New energy field safety monitoring method and system based on digital twinborn technology

Abstract

The invention discloses a new energy field safety monitoring method and system based on a digital twinborn technology, and relates to the technical field of new energy monitoring. According to the main technical scheme, the method comprises the steps of establishing a plurality of fault scenes of electrical equipment in a new energy field and a power generation and transmission link where the electrical equipment is located under different fault working conditions, and extracting typical feature parameters of each fault scene; simulating typical characteristic parameters of each fault scene by using a digital twin model to obtain standard operation parameters corresponding to each fault scene, and constructing a mapping relation library between the fault scenes and the standard operation parameters; acquiring actual operation parameters of each electrical device in the new energy field and the power generation and transmission link where the electrical device is located, and performing comparison in the mapping relation library based on the actual operation parameters to obtain a fault diagnosis result; and triggering a corresponding monitoring notification instruction according to the fault diagnosis result, and matching a disposal measure corresponding to the target fault scene from a preset emergency disposal scheme 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 the global demand for clean energy continues to increase, the proportion of renewable energy generation (such as wind power and solar energy) in the energy structure is gradually increasing. In order to ensure the safe and stable operation of these renewable energy sites, efficient monitoring and maintenance systems have become particularly important. As an emerging technical means, digital twin technology achieves real-time monitoring, analysis, prediction and optimization of actual systems by creating virtual copies of physical entities. However, in complex renewable energy sites, how to efficiently perform fault diagnosis and emergency response remains a challenge.

[0003] At present, most of the existing technologies that combine digital twin technology with new energy site safety monitoring systems mainly rely on traditional SCADA (Supervisory Control and Data Acquisition) and other on-site monitoring methods. Although they can monitor the operating status of equipment and warn of potential problems to a certain extent, they are insufficient in predicting the occurrence of faults, accurately determining the type and location of faults, and providing effective emergency responses. It is difficult to effectively deal with complex fault conditions, resulting in low accuracy and reliability of new energy site safety monitoring. Summary of the invention

[0004] In view of the above problems, the present 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] In order 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 of 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 of each fault scenario;

[0008] 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 build 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 in the new energy field 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, wherein the actual operating parameters represent the actual values ​​corresponding to each of the key indicators, and the fault diagnosis result at least includes 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 each type of fault scenario.

[0011] In the 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 used to establish multiple fault scenarios of each electrical device in the new energy field and the power generation and transmission link where the electrical device is located under different fault conditions, and extract typical characteristic parameters of each fault scenario;

[0013] A second processing unit is used to simulate the typical characteristic parameters of each of the fault scenarios using a digital twin model, obtain standard operating parameters corresponding to each of the fault scenarios, 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 of the fault scenarios when it occurs;

[0014] A diagnostic unit, used to collect actual operating parameters of each electrical device in the new energy field 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 the actual values ​​corresponding to each of the key indicators, and the fault diagnosis result at least includes a target fault scenario;

[0015] An alarm handling unit is used to trigger a corresponding monitoring notification instruction according to the fault diagnosis result, and match the handling measures corresponding to the target fault scenario from a preset emergency handling solution library, wherein the emergency handling solution library contains handling measures corresponding to each type of fault scenario.

[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 site 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 scheme, the present application provides a method and system for safety monitoring of new energy sites based on digital twin technology. When it is necessary to conduct safety monitoring of new energy sites based on digital twin technology, first, multiple fault scenarios of each electrical device in the new energy site and the power transmission link where the electrical device is located under different fault conditions are established, and typical characteristic parameters of each fault scenario are extracted. Then, the typical characteristic parameters of each fault scenario are simulated by using the digital twin model to obtain the 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 characterize 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 in the mapping relationship library based on the actual operating parameters to obtain the fault diagnosis results. The actual operating parameters characterize the actual values ​​corresponding to each key indicator. The fault diagnosis results at least include 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 the present application establishes different fault scenarios, 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 the fault scenarios and the 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 with 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, and improving the safety management level of the entire new energy site. It can effectively respond 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 of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying 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 A block diagram of a new energy site safety monitoring system based on digital twin technology provided in an embodiment of the present application is shown;

[0023] Figure 3 A block diagram of the composition 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 objects, 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, but the present application may also be implemented in other ways different from those described herein, and 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" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present application. The term "in one embodiment" that appears in different places in 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, reference Figure 1 , which is the first embodiment of this application, provides a new energy site safety monitoring method based on digital twin technology, which 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 typical characteristic parameters for each fault scenario.

[0029] Among them, typical characteristic parameters include comprehensive static characteristic parameters and dynamic characteristic parameter change rates, and 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, the new energy site includes but is not limited to wind farms, solar photovoltaic power stations, etc. Electrical equipment refers to various devices and technical components used for power generation, transmission, distribution and consumption in 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, distribution cabinets, etc., and the power 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, etc. There is a corresponding relationship between electrical equipment and power transmission links.

[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 at 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 operation records and maintenance logs of the existing system 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 faults, electrical faults, environmental impacts, etc.), and setting fault conditions of different severity for different development stages, there is at least one fault condition for a fault type, so as 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. Write a detailed description document for each fault scenario, including but not limited to the fault name, location, scope of impact, duration, etc., and intuitively present the state changes when the fault occurs through charts, images, etc. to help understand the fault characteristics. At the same time, record the ambient temperature, humidity, wind speed / light intensity when the fault occurs, track the change trend of output power before and after the fault, evaluate the degree of energy loss, calculate and compare the difference in energy conversion efficiency between normal working conditions and fault conditions, detect the vibration frequency of mechanical equipment, the current and voltage fluctuations in the power system, the control system's response speed to instructions, etc., and extract the environmental characteristic parameters, power generation characteristic parameters, and operating level characteristic parameters corresponding to each fault scenario based on the data corresponding to the above-mentioned key indicators. By introducing importance weights to comprehensively calculate the above-mentioned characteristic parameters, comprehensive static characteristic parameters can be obtained to comprehensively describe 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 dynamic characteristic parameter change rates corresponding to the environmental characteristic parameters, power generation characteristic parameters, and operating level characteristic parameters are obtained. After multiple fault scenarios are established, the cross-validation method can be used to test the effectiveness of the fault scenario to ensure that it can accurately reproduce the actual fault.

[0032] It should be noted that the specific execution process of establishing multiple fault scenarios of 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 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 operating conditions can be set: light wear, medium 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 change rate of dynamic characteristic parameters. The specific expressions are:

[0040]

[0041] Among them, ΔE(t), ΔP(t), and ΔR(t) respectively represent the change rates of environmental characteristic parameters, power generation characteristic parameters, and operating level characteristic parameters within the time period Δt, and 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 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), the influence of multiple factors such as environment, power generation and operation level can be fully considered, which is helpful for more accurate identification of fault scenarios in the future. By calculating the dynamic characteristic parameter change rate ΔE(t), ΔP(t) and ΔR(t), the trend and speed of fault development can be captured, so as to warn potential faults in advance, that is, the anomaly can be detected in time and accurately at the early stage of the fault, reducing the impact time of the fault 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, the wear of the gearbox can be discovered 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] Take photovoltaic power stations as an example: Inverter failure: By monitoring the temperature, current and other parameters of the inverter, inverter failure can be discovered in time to reduce power loss; Photovoltaic panel aging: By monitoring the output power changes of photovoltaic panels, the aging degree of photovoltaic panels can be predicted and maintenance plans can be optimized.

[0045] Take 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, the corresponding digital twin model can be constructed based on the terrain data, landform data, building layout data, equipment layout data, environmental status data and equipment operation status data of the new energy site to ensure that the digital twin model is completely consistent with the geometric layout, equipment configuration and operating parameters of the actual physical system, so that the digital twin model can accurately reflect the actual physical characteristics of each electrical equipment in the new energy site and its power generation and transmission link, that is, structure, state and behavior. Specifically, high-precision 3D scanners, drone aerial photography and other technical means can be used to obtain detailed terrain and equipment location information.

[0049] The digital twin model is used to simulate each fault scenario so that it reaches the typical characteristic parameters of the steady state. Through simulation, the expected values ​​of each key indicator corresponding to each typical characteristic parameter when each fault scenario occurs can be obtained, that is, the standard operating parameters. Among them, the key indicators correspond to the typical characteristic parameters in step 101, specifically including environmental indicators (temperature, humidity, wind speed / light intensity, etc.), power generation indicators (output power, energy conversion efficiency, etc.) and equipment operation level indicators (vibration frequency, current and voltage fluctuations, equipment response time, etc.). The expected value reflects the normal operation state of each electrical device and the power transmission link where the electrical device is located when the fault scenario occurs, that is, the standard operating parameter is used to define how each key indicator of each electrical device and the power transmission link where the electrical device is located "should" perform under a specific fault scenario, so as to provide a benchmark for subsequent fault diagnosis. The expected values ​​corresponding to each key indicator in the digital twin model of each fault scenario at this time are recorded as the standard operating parameters corresponding to each fault scenario. All standard operating parameters are structured and stored in a predefined format, and 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] Among them, the three-dimensional spatial data includes terrain data, landform data, building layout data and equipment layout data, and the operating environment data includes environmental status data and equipment operating status data. For terrain data, high-precision laser radar (LiDAR) or drone aerial photography technology can be used to obtain accurate three-dimensional point cloud data of terrain such as wind farms and photovoltaic power stations. Through geographic information system (GIS) software, record surface features (such as slope, altitude, etc.) to ensure that the model can reflect the actual landform. Use CAD drawings or on-site measurement tools to record the location, size and structural information of all buildings. Determine the specific location, model and installation method of each electrical equipment through on-site inspections or technical specifications provided by the manufacturer. Environmental status data: deploy a variety of sensors (such as temperature sensors, humidity sensors, anemometers, light intensity meters, etc.) to collect environmental status data in real time, ensuring that key locations of the entire site are covered to obtain comprehensive environmental information. Collect the operating status parameters of each electrical equipment (such as current voltage, power output, vibration frequency, etc.) through the SCADA system or other monitoring platforms. Ensure high-frequency data collection and real-time transmission for subsequent analysis.

[0052] In this step, professional 3D modeling software (such as BIM, AutoCAD, SolidWorks, etc.) is used to create accurate geometric models based on surveying and mapping data, including the layout and shape of all facilities such as wind farms and photovoltaic power stations. Based on the collected 3D spatial data, the initial 3D model corresponding to the new energy site is constructed, including the component-level electrical equipment model. By constructing the component-level electrical equipment model, the fault can be traced back to the specific faulty component on the specific faulty equipment when the fault occurs, and the fault location at the component level can be realized. The 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 equipment is updated in real time, including but not limited to operating parameters, fault alarms, and maintenance records. According to the changes in real-time data, the parameter settings in the model are dynamically adjusted to ensure that it can accurately reflect the actual operating behavior of each electrical equipment in the new energy site and the power generation and transmission link where the electrical equipment is located. At the same time, a user interface (UI) is developed to intuitively display the status and operating data of the digital twin model. Through graphics, charts, etc., it helps operation and maintenance personnel quickly understand the current situation of the system.

[0053] It should be noted that the specific process of constructing the 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 according to the simulation results; constructing the mapping relationship library between the fault scenario and the standard operating parameters according to the preset mapping function, wherein 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. i ) represents the typical characteristic parameter corresponding to the i-th fault scenario, y i is the standard operating parameter under this 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 is the typical characteristic parameter corresponding to the fault scenario, which is the actual observation data or simulation data under the i-th fault scenario, which contains a series of observation values ​​or simulation values ​​corresponding to each key indicator under the fault scenario, used to describe the state of the simulated fault scenario, y 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 simulating the digital twin model, which is used to characterize the ideal behavior of the device or system under a specific fault scenario. This mapping function M(f) is optimized by minimizing the loss function and historical data to obtain the optimized mapping function M opt (f) and use it as a preset mapping function. 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 the preset mapping function, so that the system can quickly find the corresponding operating parameters when encountering a new fault scenario, thereby achieving adaptive adjustment, improving the flexibility and reliability of the system, and reducing the need for manual intervention.

[0058] 103. Collect the actual operating parameters of each electrical device in the new energy site and the power generation and 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, the 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 the key indicators represented by the actual operating parameters are compared with the expected values ​​corresponding to the key indicators represented by each standard operating parameter in the mapping relationship library. Specifically, the similarity score between the two 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 is lower than the similarity threshold, it means that there is a fault scenario corresponding to the standard operating parameter at this time, and the fault scenario can be used as the target fault scenario. In order to make the subsequent alarm more accurate and detailed, the electrical equipment, power transmission link, and specific components corresponding to the fault can be further determined in the electrical equipment and power transmission link involved in the target fault scenario. Specifically, the component-level key parameters can be obtained and compared with the abnormal judgment threshold corresponding to the component-level key parameters to obtain the component-level abnormal parameters, and the target electrical equipment and target power transmission link corresponding to the component-level abnormal parameters are used as the faulty electrical equipment and faulty 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 field, 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 finely. The component-level key parameters can be associated with the 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 is more refined 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 can accurately quantify the difference between two vectors, thus helping to identify which parameters have changed significantly. The value range of S(x,y) is between [0,1], where 0 indicates complete dissimilarity and 1 indicates complete similarity. By calculating the absolute difference and the maximum value, it can resist the influence of outliers to a certain extent and ensure the stability of the results. At the same time, it takes into account the scale of different features, so that data of different dimensions can be reasonably compared, and comprehensively evaluates the changes in multiple parameters, providing a comprehensive operating status assessment, 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 of 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 transmission links, effectively reducing the false alarm rate, and improving the reliability of the system. In addition, by combining key indicators with key parameters at the component level, multi-level fault diagnosis from equipment level, link level to component level can be achieved, 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 according to the fault diagnosis results, and match the disposal measures corresponding to the target fault scenario from the preset emergency disposal solution library.

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

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

[0075] In this step, since the fault diagnosis result obtained in step 103 includes the target fault scenario, faulty electrical equipment, faulty power transmission link and similarity score. Therefore, the fault type corresponding to the target fault scenario can be determined according to the faulty electrical equipment and the faulty power transmission link, and the severity can be specifically quantified according to the similarity score, and the detailed alarm information of the new energy field area is generated in combination with the target fault scenario, fault type, severity, faulty electrical equipment and faulty power transmission link. For example, the alarm information includes the following contents: fault location (such as which wind turbine, which substation), fault type (such as gearbox wear, inverter failure, etc.), fault severity (such as minor, medium, serious), potential impact (such as possible downtime, economic loss), etc. According to the alarm information, the corresponding monitoring notification instruction is automatically triggered, and the notification channel of the instruction includes but is not limited to SMS, email, instant messaging tool, telephone, etc. The corresponding receiving object (such as operation and maintenance personnel, technical personnel, management) is pre-set, and different response levels (such as minor warning, serious warning, emergency alarm) are set according to the severity of the fault.

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

[0077] In this step, the disposal measures corresponding to 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 screen 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 fan, B. Arrange technicians to go to the site for inspection, C. Prepare the tools and materials needed to replace the gearbox, D. Precautions (such as safety protection measures, backup of important data, etc.).

[0078] Preset multiple evaluation indicators, including at least one of response speed, resource consumption, risk reduction, recovery effect and economic cost. Among them, response speed represents how quickly the disposal measures can be implemented; resource consumption represents how much manpower, material and financial resources are required; risk reduction represents how much potential risk the measure can reduce; recovery effect represents whether normal operation can be effectively restored; economic cost represents the cost-effectiveness of the disposal measures. For each candidate disposal measure, score according to the preset evaluation indicators, and comprehensively consider all evaluation indicators to determine the emergency disposal priority of each candidate disposal measure. For example, a weighted scoring method can be used to assign different weights according to the importance of different indicators. The candidate disposal measure with the highest emergency disposal priority is taken as the target disposal measure. Ensure that the measure can solve the problem to the maximum extent in the shortest time, with the minimum resource consumption, the maximum risk reduction, the best recovery effect and the lowest economic cost. After determining the target disposal measure, arrange relevant personnel to carry out the disposal according to the specific steps of the target disposal measure, and use project management tools to track the progress of the task in real time to ensure that each step is effectively executed. At the same time, record the execution time, results and feedback of each disposal step to facilitate subsequent evaluation and summary, so as to update the emergency disposal solution 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 treatment 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 It can be seen from the implementation method that the new energy site safety monitoring method based on digital twin technology provided by the present application is that when it is necessary to conduct safety monitoring of the new energy site based on digital twin technology, first, multiple fault scenarios of each electrical equipment in the new energy site and the power transmission link where the electrical equipment is located under different fault conditions are established, and the typical characteristic parameters of each fault scenario are extracted. Then, the typical characteristic parameters of each fault scenario are simulated by using the digital twin model to obtain the 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 characterize the expected values ​​corresponding to each key indicator when each fault scenario occurs. Then, the actual operating parameters of each electrical equipment in the new energy site and the power transmission link where the electrical equipment is located are collected, and compared in the mapping relationship library based on the actual operating parameters to obtain the fault diagnosis result. The actual operating parameters characterize 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 the present application establishes different fault scenarios, 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 the fault scenarios and the 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 with 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, and improving the safety management level of the entire new energy site. It can effectively respond 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 the present application, as an alternative to the above 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 safety monitoring of new energy sites. The embodiment of the 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 of each electrical device in the new energy field 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 used to simulate the typical characteristic parameters of each of the fault scenarios using a digital twin model, obtain standard operating parameters corresponding to each of the fault scenarios, 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 of the fault scenarios when the fault scenarios occur;

[0087] The diagnosis unit 23 is used to collect actual operating parameters of each electrical device in the new energy field and the power 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 the actual values ​​corresponding to each of the key indicators, and the fault diagnosis result at least includes 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, and the emergency handling solution library contains the handling measures corresponding to each type of fault condition.

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

[0090] The acquisition unit 25 is used to acquire three-dimensional spatial data and operating environment data of the new energy field before simulating the typical characteristic parameters of each fault scenario by using the digital twin model to obtain the standard operating parameters corresponding to each fault scenario and constructing a mapping relationship library between the fault condition and the standard operating parameters, wherein the three-dimensional spatial data includes terrain 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, configured to construct an initial three-dimensional model corresponding to the new energy field 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 used 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 used to classify the fault types based on the relevant fault cases, and set at least one fault condition for each fault type to obtain a plurality of fault scenarios, each of which has a different fault severity and development stage;

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

[0097] The first determination 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] The comprehensive static characteristic parameters are calculated as follows:

[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 dynamic characteristic parameter change rate, the specific expressions are:

[0103]

[0104] Among them, ΔE(t), ΔP(t), and ΔR(t) respectively represent the change rates of environmental characteristic parameters, power generation characteristic parameters, and operating level characteristic parameters within the time period Δt, and 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 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 used 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 according to the simulation results;

[0107] The construction module 222 constructs the mapping relationship library between the fault scenario and the standard operating parameter 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. i ) represents the input feature vector corresponding to the i-th fault scenario, y i is the standard operating parameter under this fault scenario, and N is the number of samples.

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

[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] The second determination module 233 is used to determine, according to the mapping relationship, the fault scenario corresponding to the standard operating parameter lower than the similarity threshold as the target fault scenario, and the electrical equipment involved in the target fault scenario as the target electrical equipment, and 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 equipment and the target power transmission link, and compares the component-level key parameters with the 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 the 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 used to determine the fault type corresponding to the target fault scenario according to the faulty electrical equipment and the faulty power generation and transmission link, and to determine the severity similarity score corresponding to the target fault scenario according to the similarity score;

[0124] The generation module 242 is used to generate alarm information corresponding to the new energy field area 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 determination module 243 is used to determine corresponding candidate disposal measures in the emergency disposal solution library according to the target fault scenario;

[0127] A sixth determination module 244 is used to determine the emergency response priority corresponding to each of the candidate response measures according to a preset evaluation index, wherein the evaluation index includes at least one of response speed, resource consumption, risk reduction, recovery effect and economic cost;

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

[0129] Furthermore, an embodiment of the present application further provides a storage medium, wherein the storage medium 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, the processor 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 emphasis. 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 understandable that the related features in the above methods and systems can be referenced to each other. In addition, the "first", "second" and the like in the above embodiments are used to distinguish the embodiments, and do not represent the advantages and disadvantages of the embodiments.

[0133] Those skilled in the art can 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 algorithm and display provided herein are not inherently related to any particular computer, virtual system or other device. Various general purpose systems can also be used together with the teachings based on this. According to the above description, it is obvious that the structure required for constructing such systems. In addition, the application is not directed to any specific programming language either. It should be understood that various programming languages ​​can be utilized to realize the content of the application described herein, and the description of the specific language above is for the purpose of disclosing the best mode of implementation of the 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 may be provided as methods, systems, or computer program products. Therefore, the present application may 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 may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include 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 generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. 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 a product including an instruction system, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A 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 operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0140] In a typical configuration, a computing device includes one or more processors (CPU), 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 include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. 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 disk read-only memory (CD-ROM), digital versatile disk (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 temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0143] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[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 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 may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0145] The above are only 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 modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should 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 of 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 of each fault scenario; 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 build 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 in the new energy field 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, wherein the actual operating parameters represent the actual values ​​corresponding to each of the key indicators, and the fault diagnosis result at least includes a target fault scenario; 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 each type of fault scenario.

2. The method according to claim 1, characterized in that Before simulating the typical characteristic parameters of each fault scenario by using the digital twin model, obtaining the standard operating parameters corresponding to each fault scenario, and constructing a mapping relationship library between the fault scenario 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 terrain 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 field 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 method of establishing multiple fault scenarios of each electrical device in the new energy field 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: 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; Classifying the fault types based on the relevant fault cases, and setting at least one fault condition for each fault type to obtain a plurality of fault scenarios, each of which has a different fault severity and development stage; Extracting the environmental characteristic parameters, power generation characteristic parameters and operation 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, power generation characteristic parameters and operation 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.

4. The method according to claim 3, characterized in that The calculating of the comprehensive static characteristic parameters and dynamic characteristic parameter change rate corresponding to each fault scenario based on the environmental characteristic parameters, the power generation characteristic parameters and the operation level characteristic parameters includes: The comprehensive static characteristic parameters are calculated as follows: 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 dynamic characteristic parameter change rate, the specific expressions are: Among them, ΔE(t), ΔP(t), and ΔR(t) respectively represent the change rates of environmental characteristic parameters, power generation characteristic parameters, and operating level characteristic parameters within the time period Δt, and 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 environmental characteristic parameters, power generation characteristic parameters, and operating level characteristic parameters at the current time and the previous time point, respectively.

5. The method according to claim 1, characterized in that The constructing of a mapping relationship library between the fault scenario and the standard operating parameters includes: Using the digital twin model to simulate the typical characteristic parameters of each of the fault scenarios, and determining the standard operating parameters corresponding to each of the fault scenarios according to the simulation results; The mapping relationship library between the fault scenario and the standard operating parameter is constructed according to a preset mapping function, wherein the mapping relationship library contains a mapping relationship between each fault scenario and each standard operating parameter, and 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. i ) represents the typical characteristic parameter corresponding to the i-th fault scenario, y i is the standard operating parameter under this fault scenario, and N is the number of samples.

6. The method according to claim 5, characterized in that The actual operating parameters of each electrical device in the new energy field and the power generation and transmission link where the electrical device is located are collected, and the actual operating parameters are compared in the mapping relationship library to obtain the fault diagnosis result, including: 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, the fault scenario corresponding to the standard operating parameters lower than the similarity threshold is determined as the target fault scenario according to the mapping relationship, and the electrical equipment involved in the target fault scenario is used as the target electrical equipment, and the power transmission link where the target electrical equipment involved in the target fault scenario is located is used as the target power transmission link; Acquire component-level key parameters corresponding to the target electrical equipment and the target power generation and transmission link, and compare the component-level key parameters with the 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.

7. The method according to claim 6, 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.

8. The method according to claim 6, characterized in that The triggering of corresponding monitoring notification instructions according to the fault diagnosis result includes: Determining the fault type corresponding to the target fault scenario according to the faulty electrical equipment and the faulty power generation and transmission link, and determining the severity corresponding to the target fault scenario according to 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 based on the alarm information, the corresponding monitoring notification instruction is executed.

9. The method according to claim 8, characterized in that The matching of the disposal measures corresponding to the target fault scenario from the preset emergency disposal solution library includes: Determine corresponding candidate disposal measures in the emergency disposal solution library according to the target failure scenario; Determine 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 taken as the target disposal measure, and the disposal steps included in the target disposal measure are executed.

10. A new energy field safety monitoring system based on digital twin technology, characterized in that: The system comprises: The first processing unit is used to establish multiple fault scenarios of each electrical device in the new energy field and the power generation and transmission link where the electrical device is located under different fault conditions, and extract typical characteristic parameters of each fault scenario; A second processing unit is used to simulate the typical characteristic parameters of each of the fault scenarios using a digital twin model, obtain standard operating parameters corresponding to each of the fault scenarios, 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 of the fault scenarios when it occurs; A diagnostic unit, used to collect actual operating parameters of each electrical device in the new energy field 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 the actual values ​​corresponding to each of the key indicators, and the fault diagnosis result at least includes a target fault scenario; An alarm handling unit is used to trigger a corresponding monitoring notification instruction according to the fault diagnosis result, and match the handling measures corresponding to the target fault scenario from a preset emergency handling solution library, wherein the emergency handling solution library contains handling measures corresponding to each type of fault scenario.

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