Typhoon weather power failure emergency plan method and system based on big data analysis

Abstract

The invention provides a typhoon weather power failure emergency plan method and system based on big data analysis, and relates to the technical field of power failure monitoring, and the method comprises the steps: obtaining power failure data of a power system synchronously corresponding to typhoon weather time, and generating a correlation result of typhoon weather and a power failure type; analyzing the relevance result to obtain a corresponding relation between the meteorological variable and the power fault type; establishing a fault probability prediction model corresponding to the power fault type based on the corresponding relationship between the meteorological variable and the power fault type; and according to a prediction result of the fault probability prediction model, formulating a risk level and a corresponding pre-plan measure. The typhoon weather condition is analyzed based on big data, the probability that power failures occur frequently in the corresponding typhoon weather is output, a plan is made in advance according to the output failure probability result, and the power failures under the typhoon weather condition can be predicted, dealt with and reduced through real-time data analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of power failure monitoring, and in particular to a typhoon weather power failure emergency plan method and system based on big data analysis. Background Art

[0002] Typhoons are a powerful natural disaster that frequently occurs and has a serious impact on coastal areas. The extreme weather conditions such as strong winds, heavy rains and storm surges brought by typhoons pose a huge threat to power facilities, resulting in frequent power failures. These failures not only affect the normal lives of residents, but may also cause significant losses to industrial production, transportation and other fields, and even threaten people's lives and property.

[0003] In view of the frequent power failures during typhoon weather, traditional emergency response plans often fail to fully meet the complex needs of modern society. Therefore, an emergency response plan based on big data analysis has emerged. This method collects real-time data related to typhoon weather, such as meteorological data and power failure data, and uses big data analysis to predict and evaluate potential power failure risks, and formulate more scientific and reasonable emergency response plans to deal with power failures that may be caused by typhoon weather.

[0004] In view of this, there is a need for a typhoon weather power failure emergency plan method and system based on big data analysis. Summary of the invention

[0005] In view of the problem of frequent power failures in typhoon weather in the prior art, the traditional emergency plan methods are often difficult to fully meet the complex needs of modern society. The present invention provides a typhoon weather power failure emergency plan method and system based on big data analysis, which can predict, respond to and reduce power failures in typhoon weather conditions through real-time data analysis. The specific technical solution is as follows:

[0006] A typhoon weather power failure emergency plan method based on big data analysis, comprising:

[0007] Obtain power failure data corresponding to the power system and typhoon weather time synchronization, and generate correlation results between typhoon weather and power failure types;

[0008] Analyzing the correlation results to obtain the corresponding relationship between meteorological variables and power failure types;

[0009] Based on the corresponding relationship between meteorological variables and power failure types, a fault probability prediction model corresponding to the power failure type is established;

[0010] According to the prediction results of the failure probability prediction model, risk levels and corresponding emergency measures are formulated.

[0011] Preferably, the acquiring of power failure data corresponding to the power system and the typhoon weather time synchronization and generating the correlation result between the typhoon weather and the power failure type includes:

[0012] Acquiring power fault data in the power system and classifying the power fault data;

[0013] Obtain typhoon weather data from the meteorological platform, including typhoon path, wind speed and rainfall;

[0014] The classified power grid fault data and the typhoon meteorological data are time-matched to obtain power grid fault types that are synchronized with the typhoon meteorological time and power grid fault types that are not synchronized with the typhoon meteorological time, and generate correlation results between typhoon weather and power fault types.

[0015] Preferably, the analysis of the correlation results to obtain the corresponding relationship between the meteorological variables and the power fault types includes:

[0016] Performing mathematical statistical analysis on the correlation results, extracting the corresponding frequency of each meteorological variable and the power fault type in a preset period, and obtaining the corresponding relationship between the meteorological variable and the power fault type;

[0017] The corresponding relationships between meteorological variables and power failure types include the following:

[0018] The correspondence between wind speed and power tower failure;

[0019] The correspondence between rainfall and power tower failures;

[0020] The corresponding relationship between the combined effects of typhoon and rainfall and transmission lines.

[0021] Preferably, the establishing of the fault probability prediction model corresponding to the power fault type based on the correspondence between the meteorological variable and the power fault type includes establishing a first fault probability prediction model for the power tower fault based on the correspondence between the wind speed and the power tower fault;

[0022] The first fault probability prediction model for establishing a power tower fault comprises:

[0023] According to the influence of damage on power towers under typhoon, a fatigue damage model of power towers under typhoon is established. The fatigue damage model is expressed as:

[0024]

[0025] Where: a and b are the model coefficients of the power tower material; V j V is the typhoon wind speed that the tower receives in the jth time period; cr V is the critical wind speed for the initiation of fatigue cracks in the tower caused by typhoon;m D is the limit wind speed of a typhoon causing one-time loading damage to the tower without time accumulation effect; j is the plastic fatigue damage of the tower per minute;

[0026] According to the relationship between the failure rate of power towers and the cumulative time of fatigue damage, the probability model of the tower not collapsing in the first i time intervals is established as follows:

[0027]

[0028] Where: Δt is the length of each time interval; is the cumulative damage suffered by the tower in the previous j-1 time intervals;

[0029] Then the first failure probability model of the power tower failure that occurred in the first i time intervals is:

[0030] P 1 =1-P toi

[0031] Preferably, the establishing of the fault probability prediction model corresponding to the power fault type based on the correspondence between the meteorological variable and the power fault type includes establishing a second fault probability prediction model for the power tower fault based on the correspondence between the rainfall and the power tower fault;

[0032] The second fault probability prediction model for establishing the power tower fault comprises:

[0033] The expression for predicting the cumulative effective rainfall is:

[0034]

[0035] Where: R eff，i represents the predicted cumulative effective rainfall on the i-th day; P pre，i represents the predicted rainfall on the i-th day; P act，n represents the actual rainfall on the nth day; k is the attenuation coefficient, which depends on the evaporation loss properties of rainwater in different soils;

[0036] According to the exponential relationship between effective rainfall and the occurrence of geological disasters, the landslide probability expression is determined as:

[0037]

[0038] Where a, b, c are Wei fitting constants; R m is the maximum effective rainfall;

[0039] The sliding process of the landslide is simplified into a particle kinematic model. According to the law of conservation of energy, the impact energy borne by the transmission tower is equal to the gravity potential energy of the landslide minus the work done by the friction force, that is:

[0040]

[0041] m=ρD 2 d

[0042] Where: m is the mass of the landslide; D is the thickness of the landslide impact body; d is the width of the impact surface of the transmission tower; ρ is the density of the landslide; v is the impact velocity; h is the height of the center of mass of the landslide; f 1 is the slope friction coefficient; θ is the slope angle;

[0043] If the power pole tower is simplified as a cantilever arm structure, the maximum horizontal impact force that the power pole tower can withstand is:

[0044]

[0045] E is the elastic modulus of the tower; I is the section inertia moment of the tower; ω lim is the deflection value of the power tower itself; x o is the height from the impact point to the tower base; H is the height of the transmission tower;

[0046] The bending deformation energy of the power transmission tower under the maximum horizontal impact force, that is, the maximum impact energy expression is:

[0047]

[0048] The degree of damage to power towers is expressed as the ratio of impact energy to anti-impact energy, namely:

[0049]

[0050] According to the degree of damage to the power tower and the probability of landslide, the second failure probability prediction model of the power tower failure is obtained as follows:

[0051] P 2 =P r *P l

[0052] Preferably, the establishing of the fault probability prediction model corresponding to the power fault type based on the correspondence between the meteorological variables and the power fault type includes establishing a third fault probability prediction model for the transmission line fault based on the correspondence between the typhoon and rainfall combined with the power transmission line fault;

[0053] According to the first fault probability prediction model and the second fault probability prediction model of power tower failure, the fault probability prediction model of power tower failure under the combined action of typhoon and rainfall is expressed as:

[0054] P tower =1-(1-P 1 )(1-P 2 )

[0055] Assume that the failure probability of each power tower in the transmission line is P tower1 , P tower2 ,…,P towern , then the failure probability of the transmission line is obtained according to the series reliability formula of the transmission line, which is the third failure probability prediction model of the transmission line failure, expressed as:

[0056]

[0057] Preferably, outputting the risk level and corresponding contingency measures according to the prediction results of the fault probability prediction model includes:

[0058] When the prediction result output by the fault probability prediction model is greater than the first risk threshold, a high-level risk level is output, and a contingency plan measure in the form of an electronic document is generated;

[0059] When the prediction result output by the fault probability prediction model is greater than the second risk threshold and not greater than the first risk threshold, an intermediate risk level is output and a contingency plan measure is generated in the form of an electronic document;

[0060] When the prediction result output by the fault probability prediction model is not greater than the third risk threshold, a low risk level is output and no contingency measures are generated.

[0061] A typhoon weather power failure emergency plan system based on big data analysis, applied to the above-mentioned typhoon weather power failure emergency plan method based on big data analysis, comprising:

[0062] A data acquisition unit, used to acquire power failure data corresponding to the power system and the typhoon weather time synchronization, and generate a correlation result between the typhoon weather and the power failure type;

[0063] A correlation analysis unit, used to analyze the correlation result to obtain a corresponding relationship between the meteorological variable and the power fault type;

[0064] A model building unit, used for building a fault probability prediction model corresponding to the power fault type based on the corresponding relationship between the meteorological variables and the power fault type;

[0065] The risk level and contingency plan generation unit is used to formulate risk levels and corresponding contingency plans according to the prediction results of the failure probability prediction model.

[0066] Compared with the prior art, the present invention has the following beneficial effects:

[0067] The present invention provides a typhoon weather power failure emergency plan method based on big data analysis, which obtains power failure data corresponding to the power system and typhoon meteorological time synchronization, and generates correlation results between typhoon meteorology and power failure types; analyzes the correlation results to obtain the correspondence between meteorological variables and power failure types; establishes a fault probability prediction model corresponding to the power failure type based on the correspondence between meteorological variables and power failure types; and formulates risk levels and corresponding emergency plan measures according to the prediction results of the fault probability prediction model. The present invention outputs the probability of frequent power failures in typhoon weather based on typhoon weather conditions analyzed by big data, and makes advance plans based on the output fault probability results. It can predict, respond to and reduce power failures under typhoon weather conditions through real-time data analysis, improve the disaster prevention and mitigation capabilities of the power system, and reduce the incidence and loss of power failures. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0069] Figure 1 The present invention is a flow chart of a method for preparing an emergency plan for power failures in typhoon weather based on big data analysis.

[0070] Figure 2 This is a schematic diagram of a typhoon weather power failure emergency plan based on big data analysis for the system of the present invention. DETAILED DESCRIPTION

[0071] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0072] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0073] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an" and "the" are intended to include plural forms unless the context clearly indicates otherwise.

[0074] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0075] Since the most direct power failures affected by typhoons are power failures such as power outages caused by damage to power poles and towers, the following embodiments provide a detailed description of this phenomenon.

[0076] See also Figure 1 The embodiment of the present invention provides a typhoon weather power failure emergency plan method based on big data analysis, including:

[0077] Step S01, obtaining power fault data corresponding to the power system and typhoon weather time synchronization, and generating a correlation result between typhoon weather and power fault type;

[0078] Specifically, the obtaining of power failure data corresponding to the power system and the typhoon weather time synchronization and generating the correlation result between the typhoon weather and the power failure type includes:

[0079] Acquiring power fault data in the power system and classifying the power fault data;

[0080] By obtaining the corresponding power fault data from the power company's fault recording system, monitoring center and other data recording platforms, the power data should include the time of the fault, the location of the fault, the type of fault, the degree of damage to the power tower, etc.

[0081] Obtain typhoon weather data from the meteorological platform, including typhoon path, wind speed and rainfall;

[0082] The classified power grid fault data and the typhoon meteorological data are time-matched to obtain power grid fault types that are synchronized with the typhoon meteorological time and power grid fault types that are not synchronized with the typhoon meteorological time, and generate correlation results between typhoon weather and power fault types.

[0083] The power failure data is matched with the typhoon meteorological data according to the timestamp to find out the power failures that occurred during the typhoon period (such as a period before and after the typhoon landed), and the matched power failure data is further divided into the types of power grid failures that are synchronized with the typhoon meteorological time and the types of power grid failures that are not synchronized with the typhoon meteorological time. The power failures that are synchronized with the typhoon time are statistically analyzed, including the number, proportion, and occurrence time of each type of failure.

[0084] Combined with the meteorological characteristics of typhoons (such as wind speed, rainfall, path, etc.), analyze the correlation between different types of power failures and typhoon meteorological conditions. Statistical methods such as correlation analysis and regression analysis can be used, and spatial analysis can also be combined with geographic information systems (GIS). The results of the correlation analysis are presented in the form of charts, reports, etc.

[0085] Step S02: Analyze the correlation results to obtain the corresponding relationship between meteorological variables and power fault types;

[0086] Specifically, the correlation results are analyzed to obtain the corresponding relationship between meteorological variables and power failure types, including:

[0087] Performing mathematical statistical analysis on the correlation results, extracting the corresponding frequency of each meteorological variable and the power fault type in a preset period, and obtaining the corresponding relationship between the meteorological variable and the power fault type;

[0088] The corresponding relationships between meteorological variables and power failure types include the following:

[0089] The first one is the correspondence between wind speed and power tower failure;

[0090] The second is the correspondence between rainfall and power tower failures;

[0091] The third type is the corresponding relationship between the combined effects of typhoon and rainfall and transmission lines.

[0092] Step S03: Based on the correspondence between meteorological variables and power failure types, a fault probability prediction model corresponding to the power failure type is established, including:

[0093] 1. Based on the corresponding relationship between wind speed and power tower failure, a first fault probability prediction model for power tower failure is established;

[0094] The first fault probability prediction model for establishing a power tower fault comprises:

[0095] Under the action of strong typhoons, the towers will suffer certain damage or even be destroyed directly in a very short time. According to the experimental results of existing technologies, there is an exponential relationship between the fatigue damage of the structure and the wind load, and the wind load is proportional to the square of the wind speed.

[0096] According to the influence of damage on power towers under typhoon, a fatigue damage model of power towers under typhoon is established. The fatigue damage model is expressed as:

[0097]

[0098] Where: a and b are the model coefficients of the power tower material. Different materials have different fatigue damage values ​​under the same wind speed, so the coefficients are also different. For towers of specific materials, the fatigue test data during tower production design can be used to determine; V j V is the typhoon wind speed that the tower receives in the jth time period; cr V is the critical wind speed at which fatigue cracks of the tower are initiated by typhoons. The critical wind speed values ​​corresponding to towers made of different materials are different. m D is the limit wind speed of a typhoon causing one-time loading damage to the tower without time accumulation effect; j is the plastic fatigue damage of the tower per minute;

[0099] It should be noted that the total fatigue cumulative damage D of the rod can be obtained by superimposing single fatigue damage. When D is equal to 0, the rod is considered to have no damage. When D is equal to 1, the rod is considered to have suffered fatigue failure.

[0100] The failure rate of transmission towers in strong typhoon weather is related to the cumulative time of fatigue damage. Considering that the Poisson model can effectively predict the failure rate of components under short-term weather conditions, according to the relationship between the failure rate of power towers and the cumulative time of fatigue damage, the probability model of the tower not collapsing in the first i time intervals is established as follows:

[0101]

[0102] Where: Δt is the length of each time interval; is the cumulative damage suffered by the tower in the previous j-1 time intervals; And D j = 0, that is, the tower has not been damaged in the j-1th time interval, and no new fatigue damage has occurred in the tower in the jth time interval. In this case, no matter how long the action lasts, the tower collapse is an impossible event. When , there is no need to consider the time accumulation effect in the current time interval, and the collapse of the tower is an inevitable event.

[0103] Then the first failure probability model of the power tower failure that occurred in the first i time intervals is:

[0104] P 1 =1-P toi

[0105] 2. Based on the corresponding relationship between rainfall and power tower failure, a second fault probability prediction model for power tower failure is established;

[0106] The second fault probability prediction model for establishing the power tower fault comprises:

[0107] The expression for predicting the cumulative effective rainfall is:

[0108]

[0109] Where: R eff，i represents the predicted cumulative effective rainfall on the i-th day; P pre，i represents the predicted rainfall on the i-th day; P act，n represents the actual rainfall on the nth day; k is the attenuation coefficient, which depends on the evaporation loss properties of rainwater in different soils;

[0110] Statistics from existing technical data show that under different geological and climatic conditions, there is a certain relationship between the cumulative effective rainfall and the possibility of landslides. Based on the exponential relationship between effective rainfall and the occurrence of geological disasters, the landslide probability expression is determined as:

[0111]

[0112] According to the statistical data fitting, the value of a is 0.0035, the value of b is 0.0266, and the value of c is 0.1183; R m is the maximum effective rainfall; R m Set a threshold value, when exceeding this value, landslide is inevitable. These coefficients are related to geological conditions, and the size of the coefficients varies with different geological conditions.

[0113] The cumulative effective rainfall takes into account both rainfall intensity and rainfall time. It is updated daily with the rainfall forecast and can predict in advance when a landslide may occur based on the probability of it causing a landslide.

[0114] The process of landslide disasters acting on transmission towers can be divided into the unstable sliding stage and the impact damage stage. In the unstable sliding stage, the landslide body continuously releases gravitational potential energy and converts it into kinetic energy and friction internal energy. In the impact damage stage, the kinetic energy is transferred to the transmission tower. At this time, the transmission tower receives energy and produces a certain deformation.

[0115] The sliding process of the landslide is simplified into a particle kinematic model. According to the law of conservation of energy, the impact energy borne by the transmission tower is equal to the gravity potential energy of the landslide minus the work done by the friction force, that is:

[0116]

[0117] m=ρD 2 d

[0118] Where: m is the mass of the landslide; D is the thickness of the landslide impact body; d is the width of the impact surface of the transmission tower; ρ is the density of the landslide; v is the impact velocity; h is the height of the center of mass of the landslide; f 1 is the slope friction coefficient; θ is the slope angle;

[0119] The impact of the landslide is equivalent to applying a horizontal thrust to the transmission tower. The transmission tower dissipates energy through its own deformation. The horizontal distance it deviates from the center line is the deflection. When the deflection exceeds a certain limit, the transmission tower will be completely destroyed. If the power tower is simplified as a cantilever arm structure, the maximum horizontal impact force that the power tower can withstand is:

[0120]

[0121] E is the elastic modulus of the tower; I is the section inertia moment of the tower; ω lim is the deflection value of the power tower itself; x o is the height from the impact point to the tower base; H is the height of the transmission tower;

[0122] The bending deformation energy of the power transmission tower under the maximum horizontal impact force, that is, the maximum impact energy expression is:

[0123]

[0124] The degree of damage to power towers is expressed as the ratio of impact energy to anti-impact energy, namely:

[0125]

[0126] According to the degree of damage to the power tower and the probability of landslide, the second failure probability prediction model of the power tower failure is obtained as follows:

[0127] P 2 =P r *P l

[0128] 3. Based on the corresponding relationship between the combined effects of typhoon and rainfall and the transmission line fault, a third fault probability prediction model for transmission line fault is established;

[0129] According to the first fault probability prediction model and the second fault probability prediction model of power tower failure, the fault probability prediction model of power tower failure under the combined action of typhoon and rainfall is expressed as:

[0130] P tower =1-(1-P 1 )(1-P 2 )

[0131] Assume that the failure probability of each power tower in the transmission line is P tower1 , Ptower2 ,…,P towern , then the failure probability of the transmission line is obtained according to the series reliability formula of the transmission line, which is the third failure probability prediction model of the transmission line failure, expressed as:

[0132]

[0133] Step S04: formulate risk levels and corresponding emergency measures according to the prediction results of the failure probability prediction model.

[0134] Specifically, outputting the risk level and corresponding contingency measures according to the prediction result of the fault probability prediction model includes:

[0135] When the prediction result output by the fault probability prediction model is greater than the first risk threshold, a high-level risk level is output, and a contingency plan measure in the form of an electronic document is generated;

[0136] When the prediction result output by the fault probability prediction model is greater than the second risk threshold and not greater than the first risk threshold, an intermediate risk level is output and a contingency plan measure is generated in the form of an electronic document;

[0137] When the prediction result output by the fault probability prediction model is not greater than the third risk threshold, a low risk level is output and no contingency measures are generated.

[0138] In this example, by setting different risk thresholds (first risk threshold, second risk threshold, third risk threshold), different risk thresholds are set for any of the first fault probability prediction model, the second fault probability prediction model, and the third fault probability prediction model. Through the risk threshold system, the predicted fault probability can be automatically divided into high, medium, and low risk levels. This clear level division helps power companies quickly understand the risk status of the current system and make appropriate decisions.

[0139] For high and medium risk levels, the system can automatically generate contingency plans in the form of electronic documents. These contingency plans usually include detailed steps such as fault prevention, troubleshooting, and emergency response, providing technicians with clear action guidelines. This helps to take prompt action when a fault occurs and reduce the impact and loss of the fault on the system. For low risk levels, the system does not generate contingency plans because the probability of failure at this risk level is low and will not have a significant impact on the system, or the existing monitoring and maintenance measures are sufficient to deal with it. By automatically generating contingency plans, the response speed and processing efficiency to potential faults are improved. Before or in the early stages of a fault, power companies can take preventive and troubleshooting measures based on contingency plans to avoid the expansion of the fault.

[0140] The present invention provides a typhoon weather power failure emergency plan method based on big data analysis, which obtains power failure data corresponding to the power system and typhoon meteorological time synchronization, and generates correlation results between typhoon meteorology and power failure types; analyzes the correlation results to obtain the correspondence between meteorological variables and power failure types; establishes a fault probability prediction model corresponding to the power failure type based on the correspondence between meteorological variables and power failure types; and formulates risk levels and corresponding emergency plan measures according to the prediction results of the fault probability prediction model. The present invention outputs the probability of frequent power failures in typhoon weather based on typhoon weather conditions analyzed by big data, and makes advance plans based on the output fault probability results. It can predict, respond to and reduce power failures under typhoon weather conditions through real-time data analysis, improve the disaster prevention and mitigation capabilities of the power system, and reduce the incidence and loss of power failures.

[0141] See also Figure 2 The embodiment of the present application further provides a typhoon weather power failure emergency plan system based on big data analysis, which is applied to the aforementioned typhoon weather power failure emergency plan method based on big data analysis, including:

[0142] A data acquisition unit, used to acquire power failure data corresponding to the power system and the typhoon weather time synchronization, and generate a correlation result between the typhoon weather and the power failure type;

[0143] A correlation analysis unit, used to analyze the correlation result to obtain a corresponding relationship between the meteorological variable and the power fault type;

[0144] A model building unit, used for building a fault probability prediction model corresponding to the power fault type based on the corresponding relationship between the meteorological variables and the power fault type;

[0145] The risk level and contingency plan generation unit is used to formulate risk levels and corresponding contingency plans according to the prediction results of the failure probability prediction model.

[0146] In this embodiment, the functional explanation corresponding to each unit is the same as the explanation of the aforementioned typhoon weather power failure emergency plan method based on big data analysis, and the technical effect is also the same, so it will not be repeated here.

[0147] Those of ordinary skill in the art will appreciate that the units of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0148] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical function division, and there may be other division methods in actual implementation, for example, multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored, etc.

[0149] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0150] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nlyMemory), random access memory (RAM, RandomAccessMemory), mobile hard disk, magnetic disk or optical disk, etc., which can store program code.

[0151] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A typhoon weather power failure emergency plan method based on big data analysis, characterized in that: include: Obtain power failure data corresponding to the power system and typhoon weather time synchronization, and generate correlation results between typhoon weather and power failure types; Analyzing the correlation results to obtain the corresponding relationship between meteorological variables and power failure types; Based on the corresponding relationship between meteorological variables and power failure types, a fault probability prediction model corresponding to the power failure type is established; According to the prediction results of the failure probability prediction model, risk levels and corresponding emergency measures are formulated.

2. According to the method of claim 1, a typhoon weather power failure emergency plan method based on big data analysis is characterized in that: The obtaining of power failure data corresponding to the power system and the typhoon weather time synchronization and generating the correlation result between the typhoon weather and the power failure type includes: Acquiring power fault data in the power system and classifying the power fault data; Obtain typhoon weather data from the meteorological platform, including typhoon path, wind speed and rainfall; The classified power grid fault data and the typhoon meteorological data are time-matched to obtain power grid fault types that are synchronized with the typhoon meteorological time and power grid fault types that are not synchronized with the typhoon meteorological time, and generate correlation results between typhoon weather and power fault types.

3. The method for preparing an emergency plan for power failure in typhoon weather based on big data analysis according to claim 2 is characterized in that: The analysis of the correlation results to obtain the corresponding relationship between meteorological variables and power failure types includes: Performing mathematical statistical analysis on the correlation results, extracting the corresponding frequency of each meteorological variable and the power fault type in a preset period, and obtaining the corresponding relationship between the meteorological variable and the power fault type; The corresponding relationships between meteorological variables and power failure types include the following: The correspondence between wind speed and power tower failure; The correspondence between rainfall and power tower failures; The corresponding relationship between the combined effects of typhoon and rainfall and transmission lines.

4. The method for preparing an emergency plan for power failure in typhoon weather based on big data analysis according to claim 3 is characterized in that: The establishing of a fault probability prediction model corresponding to the power fault type based on the correspondence between the meteorological variables and the power fault type includes establishing a first fault probability prediction model for the power tower fault based on the correspondence between the wind speed and the power tower fault; The first fault probability prediction model for establishing a power tower fault comprises: According to the influence of damage on power towers under typhoon, a fatigue damage model of power towers under typhoon is established. The fatigue damage model is expressed as: Where: a and b are the model coefficients of the power tower material; V j V is the typhoon wind speed that the tower receives in the jth time period; cr V is the critical wind speed for the initiation of fatigue cracks in the tower caused by typhoon; m D is the limit wind speed of a typhoon causing one-time loading damage to the tower without time accumulation effect; j is the plastic fatigue damage of the tower per minute; According to the relationship between the failure rate of power towers and the cumulative time of fatigue damage, the probability model of the tower not collapsing in the first i time intervals is established as follows: Where: Δt is the length of each time interval; is the cumulative damage suffered by the tower in the previous j-1 time intervals; Then the first failure probability model of the power tower failure that occurred in the first i time intervals is: P1=1-P toi 。 5. The method for preparing an emergency plan for power failure in typhoon weather based on big data analysis according to claim 4 is characterized in that: The establishing of a fault probability prediction model corresponding to the power fault type based on the correspondence between the meteorological variable and the power fault type includes establishing a second fault probability prediction model for the power tower fault based on the correspondence between the rainfall and the power tower fault; The second fault probability prediction model for establishing the power tower fault comprises: The expression for predicting the cumulative effective rainfall is: Where: R eff，i represents the predicted cumulative effective rainfall on the i-th day; P pre，i represents the predicted rainfall on the i-th day; P act，n represents the actual rainfall on the nth day; k is the attenuation coefficient, which depends on the evaporation loss properties of rainwater in different soils; According to the exponential relationship between effective rainfall and the occurrence of geological disasters, the landslide probability expression is determined as: According to the statistical data fitting, the value of a is 0.0035, the value of b is 0.0266, and the value of c is 0.1183; R m is the maximum effective rainfall; The sliding process of the landslide is simplified into a particle kinematic model. According to the law of conservation of energy, the impact energy borne by the transmission tower is equal to the gravity potential energy of the landslide minus the work done by the friction force, that is: m=ρD 2 d Where: m is the mass of the landslide; D is the thickness of the landslide impact body; d is the width of the impact surface of the transmission tower; ρ is the density of the landslide; v is the impact velocity; h is the height of the center of mass of the sliding body; f1 is the friction coefficient of the slope; θ is the slope angle; If the power pole tower is simplified as a cantilever arm structure, the maximum horizontal impact force that the power pole tower can withstand is: E is the elastic modulus of the tower; I is the section inertia moment of the tower; ω lim is the deflection value of the power tower itself; x o is the height from the impact point to the tower base; H is the height of the transmission tower; The bending deformation energy of the power transmission tower under the maximum horizontal impact force, that is, the maximum impact energy expression is: The degree of damage to power towers is expressed as the ratio of impact energy to anti-impact energy, namely: According to the degree of damage to the power tower and the probability of landslide, the second failure probability prediction model of the power tower failure is obtained as follows: P2=P r *P l 。 6. The method for preparing an emergency plan for power failure in typhoon weather based on big data analysis according to claim 5 is characterized in that: The establishing of the fault probability prediction model corresponding to the power fault type based on the correspondence between the meteorological variables and the power fault type includes establishing a third fault probability prediction model for the transmission line fault based on the correspondence between the combined effect of typhoon and rainfall and the transmission line fault; According to the first fault probability prediction model and the second fault probability prediction model of power tower failure, the fault probability prediction model of power tower failure under the combined action of typhoon and rainfall is expressed as: P tower =1-(1-P1)(1-P2) Assume that the failure probability of each power tower in the transmission line is P tower1 , P tower2 ,…,P towern , then the failure probability of the transmission line is obtained according to the series reliability formula of the transmission line, which is the third failure probability prediction model of the transmission line failure, expressed as:

7. The method for preparing an emergency plan for power failure in typhoon weather based on big data analysis according to claim 1 is characterized in that: Outputting the risk level and corresponding contingency measures according to the prediction results of the fault probability prediction model includes: When the prediction result output by the fault probability prediction model is greater than the first risk threshold, a high-level risk level is output, and a contingency plan measure in the form of an electronic document is generated; When the prediction result output by the fault probability prediction model is greater than the second risk threshold and not greater than the first risk threshold, an intermediate risk level is output and a contingency plan measure is generated in the form of an electronic document; When the prediction result output by the fault probability prediction model is not greater than the third risk threshold, a low risk level is output and no contingency measures are generated.

8. A typhoon weather power failure emergency plan system based on big data analysis, characterized in that: The method for emergency response plan for power failure in typhoon weather based on big data analysis applied to any one of claims 1 to 7 comprises: A data acquisition unit, used to acquire power failure data corresponding to the power system and the typhoon weather time synchronization, and generate a correlation result between the typhoon weather and the power failure type; A correlation analysis unit, used to analyze the correlation result to obtain a corresponding relationship between the meteorological variable and the power fault type; A model building unit, used for building a fault probability prediction model corresponding to the power fault type based on the corresponding relationship between the meteorological variables and the power fault type; The risk level and contingency plan generation unit is used to formulate risk levels and corresponding contingency plans according to the prediction results of the failure probability prediction model.

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