Distribution network equipment operation risk assessment method and system based on digital twinning

Abstract

The invention provides a distribution network equipment operation risk assessment method and system based on digital twinning, and the method comprises the steps: calculating an overall potential risk assessment value of equipment according to a pre-established future risk analysis model based on meteorological prediction based on meteorological data and historical operation data of each piece of equipment, judging whether the overall equipment has potential risks according to the overall potential risk assessment value of the equipment; based on the meteorological data, the overall potential risk assessment value of the equipment and the risk weight of the equipment in the grid, judging whether the whole power distribution network has a risk or not according to a pre-established combined assessment model of the influence of the weather on the grid risk; if the overall equipment has the potential risk and the overall power distribution network has no risk, adjusting a risk level threshold value of the equipment, otherwise, obtaining a risk assessment result of the equipment according to a pre-established environmental factor weighted health state assessment model based on the meteorological data, the historical operation data of the equipment and the weight coefficient of the operation data; and the accuracy and timeliness of risk assessment are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution network operation analysis, and specifically relates to a distribution network equipment operation risk assessment method and system based on digital twins. Background Art

[0002] With the rapid development of power systems and the continuous improvement of distribution network reliability, the operational status monitoring and risk assessment of distribution network equipment have become core issues for the safe operation of power grids. With the large-scale integration of distributed power sources and renewable energy sources such as photovoltaics, grid load volatility has increased. Photovoltaic power generation is significantly affected by meteorological fluctuations. Extreme weather can cause PV output to drop sharply or lead to sudden overloads, increasing the risk of failure of distribution network equipment.

[0003] Traditional distribution network equipment risk assessment methods primarily rely on the equipment's operational data, using static risk grading and simple threshold determination. These methods also have limitations in complex power grid environments, particularly when faced with long-term equipment operation, changing environments, and diverse failure modes, making it difficult to accurately capture potential equipment risks.

[0004] In actual operation, meteorological factors have a significant impact on the health status and failure risk of equipment. For example, high temperatures can cause equipment overload and accelerate transformer aging; high humidity or strong winds can affect the insulation performance of equipment, increasing the likelihood of equipment failure. However, assessment models that rely solely on equipment data still neglect environmental factors, especially the impact of meteorological changes on equipment operation. For example, the potential risks of extreme weather to equipment have not received sufficient attention. Combining meteorological factors with equipment health data to establish a more comprehensive and dynamic risk assessment model will improve the accuracy and reliability of the system's early warning system.

[0005] In summary, when comparing the traditional distribution network operation risk assessment method with the risk assessment method integrated with digital twin technology, the traditional distribution network operation risk assessment method exposes the following three deficiencies:

[0006] (1) Risk assessment is based on a single dimension, lacking a comprehensive assessment of environmental factors. Risk assessment is based on a single piece of equipment operating data, ignoring the combined effects of multiple factors. The interaction between the external environment and the equipment also affects the health of the equipment. Single-dimensional analysis can easily lead to one-sided risk assessments and misjudgments.

[0007] (2) Static assessment models lack a real-time dynamic feedback mechanism and rely on historical data and static thresholds to assess the health status of equipment. Equipment risk assessment is usually a post-analysis and lacks a real-time dynamic feedback mechanism. This model cannot reflect changes in equipment health status in a timely manner. When faced with rapidly changing loads or sudden failures, traditional methods cannot provide effective early warnings.

[0008] (3) The volatility of distributed power and photovoltaic power generation is not taken into account, making it difficult to adapt to the rapidly changing grid environment. Static thresholds and fixed rules are used for risk assessment. However, factors such as environmental changes in the grid, fluctuations in equipment loads, and the access of distributed power sources have made grid operation increasingly complex. The inability to flexibly adapt to the rapid changes in the grid environment has led to an inability to accurately assess risks in complex scenarios. Summary of the Invention

[0009] In order to solve the problems in the prior art of single risk assessment dimension, lack of real-time dynamic feedback mechanism in static assessment models, and difficulty in adapting to grid load fluctuations, the present invention provides a distribution network equipment operation risk assessment method based on digital twins. The improvement lies in that the method includes:

[0010] Based on meteorological data and the historical operating data of each device, the overall potential risk assessment value of the device is calculated according to the pre-established future risk analysis model based on meteorological forecasts, and the overall potential risk assessment value of the device is used to determine whether the device as a whole has potential risks;

[0011] Based on meteorological data, the overall potential risk assessment value of the equipment and the risk weight of the equipment in the grid, the pre-established joint assessment model of the impact of meteorological conditions on grid risk is used to determine whether the distribution network as a whole is at risk;

[0012] If there is a potential risk for the equipment as a whole and the distribution network as a whole is risk-free, the risk level threshold of the equipment is adjusted. Otherwise, the risk assessment result of the equipment is obtained based on the meteorological data, the historical operation data of the equipment, and the weight coefficient of the operation data according to the pre-established environmental factor weighted health status assessment model;

[0013] Among them, the weight coefficient of the operating data can be dynamically adjusted according to the characteristics and operating status of the equipment. The future risk analysis model based on meteorological forecasts, the joint assessment model of the impact of meteorological conditions on grid risks, and the weighted health status assessment model of environmental factors are digital twin models that map and interact with distribution network entities and are synchronized with the operating status of the distribution network entities.

[0014] Preferably, the meteorological data includes one or more of the following:

[0015] Current temperature value, current humidity value, current wind speed value, predicted temperature value, or predicted humidity value.

[0016] Preferably, the expression of the environmental factor weighted health status assessment model is:

[0017]

[0018] Among them, h(t) is the comprehensive health status index function, t is time, ω i(t) is the weight coefficient of the operating data, which indicates the influence of the parameter of the i-th operating data in the equipment health assessment, f i ((I,U,P,Q) i ,t) represents the contribution value of the specific parameters of the equipment's current, voltage, power and load operation data to the health status at time t, U represents the voltage level of the equipment, I represents the current value, P represents the power actually consumed or generated by the equipment, Q represents the power required to maintain the electric field in the power system, T represents temperature, H represents humidity, W represents wind speed, α1 represents the influence coefficient of the meteorological factor with the largest influencing factor, M(T,H,W) represents the meteorological factor function, i represents the parameter of the i-th operation data, and m represents the number of parameters of the operation data.

[0019] Preferably, the expression of the future risk analysis model based on meteorological forecast is:

[0020]

[0021] Where P(t) represents the overall potential risk assessment value of the equipment at time t, represents the risk assessment of equipment j at time t, ω j represents the risk probability weight of device j, T 预测 is the predicted value of temperature, H 预测 is the predicted value of humidity, M 预测 (T 预测 ,H 预测 ) is the future meteorological factor function, f(M 预测 (T 预测 ,H 预测 )) is the contribution value of the future meteorological factor function, and n is the number of devices.

[0022] Preferably, the expression of the joint assessment model of the impact of meteorological conditions on grid risk is:

[0023]

[0024] Among them, R 网架 represents the overall risk assessment value of the distribution network, M(T,H,W) is the meteorological factor function, α2 is the influence coefficient of the meteorological factor in the distribution network grid, and β is the weight of the overall potential risk assessment value of the equipment.

[0025] Preferably, based on meteorological data and historical operating data of each device, the overall potential risk assessment value of the device is calculated according to a pre-established future risk analysis model based on meteorological forecasts, and judging whether the device as a whole has potential risks according to the overall potential risk assessment value of the device includes:

[0026] Based on the historical operating data of each device, a risk assessment model is used to obtain the current risk assessment of each device;

[0027] Based on the predicted temperature and humidity values, the risk probability weights of each device, and the current risk assessment of each device, the overall potential risk assessment value of the device is obtained according to the pre-established future risk analysis model based on meteorological forecasts;

[0028] Determine whether the overall potential risk assessment value of the device exceeds the potential risk threshold. If so, it is determined that the device as a whole has a potential risk; otherwise, there is no potential risk.

[0029] Preferably, based on meteorological data, the overall potential risk assessment value of the equipment and the risk weight of the equipment in the grid, judging whether the distribution network as a whole is at risk according to a pre-established joint assessment model of meteorological impact on grid risk includes:

[0030] Determine the risk weight of the equipment in the grid based on the operating parameters of each equipment and meteorological factors;

[0031] Based on the overall potential risk assessment value of the equipment, meteorological data and the risk weight of the equipment in the grid, the overall risk assessment value of the distribution network is obtained according to the pre-established joint assessment model of the impact of meteorological factors on the grid risk.

[0032] Determine whether the risk assessment value of the distribution network as a whole exceeds a risk status threshold, if so, determine that the distribution network as a whole is at risk, otherwise, there is no risk;

[0033] Among them, the risk weight of the equipment in the grid includes the influence coefficient of meteorological factors in the distribution network grid and the weight of the overall potential risk assessment value of the equipment.

[0034] Based on the same inventive concept, the present invention also provides a distribution network equipment operation risk assessment system based on digital twins, the system comprising:

[0035] The potential risk judgment module is used to calculate the overall potential risk assessment value of the equipment based on meteorological data and the historical operating data of each device according to a pre-established future risk analysis model based on meteorological forecasts, and to judge whether the equipment as a whole has potential risks based on the overall potential risk assessment value of the equipment;

[0036] The distribution network risk assessment module is used to determine whether the distribution network as a whole is at risk based on meteorological data, the overall potential risk assessment value of the equipment and the risk weight of the equipment in the grid, and according to a pre-established joint assessment model of the impact of meteorological conditions on grid risk;

[0037] The risk assessment module is used to adjust the risk level threshold of the equipment if there is a potential risk for the equipment as a whole and the distribution network as a whole is risk-free. Otherwise, the risk assessment result of the equipment is obtained based on the meteorological data, the historical operation data of the equipment and the weight coefficient of the operation data according to the pre-established environmental factor weighted health status assessment model;

[0038] Among them, the weight coefficient of the operating data can be dynamically adjusted according to the characteristics and operating status of the equipment. The future risk analysis model based on meteorological forecasts, the joint assessment model of the impact of meteorological conditions on grid risks, and the weighted health status assessment model of environmental factors are digital twin models that map and interact with distribution network entities and are synchronized with the operating status of the distribution network entities.

[0039] Preferably, the meteorological data in the potential risk determination module includes one or more of the following:

[0040] Current temperature value, current humidity value, current wind speed value, predicted temperature value, or predicted humidity value.

[0041] Preferably, the expression of the environmental factor weighted health status assessment model in the risk assessment module is:

[0042]

[0043] Among them, h(t) is the comprehensive health status index function, t is time, ω i (t) is the weight coefficient of the operating data, which indicates the influence of the parameter of the i-th operating data in the equipment health assessment, f i ((I,U,P,Q) i ,t) represents the contribution value of the specific parameters of the equipment's current, voltage, power and load operation data to the health status at time t, U represents the voltage level of the equipment, I represents the current value, P represents the power actually consumed or generated by the equipment, Q represents the power required to maintain the electric field in the power system, T represents temperature, H represents humidity, W represents wind speed, α1 represents the influence coefficient of the meteorological factor with the largest influencing factor, M(T,H,W) represents the meteorological factor function, i represents the parameter of the i-th operation data, and m represents the number of parameters of the operation data.

[0044] Preferably, the expression of the future risk analysis model based on meteorological forecast in the potential risk judgment module is:

[0045]

[0046] Where P(t) represents the overall potential risk assessment value of the equipment at time t, represents the risk assessment of equipment j at time t, ω j represents the risk probability weight of device j, T 预测 is the predicted value of temperature, H 预测is the predicted value of humidity, M 预测 (T 预测 ,H 预测 ) is the future meteorological factor function, f(M 预测 (T 预测 ,H 预测 )) is the contribution value of the future meteorological factor function, and n is the number of devices.

[0047] Preferably, the expression of the joint assessment model for judging the impact of meteorological conditions on grid risk in the distribution network risk module is:

[0048]

[0049] Among them, R 网架 represents the overall risk assessment value of the distribution network, M(T,H,W) is the meteorological factor function, α2 is the influence coefficient of the meteorological factor in the distribution network grid, and β is the weight of the overall potential risk assessment value of the equipment.

[0050] Preferably, the potential risk determination module calculates the overall potential risk assessment value of the equipment based on meteorological data and historical operating data of each device according to a pre-established future risk analysis model based on meteorological forecasts, and determines whether the equipment as a whole has potential risks based on the overall potential risk assessment value of the equipment, including:

[0051] Based on the historical operating data of each device, a risk assessment model is used to obtain the current risk assessment of each device;

[0052] Based on the predicted temperature and humidity values, the risk probability weights of each device, and the current risk assessment of each device, the overall potential risk assessment value of the device is obtained according to the pre-established future risk analysis model based on meteorological forecasts;

[0053] Determine whether the overall potential risk assessment value of the device exceeds the potential risk threshold. If so, it is determined that the device as a whole has a potential risk; otherwise, there is no potential risk.

[0054] Preferably, the distribution network risk module determines whether the distribution network as a whole is at risk based on meteorological data, the overall potential risk assessment value of the equipment, and the risk weight of the equipment in the grid, and according to a pre-established joint assessment model of the impact of meteorological conditions on grid risk, including:

[0055] Determine the risk weight of the equipment in the grid based on the operating parameters of each equipment and meteorological factors;

[0056] Based on the overall potential risk assessment value of the equipment, meteorological data and the risk weight of the equipment in the grid, the overall risk assessment value of the distribution network is obtained according to the pre-established joint assessment model of the impact of meteorological factors on the grid risk.

[0057] Determine whether the risk assessment value of the distribution network as a whole exceeds a risk status threshold, if so, determine that the distribution network as a whole is at risk, otherwise, there is no risk;

[0058] Among them, the risk weight of the equipment in the grid includes the influence coefficient of meteorological factors in the distribution network grid and the weight of the overall potential risk assessment value of the equipment.

[0059] In another aspect, the present application further provides a computing device comprising: at least one processor and a memory;

[0060] The memory is used to store one or more programs;

[0061] When the one or more programs are executed by the one or more processors, a distribution network equipment operation risk assessment method based on digital twins as described above is implemented.

[0062] On the other hand, the present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed, it implements a distribution network equipment operation risk assessment method based on digital twins as described above.

[0063] Compared with the prior art, the present invention has the following beneficial effects:

[0064] The present invention provides a distribution network equipment operation risk assessment method and system based on digital twins, including: based on meteorological data and historical operation data of each device, calculating the overall potential risk assessment value of the device according to a pre-established future risk analysis model based on meteorological forecasts, and judging whether the device as a whole has potential risks according to the overall potential risk assessment value of the device; based on meteorological data, the overall potential risk assessment value of the device and the risk weight of the device in the grid, judging whether the distribution network as a whole has risks according to a pre-established joint assessment model of meteorological impact on grid risk; if there is a potential risk for the device as a whole and the distribution network as a whole has no risk, adjusting the risk level threshold of the device, otherwise, based on meteorological data, historical operation data of the device and the weight coefficient of the operation data, obtaining the risk assessment result of the device according to a pre-established environmental factor weighted health status assessment model; wherein the weight coefficient of the operation data can be determined by the characteristics and operation status of the device. Dynamic adjustment: The future risk analysis model based on meteorological forecasts, the joint assessment model of meteorological impact on grid risk, and the weighted health status assessment model of environmental factors are digital twin models that map and interact with distribution network entities and are synchronized with the operating status of distribution network entities. Combining meteorological data with the historical operating data of equipment solves the problem of relying on a single operating data of equipment in traditional risk assessment methods. Dynamic weight adjustment is adopted to adjust the risk level threshold of equipment in real time, dynamically adjust the risk warning mechanism, timely capture potential high-risk equipment, predict the potential risks of equipment in advance, accurately assess the impact of faults on grid stability, and improve the safety and reliability of the distribution network. Digital twin technology is introduced to construct a dynamic and refined distribution network equipment operation risk assessment model to achieve real-time updating and monitoring of equipment status, flexibly adapt to grid load fluctuations, meteorological changes and the access of distributed power sources, and improve the accuracy and timeliness of risk assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 A schematic flow chart of a distribution network equipment operation risk assessment method based on digital twins provided by the present invention;

[0066] Figure 2 A specific example diagram of a distribution network equipment operation risk assessment method based on digital twins provided by the present invention;

[0067] Figure 3 This is a diagram of the distribution network equipment operation risk assessment system based on digital twins provided by the present invention;

[0068] Figure 4 The present invention provides an electronic device. DETAILED DESCRIPTION

[0069] The specific embodiments of the present invention are further described in detail below with reference to the accompanying drawings.

[0070] Example 1

[0071] The present invention provides a distribution network equipment operation risk assessment method based on digital twins, such as Figure 1 ,include:

[0072] Based on meteorological data and the historical operating data of each device, the overall potential risk assessment value of the device is calculated according to the pre-established future risk analysis model based on meteorological forecasts, and the overall potential risk assessment value of the device is used to determine whether the device as a whole has potential risks;

[0073] Based on meteorological data, the overall potential risk assessment value of the equipment and the risk weight of the equipment in the grid, the pre-established joint assessment model of the impact of meteorological conditions on grid risk is used to determine whether the distribution network as a whole is at risk;

[0074] If there is a potential risk for the equipment as a whole and the distribution network as a whole is risk-free, the risk level threshold of the equipment is adjusted. Otherwise, the risk assessment result of the equipment is obtained based on the meteorological data, the historical operation data of the equipment, and the weight coefficient of the operation data according to the pre-established environmental factor weighted health status assessment model;

[0075] Among them, the weight coefficient of the operating data can be dynamically adjusted according to the characteristics and operating status of the equipment. The future risk analysis model based on meteorological forecasts, the joint assessment model of the impact of meteorological conditions on grid risks, and the weighted health status assessment model of environmental factors are digital twin models that map and interact with distribution network entities and are synchronized with the operating status of the distribution network entities.

[0076] Preferably, the meteorological data includes one or more of the following:

[0077] Current temperature value, current humidity value, current wind speed value, predicted temperature value, or predicted humidity value.

[0078] Preferably, the expression of the environmental factor weighted health status assessment model is:

[0079]

[0080] Among them, h(t) is the comprehensive health status index function, t is time, ω i (t) is the weight coefficient of the operating data, which indicates the influence of the parameter of the i-th operating data in the equipment health assessment and reflects the relative importance of each assessment parameter of the equipment health status to the final risk assessment. i ((I,U,P,Q) i,t) represents the contribution value of the specific parameters of the equipment's current, voltage, power and load operation data to the health status at time t, U represents the voltage level of the equipment, I represents the current value, P represents the power actually consumed or generated by the equipment, Q represents the power required to maintain the electric field in the power system, T represents temperature, H represents humidity, W represents wind speed, α1 represents the influence coefficient of the meteorological factor with the largest influencing factor, M(T,H,W) represents the meteorological factor function, i represents the parameter of the i-th operation data, and m represents the number of parameters of the operation data.

[0081] Among them, h(t) represents the current overall health level of the device. The health status index is usually between 0 and 1 (or other intervals). The higher the value, the worse the health of the device and the greater the risk; the lower the value, the better the device is running.

[0082] Among them, the weight coefficient is dynamically adjusted according to the degree of influence of different parameters when calculating the health status of the equipment. Abnormal changes in certain parameters may have a greater impact on the equipment risk, so their weight should be higher.

[0083] Preferably, the weight coefficient of the operating data is determined as follows:

[0084] (1) Based on the characteristics and historical data of the equipment, by analyzing the performance of the equipment under different operating conditions and combining it with historical fault data, it can be determined which parameters have a greater impact on the health of the equipment. For example, the temperature and current of a transformer usually have a greater impact on the health status than the voltage, so their weights should be set higher;

[0085] (2) Expert experience. The operating standards and empirical data of some equipment (such as the risk of certain equipment being overloaded) can also be used as the basis for determining the weight coefficient;

[0086] (3) Adaptive adjustment can be dynamically adjusted according to actual operating conditions. For example, if the device is operating under heavy load for a long time, the current weight may need to be increased, and vice versa.

[0087] For example, for a transformer, the parameters for equipment health assessment include current I, temperature T, and voltage U. At room temperature, current has a greater impact on the equipment, while at high temperatures, the impact of temperature is even more pronounced. Therefore, the weighting factor for current may be higher at room temperature, while the weighting factor for temperature may need to be increased in high temperatures.

[0088] f i ((I,U,P,Q) i ,t) represents the current, voltage, power and load operating data of the equipment, the contribution value of a specific parameter to the health status at a certain moment, and changes with time and the operating status of the equipment.

[0089] U reflects the voltage level of the equipment. Excessively high or low voltage may affect the performance and life of the equipment. I is the current fluctuation or overload that may cause the equipment to heat up or be damaged. P is the actual power consumed or generated by the equipment. Abnormal power may mean that the equipment load is unbalanced or overloaded. Q represents the power required to maintain the electric field in the power system. A poor power factor may cause unstable operation of power equipment.

[0090] At a certain moment t, the device's current I is high, while the voltage U is normal, and the power factor is poor (active power P and reactive power Q are unbalanced). Furthermore, the current temperature is high. The system dynamically calculates the device's health index h(t) based on the changes and weights of these parameters. If these parameters indicate that the device is overloaded or significantly affected by the environment, the system adjusts the health index to improve risk assessment.

[0091] The environmentally weighted health assessment model combines equipment operating data, environmental meteorological data, and dynamic weighting coefficients to comprehensively evaluate equipment health through a weighted summation method. The inclusion of meteorological factors makes the assessment more comprehensive, especially in extreme weather conditions, and enables real-time adjustments to equipment risk assessment results, thereby providing more accurate risk warnings for power grid operations.

[0092] Preferably, the expression of the future risk analysis model based on meteorological forecast is:

[0093]

[0094] Among them, P(t) represents the overall potential risk assessment value of the equipment at time t, which is a comprehensive assessment of the probability or severity of failure or risk events that may occur in the equipment at future moments. represents the risk assessment of device j at time t, calculated based on the device's health status (such as current, voltage, temperature, etc.). is the device health status index or the probability of device failure, depending on the specific risk assessment model, ω j represents the risk probability weight of device j, T 预测 is the predicted value of temperature, H 预测 is the predicted value of humidity, M 预测 (T 预测 ,H 预测 ) is the future meteorological factor function, f(M 预测 (T 预测 ,H 预测 )) is the contribution value of the future meteorological factor function, and n is the number of devices.

[0095] Combined with weather forecast data, we can assess the impact of future weather changes on equipment operations in advance and conduct forward-looking risk analysis. For example, we can use weather forecast models (temperature, humidity, precipitation, etc.) to predict equipment risks over a period of time, allowing us to take protective measures or adjust operating strategies in advance.

[0096] Dynamic risk level adjustment is performed in combination with meteorological data (such as high temperature, low temperature, humidity changes, etc.). The risk level threshold of the equipment is adjusted under different weather conditions, especially for those equipment that are more affected by the weather, such as transformers, switchgear, etc.

[0097] The risk level threshold is adjusted based on environmental factors such as temperature and humidity. For example, high temperatures may increase transformer load, causing the risk level threshold to decrease.

[0098] By dynamically adjusting the risk level threshold, potential risks of equipment can be identified in advance under extreme weather conditions (such as high temperature, heavy rain, etc.), avoiding misjudgments caused by weather changes.

[0099] By combining future meteorological conditions (such as temperature and humidity) with equipment health assessments, the system can predict the potential impact of severe weather on equipment health in advance and assess the risk to the power grid in that environment. If temperatures are forecast to be higher in the coming days, the system may issue higher risk warnings for temperature-sensitive equipment (such as transformers). The risk assessment of each device changes as the device's operating status changes, reflecting the device's health status and failure risk in real time. By weighting and summing meteorological forecast data with the device's current risk assessment, a formula can be used to determine the overall risk assessment of the device in the future. This provides forward-looking information to grid operators, enabling them to predict, issue warnings, and address potential risks before they occur.

[0100] Preferably, the expression of the joint assessment model of the impact of meteorological conditions on grid risk is:

[0101]

[0102] Among them, R 网架 It represents the overall risk assessment value of the distribution network, which integrates the risk assessment of all equipment and the influence of environmental factors, reflects the risk status of the distribution network at a certain moment, and performs weighted summation of the risk assessment values ​​of all n equipment in the distribution network. M(T,H,W) is the meteorological factor function, α2 is the influence coefficient of the meteorological factor in the distribution network grid, and β is the weight of the overall potential risk assessment value of the equipment.

[0103] M(T,H,W) is a meteorological factor function, typically determined by meteorological variables such as temperature T, humidity H, and wind speed W. This factor can be used to measure the overall impact of different weather conditions (such as extreme temperature, humidity, and wind speed) on equipment and grids. For example, extreme temperature may cause equipment overload or accelerated aging, while storms may cause equipment damage or outage.

[0104] In summary, the device health assessment model determines the current health status of a device by monitoring its operating data (such as current, voltage, and power) in real time. However, this assessment result is not isolated. To more comprehensively reflect the device's operating status, the system combines the device's operating parameters with external meteorological factors (such as temperature, humidity, and wind speed) to form a weighted health assessment model. By adjusting the weight coefficient of each device parameter under different meteorological conditions, the system can more accurately calculate the device's health status.

[0105] Meteorological factors directly impact equipment load and operating status. The risk level adjustment mechanism dynamically optimizes risk assessments by sensing weather changes, ensuring equipment remains within a reasonable safety range. Future weather conditions are linked to the current health of equipment, predicting potential risks to equipment based on future weather changes. The risk status of each device is weighted and linked to the overall grid risk. Changes in meteorological factors simultaneously impact the risk level of the entire distribution network, ensuring that grid risks can be effectively predicted and controlled in extreme weather conditions.

[0106] Combine meteorological data with overall grid risk assessment. When multiple devices are affected by meteorological factors (such as storms and low temperatures), widespread failures can occur. Meteorological factors can be incorporated as independent variables for grid risk assessment, linking them with equipment risk.

[0107] Preferably, based on meteorological data and historical operating data of each device, the overall potential risk assessment value of the device is calculated according to a pre-established future risk analysis model based on meteorological forecasts, and judging whether the device as a whole has potential risks according to the overall potential risk assessment value of the device includes:

[0108] Based on the historical operating data of each device, a risk assessment model is used to obtain the current risk assessment of each device;

[0109] Based on the predicted temperature and humidity values, the risk probability weights of each device, and the current risk assessment of each device, the overall potential risk assessment value of the device is obtained according to the pre-established future risk analysis model based on meteorological forecasts;

[0110] Determine whether the overall potential risk assessment value of the device exceeds the potential risk threshold. If so, it is determined that the device as a whole has a potential risk; otherwise, there is no potential risk.

[0111] Preferably, based on meteorological data, the overall potential risk assessment value of the equipment and the risk weight of the equipment in the grid, judging whether the distribution network as a whole is at risk according to a pre-established joint assessment model of meteorological impact on grid risk includes:

[0112] Determine the risk weight of the equipment in the grid based on the operating parameters of each equipment and meteorological factors;

[0113] Based on the overall potential risk assessment value of the equipment, meteorological data and the risk weight of the equipment in the grid, the overall risk assessment value of the distribution network is obtained according to the pre-established joint assessment model of the impact of meteorological factors on the grid risk.

[0114] Determine whether the risk assessment value of the distribution network as a whole exceeds a risk status threshold, if so, determine that the distribution network as a whole is at risk, otherwise, there is no risk;

[0115] Among them, the risk weight of the equipment in the grid includes the influence coefficient of meteorological factors in the distribution network grid and the weight of the overall potential risk assessment value of the equipment.

[0116] Preferably, if the device as a whole has potential risks and the distribution network as a whole has no risks, adjusting the risk level threshold of the device includes:

[0117] Based on known topological relationships, devices with potential risks are identified, and the risk level threshold of the devices is adjusted according to environmental factors, and risk warnings are issued. For example, if the temperature forecast for the next few days is high, high temperatures may cause the transformer load to increase, thereby lowering the risk level threshold of the devices and issuing higher risk warnings for temperature-sensitive equipment transformers.

[0118] A specific example of a distribution network equipment operation risk assessment method based on digital twins, such as Figure 2 ,include:

[0119] Combining equipment operating parameters with meteorological factors to form a health status assessment model weighted by environmental factors, a future risk analysis model based on meteorological forecasts, and a joint assessment model of the impact of meteorological conditions on grid risks;

[0120] Calculate the overall potential risk assessment value of the equipment based on the future risk analysis model based on meteorological forecasts, and determine whether the equipment as a whole has potential risks based on the overall potential risk assessment value of the equipment;

[0121] Based on the risk weights of the equipment in the grid, a joint assessment model of meteorological impact on grid risk is used to determine whether the distribution network as a whole is at risk;

[0122] If there is a potential risk in the equipment as a whole and the distribution network as a whole is risk-free, the risk level threshold of the equipment is adjusted. Otherwise, the risk assessment result of the equipment is obtained based on the environmental factor weighted health status assessment model.

[0123] With the development of digital twin technology, the combination of real-time equipment data and virtual models for dynamic monitoring and evaluation can improve the accuracy of distribution equipment health assessments. By creating a virtual model of the equipment, digital twin technology can synchronize the real-time status of the equipment and predict the future operating trends of the equipment.

[0124] Example 2

[0125] Based on the same inventive concept, the present invention also provides a distribution network equipment operation risk assessment system based on digital twins, such as Figure 3 , the system comprising:

[0126] The potential risk judgment module is used to calculate the overall potential risk assessment value of the equipment based on meteorological data and the historical operating data of each device according to a pre-established future risk analysis model based on meteorological forecasts, and to judge whether the equipment as a whole has potential risks based on the overall potential risk assessment value of the equipment;

[0127] The distribution network risk assessment module is used to determine whether the distribution network as a whole is at risk based on meteorological data, the overall potential risk assessment value of the equipment and the risk weight of the equipment in the grid, and according to a pre-established joint assessment model of the impact of meteorological conditions on grid risk;

[0128] The risk assessment module is used to adjust the risk level threshold of the equipment if there is a potential risk for the equipment as a whole and the distribution network as a whole is risk-free. Otherwise, the risk assessment result of the equipment is obtained based on the meteorological data, the historical operation data of the equipment and the weight coefficient of the operation data according to the pre-established environmental factor weighted health status assessment model;

[0129] Among them, the weight coefficient of the operating data can be dynamically adjusted according to the characteristics and operating status of the equipment. The future risk analysis model based on meteorological forecasts, the joint assessment model of the impact of meteorological conditions on grid risks, and the weighted health status assessment model of environmental factors are digital twin models that map and interact with distribution network entities and are synchronized with the operating status of the distribution network entities.

[0130] Preferably, the meteorological data in the potential risk determination module includes one or more of the following:

[0131] Current temperature value, current humidity value, current wind speed value, predicted temperature value, or predicted humidity value.

[0132] Preferably, the expression of the environmental factor weighted health status assessment model in the risk assessment module is:

[0133]

[0134] Among them, h(t) is the comprehensive health status index function, t is time, ω i (t) is the weight coefficient of the operating data, which indicates the influence of the parameter of the i-th operating data in the equipment health assessment, f i ((I,U,P,Q) i ,t) represents the contribution value of the specific parameters of the equipment's current, voltage, power and load operation data to the health status at time t, U represents the voltage level of the equipment, I represents the current value, P represents the power actually consumed or generated by the equipment, Q represents the power required to maintain the electric field in the power system, T represents temperature, H represents humidity, W represents wind speed, α1 represents the influence coefficient of the meteorological factor with the largest influencing factor, M(T,H,W) represents the meteorological factor function, i represents the parameter of the i-th operation data, and m represents the number of parameters of the operation data.

[0135] Preferably, the expression of the future risk analysis model based on meteorological forecast in the potential risk judgment module is:

[0136]

[0137] Where P(t) represents the overall potential risk assessment value of the equipment at time t, represents the risk assessment of equipment j at time t, ω j represents the risk probability weight of device j, T 预测 is the predicted value of temperature, H 预测 is the predicted value of humidity, M 预测 (T 预测 ,H 预测 ) is the future meteorological factor function, f(M 预测 (T 预测 ,H 预测 )) is the contribution value of the future meteorological factor function, and n is the number of devices.

[0138] Preferably, the expression of the joint assessment model for judging the impact of meteorological conditions on grid risk in the distribution network risk module is:

[0139]

[0140] Among them, R 网架 represents the overall risk assessment value of the distribution network, M(T,H,W) is the meteorological factor function, α2 is the influence coefficient of the meteorological factor in the distribution network grid, and β is the weight of the overall potential risk assessment value of the equipment.

[0141] Preferably, the potential risk determination module calculates the overall potential risk assessment value of the equipment based on meteorological data and historical operating data of each device according to a pre-established future risk analysis model based on meteorological forecasts, and determines whether the equipment as a whole has potential risks based on the overall potential risk assessment value of the equipment, including:

[0142] Based on the historical operating data of each device, a risk assessment model is used to obtain the current risk assessment of each device;

[0143] Based on the predicted temperature and humidity values, the risk probability weights of each device, and the current risk assessment of each device, the overall potential risk assessment value of the device is obtained according to the pre-established future risk analysis model based on meteorological forecasts;

[0144] Determine whether the overall potential risk assessment value of the device exceeds the potential risk threshold. If so, it is determined that the device as a whole has a potential risk; otherwise, there is no potential risk.

[0145] Preferably, the distribution network risk module determines whether the distribution network as a whole is at risk based on meteorological data, the overall potential risk assessment value of the equipment, and the risk weight of the equipment in the grid, and according to a pre-established joint assessment model of the impact of meteorological conditions on grid risk, including:

[0146] Determine the risk weight of the equipment in the grid based on the operating parameters of each equipment and meteorological factors;

[0147] Based on the overall potential risk assessment value of the equipment, meteorological data and the risk weight of the equipment in the grid, the overall risk assessment value of the distribution network is obtained according to the pre-established joint assessment model of the impact of meteorological factors on the grid risk.

[0148] Determine whether the risk assessment value of the distribution network as a whole exceeds a risk status threshold, if so, determine that the distribution network as a whole is at risk, otherwise, there is no risk;

[0149] Among them, the risk weight of the equipment in the grid includes the influence coefficient of meteorological factors in the distribution network grid and the weight of the overall potential risk assessment value of the equipment.

[0150] Example 3

[0151] The present invention also provides an electronic device, such as Figure 4 As shown, the electronic device may be a computer, a single-chip microcomputer, a smart mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, processor, and transceiver component are connected via a bus; the memory may be used to store an execution program, which may include instructions; and the processor is used to execute the instructions stored in the memory. The memory may also be used to store data, which may be accessed and / or modified during the execution of the instructions.

[0152] The processor may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the storage medium to implement the corresponding method flow or corresponding function, so as to realize the steps of a distribution network equipment operation risk assessment method based on digital twins in the above embodiment.

[0153] Example 4

[0154] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory), which is a memory device in the electronic device for storing programs and data. It can be understood that the storage medium here can include both the built-in storage medium in the electronic device and the extended storage medium supported by the electronic device. The storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space. These instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one disk storage. The processor loads and executes one or more instructions stored in the storage medium, which can implement the steps of a distribution network equipment operation risk assessment method based on digital twins in the above embodiment.

[0155] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0156] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0157] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0158] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0159] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. A distribution network equipment operation risk assessment method based on digital twins, characterized in that: include: Based on meteorological data and the historical operating data of each device, the overall potential risk assessment value of the device is calculated according to the pre-established future risk analysis model based on meteorological forecasts, and the overall potential risk assessment value of the device is used to determine whether the device as a whole has potential risks; Based on meteorological data, the overall potential risk assessment value of the equipment and the risk weight of the equipment in the grid, the pre-established joint assessment model of the impact of meteorological conditions on grid risk is used to determine whether the distribution network as a whole is at risk; If there is a potential risk for the equipment as a whole and the distribution network as a whole is risk-free, the risk level threshold of the equipment is adjusted. Otherwise, the risk assessment result of the equipment is obtained based on the meteorological data, the historical operation data of the equipment, and the weight coefficient of the operation data according to the pre-established environmental factor weighted health status assessment model; Among them, the weight coefficient of the operating data can be dynamically adjusted according to the characteristics and operating status of the equipment. The future risk analysis model based on meteorological forecasts, the joint assessment model of the impact of meteorological conditions on grid risks, and the weighted health status assessment model of environmental factors are digital twin models that map and interact with distribution network entities and are synchronized with the operating status of the distribution network entities.

2. A distribution network equipment operation risk assessment method based on digital twins according to claim 1, characterized in that: The meteorological data includes one or more of the following: Current temperature value, current humidity value, current wind speed value, predicted temperature value, or predicted humidity value.

3. A distribution network equipment operation risk assessment method based on digital twins according to claim 1, characterized in that: The expression of the environmental factor weighted health status assessment model is: Among them, h(t) is the comprehensive health status index function, t is time, ω i (t) is the weight coefficient of the operating data, which indicates the influence of the parameter of the i-th operating data in the equipment health assessment, f i ((I,U,P,Q) i ,t) represents the contribution value of the specific parameters of the equipment's current, voltage, power and load operation data to the health status at time t, U represents the voltage level of the equipment, I represents the current value, P represents the power actually consumed or generated by the equipment, Q represents the power required to maintain the electric field in the power system, T represents temperature, H represents humidity, W represents wind speed, α1 represents the influence coefficient of the meteorological factor with the largest influencing factor, M(T,H,W) represents the meteorological factor function, i represents the parameter of the i-th operation data, and m represents the number of parameters of the operation data.

4. A distribution network equipment operation risk assessment method based on digital twins according to claim 3, characterized in that: The expression of the future risk analysis model based on meteorological forecast is: Where P(t) represents the overall potential risk assessment value of the equipment at time t, represents the risk assessment of equipment j at time t, ω j represents the risk probability weight of device j, T 预测 is the predicted value of temperature, H 预测 is the predicted value of humidity, M 预测 (T 预测 ,H 预测 ) is the future meteorological factor function, f(M 预测 (T 预测 ,H 预测 )) is the contribution value of the future meteorological factor function, and n is the number of devices.

5. A distribution network equipment operation risk assessment method based on digital twins according to claim 4, characterized in that: The expression of the joint assessment model of the impact of meteorological conditions on grid risk is: Among them, R 网架 represents the overall risk assessment value of the distribution network, M(T,H,W) is the meteorological factor function, α2 is the influence coefficient of the meteorological factor in the distribution network grid, and β is the weight of the overall potential risk assessment value of the equipment.

6. A distribution network equipment operation risk assessment method based on digital twins according to claim 5, characterized in that: The calculation of the overall potential risk assessment value of the equipment based on the meteorological data and the historical operating data of each device according to a pre-established future risk analysis model based on meteorological forecasts, and the determination of whether the equipment as a whole has potential risks according to the overall potential risk assessment value of the equipment include: Based on the historical operating data of each device, a risk assessment model is used to obtain the current risk assessment of each device; Based on the predicted temperature and humidity values, the risk probability weights of each device, and the current risk assessment of each device, the overall potential risk assessment value of the device is obtained according to the pre-established future risk analysis model based on meteorological forecasts; Determine whether the overall potential risk assessment value of the device exceeds the potential risk threshold. If so, it is determined that the device as a whole has a potential risk; otherwise, there is no potential risk.

7. A distribution network equipment operation risk assessment method based on digital twins according to claim 6, characterized in that: The method of determining whether the distribution network as a whole is at risk based on a pre-established joint assessment model of meteorological impact on grid risk, based on meteorological data, the overall potential risk assessment value of the equipment, and the risk weight of the equipment in the grid, includes: Determine the risk weight of the equipment in the grid based on the operating parameters of each equipment and meteorological factors; Based on the overall potential risk assessment value of the equipment, meteorological data and the risk weight of the equipment in the grid, the overall risk assessment value of the distribution network is obtained according to the pre-established joint assessment model of the impact of meteorological factors on the grid risk. Determine whether the risk assessment value of the distribution network as a whole exceeds a risk status threshold, if so, determine that the distribution network as a whole is at risk, otherwise, there is no risk; Among them, the risk weight of the equipment in the grid includes the influence coefficient of meteorological factors in the distribution network grid and the weight of the overall potential risk assessment value of the equipment.

8. A distribution network equipment operation risk assessment system based on digital twins, characterized by: include: The potential risk judgment module is used to calculate the overall potential risk assessment value of the equipment based on meteorological data and the historical operating data of each device according to a pre-established future risk analysis model based on meteorological forecasts, and to judge whether the equipment as a whole has potential risks based on the overall potential risk assessment value of the equipment; The distribution network risk assessment module is used to determine whether the distribution network as a whole is at risk based on meteorological data, the overall potential risk assessment value of the equipment and the risk weight of the equipment in the grid, and according to a pre-established joint assessment model of the impact of meteorological conditions on grid risk; The risk assessment module is used to adjust the risk level threshold of the equipment if there is a potential risk for the equipment as a whole and the distribution network as a whole is risk-free. Otherwise, the risk assessment result of the equipment is obtained based on the meteorological data, the historical operation data of the equipment and the weight coefficient of the operation data according to the pre-established environmental factor weighted health status assessment model; Among them, the weight coefficient of the operating data can be dynamically adjusted according to the characteristics and operating status of the equipment. The future risk analysis model based on meteorological forecasts, the joint assessment model of the impact of meteorological conditions on grid risks, and the weighted health status assessment model of environmental factors are digital twin models that map and interact with distribution network entities and are synchronized with the operating status of the distribution network entities.

9. A distribution network equipment operation risk assessment system based on digital twins according to claim 8, characterized in that: The meteorological data in the potential risk determination module includes one or more of the following: Current temperature value, current humidity value, current wind speed value, predicted temperature value, or predicted humidity value.

10. A distribution network equipment operation risk assessment system based on digital twins according to claim 8, characterized in that: The expression of the environmental factor weighted health status assessment model in the risk assessment module is: Among them, h(t) is the comprehensive health status index function, t is time, ω i (t) is the weight coefficient of the operating data, which indicates the influence of the parameter of the i-th operating data in the equipment health assessment, f i ((I,U,P,Q) i ,t) represents the contribution value of the specific parameters of the equipment's current, voltage, power and load operating data to the health status at time t, U represents the voltage level of the equipment, I represents the current value, P represents the power actually consumed or generated by the equipment, Q represents the power required to maintain the electric field in the power system, T represents temperature, H represents humidity, W represents wind speed, ɑ1 represents the influence coefficient of the meteorological factor with the largest influencing factor, M(T,H,W) represents the meteorological factor function, i represents the parameter of the i-th operating data, and m represents the number of parameters of the operating data.

11. A distribution network equipment operation risk assessment system based on digital twins according to claim 10, characterized in that: The expression of the future risk analysis model based on meteorological forecast in the potential risk judgment module is: Where P(t) represents the overall potential risk assessment value of the equipment at time t, represents the risk assessment of equipment j at time t, ω j represents the risk probability weight of device j, T 预测 is the predicted value of temperature, H 预测 is the predicted value of humidity, M 预测 (T 预测 ,H 预测 ) is the future meteorological factor function, f(M 预测 (T 预测 ,H 预测 )) is the contribution value of the future meteorological factor function, and n is the number of devices.

12. A distribution network equipment operation risk assessment system based on digital twins according to claim 11, characterized in that: The expression of the joint assessment model of the impact of meteorological conditions on grid risk in the distribution network risk judgment module is: Among them, R 网架 represents the overall risk assessment value of the distribution network, M(T,H,W) is the meteorological factor function, α2 is the influence coefficient of the meteorological factor in the distribution network grid, and β is the weight of the overall potential risk assessment value of the equipment.

13. A distribution network equipment operation risk assessment system based on digital twins according to claim 12, characterized in that: The potential risk determination module calculates the overall potential risk assessment value of the equipment based on meteorological data and historical operating data of each device according to a pre-established future risk analysis model based on meteorological forecasts, and determines whether the equipment as a whole has potential risks based on the overall potential risk assessment value of the equipment, including: Based on the historical operating data of each device, a risk assessment model is used to obtain the current risk assessment of each device; Based on the predicted temperature and humidity values, the risk probability weights of each device, and the current risk assessment of each device, the overall potential risk assessment value of the device is obtained according to the pre-established future risk analysis model based on meteorological forecasts; Determine whether the overall potential risk assessment value of the device exceeds the potential risk threshold. If so, it is determined that the device as a whole has a potential risk; otherwise, there is no potential risk.

14. A distribution network equipment operation risk assessment system based on digital twins according to claim 13, characterized in that: The distribution network risk assessment module determines whether the distribution network as a whole is at risk based on meteorological data, the overall potential risk assessment value of the equipment, and the risk weight of the equipment in the grid, and according to a pre-established joint assessment model of the impact of meteorological conditions on grid risk. The method includes: Determine the risk weight of the equipment in the grid based on the operating parameters of each equipment and meteorological factors; Based on the overall potential risk assessment value of the equipment, meteorological data and the risk weight of the equipment in the grid, the overall risk assessment value of the distribution network is obtained according to the pre-established joint assessment model of the impact of meteorological factors on the grid risk. Determine whether the risk assessment value of the distribution network as a whole exceeds a risk status threshold, if so, determine that the distribution network as a whole is at risk, otherwise, there is no risk; Among them, the risk weight of the equipment in the grid includes the influence coefficient of meteorological factors in the distribution network grid and the weight of the overall potential risk assessment value of the equipment.

15. A computer device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, a distribution network equipment operation risk assessment method based on digital twins as described in any one of claims 1 to 7 is implemented.

16. A computer-readable storage medium, characterized in that An execution program is stored thereon, and when the execution program is executed, a distribution network equipment operation risk assessment method based on digital twins as described in any one of claims 1 to 7 is implemented.

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