Loss assessment method and device based on power target simulation monitoring

Abstract

The invention relates to a loss assessment method and device based on power target simulation monitoring. The method comprises the steps of obtaining real-time power operation data of a multi-source data acquisition device from a power target simulation scene, and integrating static power data and the real-time power operation data in a power system to obtain processed multi-source data; obtaining simulation monitoring information according to the power system simulation model and the processed multi-source data; the simulation monitoring information is used for representing the actual operation state of the power target in the power system; constructing a loss evaluation model based on power multi-factor coupling according to the loss evaluation index system; the loss evaluation index system is used for quantifying the loss condition after the power target is attacked or broken down from different angles; and processing the simulation monitoring information by adopting a loss evaluation model, and outputting loss evaluation results for representing predicted loss conditions of the power system in different fault scenes. By adopting the method, the precision and efficiency of loss evaluation can be improved, and the real-time performance and the dynamic performance are enhanced.

Description

Technical Field

[0001] The present application relates to the technical field of electric power, and particularly to a loss assessment method, device, computer device, computer-readable storage medium, and computer program product based on power target simulation monitoring. Background Art

[0002] With the digital and intelligent development of the power system, the security and reliability of the power system are facing increasingly severe challenges. The industrial control security network range has become an important tool for evaluating and improving the security of industrial control systems, which can be used to simulate the operating environment and potential attack scenarios of the power system for security testing and loss assessment.

[0003] However, traditional power target simulation monitoring technologies have many deficiencies in dealing with complex power system models and real-time loss assessment. For example, the loss assessment accuracy is low, and it is impossible to accurately quantify the loss situation of power targets after being attacked or failing; the real-time and dynamic performance of loss assessment is poor, and it is impossible to timely reflect the loss situation of power targets, affecting the timeliness of security decisions; the loss assessment efficiency is low, the assessment process is complex and time-consuming, increasing the assessment cost. Summary of the Invention

[0004] Based on this, in view of the above technical problems, it is necessary to provide a loss assessment method, device, computer device, computer-readable storage medium, and computer program product based on power target simulation monitoring that can improve the loss assessment effect of the power system.

[0005] In a first aspect, the present application provides a loss assessment method based on power target simulation monitoring, the method comprising:

[0006] Obtain the real-time power operation data of the multi-source data acquisition device from the power target simulation scenario, and integrate the static power data and the real-time power operation data in the power system to obtain the processed multi-source data;

[0007] Obtain simulation monitoring information according to the power system simulation model and the processed multi-source data; the simulation monitoring information is used to characterize the actual operating state of the power target in the power system; the power system simulation model is constructed based on the operating characteristics of the power system and the dynamic characteristics of power equipment;

[0008] Construct a loss assessment model based on power multi-factor coupling according to the loss assessment index system; the loss assessment index system is used to quantify the loss situation of the power target after being attacked or failing from different perspectives;

[0009] Process the simulation monitoring information using the loss assessment model and output the loss assessment result; the loss assessment result is used to characterize the predicted loss situation of the power system under different fault scenarios.

[0010] In one embodiment, obtaining the real-time power operation data of the multi-source data acquisition device from the power target simulation scenario, integrating the static power data and the real-time power operation data in the power system to obtain the processed multi-source data, including:

[0011] Obtain the service data of the power target in the power system through the multi-source data acquisition device for data association to obtain the real-time power operation data;

[0012] Fuse the static power data and the real-time power operation data, and preprocess the data fusion result to obtain the processed multi-source data;

[0013] Among them, the preprocessing includes any one or more of the following:

[0014] Removing outliers, removing noise, removing redundant data, and filtering and smoothing processing.

[0015] In one embodiment, the method further includes:

[0016] Determine the detailed information of the operation link based on the physical characteristics and operation rules of the power system;

[0017] Establish a dynamic characteristic model of different types of power equipment in the power system; the dynamic characteristic model is obtained by simulating the response of the power equipment under different working conditions;

[0018] Obtain the power system simulation model according to the detailed information of the operation link and the dynamic characteristic models of different types of power equipment.

[0019] In one embodiment, the method further includes:

[0020] Determine evaluation indicators in multiple dimensions according to the operation characteristics and safety requirements of the power system;

[0021] Establish the loss assessment index system by performing index definition and quantitative analysis processing on each of the evaluation indicators; the quantitative analysis processing includes qualitative index quantization, data standardization, and quantitative model processing.

[0022] In one embodiment, constructing the loss assessment model based on power multi-factor coupling according to the loss assessment index system includes:

[0023] Determine the target factors of the power target;

[0024] Construct the loss assessment model by combining multiple mathematical modeling algorithms, the target factors of the power target, and the loss assessment index system;

[0025] Among them, the target factors include any one or more of the following:

[0026] Equipment characteristics, operating status, network topology, attack type, attack intensity.

[0027] In one embodiment, processing the simulation monitoring information by using the loss assessment model and outputting a loss assessment result, including:

[0028] Process the simulation monitoring information by using the loss assessment model to obtain a model output result;

[0029] Convert the model output result into a standardized data format and generate a structured report document as the loss assessment result.

[0030] In a second aspect, the present application also provides a loss assessment device based on power target simulation monitoring, and the device includes:

[0031] A multi-source data processing module, configured to obtain real-time power operation data of multi-source data acquisition devices from a power target simulation scenario, and integrate static power data and the real-time power operation data in the power system to obtain processed multi-source data;

[0032] A simulation monitoring module, configured to obtain simulation monitoring information according to a power system simulation model and the processed multi-source data; the simulation monitoring information is used to characterize the actual operating status of a power target in the power system; the power system simulation model is constructed based on the operating characteristics of the power system and the dynamic characteristics of power equipment;

[0033] A loss assessment model construction module, configured to construct a loss assessment model based on power multi-factor coupling according to a loss assessment index system; the loss assessment index system is used to quantify the loss situation after the power target is attacked or fails from different perspectives;

[0034] A loss assessment result obtaining module, configured to process the simulation monitoring information by using the loss assessment model and output a loss assessment result; the loss assessment result is used to characterize the predicted loss situation of the power system under different fault scenarios.

[0035] In a third aspect, the present application also provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the above method are implemented.

[0036] Fourthly, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned method are implemented.

[0037] Fifthly, the present application also provides a computer program product, including a computer program. When the computer program is executed by a processor, the steps of the above-mentioned method are implemented.

[0038] The above loss assessment method, device, computer device, computer-readable storage medium and computer program product based on power target simulation monitoring obtain the real-time power operation data of multi-source data acquisition devices from the power target simulation scenario, integrate the static power data and real-time power operation data in the power system to obtain the processed multi-source data, and then obtain the simulation monitoring information according to the power system simulation model and the processed multi-source data. The simulation monitoring information is used to characterize the actual operation state of the power target in the power system. The power system simulation model is constructed based on the operation characteristics of the power system and the dynamic characteristics of power equipment. A loss assessment model based on multi-factor coupling of electricity is constructed according to the loss assessment index system. The loss assessment index system is used to quantify the loss situation after the power target is attacked or fails from different angles. Then, the loss assessment model is used to process the simulation monitoring information and output the loss assessment result. The loss assessment result is used to characterize the predicted loss situation of the power system under different fault scenarios, realizing the optimization of loss assessment based on power target simulation monitoring, being able to accurately quantify the loss situation of the power target after being attacked or failing, improving the accuracy of loss assessment, enhancing the real-time and dynamic nature of loss assessment, and effectively improving the loss assessment efficiency. Description of the Drawings

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required to be used in the description of the embodiments of the present application or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0040] Figure 1 It is a schematic flowchart of a loss assessment method based on power target simulation monitoring in an embodiment;

[0041] Figure 2 It is a schematic diagram of the loss assessment process in an embodiment;

[0042] Figure 3 It is a schematic flowchart of a loss assessment method based on power target simulation monitoring in another embodiment;

[0043] Figure 4The structural block diagram of a loss assessment device based on power target simulation monitoring in an embodiment;

[0044] Figure 5 The internal structure diagram of a computer device in an embodiment. Detailed implementation manners

[0045] In order to make the objectives, technical solutions and advantages of the present application more clear and understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0046] Currently, a variety of power system simulation technologies and target loss assessment technologies have been applied to the safety research and testing of power systems, but still face prominent problems such as inflexible resource scheduling and limited system scalability.

[0047] Traditional power network attack and defense range solutions can possess multi-dimensional and highly realistic twin simulation capabilities, simulate real equipment, business systems and various working scenarios in the power industry, and achieve full-level and full-business process simulation in a virtual-real combination manner. However, this solution has certain limitations in loss assessment. It mainly focuses on attack and defense drills and vulnerability mining, and the specific loss assessment of power targets after being attacked is not accurate and comprehensive enough.

[0048] Another traditional range solution with the main business ability of simulating power business scenarios can meet the application requirements such as network security research, testing, and drills in the power industry by simulating the business scenarios of power generation, power transmission, power transformation and power distribution in the power industry. However, this solution lacks targeted optimization in loss assessment modeling, and the loss assessment accuracy for complex power systems needs to be improved.

[0049] The present application proposes a loss assessment method based on power target simulation monitoring, which can provide comprehensive power target loss assessment capabilities. By adopting multi-source data fusion and cleaning technologies, the accuracy and reliability of data can be ensured; by using a high-precision power system simulation model to monitor the target state in real time and combining algorithms such as Monte Carlo simulation to construct a multi-factor coupling loss assessment model, losses can be accurately quantified, and then the losses of power targets after being attacked or failing can be quickly and accurately evaluated, providing strong support for power system security management and decision-making, and helping to improve the security and reliability of power systems.

[0050] In an exemplary embodiment, as Figure 1As shown, a loss assessment method based on power target simulation monitoring is provided. This embodiment uses the method applied to a terminal as an example for illustration. It is understandable that the method can also be applied to a server, or to a system including a terminal and a server, and implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps 101 to 104. Among them:

[0051] Step 101 : acquiring real-time power operation data of a multi-source data acquisition device from a power target simulation scenario, integrating static power data in the power system and the real-time power operation data to obtain processed multi-source data.

[0052] As an example, a power target may be a target device or network in a power system for testing, evaluation, or attack, such as a substation, a power plant, or a distribution network.

[0053] In practical applications, such as Figure 2 As shown, the loss assessment modeling technology based on power target simulation monitoring in this embodiment may include a data acquisition and preprocessing module and key steps, a power target simulation monitoring module and key steps, a loss assessment modeling module and key steps, and a loss assessment result output and visualization module and key steps.

[0054] Specifically, if Figure 2 As shown in Figure 1, key steps in the data acquisition and preprocessing process include multi-source data fusion, data cleaning, and preprocessing. For multi-source data fusion, real-time power operation data is acquired from various sensors, smart meters, monitoring and data acquisition systems, and other multi-source data acquisition devices within the power target simulation scenario. This data may include, but is not limited to, electrical parameters such as voltage, current, power, and frequency, as well as information such as device status and network traffic. Furthermore, this data can be integrated with static power data such as the power system's topology and device parameters to ensure comprehensiveness and accuracy.

[0055] For data cleaning and preprocessing, the collected raw data can be cleaned, such as removing outliers, noise data, and redundant data, and preprocessing the data through filtering, smoothing and other algorithms, thereby improving the quality and availability of the data and providing a reliable data basis for subsequent simulation monitoring and loss assessment.

[0056] Step 102: obtaining simulation monitoring information based on the power system simulation model and the processed multi-source data.

[0057] Among them, the simulation monitoring information can be used to characterize the actual operating status of the power target in the power system; the power system simulation model can be constructed based on the power system operating characteristics and the dynamic characteristics of the power equipment.

[0058] In specific implementation, such as Figure 2 shown, the key steps of the power target simulation monitoring process may include high-precision power system modeling, real-time simulation, and monitoring. For high-precision power system modeling, based on the physical characteristics and operating rules of the power system, a high-precision power system simulation model can be constructed, which may include detailed models of various links in the power system such as power generation, transmission, transformation, and distribution, as well as dynamic characteristic models of various power equipment. Furthermore, through the precise setting and calibration of model parameters, it can be ensured that the simulation model can accurately reflect the actual operating state of the power target.

[0059] For real-time simulation and monitoring, real-time simulation technology can be used to conduct real-time simulation and monitoring of the operating state of the power target. Through data interaction with the actual power system, synchronous operation of the simulation model and the actual system can be achieved. During the simulation process, various operating parameters and state variables of the power target can be monitored in real time to promptly discover abnormal situations and potential safety hazards. Optionally, a real-time data monitoring system based on real-time simulation software can be adopted in this process to track and record the data during the simulation process in real time, ensuring the accuracy and reliability of the simulation results.

[0060] Step 103: Construct a loss assessment model based on multi-factor coupling of electricity according to the loss assessment index system.

[0061] Among them, a loss assessment index system can be established based on the operating characteristics and safety requirements of the power system, and this loss assessment index system can be used to quantify the loss situation of the power target after being attacked or having a fault from different perspectives.

[0062] In an example, such as Figure 2 shown, the key steps of the loss assessment modeling process may include constructing a loss assessment index system and constructing a loss assessment model based on multi-factor coupling. For constructing the loss assessment index system, according to the operating characteristics and safety requirements of the power system, a comprehensive loss assessment index system can be constructed, which may include indicators in multiple dimensions such as economic loss, power supply reliability loss, equipment damage degree, and environmental impact. Each indicator has a clear definition and calculation method, and can quantify the loss situation of the power target after being attacked or having a fault from different perspectives. Optionally, methods such as expert investigation, historical data analysis, and on-site inspection can be adopted in this process, combined with the actual operating data and expert experience of the power system, to ensure the comprehensiveness and practicality of the index system.

[0063] For the construction of a loss assessment model based on multi-factor coupling, by comprehensively considering various factors such as the equipment characteristics, operating status, network topology, attack type and intensity of the power target, a loss assessment model based on multi-factor coupling can be established; this loss assessment model can adopt mathematical modeling methods and algorithms, such as Monte Carlo simulation, fuzzy logic, neural network, etc., to accurately quantify and predict the loss degree and change trend of the power target after being attacked or failing. For example, Monte Carlo simulation can simulate the state distribution of the system through random sampling methods and evaluate the losses of the system under different fault scenarios; fuzzy logic can process uncertainty and ambiguity, quantify and reason about fuzzy concepts in loss assessment; neural network can predict and classify losses by learning the patterns and rules of historical data. Thus, the loss of the power target can be accurately evaluated, and the loss degree and change trend under different scenarios can be accurately predicted.

[0064] Step 104, process the simulation monitoring information by using the loss assessment model and output a loss assessment result; the loss assessment result is used to characterize the predicted loss situation of the power system under different fault scenarios.

[0065] Exemplarily, the predicted loss situation of the power system under different fault scenarios can be displayed according to the loss assessment result, such as Figure 2 shown, the key steps of the loss assessment result output and visualization process can include result output and visualization display; for result output, the result of the loss assessment can be output in an intuitive form, such as including the numerical value of the loss, the distribution of the loss, the change trend of the loss over time, etc., which can provide strong data support for the safety management and decision-making of the power system. For visualization display, through the graphical interface and visualization technology, the result of the loss assessment can be intuitively displayed, such as drawing a loss distribution map, a curve graph of the loss changing over time, a heat map of the equipment damage degree, etc., so that users can quickly understand and master the loss situation of the power target and provide a decision-making basis for taking corresponding safety measures. This process can convert complex data information into intuitive visual information in the form of charts, graphs, etc., for the convenience of users to quickly understand and analyze.

[0066] Compared with the traditional method which has problems such as low accuracy, poor real-time performance and dynamics, and low efficiency in loss assessment, the technical solution of this embodiment constructs a loss assessment model with multi-factor coupling through a loss assessment modeling technology based on power target simulation monitoring. Combining algorithms such as Monte Carlo simulation, fuzzy logic, and neural network, it can accurately quantify the loss situation of the power target after being attacked or having a fault, effectively improving the accuracy of loss assessment and providing more reliable data support for the security protection of the power system; through real-time monitoring and dynamic modeling, it can monitor the operating state of the power target in real time and perform dynamic modeling, timely reflecting the loss situation of the power target, enhancing the real-time performance and dynamics of loss assessment, and providing more timely support for the security decision-making of the power system; through optimizing the modeling algorithm and simulation technology, it can complete loss assessment more efficiently, simplify the assessment process, reduce the assessment cost, and effectively improve the efficiency of loss assessment.

[0067] In the above loss assessment method based on power target simulation monitoring, by obtaining the real-time power operation data of multi-source data acquisition devices from the power target simulation scenario, integrating the static power data and the real-time power operation data in the power system to obtain the processed multi-source data, then obtaining simulation monitoring information according to the power system simulation model and the processed multi-source data, constructing a loss assessment model based on power multi-factor coupling according to the loss assessment index system, and then using the loss assessment model to process the simulation monitoring information and output the loss assessment result, the optimization of loss assessment based on power target simulation monitoring is realized, which can accurately quantify the loss situation of the power target after being attacked or having a fault, improve the accuracy of loss assessment, enhance the real-time performance and dynamics of loss assessment, and effectively improve the efficiency of loss assessment.

[0068] In an exemplary embodiment, the step of obtaining the real-time power operation data of multi-source data acquisition devices from the power target simulation scenario, integrating the static power data and the real-time power operation data in the power system to obtain the processed multi-source data may include the following steps:

[0069] Obtain the service data of the power target in the power system through the multi-source data acquisition device for data association to obtain the real-time power operation data; fuse the static power data and the real-time power operation data, and preprocess the data fusion result to obtain the processed multi-source data;

[0070] Among them, the preprocessing includes any one or more of the following:

[0071] Removing outliers, removing noise, removing redundant data, as well as filtering and smoothing processing.

[0072] As an example, real-time power operation data may include electrical parameters, power equipment status information, and power network traffic information; static power data may include the power system topology and power equipment parameters.

[0073] In practical applications, when performing multi-source data fusion, further processing such as data source integration, data correlation analysis, and static data fusion can be carried out. Among them, for data source integration, real-time operation data can be obtained from various sensors of power targets, smart meters, monitoring and data acquisition systems, and other multi-source data acquisition devices, including electrical parameters such as voltage, current, power, and frequency, as well as information such as equipment status and network traffic; business data of power targets can also be obtained through other acquisition means.

[0074] For data correlation analysis, data correlation analysis techniques can be used to correlate data from different sources to ensure data consistency and integrity; for static data fusion, static data such as the topology of the power system and equipment parameters can be combined, and multi-source data integration can be achieved through data fusion algorithms (such as Kalman filtering and data correlation analysis). For example, the Kalman filtering algorithm can be used to perform real-time estimation and prediction of dynamic data to reduce the impact of noise and errors. Thus, through multi-source data fusion, the comprehensiveness and diversity of data can be ensured, providing information for subsequent analysis; through data correlation analysis and Kalman filtering, the accuracy and reliability of data can be improved, and errors can be reduced.

[0075] In one example, after data acquisition, data cleaning and preprocessing can be performed. During data cleaning and preprocessing, further outlier detection can be carried out. Outliers can be detected and removed by using statistical methods (such as the 3σ principle) or machine learning algorithms (such as Isolation Forest, which detects outliers by constructing isolation trees and is suitable for high-dimensional data); noise removal can be performed. The data can be smoothed by using filtering algorithms (such as low-pass filtering, which removes high-frequency noise through a low-pass filter and retains low-frequency useful signals; median filtering) to remove noise; redundant data removal can be carried out. Redundant data can be removed by using data compression techniques such as PCA (Principal Component Analysis), which reduces the dimensionality of high-dimensional data through linear transformation and removes redundant information to reduce the amount of data.

[0076] Optionally, 3 The principle is based on the normal distribution hypothesis. Data points exceeding 3 times the standard deviation are regarded as outliers and removed. Based on the 3 principle formula, in a normal distribution:

[0077] 1 Range: Approximately 68.27% of the data points fall within the range of the mean ±1 range.

[0078] 2 Range: Approximately 95.45% of the data points fall within the mean ± 2 range.

[0079] 3 Range: Approximately 99.73% of the data points fall within the mean ± 3 range.

[0080] Among them, the 3 principle can show that almost all data points (99.73%) fall within the mean ± 3 range. Data points outside this range can be considered outliers.

[0081] The normal distribution probability density function can be used:

[0082]

[0083] Among them, x is the value of the data point; is the mean; is the standard deviation; e is the base of the natural logarithm (approximately equal to 2.71828).

[0084] According to the 3 principle, the probability that a data point falls within the mean ± 3 range is 99.73%. The calculation formula for the specific range is:

[0085]

[0086] In data cleaning, outliers can be detected through the following formula:

[0087] If or , then x is an outlier.

[0088] Judge whether the data is abnormal through the formula, and then perform the following other processing. Thus, through data cleaning and preprocessing, outliers, noise, and redundant data can be removed, and the quality and usability of the data can be improved; through data compression and dimensionality reduction, the amount of data can be reduced, and the efficiency of subsequent calculations and analyses can be improved.

[0089] In an exemplary embodiment, the following steps may further be included:

[0090] Based on the physical characteristics and operating rules of the power system, determine the detailed information of the operating links; establish the dynamic characteristic models of different types of power equipment in the power system; the dynamic characteristic models are obtained by simulating the responses of the power equipment under different working conditions; according to the detailed information of the operating links and the dynamic characteristic models of different types of power equipment, obtain the power system simulation model.

[0091] In specific implementation, when modeling a high-precision power system, physical characteristic modeling can be further carried out. For example, based on the physical characteristics and operating rules of the power system, a detailed model including links such as power generation, transmission, transformation, and distribution (i.e., detailed information on operating links) can be constructed, and power flow calculation, short-circuit calculation, and stability analysis can be performed by applying power system analysis theory; dynamic characteristic modeling can be carried out. For example, differential equations and algebraic equations can be used to describe the dynamic characteristics of power equipment and simulate its responses under different operating conditions to establish dynamic characteristic models of various power equipment (such as generators, transformers, transmission lines, etc.), which can include transient and steady-state responses; furthermore, parameter calibration can be carried out. For example, optimization algorithms (such as the least squares method, genetic algorithm) can be adopted to accurately set and calibrate model parameters through historical data and actual operating data to improve the accuracy of the model. Thus, through detailed modeling and parameter calibration, it can be ensured that the simulation model accurately reflects the actual operating state of the power target, effectively improving the model accuracy; through the dynamic characteristic model, the responses of power equipment under different operating conditions can be accurately simulated, improving the authenticity of the simulation.

[0092] In an example, during real-time simulation and monitoring, the real-time simulation platform can be further used to perform real-time simulation on the operating state of the power target; data interaction between the simulation model and the actual power system can be realized through a data interface to ensure the synchronous operation of the simulation model and the actual system; also, during the simulation process, various operating parameters (such as voltage, current, power) and state variables (such as equipment state, network state) of the power target can be monitored in real time to discover abnormalities and potential hazards in a timely manner.

[0093] In an alternative embodiment, by using a real-time simulation platform for high-speed calculation, it can be ensured that the simulation process is synchronized with the actual system; by applying anomaly detection algorithms (such as rule-based detection, machine learning algorithms) to monitor operating parameters and state variables in real time, abnormal situations can be effectively identified. Thus, through real-time simulation technology, ensuring the synchronization of the simulation process with the actual system can provide immediate monitoring and evaluation with strong real-time performance; through a standardized data interface protocol, accurate data interaction between the simulation model and the actual system can be ensured; through real-time monitoring and anomaly detection algorithms, abnormalities and potential hazards of the power target can be discovered in a timely manner, improving the security of the system.

[0094] In an exemplary embodiment, the following steps may further be included:

[0095] According to the operating characteristics and safety requirements of the power system, determine evaluation indicators in multiple dimensions; through index definition and quantitative analysis processing for each of the evaluation indicators, establish the loss evaluation index system; the quantitative analysis processing includes quantitative processing of qualitative indicators, data standardization, and quantitative model processing.

[0096] Specifically, when constructing the loss assessment index system, the index system design can be further carried out. For example, according to the operating characteristics and safety requirements of the power system, a loss assessment index system including multiple dimensions such as economic loss, power supply reliability loss, equipment damage degree, and environmental impact can be constructed to comprehensively evaluate the loss situation of the power system from multiple dimensions; define the indicators and calculation methods, such as providing clear definitions and calculation methods for each indicator to ensure the objectivity and repeatability of the evaluation results. By adopting quantitative analysis methods, qualitative indicators can be transformed into quantitative indicators, which is convenient for calculation and comparison. Thus, through the multi-dimensional index system, the comprehensiveness of the evaluation can be improved, and through clear definitions and calculation methods, the objectivity of the evaluation can be improved.

[0097] For example, the quantitative analysis methods can include: qualitative index quantification. Through methods such as the expert scoring method and the fuzzy comprehensive evaluation method, qualitative indicators can be transformed into quantitative indicators; data standardization. By adopting the minimum-maximum standardization or Z-score standardization method, the index data with different dimensions can be converted into unified standard values; quantitative model construction. By constructing a quantitative evaluation model based on the quantified index data, such as a linear regression model, a principal component analysis (PCA) model, etc.

[0098] The minimum-maximum standardization can be:

[0099]

[0100] Z-score standardization:

[0101]

[0102] Among them, is the mean; is the standard deviation.

[0103] Linear regression model:

[0104]

[0105] Among them, Y is the dependent variable (evaluation result); X1, X2, …, Xn are independent variables (quantified indicators); β0, β1, …, βn are regression coefficients; is the error term.

[0106] Principal component analysis (PCA) model:

[0107] 1. Calculate the covariance matrix

[0108]

[0109] 2. Perform eigenvalue decomposition on the covariance matrix to obtain eigenvalues and eigenvectors.

[0110] 3. Select the top k principal components to construct the data matrix Z after dimensionality reduction:

[0111]

[0112] where XX is the original data matrix; Vk is the matrix composed of the top k eigenvectors.

[0113] In one example, by constructing a multi-dimensional evaluation framework, multiple dimensions such as economic loss, power supply reliability loss, equipment damage degree, and environmental impact can be covered; through index weight assignment, according to the importance of each dimension, methods such as AHP (Analytic Hierarchy Process) or entropy method can be used to assign weights to each index; through the comprehensive evaluation model, based on the method of weighted summation or weighted average, the evaluation results of each dimension can be synthesized into an overall evaluation value.

[0114] For example, the weight calculation method of the analytic hierarchy process is as follows:

[0115] Construct the judgment matrix AA, where a ij represents the importance of index ii relative to index jj.

[0116] Calculate the weight vector W i :

[0117]

[0118] Comprehensive evaluation model:

[0119]

[0120] where W i is the weight of the ii-th index; S i is the score of the ii-th index.

[0121] In an exemplary embodiment, the constructing a loss evaluation model based on multi-factor coupling of electricity according to the loss evaluation index system may include the following steps:

[0122] Determine the target factors of the electricity target; combine multiple mathematical modeling algorithms, the target factors of the electricity target, and the loss evaluation index system to construct the loss evaluation model.

[0123] where the target factors include any one or more of the following:

[0124] Equipment characteristics, operating status, network topology, attack type, attack intensity.

[0125] As an example, multiple mathematical modeling algorithms may include Monte Carlo simulation, fuzzy logic, and neural networks.

[0126] In practical applications, when constructing a loss assessment model based on multi-factor coupling, multi-factor analysis can be further carried out. For example, by comprehensively considering various factors such as the equipment characteristics, operating status, network topology, attack type, and intensity of the power target, a loss assessment model based on multi-factor coupling can be established. By using mathematical modeling methods such as Monte Carlo simulation, fuzzy logic, and neural networks, the loss of the power target can be accurately evaluated. Thus, based on multi-factor coupling and mathematical modeling methods, the evaluation accuracy and precision can be improved, and the loss degree and change trend in different scenarios can be accurately predicted, providing a scientific basis for decision-making.

[0127] Exemplarily, through Monte Carlo simulation, based on random sampling and statistical analysis methods, the loss situations in different scenarios can be simulated, providing probability distributions and confidence intervals. The Monte Carlo simulation process is as follows:

[0128] Define the probability distribution P(X) of the input variables;

[0129] Randomly sample N times to generate samples X1, X2, …, X N ;

[0130] Calculate the loss function L(Xi);

[0131] Statistically analyze the distribution of the loss function and calculate the mean and the standard deviation :

[0132]

[0133] By applying fuzzy logic theory, uncertainty and ambiguity can be handled, and the robustness of the evaluation model can be improved. The fuzzy logic process is as follows:

[0134] Define fuzzy sets and membership functions ;

[0135] Apply fuzzy rules for reasoning and calculate the membership degree of the output variables.

[0136] Calculate the output value through defuzzification methods (such as the centroid method):

[0137]

[0138] By utilizing the self-learning and adaptive capabilities of neural networks, a complex non-linear relationship model can be established to improve the accuracy of evaluation. The neural network process is as follows:

[0139] Define the neural network structure, including the input layer, hidden layer, and output layer;

[0140] Calculate the output value through forward propagation:

[0141]

[0142] where f is an activation function (such as Sigmoid, ReLU); w i is the weight; b is the bias.

[0143] Update the weights and biases through the backpropagation algorithm to minimize the loss function:

[0144]

[0145]

[0146] where is the learning rate.

[0147] In an exemplary embodiment, processing the simulation monitoring information by using the loss evaluation model and outputting a loss evaluation result may include the following steps:

[0148] Process the simulation monitoring information by using the loss evaluation model to obtain a model output result; convert the model output result into a standardized data format to generate a structured report document as the loss evaluation result.

[0149] As an example, the loss evaluation result may include a predicted loss value, a predicted loss distribution, and a predicted loss change trend.

[0150] In an example, data formatting may be further performed when outputting the result. For example, the result of the loss evaluation is stored and transmitted in a structured data format (such as JSON, XML) for subsequent processing and display. By applying data formatting techniques to convert the evaluation result into a standardized data format, the compatibility and readability of the data can be ensured; an automatic loss evaluation report is generated, including the value of the loss, the distribution of the loss, the change trend of the loss over time, etc. For example, the evaluation result is automatically sorted and typeset through a report generation algorithm to generate a structured report document, which can provide comprehensive evaluation information. Thus, through the structured data format and the automatic generation of reports, data support can be provided and information comprehensiveness can be achieved, providing strong data support for the safety management and decision-making of the power system.

[0151] In yet another example, when making a visual display, a user-friendly graphical interface can be further designed to display the loss assessment results in the forms of charts, curves, heat maps, etc. By converting complex data information into intuitive graphs and charts, it is convenient for users to understand and analyze. Visualization technology can be applied to visually display the loss assessment results, such as drawing a loss distribution map, a curve graph of the change of loss over time, a heat map of the degree of equipment damage, etc. For example, users can interact with the visual interface through operations such as clicking and dragging to obtain more detailed information. Thus, through the graphical interface and visualization technology, an intuitive display effect can be achieved, which is convenient for users to quickly understand and master the loss situation of the power target. Through the intuitive visual display, it provides a decision-making basis for taking corresponding safety measures and helps to improve the scientificity and effectiveness of decision-making.

[0152] In an exemplary embodiment, as Figure 3 shown, a schematic flowchart of another loss assessment method based on power target simulation monitoring is provided. In this embodiment, the method includes the following steps:

[0153] In step 301, through a multi-source data acquisition device, the service data of the power target in the power system is acquired for data association to obtain real-time power operation data. In step 302, the static power data and the real-time power operation data are fused, and the data fusion result is preprocessed to obtain the processed multi-source data. In step 303, based on the physical characteristics and operation rules of the power system, the detailed information of the operation link is determined, and a dynamic characteristic model of different types of power equipment in the power system is established. In step 304, according to the detailed information of the operation link and the dynamic characteristic models of different types of power equipment, a power system simulation model is obtained. According to the power system simulation model and the processed multi-source data, simulation monitoring information is obtained. In step 305, according to the operation characteristics and safety requirements of the power system, multiple dimensions of evaluation indicators are determined. By performing index definition and quantitative analysis processing on each evaluation indicator, a loss assessment index system is established. In step 306, the target factors of the power target are determined, and combined with multiple mathematical modeling algorithms, the target factors of the power target, and the loss assessment index system, a loss assessment model is constructed. In step 307, the loss assessment model is used to process the simulation monitoring information, the model output result is converted into a standardized data format, and a structured report document is generated as the loss assessment result.

[0154] It should be noted that the specific limitations of the above steps can be referred to the specific limitations of a loss assessment method based on power target simulation monitoring described above, and will not be elaborated here.

[0155] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are sequentially shown according to the indications of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, there is no strict order limit for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same moment, but can be executed at different moments. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0156] Based on the same inventive concept, an embodiment of the present application further provides a loss assessment device for power target simulation monitoring for implementing the loss assessment method for power target simulation monitoring described above. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the loss assessment device for power target simulation monitoring provided below can refer to the limitations on the loss assessment method for power target simulation monitoring in the above text, and will not be repeated here.

[0157] In an exemplary embodiment, as Figure 4 shown, a loss assessment device for power target simulation monitoring is provided, including:

[0158] A multi-source data processing module 401, configured to obtain real-time power operation data of a multi-source data acquisition device from a power target simulation scenario, and integrate static power data and the real-time power operation data in the power system to obtain processed multi-source data;

[0159] A simulation monitoring module 402, configured to obtain simulation monitoring information according to a power system simulation model and the processed multi-source data; the simulation monitoring information is used to characterize the actual operation state of a power target in the power system; the power system simulation model is constructed based on the operation characteristics of the power system and the dynamic characteristics of power equipment;

[0160] A loss assessment model construction module 403, configured to construct a loss assessment model based on power multi-factor coupling according to a loss assessment index system; the loss assessment index system is used to quantify the loss situation of the power target after being attacked or malfunctioning from different perspectives;

[0161] A loss assessment result obtaining module 404, configured to process the simulation monitoring information by using the loss assessment model and output a loss assessment result; the loss assessment result is used to characterize the predicted loss situation of the power system under different fault scenarios.

[0162] In one embodiment, the multi-source data processing module 401 is specifically configured to obtain the service data of the power target in the power system through the multi-source data acquisition device, perform data association to obtain the real-time power operation data; fuse the static power data and the real-time power operation data, and preprocess the data fusion result to obtain the processed multi-source data.

[0163] Among them, the preprocessing includes any one or more of the following:

[0164] Removing outliers, removing noise, removing redundant data, as well as filtering processing and smoothing processing.

[0165] In one embodiment, the device further includes:

[0166] A simulation model construction module, configured to determine the detailed information of the operation link based on the physical characteristics and operation rules of the power system; establish a dynamic characteristic model of different types of power equipment in the power system; the dynamic characteristic model is obtained by simulating the response of the power equipment under different working conditions; according to the detailed information of the operation link and the dynamic characteristic models of different types of power equipment, obtain the power system simulation model.

[0167] In one embodiment, the device further includes:

[0168] An index system construction module, configured to determine evaluation indexes in multiple dimensions according to the operation characteristics and safety requirements of the power system; establish the loss evaluation index system by performing index definition and quantitative analysis processing on each of the evaluation indexes; the quantitative analysis processing includes qualitative index quantification, data standardization, and quantitative model processing.

[0169] In one embodiment, the loss evaluation model construction module 403 is specifically configured to determine the target factors of the power target; combine multiple mathematical modeling algorithms, the target factors of the power target, and the loss evaluation index system to construct the loss evaluation model;

[0170] Among them, the target factors include any one or more of the following:

[0171] Equipment characteristics, operating status, network topology, attack type, attack intensity.

[0172] In one embodiment, the loss evaluation result obtaining module 404 is specifically configured to use the loss evaluation model to process the simulation monitoring information to obtain a model output result; convert the model output result into a standardized data format to generate a structured report document as the loss evaluation result.

[0173] Each module in the above loss assessment device based on power target simulation monitoring can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or stored in a memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0174] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as Figure 5 shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communicating with external terminals in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a loss assessment method based on power target simulation monitoring. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covered on the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, a touchpad, or a mouse, etc.

[0175] Those skilled in the art can understand that Figure 5 the structure shown in

[0176] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the following steps are implemented:

[0177] In a power target simulation scenario, obtain the real-time power operation data of multi-source data acquisition devices, and integrate the static power data in the power system and the real-time power operation data to obtain processed multi-source data;

[0178] According to the power system simulation model and the processed multi-source data, obtain simulation monitoring information; the simulation monitoring information is used to characterize the actual operation state of the power target in the power system; the power system simulation model is constructed based on the operation characteristics of the power system and the dynamic characteristics of power equipment;

[0179] Construct a loss assessment model based on power multi-factor coupling according to the loss assessment index system; the loss assessment index system is used to quantify the loss situation of the power target after being attacked or failing from different perspectives;

[0180] Use the loss assessment model to process the simulation monitoring information and output a loss assessment result; the loss assessment result is used to characterize the predicted loss situation of the power system under different fault scenarios.

[0181] In one embodiment, when the processor executes the computer program, it also implements the steps of the loss assessment method for power target simulation monitoring in the above other embodiments.

[0182] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0183] In a power target simulation scenario, obtain the real-time power operation data of multi-source data acquisition devices, and integrate the static power data in the power system and the real-time power operation data to obtain processed multi-source data;

[0184] According to the power system simulation model and the processed multi-source data, obtain simulation monitoring information; the simulation monitoring information is used to characterize the actual operation state of the power target in the power system; the power system simulation model is constructed based on the operation characteristics of the power system and the dynamic characteristics of power equipment;

[0185] Construct a loss assessment model based on power multi-factor coupling according to the loss assessment index system; the loss assessment index system is used to quantify the loss situation of the power target after being attacked or failing from different perspectives;

[0186] Use the loss assessment model to process the simulation monitoring information and output a loss assessment result; the loss assessment result is used to characterize the predicted loss situation of the power system under different fault scenarios.

[0187] In one embodiment, when the computer program is executed by a processor, it also implements the steps of the loss assessment method based on power target simulation monitoring in the above-mentioned other embodiments.

[0188] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the following steps:

[0189] Obtain the real-time power operation data of the multi-source data acquisition device from the power target simulation scenario, and integrate the static power data and the real-time power operation data in the power system to obtain the processed multi-source data;

[0190] Obtain simulation monitoring information according to the power system simulation model and the processed multi-source data; the simulation monitoring information is used to characterize the actual operating state of the power target in the power system; the power system simulation model is constructed based on the operating characteristics of the power system and the dynamic characteristics of power equipment;

[0191] Construct a loss assessment model based on power multi-factor coupling according to the loss assessment index system; the loss assessment index system is used to quantify the loss situation of the power target after being attacked or failed from different perspectives;

[0192] Process the simulation monitoring information using the loss assessment model and output a loss assessment result; the loss assessment result is used to characterize the predicted loss situation of the power system under different fault scenarios.

[0193] In one embodiment, when the computer program is executed by a processor, it also implements the steps of the loss assessment method based on power target simulation monitoring in the above-mentioned other embodiments.

[0194] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0195] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in this application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., without limitation.

[0196] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this application.

[0197] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A loss assessment method based on power target simulation monitoring, characterized in that, The method includes: Obtaining real-time power operation data of a multi-source data acquisition device from a power target simulation scenario, integrating static power data in the power system and the real-time power operation data to obtain processed multi-source data; Obtaining simulation monitoring information according to a power system simulation model and the processed multi-source data; the simulation monitoring information is used to characterize the actual operation state of the power target in the power system; the power system simulation model is constructed based on the operation characteristics of the power system and the dynamic characteristics of power equipment; Constructing a loss assessment model based on power multi-factor coupling according to a loss assessment index system; the loss assessment index system is used to quantify the loss situation after the power target is attacked or fails from different perspectives; Processing the simulation monitoring information by using the loss assessment model and outputting a loss assessment result; the loss assessment result is used to characterize the predicted loss situation of the power system under different fault scenarios.

2. The method according to claim 1, wherein The step of obtaining real-time power operation data of a multi-source data acquisition device from a power target simulation scenario, integrating static power data in the power system and the real-time power operation data to obtain processed multi-source data includes: Obtaining service data of a power target in the power system through the multi-source data acquisition device for data association to obtain the real-time power operation data; Fusing the static power data and the real-time power operation data, and preprocessing the data fusion result to obtain the processed multi-source data; Wherein, the preprocessing includes any one or more of the following: Removing outliers, removing noise, removing redundant data, as well as filtering processing and smoothing processing.

3. The method according to claim 1, wherein The method further includes: Determining detailed information of operation links based on the physical characteristics and operation rules of the power system; Establishing a dynamic characteristic model of different types of power equipment in the power system; the dynamic characteristic model is obtained by simulating the response of the power equipment under different working conditions; Obtaining the power system simulation model according to the detailed information of the operation links and the dynamic characteristic models of different types of power equipment.

4. The method according to claim 1, wherein The method further includes: Determining evaluation indexes in multiple dimensions according to the operation characteristics and security requirements of the power system; Establishing the loss assessment index system by performing index definition and quantitative analysis processing on each of the evaluation indexes; the quantitative analysis processing includes qualitative index quantification, data standardization, and quantitative model processing.

5. The method according to claim 1, characterized in that, The step of constructing a loss assessment model based on power multi-factor coupling according to a loss assessment index system includes: Determining the target factors of the power target; Combining multiple mathematical modeling algorithms, the target factors of the power target, and the loss assessment index system to construct the loss assessment model; Wherein, the target factors include any one or more of the following: Equipment characteristics, operation status, network topology structure, attack type, attack intensity.

6. The method according to any one of claims 1 to 5, characterized in that, The step of processing the simulation monitoring information by using the loss assessment model and outputting a loss assessment result includes: Processing the simulation monitoring information by using the loss assessment model to obtain a model output result; Convert the model output result into a standardized data format to generate a structured report document as the loss assessment result.

7. A loss assessment device based on power target simulation monitoring, characterized in that, The device includes: A multi-source data processing module, configured to obtain real-time power operation data of a multi-source data acquisition device from a power target simulation scenario, and integrate static power data and the real-time power operation data in the power system to obtain processed multi-source data; A simulation monitoring module, configured to obtain simulation monitoring information according to a power system simulation model and the processed multi-source data; the simulation monitoring information is used to characterize the actual operation state of a power target in the power system; the power system simulation model is constructed based on the operation characteristics of the power system and the dynamic characteristics of power equipment; A loss assessment model construction module, configured to construct a loss assessment model based on power multi-factor coupling according to a loss assessment index system; the loss assessment index system is used to quantify the loss situation after the power target is attacked or fails from different perspectives; A loss assessment result obtaining module, configured to process the simulation monitoring information by using the loss assessment model and output a loss assessment result; the loss assessment result is used to characterize the predicted loss situation of the power system under different fault scenarios.

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

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

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

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