Power grid risk dynamic assessment method, system and device

Abstract

The invention discloses a power grid risk dynamic assessment method, system and device, and relates to the technical field of power grid safety and risk management, and the method comprises the steps: obtaining geographic position data of preset power grid assets and corresponding weather data containing at least two disaster variables; based on historical data, determining marginal probability distribution of each disaster variable; transforming each disaster variable at any time point through a cumulative distribution function of marginal probability distribution to generate a probability value; inputting the probability values into a Copula function, and calculating a joint exceeding probability that the values of the at least two disaster variables simultaneously exceed respective preset thresholds in a preset time window; and taking the calculated joint surpassing probability as a dynamic risk index of the power grid assets so as to realize power grid risk dynamic assessment. According to the method, a Copula function is introduced, and the complex nonlinearity and tail dependency relationship between different disaster variables is accurately captured, so that the coupling risk of multiple disaster concurrence to the power grid is more accurately quantified.

Description

Technical Field

[0001] This invention relates to the field of power grid safety and risk management technology, specifically to a dynamic real-time multi-hazard power grid risk dynamic assessment method, system, and device. Background Technology

[0003] Existing power grid risk assessment methods often have certain limitations. On the one hand, many assessment methods tend to analyze the impact of a single disaster in isolation, such as assessing the risk of strong winds or heavy rainstorms alone, while ignoring the common coupling effects and interactions between different disasters. In the real world, multiple disasters often occur concurrently or in succession; for example, typhoons are often accompanied by heavy rainfall and storm surges. This coupling effect can significantly amplify the damage to the power grid, and single-disaster assessment models cannot accurately quantify such complex risks. On the other hand, many existing risk assessment models are static, mainly based on historical statistical data for probabilistic analysis. Their assessment results are mostly used for long-term planning, design, and reinforcement decisions for the power grid. While these static assessments are crucial for improving the inherent resilience of the power grid, they are insufficient to meet the needs of real-time operation and dispatching of the power grid. The power grid dispatch center needs a tool that can dynamically reflect the risk evolution trend in the near future (e.g., several hours to several days) so that proactive defensive measures such as early warning, adjustment of operating modes, and preparation of emergency repair resources can be taken in advance.

[0004] Furthermore, existing technologies face challenges in modeling complex dependencies among multiple hazards. Relationships between hazard variables are often non-linear and exhibit stronger correlations in extreme cases (i.e., tail events). Traditional linear correlation analysis methods struggle to capture such complex dependency structures, potentially leading to an underestimation of coupling risks. Summary of the Invention

[0005] To address the shortcomings of existing technologies in capturing the complex dependency structures between disasters, which leads to an underestimation of coupling risks, this invention proposes a method, system, and device for dynamic assessment of power grid risks. This method can accurately model the nonlinear and tail-dependent relationships between various disasters and dynamically and proactively assess the coupling risks faced by the power grid based on real-time and predictive weather data, thereby solving the problems existing in the prior art.

[0006] A method for dynamic assessment of power grid risk includes the following steps: Acquire real-time weather data corresponding to the geographical location of power grid assets; the real-time weather data includes at least two types of disaster variables; Based on real-time weather data, the marginal probability distribution of each disaster variable is determined; the cumulative distribution function of the marginal probability distribution is used to transform each disaster variable at any time point to generate a probability value; each probability value is input into the Copula function to calculate the joint exceedance probability that the values ​​of at least two disaster variables simultaneously exceed their respective preset thresholds within a preset time window. The joint exceedance probability is used as the dynamic risk index of power grid assets, and the dynamic risk index of power grid assets is updated at preset time intervals to achieve dynamic assessment of power grid risk.

[0007] Furthermore, the marginal probability distribution is determined by fitting historical disaster data to one or more candidate probability distribution functions.

[0008] Furthermore, the Copula function is a Gaussian Copula, t-Copula, Gumbel Copula, or Clayton Copula function.

[0009] Furthermore, the selection process of the Copula function specifically includes the following steps: Based on professional judgment of the disaster mechanism, several candidate families of Copula functions were initially selected; Based on the Akaike Information Criterion (AIC) or the Bayesian Information Criterion (BIC), the goodness of fit of candidate Copula functions is evaluated, and the optimal Copula function is selected to calculate the joint transcendence probability.

[0010] Furthermore, the joint transcendence probability is specifically expressed as: ; in and These are the cumulative probability values ​​of the two disaster variables at their respective preset thresholds. It is the value of the Copula function. , These are the uniformly distributed variables resulting from the cumulative distribution function (CDF) transformation of two disaster variables.

[0011] The present invention also includes a power grid risk dynamic assessment system, comprising: The acquisition module is used to acquire real-time weather data corresponding to the geographical location of power grid assets; the real-time weather data includes at least two types of disaster variables; The joint exceedance probability determination module is used to determine the marginal probability distribution of each disaster variable based on real-time weather data; transform each disaster variable at any time point through the cumulative distribution function of the marginal probability distribution to generate a probability value; input each probability value into the Copula function to calculate the joint exceedance probability when the values ​​of at least two disaster variables simultaneously exceed their respective preset thresholds within a preset time window; The assessment module is used to use the joint exceedance probability as a dynamic risk index for power grid assets and to update the dynamic risk index of power grid assets at preset time intervals to achieve dynamic assessment of power grid risks.

[0012] The present invention also includes a computer device for dynamic assessment of power grid risks, comprising: a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the dynamic assessment of power grid risks method.

[0013] The present invention also includes a readable storage medium storing a computer program, the computer program including program instructions, which, when executed by a processor, are used to perform the steps of the power grid risk dynamic assessment method.

[0014] This invention provides a method for dynamic assessment of power grid risks, which has the following beneficial effects: This invention integrates real-time and predictive weather data and introduces a Copula function based on the physical characteristics of different disaster combinations. This function can accurately capture the complex nonlinear and tail-dependent relationships between different disaster variables, thereby quantifying the coupling risks of multiple disasters occurring simultaneously on the power grid in a more scientific and accurate manner. At the same time, this method can dynamically assess the evolution trend of power grid risks, providing power grid operators with forward-looking situational awareness capabilities and supporting the shift from passive response to proactive defense. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating the dynamic assessment method for power grid risks in an embodiment of the present invention. Detailed Implementation

[0016] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0017] This invention proposes a dynamic risk assessment method for power grids. This method combines geographic information, meteorological data, and a probabilistic modeling technique based on Copula functions to dynamically and quantitatively assess the complex natural disaster risks faced by power grid assets.

[0018] like Figure 1As shown, the method specifically includes the following steps: S1. Obtain the geographic location data of the preset power grid assets, and the weather data corresponding to the geographic location data; the weather data includes the values ​​of at least two disaster variables.

[0019] Geographic information data, including unique identifiers (such as area codes) and geographic coordinate ranges (such as latitude and longitude) of key assets in the power grid (such as transmission towers, substations, and line segments), can be imported into this method in a standard format.

[0020] Weather data includes historical data, real-time weather conditions, and weather forecasts for the next few days. The data structure contains multiple disaster-related variables, such as windpower, dayweather, and temperature.

[0021] During processing, some qualitative textual descriptions in the weather data need to be transformed into quantitative disaster intensity indicators through preset mapping rules. For example, texts such as "heavy rain," "heavy rain," and "moderate rain" in the "weather phenomenon" section are mapped to corresponding equivalent rainfall intensity values ​​(e.g., in millimeters) to facilitate subsequent probabilistic statistical modeling.

[0022] S2. Based on historical data, such as windpower, dayweather, and temperature, determine the marginal probability distribution for each hazard variable; select and construct a Copula function to model the dependency structure between at least two hazard variables; convert the numerical values ​​of hazard variables in the weather data into probability values ​​through their respective marginal probability distributions; use the Copula function to calculate the joint exceedance probability that the values ​​of at least two hazard variables simultaneously exceed their respective preset thresholds within a preset time window. The step of converting hazard variable numerical values ​​into probability values ​​includes performing a probability integral transformation on the numerical values.

[0023] For each independent disaster variable (such as quantified wind force, rainfall intensity, etc.), it is first necessary to determine its own probability distribution characteristics, i.e., marginal probability distribution, based on its long-term historical observation data. This can be achieved by fitting historical data to multiple candidate probability distribution functions; this invention employs a non-parametric kernel density estimation method.

[0024] Marginal probability distributions are determined by fitting historical disaster data to one or more candidate probability distribution functions. This invention employs a non-parametric method, without pre-assuming that the data follows any specific theoretical distribution, but directly estimating its distribution from the data itself. It mainly uses kernel density estimation (KDE). The historical disaster data is transformed through its own cumulative distribution function (CDF), and the result will follow a uniform distribution over the interval, i.e., a probability value.

[0025] To accurately model the complex dependencies between different hazard variables, this invention employs Copula function theory. The advantage of the Copula function lies in its ability to decompose the problem of describing the joint probability distribution of multiple variables into two independent parts: describing the marginal distributions of each variable and describing the dependency structure between them. The Copula function is selected from one of Gaussian Copula, t-Copula, Gumbel Copula, or Clayton Copula. The selection of the Copula function is based on at least one of the Akaike Information Criterion (AIC) or Bayesian Information Criterion (BIC), and the goodness of fit of candidate Copula functions is evaluated.

[0026] Choose an appropriate family of Copula functions based on the physical characteristics of different disaster combinations. For example: Gumbel Copula: suitable for simulating disaster combinations with upper-tail correlation, i.e., when one variable shows an extreme high value, another variable also tends to show an extreme high value, such as the combination of typhoon and heavy rainfall. Clayton Copula: suitable for simulating disaster combinations with lower-tail correlation, i.e., multiple variables tend to show extreme low values ​​simultaneously. t-Copula: suitable for disasters with symmetric "fat-tailed" dependencies, effectively capturing scenarios where multiple variables simultaneously show extreme values ​​(regardless of whether they are high or low).

[0027] The selection of the Copula function involves the following steps: First, based on professional judgment of the disaster mechanism, several candidate Copula function families are initially selected. Then, parameters are estimated for each family using historical data. The selection of the Copula function employs parameter estimation, which can utilize the maximum likelihood estimation (MLE) method. Finally, the goodness of fit of each model is evaluated using statistical indicators such as the Akaike Information Criterion (AIC) or the Bayesian Information Criterion (BIC), and the optimal Copula function and its parameters are selected. The AIC and BIC criteria can strike a balance between the model's fitting accuracy and complexity, avoiding overfitting due to excessive model complexity.

[0028] Thus, a complete probabilistic risk model for quantifying the coupled risks of multiple hazards has been constructed. The risk model is a composite model, its core consisting of two parts: the first part is the marginal probability distribution function determined for each independent hazard variable (such as quantified wind force, rainfall intensity, etc.); the second part is the optimal Copula function, determined through parameter estimation and goodness-of-fit testing, used to describe the dependency structure between hazard variables. This risk model provides the core mathematical foundation for subsequent dynamic risk calculations.

[0029] Dynamic risk calculation involves inputting real-time or predicted weather data into the pre-built risk model to calculate dynamic risk. The numerical values ​​of hazard variables at the current or future point in time (e.g., the predicted maximum wind speed for the next 24 hours) obtained from weather data are transformed using the cumulative distribution function (CDF) of their marginal probability distributions, which were determined in the second step. The result of this transformation is a probability value within an interval, representing the quantile level of that hazard intensity value in its historical data.

[0030] The joint exceedance probability calculation utilizes a pre-constructed Copula function, taking the probability values ​​of multiple hazard variables after probability integral transformation as input, and calculates the joint probability that the intensity of these hazard variables simultaneously exceeds their respective preset danger thresholds. This probability is called the "joint exceedance probability," which directly quantifies the coupled risk of multiple concurrent hazardous events. For example, for a two-dimensional Copula model, its joint exceedance probability can be expressed by the formula... Calculation, where and The cumulative probability values ​​corresponding to the two disaster variables at their respective preset thresholds are called the non-exceedance probability. , These are the uniformly distributed variables obtained after transforming two hazard variables using the cumulative distribution function (CDF). This is the value of the Copula function. If the risk threshold for rainfall-related disasters is set at the 95th percentile of its historical data, then the corresponding risk level for that disaster is... The value is 0.95.

[0031] S3. Based on the calculated joint exceedance probability, generate a dynamic risk index for the power grid assets; dynamically update and visualize the risk index of the power grid assets on a geographic map.

[0032] Risk index generation and visualization involves using the joint exceedance probability calculated in the previous step as the core to generate a quantitative, dynamic, multi-risk index. In a simple implementation, the joint exceedance probability can be directly used as the risk index. In a more complex implementation, the risk index can be weighted or adjusted by incorporating vulnerability information of specific power grid assets (e.g., tower design wind resistance level, service life, health status, etc.) to better reflect the disaster resilience of specific assets, thereby obtaining more refined risk assessment results.

[0033] Based on a dynamic risk index, power grid assets on a geographic map are rendered using color or size, and then visualized on the geographic map. In practice, the graphic elements (such as points or line segments) representing each power grid asset on the map can be visually encoded with a value associated with the risk index. For example, different colors (such as blue to yellow representing low to high risk) or different sizes (such as the size of the point being proportional to the risk index) can be used for rendering, forming an intuitive risk heat map.

[0034] S4. Repeat the above steps at preset time intervals to achieve dynamic risk assessment.

[0035] To achieve dynamic risk assessment, the entire process from "data acquisition and processing" to "risk index generation and visualization" is placed in a loop and repeated at preset time intervals (e.g., every 2 minutes or every hour). In each loop, the system acquires the latest weather data, recalculates and updates the risk index of all assets on the map, thereby achieving continuous and dynamic monitoring of the power grid risk situation. The method of this invention also includes a step of mapping textual descriptions in the weather data into quantified disaster intensity indicators.

[0036] The beneficial effects of this invention are as follows: By systematically integrating geographic information, meteorological data, and advanced Copula probabilistic modeling technology, this invention constructs a complete and operable dynamic risk assessment process, effectively addressing the shortcomings of existing technologies in assessing the coupled risks of multiple disasters. This provides strong technical support for ensuring the safe operation of the power grid in increasingly severe natural disaster environments. By introducing Copula functions, this invention can surpass traditional linear correlation analysis, accurately capturing the complex nonlinear and tail-dependent relationships between different disaster variables, thereby more scientifically and accurately quantifying the coupled risks of multiple disasters on the power grid. The method and system of this invention are designed to operate periodically, integrating real-time and predictive weather data to dynamically assess and display the evolution trend of risks, providing power grid operators with forward-looking situational awareness capabilities and supporting the shift from passive response to proactive defense. By transforming abstract risk probabilities into intuitive, geographically based, visualized risk maps, this invention can provide clear and quantitative basis for operational decisions such as power grid dispatching, emergency response, and asset management, helping to optimize resource allocation and improve the overall operational resilience of the power grid.

[0037] Based on the same inventive concept, this invention also proposes a dynamic power grid risk assessment system, comprising: The acquisition module is used to acquire real-time weather data corresponding to the geographical location of power grid assets; the real-time weather data contains at least two disaster variables.

[0038] The joint exceedance probability determination module is used to determine the marginal probability distribution of each disaster variable based on real-time weather data; transform each disaster variable at any time point through the cumulative distribution function of the marginal probability distribution to generate a probability value; input each probability value into the Copula function to calculate the joint exceedance probability when the values ​​of at least two disaster variables simultaneously exceed their respective preset thresholds within a preset time window.

[0039] The assessment module is used to use the joint exceedance probability as a dynamic risk index for power grid assets and to update the dynamic risk index of power grid assets at preset time intervals to achieve dynamic assessment of power grid risks.

[0040] The present invention also proposes a computer device for dynamic assessment of power grid risks, comprising: a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the dynamic assessment method for power grid risks.

[0041] The present invention also proposes a readable storage medium storing a computer program, the computer program including program instructions, which, when executed by a processor, are used to perform the steps of the power grid risk dynamic assessment method.

[0042] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for dynamic assessment of power grid risk, characterized in that, Includes the following steps: Acquire real-time weather data corresponding to the geographical location of power grid assets; the real-time weather data includes at least two types of disaster variables; Based on real-time weather data, the marginal probability distribution of each disaster variable is determined; the cumulative distribution function of the marginal probability distribution is used to transform each disaster variable at any time point to generate a probability value; each probability value is input into the Copula function to calculate the joint exceedance probability that the values ​​of at least two disaster variables simultaneously exceed their respective preset thresholds within a preset time window. The joint exceedance probability is used as the dynamic risk index of power grid assets, and the dynamic risk index of power grid assets is updated at preset time intervals to achieve dynamic assessment of power grid risk.

2. The method for dynamic assessment of power grid risk according to claim 1, characterized in that, The Copula function is a Gaussian Copula, t-Copula, Gumbel Copula, or Clayton Copula function.

3. The method for dynamic assessment of power grid risk according to claim 2, characterized in that, The selection process for the Copula function specifically includes the following steps: Based on professional judgment of the disaster mechanism, several candidate families of Copula functions were initially selected; Based on the Akaike Information Criterion (AIC) or the Bayesian Information Criterion (BIC), the goodness of fit of candidate Copula functions is evaluated, and the optimal Copula function is selected to calculate the joint transcendence probability.

4. The method for dynamic assessment of power grid risk according to claim 1, characterized in that, The marginal probability distribution is determined by fitting weather data to one or more candidate probability distribution functions.

5. The method for dynamic assessment of power grid risk according to claim 1, characterized in that, The joint transcendence probability is specifically expressed as: ； in and These are the cumulative probability values ​​of the two disaster variables at their respective preset thresholds. It is the value of the Copula function. , These are the uniformly distributed variables resulting from the cumulative distribution function (CDF) transformation of two disaster variables.

6. A dynamic risk assessment system for power grids, characterized in that, include: The acquisition module is used to acquire real-time weather data corresponding to the geographical location of power grid assets; the real-time weather data includes at least two types of disaster variables; The joint exceedance probability determination module is used to determine the marginal probability distribution of each disaster variable based on real-time weather data; transform each disaster variable at any time point through the cumulative distribution function of the marginal probability distribution to generate a probability value; input each probability value into the Copula function to calculate the joint exceedance probability when the values ​​of at least two disaster variables simultaneously exceed their respective preset thresholds within a preset time window; The assessment module is used to use the joint exceedance probability as a dynamic risk index for power grid assets and to update the dynamic risk index of power grid assets at preset time intervals to achieve dynamic assessment of power grid risks.

7. A computer device for dynamic assessment of power grid risks, characterized in that, include: A memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the power grid risk dynamic assessment method according to any one of claims 1-5.

8. A readable storage medium, characterized in that, The readable storage medium stores a computer program, which includes program instructions that, when executed by a processor, perform the steps of the power grid risk dynamic assessment method according to any one of claims 1-5.

Images