Quantitative short-term and temporary early warning method and device for extreme freezing rain of power grid

Abstract

The invention relates to the technical field of electric power meteorological early warning, and discloses a power grid extreme freezing rain quantitative short-term and temporary early warning method and device, and the method comprises the steps: determining a freezing rain region through a multi-source data fusion model, breaking through the resolution limitation of a single data source, and precisely capturing the characteristics of a freezing rain local under a complex terrain; the freezing probability is determined through temperature and wind speed, and the defect that phase state recognition depends on a single ground parameter is avoided; the target liquid water content is calculated through the micro-rain radar reflectivity and particle size spectrum parameters, the icing thickness increase amount is obtained in combination with the freezing probability, and the quantitative data missing short plate is supplemented; and finally, early warning information is generated according to the quantitative indexes, so that the matching degree between an early warning result and an actual risk is greatly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power weather early warning, in particular to a power grid extreme freezing rain quantitative short-term early warning method and device. BACKGROUND

[0002] Extreme freezing rain is a major meteorological disaster for the safe operation of power grids in winter, and often occurs in southern mountainous and hilly areas. It is formed by supercooled liquid precipitation contacting power transmission lines, towers and other facilities below 0 DEG C, rapidly freezing to form dense ice cover, leading to over-limit conductor tension, tower tilt, and even line breakage, tower collapse, line tripping and regional power outage, resulting in significant economic losses and power supply interruption accidents in history.

[0003] However, the current power grid freezing rain monitoring and early warning method is mainly realized through the technical path of data acquisition, analysis and discrimination, and early warning output. First, relying on low-resolution observation data and numerical prediction products of meteorological departments, supplemented by some online monitoring data of lines, traditional algorithms such as temperature threshold method are used to distinguish the phase state of precipitation, and combined with experience index, the grade or probability early warning is output. However, the above method has insufficient data resolution, which is difficult to capture the local characteristics of freezing rain in complex terrain, and the phase state recognition relies on a single ground parameter, resulting in a lack of quantitative data such as liquid water content and ice thickness growth in early warning, and poor matching between early warning results and actual risks. SUMMARY

[0004] The present application provides a power grid extreme freezing rain quantitative short-term early warning method and device to solve the problem of insufficient data resolution in the prior art, which is difficult to capture the local characteristics of freezing rain in complex terrain, and the phase state recognition relies on a single ground parameter, resulting in a lack of quantitative data such as liquid water content and ice thickness growth in early warning, and poor matching between early warning results and actual risks.

[0005] In a first aspect, the present application provides a power grid extreme freezing rain quantitative short-term early warning method, which comprises: inputting the feature vector of the target multi-source data of the power grid to be measured into a preset multi-source fusion model, and determining the freezing rain area according to the output result; based on the freezing rain area, using the temperature and wind speed of the target multi-source data to determine the freezing probability; using the target micro rain radar reflectivity and particle size spectrum parameters of the target multi-source data to calculate the target liquid water content; determining the ice thickness growth in a preset time period according to the freezing probability and the target liquid water content, and generating early warning information according to the target liquid water content and the ice thickness growth.

[0006] The application determines the freezing rain area through a multi-source data fusion model, breaks through the resolution limitation of a single data source, and accurately captures the local characteristics of freezing rain under complex terrain; determines the freezing probability based on temperature and wind speed, avoids the disadvantages of relying on a single ground parameter for phase state identification; calculates the target liquid water content using the reflectivity and particle size spectrum parameters of the micro rain radar, and combines the freezing probability to obtain the icing thickness growth amount, thereby complementing the missing short board of quantitative data; and finally generates early warning information according to the quantitative indicators, thereby greatly improving the matching degree of the early warning result and the actual risk.

[0007] In an optional implementation, the feature vector of the target multi-source data of the to-be-tested power grid is input into a preset multi-source fusion model, and a freezing rain area is determined according to an output result, and the method comprises the following steps: Obtaining initial multi-source data of a to-be-tested power grid; Performing time synchronization processing, spatial interpolation processing and coordinate conversion processing on the initial multi-source data in sequence to obtain target multi-source data; Extracting a feature vector of the target multi-source data and inputting the feature vector into a preset multi-source fusion model to output a plurality of phase state posterior probabilities; Selecting a freezing rain phase state posterior probability from all the phase state posterior probabilities; Determining whether the freezing rain phase state posterior probability is greater than or equal to a preset probability threshold; If yes, the area where the to-be-tested power grid is located is determined as a freezing rain area.

[0008] The application performs time synchronization, spatial interpolation and coordinate conversion processing on the initial multi-source data, guarantees data consistency and integrity, effectively makes up for the defects of insufficient resolution of a single data source, accurately captures the local characteristics of freezing rain under complex terrain, outputs phase state posterior probabilities based on a multi-source fusion model, determines a freezing rain area by comparing the freezing rain phase state posterior probability with a preset threshold, gets rid of the dependence of traditional phase state identification on a single ground parameter, and significantly improves the freezing rain identification accuracy under complex conditions.

[0009] In an optional implementation, the freezing probability is determined based on the temperature and the wind speed of the target multi-source data in the freezing rain area, and the method comprises the following steps: Extracting the temperature and the wind speed in the target multi-source data based on the freezing rain area; Determining a freezing coefficient based on a preset temperature interval where the temperature is located and a preset wind speed interval where the wind speed is located; Determining the freezing probability of the to-be-tested power grid according to the freezing coefficient.

[0010] The application extracts temperature and wind speed in target multi-source data, accurately determines freezing coefficient according to preset interval, and further deduces freezing probability. This mode discards the traditional rough estimation mode depending on a single parameter, fully combines the synergistic effect of temperature and wind speed on the freezing process, and greatly improves the calculation accuracy of freezing probability.

[0011] In an optional implementation, the target liquid water content is calculated by using the target micro rain radar reflectivity and particle size spectrum parameters of the target multi-source data, and includes: obtaining initial micro rain radar reflectivity, particle size spectrum parameters and dual-polarization radar parameters from the target multi-source data; calculating initial liquid water content by using empirical coefficients corresponding to the initial micro rain radar reflectivity and the particle size spectrum parameters; correcting the micro rain radar reflectivity by using the dual-polarization radar parameters to obtain target micro rain radar reflectivity; updating the initial liquid water content according to the target micro rain radar reflectivity to obtain target liquid water content.

[0012] The application first calculates initial liquid water content by using initial micro rain radar reflectivity, particle size spectrum parameters and corresponding empirical coefficients, and then updates target liquid water content by correcting micro rain radar reflectivity through dual-polarization radar parameters. This process effectively eliminates system deviation and clutter interference of radar observation, and solves the problem of insufficient calculation accuracy of traditional liquid water content.

[0013] In an optional implementation, the icing thickness growth amount in a preset time period is determined according to the freezing probability and the target liquid water content, and the warning information is generated according to the target liquid water content and the icing thickness growth amount, and includes: determining effective liquid water content by using freezing coefficient of the freezing probability and the target liquid water content; inputting preset freezing rain process time, process precipitation rate, water density, freezing rain density, unit adjustment coefficient, air flow rate and the effective liquid water content into a preset icing thickness model to output icing thickness growth information; determining the icing thickness growth amount of the to-be-measured power grid in a preset time period based on the icing thickness growth information; determining warning information based on the freezing rain risk warning level in which the target liquid water content and the icing thickness growth amount are located; updating the target multi-source data in real time according to a preset period to obtain new target multi-source data, and jumping to execute the steps of extracting the feature vector of the target multi-source data and inputting the preset multi-source fusion model to output a plurality of phase posterior probabilities until new warning information is obtained.

[0014] The application determines the effective liquid water content by freezing the probability and the target liquid water content, combines the icing thickness model with the multi-key parameter input, and accurately outputs the icing thickness growth information. The target liquid water content and the icing thickness growth amount are used to determine the early warning level, and the data is updated in real time and the early warning result is iteratively optimized according to the preset period, so that the problems of lack of quantitative data and untimely updating in the traditional early warning are solved, and the early warning accuracy and timeliness are greatly improved.

[0015] In an optional implementation, the method further includes: sending the early warning information to a geographic information system platform; rendering the power transmission lines, the substations and the corresponding topographic information of the to-be-tested power grid through the geographic information system platform to obtain the freezing rain intensity information corresponding to the power transmission lines and the substations of the to-be-tested power grid respectively.

[0016] The application inputs the early warning information into the geographic information system platform, renders the power transmission lines, the substations and the topographic information, and intuitively presents the freezing rain intensity corresponding to each facility. The application breaks the limitation of abstract presentation of the early warning information, accurately associates the freezing rain risk with the power grid facilities and the topography, realizes the spatial visual expression of the risk, facilitates the staff to quickly locate the high-risk areas and the key facilities, and greatly improves the readability and practicability of the early warning information.

[0017] In a second aspect, the application provides a power grid extreme freezing rain quantitative short-term early warning device, which includes: An input module is configured to input the feature vector of target multi-source data of a to-be-tested power grid into a preset multi-source fusion model, and determine a freezing rain area according to an output result; A freezing module is configured to determine a freezing probability based on the freezing rain area and using the temperature and the wind speed of the target multi-source data; A calculation module is configured to calculate a target liquid water content using a target micro rain radar reflectivity and a particle size spectrum parameter of the target multi-source data; An early warning module is configured to determine an icing thickness growth amount in a preset time period according to the freezing probability and the target liquid water content, and generate early warning information according to the target liquid water content and the icing thickness growth amount.

[0018] In a third aspect, the application provides an electronic device, which includes a memory and a processor, the memory and the processor are communicatively connected, the memory stores computer instructions, and the processor executes the computer instructions to perform the power grid extreme freezing rain quantitative short-term early warning method of the first aspect or any of the corresponding embodiments.

[0019] In a fourth aspect, the present application provides a computer readable storage medium, which stores computer instructions for causing a computer to execute the power grid extreme freezing rain quantitative short-term early warning method of the first aspect or any of the corresponding embodiments thereof.

[0020] In a fifth aspect, the present application provides a computer program product comprising computer instructions for causing a computer to execute the power grid extreme freezing rain quantitative short-term early warning method of the first aspect or any of the corresponding embodiments thereof. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the drawings needed in the specific embodiments or prior art description will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0022] Figure 1 is the first flowchart of the power grid extreme freezing rain quantitative short-term early warning method according to an embodiment of the present application; Figure 2 is the second flowchart of the power grid extreme freezing rain quantitative short-term early warning method according to an embodiment of the present application; Figure 3 is the third flowchart of the power grid extreme freezing rain quantitative short-term early warning method according to an embodiment of the present application; Figure 4 is the structural block diagram of the power grid extreme freezing rain quantitative short-term early warning device according to an embodiment of the present application; Figure 5 is the hardware structure schematic diagram of the electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0024] It can be understood that before using the technical solutions disclosed in the embodiments of the present application, the type, use range, use scenario, etc. of the personal information involved in the present application should be informed to the user and the authorization of the user should be obtained through appropriate means according to relevant laws and regulations.

[0025] The embodiment of the present application provides a power grid extreme freezing rain quantitative short-term early warning method, a freezing rain area is determined through a multi-source data fusion model, the resolution limitation of a single data source is broken through, and local characteristics of freezing rain under complex terrain are accurately captured; freezing probability is determined relying on temperature and wind speed, and the disadvantage that phase state identification depends on a single ground parameter is avoided; target liquid water content is calculated by using micro rain radar reflectivity and particle size spectrum parameters, and an icing thickness growth amount is obtained in combination with the freezing probability, so that quantitative data missing short boards are supplemented; and finally, early warning information is generated according to quantitative indexes, so that the matching degree of early warning results and actual risks is improved.

[0026] According to the embodiment of the present application, a power grid extreme freezing rain quantitative short-term early warning method is provided, and it should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a group of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that here.

[0027] In the embodiment, a power grid extreme freezing rain quantitative short-term early warning method is provided, Figure 1 The flowchart of the power grid extreme freezing rain quantitative short-term early warning method according to the embodiment of the present application is shown in the flowchart as Figure 1 The flowchart includes the following steps: Step S101, input the feature vector of the target multi-source data of the power grid to be measured into a preset multi-source fusion model, and determine the freezing rain area according to the output result.

[0028] It should be noted that the target multi-source data refers to a plurality of types of data such as initial collected ground meteorological observation, radar detection, satellite remote sensing, etc.

[0029] The feature vector refers to a set of key elements selected from the target multi-source data, which can effectively distinguish the precipitation phase state.

[0030] The preset multi-source fusion model refers to a statistical classification model constructed based on a Bayesian algorithm.

[0031] The freezing rain area refers to a freezing rain phase state posterior probability reaching or exceeding a preset probability threshold determined by the multi-source fusion model.

[0032] In the embodiment of the present application, the temperature, wet bulb temperature, radar polarization parameter, satellite brightness temperature difference and ground precipitation type and other elements representing weather and precipitation characteristics in the target multi-source data of the power grid to be measured are extracted and composed into a feature vector, the feature vector is input into a preset Bayesian multi-source fusion model, the model calculates the posterior probability of candidate phase states such as freezing rain, sleet, wet snow and pure rain, and based on the posterior probability of each phase state, the corresponding area where the power grid to be measured is located can be determined as the freezing rain area.

[0033] Step S102, based on the freezing rain area, the temperature and wind speed of the target multi-source data are used to determine the freezing probability.

[0034] It should be noted that the temperature refers to the actual air temperature near the ground in the freezing rain area.

[0035] The wind speed refers to the horizontal wind speed perpendicular to the power transmission conductor in the freezing rain area.

[0036] The freezing probability refers to the possibility of successfully freezing to form ice on the power grid conductor after the liquid water droplets in the freezing rain area hit the conductor.

[0037] In the embodiment of the present application, the near-surface temperature and horizontal wind speed corresponding to the freezing rain area are extracted from the target multi-source data, the freezing coefficient is determined by comparing with the preset temperature-wind speed combination threshold table, the freezing coefficient is mapped to the possibility of successfully freezing after the liquid water droplets in the freezing rain area hit the conductor through the probabilistic conversion model, and finally the freezing probability corresponding to the freezing rain area is obtained.

[0038] Step S103, the target micro rain radar reflectivity and particle size spectrum parameters of the target multi-source data are used for calculation to obtain the target liquid water content.

[0039] It should be noted that the target micro rain radar reflectivity refers to the accurate reflectivity data after the initial micro rain radar reflectivity is corrected by the dual-polarization radar parameter, and the clutter and system bias are eliminated.

[0040] The particle size spectrum parameter refers to a parameter set representing the particle size distribution, spectrum width and falling speed characteristics of the precipitation particles.

[0041] The target liquid water content refers to a physical quantity accurately reflecting the mass of liquid water per unit volume of air in the freezing rain area.

[0042] In the embodiment of the present application, the target micro rain radar reflectivity corrected by the dual-polarization radar parameter in the target multi-source data is used, and the particle size spectrum parameters (including spectrum width and particle falling speed spectrum) obtained at the same time are used to be substituted into the preset liquid water content empirical formula for calculation, while the non-freezing rain liquid water interference is removed by referring to the phase identification result of the freezing rain area, and finally the target liquid water content accurately representing the freezing rain intensity is obtained.

[0043] Step S104, according to the freezing probability and the target liquid water content, the ice thickness growth amount in the preset time period is determined, and according to the target liquid water content and the ice thickness growth amount, the warning information is generated.

[0044] It should be noted that the ice thickness growth amount refers to the ice thickness increment accumulated on the surface of the power transmission conductor due to the freezing rain in the preset time period.

[0045] The early warning information refers to information generated based on matching risk levels according to quantitative indexes, and includes a risk level, an influence area, and early warning time limit.

[0046] In the embodiment of the present application, according to the freezing probability and the target liquid water content corresponding to the freezing rain area, combined with the preset freezing rain process time, water density, freezing rain density, unit adjustment coefficient and horizontal wind speed perpendicular to the conductor and other parameters, the icing thickness growth of the power transmission conductor in the preset time period is calculated by substituting the icing thickness model based on the physical mechanism, and then the risk level of the area is matched and determined by comparing the four-grade freezing rain risk early warning standard corresponding to the liquid water content and the icing thickness growth, and finally the early warning information including the risk level, the influence range and the early warning time limit is generated.

[0047] In the embodiment of the present application, a power grid extreme freezing rain quantitative short-term early warning method is provided, Figure 2 The flowchart of the power grid extreme freezing rain quantitative short-term early warning method according to the embodiment of the present application is shown in Figure 2 , and the flowchart includes the following steps: Step S201, input the feature vector of the target multi-source data of the power grid to be measured into the preset multi-source fusion model, and determine the freezing rain area according to the output result.

[0048] Specifically, the above step S201 includes: Step S2011, obtain the initial multi-source data of the power grid to be measured.

[0049] It should be noted that the initial multi-source data refers to a set of original data collected from different observation devices such as ground meteorological stations, radars, satellites and the like, which have not been standardized.

[0050] In the embodiment of the present application, as shown in Figure 3 , the data of the transformer substation and the power transmission line where the power grid to be measured is located are collected, and the data includes multi-source observation data such as ground automatic weather stations, dual-polarization weather radars, micro-rain radars, geostationary satellites and high-resolution numerical models; wherein the air temperature T , relative humidity RH , wind speed U , precipitation type , surface temperature measured by the ground automatic weather station; reflectivity , differential reflectivity , correlation coefficient measured by the dual-polarization radar; reflectivity profile , spectral width and falling velocity spectrum V(D) measured by the micro-rain radar; brightness temperature difference (BTD) and cloud phase products observed by the geostationary satellite; and temperature , wet-bulb temperature ), liquid water content ( LWC and wind field information U (z) etc.

[0051] Step S2012: The initial multi-source data is processed sequentially by time synchronization, spatial interpolation and coordinate transformation to obtain the target multi-source data.

[0052] It should be noted that time synchronization processing refers to calibrating the initial multi-source data collection times from different devices to the same time reference.

[0053] Spatial interpolation refers to using existing observation data to extrapolate information about the observation gaps through algorithms.

[0054] Coordinate transformation refers to converting spatial coordinates from different data sources into the same coordinate system.

[0055] In this embodiment of the invention, the initial multi-source data is time-synchronized, spatially interpolated, and georeferenced to establish a unified four-dimensional dataset (x,y,z,t), ensuring the consistency and high-precision fusion of multi-source data in complex terrain areas, and obtaining the target multi-source data.

[0056] Step S2013: Extract the feature vectors of the target multi-source data and input them into the preset multi-source fusion model to output multiple phase posterior probabilities.

[0057] It should be noted that the feature vector refers to the set of key elements extracted from the target multi-source data that can characterize the differences in precipitation phase.

[0058] The posterior probability of a phase refers to the probability value of a certain type of precipitation phase actually occurring, output by a multi-source fusion model.

[0059] In this embodiment of the invention, based on the registration data (i.e., target multi-source data), a Bayesian multi-source fusion algorithm is used to identify the precipitation phase. Let the feature vector of the target multi-source data be... X Its posterior probability is calculated as follows:

[0060] In the formula, It represents candidate phases such as freezing rain, sleet, wet snow, and pure rain; X express Multi-source feature vectors; Indicates the first i Likelihood function of eigenvectors of multi-source data under phase-like conditions; Indicates the first i Prior probabilities of phase-like states; denotes the normalization factor for all candidate phase state categories, i.e., the joint probability of all candidate phase state categories is normalized and summed to ensure that the sum of posterior probabilities is 1; i denotes the target phase state category index (such as freezing rain, sleet, wet snow, or pure rain, etc.) to be determined at present.

[0061] Due to different values of the prior probability of different phase states and the corresponding likelihood function , the value of the summation term changes with the sample feature vector X and is not equal to 1. It reflects the overall possibility of the current observation sample under each candidate phase state, and is an important normalization factor to ensure that the model output result has a probability explanation. Through this calculation, the posterior probability of each phase state satisfies , thereby obtaining a phase state discrimination result with statistical significance.

[0062] Step S2014, selecting the freezing rain phase state posterior probability from all phase state posterior probabilities.

[0063] It should be noted that the freezing rain phase state posterior probability refers to the probability data in the phase state posterior probability set that is specifically for the freezing rain type of precipitation phase state.

[0064] In the embodiment of the present application, the probability data corresponding to the freezing rain phase state is accurately selected from the posterior probability set output by the multi-source fusion model, which contains multiple precipitation phase states such as freezing rain, sleet, pure rain, and wet snow.

[0065] Step S2015, determining whether the freezing rain phase state posterior probability is greater than or equal to a preset probability threshold.

[0066] It should be noted that the preset probability threshold refers to a probability determination standard that is pre-set based on historical freezing rain observation data and power grid icing risk prevention and control needs, and the present application is set to 0.6.

[0067] In the embodiment of the present application, the freezing rain phase state posterior probability is compared with 0.6.

[0068] Step S2016, if yes, then the region where the power grid to be measured is determined as a freezing rain region.

[0069] It should be noted that the freezing rain region refers to the region where the power grid to be measured has a freezing rain possibility reaching the preset standard, which is determined by comparing and judging the freezing rain phase state posterior probability with the preset probability threshold.

[0070] In the embodiment of the present application, if , then it is determined that the region where the power grid to be measured is a freezing rain region.

[0071] Step S202, based on the freezing rain area, using the temperature and wind speed of the target multi-source data, determining the freezing probability.

[0072] In some optional embodiments, the step S202 comprises: Step S2021, based on the freezing rain area, extracting the temperature and wind speed in the target multi-source data.

[0073] It should be noted that the temperature refers to the actual air temperature data near the ground in the freezing rain area.

[0074] The wind speed refers to the horizontal wind speed data perpendicular to the direction of the power transmission conductor in the freezing rain area.

[0075] In the embodiments of the present application, after determining that the area where the to-be-tested power grid is located is a freezing rain area, the temperature , wind speed U and other meteorological elements in the target multi-source data are extracted.

[0076] Step S2022, based on the preset temperature interval where the temperature is located and the preset wind speed interval where the wind speed is located, determining the freezing coefficient.

[0077] It should be noted that the preset temperature interval refers to a temperature range segment pre-divided based on the influence law of temperature on the freezing process according to historical freezing rain freezing observation data.

[0078] The preset wind speed interval refers to a wind speed range segment pre-divided according to the action effect of wind speed on the freezing process in combination with the icing formation mechanism of freezing rain.

[0079] The freezing coefficient refers to an efficiency coefficient representing the efficiency of the liquid water droplets in the freezing rain area successfully freezing after impacting the power transmission conductor.

[0080] In the embodiments of the present application, the threshold determination method is adopted to determine the value range of the freezing coefficient through the combined relationship of the temperature and wind speed U . The freezing coefficient is used to represent the efficiency of the liquid water droplets freezing on the conductor surface, and its value is between 0 and 1. The specific determination principle is shown in Table 1 as follows: Table 1. Freezing coefficient value determination table

[0081] Step S2023, determining the freezing probability of the to-be-tested power grid according to the freezing coefficient.

[0082] In the embodiments of the present application, based on the determined freezing coefficient, a scene uncertainty factor is introduced in combination with the freezing rain phase posterior probability, and the freezing coefficient ​​​The preset probabilistic conversion model is substituted, and the possibility that liquid water droplets hit the power transmission conductor of the to-be-tested power grid in the freezing rain area and are successfully frozen is calculated by the model to determine the freezing probability corresponding to the to-be-tested power grid.

[0083] In step S203, the target liquid water content is calculated by using the target micro rain radar reflectivity and particle size spectrum parameters of the target multi-source data.

[0084] Specifically, the step S203 includes: In step S2031, the initial micro rain radar reflectivity, particle size spectrum parameters and dual-polarization radar parameters are obtained from the target multi-source data.

[0085] It should be noted that the initial micro rain radar reflectivity refers to the original reflectivity data directly collected by the micro rain radar without correction.

[0086] The dual-polarization radar parameters refer to the parameters collected by the dual-polarization radar, including reflectivity, differential reflectivity, correlation coefficient, etc.

[0087] In the embodiment of the present application, after the freezing rain area is determined, the initial micro rain radar reflectivity, particle size spectrum parameters related to the calculation of liquid water content and dual-polarization radar parameters for reflectivity correction in the area are accurately extracted from the standardized target multi-source data.

[0088] In step S2032, the initial liquid water content is calculated by using the empirical coefficients corresponding to the initial micro rain radar reflectivity and particle size spectrum parameters.

[0089] It should be noted that the empirical coefficient refers to a coefficient obtained by statistical analysis or fitting based on a large amount of historical precipitation observation data (commonly used a and b Different particle size spectrum characteristics correspond to different empirical coefficients, which are key parameters of the liquid water content empirical formula.

[0090] The initial liquid water content refers to the original result of the liquid water content calculated by using the initial micro rain radar reflectivity and the calibrated empirical coefficient.

[0091] In the embodiment of the present application, the observed liquid water content (i.e. the initial liquid water content) is retrieved according to the micro rain radar reflectivity and the dual-polarization radar observation results. The initial liquid water content is calculated by using the radar reflectivity empirical relationship, and the specific formula is as follows:

[0092] In the formula, Q represents the initial liquid water content; a and b represents the empirical coefficient determined by the particle size spectrum parameters, ZThe reflectivity factor represents the reflectivity of radar echoes from light rain, reflecting the scattering intensity of radar signals by the size and number of water droplets per unit volume.

[0093] Step S2033: Correct the radar reflectivity of the light rain using dual-polarization radar parameters to obtain the radar reflectivity of the target light rain.

[0094] In this embodiment of the invention, to eliminate ground clutter and system bias, radar correction is performed using dual-polarization radar parameters, as detailed in the following formula:

[0095] In the formula, This represents the reflectivity measured by dual-polarization radar. k This represents an empirical coefficient, determined based on site calibration. This represents the zero-bias value of the differential reflectivity of the radar system, obtained by performing a zero-bias test on the radar system under conditions of no precipitation. Benchmark value.

[0096] Step S2034: Update the initial liquid water content based on the target light rain radar reflectivity to obtain the target liquid water content.

[0097] In this embodiment of the invention, the target liquid water content is obtained by correcting / updating the initial liquid water content using the corrected target light rain radar reflectivity. .

[0098] Specifically, dual-polarization radar parameters ( , , → Correcting the radar reflectivity of light rain using a correction formula ( → → Use The inversion yields an accurate LWC.

[0099] Step S204: Determine the increase in ice thickness within a preset time period based on the freezing probability and the target liquid water content, and generate early warning information based on the target liquid water content and the increase in ice thickness.

[0100] In some optional implementations, step S204 above includes: Step S2041: Determine the effective liquid water content using the freezing coefficient of the freezing probability and the target liquid water content.

[0101] It should be noted that the effective liquid water content refers to the total amount of liquid water that can actually participate in the formation of ice on power transmission lines, obtained by weighting the target liquid water content using the freezing coefficient.

[0102] In the embodiment of the present application, after the freezing probability corresponds to the freezing coefficient and the accurate target liquid water content, the target liquid water content is weighted and corrected by the freezing coefficient (i.e. the part of liquid water that does not freeze is removed), so as to determine the total amount of liquid water that can participate in the formation of icing of the power transmission conductor in the freezing rain area, i.e. the effective liquid water content, wherein the specific formula of the effective liquid water content is:

[0103] In the formula, f represents the freezing coefficient; and LWC represents the target liquid water content.

[0104] In step S2042, the preset freezing rain process time, the process precipitation rate, the water density, the freezing rain density, the unit adjustment coefficient, the air flow rate and the effective liquid water content are input into the preset icing thickness model, and the icing thickness growth information is output.

[0105] It should be noted that the freezing rain process time refers to the freezing rain duration for evaluating the icing accumulation condition, which is preset based on the power grid disaster prevention and early warning demand.

[0106] The process precipitation rate refers to the precipitation amount per unit time in the freezing rain process.

[0107] The water density refers to the density of liquid water under standard state, which is 1 g / cm 3 .

[0108] The freezing rain density (or the density of glaze and rime) refers to the density of the icing formed after the freezing of the freezing rain.

[0109] The unit adjustment coefficient refers to the correction coefficient preset for unifying the order of magnitude of each parameter and ensuring the calculation accuracy of the model.

[0110] The air flow rate refers to the horizontal wind speed perpendicular to the direction of the power transmission conductor in the freezing rain area.

[0111] The icing thickness model refers to a mathematical model trained based on the physical formation mechanism of icing and a large amount of historical icing observation data.

[0112] The icing thickness growth information refers to a set of information reflecting the characteristics of the icing accumulation of the power transmission conductor, which is output by the icing thickness model.

[0113] In the embodiment of the present application, the key parameters such as the preset freezing rain process time, the process precipitation rate, the water density, the freezing rain density, the unit adjustment coefficient and the air flow rate are determined, and then these parameters and the obtained effective liquid water content are input into the icing thickness model trained based on the physical formation mechanism of icing to carry out real-time calculation and prediction of the icing growth.

[0114] ​Specifically, the ice thickness of the conductor (equivalent ice thickness, i.e., information on the increase in ice thickness) is calculated using the following formula:

[0115] In the formula, Indicates the equivalent icing thickness; N Indicates the duration of the freezing rain process; j Represents a time variable; Indicates the precipitation rate during the process; Indicates the density of water; Indicates the density of freezing rain, rime, or hoarfrost; K Indicates the unit adjustment factor; This indicates the horizontal wind speed perpendicular to the power transmission line; express j The effective liquid water content at any given time.

[0116] Step S2043: Based on the ice thickness growth information, determine the amount of ice thickness growth of the power grid under test within a preset time period.

[0117] It should be noted that the preset time period refers to a time range pre-set based on the power grid disaster prevention and early warning needs, used to assess the icing accumulation situation. In this invention, it refers to... j Time to The hour corresponding to the time.

[0118] In this embodiment of the invention, the future... j The hourly increase in ice thickness is:

[0119] In the formula, Indicates the increase in ice thickness; N Indicates the duration of the freezing rain process; j Represents a time variable; Indicates the precipitation rate during the process; Indicates the density of water; Indicates the density of freezing rain, rime, or hoarfrost; K Indicates the unit adjustment factor; This indicates the horizontal wind speed perpendicular to the power transmission line; express j The effective liquid water content at any given time.

[0120] Specifically, the above calculation takes into account the spatiotemporal characteristics of changes in liquid water content and freezing rate, and realizes quantitative prediction of the cumulative amount of ice accumulation.

[0121] Step S2044: Determine the warning information based on the freezing rain risk warning level corresponding to the target liquid water content and the increase in ice thickness.

[0122] It should be noted that the freezing rain risk warning level refers to the risk level (usually divided into blue, yellow, orange and red four levels) pre-divided based on the power grid safe operation threshold and historical freezing rain disaster data.

[0123] In the embodiment of the present application, after obtaining the accurate target liquid water content and the icing thickness growth amount in the preset time period, the target liquid water content LWC and the hourly icing growth amount are matched according to the target liquid water content LWC and the hourly icing growth amount The four-level freezing rain risk warning standard is established, and the warning levels of the target liquid water content and the icing thickness growth amount are matched out respectively. The two warning levels are fused and judged in combination with the actual disaster prevention demand of the power grid, and finally the warning information that fits the actual freezing rain icing risk condition of the to-be-tested power grid is determined. The defined freezing rain risk warning level is shown in Table 2 as follows: Table 2. Freezing rain risk warning level

[0124] Step S2045, real-time update the target multi-source data according to the preset period, obtain new target multi-source data, and jump to execute the step of extracting the feature vector of the target multi-source data and inputting the preset multi-source fusion model to output multiple phase state posterior probabilities until new warning information is obtained.

[0125] It should be noted that the preset period refers to the data update time interval pre-set based on the meteorological data change rate and the power grid operation and maintenance response time requirement. The present application is set to include but not limited to hourly.

[0126] The new target multi-source data refers to the standardized data set reflecting the latest weather and precipitation conditions of the to-be-tested area after updating according to the preset period.

[0127] The new warning information refers to the warning result generated after completing the whole process analysis based on the new target multi-source data, which fits the current freezing rain icing risk condition.

[0128] In the embodiment of the present application, in order to maintain the continuity and timeliness of the warning, the hourly rolling update mechanism is adopted, that is, the latest observation data is used to correct the model output hourly. The specific correction process adopts the space-time weighted residual algorithm:

[0129] In the formula, represents the corrected result at the target time; represents the original model prediction value at the target time; n represents the number of observation data participating in the correction; represents the model prediction value of the i observation data; represents the measured value; a weight of the first observation data, i a weight of the first observation data, i a weight of the first observation data, i a horizontal distance between the observation data and the grid point to be corrected; a spatial correlation length; a time difference between the observation time and the target time, a time correlation scale, and the spatial and temporal kernel with exponential decay is used to realize smooth, stable and real-time multi-source correction in the hourly cycle.

[0130] After the latest observation data is corrected, new target multi-source data is obtained, and the above steps need to be re-executed from step S2013 until the latest warning information is obtained.

[0131] Step S205, the warning information is sent to the geographic information system platform.

[0132] It should be noted that the geographic information system platform refers to a computer system with functions of geographic spatial data management, spatial analysis and visualization display.

[0133] In the embodiment of the application, the obtained latest warning information is sent to the geographic information system platform.

[0134] Step S206, the transmission line and the transformer substation of the power grid to be measured are rendered by the geographic information system platform, and the corresponding frozen rain intensity information of the transmission line and the transformer substation of the power grid to be measured is obtained.

[0135] It should be noted that the transmission line refers to the general term of the conductor and the supporting tower and other facilities for transmitting electric energy, which is the main affected object of the icing disaster of the power grid.

[0136] The transformer substation refers to an electric power facility that undertakes the functions of electric power transformation, distribution and control.

[0137] The topographic information refers to spatial data representing the terrain undulation and landform characteristics of the region where the power grid to be measured is located.

[0138] Rendering refers to the process of converting abstract frozen rain related data into visual graphics with color classification and legend identification by the algorithm of the geographic information system platform.

[0139] The frozen rain intensity information refers to the frozen rain threat degree data corresponding to the transmission line section and the transformer substation position in the form of visualization, which includes the core contents of the frozen rain occurrence probability, the liquid water content and the icing growth trend. ​​

[0140] In the embodiment of the present application, after the early warning information is sent to the geographic information system platform, the transmission line direction of the to-be-tested power grid, the substation position information and the corresponding topographic elevation data are superimposed and integrated in the form of a grid map built in the platform, while the core data such as the freezing rain phase posterior probability, the target liquid water content and the ice thickness growth of each region are associated, the transmission line, the substation and the surrounding terrain are rendered and processed according to the preset color classification rendering rule, and the freezing rain intensity information corresponding to the transmission line and the substation of the to-be-tested power grid is obtained, which can directly reflect the freezing rain threat degree of different sections of the transmission line and different positions of the substation. The user can select the time step to view the freezing rain evolution trend or generate a single-point risk curve.

[0141] When the system detects that the freezing rain level reaches the threshold or the ice growth rate exceeds the set upper limit, a text and graphical warning report is automatically generated, which is published through the internal platform of the power grid, short message, email and dispatching system, to realize business linkage and rapid response.

[0142] In the embodiment, an extreme freezing rain quantitative short-term early warning device for a power grid is also provided, which is used to implement the above-mentioned embodiments and preferred embodiments, and will not be described again. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware or a combination of software and hardware is also possible and contemplated.

[0143] The embodiment provides an extreme freezing rain quantitative short-term early warning device for a power grid, as shown in Figure 4 The device comprises: An input module 301 is configured to input a feature vector of target multi-source data of a to-be-tested power grid into a preset multi-source fusion model, and determine a freezing rain area according to an output result; A freezing module 302 is configured to determine a freezing probability based on the freezing rain area and using temperature and wind speed of the target multi-source data; A calculation module 303 is configured to calculate a target liquid water content by using target micro rain radar reflectivity and particle size spectrum parameters of the target multi-source data; An early warning module 304 is configured to determine an ice thickness growth in a preset time period according to the freezing probability and the target liquid water content, and generate early warning information according to the target liquid water content and the ice thickness growth.

[0144] In some optional embodiments, the input module 301 comprises: An acquisition unit is configured to acquire initial multi-source data of a to-be-tested power grid; A preprocessing unit is configured to sequentially perform time synchronization processing, spatial interpolation processing and coordinate conversion processing on the initial multi-source data to obtain target multi-source data; an extraction unit configured to extract a feature vector of target multi-source data and input a preset multi-source fusion model, and output a plurality of phase state posterior probabilities; a selection unit configured to select a freezing rain phase state posterior probability from all phase state posterior probabilities; a judgment unit configured to judge whether the freezing rain phase state posterior probability is greater than or equal to a preset probability threshold; a freezing rain unit configured to, if yes, determine a region where the to-be-tested power grid is located as a freezing rain region.

[0145] In some optional embodiments, the freezing module 302 includes: an extraction wind speed unit configured to extract temperature and wind speed in the target multi-source data based on the freezing rain region; a freezing unit configured to determine a freezing coefficient based on a preset temperature interval where the temperature is located and a preset wind speed interval where the wind speed is located; a probability unit configured to determine a freezing probability of the to-be-tested power grid according to the freezing coefficient.

[0146] In some optional embodiments, the calculation module 303 includes: an acquisition data unit configured to acquire initial micro rain radar reflectivity, particle size spectrum parameters and dual-polarization radar parameters from the target multi-source data; a calculation unit configured to calculate initial liquid water content by using an empirical coefficient corresponding to the initial micro rain radar reflectivity and the particle size spectrum parameters; a correction unit configured to correct the micro rain radar reflectivity by using the dual-polarization radar parameters to obtain target micro rain radar reflectivity; an update unit configured to update the initial liquid water content according to the target micro rain radar reflectivity to obtain target liquid water content.

[0147] In some optional embodiments, the early warning module 304 includes: an effective unit configured to determine effective liquid water content by using a freezing coefficient of the freezing probability and the target liquid water content; an input unit configured to input a preset freezing rain process time, a process precipitation rate, a water density, a freezing rain density, a unit adjustment coefficient, an air flow rate and the effective liquid water content into a preset ice thickness model, and output ice thickness growth information; a growth unit configured to determine an ice thickness growth amount of the to-be-tested power grid in a preset time period based on the ice thickness growth information; an early warning unit configured to determine early warning information based on a freezing rain risk early warning level where the target liquid water content and the ice thickness growth amount are located; The jump unit is used for updating the target multi-source data in real time according to a preset period, obtaining new target multi-source data, and jumping to execute the steps of extracting a feature vector of the target multi-source data and inputting a preset multi-source fusion model to output a plurality of phase state posterior probabilities until new early warning information is obtained.

[0148] In some optional embodiments, the device further comprises: The sending unit is configured to send the early warning information to a geographic information system platform. The rendering unit is configured to render the power transmission lines, the substations and the corresponding topographic information of the to-be-tested power grid by the geographic information system platform to obtain the icing rain intensity information corresponding to the power transmission lines and the substations of the to-be-tested power grid, respectively.

[0149] The power grid extreme icing rain quantitative short-term early warning device provided in the embodiments of the present application can execute the power grid extreme icing rain quantitative short-term early warning method provided in any of the embodiments of the present application, has the function modules and beneficial effects corresponding to the execution method. The further function description of the above-mentioned various modules and units is the same as that of the corresponding embodiments, and will not be described here.

[0150] Figure 5 A structural schematic diagram of an electronic device provided in the embodiments of the present application is shown.

[0151] The following will be specifically described with reference to Figure 5 which shows a structural schematic diagram of an electronic device suitable for being used to implement the electronic device in the embodiments of the present application. The electronic device can include a processor (such as a central processor, a graphics processor, etc.) 401, which can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 402 or programs loaded from a storage 408 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the electronic device are also stored. The processor 401, the ROM 402 and the RAM 403 are connected to each other through a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0152] Generally, the following devices can be connected to the I / O interface 405: an input device 406 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 407 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage 408 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 409. The communication device 409 can allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although Figure 5 The electronic device with various devices is shown, but it should be understood that it is not required to implement or have all the shown devices, and more or less devices can be alternatively implemented or had.

[0153] In particular, the processes described above with reference to the flowcharts can be implemented as a computer software program according to embodiments of the present application. For example, embodiments of the present application include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for carrying out the methods illustrated by the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network by the communication device 409, or installed from the memory 408, or installed from the ROM 402. When the computer program is executed by the processor 401, the above-mentioned functions defined in the power grid extreme freezing rain quantitative short-term warning method according to embodiments of the present application are performed.

[0154] Figure 5 The electronic device shown is merely an example and should not impose any limitation on the functions and use range of embodiments of the present application.

[0155] Embodiments of the present application also provide a computer-readable storage medium, the above-mentioned method according to embodiments of the present application can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented by downloading and originally storing in a remote storage medium or non-transitory machine-readable storage medium and then storing in a local storage medium, so that the method described herein can be processed by such software on a storage medium using a general-purpose computer, a special-purpose processor, or programmable or special-purpose hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk or a solid state disk, etc.; further, the storage medium can also include a combination of the above-mentioned types of memories. It can be understood that the computer, processor, microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code, which, when accessed and executed by the computer, processor or hardware, implements the power grid extreme freezing rain quantitative short-term warning method shown in the above-mentioned embodiments.

[0156] Part of the present application can be applied as a computer program product, for example, computer program instructions, when executed by a computer, the operation of the computer can invoke or provide the method and / or technical solutions according to the present application. Those skilled in the art should understand that the form of computer program instructions in computer-readable medium includes but is not limited to source file, executable file, installation package file, etc., and accordingly, the way of computer program instructions executed by computer includes but is not limited to: the computer directly executes the instructions, or the computer executes the corresponding compiled program after compiling the instructions, or the computer reads and executes the instructions, or the computer reads and installs the instructions and then executes the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to the computer.

[0157] While embodiments of the present application have been described in conjunction with the appended drawings, various modifications and changes can be suggested by persons skilled in the art, and all such modifications and changes are believed to fall within the scope of the present application as defined by the appended claims.

Claims

1. A quantitative short-term early warning method for extreme freezing rain in power grids, characterized in that, The method includes: The feature vectors of the target multi-source data of the power grid under test are input into a preset multi-source fusion model, and the freezing rain area is determined based on the output results. Based on the freezing rain area, the freezing probability is determined using the temperature and wind speed data from the target multi-source data. The target liquid water content is obtained by calculating the target micro-rain radar reflectivity and particle size spectrum parameters using the target multi-source data. Based on the freezing probability and the target liquid water content, the increase in ice thickness within a preset time period is determined, and an early warning message is generated based on the target liquid water content and the increase in ice thickness.

2. The method according to claim 1, characterized in that, The step of inputting the feature vector of the target multi-source data of the power grid under test into a preset multi-source fusion model, and determining the freezing rain area based on the output results, includes: Acquire initial multi-source data of the power grid under test; The initial multi-source data is sequentially processed by time synchronization, spatial interpolation, and coordinate transformation to obtain the target multi-source data; Extract the feature vectors of the target multi-source data and input them into a preset multi-source fusion model to output multiple phase posterior probabilities; Select the posterior probability of the freezing rain phase from all the posterior probabilities of the stated phases; Determine whether the posterior probability of the freezing rain phase is greater than or equal to a preset probability threshold; If so, the area where the power grid under test is located is identified as a freezing rain area.

3. The method according to claim 1, characterized in that, The determination of the freezing probability based on the freezing rain area, using the target multi-source data for temperature and wind speed, includes: Based on the freezing rain area, extract the temperature and wind speed from the target multi-source data; The freezing coefficient is determined based on the preset temperature range where the temperature is located and the preset wind speed range where the wind speed is located. The freezing probability of the power grid under test is determined based on the freezing coefficient.

4. The method according to claim 1, characterized in that, The target liquid water content is calculated by using the target micro-rain radar reflectivity and particle size spectrum parameters from the multi-source data of the target, including: Initial micro-rain radar reflectivity, particle size spectrum parameters, and dual-polarization radar parameters are obtained from the target multi-source data. The initial liquid water content is calculated using the empirical coefficients corresponding to the initial micro-rain radar reflectivity and the particle size spectrum parameters. The radar reflectivity of the light rain is corrected using the dual polarization radar parameters to obtain the radar reflectivity of the target light rain. The initial liquid water content is updated based on the target light rain radar reflectivity to obtain the target liquid water content.

5. The method according to claim 2, characterized in that, The step involves determining the increase in ice thickness over a preset time period based on the freezing probability and the target liquid water content, and generating early warning information based on the target liquid water content and the increase in ice thickness, including: The effective liquid water content is determined by using the freezing coefficient of the freezing probability and the target liquid water content; Input the preset freezing rain process time, process precipitation rate, water density, freezing rain density, unit adjustment coefficient, air velocity and the effective liquid water content into the preset icing thickness model, and output the icing thickness growth information. Based on the ice thickness growth information, the amount of ice thickness growth of the power grid under test within a preset time period is determined; Based on the target liquid water content and the freezing rain risk warning level at which the ice thickness increase is located, the warning information is determined; According to a preset cycle, the target multi-source data is updated in real time to obtain new target multi-source data. Then, the process jumps to the steps of extracting the feature vector of the target multi-source data and inputting it into a preset multi-source fusion model, and outputting multiple phase posterior probabilities until new warning information is obtained.

6. The method according to claim 1, characterized in that, The method further includes: The warning information is sent to the geographic information system platform; The geographic information system platform is used to render the transmission lines, substations, and corresponding terrain information of the power grid under test, thereby obtaining the freezing rain intensity information corresponding to the transmission lines and substations of the power grid under test.

7. A quantitative short-term early warning device for extreme freezing rain in power grids, characterized in that, The device includes: The input module is used to input the feature vector of the target multi-source data of the power grid under test into the preset multi-source fusion model, and determine the freezing rain area based on the output results; The freezing module is used to determine the freezing probability based on the freezing rain area and the temperature and wind speed of the target multi-source data. The calculation module is used to calculate the target liquid water content by using the target micro-rain radar reflectivity and particle size spectrum parameters from the target multi-source data. The early warning module is used to determine the increase in ice thickness within a preset time period based on the freezing probability and the target liquid water content, and to generate early warning information based on the target liquid water content and the increase in ice thickness.

8. An electronic device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the power grid extreme freezing rain quantitative short-term early warning method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the quantitative short-term early warning method for extreme freezing rain in power grids as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, It includes computer instructions for causing a computer to execute the quantitative short-term early warning method for extreme freezing rain in power grids as described in any one of claims 1 to 6.

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