Disaster prevention early warning and production decision support method and system for power distribution network

Abstract

The present invention relates to the technical field of power distribution networks. Disclosed are a disaster prevention early warning and production decision support method and system for a power distribution network. According to the present invention, multiple types of data from a data center can be integrated, and classification, analysis and feature extraction are performed on multi-source data of a power distribution network, and then without the need to install additional meteorological environment monitoring devices, by means of calculating a maximum wind load, a lightning trip-out rate, a maximum current carrying capacity at a highest allowable operating temperature, and an average failure frequency and an average failure time which consider environmental factors, a power outage risk of the power distribution network is evaluated, and risk grading is performed. After an emergency occurs, by means of analyzing a power grid topology structure after device shutdown, a device shutdown probability can be further calculated, and a load power outage probability and the power outage risk can be obtained. According to the present invention, pre-disaster early warning, in-disaster monitoring, and post-disaster emergency repair auxiliary decision-making for the power distribution network under disasters such as typhoons, thunder and lightning, floods and icing are implemented, providing data and service support for safety production, planning and management, and commanding and decision-making of the power distribution network.

Description

A distribution network disaster prevention early warning and production decision support method and system Technical Field

[0001] The present invention relates to the technical field of distribution networks, and in particular to a method and system for supporting disaster prevention, early warning, and production decisions of distribution networks. Background Art

[0002] Frequent natural disasters, especially extreme weather events such as typhoons, lightning, hail, rain, snow, and severe convective weather, pose significant risks to the safe and stable operation of distribution networks, easily leading to large-scale power outages. The distribution network, at the end of the power system, is responsible for directly supplying power to users. When a disaster strikes, if the distribution network is affected by a disaster and causes a power outage, it can cause significant losses to users. While research on improving the disaster resilience of power grids has been continuously enriched both domestically and internationally, current research on power grid disaster prevention focuses primarily on transmission lines, with less research on disaster prevention in distribution networks and even less on intelligent disaster warning for distribution networks. In fact, compared with transmission equipment, due to the large number of distribution equipment and the complexity of the distribution network, when affected by natural disasters such as typhoons, wildfires, lightning, waterlogging, geological disasters, and icing, the impact process is more complex and changeable, and multiple failures are prone to occur, resulting in difficulties in prediction and processing. In terms of meteorological and environmental monitoring, the existing technologies for disaster warning and processing mostly install actual meteorological and environmental monitoring devices on the power grid, and then receive and collect the data transmitted back by these devices for further processing. The disadvantage of this method is that not all areas are equipped with actual meteorological and environmental monitoring devices, which makes it impossible to obtain meteorological and environmental data in some areas, thereby affecting the accuracy of the final output results.

[0003] In view of this, a distribution network disaster prevention early warning and production decision support method and system is needed.

[0004] Summary of the Invention

[0005] In view of the current research on power grid disaster prevention in the existing technology, it is mainly focused on transmission lines, while there is less research on disaster prevention of distribution networks, and even less research on intelligent disaster warning of distribution networks. Compared with transmission equipment, due to the large number of distribution equipment and the complexity of the distribution network, when affected by natural disasters such as typhoons, wildfires, lightning, waterlogging, geology, and ice, the impact process is more complex and changeable, and multiple failures are prone to occur. In terms of meteorological environment monitoring, the existing technologies for disaster warning and processing are mostly to install actual meteorological environment monitoring devices on the power grid, and then receive and collect the data transmitted back by these devices for further processing. The disadvantage of this method is that not all areas are equipped with actual meteorological environment monitoring devices, which leads to the inability to obtain meteorological environment data in some areas, thereby affecting the accuracy of the final output results. The present invention provides a distribution network disaster prevention warning and production decision support method and system, which can judge the risk of power outage in the distribution network based on the collected diverse data, and then further derive the load power outage probability and power outage risk after the occurrence of emergencies such as disasters. The specific technical solution is as follows:

[0006] A method for disaster prevention, early warning and production decision support of a distribution network, comprising the following steps:

[0007] S1: Collect information on potential emergencies, including typhoons, lightning, high temperatures, and adverse environmental conditions;

[0008] S2: Based on emergency information, assess the risk of power outages in the distribution network, derive the distribution network's ability to respond to emergencies, and perform damage classification.

[0009] S3: Determine whether to initiate an early warning based on the distribution network outage risk. If not, continue to collect information on potential emergencies and conduct a distribution network outage risk assessment. If an early warning is required, implement early warning measures.

[0010] S4: After the incident occurs, the equipment is shut down and the backup power supply path (or equipment) designed in the emergency management plan is activated after the equipment is shut down, thereby updating the grid topology.

[0011] S5: Analyze the updated grid topology. If an island grid is disconnected from the main grid, calculate the power generation and load difference for the island grid, and then obtain the power outage load. If the grid remains intact, solve the minimum loss load shedding, that is, find the load that needs to be shelved to maintain power supply to the system's critical loads while meeting grid operation requirements and equipment capacity constraints.

[0012] S6: Calculate the equipment outage probability, and calculate the load outage probability and power outage risk based on the power outage load and equipment outage probability.

[0013] Preferably, the assessment of the power outage risk of the distribution network specifically includes calculating the maximum wind load, lightning trip rate, maximum current carrying capacity at the highest allowable operating temperature, average failure frequency and average failure time considering environmental factors.

[0014] Preferably, the calculation of the maximum wind load is as follows:

[0015] The calculation formula for line wind load is as follows: W x =6.25×10 -4 αμ z μ sc β c dl H v 2 sin 2 θ

[0016] Where W x is the standard value of conductor wind load, N; α is the conductor wind pressure unevenness coefficient; μ sc β is the wire shape coefficient. According to GB 50545, it is 1.2 when d < 17 mm and 1.1 when d > = 17 mm. c is the wind load adjustment coefficient, the voltage level is 500kV / 750kV, and other values ​​are directly taken as the conductor outer diameter, mm; l H is the horizontal span of the tower, m; μ z is the wind pressure height variation coefficient; θ is the angle between the wind direction and the conductor;

[0017] The probability distribution is fitted using the generalized extreme value distribution. The generalized extreme value distribution is divided into type I extreme value distribution, type II extreme value distribution, and type III extreme value distribution. The three extreme value distributions of different types are generalized, and the generalized extreme value distribution function expression is:

[0018] Where: r is the shape parameter; a is the scale parameter; b is the location parameter;

[0019] The coefficient of variation of the feeder design wind load is:

[0020] Where: μ is the mean of the feeder design wind load; σ is the standard deviation of the line design wind load;

[0021] The probability distribution of the design wind load of the overhead feeder is calculated using the stress-strength interference area method, which is a normal distribution:

[0022] Therefore, the feeder outage probability prediction model based on wind disasters is as follows:

[0023] The fatigue loss factor is:

[0024] The maximum wind load that the damaged line can withstand is:

[0025] Where: ζ is the fatigue loss coefficient; W d ' is the actual wind load that can be sustained; W d is the wind load that the design can withstand; ζ2 is the fatigue loss coefficient when the line reaches the end of its service life; β is the shape parameter; α is the scale parameter.

[0026] Preferably, it is characterized in that the lightning trip rate is calculated as follows:

[0027] The calculation formula for the lightning trip rate is: η=N g Sξσ

[0028] Where: η is the lightning trip rate, times (100km·a)-1; N g is the ground-to-ground lightning density, indicating the intensity of lightning activity and only related to the characteristics of lightning activity itself, times km-2·a-1; S is the effective lightning-affected area that causes line tripping, generally within a range of 0.5 km from one side of the line, km; ξ is the probability of insulator flashover caused by lightning in the effective area.

[0029] Preferably, the maximum current carrying capacity at the maximum allowable operating temperature is calculated as follows:

[0030] The WRF numerical weather forecast system is used to obtain the predicted values ​​of meteorological environmental parameters along the overhead lines;

[0031] The dynamic current-carrying value of the line at future moments is calculated using the conductor thermal balance equation. The formula for calculating the maximum current-carrying capacity I of the line is as follows:

[0032] Where Ta is the ambient temperature, Tc is the conductor temperature, V dw is the error between the predicted wind speed and the actual data, R(Tc) is a constant, and the other parameters are the meteorological environment data along the route.

[0033] Preferably, the average failure frequency and average failure time considering environmental factors are calculated as follows: to =f ad P ad +f no (1-P ad ); r to =r ad P ad +r no (1-P ad );

[0034] Where: f adand f no are the failure frequencies under severe and normal climate conditions, respectively; f to is the average failure frequency; r ad and r no are the average repair time under two climatic conditions; r to is the average repair time during the entire period; P ad and (1-P ad ) are the probabilities of severe and normal weather conditions.

[0035] Preferably, steps S4-S6 are specifically as follows:

[0036] The equipment is shut down and the backup power supply path (or equipment) designed in the emergency management plan to be activated after the equipment is shut down is activated. The grid topology is then updated and analyzed. If an island grid is disconnected from the main grid, the power generation and load difference is calculated for the island grid to obtain the power outage load. If the grid remains intact, the minimum loss load shedding optimization problem is calculated to determine the load required to maintain power supply to the system's critical loads while meeting grid operation requirements and equipment capacity constraints. The calculated load to be shelved is then updated with its non-outage probability until all affected equipment sets are traversed. The power outage probability of each load is then calculated, and finally the power outage risk of the urban distribution network in the event of an emergency is calculated.

[0037] The probability p of disaster i occurring is used i and severity of harm C i The risk R is calculated by multiplying the power outage load and the load importance. The severity of the disaster can be expressed by the power outage load or the value of the load importance:

[0038] Value coefficient c i This is usually determined manually based on the power supply needs in emergency management. For example, under normal circumstances, the load values ​​of municipal governments, hospitals, and transportation systems are relatively high. However, during major public events such as the Olympic Games, a higher value coefficient should be set for related venues and transportation facilities.

[0039] Analyze the updated grid topology. If an island grid is disconnected from the main grid, calculate the power generation and load difference for the island grid to determine the outage load. If the grid remains intact, use the minimum loss load shedding optimization problem to determine the load required to maintain power supply to the system's critical loads while meeting grid operation requirements and equipment capacity constraints.

[0040] The objective function of the minimum loss load shedding problem is:

[0041] The constraints are:

[0042] Where, P i is the initial active power of load i, P i * is the active power of load i after taking emergency load shedding measures; f is the network power flow equation; V and θ are the voltage and phase angle vectors of all nodes; P * and Q * are the active and reactive power vectors of all loads after load shedding measures; V k is the node voltage amplitude, Ω is the node set; F l is the transmission power of branch l, and Ψ is the branch set.

[0043] A distribution network disaster prevention warning and production decision support system, applying the above-mentioned method, comprises a multi-data access module, an external public data module, a data fusion module and a display module; the multi-data access module is used to access meteorological data, typhoon data, lightning and wildfire data; the external public data module is used to store public warning data; the data fusion module is used to process various collected data and apply model analysis to obtain results; the display module is used to interact with a client and display to the client various raw data collected by the multi-data access module, the result data produced by the data fusion module and the data stored in the external public data module.

[0044] Preferably, the meteorological data include real-time data, forecast data and historical data of humidity, precipitation, temperature, wind direction, wind speed and air pressure; the typhoon data include basic typhoon information, typhoon level, forecast path, implementation path and forecast level; the lightning and mountain product data include lightning strike points and fire points; the public warning data include warning information title, type, level, warning text information, warning status, release time and end time.

[0045] Preferably, the system further includes a business domain data module, which includes a production domain module, a dispatching automation module, a marketing domain module, a distribution automation module, a customer service module, a metering automation module, and a GIS module; the production domain module stores equipment records, faults, defects, plans, and power outage data; the dispatching automation module stores switch actions, fault types, and fault times; the marketing domain module stores customer records, distribution transformer records, and power outage data; the distribution automation module stores switch actions, fault types, and fault times; the customer service module stores geographic locations, distribution network topology, and risk points; the metering automation module stores customer records, distribution transformer records, and power outage data; and the GIS module is provided with GIS topology, multi-functional layer waterlogging annotation, and distribution network line tower marking services.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] The present invention can integrate multiple types of data from the data center, and classify, analyze, and extract features from multiple sources of data in the distribution network (such as meteorological data, equipment inventory data, operating data, etc.). Then, without the need to install additional meteorological environment monitoring devices, the meteorological environment data can be obtained and further processed through the WRF numerical forecast product. By calculating the maximum wind load, lightning trip rate, maximum current carrying capacity at the highest allowable operating temperature, average failure frequency and average failure time considering environmental factors, the power outage risk of the distribution network is evaluated and risk classification is performed, so that staff can upgrade equipment or distribution networks with high risk levels before disasters and other emergencies occur. After disasters and other emergencies occur, by analyzing the power grid topology after the equipment is out of service, the probability of equipment outage is further calculated. Based on the power outage load and the equipment outage probability, the load power outage probability and the power outage risk are calculated, and pre-disaster warning, mid-disaster monitoring, and post-disaster emergency repair auxiliary decision-making for distribution networks under disasters such as typhoons, lightning, floods, and ice cover are realized, providing data and business support for the safe production, planning management, and command decision-making of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly describes the drawings required for the specific embodiments or the description of the prior art. Similar elements or parts are generally identified by similar reference numerals throughout the drawings. Elements or parts in the drawings are not necessarily drawn to scale.

[0049] FIG1 is a flow chart of the power outage risk determination process of the distribution network according to the present invention;

[0050] FIG2 is a flow chart of emergency handling according to the present invention;

[0051] FIG3 is a schematic diagram of trip rate calculation of the present invention;

[0052] FIG4 is a schematic diagram of data collection and result classification according to the present invention;

[0053] Figure 5 is a schematic diagram of a distribution network disaster prevention warning and production decision support system. DETAILED DESCRIPTION

[0054] 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 them. 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.

[0055] It will be understood that when used in this specification and the appended claims, the terms “comprises” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0056] It should also be understood that the terms used in the present specification are only for the purpose of describing particular 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 the plural forms unless the context clearly indicates otherwise.

[0057] 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.

[0058] This embodiment provides a distribution network disaster prevention early warning and production decision support method, including the following steps:

[0059] S1: Collect information on potential emergencies, including typhoons, lightning, high temperatures, and adverse environmental conditions;

[0060] S2: Based on emergency information, assess the risk of power outages in the distribution network, derive the distribution network's ability to respond to emergencies, and perform damage classification.

[0061] S3: Determine whether to initiate an early warning based on the distribution network outage risk. If not, continue to collect information on potential emergencies and conduct a distribution network outage risk assessment. If an early warning is required, implement early warning measures.

[0062] S4: After the incident occurs, the equipment is shut down and the backup power supply path (or equipment) designed in the emergency management plan is activated after the equipment is shut down, thereby updating the grid topology.

[0063] S5: Analyze the updated grid topology. If an island grid is disconnected from the main grid, calculate the power generation and load difference for the island grid, and then obtain the power outage load. If the grid remains intact, solve the minimum loss load shedding, that is, find the load that needs to be shelved to maintain power supply to the system's critical loads while meeting grid operation requirements and equipment capacity constraints.

[0064] S6: Calculate the equipment outage probability, and calculate the load outage probability and power outage risk based on the power outage load and equipment outage probability.

[0065] The following is a detailed introduction to the principles and detailed process of each step:

[0066] In order to better share network data resources and avoid the problem of excessive transmission pressure on meteorological big data networks, the present invention sorts out typical disaster loss scenarios and disaster loss information of distribution networks under different natural disaster environments such as historical typhoons, lightning, floods, high temperatures, and ice disasters, and combines key disaster factors such as geographical environment, wind speed, temperature, precipitation, time, etc. with damage to distribution network equipment, line tripping, load loss, etc. to conduct correlation analysis, and study the distribution network loss mechanism under different natural disaster environments; combined with relevant regulations such as national disaster prevention and warning classification, power grid company wind and flood prevention and emergency response, the disaster loss conditions of distribution networks in different disaster environments are classified, and a distribution network loss classification library corresponding to different natural disaster conditions is established to evaluate the distribution network disaster response capabilities.

[0067] Firstly, the key influencing factors of distribution network disaster losses, such as temperature and humidity, wind speed, rainfall, typhoon level, and lightning density, are extracted. Combined with the actual distribution network grid structure, ledger information, and equipment operation data, a dynamic evaluation result of the distribution network's own pre-disaster response capability is obtained considering the characteristics of distribution network disaster losses.

[0068] (1) Time-varying outage model of overhead feeders under typhoon conditions:

[0069] Calculation formula for line wind load: W x =6.25×10 -4 αμ z μ sc β c dl H v 2 sin 2 θ (1)

[0070] Where Wx is the standard value of the conductor wind load, N; α is the conductor wind pressure unevenness coefficient; μsc is the wire body coefficient, which is specified in GB 50545 as 1.2 when d < 17 mm and 1.1 when d >= 17 mm; βc is the wind load adjustment coefficient, which is directly taken as the conductor outer diameter for voltage levels of 500 kV / 750 kV and for other voltage levels, in mm; lH is the horizontal tower spacing, in m; μz is the wind pressure height variation coefficient; and θ is the angle between the wind direction and the conductor, (.).

[0071] From the above formula, we can see that the wind load on the line is closely related to wind speed and wind direction. Since wind speed and wind direction are random variables, the wind load on the line is also a random variable. The probability distribution is fitted using the generalized extreme value distribution. The generalized extreme value distribution is divided into type I extreme value distribution, type II extreme value distribution, and type III extreme value distribution. By generalizing the three extreme value distributions of different types, the generalized extreme value distribution function expression is:

[0072] Where r is the shape parameter, a is the scale parameter, and b is the location parameter.

[0073] At present, most countries (including my country) use Type I extreme value distribution for wind speed probability simulation. Therefore, this method of wind speed probability simulation is applied to predicting wind load probability distribution, that is, using Type I extreme value distribution to determine the wind load probability distribution function:

[0074] Currently, there are three methods for estimating the two parameters a and b in formula (3): distance method, least squares method, maximum likelihood method, and probability weighted distance method. The distance method is used to determine the two parameters, and the calculation results are obtained by the calculation formula of root variance and mathematical expectation:

[0075] Where: is the average value of the feeder wind load.

[0076] The specific steps of the line wind load probability distribution function are:

[0077] 1) Calculate the wind load on the line based on the observed wind speed and direction data;

[0078] 2) Select the maximum wind load within the interval t, repeat this process, and select several groups of maximum wind loads to form a series of extreme value sample sequences of wind loads;

[0079] 3) Based on the extreme value sample sequence of wind load in step 2), the two parameters a and b of wind load are estimated by distance method;

[0080] 4) Based on the two parameters a and L estimated each time, the probability distribution function of the line wind load under wind disaster can be estimated.

[0081] The coefficient of variation of the feeder design wind load is:

[0082] Where: μ is the mean of the feeder design wind load; σ is the standard deviation of the line design wind load.

[0083] In fact, the actual wind load and the designed wind load of the line are random variables. When the stress-strength interference area method is used to calculate the probability of failure of the overhead feeder, the probability distribution of the designed wind load of the feeder is a normal distribution:

[0084] Therefore, the present invention proposes a feeder outage probability prediction model based on wind disasters:

[0085] The fatigue loss factor is

[0086] The maximum wind load that the damaged line can withstand is

[0087] Where: ζ is the fatigue loss coefficient; Wd' is the actual wind load that can be sustained; Wd is the designed wind load that can be sustained; ζ2 is the fatigue loss coefficient when the line reaches the end of its service life; β is the shape parameter; α is the scale parameter.

[0088] (2) Lightning trip probability analysis method and framework;

[0089] Research shows that for a line to trip due to a lightning strike, the following conditions must be met: ① The overvoltage caused by the lightning strike is greater than the insulator's lightning impulse withstand voltage value U50%, resulting in an impulse flashover; ② After the lightning disappears, the impulse flashover develops into a short-circuit current arc and persists, causing the relay protection device to operate and the line to trip. The line's lightning resistance level and grounding method are two important factors that affect the line trip rate. In addition, it is closely related to the frequency of local lightning activity. Therefore, the calculation of the lightning trip rate needs to consider the following three important calculation parameters: ground flash density, flashover rate, and arcing rate. Therefore, the calculation formula for the lightning trip rate is: η=N g Sξσ

[0090] Where: η is the lightning trip rate, times (100 km a)-1; Ng is the ground-to-ground flash density, which indicates the intensity of lightning activity and is only related to the characteristics of the lightning activity itself, times km-2 a-1; S is the effective lightning-affected area that causes line tripping, generally within a range of 0.5 km from one side of the line, km; ξ is the probability of insulator flashover caused by lightning within the effective area, which is mainly related to the line's lightning withstand level (related to the tower structure, grounding resistance, the presence of lightning conductors, insulator model, etc.), lightning current, and lightning strike location, and is recorded as the flashover rate; σ is the arcing rate, which is related to the tower structure itself and the grounding method.

[0091] From the above analysis, it can be seen that ground-to-ground lightning density, lightning current magnitude, lightning strike location, ground resistance, tower structure, etc. will affect the lightning trip rate. From a statistical point of view, it can be divided into deterministic factors (such as ground resistance, span, insulator model, etc.) and uncertain factors (lightning current amplitude, ground-to-ground lightning frequency, lightning strike point, etc.). Among these uncertain factors, the statistical values ​​and probability distribution models of relevant lightning parameters are obtained based on the statistical analysis of lightning activity characteristics, and the Monte Carlo method is used to calculate the flashover rate. Based on the Monte Carlo method for calculating the trip rate, the present invention proposes an improved method for calculating the lightning trip rate based on the characteristics of lightning activity.

[0092] (3) A time-varying outage model of conductors in high temperature weather based on numerical weather forecasts;

[0093] The static current-carrying capacity of overhead lines is calculated based on a steady-state thermal balance equation and the conductor's maximum allowable operating temperature (70°C for steel-core aluminum stranded conductors in my country), using very conservative meteorological factors as boundary conditions. This pre-set boundary condition cannot dynamically reflect real-time changes in the line's operating status. In contrast to static current-carrying capacity, dynamic current-carrying capacity is calculated online using the conductor's thermal balance equation based on real-time meteorological parameters, providing real-time insights into the line's operating status.

[0094] Currently, dynamic current-carrying capacity (DCC) is not widely applied to overhead lines because conductor temperature measurement and micrometeorological parameter monitoring devices are not widely installed throughout the entire overhead line corridor. However, if grid dispatchers and substation operators can use relevant technologies to predict the dynamic DCC of overhead lines during the peak summer electricity demand period, this can provide a decision-making basis for scheduling and optimizing operation modes, thereby fully utilizing the dynamic DCC of overhead lines while ensuring their safe operation.

[0095] Numerical weather prediction (NWP) is a method based on the current state of the atmosphere. It uses mathematical models of the atmosphere, sets appropriate initial values ​​and boundary conditions, and utilizes large computers to perform numerical calculations on massive amounts of meteorological data. By solving the physical equations that describe weather evolution, the method predicts atmospheric motion and weather for a specific time period in the future. Currently, the following numerical weather prediction models are widely used: 1) WRF, the Weather Research and Forecasting Model, supported by the National Center for Meteorological Research, the National Oceanic and Atmospheric Administration, and the Air Force Weather Service; 2) RAMS, the Regional Atmospheric Model System, developed and researched by Colorado State University; 3) GEM-LAM, the Global Environmental Multiscale Limited Area Model, a Canadian meteorological service; 4) HIRLAM, the High-Resolution Limited Area Model, supported by the European Meteorological Cooperation Institute; and 5) ALADIN, supported by a consortium of several European and North African countries led by the French Meteorological Center. Because the WRF model comprehensively considers the influencing factors of micrometeorological processes, its forecasts include wind speed, temperature, solar radiation, humidity, and precipitation, and have a relative error limit of 8%.

[0096] Therefore, we used WRF numerical weather forecast products to obtain 24-hour meteorological environmental data along overhead lines and studied their current-carrying capacity at future moments. The proposed method uses meteorological parameters from numerical weather forecasts to predict dynamic current-carrying values. This method can provide a reference for emergency response personnel to conduct risk assessments during periods of continuous high temperatures, without the need for actual meteorological monitoring equipment.

[0097] To predict the dynamic current-carrying capacity of a conductor at a future time, it is necessary to use the WRF numerical weather forecast system to obtain the predicted values ​​of meteorological environmental parameters along the overhead line, and use the conductor heat balance equation to calculate the dynamic current-carrying value of the line at a future time.

[0098] The calculation basis of the dynamic current carrying capacity of the conductor is:

[0099] When the conductor temperature Tc is the maximum allowable operating temperature of the line, 70°C, R(Tc) is a constant. Substituting the meteorological environment data along the overhead line for the next 24 hours into formula (3.1), the calculated value of the maximum current carrying capacity I of the line for the next 24 hours at the maximum allowable operating temperature can be calculated.

[0100] Taking into account the errors between the ambient temperature forecast Ta, the forecast wind speed and the actual data, the ambient temperature forecast and the forecast wind speed along the overhead line in the next 24 hours are T a ∈[T amin ,T amax ],v a ∈[v amin ,v amax ], the predicted dynamic quantities of the line have maximum values ​​Imax and Imin. The ideal values ​​provided by numerical prediction technology have errors, which are used to calculate the prediction of the line in the next 24 hours.

[0101] Dynamic current carrying capacity has significant errors, so the volatility of numerical forecasting technology needs to be considered to improve the accuracy of dynamic current carrying capacity predictions. With the development of numerical forecasting technology, the numerical forecast intervals for the next 24 hours of weather conditions have become relatively accurate. Therefore, it can be assumed that the actual weather data is randomly distributed within the numerical forecast interval, and therefore the actual dynamic current carrying capacity of overhead lines is also randomly distributed within the predicted dynamic current carrying capacity interval. By considering that overhead lines follow a certain probability distribution in numerical weather forecasts, a probabilistic outage model due to high temperature weather can be obtained.

[0102] (4) Equipment outage models for various weather conditions;

[0103] Many system components may be exposed to harsh, even catastrophic, environments. Although these conditions are infrequent and short-lived, the probability of component failure increases significantly during these periods, potentially leading to the cascading failure of multiple components or even the failure of entire subsystems. It is important to note that the failure process of cascading outages in harsh or catastrophic environments differs from the concept of common-cause outages. Essentially, environmentally dependent outages involve only dependencies between system components on their environment, rather than correlations between components. While cascading component failures remain independent, they are exacerbated by the impact of the shared environment.

[0104] Harsh environments typically refer to unfavorable climatic conditions such as wind, rain, and snow, while catastrophic environments refer to natural disasters such as blizzards, tornadoes, fires, floods, and earthquakes. Because the probability of catastrophic environments and their impact can only be roughly estimated, accurately modeling them is difficult. However, once a certain estimate is accepted in the analysis, it is reasonable to assume that all components will fail simultaneously, such as in a common-cause outage, within this estimated range. This assumption enables a system risk assessment that incorporates the impact of catastrophic environments. Although this impact is simplified and may be imprecise, this approach provides some quantitative information useful for decision-making.

[0105] For general climatic conditions, since meteorological statistics are always available, better simulation methods can be designed. The traditional method is to divide the climate into two basic states: normal and severe. The probabilities of normal and severe climatic states can be calculated based on meteorological data. If the failure frequency and repair time of components under normal and severe climatic conditions can be distinguished, the system risk under the two climatic conditions can be evaluated separately, and the final risk index is obtained by weighting the probabilities of the two climatic conditions. However, most data acquisition systems do not distinguish between failure events under normal and severe climatic conditions, but only have the average failure frequency and average repair time of the past years. In this case, the following formulas (12) to (13) can be used to calculate the failure frequency and repair time under the two climatic conditions. f to =f ad P ad +f no (1-P ad ) (12) r to =r ad P ad +r no (1-P ad ) (13)

[0106] Where: fad and fno are the failure frequencies under severe and normal climate conditions, respectively; fto is the average failure frequency; rad and rno are the average repair times under the two climate conditions, respectively; rto is the average repair time over the entire period; Pad and (1-Pad) are the probabilities of severe and normal climate conditions.

[0107] Research on distribution network disaster warning and risk assessment technology integrating multi-source data

[0108] Study the factors causing wind and flood disasters in distribution networks and the day-ahead prediction methods, and establish a day-ahead prediction probability model for wind and flood disaster losses in distribution networks; under different natural disaster conditions such as typhoons, lightning, high temperatures, icing, severe convective weather, etc., conduct a fusion analysis of meteorological environment data, distribution equipment ledgers, distribution network operation, GIS maps and other data, establish a probability warning model for distribution network equipment disaster damage, and issue warnings for distribution network damage under disaster environments; study distribution network disaster risk assessment technology, establish a distribution equipment outage probability model, and analyze and calculate the corresponding power outage load after each equipment is shut down.

[0109] The equipment is shut down and the backup power supply path (or equipment) designed in the emergency management plan for this equipment's shutdown is activated, thereby updating the grid topology. The updated grid topology is analyzed. If an island grid is disconnected from the main grid, the power generation and load difference is calculated for the island grid to determine the outage load. If the grid remains intact, a minimum loss load shedding optimization problem is calculated to determine the load required to maintain power to the critical loads while meeting grid operating requirements and equipment capacity constraints. The calculated load shedding probability is then updated with the non-outage probability. This process continues until all affected equipment sets are traversed, and the outage probability of each load is calculated. Finally, the outage risk of the urban distribution network in the event of an emergency is calculated.

[0110] The probability p of disaster i occurring is generally used i and severity of harm C i The risk R is calculated by multiplying the power outage load and the load importance. The severity of the disaster can be expressed by the power outage load or the value of the load importance:

[0111] Value coefficient c i It is generally determined artificially based on the power supply needs in emergency management. For example, under normal circumstances, the load values ​​of the municipal government, hospitals, and transportation systems are relatively large. During major public events such as the Olympic Games, a larger value coefficient should be set for related venues and transportation facilities.

[0112] Analyze the updated grid topology. If an island grid is disconnected from the main grid, calculate the power generation and load difference for the island grid to obtain the power outage load. If the grid remains intact, use the minimum loss load shedding optimization problem to determine the load required to maintain power supply to the system's critical loads while meeting grid operation requirements and equipment capacity constraints.

[0113] The objective function of the minimum loss load shedding problem is:

[0114] The constraints are

[0115] Where, Pi is the initial active power of load i, P i * is the active power of load i after taking emergency load shedding measures; f is the network power flow equation; V and θ are the voltage and phase angle vectors of all nodes; P* and Q* are the active and reactive power vectors of all loads after taking emergency load shedding measures; V k is the node voltage amplitude, Ω is the node set; F l is the transmission power of branch l, and Ψ is the branch set.

[0116] Research on Operational Disaster Loss Analysis Technology of Distribution Network Equipment

[0117] Research on rapid statistical technology for the extent of damage caused by distribution network disasters. Based on the existing production business distribution network equipment operation data, including power outage information, tripping information, equipment defects, etc., integrating multi-source data such as metering automation, GIS topology, marketing user files, and applying big data technology, rapid statistics and analysis of the extent of damage caused by disasters such as damage to distribution equipment, line tripping, and load loss can be achieved.

[0118] The main method is to extract key influencing factors of distribution network disaster losses such as temperature and humidity, wind speed, rainfall, typhoon level, and lightning density, and combine the actual distribution network grid structure, ledger information, and equipment operation data to study the rapid statistical technology of distribution network damage considering the characteristics of distribution network disaster losses.

[0119] Taking typhoon disaster damage statistics as an example, we analyzed factors related to collapsed / broken towers and collapsed / broken poles based on wind field forecasts, tower-tolerable wind speeds, tower altitudes, etc., and studied tower damage warning models. Before the typhoon arrives, we issued an early warning list of towers with risks of collapsed / broken towers and collapsed / broken poles based on typhoon forecast data, and guided operation and maintenance staff to carry out wind-proof reinforcement work.

[0120] In addition, this embodiment also provides a distribution network disaster prevention warning and production decision support system, which utilizes the above-described method and includes a multi-data access module, an external public data module, a data fusion module, and a display module. The multi-data access module is used to access meteorological data, typhoon data, lightning and wildfire data; the external public data module is used to store public warning data; the data fusion module is used to process various collected data and apply model analysis to derive results; and the display module is used to interact with the client and display the raw data collected by the multi-data access module, the result data generated by the data fusion module, and the data stored in the external public data module to the client. The meteorological data includes real-time data, forecast data, and historical data on humidity, precipitation, temperature, wind direction, wind pressure, and air pressure; the typhoon data includes basic typhoon information, typhoon level, forecast path, implementation path, and forecast level; the lightning and wildfire data includes lightning strike points and fire points; and the public warning data includes the warning information title, type, level, warning text, warning status, release time, and end time. It also includes a business domain data module, which includes a production domain module, a dispatching automation module, a marketing domain module, a distribution automation module, a customer service module, a metering automation module, and a GIS module. The production domain module stores equipment records, faults, defects, plans, and power outage data. The dispatching automation module stores switch actions, fault types, and fault times. The marketing domain module stores customer records, distribution transformer records, and power outage data. The distribution automation module stores switch actions, fault types, and fault times. The customer service module stores geographic locations, distribution network topology, and risk points. The metering automation module stores customer records, distribution transformer records, and power outage data. The GIS module provides GIS topology, multi-functional layer waterlogging annotation, and distribution network line tower marking services.

[0121] In summary, the present invention can integrate multiple types of data from the data center, and perform classification, analysis, and feature extraction of multi-source data of the distribution network (such as meteorological data, equipment inventory data, operation data, etc.), and then obtain meteorological environment data through the WRF numerical forecast product and further process it without the need to install a separate meteorological environment monitoring device. By calculating the maximum wind load, lightning trip rate, maximum current carrying capacity at the highest allowable operating temperature, average failure frequency and average failure time considering environmental factors, the distribution network power outage risk is evaluated and risk classification is performed, so that staff can upgrade equipment or distribution networks with high risk levels before disasters and other emergencies occur. After disasters and other emergencies occur, by analyzing the power grid topology after the equipment is out of service, the equipment outage probability is further calculated, and the load power outage probability and power outage risk are calculated based on the power outage load and the equipment outage probability. The present invention realizes pre-disaster warning, mid-disaster monitoring, and post-disaster emergency repair auxiliary decision-making for distribution networks under disasters such as typhoons, lightning, floods, and ice cover, and provides data and business support for production safety, planning management, and command decision-making of distribution networks.

[0122] Those skilled in the art will appreciate that the modules of the various examples 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.

[0123] In the embodiments provided by the present invention, it should be understood that the division of units is merely 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.

[0124] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0125] 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, 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. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or 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-0nly Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc., various media that can store program code.

[0126] 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 above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions 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 description of the present invention.

Claims

1. A method for disaster prevention, early warning and production decision support of a distribution network, characterized in that: The following steps are involved: S1: Collecting information on potential emergencies, including typhoons, lightning, high temperatures, and adverse environmental conditions; S2: Based on the emergency information, the power outage risk of the distribution network is assessed, and then the response capability of the distribution network to the emergency is obtained, and the disaster loss is graded; S3: Determine whether to enter early warning based on the risk of power outage in the distribution network. If it is not necessary to enter early warning, continue to collect information on potential emergencies and conduct risk assessment of power outage in the distribution network. If it is necessary to enter early warning, implement early warning measures; S4: After the incident occurs, the equipment is shut down, and the backup power supply path (or equipment) designed in the emergency management plan that can be activated after the equipment is shut down is activated, thereby updating the grid topology; S5: Analyze the updated grid topology. If an isolated grid is separated from the main grid, calculate the difference between power generation and load for the isolated grid, and then obtain the power outage load. If the grid remains intact, solve the minimum loss load shedding, that is, find the load that needs to be shelved to maintain the power supply of the system's key loads while meeting the grid operation requirements and equipment capacity constraints. S6: Calculate the equipment outage probability, and calculate the load outage probability and power outage risk based on the power outage load and equipment outage probability.

2. A method for disaster prevention, early warning and production decision support of a distribution network according to claim 1, characterized in that: The evaluation of the power outage risk of the distribution network specifically includes calculating the maximum wind load, lightning tripping rate, maximum current carrying capacity at the highest allowable operating temperature, average failure frequency and average failure time considering environmental factors.

3. A method for disaster prevention, early warning and production decision support of a distribution network according to claim 2, characterized in that: The calculation of the maximum wind load is as follows: The calculation formula of line wind load is as follows: W x =6.25×10 -4 am z m sc b c d.l. H v 2 sin 2 i Where W x is the standard value of conductor wind load, N; α is the conductor wind pressure unevenness coefficient; μ sc β is the wire body coefficient. According to GB 50545, when d < 17 mm, it is 1.2; when d > = 17 mm, it is 1.1; c is the wind load adjustment coefficient, the voltage level is 500kV / 750kV, and the others are directly taken as the conductor outer diameter, mm; l H is the horizontal span of the tower, m; μ z is the wind pressure height variation coefficient; θ is the angle between the wind direction and the conductor; The probability distribution is fitted using the generalized extreme value distribution. The generalized extreme value distribution is divided into type I extreme value distribution, type II extreme value distribution, and type III extreme value distribution. The three extreme value distributions of different types are generalized, and the generalized extreme value distribution function expression is obtained as follows: Where: r is the shape parameter; a is the scale parameter; b is the location parameter; The coefficient of variation of the feeder design wind load is: Where: μ is the mean of the feeder design wind load; σ is the standard deviation of the line design wind load; The probability distribution of the design wind load of the overhead feeder is normally distributed when the probability of failure of the overhead feeder is calculated using the stress-strength interference area method: Therefore, the feeder outage probability prediction model based on wind disasters is as follows: The fatigue loss factor is: The maximum wind load that the damaged line can withstand is: Where: ζ is the fatigue damage coefficient; W d ' is the actual wind load that can be sustained; W d is the wind load that the design can withstand; ζ2 is the fatigue loss coefficient when the line reaches the end of its service life; β is the shape parameter; α is the scale parameter.

4. A method for supporting disaster prevention, early warning and production decision-making of distribution network according to claim 2, characterized in that: The lightning trip rate is calculated as follows: The calculation formula for the lightning trip rate is: n=N g Sex Where: η is the lightning trip rate, times (100km·a)-1; N g The density of ground lightning indicates the intensity of lightning activity. The characteristics of lightning activity are related to themselves, times km-2·a-1; S is the effective lightning-affected area that causes line tripping, generally within a range of 0.5 km from one side of the line, km; ξ is the probability of insulator flashover caused by lightning in the effective area.

5. A method for disaster prevention, early warning and production decision support of distribution network according to claim 2, characterized in that: The maximum current carrying capacity at the highest allowable operating temperature is calculated as follows: The predicted values ​​of meteorological environmental parameters along the overhead lines are obtained with the help of WRF numerical weather forecast system; The dynamic current carrying value of the line at the future moment is calculated using the conductor heat balance equation. The calculation formula for the maximum current carrying capacity I of the line is as follows: Where Ta is the predicted ambient temperature, Tc is the conductor temperature, V dw is the error between the predicted wind speed and the actual data, R(Tc) is a constant, and the other parameters are the meteorological environment data along the line.

6. A method for disaster prevention, early warning and production decision support for distribution network according to claim 2, characterized in that: The average failure frequency and average failure time considering the environmental factors are calculated as follows: to =f ad P ad +f no (1-P ad ); to =r ad P ad +r no (1-P ad ); Where: f ad and f no are the failure frequencies under severe and normal climate conditions, respectively; f to is the average failure frequency; r ad and r no are the average repair time under the two climate conditions; r to is the average repair time over the entire period; P ad and (1-P ad ) are the probabilities of severe and normal weather conditions.

7. A method for disaster prevention, early warning and production decision support for distribution network according to claim 2, characterized in that: The steps S4-S6 are specifically as follows: Shut down the equipment and activate the backup power supply path (or equipment) designed in the emergency management plan that can be activated after the equipment is shut down, and then update the grid topology. Analyze the updated grid topology. If an isolated grid is separated from the main grid, calculate the difference between power generation and load for the isolated grid, and then obtain the power outage load. If the power grid remains intact, the minimum loss load shedding optimization problem is calculated to obtain the load that needs to be removed to maintain the power supply of the system's key loads under the conditions of meeting the power grid operation requirements and equipment capacity constraints. The calculated load to be removed updates its power outage probability until all affected equipment sets are traversed, and then the power outage probability of each load is calculated. Finally, the urban distribution network under emergency conditions is calculated. Risk of power outages; The probability p of disaster i occurring is used i and severity of harm C i The risk R is calculated by multiplying the product of . The severity of the disaster can be expressed by the power outage load or by the value that represents the importance of the load: Value coefficient c i It is usually determined artificially based on the power supply demand in emergency management. For example, in daily situations, the load values ​​of municipal governments, hospitals, and transportation systems are relatively large, while during major public events such as the Olympic Games, a larger value coefficient should be set for related venues and transportation facilities; Analyze the updated grid topology. If an isolated grid is disconnected from the main grid, calculate the power generation and load difference for the isolated grid and obtain the power outage load. If the power grid remains intact, the minimum loss load shedding optimization problem can be used to find the load that needs to be shelved to maintain the power supply of the system's key loads while meeting the power grid operation requirements and equipment capacity constraints; The objective function of the minimum loss load shedding problem is: The constraints are: Where P i is the initial active power of load i, P i * is the active power of load i after taking emergency load shedding measures; f is the network power flow equation; V and θ are the voltage and phase angle vectors of all nodes; P * and Q * are the active and reactive power vectors of all loads after load shedding measures; V k is the node voltage amplitude, Ω is the node set; F l is the transmission power of branch l, and Ψ is the branch set.

8. A distribution network disaster prevention warning and production decision support system, characterized in that: The method described in any one of claims 1 to 7 comprises a multi-data access module, an external public data module, a data fusion module and a display module; the multi-data access module is used to access meteorological data, typhoon data, lightning and wildfire data; the external public data module is used to store public warning data; the data fusion module is used to process various collected data and apply model analysis to obtain results; the display module is used to interact with the client and display to the client various original data collected by the multi-data access module, the result data produced in the data fusion module and the data stored in the external public data module.

9. A distribution network disaster prevention early warning and production decision support system according to claim 8, characterized in that: The meteorological data include real-time data, forecast data and historical data of humidity, precipitation, temperature, wind direction, wind speed and air pressure; the typhoon data include basic typhoon information, typhoon level, forecast path, implementation path and forecast level; the lightning and mountain product data include lightning strike points and fire points; the public warning data include warning information title, type, level, warning text information, warning status, release time and end time.

10. A distribution network disaster prevention early warning and production decision support system according to claim 8, characterized in that: It also includes a business domain data module, which includes a production domain module, a dispatching automation module, a marketing domain module, a distribution automation module, a customer service module, a metering automation module and a GIS module; the production domain module stores equipment records, faults, defects, plans and power outage data; the dispatching automation module stores switch actions, fault types and fault times; the marketing domain module stores customer records, distribution transformer records and power outage data; the distribution automation module stores switch actions, fault types and fault times; the customer service module stores geographic locations. Distribution network topology. Risk potential points; the metering automation module stores customer records, distribution transformer records and power outage data; the GIS module is provided with GIS topology, multi-functional layer waterlogging annotation, and distribution network line tower marking services.

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