flow chart of a control method for a power transmission system in another embodiment;

[0055] Figure 8 A comparison diagram of the effects of a traditional power grid operation and maintenance management system and the smart power grid operation and maintenance management system in this embodiment;

[0056] Fig. 9FIG. 4 is a structural block diagram of a control device for a power transmission system in an embodiment. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0058] In the operation of modern power systems, the efficient and reliable operation of the transmission network is crucial to ensure the stability of power supply. However, traditional transmission management and monitoring methods face several major technical challenges: Data island problem: Different transmission and monitoring equipment often operate independently, and the data sharing and integration capabilities are insufficient, resulting in inefficient data utilization. Insufficient prediction accuracy: There is a lack of effective data analysis tools to predict potential problems in the transmission system, such as load anomalies, equipment failures, etc., making it difficult to achieve early warning and prevention. Insufficient support for optimization decisions: In the operation and maintenance decision-making of the transmission system, there is a lack of support based on big data analysis, resulting in low optimization of operation and maintenance efficiency and resource allocation. Insufficient safety risk management: In the face of complex external environments and internal equipment status, it is difficult for existing technologies to comprehensively evaluate and manage safety risks in the transmission process. Through the above analysis, the problems and defects of the existing technology are: the existing technology has problems such as data islands, insufficient prediction accuracy, insufficient optimization decision support, and insufficient safety risk management. The present application provides a control method for a transmission system, which aims to solve the above technical problems. The following embodiments will specifically illustrate the control method for the transmission system described in the present application.

[0059] The control method of the power transmission system provided in the embodiment of the present application can be applied to Figure 1 The computer device shown in the figure can be a terminal or a server, and its internal structure diagram can be as shown in Figure 1As shown, the computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a control method of a power transmission system is realized. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0060] Those skilled in the art will understand that Figure 1 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0061] In one embodiment, Figure 2 As shown, a control method for a power transmission system is provided, and the method is applied to Figure 1 The computer device in the example is used to illustrate, including the following steps:

[0062] S101, acquiring sensor data, external environment data and historical operation and maintenance data in the power transmission system.

[0063] Among them, sensor data includes at least one of temperature, humidity, current, voltage, etc. External environment data includes at least one of meteorological data, weather data, environmental data, etc. Historical operation and maintenance data includes multi-dimensional data such as historical transmission load data, weather conditions, date types (weekdays, weekends, holidays), etc.

[0064] In the embodiment of the present application, various sensors can be pre-installed at various key nodes of the power transmission system, such as temperature sensors, humidity sensors, current sensors, voltage sensors, data collection devices, etc. When it is necessary to monitor the working status of the power transmission system, the computer device can obtain sensor data in the power transmission system through the pre-installed various sensors, and use its own data collection software to automatically collect external environment data and historical operation and maintenance data.

[0065] S102, inputting sensor data and external environment data into a monitoring model to monitor the working status and obtain monitoring results.

[0066] Among them, the monitoring model is used to monitor the working status of the power transmission system. The monitoring model can be a neural network model or a machine learning model, a supervised network model or an unsupervised network model. For example, the monitoring model can adopt a long short-term memory network (LSTM) model, or a model developed based on machine learning frameworks such as TensorFlow and PyTorch. The monitoring results include normal power transmission system and abnormal power transmission system.

[0067] In the embodiment of the present application, the computer device can pre-train the initial monitoring model based on the sensor sample data and the external environment sample data to obtain a trained monitoring model. After the computer device obtains the sensor data, external environment data and historical operation and maintenance data in the power transmission system based on the above steps, the sensor data and external environment data can be directly input into the monitoring model to monitor the working status and obtain the monitoring results.

[0068] S103: When the monitoring result indicates that there is an abnormality in the power transmission system, the historical operation and maintenance data is input into the resource scheduling model for resource scheduling.

[0069] Among them, the resource scheduling model is used to schedule power resources. The resource scheduling model can be a neural network model or a machine learning model, a supervised network model or an unsupervised network model. The monitoring results include normal transmission system and abnormal transmission system.

[0070] In the embodiment of the present application, the computer device can pre-train the initial resource scheduling model based on the historical operation and maintenance sample data to obtain a trained resource scheduling model. After the computer device obtains the monitoring results based on the above steps, if the monitoring results indicate that there is an abnormality in the power transmission system, the historical operation and maintenance data is input into the resource scheduling model for resource scheduling.

[0071] The control method of the power transmission system provided in the embodiment of the present application obtains sensor data, external environment data and historical operation and maintenance data in the power transmission system, and then inputs the sensor data and external environment data into the monitoring model to monitor the working status, obtains the monitoring results, and when the monitoring results indicate that there is an abnormality in the power transmission system, inputs the historical operation and maintenance data into the resource scheduling model for resource scheduling. In the above method, since the method combines multi-dimensional data such as real-time monitoring data and historical operation and maintenance data to analyze the working status of the power transmission system, it realizes data fusion and efficient processing, so it improves the accuracy and completeness of the data to a certain extent, and also provides strong data support for subsequent decision support.

[0072] In one embodiment, a control method for a power transmission system is also provided. Figure 3 As shown, Figure 2 The method described in the embodiment also includes:

[0073] S201, input the monitoring results into the risk assessment model to perform risk assessment and obtain the assessment results.

[0074] The risk assessment model can be a neural network model or a machine learning model, and can be a supervised network model or an unsupervised network model. The assessment results include whether the transmission system is in a normal state, the transmission system is in an abnormal state, and whether the risk value when the transmission system is in an abnormal state is greater than a preset risk threshold.

[0075] In the embodiment of the present application, the computer device can pre-train the initial risk assessment model based on the sensor sample data and the external environment sample data to obtain a trained risk assessment model. After obtaining the monitoring results, the computer device can input the monitoring results into the risk assessment model for risk assessment to obtain the assessment results.

[0076] Correspondingly, when the computer device executes step S103 "when the monitoring result indicates that there is an abnormality in the power transmission system, inputting the historical operation and maintenance data into the resource scheduling model for resource scheduling", it specifically executes step S202:

[0077] S202: When the evaluation result indicates that an abnormality exists in the power transmission system and the risk value corresponding to the abnormality is greater than a preset risk threshold, the historical operation and maintenance data and the risk value corresponding to the abnormality are input into a resource scheduling model for resource scheduling.

[0078] In an embodiment of the present application, after the computer device obtains the evaluation result based on the above steps, it can further analyze whether the evaluation result is abnormal, and whether it is greater than a preset risk threshold under abnormal circumstances. If the evaluation result indicates that there is an abnormality in the transmission system and the risk value corresponding to the abnormality is greater than the preset risk threshold, the historical operation and maintenance data and the risk value corresponding to the abnormality are input into the resource scheduling model for resource scheduling. Specifically, the transformer load can be adjusted and the transmission path can be replanned.

[0079] In one embodiment, a control method for a power transmission system is also provided. Figure 4 As shown, Figure 3 The method described in the embodiment also includes:

[0080] S301, when the evaluation result indicates that there is an abnormality in the power transmission system and the risk value corresponding to the abnormality is greater than a preset risk threshold, display the scheduling strategy selection items.

[0081] Among them, the dispatch strategy options include at least one of adjusting transformer load, replanning transmission path, etc.

[0082] In an embodiment of the present application, after the computer device obtains the evaluation result based on the above steps, it can further analyze whether the evaluation result is abnormal, and whether it is greater than a preset risk threshold under abnormal circumstances. If the evaluation result indicates that there is an abnormality in the transmission system and the risk value corresponding to the abnormality is greater than the preset risk threshold, the scheduling strategy selection items can be displayed on the display interface to instruct the user to make a selection, wherein the display page can be a display interface on the computer device, or a display interface of other devices connected to the computer device, or a visual interactive platform developed using Web development technology (such as HTML5, CSS3, JavaScript), for example, using a data visualization library (such as D3.js, ECharts) to display the real-time operating status, historical trends, risk warnings and decision-making recommendations of the transmission system.

[0083] S302, in response to the user's selection operation based on the scheduling policy selection item, performing resource scheduling according to the scheduling policy indicated by the selection operation, and not executing step S202.

[0084] In the embodiment of the present application, after the user triggers the selection operation of the scheduling policy selection item, the computer device can perform resource scheduling according to the scheduling policy indicated by the selection operation without executing step S202.

[0085] In one embodiment, a control method for a power transmission system is also provided. Figure 5 As shown, Figure 2 The method described in the embodiment also includes:

[0086] S104, displaying the monitoring results, or outputting an alarm message when the monitoring results indicate that an abnormality exists in the power transmission system.

[0087] In the embodiment of the present application, after obtaining the monitoring results, the computer device can display the monitoring results on the display interface, such as viewing detailed data reports, adjusting operation and maintenance decision parameters, etc. Optionally, when the monitoring results indicate that there is an abnormality in the power transmission system, an alarm message can be output. Specifically, the alarm content can be displayed on the display interface, the alarm device can be alarmed, and relevant staff can be notified through text messages, emails, etc.

[0088] In one embodiment, a control method for a power transmission system is also provided. Figure 6 As shown, Figure 2 The method described in the embodiment also includes:

[0089] S401, preprocessing sensor data, external environment data and historical operation and maintenance data in the power transmission system to obtain preprocessed sensor data, external environment data and historical operation and maintenance data.

[0090] The preprocessing includes at least one of cleaning, normalization, missing value filling and outlier removal.

[0091] In the embodiment of the present application, after obtaining the sensor data, external environment data and historical operation and maintenance data in the power transmission system, the computer device can pre-process the sensor data, external environment data and historical operation and maintenance data in the power transmission system to obtain the pre-processed sensor data, external environment data and historical operation and maintenance data. Specifically, data cleaning processing, normalization processing, data fusion processing of data from different sources, missing value filling processing, outlier removal and other processing can be performed.

[0092] Correspondingly, when the computer device executes step S102 "inputting the sensor data and the external environment data into the monitoring model to monitor the working state and obtain the monitoring result", it specifically executes step S402:

[0093] S402, inputting the preprocessed sensor data, the preprocessed external environment data and the preprocessed historical operation and maintenance data into the monitoring model for monitoring to obtain monitoring results.

[0094] In an embodiment of the present application, after the computer device obtains the preprocessed sensor data, preprocessed external environment data and preprocessed historical operation and maintenance data in the power transmission system based on the above steps, the preprocessed sensor data, preprocessed external environment data and preprocessed historical operation and maintenance data can be input into the monitoring model for monitoring to obtain monitoring results.

[0095] In one embodiment, a method for training a monitoring model is also provided. Figure 7 As shown, the method includes:

[0096] S501, obtaining sensor sample data, external environment sample data and historical operation and maintenance sample data in the power transmission system.

[0097] In the embodiment of the present application, the computer device can obtain sensor sample data, external environment sample data and historical operation and maintenance sample data in the power transmission system through sensors installed on key power grid equipment. Specifically, it can collect current, voltage, temperature and other operating data in real time, and can collect multi-dimensional data such as power transmission load data, weather conditions, date types (weekdays, weekends, holidays) in the past year, and can integrate historical operation and maintenance records, including equipment maintenance, replacement and other information. In addition, external environment data such as weather conditions and geographic location are also collected. Optionally, big data technology can be used to pre-process the collected data to remove noise and outliers to ensure the accuracy of the data.

[0098] S502, training an initial monitoring model based on sensor sample data, external environment sample data and historical operation and maintenance sample data to obtain a monitoring model.

[0099] In an embodiment of the present application, the computer device can pre-use programming languages ​​such as Python and select machine learning frameworks such as TensorFlow and PyTorch to build an initial monitoring model. Specifically, it can use programming languages ​​such as Python and select machine learning frameworks such as TensorFlow and PyTorch to perform time series analysis on the collected historical operation and maintenance sample data, real-time sensor sample data, and external environment sample data, train a monitoring model for specific transmission lines and equipment, and automatically update the model regularly to adapt to changes in the transmission system and new data patterns.

[0100] In one embodiment, a method for training a resource scheduling model is also provided, including that a computer device can monitor the current, voltage, temperature and other parameters of a transmission line in real time through sensors, and then use big data analysis tools to analyze the real-time monitoring data and historical operation and maintenance records to identify deficiencies in resource allocation, and then apply a genetic algorithm to dynamically optimize the resource allocation of the transmission line according to the data analysis results, such as adjusting transformer loads and replanning transmission routes. Finally, through simulation and actual application, the effect of optimal resource allocation is evaluated, such as reducing energy consumption and improving transmission efficiency.

[0101] Based on all the above embodiments, a control method for a power transmission system is also provided, the method comprising:

[0102] S601, obtaining sensor sample data, external environment sample data and historical operation and maintenance sample data in the power transmission system.

[0103] S602, training an initial monitoring model based on sensor sample data, external environment sample data and historical operation and maintenance sample data to obtain a monitoring model.

[0104] S603, acquiring sensor data, external environment data and historical operation and maintenance data in the power transmission system.

[0105] S604, preprocessing the sensor data, external environment data and historical operation and maintenance data in the power transmission system to obtain preprocessed sensor data, external environment data and historical operation and maintenance data. The preprocessing includes at least one of cleaning, normalization, missing value filling and outlier removal.

[0106] S605, input the preprocessed sensor data, the preprocessed external environment data and the preprocessed historical operation and maintenance data into the monitoring model for monitoring to obtain monitoring results.

[0107] S606, input the monitoring results into the risk assessment model for risk assessment to obtain an assessment result. If the assessment result indicates that there is an abnormality in the power transmission system and the risk value corresponding to the abnormality is greater than the preset risk threshold, execute S607, otherwise execute S608.

[0108] S607, inputting historical operation and maintenance data and the risk value corresponding to the abnormality into the resource scheduling model for resource scheduling.

[0109] S608, displaying the scheduling policy selection items, and responding to the user's selection operation based on the scheduling policy selection items, performing resource scheduling according to the scheduling policy indicated by the selection operation, and not executing step S607.

[0110] S609, displaying the monitoring result, or outputting an alarm message when the monitoring result indicates that there is an abnormality in the power transmission system.

[0111] In the embodiment of the present application, first, the real-time data, historical data and environmental data of the power grid operation are collected through the sensors installed inside the power grid system, historical operation and maintenance records and external environmental data sources, and the data are preprocessed, including data cleaning, denoising and standardization; then, a load prediction model based on time series, an equipment status prediction model based on machine learning and a fault prediction model are constructed, and algorithms such as ARIMA, long short-term memory network (LSTM) and random forest are used to perform model training and prediction based on the preprocessed data; then, an optimization algorithm including decision trees and genetic algorithms is developed to automatically generate the optimal operation and maintenance scheduling and resource allocation plan according to the output of the above prediction model and the current power grid system state; then, an anomaly detection algorithm such as an isolation forest-based model is used to combine real-time monitoring data and historical accident data to construct a safety risk assessment model for power grid operation; finally, a user interaction platform is implemented, which provides functions such as detailed data report viewing and adjustment of operation and maintenance decision parameters, and displays load prediction, equipment status, fault prediction and safety risk assessment results through visualization tools for users to understand and apply.

[0112] The specific implementation of the optimization algorithm includes: using genetic algorithms to globally optimize the operation and maintenance scheduling and resource allocation of the power grid, and by defining the cost and risk functions of power grid operation and maintenance, with the goal of minimizing maintenance costs and reducing failure risks, automatically generating optimized scheduling plans including equipment maintenance plans; and the implementation details of the safety risk assessment model are: applying an anomaly detection algorithm based on isolation forests to analyze real-time monitoring data of the power grid to identify potential safety risks and abnormal conditions, and to evaluate and warn of risk factors that affect the stable operation of the power grid in real time, providing a scientific basis and early warning support for the safe operation of the power grid.

[0113] For a specific embodiment, the following will describe in detail how to use the collected sensor data, historical operation and maintenance data, and external environment data to implement the application of transmission algorithms based on big data analysis to develop a smart grid operation and maintenance management system.

[0114] (1) Data collection and preprocessing:

[0115] Sensor data: Install sensors at key nodes of the power grid to collect real-time data such as current, voltage, power factor, etc. For example, data can be collected every 5 minutes to ensure the real-time and accuracy of the data.

[0116] Historical operation and maintenance records: Organize the operation and maintenance records in the past 5 years, including equipment replacement, maintenance activities, fault records, etc. The data is stored in the form of a spreadsheet, and each record includes information such as date, equipment type, fault description, and treatment measures.

[0117] External environmental data: Obtain external environmental data related to power grid operation from the public weather service API, such as temperature, humidity, wind speed, etc. Considering the impact of meteorological conditions on power grid operation, the data is updated every hour.

[0118] (2) Building a monitoring model:

[0119] Load forecasting model: Using the load data of the past five years and relevant external factor data (such as temperature and holiday information), a machine learning algorithm (such as LSTM network) is used to build a load monitoring model to predict the grid load at various time points in the next 24 hours.

[0120] Equipment status prediction model: Analyze historical operation and maintenance records and sensor data, use random forest models to predict the future operating status of key equipment, and identify emerging failure trends.

[0121] (3) Development of optimization algorithms:

[0122] Operation and maintenance scheduling optimization: Based on load forecasting and equipment status prediction results, a genetic algorithm is used to generate the optimal equipment maintenance and replacement plan, with the goal of minimizing operation and maintenance costs while ensuring reliable operation of the power grid.

[0123] (4) Risk assessment model:

[0124] Anomaly detection: Utilize an anomaly detection algorithm based on isolation forest to analyze sensor data in real time, identify abnormal conditions in power grid operation, and promptly discover potential safety risks.

[0125] (5) Platform and user interaction function:

[0126] User interface: Develop a graphical user interface (GUI) to display real-time monitoring data, load forecasting results, equipment status and fault prediction information, and safety risk assessment reports. Users can adjust operation and maintenance decision parameters through the interface, such as adjusting the priority of maintenance plans, etc.

[0127] The comparison of the effects of the traditional power grid operation and maintenance management system and the smart grid operation and maintenance management system developed based on the method described in this embodiment is shown in the figure. Figure 8 As shown, where:

[0128] X-axis: Technical indicators (5 items in total)

[0129] - Data timeliness

[0130] - Data visualization level

[0131] - Heterogeneous multi-source data fusion

[0132] - Resource allocation and energy saving and consumption reduction optimization

[0133] - Safety and reliability of power grid operation

[0134] Y-axis: Performance rating (e.g., using a 1-10 rating scale)

[0135] Histogram content:

[0136] For each technical indicator, a bar chart of different colors is used to represent the effectiveness scores of existing technologies and power transmission algorithm application development solutions based on big data analysis. For example:

[0137] - Data timeliness: Existing technologies score 5, new solutions score 9 (light gray represents existing technologies, dark blue represents new solutions)

[0138] - Data visualization level: Existing technology score 6, new solution score 8 (also using different colors to distinguish)

[0139] - Heterogeneous multi-source data fusion: Existing technology score 4, new solution score 9

[0140] - Resource allocation and energy saving optimization: 7 points for existing technology and 10 points for new solutions

[0141] - Grid operation safety and reliability: Existing technology scored 6, and new solutions scored 9.

[0142] See the table below for details:

[0143]

[0144] The method described in the embodiments of the present application realizes data integration and sharing through the construction of a unified data platform, greatly improves the utilization efficiency and value of data, and can improve data utilization efficiency; uses intelligent prediction models to accurately predict potential problems in the power transmission system, realize early warning and prevention of accidents, improve system reliability, and enhance prediction and warning capabilities; the optimization decision-making algorithm based on big data analysis provides scientific decision-making support for the operation and maintenance scheduling and resource allocation of the power transmission system, improves operation and maintenance efficiency and resource utilization, and can optimize operation and maintenance decisions; a comprehensive safety risk assessment system can timely discover and assess safety risks in the power transmission process, provide strong support for the formulation of response measures, and strengthen safety risk management; through a visual interaction module, the interaction function between the platform and the user is realized, which can improve the management interaction experience; by adopting advanced data storage and processing technologies, it is possible to realize the The efficient storage and real-time processing of data ensures that the smart grid can obtain and analyze key information in a timely manner when facing massive data, thereby improving the response speed and decision-making efficiency of the power grid and solving the problem of data timeliness; by adopting more intuitive data display methods, such as text, pictures, etc., the complex power grid operation data is converted into an easy-to-understand form, which not only helps the operation and maintenance personnel to better understand the power grid status, but also improves the interaction efficiency with the system, and can improve the level of data visualization; the smart grid involves a variety of data sources and data structures. The above scheme realizes the fusion and efficient processing of data by constructing a heterogeneous multi-source data model, which not only improves the accuracy and integrity of the data, but also provides strong data support for subsequent decision support, and can realize the fusion of heterogeneous multi-source data; based on big data analysis, this embodiment can accurately predict the load changes and energy demand of the power grid, thereby realizing the optimal allocation of resources. At the same time, by real-time monitoring of the energy consumption of the power grid, it can also effectively evaluate the effect of energy saving and consumption reduction, promote the sustainable development of the power industry, and optimize resource allocation and energy saving and consumption reduction; by real-time monitoring of the power grid status and fault warning, the above scheme can timely discover and deal with potential safety hazards and reduce the probability of faults. At the same time, the optimization algorithm based on big data analysis can also help operation and maintenance personnel formulate more reasonable operation strategies, improve the operation efficiency and reliability of the power grid, and improve the safety and reliability of the power grid operation. In summary, this embodiment has achieved remarkable results in solving the technical problems existing in the prior art and provided strong technical support for the development of smart grids.

[0145] The methods described in the above steps are all described in the above embodiments. Please refer to the above description for details and will not be repeated here.

[0146] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0147] Based on the same inventive concept, the embodiment of the present application also provides a control device for a power transmission system for implementing the control method for a power transmission system involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of one or more power transmission system control devices provided below can refer to the limitations of the control method for the power transmission system above, and will not be repeated here.

[0148] In one embodiment, Fig. 9 As shown, a control device for a power transmission system is provided, comprising:

[0149] The acquisition module 10 is used to acquire sensor data, external environment data and historical operation and maintenance data in the power transmission system.

[0150] The monitoring module 11 is used to input the sensor data and the external environment data into the monitoring model to monitor the working status and obtain the monitoring results.

[0151] The scheduling module 12 is used to input the historical operation and maintenance data into the resource scheduling model for resource scheduling when the monitoring results indicate that there is an abnormality in the power transmission system.

[0152] In one embodiment, the control of the power transmission system further includes:

[0153] The evaluation module is used to input the monitoring results into the risk assessment model to perform risk assessment and obtain the evaluation results.

[0154] Correspondingly, the above-mentioned scheduling module is specifically used to input historical operation and maintenance data and the risk value corresponding to the abnormality into the resource scheduling model for resource scheduling when the evaluation result indicates that there is an abnormality in the transmission system and the risk value corresponding to the abnormality is greater than the preset risk threshold.

[0155] In one embodiment, the control of the power transmission system further includes:

[0156] The display unit is used to display the scheduling strategy selection items when the evaluation result indicates that there is an abnormality in the transmission system and the risk value corresponding to the abnormality is greater than a preset risk threshold.

[0157] The scheduling unit is used to respond to the user's selection operation based on the scheduling policy selection item, perform resource scheduling according to the scheduling policy indicated by the selection operation, and does not execute the step of inputting historical operation and maintenance data into the resource scheduling model for resource scheduling.

[0158] In one embodiment, the control of the power transmission system further includes any of the following modules:

[0159] The display module is used to display the monitoring results.

[0160] The alarm module is used to output alarm information when the monitoring results indicate that there is an abnormality in the power transmission system.

[0161] In one embodiment, the control of the power transmission system further includes:

[0162] The processing module is used to preprocess the sensor data, external environment data and historical operation and maintenance data in the power transmission system to obtain the preprocessed sensor data, external environment data and historical operation and maintenance data; the preprocessing includes at least one of cleaning processing, normalization processing, missing value filling processing and outlier removal processing.

[0163] Correspondingly, the above-mentioned monitoring module is specifically used to input the preprocessed sensor data, the preprocessed external environment data and the preprocessed historical operation and maintenance data into the monitoring model for monitoring to obtain monitoring results.

[0164] In one embodiment, the control of the power transmission system further includes:

[0165] The sample acquisition module is used to obtain sensor sample data, external environment sample data and historical operation and maintenance sample data in the power transmission system.

[0166] The training module is used to train the initial monitoring model based on sensor sample data, external environment sample data and historical operation and maintenance sample data to obtain a monitoring model.

[0167] Each module in the control device of the above power transmission system can be implemented in whole or in part by software, hardware or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each module.

[0168] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0169] Obtain sensor data, external environment data, and historical operation and maintenance data in the power transmission system;

[0170] Input the sensor data and external environment data into the monitoring model to monitor the working status and obtain the monitoring results;

[0171] When the monitoring results indicate that there are abnormalities in the transmission system, the historical operation and maintenance data is input into the resource scheduling model for resource scheduling.

[0172] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0173] Input the monitoring results into the risk assessment model to conduct risk assessment and obtain the assessment results;

[0174] When the monitoring results indicate that there is an abnormality in the transmission system, the historical operation and maintenance data is input into the resource scheduling model for resource scheduling, including:

[0175] When the evaluation result indicates that there is an abnormality in the transmission system and the risk value corresponding to the abnormality is greater than the preset risk threshold, the historical operation and maintenance data and the risk value corresponding to the abnormality are input into the resource scheduling model for resource scheduling.

[0176] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0177] When the evaluation result indicates that there is an abnormality in the transmission system and the risk value corresponding to the abnormality is greater than the preset risk threshold, the dispatch strategy selection options are displayed;

[0178] In response to the user's selection operation based on the scheduling policy selection item, resource scheduling is performed according to the scheduling policy indicated by the selection operation, and the step of inputting historical operation and maintenance data into the resource scheduling model for resource scheduling is not performed.

[0179] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0180] Display monitoring results;

[0181] When the monitoring results indicate that there is an abnormality in the power transmission system, an alarm message is output.

[0182] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0183] Preprocessing sensor data, external environment data and historical operation and maintenance data in the power transmission system to obtain preprocessed sensor data, external environment data and historical operation and maintenance data; the preprocessing includes at least one of cleaning processing, normalization processing, missing value filling processing and outlier removal processing;

[0184] Input sensor data and external environment data into the monitoring model to monitor the working status and obtain monitoring results, including:

[0185] The preprocessed sensor data, the preprocessed external environment data and the preprocessed historical operation and maintenance data are input into the monitoring model for monitoring to obtain the monitoring results.

[0186] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0187] Obtain sensor sample data, external environment sample data and historical operation and maintenance sample data in the power transmission system;

[0188] The initial monitoring model is trained based on sensor sample data, external environment sample data and historical operation and maintenance sample data to obtain a monitoring model.

[0189] The above embodiment provides a computer device, whose implementation principle and technical effect are similar to those of the above method embodiment, and will not be repeated here.

[0190] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0191] Obtain sensor data, external environment data, and historical operation and maintenance data in the power transmission system;

[0192] Input the sensor data and external environment data into the monitoring model to monitor the working status and obtain the monitoring results;

[0193] When the monitoring results indicate that there are abnormalities in the transmission system, the historical operation and maintenance data is input into the resource scheduling model for resource scheduling.

[0194] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0195] Input the monitoring results into the risk assessment model to conduct risk assessment and obtain the assessment results;

[0196] When the monitoring results indicate that there is an abnormality in the transmission system, the historical operation and maintenance data is input into the resource scheduling model for resource scheduling, including:

[0197] When the evaluation result indicates that there is an abnormality in the transmission system and the risk value corresponding to the abnormality is greater than the preset risk threshold, the historical operation and maintenance data and the risk value corresponding to the abnormality are input into the resource scheduling model for resource scheduling.

[0198] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0199] When the evaluation result indicates that there is an abnormality in the transmission system and the risk value corresponding to the abnormality is greater than the preset risk threshold, the dispatch strategy selection options are displayed;

[0200] In response to the user's selection operation based on the scheduling policy selection item, resource scheduling is performed according to the scheduling policy indicated by the selection operation, and the step of inputting historical operation and maintenance data into the resource scheduling model for resource scheduling is not performed.

[0201] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0202] Display monitoring results;

[0203] When the monitoring results indicate that there is an abnormality in the power transmission system, an alarm message is output.

[0204] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0205] Preprocessing sensor data, external environment data and historical operation and maintenance data in the power transmission system to obtain preprocessed sensor data, external environment data and historical operation and maintenance data; the preprocessing includes at least one of cleaning processing, normalization processing, missing value filling processing and outlier removal processing;

[0206] Input sensor data and external environment data into the monitoring model to monitor the working status and obtain monitoring results, including:

[0207] The preprocessed sensor data, the preprocessed external environment data and the preprocessed historical operation and maintenance data are input into the monitoring model for monitoring to obtain the monitoring results.

[0208] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0209] Obtain sensor sample data, external environment sample data and historical operation and maintenance sample data in the power transmission system;

[0210] The initial monitoring model is trained based on sensor sample data, external environment sample data and historical operation and maintenance sample data to obtain a monitoring model.

[0211] The above embodiment provides a computer-readable storage medium, whose implementation principle and technical effect are similar to those of the above method embodiment, and will not be repeated here.

[0212] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0213] Obtain sensor data, external environment data, and historical operation and maintenance data in the power transmission system;

[0214] Input the sensor data and external environment data into the monitoring model to monitor the working status and obtain the monitoring results;

[0215] When the monitoring results indicate that there are abnormalities in the transmission system, the historical operation and maintenance data is input into the resource scheduling model for resource scheduling.

[0216] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0217] Input the monitoring results into the risk assessment model to conduct risk assessment and obtain the assessment results;

[0218] When the monitoring results indicate that there is an abnormality in the transmission system, the historical operation and maintenance data is input into the resource scheduling model for resource scheduling, including:

[0219] When the evaluation result indicates that there is an abnormality in the transmission system and the risk value corresponding to the abnormality is greater than the preset risk threshold, the historical operation and maintenance data and the risk value corresponding to the abnormality are input into the resource scheduling model for resource scheduling.

[0220] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0221] When the evaluation result indicates that there is an abnormality in the transmission system and the risk value corresponding to the abnormality is greater than the preset risk threshold, the dispatch strategy selection options are displayed;

[0222] In response to the user's selection operation based on the scheduling policy selection item, resource scheduling is performed according to the scheduling policy indicated by the selection operation, and the step of inputting historical operation and maintenance data into the resource scheduling model for resource scheduling is not performed.

[0223] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0224] Display monitoring results;

[0225] When the monitoring results indicate that there is an abnormality in the power transmission system, an alarm message is output.

[0226] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0227] Preprocessing sensor data, external environment data and historical operation and maintenance data in the power transmission system to obtain preprocessed sensor data, external environment data and historical operation and maintenance data; the preprocessing includes at least one of cleaning processing, normalization processing, missing value filling processing and outlier removal processing;

[0228] Input sensor data and external environment data into the monitoring model to monitor the working status and obtain monitoring results, including:

[0229] The preprocessed sensor data, the preprocessed external environment data and the preprocessed historical operation and maintenance data are input into the monitoring model for monitoring to obtain the monitoring results.

[0230] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0231] Obtain sensor sample data, external environment sample data and historical operation and maintenance sample data in the power transmission system;

[0232] The initial monitoring model is trained based on sensor sample data, external environment sample data and historical operation and maintenance sample data to obtain a monitoring model.

[0233] The above embodiment provides a computer program product, whose implementation principle and technical effect are similar to those of the above method embodiment, and will not be repeated here.

[0234] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0235] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0236] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be construed as limiting the scope of the present application. It should be noted that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A control method for a power transmission system, It is characterized in that The method comprises: Obtain sensor data, external environment data, and historical operation and maintenance data in the power transmission system; Inputting the sensor data and the external environment data into a monitoring model to monitor the working state and obtain a monitoring result; When the monitoring result indicates that an abnormality exists in the power transmission system, the historical operation and maintenance data is input into a resource scheduling model for resource scheduling.

2. The method according to claim 1, It is characterized in that The method further comprises: Inputting the monitoring results into a risk assessment model to perform risk assessment and obtain an assessment result; When the monitoring result indicates that the power transmission system is abnormal, the historical operation and maintenance data is input into a resource scheduling model for resource scheduling, including: When the evaluation result indicates that an abnormality exists in the power transmission system and the risk value corresponding to the abnormality is greater than a preset risk threshold, the historical operation and maintenance data and the risk value corresponding to the abnormality are input into a resource scheduling model for resource scheduling.

3. The method according to claim 2, It is characterized in that The method further comprises: When the evaluation result indicates that an abnormality exists in the power transmission system and the risk value corresponding to the abnormality is greater than a preset risk threshold, displaying a scheduling strategy selection item; In response to the user's selection operation based on the scheduling policy selection item, resource scheduling is performed according to the scheduling policy indicated by the selection operation, and the step of inputting the historical operation and maintenance data into the resource scheduling model for resource scheduling is not performed.

4. The method according to claims 1-3, It is characterized in that The method further comprises any of the following: Displaying the monitoring results; When the monitoring result indicates that an abnormality exists in the power transmission system, an alarm message is output.

5. The method according to claim 1, It is characterized in that The method further comprises: Preprocessing the sensor data, external environment data and historical operation and maintenance data in the power transmission system to obtain preprocessed sensor data, external environment data and historical operation and maintenance data; the preprocessing includes at least one of cleaning processing, normalization processing, missing value filling processing and outlier removal processing; The step of inputting the sensor data and the external environment data into a monitoring model to monitor the working state and obtain a monitoring result includes: The preprocessed sensor data, the preprocessed external environment data and the preprocessed historical operation and maintenance data are input into the monitoring model for monitoring to obtain monitoring results.

6. The method according to claim 1, It is characterized in that The method further comprises: Acquire sensor sample data, external environment sample data and historical operation and maintenance sample data in the power transmission system; The initial monitoring model is trained based on the sensor sample data, the external environment sample data and the historical operation and maintenance sample data to obtain the monitoring model.

7. A control device for a power transmission system, It is characterized in that The device comprises: An acquisition module is used to acquire sensor data, external environment data, and historical operation and maintenance data in the power transmission system; A monitoring module, used for inputting the sensor data and the external environment data into a monitoring model to monitor the working state and obtain a monitoring result; The scheduling module is used to input the historical operation and maintenance data into a resource scheduling model for resource scheduling when the monitoring result indicates that there is an abnormality in the power transmission system.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program. It is characterized in that When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, It is characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, It is characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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