A method and system for optimizing the layout of an electric power meteorological monitoring network

Abstract

The present invention discloses a method and system for optimizing the layout of a power meteorological monitoring network. The method comprises: determining a numerical simulation test period for typical power meteorological disasters, and based on the numerical simulation test period, conducting high-resolution and high-precision numerical simulation tests, constructing a real meteorological field for the numerical simulation test, and determining real meteorological field data; extracting power meteorological monitoring data and diagnosing various power meteorological monitoring elements based on the real meteorological data, and constructing a simulated monitoring set of hypothetical sites for the numerical simulation test; conducting a data assimilation numerical simulation test of the simulated monitoring data based on the simulated monitoring set through control simulation tests and multiple sensitivity simulation tests, and determining the data assimilation numerical simulation test results of the simulated monitoring data; and conducting data assimilation effect evaluation and verification based on the data assimilation numerical simulation test results of the simulated monitoring data in combination with real meteorological field data to determine the optimal power meteorological monitoring network layout plan.

Description

Technical Field

[0001] The present invention relates to the field of electric power meteorology technology, and more particularly to a method and system for optimizing the layout of an electric power meteorology monitoring network. Background Art

[0002] With the intensification of global warming and the construction of new power systems based on new energy sources, extreme weather disasters pose severe challenges to the safe and stable operation of power. At present, with the exception of lightning monitoring networks (lightning locators), the power meteorological monitoring network has not yet matured. It is still mainly based on meteorological monitoring by meteorological departments, supplemented by very sparse online monitoring devices for transmission lines. As a result, the role of meteorological forecasting and early warning technology in supporting power safety production has not been fully utilized. The defects in the layout of the power meteorological monitoring network are one of the main reasons for the large errors in power meteorological forecasts, especially forecasts of extreme weather processes. Scientifically planned meteorological monitoring is the basis for carrying out accurate weather forecasts and early warnings, and is the key to supporting power production in responding to extreme weather disasters. Summary of the Invention

[0003] According to the present invention, a method and system for optimizing the layout of an electric power meteorological monitoring network are provided to solve the technical problem of how to perform the layout of an electric power meteorological monitoring network.

[0004] According to a first aspect of the present invention, a method for optimizing the layout of an electric power meteorological monitoring network is provided, comprising:

[0005] For the power grid area of ​​interest, analyze and obtain the main characteristics of power meteorological disasters in the power grid area, select typical disaster processes, determine the numerical simulation test period of typical power meteorological disasters, and conduct high-resolution and high-precision numerical simulation tests based on the numerical simulation test period, construct a real meteorological field for the numerical simulation test, and determine the real meteorological field data;

[0006] Based on the real meteorological data, extracting power meteorological monitoring data and diagnosing various power meteorological monitoring elements according to actual meteorological monitoring equipment, and constructing a simulated monitoring set of hypothetical sites for numerical simulation tests;

[0007] Based on the simulated monitoring set of the hypothetical station, a data assimilation numerical simulation test of the simulated monitoring data is carried out through a control simulation test and a variety of sensitivity simulation tests, and the data assimilation numerical simulation test results of the simulated monitoring data are determined;

[0008] Based on the data assimilation numerical simulation test results of the simulated monitoring data and combined with the real meteorological field data, data assimilation effect evaluation and verification are carried out to determine the optimal power meteorological monitoring network layout plan.

[0009] Optionally, for the power grid area of ​​interest, main characteristics of power meteorological disasters in the power grid area are analyzed and obtained, typical disaster processes are selected, and a numerical simulation test period for typical power meteorological disasters is determined. Based on the numerical simulation test period for the typical power meteorological disasters, high-resolution and high-precision numerical simulation tests are carried out to construct a real meteorological field for the numerical simulation test, and determine real meteorological field data, including:

[0010] For the power grid area of ​​interest, based on the power meteorological disaster loss data in the power grid area, statistically analyze the disaster type and spatiotemporal distribution characteristics, determine the typical power meteorological disaster process in the power grid area and the numerical simulation test period;

[0011]

[0012] F=max{P1,P2,…,P k}

[0013] Where, P i represents the total number of times the i-th type of power meteorological disaster occurred during the statistical period, j = 1, ..., n is the number of times the power meteorological disaster p occurred, i = 1, ..., k represents that a total of k types of disasters occurred in the region, and F is the meteorological disaster with the highest frequency among the statistical power meteorological disasters;

[0014] According to the meteorological disasters with the highest frequency, the most typical disaster cases are selected to determine the numerical simulation test period of typical power meteorological disasters;

[0015] Based on a high-resolution numerical weather prediction model, and according to the numerical simulation test period of the typical power meteorological disaster, the assimilation of existing power meteorological monitoring data and the localization of the physical parameterization scheme of the numerical model are carried out;

[0016] Complete the predetermined high-resolution and high-precision numerical simulation of the test period covering the typical power meteorological disaster, determine the numerical simulation results of the period, and use the numerical simulation results of the period as real meteorological field data.

[0017] Optionally, based on the real meteorological data, various power meteorological monitoring elements are extracted and diagnosed according to actual meteorological monitoring equipment to construct a simulated monitoring set of a hypothetical site for numerical simulation experiments, including:

[0018] Determine hypothetical power meteorological monitoring sites based on the latitude and longitude information of important transmission lines of the power grid of interest and in combination with the spatiotemporal distribution characteristics;

[0019] Extracting the assumed power meteorological monitoring data for the numerical simulation test period using the current power meteorological monitoring equipment based on the real meteorological field data and the assumed power meteorological monitoring site, wherein the monitoring elements of the current power meteorological monitoring equipment are seven elements, namely wind speed, wind direction, temperature, humidity, air pressure, precipitation, and irradiance;

[0020] Based on the main characteristics of power meteorological disasters and combined with the power meteorological monitoring data, the correlation coefficient is used to diagnose the observed variables that are significantly correlated with the disaster type;

[0021] Correlation coefficient calculation formula:

[0022]

[0023] In the formula, x and y represent the power meteorological disaster index and the observation variable sequence respectively. and are their respective average values.

[0024] Optionally, based on the simulated monitoring set of the hypothetical site, a data assimilation numerical simulation test of the simulated monitoring data is carried out through a control simulation test and multiple sensitivity simulation tests, and the data assimilation numerical simulation test results of the simulated monitoring data are determined, including:

[0025] According to the numerical simulation test period, the numerical model is adjusted to balance time, and the numerical simulation system is constructed using the background field data of the model. The control simulation test is carried out by aligning the numerical simulation test period with the high-resolution and high-precision numerical simulation test;

[0026] Based on the characteristics of the main power meteorological disasters, sensitivity simulation tests were conducted from the aspects of spatial density, monitoring types, and monitoring elements of power meteorological monitoring stations, combined with data assimilation of the power meteorological monitoring data;

[0027] Aiming at the characteristics of the main power meteorological disasters and important power grid areas, combined with the longitude and latitude information of the transmission lines, sensitivity simulation tests were carried out from the aspects of the spatial density of the layout of monitoring stations along the transmission channel, the spatial density of the layout at different distances along and around the line, and the influence of terrain height, combined with the data assimilation of the power meteorological monitoring data, to determine the data assimilation numerical simulation test results of the simulated monitoring data.

[0028] Optionally, based on the data assimilation numerical simulation test results of the simulated monitoring data and combined with the real meteorological field data, data assimilation effect evaluation and verification are carried out to determine the optimal power meteorological monitoring network layout plan, including:

[0029] Based on the real meteorological field data of each grid point and the data assimilation numerical simulation test results of the simulated monitoring data, the forecast effect under different power meteorological monitoring station layouts is tested and evaluated;

[0030] Analyze and evaluate the effects of various assimilation tests for numerical simulation of power meteorological disasters;

[0031] The hypothetical power meteorological monitoring data is used as the standard for numerical simulation tests. Multiple sensitivity simulation tests assimilate the hypothetical power meteorological monitoring simulation data of different layouts and types. Based on the data assimilation of the numerical simulation test results of the simulated monitoring data, the optimal power meteorological monitoring site layout plan is comprehensively determined.

[0032] Optionally, for the numerical simulation test of power meteorological disasters, various assimilation test effects are analyzed and evaluated, and the evaluation method of the assimilation test effect includes at least one of the three evaluation methods: power grid disaster area analysis and evaluation method, power grid disaster simulation element error statistical evaluation, and power grid disaster cumulative large value area analysis;

[0033] The power grid disaster location analysis is based on the two-dimensional spatial distribution map of the disaster, selecting the early and middle and late stages of the meteorological disaster development, and analyzing the disaster development location after assimilation;

[0034] The disaster error statistical analysis is

[0035] Calculate various numerical simulation elements during the disaster period, perform statistical analysis, and calculate the root mean square error (RMSE) and mean absolute error (MAE):

[0036]

[0037]

[0038] Where, F i is the disaster value simulated by different sensitivity tests, O i In order to control the disaster value of the simulation test, N is the number of all samples during the disaster period;

[0039] The cumulative large value area analysis is

[0040] Select the area with the largest cumulative value of power grid disasters and analyze the improvement amount ME of the area with the largest value after assimilation:

[0041] ME=F max -O max

[0042] Where, F max is the value of the disaster accumulation area with large value after assimilation of different sensitivity tests, O maxTo control the cumulative large value area in the simulation test.

[0043] According to another aspect of the present invention, there is also provided a system for optimizing the layout of an electric power meteorological monitoring network, comprising:

[0044] A module for determining real meteorological field data is used to analyze and obtain the main characteristics of power meteorological disasters in the power grid area of ​​interest, select typical disaster processes, determine the numerical simulation test period of typical power meteorological disasters, and conduct high-resolution and high-precision numerical simulation tests based on the numerical simulation test period, construct a real meteorological field for the numerical simulation test, and determine the real meteorological field data;

[0045] Constructing a hypothetical site simulation monitoring set module, which is used to extract power meteorological monitoring data and diagnose various power meteorological monitoring elements according to the actual meteorological monitoring equipment based on the real meteorological data, and construct a hypothetical site simulation monitoring set for numerical simulation experiments;

[0046] a simulation test result determination module, configured to carry out a data assimilation numerical simulation test of the simulated monitoring data through a control simulation test and a plurality of sensitivity simulation tests according to the simulated monitoring set of the hypothetical site, and determine the data assimilation numerical simulation test result of the simulated monitoring data;

[0047] The module for determining the optimal network layout scheme is used to evaluate and verify the data assimilation effect based on the data assimilation numerical simulation test results of the simulated monitoring data and combine them with the real meteorological field data to determine the optimal power meteorological monitoring network layout scheme.

[0048] Optionally, determining a real meteorological field data module includes:

[0049] The power meteorological disaster process determination submodule is used to determine the typical power meteorological disaster process and numerical simulation test period in the power grid area of ​​interest, based on the power meteorological disaster loss data in the power grid area, statistically analyze the disaster type and spatiotemporal distribution characteristics;

[0050]

[0051] F=max{P1,P2,…P k}

[0052] Where, P i represents the total number of times the i-th type of power meteorological disaster occurred during the statistical period, j = 1, ..., n is the number of times the power meteorological disaster p occurred, i = 1, ..., k represents that a total of k types of disasters occurred in the region, and F is the meteorological disaster with the highest frequency among the statistical power meteorological disasters;

[0053] The submodule for determining the numerical simulation test period is used to select the most typical disaster cases based on the meteorological disasters with the highest frequency of occurrence and determine the numerical simulation test period for typical power meteorological disasters;

[0054] Develop an assimilation and localization scheme submodule, which is used to assimilate existing power meteorological monitoring data and localize the physical parameterization scheme of the numerical model based on a high-resolution numerical weather prediction model and according to the numerical simulation test period of the typical power meteorological disaster;

[0055] The real meteorological data submodule is used to complete the predetermined high-resolution and high-precision numerical simulation of the test period covering the typical power meteorological disaster, determine the numerical simulation results of the period, and use the numerical simulation results of the period as the real meteorological field data.

[0056] Optionally, determining a real meteorological field data module includes:

[0057] A submodule for determining a hypothetical power meteorological monitoring site is used to determine a hypothetical power meteorological monitoring site based on the latitude and longitude information of important transmission lines of the power grid of interest and in combination with the spatiotemporal distribution characteristics;

[0058] a submodule for determining assumed power meteorological monitoring data, configured to extract, based on the actual meteorological field data and the assumed power meteorological monitoring site, assumed power meteorological monitoring data for the numerical simulation test period through the current power meteorological monitoring equipment, wherein the monitoring elements of the current power meteorological monitoring equipment are seven elements, namely, wind speed, wind direction, temperature, humidity, air pressure, precipitation, and irradiance;

[0059] A diagnosis observation variable submodule is used to diagnose the observation variables significantly correlated with the disaster type using correlation coefficients based on the main power meteorological disaster characteristics and the power meteorological monitoring data;

[0060] Correlation coefficient calculation formula:

[0061]

[0062] In the formula, x and y represent the power meteorological disaster index and the observation variable sequence respectively. and are their respective average values.

[0063] Optionally, determining a simulation test result module includes:

[0064] a submodule for carrying out a control simulation test, for adjusting the equilibrium time according to the numerical simulation test period and taking into account the numerical model, constructing a numerical simulation system using the background field data of the model, and carrying out the control simulation test by aligning the numerical simulation test period with the high-resolution and high-precision numerical simulation test;

[0065] Conducting a sensitivity simulation test submodule, for conducting sensitivity simulation tests based on the characteristics of the main power meteorological disasters, from the aspects of the spatial density of power meteorological monitoring stations, monitoring types, and monitoring elements, combined with the data assimilation of the power meteorological monitoring data;

[0066] The submodule for determining the assimilation numerical simulation test results is used to carry out sensitivity simulation tests based on the characteristics of the main power meteorological disasters and important power grid areas, combined with the longitude and latitude information of the transmission lines, from the aspects of the spatial density of the layout of monitoring stations along the transmission channel, the spatial density of the layout at different distances along and around the line, and the influence of terrain height, combined with the data assimilation of the power meteorological monitoring data, to determine the data assimilation numerical simulation test results of the simulated monitoring data.

[0067] Optionally, the module for determining an optimal network layout solution includes:

[0068] A test and evaluation submodule is used to test and evaluate the forecast effect under different power meteorological monitoring station layouts based on the real meteorological field data of each grid point and the data assimilation numerical simulation test results of the simulated monitoring data;

[0069] The test effect analysis submodule is used to analyze and evaluate the effects of various assimilation tests for numerical simulation tests of power meteorological disasters;

[0070] The submodule for determining the optimal network layout plan is used to use the assumed power meteorological monitoring data as the standard for numerical simulation tests. Multiple sensitivity simulation tests assimilate the assumed power meteorological monitoring simulation data of different layouts and types. The optimal power meteorological monitoring site layout plan is comprehensively determined based on the data assimilation of the numerical simulation test results of the simulated monitoring data.

[0071] Optionally, the test effect analysis submodule includes at least one of the following three evaluation methods: power grid disaster area analysis and evaluation method, power grid disaster simulation element error statistical evaluation, and power grid disaster cumulative large value area analysis;

[0072] The power grid disaster location analysis is based on the two-dimensional spatial distribution map of the disaster, selecting the early and middle and late stages of the meteorological disaster development, and analyzing the disaster development location after assimilation;

[0073] The disaster error statistical analysis is

[0074] Calculate various numerical simulation elements during the disaster period, perform statistical analysis, and calculate the root mean square error (RMSE) and mean absolute error (MAE):

[0075]

[0076]

[0077] Where, F i is the disaster value simulated by different sensitivity tests, O i In order to control the disaster value of the simulation test, N is the number of all samples during the disaster period;

[0078] The cumulative large value area analysis is

[0079] Select the area with the largest cumulative value of power grid disasters and analyze the improvement amount ME of the area with the largest value after assimilation:

[0080] ME=F max -O max

[0081] Where, F max is the value of the disaster accumulation area with large value after assimilation of different sensitivity tests, O max To control the cumulative large value area in the simulation test.

[0082] Through a series of numerical simulations, we can scientifically, efficiently, and accurately quantify and evaluate the value and role of different monitoring network layouts in future power and meteorological disaster forecasting and warning. Furthermore, the proposed method can guide the selection of power and meteorological monitoring elements and the optimization of network layouts for different budgets and disasters at the root of regional power grids. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] A more complete understanding of exemplary embodiments of the present invention may be obtained by referring to the following drawings:

[0084] Figure 1 This is a flow chart of a method for optimizing the layout of an electric power meteorological monitoring network according to this embodiment;

[0085] Figure 2 This is a schematic diagram of a power meteorological monitoring network layout optimization system described in this embodiment. DETAILED DESCRIPTION

[0086] Exemplary embodiments of the present invention will now be described with reference to the accompanying drawings. However, the present invention may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided to provide a thorough and complete disclosure of the present invention and to fully convey the scope of the present invention to those skilled in the art. The terminology used in the exemplary embodiments shown in the accompanying drawings is not intended to limit the present invention. In the accompanying drawings, identical elements are denoted by the same reference numerals.

[0087] Unless otherwise specified, the terms used herein (including technical terms) have the meanings commonly understood by those skilled in the art. In addition, it is understood that terms defined in commonly used dictionaries should be understood to have the same meanings as those in the context of the relevant fields, and should not be understood as idealized or overly formal meanings.

[0088] According to a first aspect of the present invention, a method 100 for optimizing the layout of an electric power meteorological monitoring network is provided. Figure 1 As shown, the method 100 includes:

[0089] S101: Analyze and obtain the main power meteorological disaster characteristics of the power grid area of ​​interest, select typical disaster processes, determine the numerical simulation test period of typical power meteorological disasters, and conduct high-resolution and high-precision numerical simulation tests based on the numerical simulation test period to construct a real meteorological field for the numerical simulation test and determine real meteorological field data;

[0090] S102: Based on the real meteorological data, extracting power meteorological monitoring data and diagnosing various power meteorological monitoring elements according to actual meteorological monitoring equipment, and constructing a simulated monitoring set of a hypothetical site for numerical simulation experiments;

[0091] S103: Based on the simulated monitoring set of the hypothetical site, a data assimilation numerical simulation test is conducted through a control simulation test and multiple sensitivity simulation tests to determine the data assimilation numerical simulation test results of the simulated monitoring data;

[0092] S104: Based on the data assimilation numerical simulation test results of the simulated monitoring data and combined with the real meteorological field data, carry out data assimilation effect evaluation and verification to determine the optimal power meteorological monitoring network layout plan.

[0093] Specifically, it includes the following 4 steps:

[0094] Step 1: For the power grid area of ​​concern, analyze and obtain the main types of power meteorological disasters in the area, select typical disaster processes, carry out high-resolution and high-precision numerical simulation experiments, and construct a real meteorological field for the numerical simulation experiments.

[0095] Step 2: Based on the real meteorological data output in step 1, various power meteorological monitoring elements are extracted and diagnosed according to the actual meteorological monitoring equipment, and a simulation monitoring set of hypothetical sites for numerical simulation experiments is constructed.

[0096] Step 3: Based on the simulated monitoring set of the hypothetical power meteorological monitoring station extracted in step 2, a data assimilation numerical simulation test of the simulated monitoring data is carried out through control simulation tests and multiple sensitivity simulation tests.

[0097] Step 4: Based on the data assimilation numerical simulation test results of the simulated monitoring data in step 3 and combined with the real meteorological field data in step 1, carry out data assimilation effect evaluation and verification, and then determine the optimal power meteorological monitoring network layout plan.

[0098] The step 1 specifically includes the following steps:

[0099] Step 1-1: For the power grid area of ​​concern, based on the power meteorological disaster loss data in the area, statistically analyze the disaster type and spatiotemporal distribution characteristics, and identify the typical power meteorological disaster process in the area and the numerical simulation test period.

[0100]

[0101] F=max{P1,P2,…,P k}

[0102] In the above formula, P i represents the total number of times the i-th type of power meteorological disaster occurs during the statistical period, j = 1,…, n is the number of times the power meteorological disaster p occurs, i = 1,…, k represents that a total of k types of disasters have occurred in the region, and F is the meteorological disaster with the highest frequency among the statistical power meteorological disasters.

[0103] Based on the meteorological disasters with the highest frequency, the most typical disaster cases are selected to determine the period of numerical simulation tests.

[0104] Step 1-2: Based on the high-resolution numerical weather prediction model, according to the numerical simulation test period determined in step 1-1, the assimilation of existing power meteorological monitoring data and the localization of the physical parameterization scheme of the numerical model are carried out.

[0105] Step 1-3: Based on step 1-2, a 3km×3km high-resolution and high-precision numerical simulation covering a test period of a typical power meteorological disaster is completed, and the numerical simulation results of this period are used as the real meteorological field data of the present invention.

[0106] The step 2 specifically includes the following steps:

[0107] Step 2-1: Based on the latitude and longitude information of important transmission lines of the power grid of concern and combined with the statistical characteristics of power meteorological disasters in step 1, determine the hypothetical power meteorological monitoring sites.

[0108] Step 2-2: Based on the actual meteorological data from Step 1 and the assumed power meteorological monitoring station locations from Step 2-1, extract the hypothetical power meteorological monitoring data for the numerical simulation test period. Current power meteorological monitoring equipment monitors seven elements: wind speed, wind direction, temperature, humidity, air pressure, precipitation, and irradiance.

[0109] Step 2-3: Based on the main types of power meteorological disasters in step 1, combined with various power meteorological monitoring equipment, use correlation coefficients to diagnose observed variables that are significantly correlated with disaster types.

[0110] Correlation coefficient calculation formula:

[0111]

[0112] In the formula, x and y represent the power meteorological disaster index and the observation variable sequence respectively. and are their respective average values.

[0113] The step 3 specifically includes the following steps:

[0114] Step 3-1: According to the numerical simulation period in step 1, the balance time is adjusted considering the numerical model, and only the background field data of the model is used to construct the numerical simulation system. The available simulation period is consistent with the high-resolution simulation in step 1, which is the control simulation test of the present invention.

[0115] Step 3-2: Based on the main characteristics of power meteorological disasters counted in step 1, sensitivity simulation tests are carried out from the aspects of spatial density of power meteorological monitoring stations, monitoring types, monitoring elements, etc., combined with the data assimilation of power meteorological monitoring data extracted in step 2.

[0116] The specific settings of the sensitivity test are as follows:

[0117] (1) Ground stations at 6 km intervals (every 2 grid points), including observations of temperature, humidity, and air pressure at 2 m above the ground, and wind direction and speed at 10 m; referred to as "INT2sfc"

[0118] (2) Same as (1), but every 15 km; abbreviated as "INT5sfc"

[0119] (3) Same as (1), but every 30 km; abbreviated as "INT10sfc"

[0120] (4) Balloon observations at 30 km intervals (every 2 grid points), including temperature, humidity, air pressure, wind direction, and wind speed observations from the ground to 18 km at different altitudes; referred to as "INT10upr"

[0121] (5) Same as (2), but only observes the temperature from 2m above the ground; abbreviated as "INT5sfcT"

[0122] (6) Same as (2), but only observes humidity from 2m above the ground; abbreviated as "INT5sfcQ"

[0123] (7) Same as (2), but only observes wind direction and speed 10m above the ground; referred to as “INT5sfcWind”

[0124] (8) Same as (2), but only observes the temperature and humidity from 2m above the ground; abbreviated as "INT5sfcTQ"

[0125] Comparing the assimilation and forecasting experiments of observation data (1)-(3) can reveal the role of ground observations and the influence of the spatial density of observation sites; comparing the assimilation experiments of observation data (3) and (4) can explain the role of observation type; comparing the assimilation and forecasting experiments of observation data (2), (5), (6), (7), and (8) can reveal the role of observed variables (temperature and humidity at two meters above the ground, and wind field at ten meters above the ground).

[0126] Step 3-3: For the major disasters and important power grid areas counted in Step 1, combined with the longitude and latitude information of the transmission lines, sensitivity simulation tests are carried out from the aspects of the spatial density of the layout of monitoring stations along the transmission channel, the spatial density of the layout at different distances along and around the line, the influence of terrain height, etc., combined with the data assimilation of the power meteorological monitoring data extracted in Step 2.

[0127] In the station layout test, the sensitivity simulation test settings are as follows:

[0128] (9) Ground stations at intervals of 3 km along the power grid, with the same observation contents as (2); referred to as “INT1line”

[0129] (10) Ground stations along the power grid and at intervals of 9 km around it, with the same observation content as (2); referred to as "INT3lineWrap1"

[0130] (11) Same as (10), but with intervals of 15 km; abbreviated as “INT5line_Wrap1”

[0131] (12) Same as (11), but the ground station covers two circuits along the power grid; referred to as “INT5line_Wrap2”

[0132] (13) Same as (11), but ignores observations with terrain heights below 450 meters; abbreviated as "INT5line_Wrap1_H"

[0133] (14) Same as (12), but ignores observations with terrain heights below 450 meters; abbreviated as "INT5line_Wrap2_H"

[0134] Among them, the station layouts of (10) and (11) are called single-layer envelope layout, and the station layout of (12) is called double-layer envelope layout. Comparing the assimilation and forecasting experiments of observation data of (9) and (10) can reveal the role of envelope layout; comparing the assimilation and forecasting experiments of observation data of (10)-(12) can reveal the influence of observation spacing / envelope layer number; the comparison experiments (11)-(14) are to explore whether the observations of stations in low terrain can be ignored.

[0135] The step 4 specifically includes the following steps:

[0136] Step 4-1: Based on the real meteorological field data of each grid point in step 1, as well as the control simulation test and various sensitivity simulation test results in step 3, carry out the inspection and evaluation of the forecast effect under different power meteorological monitoring station layouts.

[0137] Step 4-2: For the numerical simulation test of power meteorological disasters, three evaluation methods are proposed, including grid disaster area analysis, grid disaster simulation element error statistics, and grid disaster cumulative large value area analysis, to comprehensively analyze and evaluate the effects of various assimilation tests.

[0138] (1) Analysis of power grid disaster areas

[0139] Based on the two-dimensional spatial distribution map of disasters, the early, middle and late stages of meteorological disaster development were selected to analyze the disaster development distribution after assimilation.

[0140] (2) Statistical analysis of disaster errors

[0141] Various numerical simulation elements during the disaster period were calculated, statistical analysis was performed, and the root mean square error (RMSE) and mean absolute error (MAE) were calculated.

[0142]

[0143]

[0144] Where, F i is the disaster value simulated by different sensitivity tests, O i In order to control the disaster value of the simulation experiment, N is the number of all samples during the disaster period.

[0145] (3) Analysis of cumulative large value areas

[0146] The areas with large cumulative values ​​of power grid disasters are selected, and the improvement amount (ME) of the large value areas after assimilation is analyzed.

[0147] ME=F max -O max

[0148] Where, Fmax is the value of the disaster accumulation area with large value after assimilation of different sensitivity tests, O max To control the cumulative large value area in the simulation test.

[0149] Step 4-3: The control simulation test is for the power meteorological monitoring data without assimilation assumptions. As the standard for the numerical simulation test, multiple sensitivity simulation tests assimilate the assumed power meteorological monitoring simulation data of different layouts and types. Based on the sensitivity test evaluation results, the optimal power meteorological monitoring station layout plan is comprehensively determined.

[0150] Through a series of numerical simulations, we can scientifically, efficiently, and accurately quantify and evaluate the value and role of different monitoring network layouts in future power and meteorological disaster forecasting and warning. Furthermore, the proposed method can guide the selection of power and meteorological monitoring elements and the optimization of network layouts for different budgets and disasters at the root of regional power grids.

[0151] Optionally, for the power grid area of ​​interest, main characteristics of power meteorological disasters in the power grid area are analyzed and obtained, typical disaster processes are selected, and a numerical simulation test period for typical power meteorological disasters is determined. Based on the numerical simulation test period for the typical power meteorological disasters, high-resolution and high-precision numerical simulation tests are carried out to construct a real meteorological field for the numerical simulation test, and determine real meteorological field data, including:

[0152] For the power grid area of ​​interest, based on the power meteorological disaster loss data in the power grid area, statistically analyze the disaster type and spatiotemporal distribution characteristics, determine the typical power meteorological disaster process in the power grid area and the numerical simulation test period;

[0153]

[0154] F=max{P1,P2,…,P k}

[0155] Where, P i represents the total number of times the i-th type of power meteorological disaster occurred during the statistical period, j = 1, ..., n is the number of times the power meteorological disaster p occurred, i = 1, ..., k represents that a total of k types of disasters occurred in the region, and F is the meteorological disaster with the highest frequency among the statistical power meteorological disasters;

[0156] According to the meteorological disasters with the highest frequency, the most typical disaster cases are selected to determine the numerical simulation test period of typical power meteorological disasters;

[0157] Based on a high-resolution numerical weather prediction model, and according to the numerical simulation test period of the typical power meteorological disaster, the assimilation of existing power meteorological monitoring data and the localization of the physical parameterization scheme of the numerical model are carried out;

[0158] Complete the predetermined high-resolution and high-precision numerical simulation of the test period covering the typical power meteorological disaster, determine the numerical simulation results of the period, and use the numerical simulation results of the period as real meteorological field data.

[0159] Optionally, based on the real meteorological data, various power meteorological monitoring elements are extracted and diagnosed according to actual meteorological monitoring equipment to construct a simulated monitoring set of a hypothetical site for numerical simulation experiments, including:

[0160] Determine hypothetical power meteorological monitoring sites based on the latitude and longitude information of important transmission lines of the power grid of interest and in combination with the spatiotemporal distribution characteristics;

[0161] Extracting the assumed power meteorological monitoring data for the numerical simulation test period using the current power meteorological monitoring equipment based on the real meteorological field data and the assumed power meteorological monitoring site, wherein the monitoring elements of the current power meteorological monitoring equipment are seven elements, namely wind speed, wind direction, temperature, humidity, air pressure, precipitation, and irradiance;

[0162] Based on the main characteristics of power meteorological disasters and combined with the power meteorological monitoring data, the correlation coefficient is used to diagnose the observed variables that are significantly correlated with the disaster type;

[0163] Correlation coefficient calculation formula:

[0164]

[0165] In the formula, x and y represent the power meteorological disaster index and the observation variable sequence respectively. and are their respective average values.

[0166] Optionally, based on the simulated monitoring set of the hypothetical site, a data assimilation numerical simulation test of the simulated monitoring data is carried out through a control simulation test and multiple sensitivity simulation tests, and the data assimilation numerical simulation test results of the simulated monitoring data are determined, including:

[0167] According to the numerical simulation test period, the numerical model is adjusted to balance time, and the numerical simulation system is constructed using the background field data of the model. The control simulation test is carried out by aligning the numerical simulation test period with the high-resolution and high-precision numerical simulation test;

[0168] Based on the characteristics of the main power meteorological disasters, sensitivity simulation tests were conducted from the aspects of spatial density, monitoring types, and monitoring elements of power meteorological monitoring stations, combined with data assimilation of the power meteorological monitoring data;

[0169] Aiming at the characteristics of the main power meteorological disasters and important power grid areas, combined with the longitude and latitude information of the transmission lines, sensitivity simulation tests were carried out from the aspects of the spatial density of the layout of monitoring stations along the transmission channel, the spatial density of the layout at different distances along and around the line, and the influence of terrain height, combined with the data assimilation of the power meteorological monitoring data, to determine the data assimilation numerical simulation test results of the simulated monitoring data.

[0170] Optionally, based on the data assimilation numerical simulation test results of the simulated monitoring data and combined with the real meteorological field data, data assimilation effect evaluation and verification are carried out to determine the optimal power meteorological monitoring network layout plan, including:

[0171] Based on the real meteorological field data of each grid point and the data assimilation numerical simulation test results of the simulated monitoring data, the forecast effect under different power meteorological monitoring station layouts is tested and evaluated;

[0172] Analyze and evaluate the effects of various assimilation tests for numerical simulation of power meteorological disasters;

[0173] The hypothetical power meteorological monitoring data is used as the standard for numerical simulation tests. Multiple sensitivity simulation tests assimilate the hypothetical power meteorological monitoring simulation data of different layouts and types. Based on the data assimilation of the numerical simulation test results of the simulated monitoring data, the optimal power meteorological monitoring site layout plan is comprehensively determined.

[0174] Optionally, for the numerical simulation test of power meteorological disasters, various assimilation test effects are analyzed and evaluated, and the evaluation method of the assimilation test effect includes at least one of the three evaluation methods: power grid disaster area analysis and evaluation method, power grid disaster simulation element error statistical evaluation, and power grid disaster cumulative large value area analysis;

[0175] The power grid disaster location analysis is based on the two-dimensional spatial distribution map of the disaster, selecting the early and middle and late stages of the meteorological disaster development, and analyzing the disaster development location after assimilation;

[0176] The disaster error statistical analysis is

[0177] Calculate various numerical simulation elements during the disaster period, perform statistical analysis, and calculate the root mean square error (RMSE) and mean absolute error (MAE):

[0178]

[0179]

[0180] Where, F i is the disaster value simulated by different sensitivity tests, O iIn order to control the disaster value of the simulation test, N is the number of all samples during the disaster period;

[0181] The cumulative large value area analysis is

[0182] Select the area with the largest cumulative value of power grid disasters and analyze the improvement amount ME of the area with the largest value after assimilation:

[0183] ME=F max -O max

[0184] Where, F max is the value of the disaster accumulation area with large value after assimilation of different sensitivity tests, O max To control the cumulative large value area in the simulation test.

[0185] Through a series of numerical simulations, we can scientifically, efficiently, and accurately quantify and evaluate the value and role of different monitoring network layouts in future power and meteorological disaster forecasting and warning. Furthermore, the proposed method can guide the selection of power and meteorological monitoring elements and the optimization of network layouts for different budgets and disasters at the root of regional power grids.

[0186] According to another aspect of the present invention, a power meteorological monitoring network layout optimization system 200 is provided. Figure 2 As shown, the system 200 includes:

[0187] The module 210 for determining real meteorological field data is used to analyze and obtain the main characteristics of power meteorological disasters in the power grid area of ​​interest, select typical disaster processes, determine the numerical simulation test period of typical power meteorological disasters, and conduct high-resolution and high-precision numerical simulation tests based on the numerical simulation test period to construct a real meteorological field for the numerical simulation test and determine the real meteorological field data.

[0188] A module 220 for constructing a hypothetical site simulation monitoring set is used to extract power meteorological monitoring data and diagnose various power meteorological monitoring elements according to the actual meteorological monitoring equipment based on the real meteorological data, and to construct a hypothetical site simulation monitoring set for the numerical simulation test;

[0189] The simulation test result determination module 230 is configured to carry out a data assimilation numerical simulation test of the simulated monitoring data through a control simulation test and a plurality of sensitivity simulation tests based on the simulated monitoring set of the hypothetical site, and determine the data assimilation numerical simulation test result of the simulated monitoring data;

[0190] The module 240 for determining the optimal network layout scheme is used to evaluate and verify the data assimilation effect based on the data assimilation numerical simulation test results of the simulated monitoring data and the real meteorological field data, so as to determine the optimal power meteorological monitoring network layout scheme.

[0191] Optionally, determining a real meteorological field data module includes:

[0192] The power meteorological disaster process determination submodule is used to determine the typical power meteorological disaster process and numerical simulation test period in the power grid area of ​​interest, based on the power meteorological disaster loss data in the power grid area, statistically analyze the disaster type and spatiotemporal distribution characteristics;

[0193]

[0194] F=max{P1,P2,…,P k}

[0195] Where, P i represents the total number of times the i-th type of power meteorological disaster occurred during the statistical period, j = 1, ..., n is the number of times the power meteorological disaster p occurred, i = 1, ..., k represents that a total of k types of disasters occurred in the region, and F is the meteorological disaster with the highest frequency among the statistical power meteorological disasters;

[0196] The submodule for determining the numerical simulation test period is used to select the most typical disaster cases based on the meteorological disasters with the highest frequency of occurrence and determine the numerical simulation test period for typical power meteorological disasters;

[0197] Develop an assimilation and localization scheme submodule, which is used to assimilate existing power meteorological monitoring data and localize the physical parameterization scheme of the numerical model based on a high-resolution numerical weather prediction model and according to the numerical simulation test period of the typical power meteorological disaster;

[0198] The real meteorological data submodule is used to complete the predetermined high-resolution and high-precision numerical simulation of the test period covering the typical power meteorological disaster, determine the numerical simulation results of the period, and use the numerical simulation results of the period as the real meteorological field data.

[0199] Optionally, determining a real meteorological field data module includes:

[0200] A submodule for determining a hypothetical power meteorological monitoring site is used to determine a hypothetical power meteorological monitoring site based on the latitude and longitude information of important transmission lines of the power grid of interest and in combination with the spatiotemporal distribution characteristics;

[0201] a submodule for determining assumed power meteorological monitoring data, configured to extract, based on the actual meteorological field data and the assumed power meteorological monitoring site, assumed power meteorological monitoring data for the numerical simulation test period through the current power meteorological monitoring equipment, wherein the monitoring elements of the current power meteorological monitoring equipment are seven elements, namely, wind speed, wind direction, temperature, humidity, air pressure, precipitation, and irradiance;

[0202] A diagnosis observation variable submodule is used to diagnose the observation variables significantly correlated with the disaster type using correlation coefficients based on the main power meteorological disaster characteristics and the power meteorological monitoring data;

[0203] Correlation coefficient calculation formula:

[0204]

[0205] In the formula, x and y represent the power meteorological disaster index and the observation variable sequence respectively. and are their respective average values.

[0206] Optionally, determining a simulation test result module includes:

[0207] a submodule for carrying out a control simulation test, for adjusting the equilibrium time according to the numerical simulation test period and taking into account the numerical model, constructing a numerical simulation system using the background field data of the model, and carrying out the control simulation test by aligning the numerical simulation test period with the high-resolution and high-precision numerical simulation test;

[0208] Conducting a sensitivity simulation test submodule, for conducting sensitivity simulation tests based on the characteristics of the main power meteorological disasters, from the aspects of the spatial density of power meteorological monitoring stations, monitoring types, and monitoring elements, combined with the data assimilation of the power meteorological monitoring data;

[0209] The submodule for determining the assimilation numerical simulation test results is used to carry out sensitivity simulation tests based on the characteristics of the main power meteorological disasters and important power grid areas, combined with the longitude and latitude information of the transmission lines, from the aspects of the spatial density of the layout of monitoring stations along the transmission channel, the spatial density of the layout at different distances along and around the line, and the influence of terrain height, combined with the data assimilation of the power meteorological monitoring data, to determine the data assimilation numerical simulation test results of the simulated monitoring data.

[0210] Optionally, the module for determining an optimal network layout solution includes:

[0211] A test and evaluation submodule is used to test and evaluate the forecast effect under different power meteorological monitoring station layouts based on the real meteorological field data of each grid point and the data assimilation numerical simulation test results of the simulated monitoring data;

[0212] The test effect analysis submodule is used to analyze and evaluate the effects of various assimilation tests for numerical simulation tests of power meteorological disasters;

[0213] The submodule for determining the optimal network layout plan is used to use the assumed power meteorological monitoring data as the standard for numerical simulation tests. Multiple sensitivity simulation tests assimilate the assumed power meteorological monitoring simulation data of different layouts and types. The optimal power meteorological monitoring site layout plan is comprehensively determined based on the data assimilation of the numerical simulation test results of the simulated monitoring data.

[0214] Optionally, the test effect analysis submodule includes at least three evaluation methods: at least one of a power grid disaster area analysis and evaluation method, a power grid disaster simulation element error statistical evaluation method, and a power grid disaster cumulative large value area analysis method;

[0215] The power grid disaster location analysis is based on the two-dimensional spatial distribution map of the disaster, selecting the early and middle and late stages of the meteorological disaster development, and analyzing the disaster development location after assimilation;

[0216] The disaster error statistical analysis is

[0217] Calculate various numerical simulation elements during the disaster period, perform statistical analysis, and calculate the root mean square error (RMSE) and mean absolute error (MAE):

[0218]

[0219]

[0220] Where, F i is the disaster value simulated by different sensitivity tests, O i In order to control the disaster value of the simulation test, N is the number of all samples during the disaster period;

[0221] The cumulative large value area analysis is

[0222] Select the area with the largest cumulative value of power grid disasters and analyze the improvement amount (ME) of the area with the largest value after assimilation:

[0223] ME=F max -O max

[0224] Where, F max is the value of the disaster accumulation area with large value after assimilation of different sensitivity tests, O max To control the cumulative large value area in the simulation test.

[0225] An electric power meteorological monitoring network layout optimization system 200 according to an embodiment of the present invention corresponds to an electric power meteorological monitoring network layout optimization method 300 according to another embodiment of the present invention, and will not be described in detail here.

[0226] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiment of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal translation scripting language JavaScript, etc.

[0227] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0228] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0229] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0230] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0231] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A method for optimizing the layout of an electric power meteorological monitoring network, characterized in that: include: For the power grid area of ​​interest, analyze and obtain the main characteristics of power meteorological disasters in the power grid area, select typical disaster processes, determine the numerical simulation test period of typical power meteorological disasters, and conduct high-resolution and high-precision numerical simulation tests based on the numerical simulation test period, construct a real meteorological field for the numerical simulation test, and determine the real meteorological field data; Based on the real meteorological field data, extracting power meteorological monitoring data and diagnosing various power meteorological monitoring elements according to actual meteorological monitoring equipment, and constructing a simulated monitoring set of hypothetical sites for numerical simulation tests; Based on the simulated monitoring set of the hypothetical station, a data assimilation numerical simulation test of the simulated monitoring data is carried out through a control simulation test and a variety of sensitivity simulation tests, and the data assimilation numerical simulation test results of the simulated monitoring data are determined; Based on the data assimilation numerical simulation test results of the simulated monitoring data and combined with the real meteorological field data, data assimilation effect evaluation and verification are carried out to determine the optimal power meteorological monitoring network layout plan.

2. The method according to claim 1, characterized in that For the power grid area of ​​interest, analyze and obtain the main characteristics of the power meteorological disasters in the power grid area, select typical disaster processes, determine the numerical simulation test period of typical power meteorological disasters, and conduct high-resolution and high-precision numerical simulation tests based on the numerical simulation test period of typical power meteorological disasters, construct a real meteorological field for the numerical simulation test, and determine the real meteorological field data, including: For the power grid area of ​​interest, based on the power meteorological disaster loss data in the power grid area, statistically analyze the disaster type and spatiotemporal distribution characteristics, determine the typical power meteorological disaster process in the power grid area and the numerical simulation test period; F=max{P1,P2,…,P k } Where, P i represents the total number of times the i-th type of power meteorological disaster occurred during the statistical period, j = 1, ..., n is the number of times the power meteorological disaster p occurred, i = 1, ..., k represents that a total of k types of disasters occurred in the region, and F is the meteorological disaster with the highest frequency among the statistical power meteorological disasters; According to the meteorological disasters with the highest frequency, the most typical disaster cases are selected to determine the numerical simulation test period of typical power meteorological disasters; Based on a high-resolution numerical weather prediction model, and according to the numerical simulation test period of the typical power meteorological disaster, the assimilation of existing power meteorological monitoring data and the localization of the physical parameterization scheme of the numerical model are carried out; Complete the predetermined high-resolution and high-precision numerical simulation of the test period covering the typical power meteorological disaster, determine the numerical simulation results of the period, and use the numerical simulation results of the period as real meteorological field data.

3. The method according to claim 2, characterized in that Based on the real meteorological field data, various power meteorological monitoring elements are extracted and diagnosed according to actual meteorological monitoring equipment, and a simulated monitoring set of hypothetical sites for numerical simulation experiments is constructed, including: Determine hypothetical power meteorological monitoring sites based on the latitude and longitude information of important transmission lines of the power grid of interest and in combination with the spatiotemporal distribution characteristics; Extracting the assumed power meteorological monitoring data for the numerical simulation test period using the current power meteorological monitoring equipment based on the real meteorological field data and the assumed power meteorological monitoring site, wherein the monitoring elements of the current power meteorological monitoring equipment are seven elements, namely wind speed, wind direction, temperature, humidity, air pressure, precipitation, and irradiance; Based on the main characteristics of power meteorological disasters and combined with the power meteorological monitoring data, the correlation coefficient is used to diagnose the observed variables that are significantly correlated with the disaster type; Correlation coefficient calculation formula: In the formula, x and y represent the power meteorological disaster index and the observation variable sequence respectively. and are their respective average values.

4. The method according to claim 1, wherein Based on the simulated monitoring set of the hypothetical station, a data assimilation numerical simulation test of the simulated monitoring data is carried out through a controlled simulation test and a variety of sensitivity simulation tests, and the data assimilation numerical simulation test results of the simulated monitoring data are determined, including: According to the numerical simulation test period, the numerical model is adjusted to balance time, and the numerical simulation system is constructed using the background field data of the model. The control simulation test is carried out by aligning the numerical simulation test period with the high-resolution and high-precision numerical simulation test; Based on the characteristics of the main power meteorological disasters, sensitivity simulation tests were conducted from the aspects of spatial density, monitoring types, and monitoring elements of power meteorological monitoring stations, combined with data assimilation of the power meteorological monitoring data; Aiming at the characteristics of the main power meteorological disasters and important power grid areas, combined with the longitude and latitude information of the transmission lines, sensitivity simulation tests were carried out from the aspects of the spatial density of the layout of monitoring stations along the transmission channel, the spatial density of the layout at different distances along and around the line, and the influence of terrain height, combined with the data assimilation of the power meteorological monitoring data, to determine the data assimilation numerical simulation test results of the simulated monitoring data.

5. The method according to claim 1, wherein Based on the data assimilation numerical simulation test results of the simulated monitoring data and combined with the real meteorological field data, the data assimilation effect evaluation and verification are carried out to determine the optimal power meteorological monitoring network layout plan, including: Based on the real meteorological field data of each grid point and the data assimilation numerical simulation test results of the simulated monitoring data, the forecast effect under different power meteorological monitoring station layouts is tested and evaluated; Analyze and evaluate the effects of various assimilation tests for numerical simulation of power meteorological disasters; The hypothetical power meteorological monitoring data is used as the standard for numerical simulation tests. Multiple sensitivity simulation tests assimilate the hypothetical power meteorological monitoring simulation data of different layouts and types. Based on the data assimilation of the numerical simulation test results of the simulated monitoring data, the optimal power meteorological monitoring site layout plan is comprehensively determined.

6. The method according to claim 5, characterized in that For numerical simulation experiments on power meteorological disasters, various assimilation test results are analyzed and evaluated. The evaluation methods for the assimilation test results include at least three evaluation methods: at least one of the following: power grid disaster area analysis and evaluation method, power grid disaster simulation element error statistical evaluation, and power grid disaster cumulative large value area analysis; The power grid disaster location analysis is based on the two-dimensional spatial distribution map of the disaster, selecting the early and middle and late stages of the meteorological disaster development, and analyzing the disaster development location after assimilation; The disaster error statistical analysis is Calculate various numerical simulation elements during the disaster period, perform statistical analysis, and calculate the root mean square error (RMSE) and mean absolute error (MAE): Where, F i is the disaster value simulated by different sensitivity tests, O i To control the disaster value of the simulation test, N is the number of all samples during the disaster period; The cumulative large value area analysis is Select the area with the largest cumulative value of power grid disasters and analyze the improvement amount ME of the area with the largest value after assimilation: ME=F max -Oh max Where, F max is the value of the disaster accumulation area with large value after assimilation of different sensitivity tests, O max To control the cumulative large value area in the simulation test.

7. A power meteorological monitoring network layout optimization system, characterized in that: include: A module for determining real meteorological field data is used to analyze and obtain the main characteristics of power meteorological disasters in the power grid area of ​​interest, select typical disaster processes, determine the numerical simulation test period of typical power meteorological disasters, and conduct high-resolution and high-precision numerical simulation tests based on the numerical simulation test period, construct a real meteorological field for the numerical simulation test, and determine the real meteorological field data; Constructing a hypothetical site simulation monitoring set module, which is used to extract power meteorological monitoring data and diagnose various power meteorological monitoring elements according to the actual meteorological monitoring equipment based on the real meteorological field data, and construct a hypothetical site simulation monitoring set for numerical simulation experiments; a simulation test result determination module, configured to carry out a data assimilation numerical simulation test of the simulated monitoring data through a control simulation test and a plurality of sensitivity simulation tests according to the simulated monitoring set of the hypothetical site, and determine the data assimilation numerical simulation test result of the simulated monitoring data; The module for determining the optimal network layout scheme is used to evaluate and verify the data assimilation effect based on the data assimilation numerical simulation test results of the simulated monitoring data and combine them with the real meteorological field data to determine the optimal power meteorological monitoring network layout scheme.

8. The system according to claim 7, characterized in that Determine the real meteorological field data module, including: The power meteorological disaster process determination submodule is used to determine the typical power meteorological disaster process and numerical simulation test period in the power grid area of ​​interest, based on the power meteorological disaster loss data in the power grid area, statistically analyze the disaster type and spatiotemporal distribution characteristics; F=max{P1,P2,…,P k } Where, P i represents the total number of times the i-th type of power meteorological disaster occurred during the statistical period, j = 1, ..., n is the number of times the power meteorological disaster p occurred, i = 1, ..., k represents that a total of k types of disasters occurred in the region, and F is the meteorological disaster with the highest frequency among the statistical power meteorological disasters; The submodule for determining the numerical simulation test period is used to select the most typical disaster cases based on the meteorological disasters with the highest frequency of occurrence and determine the numerical simulation test period for typical power meteorological disasters; Develop an assimilation and localization scheme submodule, which is used to assimilate existing power meteorological monitoring data and localize the physical parameterization scheme of the numerical model based on a high-resolution numerical weather prediction model and according to the numerical simulation test period of the typical power meteorological disaster; The real meteorological data submodule is used to complete the predetermined high-resolution and high-precision numerical simulation of the test period covering the typical power meteorological disaster, determine the numerical simulation results of the period, and use the numerical simulation results of the period as the real meteorological field data.

9. The system according to claim 8, characterized in that Determine the real meteorological field data module, including: A submodule for determining a hypothetical power meteorological monitoring site is used to determine a hypothetical power meteorological monitoring site based on the latitude and longitude information of important transmission lines of the power grid of interest and in combination with the spatiotemporal distribution characteristics; a submodule for determining assumed power meteorological monitoring data, configured to extract, based on the actual meteorological field data and the assumed power meteorological monitoring site, assumed power meteorological monitoring data for the numerical simulation test period through the current power meteorological monitoring equipment, wherein the monitoring elements of the current power meteorological monitoring equipment are seven elements, namely, wind speed, wind direction, temperature, humidity, air pressure, precipitation, and irradiance; A diagnosis observation variable submodule is used to diagnose the observation variables significantly correlated with the disaster type using correlation coefficients based on the main power meteorological disaster characteristics and the power meteorological monitoring data; Correlation coefficient calculation formula: In the formula, x and y represent the power meteorological disaster index and the observation variable sequence respectively. and are their respective average values.

10. The system according to claim 7, wherein: Determine the simulation test result module, including: a submodule for carrying out a control simulation test, for adjusting the equilibrium time according to the numerical simulation test period and taking into account the numerical model, constructing a numerical simulation system using the background field data of the model, and carrying out the control simulation test by aligning the numerical simulation test period with the high-resolution and high-precision numerical simulation test; Conducting a sensitivity simulation test submodule, for conducting sensitivity simulation tests based on the characteristics of the main power meteorological disasters, from the aspects of the spatial density of power meteorological monitoring stations, monitoring types, and monitoring elements, combined with the data assimilation of the power meteorological monitoring data; The submodule for determining the assimilation numerical simulation test results is used to carry out sensitivity simulation tests based on the characteristics of the main power meteorological disasters and important power grid areas, combined with the longitude and latitude information of the transmission lines, from the aspects of the spatial density of the layout of monitoring stations along the transmission channel, the spatial density of the layout at different distances along and around the line, and the influence of terrain height, combined with the data assimilation of the power meteorological monitoring data, to determine the data assimilation numerical simulation test results of the simulated monitoring data.

11. The system according to claim 7, wherein: The module for determining the optimal network layout solution includes: A test and evaluation submodule is used to test and evaluate the forecast effect under different power meteorological monitoring station layouts based on the real meteorological field data of each grid point and the data assimilation numerical simulation test results of the simulated monitoring data; The test effect analysis submodule is used to analyze and evaluate the effects of various assimilation tests for numerical simulation tests of power meteorological disasters; The submodule for determining the optimal network layout plan is used to use the assumed power meteorological monitoring data as the standard for numerical simulation tests. Multiple sensitivity simulation tests assimilate the assumed power meteorological monitoring simulation data of different layouts and types. The optimal power meteorological monitoring site layout plan is comprehensively determined based on the data assimilation of the numerical simulation test results of the simulated monitoring data.

12. The system according to claim 11, characterized in that The test effect analysis submodule includes at least three evaluation methods: at least one of the following: power grid disaster area analysis and evaluation method, power grid disaster simulation element error statistical evaluation, and power grid disaster cumulative large value area analysis; The power grid disaster location analysis is based on the two-dimensional spatial distribution map of the disaster, selecting the early and middle and late stages of the meteorological disaster development, and analyzing the disaster development location after assimilation; The disaster error statistical analysis is Calculate various numerical simulation elements during the disaster period, perform statistical analysis, and calculate the root mean square error (RMSE) and mean absolute error (MAE): Where, F i is the disaster value simulated by different sensitivity tests, O i To control the disaster value of the simulation test, N is the number of all samples during the disaster period; The cumulative large value area analysis is Select the area with the largest cumulative value of power grid disasters and analyze the improvement amount ME of the area with the largest value after assimilation: ME=F max -Oh max Where, F max is the value of the disaster accumulation area with large value after assimilation of different sensitivity tests, O max To control the cumulative large value area in the simulation test.

13. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and the computer program is used to execute the method according to any one of claims 1 to 6.

Images