Power grid cascading failure probability risk assessment method considering transformer mixed failure

Abstract

The invention discloses a power grid cascading fault probability risk assessment method considering transformer mixed faults. The method comprises the following steps: S1) collecting system data; s2) carrying out life end fault modeling; s3) weather-related random fault modeling is carried out; s4) establishing a current-dependent overload protection power failure model; s5) establishing a transformer mixed fault probability model used for calculating the unavailability of a single transformer; s6), performing double-circulation non-sequence Monte Carlo simulation; and S7) calculating a reliability index. According to the power grid cascading fault probability risk assessment method considering the mixed fault of the transformer, a life end fault, a weather-related random fault and current-related overload protection outage are unified into a combined power failure probability; iteratively updating the unavailability of the remaining equipment under the new power flow / new temperature in the NMC, and explicitly simulating linkage propagation; and EENS, PLC and PCFE indexes are quantitatively output, and a decision-making basis is provided for planning and asset management.

Description

Technical Field

[0001] This invention belongs to the field of power grid risk assessment, and specifically relates to a method for assessing the probability risk of power grid cascading failures considering mixed transformer faults. Background Technology

[0002] With the continuous expansion of the power system, the number and capacity of transformers at voltage levels of 35kV and above are growing rapidly. As key equipment in the power transmission and distribution network, transformer failures can not only lead to single-point power outages, but may also trigger cascading failures due to power flow shifts, protection actions, and thermal stress, ultimately causing widespread power outages. Traditional reliability assessments often use the assumption of a constant failure rate. These static models are difficult to reflect the dynamic impact of operating conditions (load / current), environmental factors (temperature / weather), and equipment aging on the failure rate.

[0003] In recent years, physical statistical models based on thermal aging (such as the Arrhenius relation) and lifetime distributions (Weibull distribution) have been used to describe insulation degradation and end-of-life failure. At the system level, Monte Carlo simulation, especially non-sequential Monte Carlo (NMC), has been used to quantify the reliability indicators of power systems under multiple uncertainties (such as PLCs and EENS). However, existing methods often model these mechanisms in isolation or use simplified two-state device models to represent complex conditions, resulting in insufficient characterization of cascading failure propagation and multi-mechanism coupling.

[0004] Meanwhile, with the continuous expansion of the power system, the number and complexity of transformers have increased significantly. As a key piece of equipment in the power grid, transformer failures can trigger a chain reaction, causing widespread power outages and resulting in serious social, economic, and environmental losses.

[0005] Existing power grid reliability assessment methods mostly use constant failure rate models, which cannot reflect reliability changes caused by factors such as transformer load, ambient temperature, and weather conditions, leading to biased risk assessment results and a lack of a true description of dynamic environment and aging characteristics.

[0006] International research often models transformer lifespan using the Arrhenius model and Weibull distribution, and combines this with Monte Carlo methods for power system reliability analysis. However, these methods often neglect the coupling effect of dynamic loads and environmental conditions, failing to accurately reflect changes in risk during actual power grid operation.

[0007] Although domestic scholars have attempted to introduce fuzzy logic, Bayesian networks, deep learning and other methods to predict transformer faults, the accuracy and interpretability are insufficient in engineering applications, and it is difficult to quantify the propagation path and impact of cascading fault risks. Summary of the Invention

[0008] Purpose of the Invention: To overcome the above shortcomings, the purpose of this invention is to provide a power grid cascading failure probability risk assessment method that considers transformer mixed faults. This method unifies end-of-life faults, weather-related random faults, and current-related overload protection outages into a joint outage probability. It iteratively updates the unavailability of remaining equipment under new power flow / temperature conditions in the NMC, explicitly simulating cascading propagation. It quantifies and outputs EENS, PLC, and PCFE indicators, providing a basis for decision-making in planning and asset management.

[0009] Technical Solution: To achieve the above objectives, this invention provides a method for assessing the probability risk of power grid cascading faults considering transformer mixed faults, comprising: S1): System data acquisition, providing necessary input data for subsequent modeling and calculation; S2): End-of-life fault modeling, establishing a transformer end-of-life fault model; calculating the probability of power outage within the predicted future time interval of the transformer's remaining lifespan; enabling early prediction of transformer end-of-life faults, providing a basis for preventive maintenance; S3): Weather-related random fault modeling, using a two-state weather model, calculates the probability of random faults in the predicted future time interval; it can simulate the impact of weather changes on fault probability and improve the model's ability to handle randomness and uncertainty; S4): Establish a current-related overload protection outage model, adopt a relay action model with normal distribution error, and calculate the probability of outage caused by protection in the predicted future time interval; consider the relay action error, simulate the probability of outage caused by overload protection, and more comprehensively evaluate the model risk; S5): A hybrid failure probability model for transformers is established, which combines the probability of end-of-life failure, weather-related random failures, and overload protection outages to obtain a hybrid failure probability model for transformers, and calculates the unavailability of a single transformer; load, temperature, weather, and protection actions are uniformly mapped to real-time failure probabilities, overcoming the static failure rate assumption. S6): Double-loop non-sequence Monte Carlo simulation; S601): Increment the non-sequential Monte Carlo simulation (NMC) iteration count; check each unchecked transformer, generate a random number, and compare the random number with the transformer's unavailability; if the transformer's unavailability is greater than the random number, the transformer will be shut down, and the process will jump to S602, while the cascading failure event count will be incremented; if the transformer's unavailability is less than the random number, the transformer is normal, and the process continues to check the unchecked transformers; after all transformers have been checked, the process jumps to S7; Monte Carlo simulation can randomly generate fault scenarios, simulate the random behavior of the system, and improve the ability to handle uncertainty; by comparing the random number with unavailability, the failure probability of the transformer is simulated, improving the randomness of the evaluation; S602): A cascading failure simulation loop is implemented, which involves disconnecting the out-of-service transformers, calculating the power flow and new unavailability of the remaining transformers in the new network, comparing the new unavailability with the same random number from the previous step, and again determining whether there are any transformer failures in the new network, until no transformer failures are found, at which point execution jumps to S601. By disconnecting the out-of-service transformers and recalculating the power flow and unavailability, the propagation process of the fault is simulated, and the risk of cascading failures is assessed. The power flow and unavailability are dynamically updated in each iteration to ensure that the simulation results reflect the actual operating state of the system. Simultaneously, by simulating cascading failures, potential high-risk areas are identified, providing a basis for optimization and improvement. S7): Calculation of reliability indicators.

[0010] Furthermore, the reliability metrics in S7 include Expected Energy Not Powered (EENS), Probability of Load Cut-off (PLC), and Probability of Cascading Failure Events (PCFE). The formula for calculating the expected unpowered energy is as follows: in, The probability of load reduction state i is obtained by dividing the number of times state i occurs by the total number of iterations. 8760 represents the amount of load cut in state i; 8760 represents the number of hours in a year; considering all possible load cut states and their probabilities, it provides a comprehensive assessment of the expected unpowered energy of the system within a year, and combines the frequency and severity of load cuts to provide a quantitative reliability metric. The formula for calculating the load reduction probability is as follows: Where X is the number of load shedding events and MC is the total number of NMC iterations; it directly reflects the frequency of load shedding events and helps to assess the overall risk level. The probability exponent of the cascading failure event is calculated using the following formula: in, This refers to the number of cascading failure events. Specifically assessing the probability of cascading failure events helps identify potential cascading risks and provides a basis for taking preventative measures.

[0011] Furthermore, S2 specifically refers to: First, establish the Arrhenius relationship between characteristic lifetime and temperature, as shown in the following formula: Where A is an empirical constant. The apparent activation energy is given by k, Boltzmann constant is given by T, and Kelvin temperature is given by T. The conversion is based on hotspot temperature. By monitoring hotspot temperatures in real time and dynamically updating feature lifetimes, the adaptability and accuracy of the model can be improved. Secondly, the lifetime distribution follows a Weibull distribution, as shown in the following formula: in, The characteristic lifetime corresponds to the scale parameter of the Weibull distribution; Here are the shape parameters of the Weibull distribution. Let be the cumulative distribution function. The Weibull distribution is a commonly used distribution in reliability analysis, which can effectively describe the lifetime distribution of equipment. Finally, the power outage probability, including the transformer's remaining lifespan (EoL), is obtained as follows: in, For the transformer during its remaining lifespan, at time intervals Failure probability within; Let be the cumulative distribution function (CDF) of the Weibull distribution, representing the failure probability at time t of the lifetime; This represents the current running time, i.e., the time the device has been running. This is a predicted future time interval. It can predict the probability of power outages during the remaining lifespan of a transformer, providing a basis for preventative maintenance; at the same time, it provides an accurate assessment of the probability of power outages through calculation using the cumulative distribution function (CDF).

[0012] Furthermore, S3 specifically refers to: A two-state weather model is adopted, namely Where 0 represents normal and 1 represents severe weather, the failure rate amplification factor under severe weather is: The conditional failure rate is: in, This is an empirical constant, which can be obtained from historical power grid operating data or equipment reliability manuals; thus, it is obtained that... The formula for calculating the probability of random failures within the system is as follows: By incorporating weather factors and considering the impact of the external environment on transformer failures, the model becomes closer to actual operating conditions. At the same time, by using an amplification factor, the impact of severe weather on the failure rate is quantified, thereby improving the model's sensitivity.

[0013] Furthermore, S4 specifically includes: Define the relay operating current threshold To truncate the normal distribution, the formula is as follows: The probability of the necessary action, i.e., the need to trip, is calculated as follows: The probability of erroneous action without requiring any action is expressed as an empirical constant. The correct action rate is taken into account using an empirical constant. This information can be obtained from historical power grid operating data or equipment reliability manuals. The formula for calculating the probability of a power outage caused by internal protection is as follows: By truncating the normal distribution, the randomness of the relay operating current threshold is scientifically addressed, and the upper and lower limits of the threshold are considered to avoid unreasonable thresholds. At the same time, by using empirical constants and combining actual operating data, a practical assessment of false tripping and correct tripping rates is provided.

[0014] Furthermore, the calculation formula for the transformer hybrid fault probability model in S5 is as follows: A single transformer in The probability of mixed power outages within the system, i.e., the unavailability of a single transformer. It achieves comprehensive modeling of transformer life-end faults, weather-dependent random faults, and current-related overload protection outages in a single time step.

[0015] Furthermore, the random number in S601 is between 0 and... Between; the Set the value to 0.3. An excessively large range of random numbers can lead to an increased false alarm rate, meaning that a normally functioning transformer might be incorrectly identified as faulty. By limiting the range of random numbers to 0 to 0.3, the false alarm rate can be effectively reduced, and the reliability of the simulation results can be improved.

[0016] As can be seen from the above technical solution, the present invention has the following beneficial effects: 1. This invention provides a method for assessing the probability risk of power grid cascading faults that considers mixed transformer faults. It establishes a method for constructing a mixed fault probability model, which integrates transformer life-end faults, weather-dependent random faults, and current-related overload protection outages into a single time step, resulting in a more comprehensive risk assessment. 2. The present invention provides a power grid cascading failure probability risk assessment method that considers transformer mixed faults. It iteratively updates the unavailability of remaining equipment in the NMC under new power flow / new temperature, explicitly simulates cascading propagation, and quantifies and outputs EENS, PLC, and PCFE indicators, providing a basis for decision-making in planning and asset management. Attached Figure Description

[0017] Figure 1 This is a schematic diagram illustrating the steps of a power grid cascading fault probability risk assessment method considering transformer mixed faults as described in this invention. Figure 2 This is a flowchart of a power grid cascading failure probability risk assessment method considering transformer mixed faults, as described in this invention. Figure 3 This is a topology diagram of the node testing system in Example 2. Detailed Implementation

[0018] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0019] Example 1 In this embodiment, as Figure 1 This invention discloses a method for assessing the probability risk of power grid cascading failures considering transformer mixed faults, including: S1): System data acquisition; specifically, collecting power grid operation data, including information such as the load, current, ambient temperature, and service life of each transformer; S2): End-of-life fault modeling, establishing a transformer end-of-life fault model; calculating the probability of power outage within the predicted future time interval of the transformer's remaining lifespan; using the Arrhenius–Weibull distribution to establish a transformer end-of-life fault model, quantitatively describing the relationship between insulation aging and thermal stress; S3): Weather-related random fault modeling: A dual-state weather model is used to calculate the probability of random faults in the predicted future time interval; a transformer random fault model based on the dual-state weather model (normal / severe) is constructed to reflect the impact of environmental temperature, humidity, lightning strikes and other factors on the failure rate; S4): Establish a current-related overload protection outage model, adopt a relay action model with normal distribution error to describe random outage fault events caused by measurement deviation or overcurrent tripping, and calculate the probability of outage caused by protection in the predicted future time interval. S5): Establishment of a hybrid failure probability model for transformers. This model combines the probability of end-of-life failures, weather-related random failures, and overload protection outages to obtain a hybrid failure probability model for transformers, and calculates the unavailability of a single transformer. S6): Double-loop non-sequential Monte Carlo simulation; consists of two nested loops, with the traditional non-sequential Monte Carlo simulation (NMC) as the outer loop and the cascading failure event simulation loop located inside the NMC loop; Monte Carlo simulation was used to randomly disable transformers in the test network; specifically, each transformer was checked individually, generating a value between 0 and... ( A random number between a set constant and the transformer's unavailability is generated and compared with the transformer's unavailability. If the transformer's unavailability is greater than the random number, the transformer will be shut down. If the transformer's unavailability is less than the random number, the transformer will operate normally. If this step causes one or more transformers to shut down, a cascading fault simulation loop is started. This involves disconnecting the shut-down transformer, calculating the power flow and the new unavailability of the remaining transformers in the new network, comparing the new unavailability with the same random number from the previous step, and then determining again whether there are any transformers shut down due to faults in the new network. S7): Calculation of reliability indicators.

[0020] Specifically, in S6, before entering the NMC cycle, an initial system operating state is selected, which can be a normal operating state or a specific pre-fault state, and the parameters of each component are determined, such as the rated power of the transformer, impedance, and initial load distribution.

[0021] Then, the power flow is calculated to determine the load of each transformer, and its HST is obtained according to the IEC formula under steady-state load conditions. Then, the HST and the service life of the transformer are input into the transformer hybrid failure probability model to calculate the unavailability of each transformer; the service life of the transformer is randomly allocated from 10 to 60 years.

[0022] In this embodiment, the reliability indicators in S7 include Expected Energy Not Powered (EENS), Probability of Load Cut-off (PLC), and Probability of Cascading Failure Events (PCFE). The formula for calculating the expected unpowered energy is as follows: in, The probability of load reduction state i is obtained by dividing the number of times state i occurs by the total number of iterations. 8760 represents the load reduction in state i; 8760 represents the number of hours per year. The formula for calculating the load reduction probability is as follows: Where X is the number of reductions that occur, and MC is the total number of NMC iterations; The probability exponent of the cascading failure event is calculated using the following formula: in, This represents the number of times a cascading failure event occurs.

[0023] Specifically, different operating scenarios, such as peak load, off-peak load, and fault scenarios, are considered, and EENS is calculated separately to comprehensively evaluate the reliability of the system.

[0024] In this embodiment, S2 specifically refers to: First, establish the Arrhenius relationship between characteristic lifetime and temperature, as shown in the following formula: Where A is an empirical constant. The apparent activation energy is given by k, Boltzmann constant is given by T, and Kelvin temperature is given by T. The conversion is based on hotspot temperature. ); Secondly, the lifetime distribution follows a Weibull distribution, as shown in the following formula: in, The characteristic lifetime corresponds to the scale parameter of the Weibull distribution; Here are the shape parameters of the Weibull distribution. Let be the cumulative distribution function. For survival functions; Finally, the power outage probability, including the transformer's remaining lifespan (EoL), is obtained as follows: in, For the transformer during its remaining lifespan, at time intervals Failure probability within; Let be the cumulative distribution function (CDF) of the Weibull distribution, representing the failure probability at time t of the lifetime; This represents the current running time, i.e., the time the device has been running. This represents the predicted future time interval.

[0025] In particular, the impact of load shedding events at different points in time on system reliability can be considered, thus introducing a time-weighted factor as an option to make the calculation results more consistent with actual operating conditions.

[0026] In this embodiment, S3 specifically refers to: A two-state weather model is adopted, namely Where 0 represents normal and 1 represents severe weather, the failure rate amplification factor under severe weather is: The conditional failure rate is: in, This is an empirical constant, which can be obtained from historical power grid operating data or equipment reliability manuals; thus, it is obtained that... The formula for calculating the probability of random failures within the system is as follows: .

[0027] Specifically, a transformer stochastic fault model based on a dual-state weather model (normal / severe) is constructed to reflect the impact of environmental factors such as temperature, humidity, and lightning strikes on the failure rate.

[0028] In this embodiment, S4 specifically refers to: Define the relay operating current threshold To truncate the normal distribution, the formula is as follows: The probability of the necessary action, i.e., the need to trip, is calculated as follows: The probability of erroneous action without requiring any action is expressed as an empirical constant. The correct action rate is taken into account using an empirical constant. This information can be obtained from historical power grid operating data or equipment reliability manuals. The formula for calculating the probability of a power outage caused by internal protection is as follows: .

[0029] Specifically, a relay action model with a normal distribution error is used to describe random outage fault events caused by measurement deviation or overcurrent tripping, which is achieved by considering the probability of necessary / unnecessary actions caused by measurement and setting errors.

[0030] In this embodiment, the calculation formula for the transformer hybrid fault probability model in S5 is as follows: A single transformer in The probability of mixed power outages within the system, i.e., the unavailability of a single transformer. .

[0031] Specifically, the three mechanisms of end-of-life failure, weather-related random failure, and overload protection outage probability are combined to obtain a transformer hybrid failure probability model, which calculates the total unavailability of a single transformer in a specific period. The three mechanisms are independently approximated.

[0032] In this embodiment, the random number in S601 is between 0 and... Between; the Take 0.3.

[0033] Specifically, a random number between 0 and 0.3 is chosen because the unavailability of the transformer is relatively small during the testing process. If the generated random number is too large, the transformer will mostly be in normal condition and the test cannot be completed.

[0034] In particular, the stated The value of 0.3 is taken as an empirical value, and a better value can be set by human adjustment.

[0035] Example 2 Based on Example 1, in this example, as... Figure 3 The IEEE 118-node system was used as the test platform. This system is a widely used standard test system in power system research and can reasonably simulate the complex topology and operating characteristics of actual power grids. The transmission voltage levels are 138kV and 345kV. The network has 118 nodes, 53 generators, 173 lines, and 13 transformers, including 8 (345 / 138kV) step-down transformers, 3 (345 / 161kV) step-down transformers, and 2 (161 / 138kV) step-down transformers.

[0036] Each transformer is randomly assigned a remaining lifespan, ranging from 10 to 60 years. This age distribution reflects the varying degrees of aging of transformer equipment in actual power grids. Six load levels (valley to peak) and corresponding ambient temperatures are set; weather conditions are randomly assigned.

[0037] The equipment parameters are selected as follows: Arrhenius takes This is an empirical estimate (or a fit to the company's ledger). In the Weibull distribution... (Aging period) Dynamically provided by Arrhenius; Weather: (normal), (bad, ),Protect: Cut off to Given .

[0038] In practical engineering, regression estimation can be performed using operation and maintenance data. Modeling is performed by voltage level / structure grouping.

[0039] According to the method described, 100 Monte Carlo simulations were performed for each load level, and the final system reliability results are shown in the table below.

[0040] Table 1 Reliability Indicators Data shows that when considering cascading failures, the probability of load shedding (PLC) increased from 0.565000 to 0.581667, an increase of 2.95%; the expected unsupplied energy (EENS) rose from 1313.252883 MWh to 1395.151683 MWh, an increase of 6.24%; and the probability of cascading failure events (PCFE) was recorded as 0.051667.

[0041] These three indicators collectively reveal the complex impact of cascading failures on the system. The relatively small increase in PLC indicates that the system has a certain capacity to withstand initial failures, but the significant increase in EENS indicates that once a cascading failure occurs, its scope and severity will increase dramatically. This uneven impact pattern suggests that the main harm of cascading failures lies not in increasing the frequency of power outages, but in expanding the scope of outages and exacerbating energy shortages. While a PCFE value of approximately 5.17% may seem low, in a large power grid, this represents a considerable risk exposure, especially considering the potentially huge economic losses and social impacts of each cascading event.

[0042] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements can be made without departing from the principle of the present invention, and these improvements should also be considered within the scope of protection of the present invention.

Claims

1. A method for assessing the probability risk of power grid cascading faults considering transformer mixed faults, characterized in that: include: S1): System data acquisition; S2): End-of-life fault modeling, establishing a transformer end-of-life fault model; calculating the probability of power outage within the predicted future time interval of the transformer's remaining lifespan; S3): Weather-related random fault modeling, using a two-state weather model to calculate the probability of random faults in the predicted future time interval; S4): Establish a current-related overload protection outage model, adopt a relay action model with normal distribution error, and calculate the probability of outage caused by protection in the predicted future time interval; S5): The transformer hybrid failure probability model is established by combining the probability of end-of-life failure, weather-related random failure, and overload protection shutdown. This model is used to calculate the unavailability of a single transformer. S6): Double-loop non-sequence Monte Carlo simulation; S601): Increment the number of iterations of the non-sequential Monte Carlo simulation (NMC); check each unchecked transformer one by one, generate random numbers, and compare the random numbers with the unavailability of the transformers; If the transformer unavailability is greater than the random number, the transformer will be shut down and the process will jump to execute S602, while the count of cascading fault events will be incremented by one. If the unavailability of a transformer is less than the random number, then the transformer is normal, and the unchecked transformers are checked. After all transformers have been checked, the process jumps to S7. S602): Chain failure simulation loop, that is, disconnect the out-of-service transformer, calculate the power flow and the new unavailability of the remaining transformers in the new network, compare the new unavailability with the same random number in the previous step, and determine again whether there is a transformer out of service in the new network, until there are no transformers out of service, and then jump to execute S601. S7): Calculation of reliability indicators.

2. The method for assessing the probability risk of power grid cascading failures considering transformer mixed faults according to claim 1, characterized in that: The reliability metrics in S7 include Expected Energy Not Powered (EENS), Probability of Load Cut-off (PLC), and Probability of Cascading Failure Events (PCFE). The formula for calculating the expected unpowered energy is as follows: in, The probability of load reduction state i is obtained by dividing the number of times state i occurs by the total number of iterations. 8760 represents the load reduction in state i; 8760 represents the number of hours per year. The formula for calculating the load reduction probability is as follows: Where X is the number of reductions that occur, and MC is the total number of NMC iterations; The probability exponent of the cascading failure event is calculated using the following formula: in, This represents the number of times a cascading failure event occurs.

3. The method for assessing the probability risk of power grid cascading faults considering transformer mixed faults according to claim 1, characterized in that: Specifically, S2 is: First, establish the Arrhenius relationship between characteristic lifetime and temperature, as shown in the following formula: Where A is an empirical constant. The apparent activation energy is given by k, Boltzmann constant is given by T, and Kelvin temperature is given by T. The conversion is based on hotspot temperature. ); Secondly, the lifetime distribution follows a Weibull distribution, as shown in the following formula: in, The characteristic lifetime corresponds to the scale parameter of the Weibull distribution; Here are the shape parameters of the Weibull distribution. Let be the cumulative distribution function. For survival functions; Finally, the power outage probability, including the transformer's remaining lifespan (EoL), is obtained as follows: in, For the transformer during its remaining lifespan, at time intervals Failure probability within; Let be the cumulative distribution function (CDF) of the Weibull distribution, representing the failure probability at time t of the lifetime; This represents the current running time, i.e., the time the device has been running. This represents the predicted future time interval.

4. The method for assessing the probability risk of power grid cascading faults considering transformer mixed faults according to claim 3, characterized in that: Specifically, S3 is: A two-state weather model is adopted, namely Where 0 represents normal and 1 represents severe weather, the failure rate amplification factor under severe weather is: The conditional failure rate is: in, This is an empirical constant, which can be obtained from historical power grid operating data or equipment reliability manuals; thus, it is obtained that... The formula for calculating the probability of random failures within the system is as follows: 。 5. The power grid cascading fault probability risk assessment method considering transformer mixed faults according to claim 4, characterized in that: Specifically, S4 is: Define the relay operating current threshold To truncate the normal distribution, the formula is as follows: The probability of the necessary action, i.e., the need to trip, is calculated as follows: The probability of erroneous action without requiring any action is expressed as an empirical constant. The correct action rate is taken into account using an empirical constant. This information can be obtained from historical power grid operating data or equipment reliability manuals. The formula for calculating the probability of a power outage caused by internal protection is as follows: 。 6. The method for assessing the probability risk of power grid cascading faults considering transformer mixed faults according to claim 5, characterized in that: The calculation formula for the transformer hybrid fault probability model in S5 is as follows: A single transformer in The probability of mixed power outages within the system, i.e., the unavailability of a single transformer. .

7. The method for assessing the probability risk of power grid cascading faults considering transformer mixed faults according to claim 1, characterized in that: The random number in S601 is between 0 and... Between; the Take 0.3.

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