Regional power grid risk and power transformer depreciation age correlation analysis method

Abstract

The invention belongs to the technical field of power system risk assessment, and particularly relates to a regional power grid risk and power transformer depreciation age limit correlation analysis method. The influence of the transformer depreciation age limit and the regional power grid operation time on the regional power grid risk is considered at the same time, the influence of the transformer depreciation age limit on the reliability of the transformer in the regional power grid can be effectively captured, and then the influence of the transformer depreciation age limit on the regional power grid risk is quantified; the method fills the blank of the current regional power grid risk assessment method based on the transformer depreciation age limit, can be used as an important basis for a power grid company to scientifically formulate the transformer depreciation age limit, and promotes the power grid enterprise to realize cost reduction and benefit improvement.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system risk assessment, and in particular relates to a method for analyzing the correlation between regional power grid risk and the depreciation period of power transformers. Background Art

[0002] The depreciation period of transformers not only affects the reliability of transformers in regional power grids, but also influences the proportion of overaged assets within the grid and the cost accounting of transmission and distribution prices. It is a crucial factor influencing the scientific management and healthy development of power grid companies. As critical power grid equipment, the depreciation period of transformers directly impacts the risk level of the regional power grid. A shorter depreciation period results in higher transformer reliability within the regional power grid, lowering the risk of the regional grid. However, this can lead to premature transformer replacement, forcing the power grid company to incur premature purchase costs. A longer depreciation period, while reducing the purchase cost of the power grid company, also reduces transformer reliability within the regional power grid, increasing the risk of the regional grid.

[0003] In summary, there is an urgent need for a correlation analysis method between regional power grid risk and power transformer depreciation life to quantify the impact of transformer depreciation life on regional power grid risk value and provide a scientific basis for power grid companies to scientifically formulate transformer depreciation life. Summary of the Invention

[0004] The purpose of the present invention is to overcome the shortcomings of the existing technology and provide a method for analyzing the correlation between regional power grid risk and the depreciation period of power transformers, so as to solve the problem that the current power grid risk assessment method does not involve the change of regional power grid risk with the depreciation period of transformers, quantify the impact of the depreciation period of transformers on the regional power grid risk value, and provide a scientific basis for power grid companies to scientifically formulate the depreciation period of transformers.

[0005] The object of the present invention is achieved by: a method for analyzing the correlation between regional power grid risk and power transformer depreciation period, which comprises the following steps:

[0006] S1. Based on transformer fault related information, the bathtub curve is used to fit the transformer fault data and a model is constructed to show the evolution of transformer failure rate with transformer age.

[0007] S2. Based on the evolution model of transformer failure rate as the transformer age changes in step S1, determine the transformer failure rate and establish a transformer repairable forced outage model;

[0008] S3. Based on the transformer outage model in S2, the sequential Monte Carlo method is used to simulate the operating status of the transformers in the regional power grid, and the regional power grid risk sampling value I is calculated according to the operating status of the transformers in the regional power grid;

[0009] S4. If the number of simulations of the Sequential Monte Carlo method does not reach the set value, step S3 is repeated. Otherwise, the average of the regional power grid risk sampling values ​​I obtained by multiple simulations is used as the regional power grid risk sampling value II under the current regional power grid operation time and transformer depreciation period, and step S5 is executed.

[0010] S5. Change the regional power grid operation time, and re-execute steps S2 to S4 to calculate the regional power grid risk sampling value II until all possible values ​​within the regional power grid operation time definition domain are traversed, and the average value of the regional power grid risk sampling values ​​II corresponding to different regional power grid operation times is used as the regional power grid risk value under the current transformer depreciation period;

[0011] S6. Change the depreciation period of the transformer, repeat steps S2 to S5, and calculate the regional power grid risk value corresponding to different depreciation periods within the transformer depreciation period definition domain;

[0012] S7. Using the transformer depreciation period as an independent variable and the regional power grid risk values ​​corresponding to different depreciation periods as a function, a mathematical fitting method is used to fit the data obtained in step S6 to obtain a correlation curve between the regional power grid risk and the power transformer depreciation period.

[0013] Furthermore, the step S1 includes:

[0014] Determining parameters of a bathtub curve based on transformer fault information;

[0015] Determine the dividing point T according to the design life of the transformer d , when the transformer service age is less than T d The transformer failure rate model during the stable period is a constant, and its value is the bathtub curve at T d The value of the transformer is greater than T d The failure rate model of the transformer during the wear period is the bathtub curve;

[0016] The four-parameter bathtub curve is fitted based on the transformer fault related information. The four-parameter bathtub curve function is shown as follows:

[0017]

[0018] Where, α, β, η, λ>0; parameter λ is the product factor of the failure rate function; η is the interval parameter; the values ​​of shape parameters α and β jointly determine the shape of the failure rate curve, α∈(0,1), β>0, t∈(0,η); T s The service life of the transformer;

[0019] When the equipment is in service T s Greater than T d The failure rate model of the transformer during the wear period is h(T s), the transformer failure rate evolution model is shown as follows:

[0020]

[0021] Where h(t) is the four-parameter bathtub curve function, T s is the transformer service life, T d It is the dividing point between the failure rate curve in the stable period and the failure rate curve in the wear period.

[0022] Furthermore, the S2 includes:

[0023] The regional power grid operation time and transformer depreciation years are taken as input variables, and their respective definition domains are determined. The regional power grid risk is taken as the output variable. The regional power grid operation time T y The domain of is shown below,

[0024]

[0025] Among them, T y Indicates the operating life of the regional power grid and the depreciation life of the transformer T z The domain of is shown below,

[0026]

[0027] Among them, T z Indicates the depreciation period of the transformer;

[0028] The transformer age is determined based on the regional power grid operation time and transformer depreciation period, as follows:

[0029] T s =T y mod T z

[0030] Among them, T s Indicates the service life of the transformer, T y Indicates the regional power grid operation time, T z Indicates the depreciation period of the transformer, and the symbol mod represents the remainder operation;

[0031] The outage model of the transformer is a repairable forced outage model, where μ is the repair rate, which represents the probability that the transformer will switch from a fault state to an operating state. The calculation of the repair rate μ is shown in the following formula:

[0032]

[0033] Among them, M TTR is the mean repair time of the transformer.

[0034] Furthermore, step S3 includes:

[0035] Generate a random number R between (0,1) for each transformer in the regional power grid i , the specific formula for simulating the operating status of each transformer in the regional power grid is shown below:

[0036]

[0037] Where s i Indicates that transformer i is in working or failure state, Q i is the transformer failure rate, R i is a uniformly distributed random number;

[0038] Calculate the operating time and repair time of the transformer as shown below,

[0039]

[0040] Among them, f(T s ) is the transformer failure rate function, μ is the probability that the transformer changes from the fault state to the operating state, ζ1, ζ2 are random numbers uniformly distributed between [0,1];

[0041] When a device fails in the power supply path of a load node, the node load loss is calculated, and the regional power grid risk sampling value I is calculated according to the following formula:

[0042]

[0043] Among them, EENS I The regional power grid risk sampling value I, S is the state set where the regional power grid cannot meet the load demand, P i is the power reduction in state i, t i is the power reduction time in state i.

[0044] Furthermore, the step S5 includes:

[0045] Increase the regional power grid operation time, re-execute steps S2 to S4 and calculate the regional power grid risk sampling value II according to the following formula until all possible values ​​within the regional power grid operation time definition domain are traversed;

[0046]

[0047] Among them, EENS II is the regional power grid risk sampling value II, T step is the number of sequential Monte Carlo simulations;

[0048] The average value of the regional power grid risk value samples II corresponding to the operation time of different regional power grids is used as the regional power grid risk value under the current transformer depreciation period;

[0049]

[0050] Among them, EENS is the regional power grid risk sampling value II, T y，max is the maximum operating time of the regional power grid.

[0051] Furthermore, the step S7 includes:

[0052] The regional power grid risk under each depreciation period is normalized according to the following formula:

[0053]

[0054] in, represents the regional power grid risk after normalization, EENS i represents the regional power grid risk value corresponding to the i-th depreciation period, EENS max It represents the maximum value of regional power grid risk value corresponding to all depreciation years, and EENS,min represents the minimum value of regional power grid risk value corresponding to all depreciation years;

[0055] The normalized regional power grid risk and its corresponding depreciation period are fitted with the risk function based on the two-parameter Weibull distribution using the least squares method.

[0056]

[0057] Among them, EENS represents the regional power grid risk, S(m,n) is the residual sum of squares between the actual value and the fitted value, m, n, c are the risk function coefficients, t s Indicates the depreciation period of the transformer, EENS i Indicates the regional power grid risk value corresponding to the i-th depreciation period of the transformer, eens i It represents the fitting value of regional power grid risk corresponding to the i-th depreciation period of the transformer.

[0058] Beneficial effects of the present invention: The method for analyzing the correlation between regional power grid risk and power transformer depreciation life of the present invention simultaneously considers the impact of transformer depreciation life and regional power grid operation time on regional power grid risk, can effectively capture the impact of transformer depreciation life on transformer reliability in regional power grid, and then quantify the impact of transformer depreciation life on regional power grid risk, filling the gap in the current method for evaluating regional power grid risk based on transformer depreciation life, and can serve as an important basis for power grid companies to scientifically formulate transformer depreciation life, and promote power grid enterprises to reduce costs and increase efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0060] Figure 1 This is a flow chart of the model for associating transformer depreciation life with regional power grid risk in the present invention;

[0061] Figure 2 This is a schematic diagram of the regional power grid structure in the present invention;

[0062] Figure 3 This is a schematic diagram of depreciation period-regional power grid risk in the present invention. DETAILED DESCRIPTION

[0063] The present invention will be further described below with reference to the accompanying drawings.

[0064] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0065] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0066] like Figure 1-3 As shown, a method for analyzing the correlation between regional power grid risk and power transformer depreciation period of the present invention includes the following steps:

[0067] S1. Based on transformer fault related information, the bathtub curve is used to fit the transformer fault data and a model is constructed to show the evolution of transformer failure rate with transformer age.

[0068] S2. Based on the evolution model of transformer failure rate as the transformer age changes in step S1, determine the transformer failure rate and establish a transformer repairable forced outage model;

[0069] S3. Based on the transformer outage model in S2, the sequential Monte Carlo method is used to simulate the operating status of the transformers in the regional power grid, and the regional power grid risk sampling value I is calculated according to the operating status of the transformers in the regional power grid;

[0070] S4. If the number of simulations of the Sequential Monte Carlo method does not reach the set value, step S3 is repeated. Otherwise, the average of the regional power grid risk sampling values ​​I obtained by multiple simulations is used as the regional power grid risk sampling value II under the current regional power grid operation time and transformer depreciation period, and step S5 is executed.

[0071] S5. Change the regional power grid operation time, and re-execute steps S2 to S4 to calculate the regional power grid risk sampling value II until all possible values ​​within the regional power grid operation time definition domain are traversed, and the average value of the regional power grid risk sampling values ​​II corresponding to different regional power grid operation times is used as the regional power grid risk value under the current transformer depreciation period;

[0072] S6. Change the depreciation period of the transformer, repeat steps S2 to S5, and calculate the regional power grid risk value corresponding to different depreciation periods within the transformer depreciation period definition domain;

[0073] S7. Using the transformer depreciation period as an independent variable and the regional power grid risk values ​​corresponding to different depreciation periods as a function, a mathematical fitting method is used to fit the data obtained in step S6 to obtain a correlation curve between the regional power grid risk and the power transformer depreciation period.

[0074] Furthermore, the step S1 includes:

[0075] Determining parameters of a bathtub curve based on transformer fault information;

[0076] Determine the dividing point T according to the design life of the transformer d , when the transformer service age is less than T d The transformer failure rate model during the stable period is a constant, and its value is the bathtub curve at T d The value of the transformer is greater than T d The failure rate model of the transformer during the wear period is the bathtub curve;

[0077] The four-parameter bathtub curve is fitted based on the transformer fault related information. The four-parameter bathtub curve function is shown as follows:

[0078]

[0079] Where, α, β, η, λ>0; parameter λ is the product factor of the failure rate function. Changing the value of λ does not change the shape of the failure rate curve but changes the scale of the failure rate axis; η is an interval parameter. Changing the value of η does not change the shape of each curve, but only the scale of the coordinate axis changes; the values ​​of shape parameters α and β jointly determine the shape of the failure rate curve, α∈(0,1), β>0, t∈(0,η); T s The service life of the transformer;

[0080] The design life of the transformer is set at 30 years, and the dividing point between the failure rate curve in the stable period and the failure rate curve in the wear period is T d When the transformer service age is less than 18 years, the transformer stable period failure rate model is a four-parameter bathtub curve at T d The value of h(18); when the equipment age T s Greater than T d The failure rate model of the transformer during the wear period is h(T s ), the transformer failure rate evolution model is shown as follows:

[0081]

[0082] Where h(t) is the four-parameter bathtub curve function, T s is the transformer service life, T d It is the dividing point between the failure rate curve in the stable period and the failure rate curve in the wear period.

[0083] Furthermore, the S2 includes:

[0084] The regional power grid operation time and transformer depreciation years are taken as input variables, and their respective definition domains are determined. The regional power grid risk is taken as the output variable. The regional power grid operation time T y The domain of is shown below,

[0085]

[0086] Among them, T y Indicates the operating life of the regional power grid, with a value of 1 to 100. The depreciation life of the transformer is T z The domain of is shown below,

[0087]

[0088] Among them, T z Indicates the depreciation period of the transformer, ranging from 15 to 30 years;

[0089] The transformer age is determined based on the regional power grid operation time and transformer depreciation period, as follows:

[0090] T s =T y mod T z

[0091] Among them, T s Indicates the service life of the transformer, T y Indicates the regional power grid operation time, T z Indicates the depreciation period of the transformer, and the symbol mod represents the remainder operation;

[0092] The outage model of the transformer is a repairable forced outage model, where μ is the repair rate, which represents the probability that the transformer will switch from a fault state to an operating state. The calculation of the repair rate μ is shown in the following formula:

[0093]

[0094] Among them, M TTR is the average repair time of the transformer, and in this embodiment, the value is 8.

[0095] Furthermore, step S3 includes:

[0096] Generate a random number R between (0,1) for each transformer in the regional power grid i , the specific formula for simulating the operating status of each transformer in the regional power grid is shown below:

[0097]

[0098] Where s i Indicates that transformer i is in working or failure state, Q i is the transformer failure rate, R i is a uniformly distributed random number;

[0099] Calculate the operating time and repair time of the transformer as shown below,

[0100]

[0101] Among them, f(T s ) is the transformer failure rate function, μ is the probability that the transformer changes from the fault state to the operating state, ζ1, ζ2 are random numbers uniformly distributed between [0,1];

[0102] The power supply path of the load node is determined as follows:

[0103]

[0104] Among them, lp1 indicates that the power supply path that meets node 1 is transformer 1, transformer 2, and line 10; lp2 indicates that the power supply path that meets node 1 is transformer 1, transformer 3, and line 10; lp3 indicates that the power supply path that meets node 1 is transformer 1, transformer 4, and line 11; lp4 indicates that the power supply path that meets node 1 is transformer 1, transformer 5, line 10, and line 12; lp5 indicates that the power supply path that meets node 1 is transformer 1, transformer 7, line 11, and line 13; lp6 indicates that the power supply path that meets node 1 is transformer 1, transformer 6, line 10, line 12, and line 14; lp7 indicates that the power supply path that meets node 1 is transformer 1, transformer 8, line 11, line 13, and line 15.

[0105] When a device fails in the power supply path of a load node, the node load loss is calculated, and the regional power grid risk sampling value I is calculated according to the following formula:

[0106]

[0107] Among them, EENS I The regional power grid risk sampling value I, S is the state set where the regional power grid cannot meet the load demand, P i is the power reduction in state i, t i is the power reduction time in state i.

[0108] Furthermore, the step S5 includes:

[0109] Increase the regional power grid operation time, re-execute steps S2 to S4 and calculate the regional power grid risk sampling value II according to the following formula until all possible values ​​within the regional power grid operation time definition domain are traversed;

[0110]

[0111] Among them, EENS II is the regional power grid risk sampling value II, T step is the number of sequential Monte Carlo simulations;

[0112] The average value of the regional power grid risk value samples II corresponding to the operation time of different regional power grids is used as the regional power grid risk value under the current transformer depreciation period;

[0113]

[0114] Among them, EENS is the regional power grid risk sampling value II, T y，max is the maximum operating time of the regional power grid.

[0115] Furthermore, the step S7 includes:

[0116] The regional power grid risk under each depreciation period is normalized according to the following formula:

[0117]

[0118] in, represents the regional power grid risk after normalization, EENS i represents the regional power grid risk value corresponding to the i-th depreciation period, EENS max It represents the maximum value of regional power grid risk value corresponding to all depreciation years, and EENS,min represents the minimum value of regional power grid risk value corresponding to all depreciation years;

[0119] The normalized regional power grid risk and its corresponding depreciation period are fitted with the risk function based on the two-parameter Weibull distribution using the least squares method.

[0120]

[0121] Among them, EENS represents the regional power grid risk, S(m,n) is the residual sum of squares between the actual value and the fitted value, m, n, c are the risk function coefficients, t s Indicates the depreciation period of the transformer, EENS i Indicates the regional power grid risk value corresponding to the i-th depreciation period of the transformer, eens i It represents the fitting value of regional power grid risk corresponding to the i-th depreciation period of the transformer.

[0122] Final depreciation period - regional power grid risk diagram Figure 3 As shown in Figure 2, constructing a transformer failure rate evolution model based on the bathtub curve can provide more accurate transformer failure rate information, thereby improving the accuracy and reliability of risk assessment results. Calculating the transformer failure rate based on the transformer's depreciation years and equipment age can better reflect the transformer's actual status, making regional power grid risk assessment results more refined.

[0123] In summary, the present invention provides a method for analyzing the correlation between regional power grid risk and power transformer depreciation life. It simultaneously considers the impact of transformer depreciation life and regional power grid operation time on regional power grid risk. It can effectively capture the impact of transformer depreciation life on transformer reliability in the regional power grid, and further quantify the impact of transformer depreciation life on regional power grid risk. This method fills the gap in the current regional power grid risk assessment method based on transformer depreciation life. It can serve as an important basis for power grid companies to scientifically formulate transformer depreciation life, and promote power grid enterprises to reduce costs and increase efficiency.

[0124] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0125] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention, and such changes and modifications fall within the scope of the invention as claimed.

Claims

1. A method for analyzing the correlation between regional power grid risk and power transformer depreciation period, characterized in that: The following steps are involved: S1. Based on transformer fault related information, the bathtub curve is used to fit the transformer fault data and a model is constructed to show the evolution of transformer failure rate with transformer age. S2. Based on the evolution model of transformer failure rate as the transformer age changes in step S1, determine the transformer failure rate and establish a transformer repairable forced outage model; S3. Based on the transformer outage model in S2, the sequential Monte Carlo method is used to simulate the operating status of the transformers in the regional power grid, and the regional power grid risk sampling value I is calculated according to the operating status of the transformers in the regional power grid; S4. If the number of simulations of the Sequential Monte Carlo method does not reach the set value, step S3 is repeated. Otherwise, the average of the regional power grid risk sampling values ​​I obtained by multiple simulations is used as the regional power grid risk sampling value II under the current regional power grid operation time and transformer depreciation period, and step S5 is executed. S5. Change the regional power grid operation time, and re-execute steps S2 to S4 to calculate the regional power grid risk sampling value II until all possible values ​​within the regional power grid operation time definition domain are traversed, and the average value of the regional power grid risk sampling values ​​II corresponding to different regional power grid operation times is used as the regional power grid risk value under the current transformer depreciation period; S6. Change the depreciation period of the transformer, repeat steps S2 to S5, and calculate the regional power grid risk value corresponding to different depreciation periods within the transformer depreciation period definition domain; S7. Using the transformer depreciation period as an independent variable and the regional power grid risk values ​​corresponding to different depreciation periods as a function, a mathematical fitting method is used to fit the data obtained in step S6 to obtain a correlation curve between the regional power grid risk and the power transformer depreciation period.

2. The method for analyzing the correlation between regional power grid risk and power transformer depreciation period according to claim 1, characterized in that: The step S1 comprises: Determining parameters of a bathtub curve based on transformer fault information; Determine the dividing point T according to the design life of the transformer d , when the transformer service age is less than T d The transformer failure rate model during the stable period is a constant, and its value is the bathtub curve at T d The value of the transformer is greater than T d The failure rate model of the transformer during the wear period is the bathtub curve; The four-parameter bathtub curve is fitted based on the transformer fault related information. The four-parameter bathtub curve function is shown as follows: Where, α, β, η, λ>0; parameter λ is the product factor of the failure rate function; η is the interval parameter; the values ​​of shape parameters α and β jointly determine the shape of the failure rate curve, α∈(0,1), β>0, t∈(0,η); T s The service life of the transformer; When the equipment is in service T s Greater than T d The failure rate model of the transformer during the wear period is h(T s ), the transformer failure rate evolution model is shown as follows: Where h(t) is the four-parameter bathtub curve function, T s is the transformer service life, T d It is the dividing point between the failure rate curve in the stable period and the failure rate curve in the wear period.

3. The method for analyzing the correlation between regional power grid risk and power transformer depreciation period according to claim 1, characterized in that: The S2 includes: The regional power grid operation time and transformer depreciation years are taken as input variables, and their respective definition domains are determined. The regional power grid risk is taken as the output variable. The regional power grid operation time T y The domain of is shown below, Among them, T y Indicates the operating life of the regional power grid and the depreciation life of the transformer T z The domain of is shown below, Among them, T z Indicates the depreciation period of the transformer; The transformer age is determined based on the regional power grid operation time and transformer depreciation period, as follows: T s =T y mod T z Among them, T s Indicates the service life of the transformer, T y Indicates the regional power grid operation time, T z Indicates the depreciation period of the transformer, and the symbol mod represents the remainder operation; The outage model of the transformer is a repairable forced outage model, where μ is the repair rate, which represents the probability that the transformer will switch from a fault state to an operating state. The calculation of the repair rate μ is shown in the following formula: Among them, M TTR is the mean repair time of the transformer.

4. The method for analyzing the correlation between regional power grid risk and power transformer depreciation period according to claim 1, characterized in that: The step S3 comprises: Generate a random number R between (0,1) for each transformer in the regional power grid i , the specific formula for simulating the operating status of each transformer in the regional power grid is shown below: Where s i Indicates that transformer i is in working or failure state, Q i is the transformer failure rate, R i is a uniformly distributed random number; Calculate the operating time and repair time of the transformer as shown below, Among them, f(T s ) is the transformer failure rate function, μ is the probability that the transformer changes from the fault state to the operating state, ζ1, ζ2 are random numbers uniformly distributed between [0,1]; When a device fails in the power supply path of a load node, the node load loss is calculated, and the regional power grid risk sampling value I is calculated according to the following formula: Among them, EENS I The regional power grid risk sampling value I, S is the state set where the regional power grid cannot meet the load demand, P i is the power reduction in state i, t i is the power reduction time in state i.

5. The method for analyzing the correlation between regional power grid risk and power transformer depreciation period according to claim 1, characterized in that: The step S5 comprises: Increase the regional power grid operation time, re-execute steps S2 to S4 and calculate the regional power grid risk sampling value II according to the following formula until all possible values ​​within the regional power grid operation time definition domain are traversed; Among them, EENS II is the regional power grid risk sampling value II, T step is the number of sequential Monte Carlo simulations; The average value of the regional power grid risk value samples II corresponding to the operation time of different regional power grids is used as the regional power grid risk value under the current transformer depreciation period; Among them, EENS is the regional power grid risk sampling value II, T y，max is the maximum operating time of the regional power grid.

6. The method for analyzing the correlation between regional power grid risk and power transformer depreciation period according to claim 1, characterized in that: The step S7 comprises: The regional power grid risk under each depreciation period is normalized according to the following formula: in, represents the regional power grid risk after normalization, EENS i represents the regional power grid risk value corresponding to the i-th depreciation period, EENS max It represents the maximum value of regional power grid risk value corresponding to all depreciation years, and EENS,min represents the minimum value of regional power grid risk value corresponding to all depreciation years; The normalized regional power grid risk and its corresponding depreciation period are fitted with the risk function based on the two-parameter Weibull distribution using the least squares method. Among them, EENS represents the regional power grid risk, S(m,n) is the residual sum of squares between the actual value and the fitted value, m, n, c are the risk function coefficients, t s Indicates the depreciation period of the transformer, EENS i Indicates the regional power grid risk value corresponding to the i-th depreciation period of the transformer, eens i It represents the fitted value of regional power grid risk corresponding to the i-th depreciation period of the transformer.

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