CN-122026352-A - Method for identifying gridding weak points of power distribution network under strong typhoon precipitation

CN122026352ACN 122026352 ACN122026352 ACN 122026352ACN-122026352-A

Abstract

The invention discloses a method for identifying grid weak points of a power distribution network under strong typhoons and precipitation, which particularly relates to the technical field of power control and comprises the following steps: and (3) carrying out regional parameter acquisition and grid division, acquiring equipment and environment linkage parameters, carrying out weather-power parameter threshold and weight calculation to acquire parameter comprehensive response values, calculating single-point equipment risk values, and judging systematic weak points. According to the identification method for the grid weak points of the power distribution network under strong typhoons and precipitation, disclosed by the invention, a risk grade matrix is constructed through multi-dimensional parameter acquisition and cross operation, the unmanned aerial vehicle is used for carrying a laser radar system to acquire terrain elevation and gradient data, the drainage capacity parameters such as the diameter, the material and the like of a drainage pipeline are combined with on-site survey analysis, the cross operation is carried out after grading according to elevation differences, gradient and drainage efficiency, grid units are accurately divided, the duration of accumulated water is calculated, the spatial fine assessment of the risk of the power distribution network under strong typhoons and precipitation is realized, and the unilaterality of single parameter assessment is avoided.

Inventors

DAI PAN
Liu Daikai
ZHANG MENGQI
HU ZHECHENG
CHEN XILIN
XUE YOU
HUANG JINGJING
ZHU CHAO
ZHANG MANYING
LIN LING

Assignees

国网浙江省电力有限公司经济技术研究院

Dates

Publication Date: 20260512
Application Date: 20251215

Claims (8)

1. The method for identifying the gridding weak points of the power distribution network under strong typhoons and precipitation is characterized by comprising the following steps of: s1, collecting regional parameters and dividing grids, obtaining topographic feature parameters and drainage capacity parameters of each point in the region, classifying the regional parameters and the drainage capacity parameters, then performing cross operation to judge risk levels, dividing grid units according to the risk levels, and calculating the duration time of accumulated water of each grid; S2, carrying out linkage collection on equipment and environmental parameters, collecting equipment operation state parameters and environmental parameters, establishing association rules, presetting a corresponding relation between the environment and collection frequency, and dynamically adjusting the collection frequency to obtain linkage parameters; S3, calculating weather-power parameter thresholds and weights, dividing weather process stages, calculating weather element thresholds of each stage, establishing weight rules, and calculating comprehensive response values of power parameters of the equipment group; s4, calculating a single-point equipment risk value, calling a linkage acquisition parameter to calculate equipment parameter deviation degree, and adjusting deviation degree weight according to meteorological parameters to obtain a single-point equipment dynamic risk value; s5, judging systematic weak points, analyzing single-point high-risk equipment neighborhood equipment based on the topological structure of the power distribution network, judging risk propagation areas and marking the risk propagation areas as systematic weak points.
2. The method for identifying the grid weak points of the power distribution network under typhoon strong precipitation is characterized by comprising the steps of S1, collecting regional parameters and dividing grids, specifically collecting terrain characteristic parameters by using an unmanned plane-mounted laser radar system, obtaining drainage capacity parameters through field survey and pipe network data analysis, classifying the terrain characteristic parameters according to elevation differences and gradients, classifying the drainage capacity parameters according to drainage efficiency per unit area, constructing a risk grade matrix by cross operation of classification results, dividing grid units according to risk grades, calculating rainwater flow velocity and flow direction by using a Manning formula based on the terrain characteristic parameters, and calculating the duration of each grid ponding by using a continuity equation and a mass conservation law in combination with the drainage capacity parameters.
3. The method for identifying the grid weak points of the power distribution network under strong typhoons and precipitation is characterized in that S2 equipment and environmental parameters are collected in a linkage mode, specifically, equipment operation state parameters and environmental parameters are collected in real time through a sensor network, historical data are analyzed through an association rule mining algorithm to establish an equipment-environment parameter association rule, the corresponding relation between the gradient of the change of the preset environmental parameters and the gradient of the collection frequency of the working condition parameters of the equipment is preset, and when the environmental parameters reach the preset proportion of the characteristic values of historical faults, the collection frequency of the working condition parameters of the corresponding equipment is automatically improved.
4. A method for identifying grid weak points of a power distribution network under strong typhoon precipitation according to claim 1, wherein S3 comprises the steps of calculating weather-power parameter thresholds and weights, dividing a weather process into three stages including short term, medium term and long term according to time, calculating weather element thresholds according to weather element variation amplitude and trend according to each stage by adopting a statistical analysis method, establishing weather-power parameter combination weight rules, enabling single weather element to reach a threshold value and give basic weight, enabling multiple elements to reach threshold value weight superposition at the same time, converting collected real-time voltage, current and temperature parameters into standardized parameters based on a minimum-maximum normalization method, combining weather element weights according to equipment group power parameters, and obtaining the weather element combination weight rules according to a formula Calculating the comprehensive response value of the power parameter, wherein, In order to correspond to the weight coefficient, Is a standardized parameter corresponding to the real-time parameter value.
5. The method for identifying the grid weak points of the power distribution network under typhoon strong precipitation according to claim 1, wherein S4 is characterized in that the single-point equipment risk value is calculated, specifically, equipment and environment linkage acquisition parameters are called, and the parameters are calculated according to the formula Calculating the deviation degree of the real-time operation parameter and the history normal interval of the equipment to obtain a parameter deviation degree index, wherein x is a real-time parameter value, As the average value of the historical normal interval, The standard deviation of the historical normal interval is adjusted according to the real-time meteorological parameters, the deviation degree weight is adjusted according to the equipment-environment association rule and the meteorological-electric parameter weight rule, and the deviation degree weight is calculated according to the formula Calculating a single point device dynamic risk value, wherein The degree of deviation is weighted, and D is the degree of deviation.
6. The identification method for the grid weak points of the power distribution network under typhoon strong precipitation is characterized by comprising the steps of S5, judging the systematic weak points, specifically, constructing a network model based on a power distribution network topological structure, defining a device connection relation and an electrical distance, finding out neighborhood devices of feeder lines where single-point high-risk devices are located, wherein the neighborhood devices are devices which are directly connected with the high-risk devices or are connected with the high-risk devices through not more than 3 intermediate devices, and judging the region as the systematic weak points when the number of the high-risk devices in the neighborhood devices exceeds 30% and the standard deviation of risk values of the devices is smaller than a set threshold value.
7. The method for identifying the grid weak points of the power distribution network under typhoon strong precipitation according to claim 1, wherein the terrain characteristic parameters comprise terrain elevation data and gradient data, and the environment parameters comprise rainfall, wind speed, air humidity and temperature.
8. The method for identifying the grid weak points of the power distribution network under typhoon strong precipitation according to claim 1, wherein the drainage capacity parameters comprise drainage pipeline diameter, material, gradient and pump station drainage flow, and the equipment operation state parameters comprise equipment voltage, current, temperature and vibration frequency.

Description

Method for identifying gridding weak points of power distribution network under strong typhoon precipitation Technical Field The invention relates to the technical field of power control, in particular to a method for identifying gridding weak points of a power distribution network under strong typhoon precipitation. Background The technical field of power control comprises technical research and application in various aspects of power system operation, control, protection and the like. The core content of the method is that the effective management of the power system is realized by various technical means, and the safe, stable and efficient operation of the power system is ensured. In the power control, the control theory, the information technology and the like are required to be applied to accurately control all links of power generation, power transmission, transformation, power distribution, power utilization and the like, for example, the output control of power generation equipment is carried out, the stable supply of electric energy is ensured, the operation of a power transmission line is monitored, and fault hidden dangers and the like are found and processed in time, so that the good operation state of the whole power system is maintained. The method for identifying the grid weak points of the power distribution network under strong typhoon precipitation is a technical means for accurately identifying the weak points which are easy to fail after grid division is carried out on the power distribution network under the condition that the influence of severe typhoon precipitation weather on the power distribution network is pointed. In the method, a large amount of basic data such as power distribution network line parameters, equipment information and the like and historical meteorological data of typhoon strong precipitation are required to be collected, and the probability of faults caused by factors such as strong winds, storm flushing, flood soaking and the like of equipment such as lines, towers and transformers in each grid of the power distribution network under the condition of typhoon strong precipitation is researched by utilizing modes such as establishing a fault probability model and analog simulation through analysis of the data, so that the position of a weak point in the power distribution network is determined. In the prior art, the data are manually collected in the identification of the weak points of the distribution network under strong typhoon precipitation, a fault probability model is established by combining historical meteorological data, the fine analysis of the topographic features and the drainage capacity is lacked, the grid division is rough, and the actual influence of the ponding risk on equipment is difficult to accurately reflect. The data acquisition adopts fixed frequency, and a dynamic association mechanism of environmental parameters and acquisition frequency is not established, so that abnormal initial change of equipment can be missed due to insufficient data density, and fault early warning lag can be caused. The risk assessment does not divide the weather process stage, the differential weight setting is lacked, and the deviation between the assessment result and the actual fault probability is large. The single-point equipment risk adopts static threshold judgment, and the weight is dynamically adjusted without combining real-time meteorological parameters, so that missed judgment or misjudgment can be caused. The systematic weak point identification only analyzes single-point risks in isolation, a device connection relation model is not constructed, a risk propagation area cannot be identified, and fault prevention is limited to single-point maintenance. For example, a feeder area device risk accumulation may not be found, causing regional failure. Disclosure of Invention The invention mainly aims to provide a method for identifying grid weak points of a power distribution network under strong typhoons and precipitation, which can effectively solve the problems related to the background technology. In order to achieve the above purpose, the technical scheme adopted by the invention is as follows: a method for identifying gridding weak points of a power distribution network under typhoon strong precipitation comprises the following steps: s1, collecting regional parameters and dividing grids, obtaining topographic feature parameters and drainage capacity parameters of each point in the region, classifying the regional parameters and the drainage capacity parameters, then performing cross operation to judge risk levels, dividing grid units according to the risk levels, and calculating the duration time of accumulated water of each grid; S2, carrying out linkage collection on equipment and environmental parameters, collecting equipment operation state parameters and environmental parameters, establishing association rules, presetting a corresponding relati