Electric power system reliability evaluation method and device based on extreme weather events

Abstract

The invention discloses an electric power system reliability evaluation method and device based on an extreme weather event, and belongs to the field of electric power systems.The method comprises the steps that historical data of the extreme weather event are obtained, and an occurrence rate function of the extreme weather event is obtained through fitting according to the historical data; according to the incidence rate function, generating number and time nodes in the extreme weather events in one year by using a non-homogeneous Poisson process; performing random extraction from the historical data according to the number and the time nodes, and determining an intensity parameter and a duration of each extreme weather event according to an extraction result; and determining a corresponding event failure rate according to the intensity parameter of each extreme weather event, and determining a total failure rate according to the failure rate of each event, the failure rate without the event, the corresponding event and the duration without the event. According to the method, the comprehensive influence of the randomness of the extreme weather intensity on the reliability index probability distribution is considered, and an important basis is provided for evaluating the reliability of the power system in an extreme weather event high-incidence region.

Description

Technical Field

[0001] The present invention relates to the field of power systems, and in particular to a method and device for evaluating the reliability of a power system based on extreme weather events. Background Art

[0002] Accurate evaluation of reliability indicators is an important basis for quantifying the reliability differences between power supply systems and formulating reliability improvement and optimization measures. At present, user-oriented reliability indicators such as system average interruption frequency and system average interruption duration are usually used to measure the power supply reliability of power systems. Weather conditions have a great impact on the failure and recovery time of components in power systems, especially in extreme weather conditions, so the reliability indicators calculated at different times will be significantly different. Generally speaking, failures caused by weather reasons are not evenly distributed throughout the year, and the failure rate and recovery time are time-series, which has a great impact on the power shortage indicators estimated based on the time-varying load curve. For example, thunderstorms are very common in the summer thunderstorm season. At this time, if the load is large, there may be a higher failure rate.

[0003] Therefore, it is urgent to establish a power system reliability assessment method that takes extreme weather into account, which will have very important practical significance for accurately assessing the economic losses caused by power supply to users. Summary of the invention

[0004] The main purpose of the present invention is to establish a power system reliability assessment method taking extreme weather into consideration, so as to more accurately assess the economic losses caused by abnormal power supply to users.

[0005] The present invention provides a method for evaluating the reliability of an electric power system based on extreme weather events, comprising: acquiring historical data of extreme weather events, and fitting an occurrence rate function of extreme weather events based on the historical data; generating the number and time nodes of extreme weather events in a year using a non-homogeneous Poisson process based on the occurrence rate function; randomly sampling from the historical data based on the number and the time nodes, and determining the intensity parameter and duration of each extreme weather event based on the sampling result; determining the corresponding event failure rate based on the intensity parameter of each extreme weather event, and determining the total failure rate based on the failure rate of each event, the failure rate when there is no event, and the corresponding event and event-free duration; wherein the extreme weather events include rainstorm events and lightning strike events, and the corresponding intensity parameters include water accumulation depth and ground lightning density, respectively.

[0006] In the power system reliability assessment method based on extreme weather events of the present invention, the rainstorm event includes: a weather condition in which the accumulated water exceeds the elevation of the cable joint of the high-voltage switch cabinet in the station.

[0007] In the power system reliability assessment method based on extreme weather events of the present invention, for a lightning strike event, the corresponding event failure rate is determined according to the intensity parameter of each extreme weather event, including:

[0008] λ lightning (N g (t))=(βN g (t)+1)λ norm

[0009] Among them, N g (t) is the ground-to-ground lightning density at time t, β is the scale parameter, and λ norm is a constant failure rate under normal weather conditions, λ lightning (N g (t)) is the failure rate when a lightning strike occurs.

[0010] In the power system reliability assessment method based on extreme weather events of the present invention, for a rainstorm event, the corresponding event failure rate is determined according to the intensity parameter of each extreme weather event, including:

[0011]

[0012] Among them, d(t) is the water depth at time t, γ is the damping coefficient, and λ norm is the constant failure rate under normal weather conditions, D W Designed flood protection height for electrical equipment, D BW is the elevation of the cable joint of the high-voltage switch cabinet in the station, rain (d(t)) is the failure rate when a rainstorm event occurs.

[0013] In the power system reliability assessment method based on extreme weather events of the present invention, the total failure rate is determined according to the failure rate of each event, the failure rate when there is no event, and the corresponding event and no event duration, including:

[0014]

[0015] Among them, T hw 、T l and T n They represent the annual average duration of rainstorm events, lightning events and no events, respectively, T tot The total duration of one year.

[0016] In the power system reliability assessment method based on extreme weather events of the present invention, after determining the intensity parameters and duration of each extreme weather event according to the extraction results, it also includes: determining the extreme weather impact weight coefficient on the fault recovery time according to the intensity parameters and duration of the extreme weather event; determining the total fault recovery time according to the extreme weather impact weight coefficient, the hourly change weight coefficient, the daily change weight coefficient and the fault recovery time under normal weather conditions.

[0017] In the power system reliability assessment method based on extreme weather events of the present invention, the total fault recovery time is determined based on the extreme weather impact weight coefficient, the hourly change weight coefficient, the daily change weight coefficient and the fault recovery time under normal weather conditions.

[0018] In the power system reliability assessment method based on extreme weather events of the present invention, after determining the total fault recovery time, it also includes: determining the power system reliability score based on the total failure rate and the total fault recovery time.

[0019] The present invention also provides an extreme weather event-based power system reliability assessment device, comprising: an acquisition module, used to acquire extreme weather event historical data, and fit the occurrence rate function of extreme weather events based on the historical data; a simulation module, used to generate the number and time nodes of extreme weather events in a year based on the occurrence rate function using a non-homogeneous Poisson process; a processing module, used to randomly extract from the historical data according to the number and the time nodes, and determine the intensity parameters and duration of each extreme weather event based on the extraction results; an assessment module, used to determine the corresponding event failure rate based on the intensity parameters of each extreme weather event, and determine the total failure rate based on the failure rate of each event, the failure rate when there is no event, and the corresponding event and event-free duration; wherein the extreme weather events include rainstorm events and lightning strike events, and the corresponding intensity parameters include water accumulation depth and ground lightning density, respectively.

[0020] The present invention also provides a computer storage medium, which stores a computer program executable by a processor, and the computer program executes any of the above-mentioned methods for evaluating the reliability of a power system based on extreme weather events.

[0021] The beneficial effects of the present invention are: considering the failure rate of overhead lines as a function of extreme weather, effectively accounting for the comprehensive impact of the distribution pattern of extreme weather and the randomness of weather intensity on the probability distribution of reliability indicators, and providing an important basis for evaluating the reliability of power systems in areas prone to extreme weather events. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] 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 some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0023] Figure 1 is a flow chart of a method for evaluating reliability of a power system based on extreme weather events according to an embodiment of the present invention;

[0024] Figure 2 is a flow chart of a method for generating extreme weather events according to an embodiment of the present invention;

[0025] Figure 3 Schematic diagram of failure rate under four different conditions of the embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0027] Combine the following Figures 1 to 3 The power system reliability assessment method and device based on extreme weather events of the present invention are described. Figure 1 is a flow chart of a method for evaluating power system reliability based on extreme weather events according to an embodiment of the present invention, such as Figure 1 As shown, the present invention provides a method for evaluating the reliability of a power system based on extreme weather events, comprising:

[0028] S1. Obtain historical data of extreme weather events, and fit an occurrence rate function of extreme weather events based on the historical data.

[0029] In the present invention, extreme weather events include rainstorm events and lightning events. In one embodiment, the rainstorm event includes a weather condition where the water accumulation exceeds the ground elevation of the cable joint of the high-voltage switch cabinet in the station, that is, the rainstorm event can be defined as a condition where the water accumulation exceeds the ground elevation of the equipment, and the lightning event is defined as a weather condition where the ground lightning density is greater than zero. The fitted extreme weather occurrence rate function is x(t), and different events can be fitted separately.

[0030] S2. Based on the incidence function, using a non-homogeneous Poisson process, generate the number and time nodes of extreme weather events in a year.

[0031] Specifically, based on the seasonal distribution of extreme weather events, the accumulation rate X(t) of extreme weather events in different seasons can be calculated. The non-homogeneous Poisson process method can be used to generate the number of lightning strikes in a year and the time nodes of single lightning strikes.

[0032] The calculation method of extreme weather event accumulation rate X(t) is:

[0033]

[0034] In a given time interval (0, t], the probability of n extreme weather events occurring is:

[0035]

[0036] The average number of extreme weather events in the interval (0, t] is:

[0037] E(N(t))=X(t) (3)

[0038] The average number of extreme weather events within the time interval [t, t+Δt] is:

[0039]

[0040] S3. Randomly draw from the historical data according to the quantity and the time node, and determine the intensity parameters and duration of each extreme weather event according to the drawing results, and the corresponding intensity parameters include water accumulation depth and ground lightning density.

[0041] like Figure 2 As shown, based on the above time nodes and times, random sampling is performed based on the historical extreme weather meteorological database. For lightning strike events, the ground-to-ground lightning density and lightning strike event duration can be determined based on the sampling results. Among them, the ground-to-ground lightning density is the number of ground lightning strikes per square kilometer per year. For rainstorm events, the depth of accumulated water can be determined based on the sampling results.

[0042] S4. Determine the corresponding event failure rate based on the intensity parameters of each extreme weather event, and determine the total failure rate based on the failure rate of each event, the failure rate when there is no event, and the corresponding event and no-event duration.

[0043] For example, for lightning strike events, the failure rate is calculated based on the number of lightning strikes to obtain the lightning strike failure rate. For rainstorm events, the failure rate is calculated based on the depth of accumulated water d(t) to obtain the rainstorm failure rate λ rain (d(t)). Then, the total failure rate is determined by comprehensively considering the failure rates and durations of lightning events, rainstorm events, and normal events without any events.

[0044] As a preferred method, the reliability model considering the impact of heavy rain and lightning strikes on overhead lines mainly involves the impact of heavy rain and lightning strikes on failure rate and failure recovery events. The time-varying failure rate model of overhead lines can be established as follows:

[0045] λ(d(t),N g (t)) = λ hw (d(t))+λ l (N g (t))+λ n (d(t),N g (t)) (5)

[0046] Among them, λ hw (d(t)) is the failure rate when a rainstorm event occurs; λ l (N g (t)) is the failure rate when a lightning strike occurs; λ n (d(t),N g (t)) is the failure rate under normal weather conditions.

[0047] The three different failure rates are defined as:

[0048]

[0049] Among them, D BW It is the elevation of the cable joint of the high-voltage switch cabinet in the station, N g (t) is the ground-to-ground lightning density at time t.

[0050] The power system reliability assessment method based on extreme weather events of the present invention takes into account the failure rate of overhead lines as a function of extreme weather, and effectively accounts for the comprehensive impact of the distribution pattern of extreme weather and the randomness of weather intensity on the probability distribution of reliability indicators, providing an important basis for assessing the reliability of power systems in areas prone to extreme weather events.

[0051] In one embodiment, for a rainstorm event, determining the corresponding event failure rate according to the intensity parameter of each extreme weather event includes:

[0052]

[0053] Among them, d(t) is the water depth at time t, γ is the damping coefficient, and λ norm is the constant failure rate under normal weather conditions, D W Designed flood protection height for electrical equipment, D BW It is the elevation of the cable joint of the high-voltage switch cabinet in the station. The water depth and flood prevention height are both relative to the same elevation.

[0054] The impact of heavy rain events on electrical equipment is mainly caused byrain (d(t)) determines the relationship between the failure rate and the degree of water accumulation, which can be expressed by an exponential expression. The exponential expression for modeling the failure rate in a rainstorm event is shown in Formula (9).

[0055] In one embodiment, for a lightning strike event, determining the corresponding event failure rate according to the intensity parameter of each extreme weather event includes:

[0056] λ lightning (N g (t))=(βN g (t)+1)λ norm (10)

[0057] Among them, N g (t) is the ground-to-ground lightning density at time t, β is the scale parameter, and λ norm is a constant failure rate under normal weather conditions, λ lightning (N g (t)) is the failure rate when a lightning strike occurs. The failure rate under lightning strike conditions is modeled as a linear model that depends on the ground lightning density in the embodiment of the present invention, as shown in formula (10).

[0058] In one embodiment, determining the total failure rate according to the failure rate of each event, the failure rate when there is no event, and the corresponding event and no-event durations includes:

[0059]

[0060] Among them, T hw 、T l and T n They represent the annual average duration of rainstorm events, lightning events and no events, respectively, T tot is the total length of a year. It is possible that heavy rain and lightning strikes occur simultaneously, so T hw 、T l and T n The sum is greater than or equal to T tot .

[0061] Under normal weather conditions, rain (d(t)) and λ lightning (N g (t)) are all zero, and the failure rate in equation (5) is equal to the constant failure rate under normal weather conditions. In extreme weather with heavy rain and lightning, the total failure rate is the sum of the failure rates of each item. The failure rates of different failure causes are additive. Figure 3 Four different weather conditions are shown: lightning and no heavy rain, heavy rain and no lightning, both heavy rain and lightning, and normal weather conditions.

[0062] exist Figure 3 In the example, event A represents a lightning strike event, and its condition is N g >0; Event B represents a rainstorm event, and its condition is d(t)≥D BW ; Event C represents normal weather, and its condition is (d(t) <D BW )∩(N g =0), let the expected value E(λ lightning (N g (t)))=a;E(λ rain (d(t)))=b;E(λ norm )=λ norm =c. Figure 2 It can be seen that λ(d(t),N g The expected value of (t)) is:

[0063]

[0064] To consider additional extreme weather conditions, the result of equation (12) is made equal to λ(w(t),N in equation (5). g The expected value of the expression (t) is obtained by estimating the failure probability under different weather conditions as the proportion of these failures occurring in a year, and then obtaining the total failure probability of equation (11).

[0065] In one embodiment, after determining the intensity parameters and duration of each extreme weather event according to the extraction results, it also includes: determining the extreme weather impact weight coefficient regarding the fault recovery time according to the intensity parameters and duration of the extreme weather event; determining the total fault recovery time according to the extreme weather impact weight coefficient, the hourly change weight coefficient, the daily change weight coefficient and the fault recovery time under normal weather conditions.

[0066] Considering the fault recovery time of the power system under extreme weather conditions, the reliability model of the present invention regards it as time-varying. If the fault occurs at night or on weekends, the repair time is often longer. This is because the number and work efficiency of maintenance personnel vary over time. This time dependence of the recovery time can be calculated by the weight factor f h and f d To establish the model.

[0067] The failure recovery time after extreme weather is often very long, which is related to the degree of damage and the available recovery resources. In the invention, the extreme weather impact weight coefficient f is calculated by the third weight factor. w , the restoration time is modeled as a function of the intensity of extreme weather, thereby quantifying the damage caused by extreme weather to the power system.

[0068] In one embodiment, the total fault recovery time is calculated as shown in formula (13).

[0069] r(t)=f w (d(t),N g (t))f d (t)f h (t)r norm (13)

[0070] Among them, f w (d(t),N g (t)) is the weight coefficient of extreme weather impact; fh(t) is the weight coefficient of hourly change in a day; f d (t) is the daily change weight coefficient; r norm It is the reference recovery time under normal weather conditions.

[0071] In one embodiment, after determining the total fault recovery time, the method further includes: determining a power system reliability score according to the total fault rate and the total fault recovery time.

[0072] Specifically, the total failure rate and total fault recovery time can be included in the power system reliability assessment system for scoring. Both the total failure rate and the total fault recovery time are negatively correlated with the reliability score. Considering the system with the same failure rate, the shorter the total fault recovery time, the higher the reliability.

[0073] The present invention also provides an extreme weather event-based power system reliability assessment device, comprising: an acquisition module, used to acquire extreme weather event historical data, and fit the occurrence rate function of extreme weather events based on the historical data; a simulation module, used to generate the number and time nodes of extreme weather events in a year based on the occurrence rate function using a non-homogeneous Poisson process; a processing module, used to randomly extract from the historical data according to the number and the time nodes, and determine the intensity parameters and duration of each extreme weather event based on the extraction results; an assessment module, used to determine the corresponding event failure rate based on the intensity parameters of each extreme weather event, and determine the total failure rate based on the failure rate of each event, the failure rate when there is no event, and the corresponding event and event-free duration; wherein the extreme weather events include rainstorm events and lightning strike events, and the corresponding intensity parameters include water accumulation depth and ground lightning density, respectively.

[0074] The implementation principle and technical effects of the power system reliability assessment device based on extreme weather events provided in the embodiment of the present invention are the same as those of the aforementioned power system reliability assessment method based on extreme weather events. For the sake of brief description, for parts not mentioned in the embodiment of the power system reliability assessment device based on extreme weather events, reference may be made to the corresponding contents in the aforementioned power system reliability assessment method based on extreme weather events embodiment.

[0075] The present application also provides a computer-readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a disk, an optical disk, a server, an App application store, etc., on which a computer program is stored, and the program is executed by a processor to implement the corresponding function. The computer-readable storage medium of this embodiment is executed by a processor to implement the method embodiment of the power system reliability assessment method based on extreme weather events.

[0076] It should be pointed out that, according to the needs of implementation, the various steps / components described in this application can be split into more steps / components, and two or more steps / components or partial operations of steps / components can be combined into new steps / components to achieve the purpose of the present invention.

[0077] The order of execution of each step in the above embodiment does not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present application.

[0078] It should be understood that those skilled in the art can make improvements or changes based on the above description, and all these improvements and changes should fall within the scope of protection of the appended claims of the present invention.

Claims

1. A method for evaluating the reliability of a power system based on extreme weather events, characterized in that: The method includes obtaining historical data of extreme weather events, and fitting an occurrence rate function of extreme weather events based on the historical data; According to the occurrence rate function, the number and time nodes of extreme weather events in a year are generated using a non-homogeneous Poisson process; Randomly extract from the historical data according to the quantity and the time node, and determine the intensity parameters and duration of each extreme weather event according to the extraction results; According to the intensity parameters of each extreme weather event, the corresponding event failure rate is determined, and the total failure rate is determined according to the failure rate of each event, the failure rate when there is no event, and the corresponding duration of events and no events; The extreme weather events include rainstorm events and lightning strike events, and the corresponding intensity parameters include water accumulation depth and ground lightning density.

2. The power system reliability assessment method based on extreme weather events according to claim 1 is characterized in that: The rainstorm events include: weather conditions where the accumulated water exceeds the elevation of the cable joints of the high-voltage switch cabinet in the station.

3. The power system reliability assessment method based on extreme weather events according to claim 1 is characterized in that: For rainstorm events, the corresponding event failure rate is determined according to the intensity parameters of each extreme weather event, including: Among them, d(t) is the water depth at time t, γ is the damping coefficient, and λ norm is the constant failure rate under normal weather conditions, D W Designed flood protection height for electrical equipment, D BW The height of the cable joint of the high-voltage switch cabinet in the station, λ rain (d(t)) is the failure rate when a rainstorm event occurs.

4. The power system reliability assessment method based on extreme weather events according to claim 1 is characterized in that: For lightning strike events, the corresponding event failure rate is determined according to the intensity parameters of each extreme weather event, including: l lightning (N g (t))=(βN g (t)+1)λ norm Among them, N g (t) is the ground-to-ground lightning density at time t, β is the scale parameter, and λ norm is a constant failure rate under normal weather conditions, λ lightning (N g (t)) is the failure rate when a lightning strike occurs.

5. The power system reliability assessment method based on extreme weather events according to claim 1 is characterized in that: Determining the total failure rate according to the failure rate of each event, the failure rate when there is no event, and the corresponding event and no-event durations includes: Among them, T hw , T l and T n They represent the annual average duration of rainstorm events, lightning events and no events, respectively, T tot The total duration of one year.

6. The method for evaluating the reliability of a power system based on extreme weather events according to claim 1, characterized in that: After determining the intensity parameters and duration of each extreme weather event according to the extraction results, it also includes: Determine the extreme weather impact weight coefficient on the fault recovery time based on the intensity parameters and duration of the extreme weather event; The total fault recovery time is determined based on the extreme weather impact weight coefficient, the hourly change weight coefficient, the daily change weight coefficient and the fault recovery time under normal weather conditions.

7. The method for evaluating the reliability of a power system based on extreme weather events according to claim 6, characterized in that: Determining the total fault recovery time according to the extreme weather impact weight coefficient, the hourly change weight coefficient, the daily change weight coefficient and the fault recovery time under normal weather conditions includes: r(t)=f w (d(t),N g (t))f d (t)f h (t)r norm Among them, f w (d(t),N g (t)) is the weight coefficient of extreme weather impact, fh(t) is the weight coefficient of hourly change in a day, and f d (t) is the daily change weight coefficient, r norm This is the reference recovery time under normal weather conditions.

8. The method for evaluating the reliability of a power system based on extreme weather events according to claim 6, characterized in that: After determining the total fault recovery time, the method further includes: A power system reliability score is determined based on the total failure rate and the total failure recovery time.

9. A power system reliability assessment device based on extreme weather events, characterized in that: include: An acquisition module is used to acquire historical data of extreme weather events and, based on the historical data, fit an occurrence rate function of extreme weather events; A simulation module, used for generating the number and time nodes of extreme weather events in a year by using a non-homogeneous Poisson process according to the occurrence rate function; A processing module, configured to randomly extract data from the historical data according to the quantity and the time node, and determine the intensity parameter and duration of each extreme weather event according to the extraction result; An evaluation module, used for determining the corresponding event failure rate according to the intensity parameter of each extreme weather event, and determining the total failure rate according to the failure rate of each event, the failure rate when there is no event, and the corresponding event and no-event duration; The extreme weather events include rainstorm events and lightning strike events, and the corresponding intensity parameters include water accumulation depth and ground lightning density.

10. A computer storage medium, characterized in that: A computer program executable by a processor is stored therein, and the computer program executes the power system reliability assessment based on extreme weather events as described in any one of claims 1-8.

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