A new energy site safety monitoring method and system based on digital twin technology

Abstract

The present application discloses a method and system for monitoring the safety of new energy sites based on digital twin technology, which relates to the field of new energy monitoring technology. The main technical solution is: establishing multiple fault scenarios for each electrical device in the new energy site and the power transmission link where the electrical device is located under different fault conditions, and extracting the typical characteristic parameters of each fault scenario; using a digital twin model to simulate the typical characteristic parameters of each fault scenario, obtain the standard operating parameters corresponding to each fault scenario, and construct a mapping relationship library between the fault scenario and the standard operating parameters; collecting the actual operating parameters of each electrical device in the new energy site and the power transmission link where the electrical device is located, and comparing them in the mapping relationship library based on the actual operating parameters to obtain the fault diagnosis results; triggering the corresponding monitoring notification instructions according to the fault diagnosis results, and matching the disposal measures corresponding to the target fault scenario from the preset emergency disposal solution library.

Description

Technical Field

[0001] The present application relates to the field of new energy monitoring technology, and in particular to a new energy site safety monitoring method and system based on digital twin technology. Background Art

[0002] As global demand for clean energy continues to grow, the proportion of renewable energy generation (such as wind and solar power) in the energy mix is ​​gradually increasing. To ensure the safe and stable operation of these renewable energy sites, efficient monitoring and maintenance systems are becoming increasingly important. Digital twin technology, as an emerging technology, creates virtual replicas of physical entities, enabling real-time monitoring, analysis, prediction, and optimization of actual systems. However, efficient fault diagnosis and emergency response within complex renewable energy sites remains a challenge.

[0003] Currently, most existing new energy site safety monitoring systems that incorporate digital twin technology rely primarily on traditional SCADA (Supervisory Control and Data Acquisition) and other on-site monitoring methods. While these systems can monitor equipment operating status and provide early warning of potential problems to a certain extent, they lack the ability to predict faults, accurately determine fault type and location, and provide effective emergency responses. This makes it difficult to effectively address complex fault conditions, resulting in low accuracy and reliability in new energy site safety monitoring. Summary of the Invention

[0004] In view of the above problems, this application provides a new energy site safety monitoring method and system based on digital twin technology. The main purpose is to improve the accuracy of fault diagnosis and the timeliness of emergency response, effectively deal with complex fault conditions, and thus ensure the accuracy and reliability of new energy site safety monitoring.

[0005] To solve the above technical problems, this application proposes the following solutions:

[0006] In a first aspect, the present application provides a new energy site safety monitoring method based on digital twin technology, the method comprising:

[0007] Establish multiple fault scenarios for each electrical device in the new energy site and the power transmission link where the electrical device is located under different fault conditions, and extract typical characteristic parameters for each fault scenario;

[0008] Using a digital twin model to simulate the typical characteristic parameters of each fault scenario, obtain standard operating parameters corresponding to each fault scenario, and construct a mapping relationship library between the fault scenarios and the standard operating parameters, wherein the standard operating parameters are used to characterize the expected values ​​corresponding to each key indicator of each fault scenario when it occurs;

[0009] Collecting actual operating parameters of each electrical device and the power generation and transmission link where the electrical device is located within the new energy site, and comparing the actual operating parameters in the mapping relationship library to obtain a fault diagnosis result, wherein the actual operating parameters represent actual values ​​corresponding to each of the key indicators, and the fault diagnosis result includes at least a target fault scenario;

[0010] The corresponding monitoring notification instruction is triggered according to the fault diagnosis result, and the handling measures corresponding to the target fault scenario are matched from the preset emergency handling solution library, and the emergency handling solution library contains handling measures corresponding to various types of fault scenarios.

[0011] In a second aspect, the present application provides a new energy site safety monitoring system based on digital twin technology, the system comprising:

[0012] The first processing unit is configured to establish multiple fault scenarios for each electrical device in the new energy site and the power generation and transmission link where the electrical device is located under different fault conditions, and extract typical characteristic parameters for each fault scenario;

[0013] a second processing unit, configured to simulate the typical characteristic parameters of each fault scenario using a digital twin model, obtain standard operating parameters corresponding to each fault scenario, and construct a mapping relationship library between the fault scenarios and the standard operating parameters, wherein the standard operating parameters are used to characterize the expected values ​​corresponding to each key indicator of each fault scenario when it occurs;

[0014] a diagnostic unit configured to collect actual operating parameters of each electrical device within the new energy site and the power generation and transmission link where the electrical device is located, and compare the actual operating parameters in the mapping relationship library to obtain a fault diagnosis result, wherein the actual operating parameters represent actual values ​​corresponding to each of the key indicators, and the fault diagnosis result includes at least a target fault scenario;

[0015] The alarm handling unit is used to trigger the corresponding monitoring notification instruction according to the fault diagnosis result, and match the handling measures corresponding to the target fault scenario from the preset emergency handling solution library, which contains handling measures corresponding to various types of fault scenarios.

[0016] In order to achieve the above-mentioned purpose, according to the third aspect of the present application, a storage medium is provided, which includes a stored program, wherein when the program is running, the device where the storage medium is located is controlled to execute the new energy field safety monitoring method based on digital twin technology of the above-mentioned first aspect.

[0017] In order to achieve the above-mentioned purpose, according to the fourth aspect of the present application, a processor is provided, which is used to run a program, wherein when the program is running, the new energy site safety monitoring method based on digital twin technology of the above-mentioned first aspect is executed.

[0018] By means of the above technical solution, the present application provides a method and system for monitoring the safety of a new energy site based on digital twin technology. When it is necessary to monitor the safety of a new energy site based on digital twin technology, first, multiple fault scenarios are established for each electrical device and the power transmission link where the electrical device is located within the new energy site under different fault conditions, and typical characteristic parameters of each fault scenario are extracted. Then, the typical characteristic parameters of each fault scenario are simulated using a digital twin model to obtain standard operating parameters corresponding to each fault scenario, and a mapping relationship library between the fault scenarios and the standard operating parameters is constructed. The standard operating parameters are used to represent the expected values ​​corresponding to each key indicator when each fault scenario occurs. Next, the actual operating parameters of each electrical device and the power transmission link where the electrical device is located within the new energy site are collected and compared with the mapping relationship library based on the actual operating parameters to obtain a fault diagnosis result. The actual operating parameters represent the actual values ​​corresponding to each key indicator. The fault diagnosis result includes at least a target fault scenario. Finally, a corresponding monitoring notification instruction is triggered based on the fault diagnosis result, and a response measure corresponding to the target fault scenario is matched from a preset emergency response solution library. The emergency response solution library contains response measures corresponding to various fault scenarios. The technical solution provided in this application establishes different fault scenarios and uses digital twin models to simulate the actual operating behaviors of the fault scenarios, obtains the standard operating parameters corresponding to each fault scenario, and constructs a mapping relationship library between fault scenarios and standard operating parameters. It can not only identify faults more accurately, but also quickly locate the source of the fault and trigger corresponding monitoring notification instructions by comparing the actual operating parameters and the standard operating parameters, thereby improving the efficiency and safety of fault handling. In addition, it also provides the ability to automatically match the optimal emergency response plan, ensuring that the most appropriate measures can be taken when facing different types of faults, reducing unnecessary losses, improving the safety management level of the entire new energy site, and effectively responding to complex fault conditions, thereby ensuring the accuracy and reliability of safety monitoring of the new energy site.

[0019] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present application. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0021] Figure 1 A flow chart of a new energy site safety monitoring method based on digital twin technology provided in an embodiment of the present application is shown;

[0022] Figure 2 The following is a block diagram of a new energy site safety monitoring system based on digital twin technology provided in an embodiment of the present application;

[0023] Figure 3 A block diagram of another new energy site safety monitoring system based on digital twin technology provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0024] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are described in detail below in conjunction with the drawings in the specification.

[0025] In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the present application may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present application. Therefore, the present application is not limited to the specific embodiments disclosed below.

[0026] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present application. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0027] Example 1, with reference to Figure 1 This is the first embodiment of this application. This embodiment provides a new energy site safety monitoring method based on digital twin technology. This method can improve the accuracy of fault diagnosis and the timeliness of emergency response, effectively deal with complex fault conditions, and thus ensure the accuracy and reliability of new energy site safety monitoring. The method includes:

[0028] 101. Establish a fault scenario library for each electrical equipment in the new energy site and the power transmission links where the electrical equipment is located under different fault conditions, and extract the typical characteristic parameters of each fault scenario.

[0029] Among them, the typical characteristic parameters include comprehensive static characteristic parameters and dynamic characteristic parameter change rates. The comprehensive static characteristic parameters and dynamic characteristic parameter change rates are calculated based on environmental characteristic parameters, power generation characteristic parameters and operation level characteristic parameters.

[0030] In this embodiment, new energy sites include, but are not limited to, wind farms and solar photovoltaic power stations. Electrical equipment refers to the various devices and technical components used for power generation, transmission, distribution, and consumption within the new energy site, including but not limited to power generation equipment, transmission equipment, distribution equipment, and auxiliary equipment such as generators, photovoltaic modules, transformers, switchgear, and distribution cabinets. The power generation and transmission link refers to all transmission links from the power generation equipment to the end user, including but not limited to booster stations, transmission lines, step-down stations, and distribution networks. There is a corresponding relationship between electrical equipment and the power generation and transmission link.

[0031] In actual applications, high-precision sensors (such as temperature sensors, vibration sensors, current and voltage sensors, etc.) will be deployed on various electrical equipment in the new energy field and on the power generation and transmission links where they are located to collect data from all key locations in real time. The corresponding typical characteristic parameters can be determined through the collected data. The typical characteristic parameters can effectively characterize the key indicators of fault characteristics. For example, environmental characteristics are determined by environmental indicator data (temperature, humidity, wind speed / light intensity, etc.), power generation characteristics are determined by power generation indicator data (output power, energy conversion efficiency, etc.), and operating level characteristics are determined by equipment operation indicator data (vibration frequency, current and voltage fluctuations, equipment response time, etc.). In this step, the existing system's operation records and maintenance logs are used to obtain past fault cases and their related data, and the corresponding fault cases are extracted from them. By classifying the fault cases (such as mechanical failure, electrical failure, environmental impact, etc.), and setting fault conditions of different severity for different development stages, there is at least one fault condition for each fault type to obtain multiple fault scenarios. Specifically, appropriate simulation tools (such as MATLAB / Simulink, ANSYS, etc.) can be selected to simulate various fault scenarios, adjust multiple variables, and generate a series of different fault conditions. A detailed description document is written for each fault scenario, including but not limited to the fault name, location, scope of impact, duration, and other information. The state changes when the fault occurs are intuitively presented through charts, images, etc. to help understand the fault characteristics. At the same time, the ambient temperature, humidity, wind speed / light intensity at the time of the fault are recorded, the changing trend of output power before and after the fault is tracked, the degree of energy loss is assessed, the difference in energy conversion efficiency between normal operating conditions and fault conditions is calculated and compared, the vibration frequency of mechanical equipment, the current and voltage fluctuations in the power system, the response speed of the control system to instructions, etc. are detected. Based on the data corresponding to the key indicators mentioned above, the environmental characteristic parameters, power generation characteristic parameters, and operating level characteristic parameters corresponding to each fault scenario are extracted. Importance weights are introduced to comprehensively calculate the above-mentioned characteristic parameters to obtain comprehensive static characteristic parameters for a comprehensive description of each fault scenario. Considering that the above-mentioned characteristic parameters will show specific patterns over time, time series analysis is performed to capture dynamic characteristics, and the change rate of dynamic characteristic parameters corresponding to the environmental characteristic parameters, power generation characteristic parameters, and operating level characteristic parameters is obtained. After multiple fault scenarios are established, a cross-validation method can be used to test the effectiveness of the fault scenarios to ensure that they can accurately reproduce the actual faults.

[0032] It should be noted that the specific execution process of establishing multiple fault scenarios for each electrical equipment in the new energy site and the power transmission link where the electrical equipment is located under different fault conditions and extracting the typical characteristic parameters of each fault scenario is as follows:

[0033] Relevant fault cases are extracted from the historical maintenance records corresponding to each electrical equipment and the power generation and transmission link where the electrical equipment is located; the fault types are classified based on the relevant fault cases, and at least one fault operating condition is set for each fault type to obtain multiple fault scenarios, each fault scenario having different fault severity and development stages; the environmental characteristic parameters, power generation characteristic parameters and operation level characteristic parameters corresponding to each fault scenario are extracted, and the comprehensive static characteristic parameters and dynamic characteristic parameter change rates corresponding to each fault scenario are calculated based on the environmental characteristic parameters, power generation characteristic parameters and operation level characteristic parameters; the comprehensive static characteristic parameters and dynamic characteristic parameter change rates are used as typical characteristic parameters of each fault scenario.

[0034] Each fault scenario has different fault severity and development stages. For example, for gearbox wear, three conditions can be set: light wear, moderate wear, and heavy wear.

[0035] Combined with the above description of the comprehensive static characteristic parameters and the dynamic characteristic parameter change rate, the corresponding calculation expression is as follows:

[0036] Calculate the comprehensive static characteristic parameters. The specific expression is:

[0037] C(t)=ω1E(t)+ω2P(t)+ω3R(t);

[0038] Among them, C(t) represents the comprehensive static characteristic parameter at a certain time t, E(t) is the environmental characteristic parameter, P(t) is the power generation characteristic parameter, R(t) is the operation level characteristic parameter, ω1, ω2 and ω3 are the importance weights of the environmental characteristic parameter, power generation characteristic parameter and operation level characteristic parameter respectively;

[0039] Calculate the rate of change of dynamic characteristic parameters. The specific expressions are:

[0040]

[0041] Among them, ΔE(t), ΔP(t), and ΔR(t) represent the rates of change of the environmental characteristic parameters, power generation characteristic parameters, and operating level characteristic parameters within the time period Δt, respectively; E(t) and E(t-Δt), P(t) and P(t-Δt), and R(t) and R(t-Δt) are the characteristic parameter values ​​of the environmental characteristic parameters, power generation characteristic parameters, and operating level characteristic parameters at the current time and the previous time point, respectively.

[0042] It should be noted that by calculating the comprehensive static characteristic parameter C(t), we can fully consider the impact of multiple factors such as the environment, power generation, and operating level, which helps to more accurately identify fault scenarios later. By calculating the change rates of the dynamic characteristic parameters ΔE(t), ΔP(t), and ΔR(t), we can capture the trend and speed of fault development, thereby providing early warning of potential faults. This means that anomalies can be detected promptly and accurately at the early stage of a fault, reducing the duration of the fault's impact on the system.

[0043] Take wind farms as an example: Gearbox wear: By combining the static characteristic parameters and the change rate of dynamic characteristic parameters, gearbox wear can be detected in advance to avoid sudden shutdowns; blade breakage: By monitoring the vibration and stress changes of the blades, the risk of blade breakage can be predicted and damaged parts can be replaced in time.

[0044] Taking photovoltaic power stations as an example: Inverter failure: By monitoring the inverter's temperature, current and other parameters, inverter failure can be detected in a timely manner to reduce power loss; photovoltaic panel aging: By monitoring the output power changes of the photovoltaic panels, the aging degree of the photovoltaic panels can be predicted and the maintenance plan can be optimized.

[0045] Taking transformers and transmission lines as an example: Insulation damage: By monitoring the insulation resistance and oil temperature of the transformer, the risk of insulation damage can be predicted and short-circuit accidents can be avoided; Winding short circuit: By monitoring the current and voltage changes of the transmission line, winding short circuit problems can be discovered in time to ensure the safety of power transmission.

[0046] 102. Use the digital twin model to simulate the typical characteristic parameters of each fault scenario, obtain the standard operating parameters corresponding to each fault scenario, and build a mapping relationship library between fault conditions and standard operating parameters.

[0047] Among them, the standard operating parameters are used to characterize the expected values ​​corresponding to each key indicator when each fault scenario occurs.

[0048] In this step, a corresponding digital twin model is constructed based on the new energy site's topographic data, landform data, building layout data, equipment layout data, environmental status data, and equipment operating status data. This ensures that the digital twin model is completely consistent with the geometric layout, equipment configuration, and operating parameters of the actual physical system. This enables the digital twin model to accurately reflect the actual physical characteristics of each electrical device within the new energy site and the power transmission chain within it, namely, its structure, state, and behavior. Specifically, high-precision 3D scanners, drone aerial photography, and other technical means can be used to obtain detailed topographic and equipment location information.

[0049] A digital twin model is used to simulate various fault scenarios, achieving typical steady-state characteristic parameters. Through simulation, the expected values ​​of the key indicators corresponding to each typical characteristic parameter when each fault scenario occurs, i.e., standard operating parameters, can be obtained. Key indicators correspond to the typical characteristic parameters in step 101 and specifically include environmental indicators (temperature, humidity, wind speed / light intensity, etc.), power generation indicators (output power, energy conversion efficiency, etc.), and equipment operating level indicators (vibration frequency, current and voltage fluctuations, equipment response time, etc.). These expected values ​​reflect the normal operating state of each electrical device and the power transmission link in which they are located when the fault scenario occurs. In other words, these standard operating parameters define how each key indicator of each electrical device and the power transmission link in which they are located "should" perform under a specific fault scenario, providing a benchmark for subsequent fault diagnosis. The expected values ​​of each key indicator in the digital twin model for each fault scenario are recorded as the standard operating parameters corresponding to each fault scenario. All standard operating parameters are structured and stored in a predefined format. A mapping relationship library between fault scenarios and standard operating parameters is constructed to obtain a mapping relationship between each fault scenario and each standard operating parameter.

[0050] It should be noted that the specific construction process of the digital twin model corresponding to the new energy site is: before using the digital twin model to simulate the typical characteristic parameters of each fault scenario, obtain the standard operating parameters corresponding to each fault scenario, and construct a mapping relationship library between the fault scenarios and the standard operating parameters, the method also includes: collecting three-dimensional spatial data and operating environment data of the new energy site; constructing an initial three-dimensional model corresponding to the new energy site based on the three-dimensional spatial data; mapping the operating environment data to the initial three-dimensional model to obtain the digital twin model corresponding to the new energy site.

[0051] Three-dimensional spatial data includes topographic data, landform data, building layout data, and equipment layout data. Operating environment data includes environmental status data and equipment operating status data. For topographic data, high-precision LiDAR (Light Detection and Ranging) or drone aerial photography can be used to obtain precise three-dimensional point cloud data of terrain such as wind farms and photovoltaic power plants. Using Geographic Information System (GIS) software, surface features (such as slope and altitude) are recorded to ensure the model reflects the actual topography. Using CAD drawings or on-site measurement tools, the location, dimensions, and structural information of all buildings are recorded. The specific location, model, and installation method of each electrical device are determined through on-site inspections or manufacturer-provided technical specifications. Environmental status data: Various sensors (such as temperature sensors, humidity sensors, anemometers, and light intensity meters) are deployed to collect real-time environmental status data, ensuring coverage of key locations throughout the site to obtain comprehensive environmental information. Operating status parameters (such as current, voltage, power output, and vibration frequency) of each electrical device are collected through a SCADA system or other monitoring platform. High-frequency data collection and real-time transmission are ensured to facilitate subsequent analysis.

[0052] In this step, professional 3D modeling software (such as BIM, AutoCAD, and SolidWorks) is used to create precise geometric models based on survey data, including the layout and shape of all facilities, such as wind farms and photovoltaic power plants. Based on the collected 3D spatial data, an initial 3D model of the new energy site is constructed, including component-level models of electrical equipment. This allows faults to be traced back to the specific faulty component on the device, enabling component-level fault location. Environmental status data and operating status parameters are transmitted to the digital twin model in real time to keep the model state synchronized with the actual system. In the digital twin model, the status of each electrical device, including but not limited to operating parameters, fault alarms, and maintenance records, is updated in real time. Based on changes in real-time data, the model's parameter settings are dynamically adjusted to ensure that they accurately reflect the actual operating behavior of each electrical device within the new energy site and the power generation and transmission links in which they are located. A user interface (UI) is also developed to visually display the status and operating data of the digital twin model. Graphics and charts are used to help operation and maintenance personnel quickly understand the current status of the system.

[0053] It should be noted that the specific process of building a mapping relationship library between fault conditions and standard operating parameters is as follows: using the digital twin model to simulate the typical characteristic parameters of each fault scenario, and determining the standard operating parameters corresponding to each fault scenario based on the simulation results; building a mapping relationship library between fault scenarios and standard operating parameters based on a preset mapping function, the mapping relationship library contains the mapping relationship between each fault scenario and each standard operating parameter, and the specific expression of the mapping function is:

[0054]

[0055] Among them, M opt (f) is a preset mapping function used to establish the mapping relationship between each fault scenario and each standard operating parameter, M(f) is the initial mapping function, f(x i ) represents the typical characteristic parameter corresponding to the i-th fault scenario, y i is the standard operating parameter under the i-th fault scenario, and N is the number of samples.

[0056] In this step, select a suitable mapping function M(f), such as linear regression, polynomial regression, neural network, etc. Define the input variable x i and the output variable y i , where x i It is the typical characteristic parameter corresponding to the fault scenario, which is specifically the actual observation data or simulation data under the i-th fault scenario. It contains a series of observation values ​​or simulation values ​​corresponding to each key indicator under the fault scenario, which is used to describe the state of the simulated fault scenario. i It is the standard operating parameter under the fault scenario. Specifically, it is a series of expected values ​​corresponding to each key indicator under the i-th fault scenario obtained by digital twin model simulation, which is used to characterize the ideal behavior of the device or system under a specific fault scenario. By minimizing the loss function and historical data, this mapping function M(f) is optimized to obtain the optimized mapping function M opt (f) and use it as a preset mapping function. By using the optimized mapping function, the mapping relationship between each fault scenario and its standard operating parameters can be established one by one, thereby forming a mapping relationship library.

[0057] By constructing a digital twin model corresponding to the new energy site and using the digital twin model to simulate each fault scenario, the standard operating parameters can be obtained more accurately to ensure the accuracy of subsequent fault diagnosis. At the same time, a mapping relationship library between fault scenarios and standard operating parameters is constructed in combination with preset mapping functions, so that the system can quickly find the corresponding operating parameters when encountering new fault scenarios, thereby achieving adaptive adjustment, improving the flexibility and reliability of the system, and reducing the need for human intervention.

[0058] 103. Collect the actual operating parameters of each electrical equipment in the new energy site and the power transmission link where the electrical equipment is located, and compare them in the mapping relationship library based on the actual operating parameters to obtain fault diagnosis results.

[0059] The actual operating parameters represent the actual values ​​corresponding to the key indicators, and the fault diagnosis results at least include the target fault scenario.

[0060] In this step, real-time operating parameters can be collected in real time through various sensors and monitoring systems deployed on site. High-frequency collection (such as several times per second) and low-frequency collection (such as once per minute) can be set, and the data can be transmitted to the system's central server or edge computing node through wired or wireless communication networks (such as Ethernet, LoRa, NB-IoT, etc.).

[0061] The actual values ​​corresponding to each key indicator represented by the actual operating parameters are compared with the expected values ​​corresponding to each key indicator represented by each standard operating parameter in the mapping relationship library. A similarity score can be calculated using similarity formulas such as Euclidean distance, cosine distance, and Manhattan distance. A similarity threshold is set. When the similarity score between a standard operating parameter and the actual operating parameter falls below the threshold, a fault scenario corresponding to the standard operating parameter exists, and this fault scenario can be used as the target fault scenario. To ensure more accurate and detailed subsequent alerts, the faulty electrical equipment, power transmission link, and specific components associated with the fault can be further identified within the electrical equipment and power transmission links involved in the target fault scenario. Specifically, component-level key parameters can be obtained and compared with the corresponding abnormality determination thresholds to obtain component-level abnormal parameters. The target electrical equipment and power transmission link corresponding to the component-level abnormal parameters are then identified as the faulty electrical equipment and power transmission link. The target fault scenario, similarity score, faulty electrical equipment, and faulty power transmission link are summarized to obtain accurate and comprehensive fault diagnosis results.

[0062] It should be noted that the specific execution process of collecting the actual operating parameters of each electrical equipment and the power transmission link where the electrical equipment is located in the new energy site, and comparing them in the mapping relationship library based on the actual operating parameters to obtain the fault diagnosis results is as follows: calculating the similarity score between the actual operating parameters and each standard operating parameter in the mapping relationship library; judging whether the similarity score is lower than the preset similarity threshold; if so, determining the fault scenario corresponding to the standard operating parameter lower than the similarity threshold as the target fault scenario according to the mapping relationship, and taking the electrical equipment involved in the target fault scenario as the target electrical equipment, and taking the power transmission link where the target electrical equipment involved in the target fault scenario is located as the target power transmission link; obtaining the component-level key parameters corresponding to the target electrical equipment and the target power transmission link, and comparing the component-level key parameters with the abnormality judgment threshold corresponding to the component-level key parameters to obtain the component-level abnormal parameters; taking the target electrical equipment and the target power transmission link corresponding to the component-level abnormal parameters as the faulty electrical equipment and the faulty power transmission link, and taking the target fault scenario, the faulty electrical equipment, the faulty power transmission link and the similarity score as the fault diagnosis result.

[0063] In this step, the component-level key parameters can be a subset of the set composed of various key indicators, or they can be customized according to actual conditions, which are used to diagnose and locate faults more precisely. The component-level key parameters can be associated with specific components of the component-level electrical equipment model in the digital twin model, so as to more accurately identify the specific location of the fault. It is worth noting that the key indicators are data collected from various electrical equipment and the power generation and transmission link level where they are located, including but not limited to temperature, vibration frequency, current and voltage, power output, load rate, etc., while the component-level key parameters are data collected from the specific component level, which are more detailed and specific. For example, for the gearbox of a wind turbine, the component-level key parameters include the temperature, vibration frequency, lubrication condition, etc. inside the gearbox.

[0064] Calculate the similarity score. The specific expression is:

[0065]

[0066] Among them, S(x,y) represents the parameter vector x corresponding to the actual operating parameters i The parameter vector y corresponding to the standard operating parameters i The similarity score between them is n, and n is the dimension of the parameter vector.

[0067] This formula accurately quantifies the difference between two vectors, helping to identify which parameters have significantly changed. The value of S(x,y) ranges from [0 to 1], where 0 indicates complete dissimilarity and 1 indicates complete similarity. By calculating the absolute difference and maximum value, it can mitigate the influence of outliers to a certain extent, ensuring the stability of the results. It also considers the scale of different features, allowing for reasonable comparison of data across different dimensions. It also comprehensively evaluates changes in multiple parameters, providing a comprehensive assessment of operational status and effectively improving the accuracy of fault diagnosis.

[0068] Calculate the abnormality judgment threshold. The specific expression is:

[0069] T=μ+k·σ;

[0070] Among them, T is the abnormal judgment threshold corresponding to the component-level key parameter, μ is the historical mean corresponding to the component-level key parameter, σ is the historical standard deviation corresponding to the component-level key parameter, and k is the safety factor.

[0071] The threshold T is the mean μ and standard deviation σ obtained based on the historical data corresponding to the key parameters at the component level, which can make the obtained abnormal judgment threshold change accurate. By adjusting the safety factor k, the sensitivity of the threshold can be flexibly set according to actual needs to flexibly adapt to different abnormality detection needs, thereby improving the accuracy and rationality of determining faulty electrical equipment and faulty power generation and transmission links, effectively reducing the false alarm rate, and improving the reliability of the system. In addition, by combining key indicators with component-level key parameters, multi-level fault diagnosis from equipment level, link level to component level can be realized, which not only improves the accuracy of fault detection, but also can quickly locate specific faulty components, thereby ensuring the effectiveness of subsequent disposal measures.

[0072] 104. Trigger corresponding monitoring notification instructions based on the fault diagnosis results, and match the corresponding disposal measures for the target fault scenario from the preset emergency disposal solution library.

[0073] Among them, the emergency response solution library contains corresponding response measures for various fault scenarios.

[0074] It should be noted that the specific execution process of triggering the corresponding monitoring notification instructions based on the fault diagnosis results is: determining the fault type corresponding to the target fault scenario based on the faulty electrical equipment and the faulty power generation and transmission link, and determining the severity corresponding to the target fault scenario based on the similarity score; generating alarm information corresponding to the new energy field based on the fault type, severity, faulty electrical equipment and faulty power generation and transmission link, and executing the corresponding monitoring notification instructions based on the alarm information.

[0075] In this step, since the fault diagnosis results obtained in step 103 include the target fault scenario, faulty electrical equipment, faulty power transmission link, and similarity score, the fault type corresponding to the target fault scenario can be determined based on the faulty electrical equipment and faulty power transmission link. The severity can also be quantified based on the similarity score. Detailed alarm information for the new energy site is generated based on the target fault scenario, fault type, severity, faulty electrical equipment, and faulty power transmission link. For example, the alarm information may include the following: fault location (e.g., wind turbine, substation), fault type (e.g., gearbox wear, inverter failure), fault severity (e.g., minor, moderate, severe), potential impact (e.g., potential downtime, economic loss), etc. Based on the alarm information, the corresponding monitoring notification command is automatically triggered. The notification channels for this command include, but are not limited to, text messages, emails, instant messaging tools, and phone calls. The corresponding recipients (e.g., operations and maintenance personnel, technical personnel, management) are pre-defined, and different response levels (e.g., minor warning, severe warning, emergency alarm) are set based on the severity of the fault.

[0076] It should be noted that the specific execution process of automatically matching the disposal measures corresponding to the target fault scenario from the preset emergency disposal solution library is: determining the corresponding candidate disposal measures in the emergency disposal solution library according to the target fault scenario; determining the emergency disposal priority corresponding to each candidate disposal measure according to the preset evaluation indicators; taking the candidate disposal measure with the highest emergency disposal priority as the target disposal measure, and executing the disposal steps included in the target disposal measure.

[0077] In this step, the corresponding disposal measures for various fault scenarios obtained in the previous steps are collected and sorted in advance to form a detailed emergency disposal solution library. The emergency disposal solution library covers fault description, cause analysis, processing steps, required tools and materials, precautions, etc. Use intelligent matching algorithms such as rule-based matching and fuzzy matching to compare the target fault scenario with the entries in the emergency disposal solution library to filter out candidate disposal measures that match the target fault scenario from the emergency disposal solution library. Each candidate disposal measure should include specific processing steps, required resources, and expected effects. For example, candidate disposal measures: A. Stop the operation of the faulty wind turbine, B. Arrange for technical personnel to go to the site for inspection, C. Prepare the tools and materials required to replace the gearbox, D. Precautions (such as safety protection measures, backup of important data, etc.).

[0078] Multiple evaluation indicators are pre-defined, including at least one of response speed, resource consumption, risk reduction, recovery effectiveness, and economic cost. Response speed indicates how quickly the response can be implemented; resource consumption indicates the amount of manpower, material, and financial resources required; risk reduction indicates how much potential risk the response can mitigate; recovery effectiveness indicates whether normal operations can be effectively restored; and economic cost indicates the cost-effectiveness of the response. Each candidate response is scored based on the pre-defined evaluation indicators. Taking all evaluation indicators into consideration, the emergency response priority for each candidate response is determined. For example, a weighted scoring method can be used, assigning different weights based on the importance of different indicators. The candidate response with the highest emergency response priority is designated as the target response. Ensure that this response resolves the issue as quickly as possible, minimizes resource consumption, maximizes risk reduction, achieves the best recovery effect, and minimizes economic cost. After determining the target response, assign relevant personnel to carry out the response according to the specific steps of the target response. Use project management tools to track the progress of the task in real time to ensure the effective execution of each step. The execution time, results, and feedback for each step are recorded to facilitate subsequent evaluation and summary, thereby updating the emergency response plan library.

[0079] The specific expression for the emergency response priority of each candidate response measure is:

[0080]

[0081] Among them, P j represents the priority of the jth candidate disposal measure, r ij is the influence of the jth candidate disposal measure on the ith evaluation indicator, ω i is the importance weight of each evaluation indicator.

[0082] By triggering corresponding monitoring notification instructions based on the fault diagnosis results and automatically matching the target disposal measures corresponding to the target fault scenario from the preset emergency disposal solution library, it ensures that the most appropriate measures can be taken when facing different types of faults, reducing unnecessary losses, improving the safety management level of the entire new energy site, and effectively responding to complex fault conditions, thereby improving the reliability and responsiveness of the system.

[0083] Based on the above Figure 1 As can be seen from the implementation method, the present application provides a new energy site safety monitoring method based on digital twin technology. When it is necessary to perform safety monitoring of the new energy site based on digital twin technology, first, multiple fault scenarios are established for each electrical device in the new energy site and the power transmission link where the electrical device is located under different fault conditions, and typical characteristic parameters of each fault scenario are extracted. Then, the typical characteristic parameters of each fault scenario are simulated using a digital twin model to obtain standard operating parameters corresponding to each fault scenario, and a mapping relationship library between the fault scenario and the standard operating parameters is constructed. The standard operating parameters are used to represent the expected values ​​corresponding to each key indicator when each fault scenario occurs. Then, the actual operating parameters of each electrical device in the new energy site and the power transmission link where the electrical device is located are collected, and compared with the mapping relationship library based on the actual operating parameters to obtain a fault diagnosis result. The actual operating parameters represent the actual values ​​corresponding to each key indicator. The fault diagnosis result includes at least the target fault scenario. Finally, the corresponding monitoring notification instruction is triggered according to the fault diagnosis result, and the disposal measures corresponding to the target fault scenario are matched from the preset emergency disposal solution library. The emergency disposal solution library contains disposal measures corresponding to various fault scenarios. The technical solution provided in this application establishes different fault scenarios and uses digital twin models to simulate the actual operating behaviors of the fault scenarios, obtains the standard operating parameters corresponding to each fault scenario, and constructs a mapping relationship library between fault scenarios and standard operating parameters. It can not only identify faults more accurately, but also quickly locate the source of the fault and trigger corresponding monitoring notification instructions by comparing the actual operating parameters and the standard operating parameters, thereby improving the efficiency and safety of fault handling. In addition, it also provides the ability to automatically match the optimal emergency response plan, ensuring that the most appropriate measures can be taken when facing different types of faults, reducing unnecessary losses, improving the safety management level of the entire new energy site, and effectively responding to complex fault conditions, thereby ensuring the accuracy and reliability of safety monitoring of the new energy site.

[0084] Example 2, reference Figure 2 and Figure 3 , which is the second embodiment of this application, as a Figure 1 The implementation of the method embodiment shown in the figure provides a new energy site safety monitoring system based on digital twin technology. The system is used to improve the accuracy of fault diagnosis and the timeliness of emergency response, effectively deal with complex fault conditions, and thus ensure the accuracy and reliability of new energy site safety monitoring. The embodiment of this system corresponds to the aforementioned method embodiment. For ease of reading, this embodiment will no longer repeat the details of the aforementioned method embodiment one by one, but it should be clear that the system in this embodiment can correspond to all the contents of the aforementioned method embodiment. The system includes:

[0085] The first processing unit 21 is used to establish multiple fault scenarios for each electrical device in the new energy site and the power transmission link where the electrical device is located under different fault conditions, and extract typical characteristic parameters of each fault scenario;

[0086] The second processing unit 22 is configured to simulate the typical characteristic parameters of each fault scenario using a digital twin model, obtain standard operating parameters corresponding to each fault scenario, and construct a mapping relationship library between the fault conditions and the standard operating parameters, wherein the standard operating parameters are used to characterize the expected values ​​corresponding to each key indicator of each fault scenario when it occurs;

[0087] The diagnostic unit 23 is configured to collect actual operating parameters of each electrical device and the power generation and transmission link in the new energy site, and compare the actual operating parameters in the mapping relationship library to obtain a fault diagnosis result, wherein the actual operating parameters represent actual values ​​corresponding to each key indicator, and the fault diagnosis result includes at least a target fault scenario;

[0088] The alarm handling unit 24 is used to trigger the corresponding monitoring notification instruction according to the fault diagnosis result, and match the handling measures corresponding to the target fault scenario from the preset emergency handling solution library, which contains handling measures corresponding to each type of fault condition.

[0089] Further, such as Figure 3 As shown, the system further includes:

[0090] The acquisition unit 25 is configured to acquire three-dimensional spatial data and operating environment data of the new energy site before simulating the typical characteristic parameters of each fault scenario using the digital twin model to obtain standard operating parameters corresponding to each fault scenario and constructing a mapping relationship library between the fault conditions and the standard operating parameters, wherein the three-dimensional spatial data includes topographic data, landform data, building layout data, and equipment layout data, and the operating environment data includes environmental status data and equipment operating status data;

[0091] A construction unit 26 is configured to construct an initial three-dimensional model corresponding to the new energy site based on the three-dimensional spatial data, wherein the initial three-dimensional model includes a component-level electrical equipment model;

[0092] The mapping unit 27 is used to map the operating environment data to the initial three-dimensional model to obtain the digital twin model corresponding to the new energy field.

[0093] Further, such as Figure 3 As shown, the first processing unit 21 includes:

[0094] An extraction module 211 is configured to extract relevant fault cases from the historical maintenance records corresponding to each of the electrical devices and the power generation and transmission links where the electrical devices are located;

[0095] A first processing module 212 is configured to classify fault types based on the relevant fault cases, and set at least one fault operating condition for each fault type to obtain multiple fault scenarios, each of which has a different fault severity and development stage;

[0096] The second processing module 213 is configured to extract the environmental characteristic parameters, power generation characteristic parameters, and operation level characteristic parameters corresponding to each fault scenario, and calculate the comprehensive static characteristic parameters and dynamic characteristic parameter change rate corresponding to each fault scenario based on the environmental characteristic parameters, power generation characteristic parameters, and operation level characteristic parameters;

[0097] The first determining module 214 is configured to use the comprehensive static characteristic parameter and the dynamic characteristic parameter change rate as the typical characteristic parameter of each fault scenario.

[0098] Further, such as Figure 3 As shown, the second processing module 213 includes:

[0099] Calculate the comprehensive static characteristic parameters, the specific expression is:

[0100] C(t)=ω1E(t)+ω2P(t)+ω3R(t);

[0101] Among them, C(t) represents the comprehensive static characteristic parameter at a certain time t, E(t) is the environmental characteristic parameter, P(t) is the power generation characteristic parameter, R(t) is the operation level characteristic parameter, ω1, ω2 and ω3 are the importance weights corresponding to the environmental characteristic parameter, power generation characteristic parameter and operation level characteristic parameter respectively;

[0102] Calculate the change rate of the dynamic characteristic parameter, the specific expressions are:

[0103]

[0104] Among them, ΔE(t), ΔP(t), and ΔR(t) represent the rates of change of the environmental characteristic parameters, power generation characteristic parameters, and operating level characteristic parameters within the time period Δt, respectively; E(t) and E(t-Δt), P(t) and P(t-Δt), and R(t) and R(t-Δt) are the characteristic parameter values ​​of the environmental characteristic parameters, power generation characteristic parameters, and operating level characteristic parameters at the current time and the previous time point, respectively.

[0105] Further, such as Figure 3 As shown, the second processing unit 22 includes:

[0106] A simulation module 221 is configured to simulate the typical characteristic parameters of each fault scenario using the digital twin model, and determine the standard operating parameters corresponding to each fault scenario based on the simulation results;

[0107] The construction module 222 constructs the mapping relationship library between the fault scenario and the standard operating parameters according to a preset mapping function, and obtains the mapping relationship between each fault scenario and each standard operating parameter. The specific expression of the mapping function is:

[0108]

[0109] Among them, M opt (f) is a preset mapping function used to establish the mapping relationship between each fault scenario and each standard operating parameter, M(f) is the initial mapping function, f(x i ) represents the input feature vector corresponding to the i-th fault scenario, y i is the standard operating parameter under the i-th fault scenario, and N is the number of samples.

[0110] Further, such as Figure 3 As shown, the diagnosis unit 23 includes:

[0111] A calculation module 231 calculates a similarity score between the actual operating parameter and each of the standard operating parameters in the mapping relationship library;

[0112] A determination module 232 determines whether the similarity score is lower than a preset similarity threshold;

[0113] A second determining module 233 is configured to, if yes, determine the fault scenario corresponding to the standard operating parameter lower than the similarity threshold as a target fault scenario based on the mapping relationship, and use the electrical equipment involved in the target fault scenario as the target electrical equipment, and use the power transmission link where the target electrical equipment involved in the target fault scenario is located as the target power transmission link;

[0114] The detection module 234 obtains component-level key parameters corresponding to the target electrical device and the target power transmission link, and compares the component-level key parameters with abnormality determination thresholds corresponding to the component-level key parameters to obtain component-level abnormality parameters;

[0115] The third determination module 235 takes the target electrical equipment and target power transmission link corresponding to the component-level abnormal parameters as the faulty electrical equipment and the faulty power transmission link, and takes the target fault scenario, the faulty electrical equipment, the faulty power transmission link, and the similarity score as the fault diagnosis result.

[0116] Further, such as Figure 3 As shown, the similarity score is calculated, and the specific expression is:

[0117]

[0118] Among them, S(x,y) represents the parameter vector x corresponding to the actual operating parameters i The parameter vector y corresponding to the standard operating parameters i The similarity score between them, n is the dimension of the parameter vector;

[0119] Calculate the abnormality determination threshold, the specific expression is:

[0120] T=μ+k·σ;

[0121] Among them, T is the abnormal judgment threshold corresponding to the component-level key parameter, μ is the historical mean corresponding to the component-level key parameter, σ is the historical standard deviation corresponding to the component-level key parameter, and k is the safety factor.

[0122] Further, such as Figure 3 As shown, the alarm handling unit 24 includes:

[0123] A fourth determination module 241 is configured to determine a fault type corresponding to the target fault scenario based on the faulty electrical equipment and the faulty power generation and transmission link, and determine a severity similarity score corresponding to the target fault scenario based on the similarity score;

[0124] The generation module 242 is used to generate alarm information corresponding to the new energy field according to the target fault scenario, the fault type, the severity, the faulty electrical equipment and the faulty power generation and transmission link, and execute the corresponding monitoring notification instruction based on the alarm information.

[0125] Further, such as Figure 3 As shown, the alarm handling unit 24 includes:

[0126] A fifth determining module 243 is configured to determine corresponding candidate handling measures in the emergency handling solution library according to the target fault scenario;

[0127] a sixth determination module 244 configured to determine an emergency response priority corresponding to each candidate response measure based on a preset evaluation metric, wherein the evaluation metric includes at least one of response speed, resource consumption, risk reduction, recovery effect, and economic cost;

[0128] The third processing module 245 is configured to take the candidate disposal measure with the highest emergency disposal priority as a target disposal measure and execute the disposal steps included in the target disposal measure.

[0129] Furthermore, the embodiment of the present application also provides a storage medium, which is used to store a computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the above Figure 1 The new energy site safety monitoring method based on digital twin technology is described in.

[0130] Furthermore, the embodiment of the present application also provides a processor, which is used to run a program, wherein the program executes the above Figure 1 The new energy site safety monitoring method based on digital twin technology is described in.

[0131] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0132] It is understood that the related features in the above methods and systems can be referenced to each other. In addition, the terms "first" and "second" in the above embodiments are used to distinguish between the embodiments, and do not represent the advantages and disadvantages of the embodiments.

[0133] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0134] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems may also be used together with the teachings herein. Based on the above description, it is apparent that the structure required for constructing such systems is suitable. In addition, the present application is not directed to any specific programming language. It should be understood that various programming languages ​​may be utilized to implement the present application described herein, and the description of the specific languages ​​above is provided for the purpose of disclosing the preferred embodiment of the present application.

[0135] In addition, the memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0136] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0137] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or boxes.

[0138] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system that is implemented in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0139] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0140] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0141] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0142] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0143] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0144] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0145] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A new energy field safety monitoring method based on digital twin technology, characterized in that: The method comprises: Establish multiple fault scenarios for each electrical device in the new energy site and the power transmission link where the electrical device is located under different fault conditions, and extract typical characteristic parameters for each fault scenario; Using a digital twin model to simulate the typical characteristic parameters of each fault scenario, obtain standard operating parameters corresponding to each fault scenario, and construct a mapping relationship library between the fault scenarios and the standard operating parameters, wherein the standard operating parameters are used to characterize the expected values ​​corresponding to each key indicator of each fault scenario when it occurs; Collecting actual operating parameters of each electrical device and the power generation and transmission link where the electrical device is located within the new energy site, and comparing the actual operating parameters in the mapping relationship library to obtain a fault diagnosis result, wherein the actual operating parameters represent actual values ​​corresponding to each of the key indicators, and the fault diagnosis result includes at least a target fault scenario; triggering corresponding monitoring notification instructions based on the fault diagnosis results, and matching treatment measures corresponding to the target fault scenario from a preset emergency treatment solution library, wherein the emergency treatment solution library contains treatment measures corresponding to various fault scenarios; The method of establishing multiple fault scenarios for each electrical device in the new energy site and the power transmission link where the electrical device is located under different fault conditions, and extracting typical characteristic parameters of each fault scenario, includes: Extracting relevant fault cases from the historical maintenance records corresponding to each of the electrical devices and the power generation and transmission links where the electrical devices are located; Classifying fault types based on the relevant fault cases, and setting at least one fault operating condition for each fault type to obtain a plurality of fault scenarios, each of which has a different fault severity and development stage; Extracting environmental characteristic parameters, power generation characteristic parameters, and operating level characteristic parameters corresponding to each of the fault scenarios, and calculating the comprehensive static characteristic parameters and dynamic characteristic parameter change rates corresponding to each of the fault scenarios based on the environmental characteristic parameters, the power generation characteristic parameters, and the operating level characteristic parameters; The comprehensive static characteristic parameter and the change rate of the dynamic characteristic parameter are used as the typical characteristic parameters of each fault scenario.

2. The method according to claim 1, characterized in that Before simulating the typical characteristic parameters of each fault scenario using a digital twin model, obtaining standard operating parameters corresponding to each fault scenario, and constructing a mapping relationship library between the fault scenarios and the standard operating parameters, the method further includes: Collecting three-dimensional spatial data and operating environment data of the new energy site, wherein the three-dimensional spatial data includes topographic data, landform data, building layout data, and equipment layout data, and the operating environment data includes environmental status data and equipment operating status data; Constructing an initial three-dimensional model corresponding to the new energy site based on the three-dimensional spatial data, wherein the initial three-dimensional model includes a component-level electrical equipment model; The operating environment data is mapped to the initial three-dimensional model to obtain the digital twin model corresponding to the new energy field.

3. The method according to claim 1, characterized in that The calculating of the comprehensive static characteristic parameters and dynamic characteristic parameter change rates corresponding to each fault scenario based on the environmental characteristic parameters, the power generation characteristic parameters, and the operating level characteristic parameters includes: Calculate the comprehensive static characteristic parameters, the specific expression is: C(t)=ω1E(t)+ω2P(t)+ω3R(t); Among them, C(t) represents the comprehensive static characteristic parameter at a certain time t, E(t) is the environmental characteristic parameter, P(t) is the power generation characteristic parameter, R(t) is the operation level characteristic parameter, ω1, ω2 and ω3 are the importance weights corresponding to the environmental characteristic parameter, power generation characteristic parameter and operation level characteristic parameter respectively; Calculate the change rate of the dynamic characteristic parameter, the specific expressions are: Among them, ΔE(t), ΔP(t), and ΔR(t) represent the rates of change of the environmental characteristic parameters, power generation characteristic parameters, and operating level characteristic parameters within the time period Δt, respectively; E(t) and E(t-Δt), P(t) and P(t-Δt), and R(t) and R(t-Δt) are the characteristic parameter values ​​of the environmental characteristic parameters, power generation characteristic parameters, and operating level characteristic parameters at the current time and the previous time point, respectively.

4. The method according to claim 1, wherein The constructing of a mapping relationship library between the fault scenarios and the standard operating parameters includes: Using the digital twin model to simulate the typical characteristic parameters of each fault scenario, and determining the standard operating parameters corresponding to each fault scenario based on the simulation results; The mapping relationship library between the fault scenario and the standard operating parameters is constructed according to a preset mapping function. The mapping relationship library contains a mapping relationship between each fault scenario and each standard operating parameter. The specific expression of the mapping function is: Among them, M opt (f) is a preset mapping function used to establish the mapping relationship between each fault scenario and each standard operating parameter, M(f) is the initial mapping function, f(x i ) represents the typical characteristic parameter corresponding to the i-th fault scenario, y i is the standard operating parameter under the i-th fault scenario, and N is the number of samples.

5. The method according to claim 4, characterized in that The collecting of actual operating parameters of each electrical device in the new energy site and the power generation and transmission link where the electrical device is located, and comparing the actual operating parameters in the mapping relationship library to obtain a fault diagnosis result, includes: Calculating a similarity score between the actual operating parameter and each of the standard operating parameters in the mapping relationship library; Determining whether the similarity score is lower than a preset similarity threshold; If so, determining the fault scenario corresponding to the standard operating parameters lower than the similarity threshold as the target fault scenario according to the mapping relationship, and taking the electrical equipment involved in the target fault scenario as the target electrical equipment, and taking the power transmission link where the target electrical equipment involved in the target fault scenario is located as the target power transmission link; Obtaining component-level key parameters corresponding to the target electrical equipment and the target power generation and transmission link, and comparing the component-level key parameters with abnormality determination thresholds corresponding to the component-level key parameters to obtain component-level abnormality parameters; The target electrical equipment and target power transmission link corresponding to the component-level abnormal parameters are taken as the faulty electrical equipment and the faulty power transmission link, and the target fault scenario, the faulty electrical equipment, the faulty power transmission link and the similarity score are taken as the fault diagnosis result.

6. The method according to claim 5, characterized in that The similarity score is calculated as follows: Among them, S(x,y) represents the parameter vector x corresponding to the actual operating parameters i The parameter vector y corresponding to the standard operating parameters i The similarity score between them, n is the dimension of the parameter vector; Calculate the abnormality determination threshold, the specific expression is: T=μ+k·σ; Among them, T is the abnormal judgment threshold corresponding to the component-level key parameter, μ is the historical mean corresponding to the component-level key parameter, σ is the historical standard deviation corresponding to the component-level key parameter, and k is the safety factor.

7. The method according to claim 5, characterized in that The triggering of corresponding monitoring notification instructions according to the fault diagnosis result includes: Determining a fault type corresponding to the target fault scenario based on the faulty electrical equipment and the faulty power generation and transmission link, and determining a severity corresponding to the target fault scenario based on the similarity score; Based on the fault type, the severity, the faulty electrical equipment and the faulty power generation and transmission link, alarm information corresponding to the new energy field is generated, and the corresponding monitoring notification instruction is executed based on the alarm information.

8. The method according to claim 7, characterized in that The matching of the handling measures corresponding to the target fault scenario from the preset emergency handling solution library includes: Determine corresponding candidate disposal measures in the emergency disposal solution library according to the target failure scenario; Determining the emergency response priority corresponding to each candidate response measure based on preset evaluation indicators, wherein the evaluation indicators include at least one of response speed, resource consumption, risk reduction, recovery effect, and economic cost; The candidate disposal measure with the highest emergency disposal priority is used as the target disposal measure, and the disposal steps included in the target disposal measure are executed.

9. A new energy field safety monitoring system based on digital twin technology, characterized by: The system comprises: The first processing unit is configured to establish multiple fault scenarios for each electrical device in the new energy site and the power generation and transmission link where the electrical device is located under different fault conditions, and extract typical characteristic parameters for each fault scenario; a second processing unit, configured to simulate the typical characteristic parameters of each fault scenario using a digital twin model, obtain standard operating parameters corresponding to each fault scenario, and construct a mapping relationship library between the fault scenarios and the standard operating parameters, wherein the standard operating parameters are used to characterize the expected values ​​corresponding to each key indicator of each fault scenario when it occurs; a diagnostic unit configured to collect actual operating parameters of each electrical device within the new energy site and the power generation and transmission link where the electrical device is located, and compare the actual operating parameters in the mapping relationship library to obtain a fault diagnosis result, wherein the actual operating parameters represent actual values ​​corresponding to each of the key indicators, and the fault diagnosis result includes at least a target fault scenario; an alarm handling unit, configured to trigger a corresponding monitoring notification instruction according to the fault diagnosis result, and match a handling measure corresponding to the target fault scenario from a preset emergency handling solution library, wherein the emergency handling solution library contains handling measures corresponding to various fault scenarios; The first processing unit includes: An extraction module, configured to extract relevant fault cases from historical maintenance records corresponding to each of the electrical devices and the power generation and transmission links where the electrical devices are located; A first processing module is configured to classify fault types based on the relevant fault cases, and set at least one fault operating condition for each fault type to obtain a plurality of fault scenarios, each of which has a different fault severity and development stage; a second processing module, configured to extract environmental characteristic parameters, power generation characteristic parameters, and operating level characteristic parameters corresponding to each of the fault scenarios, and calculate, based on the environmental characteristic parameters, power generation characteristic parameters, and operating level characteristic parameters, a comprehensive static characteristic parameter and a dynamic characteristic parameter change rate corresponding to each of the fault scenarios; The first determining module is configured to use the comprehensive static characteristic parameter and the change rate of the dynamic characteristic parameter as the typical characteristic parameter of each fault scenario.

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