Power distribution network reliability evaluation method, device, equipment and medium

Abstract

The invention relates to a power distribution network reliability evaluation method and device, equipment and a medium, and the method comprises the steps: obtaining a weather log of a power distribution network, coordinates of power distribution nodes, and a historical power utilization state log; determining a fault evaluation coefficient of the power distribution network in a preset time period based on the fault duration, the fault frequency and the fault number and coordinates of the power distribution nodes in the historical power utilization state log; obtaining a weather anomaly coefficient of a preset time period based on the weather anomaly degree, the weather anomaly duration and the weather anomaly frequency in the weather log; determining the weather influence degree of the power distribution network according to the correlation characteristics of the fault evaluation coefficient and the weather abnormity coefficient; obtaining a fault outage coefficient of the power distribution network based on the fault frequency of the power distribution node in the historical power utilization state log in the preset time period; and obtaining the reliability of the power distribution network according to the weather influence degree, the weather abnormal coefficient and the fault outage coefficient. The method can accurately and comprehensively reflect the reliability of the power distribution network.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network monitoring, and particularly to a method, device, equipment and medium for evaluating the reliability of a distribution network. Background Art

[0002] The distribution network is an important part of the power system, responsible for distributing electric power from the substation to the user side of the distribution nodes to ensure that users such as residents, commercial and industrial users obtain stable and reliable power supply; evaluating the reliability of the distribution network refers to determining the operating state of the distribution network and the ability to maintain power supply when abnormalities occur through calculation and analysis, and its purpose is to quantify the risks that the distribution network may face during operation and the response ability in abnormal situations.

[0003] Existing technologies usually adopt evaluation indicators such as the average power outage frequency and average power outage duration of the power system. Although they can reflect the reliability of the distribution network to a certain extent, most of these indicators reflect the situation of the distribution network from the user side and the average situation, and cannot more comprehensively reflect the reliability of the distribution network, especially the impact of abnormal weather and other situations on the faults of the distribution network, resulting in relatively low accuracy of the reliability evaluation method and results of the distribution network, and affecting the judgment of the maintenance and operating state of the distribution network. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method, device, equipment and medium for evaluating the reliability of a distribution network, which can accurately and comprehensively reflect the reliability of the distribution network, and make the judgment of the maintenance and operating state of the distribution network more accurate.

[0005] The technical solution adopted by the present invention to solve its technical problems is: to provide a method for evaluating the reliability of a distribution network, including the following steps:

[0006] Obtain the weather log of the distribution network within a preset time period, as well as the coordinates of the distribution nodes and the historical power consumption status log within the preset time period;

[0007] Determine the fault evaluation coefficient of the distribution network within the preset time period based on the fault duration, fault times, and the number and coordinates of the distribution node faults in the historical power consumption status log of the distribution node within the preset time period;

[0008] Obtain the weather anomaly coefficient of the preset time period based on the weather anomaly degree, weather anomaly duration, and weather anomaly frequency in the weather log of the distribution network within the preset time period;

[0009] Determine the weather influence degree of the distribution network according to the correlation characteristics of the fault evaluation coefficient and the weather anomaly coefficient;

[0010] Obtain the fault outage coefficient of the distribution network based on the number of faults in the historical power consumption status log of the distribution nodes within a preset time period;

[0011] Obtain the reliability of the distribution network according to the weather influence degree, the weather anomaly coefficient, and the fault outage coefficient.

[0012] The determination of the fault evaluation coefficient of the distribution network within a preset time period based on the fault duration, the number of faults, the number of faults of the distribution nodes, and the coordinates in the historical power consumption status log of the distribution nodes specifically includes:

[0013] Obtain the first fault characteristic value based on the fault duration and the number of faults in the historical power consumption status log of the distribution nodes within a preset time period;

[0014] Obtain the second fault characteristic value according to the number of faults and the coordinates of the distribution nodes;

[0015] Calculate the sum of the first fault characteristic value and the second fault characteristic value, and use the obtained value as the fault evaluation coefficient of the distribution network within a preset time period.

[0016] The obtaining of the first fault characteristic value based on the fault duration and the number of faults in the historical power consumption status log of the distribution nodes within a preset time period is specifically:

[0017] Calculate the total sum of the fault durations of all distribution nodes in the distribution network within a preset time period to obtain the cumulative fault duration of the distribution network;

[0018] Calculate the product of the cumulative fault duration and the number of faults of all distribution nodes within a preset time period, and perform normalization processing to obtain the first fault characteristic value.

[0019] The obtaining of the second fault characteristic value according to the number of faults and the coordinates of the distribution nodes is specifically:

[0020] Perform clustering according to the number of faults and the coordinates of the distribution nodes to obtain different fault area clustering clusters at the same moment;

[0021] Calculate the average value of the Euclidean distances between the clustering centers of any fault area clustering cluster at the same moment and other fault area clustering clusters to obtain the discrete characteristic value of the any fault area clustering cluster;

[0022] Calculate the sum of the discrete characteristic values of all fault area clustering clusters at the same moment to obtain the distribution characteristic value;

[0023] Calculate the sum of the products of the number of fault area clustering clusters at all moments and the distribution characteristic value, and perform normalization processing to obtain the second fault characteristic value.

[0024] The abnormal weather in the weather log includes at least one of rainy weather, windy weather, and thunderstorm weather; obtaining the weather anomaly coefficient for the preset time period based on the weather anomaly degree, weather anomaly duration, and weather anomaly frequency in the weather log of the distribution network for the preset time period specifically includes:

[0025] For any rainy weather in the weather log, normalize the rainfall amount of the any rainy weather to obtain a rainfall anomaly degree value; calculate the product of the reciprocal of the average time interval between the any rainy weather and the adjacent rainy weathers before and after, the rainfall duration of the any rainy weather, and the rainfall anomaly degree value to obtain the rainfall anomaly coefficient of the any rainy weather; calculate the sum of the rainfall anomaly coefficients of all rainy weathers in the weather log, and perform normalization to obtain the weather anomaly coefficient of the rainy weather type in the preset time period; the reciprocal of the average time interval between the any rainy weather and the adjacent rainy weathers before and after is the weather anomaly frequency corresponding to the any rainy weather;

[0026] For any windy weather in the weather log that exceeds the preset wind force level, normalize the wind force level of the any windy weather to obtain a wind anomaly degree value; calculate the product of the reciprocal of the average time interval between the any windy weather and the adjacent windy weathers before and after, the windy duration of the any windy weather, and the wind anomaly degree value to obtain the wind anomaly coefficient of the any windy weather; calculate the sum of the wind anomaly coefficients of all windy weathers in the weather log, and perform normalization to obtain the weather anomaly coefficient of the windy weather type in the preset time period; the reciprocal of the average time interval between the any windy weather and the adjacent windy weathers before and after is the weather anomaly frequency corresponding to the any windy weather;

[0027] For any thunderstorm weather in the weather log that exceeds the preset peak current intensity, normalize the peak current intensity of the any thunderstorm weather to obtain a thunderstorm anomaly degree value; calculate the product of the reciprocal of the average time interval between the any thunderstorm weather and the adjacent thunderstorm weathers before and after, the thunderstorm duration of the any thunderstorm weather, and the thunderstorm anomaly degree value to obtain the thunderstorm anomaly coefficient of the any thunderstorm weather; calculate the sum of the thunderstorm anomaly coefficients of all thunderstorm weathers in the weather log, and perform normalization to obtain the weather anomaly coefficient of the thunderstorm weather type in the preset time period; the reciprocal of the average time interval between the any thunderstorm weather and the adjacent thunderstorm weathers before and after is the weather anomaly frequency corresponding to the any thunderstorm weather.

[0028] Determining the weather impact degree of the distribution network according to the correlation characteristics of the fault evaluation coefficient and the weather anomaly coefficient specifically includes:

[0029] Construct a fault evaluation sequence based on the fault evaluation coefficients of connected preset time periods, and construct a weather anomaly sequence based on the weather anomaly coefficients of the same weather types in the connected preset time periods; calculate the Pearson correlation coefficient between the fault evaluation sequence and the weather anomaly sequence and perform a positive correlation mapping to obtain the weather influence degree of the distribution network.

[0030] The method for obtaining the fault outage coefficient of the distribution network based on the number of faults in the historical power consumption status log of the distribution node within a preset time period specifically includes:

[0031] Calculate the ratio of the number of faults of the distribution node within a preset time period to the preset time period to obtain the fault outage rate of the distribution node;

[0032] Calculate the average value of the fault outage rates of all distribution nodes in the distribution network to obtain the average fault outage index;

[0033] Calculate the ratio of the fault outage rate of the overhead line to the distribution node to obtain the scaling coefficient;

[0034] Calculate the product of the scaling coefficient and the average fault outage index to obtain the fault outage rate benchmark of the distribution node;

[0035] Calculate the product of a preset first proportional coefficient and the fault outage rate to obtain a first outage characteristic value;

[0036] Calculate the product of a preset second proportional coefficient and the fault outage rate benchmark to obtain a second outage characteristic value;

[0037] Calculate the sum of the first outage characteristic value and the second outage characteristic value to obtain the fault outage characteristic value of the distribution node;

[0038] Calculate the average value of the fault outage characteristic values of all distribution nodes to obtain the fault outage coefficient of the distribution network.

[0039] The method for obtaining the reliability of the distribution network based on the weather influence degree, weather anomaly coefficient, and fault outage coefficient specifically includes:

[0040] Calculate the sum of the products of the weather influence degrees and the weather anomaly coefficients of all abnormal weather types and perform a negative correlation mapping to obtain a first reliability coefficient;

[0041] Perform a negative correlation mapping on the fault outage coefficient to obtain a second reliability coefficient;

[0042] Calculate the sum of the first reliability coefficient and the second reliability coefficient to obtain the reliability of the distribution network.

[0043] The technical solution adopted by the present invention to solve its technical problems is: to provide a distribution network reliability evaluation device, including:

[0044] An acquisition module, configured to acquire the weather log of the distribution network within a preset time period, as well as the coordinates of the distribution nodes and the historical power consumption status log within the preset time period;

[0045] A fault evaluation coefficient module, configured to determine the fault evaluation coefficient of the distribution network within a preset time period based on the fault duration, the number of faults, and the number and coordinates of the faults of the distribution nodes in the historical power consumption status log within the preset time period;

[0046] A weather anomaly coefficient module, configured to obtain the weather anomaly coefficient of the preset time period based on the degree of weather anomaly, the duration of weather anomaly, and the frequency of weather anomaly in the weather log of the distribution network within the preset time period;

[0047] A weather impact degree module, configured to determine the weather impact degree of the distribution network according to the correlation characteristics of the fault evaluation coefficient and the weather anomaly coefficient;

[0048] A fault outage coefficient module, configured to obtain the fault outage coefficient of the distribution network based on the number of faults in the historical power consumption status log of the distribution nodes within a preset time period;

[0049] A reliability evaluation module, configured to obtain the reliability of the distribution network according to the weather impact degree, the weather anomaly coefficient, and the fault outage coefficient.

[0050] The fault evaluation coefficient module includes:

[0051] A first fault characteristic value unit, configured to obtain a first fault characteristic value based on the fault duration and the number of faults in the historical power consumption status log of the distribution nodes within a preset time period;

[0052] A second fault characteristic value unit, configured to obtain a second fault characteristic value according to the number and coordinates of the faults of the distribution nodes;

[0053] A fault evaluation coefficient calculation unit, configured to calculate the sum of the first fault characteristic value and the second fault characteristic value, and use the obtained value as the fault evaluation coefficient of the distribution network within a preset time period.

[0054] The first fault characteristic value unit includes:

[0055] An accumulated fault duration calculation sub-unit, configured to calculate the sum of the fault durations of all distribution nodes in the distribution network within a preset time period to obtain the accumulated fault duration of the distribution network;

[0056] A first fault characteristic value calculation sub-unit, configured to calculate the product of the accumulated fault duration and the number of faults of all distribution nodes within a preset time period, and perform normalization processing to obtain a first fault characteristic value.

[0057] The second fault eigenvalue unit includes:

[0058] A clustering subunit, configured to cluster according to the number of faults and coordinates of distribution nodes to obtain different fault area clustering clusters at the same moment;

[0059] A distance calculation subunit, configured to calculate the average value of the Euclidean distances between the clustering centers of any fault area clustering cluster and other fault area clustering clusters at the same moment, and obtain the discrete eigenvalue of the any fault area clustering cluster;

[0060] A distribution eigenvalue calculation subunit, configured to calculate the sum of the discrete eigenvalues of all fault area clustering clusters at the same moment, and obtain the distribution eigenvalue;

[0061] A second fault eigenvalue calculation subunit, configured to calculate the sum of the products of the number of fault area clustering clusters at all moments and the distribution eigenvalue, and perform normalization processing to obtain the second fault eigenvalue.

[0062] The abnormal weather in the weather log includes at least one of rainy weather, windy weather, and thunderstorm weather; the weather anomaly coefficient module includes:

[0063] A rainy weather anomaly coefficient acquisition unit, configured to, for any rainy weather in the weather log, perform normalization processing on the rainfall amount of the any rainy weather to obtain a rainfall anomaly degree value; calculate the product of the reciprocal of the average time interval between the any rainy weather and the adjacent rainy weather before and after, the rainfall duration of the any rainy weather, and the rainfall anomaly degree value, to obtain the rainfall anomaly coefficient of the any rainy weather; calculate the sum of the rainfall anomaly coefficients of all rainy abnormal weather in the weather log, and perform normalization processing to obtain the weather anomaly coefficient of the rainy weather type in a preset period; the reciprocal of the average time interval between the any rainy weather and the adjacent rainy weather before and after is the weather anomaly frequency corresponding to the any rainy weather;

[0064] A windy weather anomaly coefficient acquisition unit, configured to, for any windy weather exceeding a preset wind force level in the weather log, perform normalization processing on the wind force level of the any windy weather to obtain a wind anomaly degree value; calculate the product of the reciprocal of the average time interval between the any windy weather and the adjacent windy weather before and after, the windy duration of the any windy weather, and the wind anomaly degree value, to obtain the windy anomaly coefficient of the any windy weather; calculate the sum of the windy anomaly coefficients of all windy weather in the weather log, and perform normalization processing to obtain the weather anomaly coefficient of the windy weather type in a preset period; the reciprocal of the average time interval between the any windy weather and the adjacent windy weather before and after is the weather anomaly frequency corresponding to the any windy weather;

[0065] A thunderstorm weather anomaly coefficient acquisition unit, which is used for any thunderstorm weather in the weather log that exceeds the preset peak current intensity, normalizes the peak current intensity of the any thunderstorm weather to obtain a thunderstorm anomaly degree value; calculates the reciprocal of the average time interval between the any thunderstorm weather and the adjacent thunderstorm weathers before and after, the thunderstorm duration of the any thunderstorm weather, and the product of the thunderstorm anomaly degree value, to obtain the thunderstorm anomaly coefficient of the any thunderstorm weather; calculates the sum of the thunderstorm anomaly coefficients of all thunderstorm weathers in the weather log, and performs normalization processing to obtain the weather anomaly coefficient of the thunderstorm weather type in the preset time period; the reciprocal of the average time interval between the any thunderstorm weather and the adjacent thunderstorm weathers before and after is the weather anomaly frequency corresponding to the any thunderstorm weather.

[0066] The weather impact degree module includes:

[0067] A fault evaluation sequence construction unit, which is used to construct a fault evaluation sequence according to the fault evaluation coefficients of connected preset time periods;

[0068] A weather anomaly sequence construction unit, which is used to construct a weather anomaly sequence according to the weather anomaly coefficients of the same weather type in connected preset time periods;

[0069] A weather impact degree calculation unit, which is used to calculate the Pearson correlation coefficient of the fault evaluation sequence and the weather anomaly sequence and perform a positive correlation mapping to obtain the weather impact degree of the distribution network.

[0070] The fault outage coefficient module includes:

[0071] A fault outage rate calculation unit, which is used to calculate the ratio of the number of faults of the distribution node in the preset time period to the preset time period to obtain the fault outage rate of the distribution node;

[0072] An average fault outage index calculation unit, which is used to calculate the average value of the fault outage rates of all distribution nodes in the distribution network to obtain an average fault outage index;

[0073] A scaling coefficient calculation unit, which is used to calculate the ratio of the fault outage rate of the overhead line to the distribution node to obtain a scaling coefficient;

[0074] A fault outage rate benchmark calculation unit, which is used to calculate the product of the scaling coefficient and the average fault outage index to obtain the fault outage rate benchmark of the distribution node;

[0075] A first outage eigenvalue calculation unit, which is used to calculate the product of a preset first proportional coefficient and the fault outage rate to obtain a first outage eigenvalue;

[0076] A second outage eigenvalue calculation unit, configured to calculate the product of a preset second proportional coefficient and the failure outage rate benchmark to obtain a second outage eigenvalue;

[0077] A failure outage eigenvalue calculation unit, configured to calculate the sum of the first outage eigenvalue and the second outage eigenvalue to obtain the failure outage eigenvalue of the distribution node;

[0078] A failure outage coefficient calculation unit, configured to calculate the average value of the failure outage eigenvalues of all distribution nodes to obtain the failure outage coefficient of the distribution network.

[0079] The reliability evaluation module includes:

[0080] A first reliability coefficient calculation unit, configured to calculate the sum of the products of the weather impact degrees and the weather anomaly coefficients of all abnormal weather types, and perform a negative correlation mapping to obtain a first reliability coefficient;

[0081] A second reliability coefficient calculation unit, configured to perform a negative correlation mapping on the failure outage coefficient to obtain a second reliability coefficient;

[0082] A reliability calculation unit, configured to calculate the sum of the first reliability coefficient and the second reliability coefficient to obtain the reliability of the distribution network.

[0083] The technical solution adopted by the present invention to solve its technical problems is: to provide an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned distribution network reliability evaluation method are implemented.

[0084] The technical solution adopted by the present invention to solve its technical problems is: to provide a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned distribution network reliability evaluation method are implemented.

[0085] Beneficial effects

[0086] Due to the adoption of the above technical solution, compared with the prior art, the present invention has the following advantages and positive effects: The present invention determines the fault evaluation coefficient of the distribution network according to the fault data of the distribution node, obtains the weather impact degree of the distribution network according to the correlation characteristics of the fault evaluation coefficient and the weather anomaly coefficient of the distribution network, analyzes the fault situation of the distribution network according to the fault degree of the distribution node to obtain the failure outage coefficient, and then combines the weather impact degree, the weather anomaly coefficient and the failure outage coefficient to obtain the reliability of the distribution network, so as to accurately and comprehensively reflect the reliability of the distribution network, making the judgment of the maintenance and operation status of the distribution network more accurate. Description of the drawings

[0087] Figure 1It is the flowchart of the reliability assessment method for the distribution network in the first embodiment of the present invention. Specific Embodiments

[0088] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and not to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of this application.

[0089] The first embodiment of the present invention relates to a reliability assessment method for a distribution network, as Figure 1 shown, specifically including the following steps:

[0090] Step 1, obtain the weather log of the distribution network within a preset time period, as well as the coordinates of the distribution nodes and the historical power consumption status log within the preset time period.

[0091] In this embodiment, the implementation scenario is to comprehensively and multi-dimensionally evaluate the reliability of the distribution network. First, obtain the weather log of the distribution network within a preset time period, the coordinates of the distribution nodes, and the historical power consumption status log within the preset time period; since abnormal weather conditions may cause associated impacts on the lines of the distribution network, the more vulnerable the distribution network is to weather, the lower the reliability means. The weather log records the daily weather types, such as sunny, rainy, and thunderstorm weather, as well as the grade status and duration of each weather type; the historical power consumption status log of the distribution nodes records the time of power failure interruption and the time of restoring power supply. In this embodiment, the preset time period is 30 days, and a weather log and the historical power consumption status logs of different distribution nodes are obtained every 30 days. The implementer can determine the preset time period according to the implementation scenario.

[0092] Step 2, determine the fault evaluation coefficient of the distribution network within the preset time period based on the fault duration, the number of faults, the number of distribution node faults, and the coordinates in the historical power consumption status log of the distribution nodes within the preset time period.

[0093] The power failure interruption of the distribution nodes at individual user ends is usually caused by the line and equipment failures at the user ends, and has little impact on the line and equipment status of the distribution network, and it is difficult to reflect the reliability of the distribution network; while the long-time power failure interruption of a large number of collective distribution nodes has a greater impact on the line and equipment status of the distribution network, and is very likely to be caused by abnormal weather, and is more likely to reflect the reliability of the distribution network.

[0094] Therefore, when evaluating the reliability of a distribution network, in order to be more comprehensive and accurate, it is necessary to consider the fault area range and fault time of distribution nodes. Therefore, in this step, a first fault feature value is obtained based on the fault duration and the number of faults in the historical power consumption status log of the distribution nodes within a preset period.

[0095] In this embodiment, the steps for obtaining the first fault feature value include: First, calculate the sum of the fault durations of all distribution nodes in the distribution network within a preset period to obtain the cumulative fault duration of the distribution network. The longer the cumulative fault duration, the more likely it is that abnormal weather causes faults in the lines and equipment of the distribution network, resulting in power failure interruptions of a large number of distribution nodes, and the more it can reflect the impact of weather conditions on the reliability of the distribution network. The shorter the cumulative fault duration, the fewer distribution nodes have faults, the lower the probability of the distribution network having faults, and the less likely it is affected by weather conditions. Then, calculate the product of the cumulative fault duration and the number of faults of all distribution nodes within a preset period, and perform normalization processing to obtain the first fault feature value. The more the cumulative fault duration and the number of faults, the larger the first fault feature value, the more likely the distribution network is to have faults, the worse the reliability, and the more likely it is affected by weather conditions.

[0096] Furthermore, when there are power failure interruptions of a large number of distribution nodes at the same time, it is more likely that the distribution network has abnormal faults. Therefore, in this step, it is also necessary to cluster according to the number and coordinates of the distribution nodes with faults to obtain different fault area clustering clusters at the same time. In this embodiment, the steps for obtaining the fault area clustering clusters include: When the number of distribution nodes with faults at the same time exceeds a preset number threshold, cluster according to the coordinates of the distribution nodes with faults through the K-means clustering algorithm to obtain different fault area clustering clusters at the same time. The preset number threshold in this embodiment is 20, and the implementer can determine it according to the implementation scenario. It should be noted that K-means belongs to the prior art, and the optimal value of the clustering cluster can be obtained through the existing elbow method, and the specific steps will not be elaborated. The fault area clustering clusters reflect the distribution area characteristics of the distribution nodes with power failure interruptions. The farther the distance between the clustering clusters and the more the number of clustering clusters, the more likely it is that the distribution network has abnormalities, and it is very likely to be caused by weather conditions. Therefore, a second fault feature value can be obtained according to the number characteristics and distance characteristics of the fault area clustering clusters.

[0097] In this embodiment, the second fault eigenvalue acquisition step includes: calculating the average value of the Euclidean distances between the clustering centers of any fault area clustering cluster and other fault area clustering clusters at the same moment to obtain the discrete eigenvalue of the any fault area clustering cluster; calculating the sum of the discrete eigenvalues of all fault area clustering clusters at the same moment to obtain the distribution eigenvalue; when the distribution eigenvalue is larger, it means that the distribution of power distribution nodes with power failure interruptions at the same moment is wider and the area is larger, and it is more likely to be a fault caused by the influence of weather on the power distribution network. Calculate the sum of the products of the number of fault area clustering clusters and the distribution eigenvalue at all moments, and perform normalization processing to obtain the second fault eigenvalue; when the number of power distribution nodes with faults at each moment is larger and the distribution area is wider, the second fault eigenvalue is larger, which means that the reliability of the power distribution network is worse and it is more vulnerable to weather conditions.

[0098] The first fault eigenvalue and the second fault eigenvalue can reflect the fault situation of the power distribution network through the fault characteristics and distribution of the overall power distribution nodes. Therefore, finally, the fault evaluation coefficient of the power distribution network in the preset time period can be obtained according to the first fault eigenvalue and the second fault eigenvalue. In this embodiment, the fault evaluation coefficient acquisition step includes: calculating the sum of the first fault eigenvalue and the second fault eigenvalue, and using the obtained value as the fault evaluation coefficient of the power distribution network in the preset time period; when the fault evaluation coefficient is larger, it means that the fault situation of the power distribution network in the preset time period is more serious. The formula for obtaining the fault evaluation coefficient can be expressed as:

[0099]

[0100] In the formula, R represents the fault evaluation coefficient of the power distribution network in the preset time period, norm represents the normalization function, K represents the number of faults of all power distribution nodes in the preset time period, T represents the cumulative fault duration of the power distribution network, norm(K*T) represents the first fault eigenvalue, I represents the number of clustering moments of the power distribution nodes with faults, H i represents the number of fault area clustering clusters at the i-th moment, represents the discrete eigenvalue of the h-th fault area clustering cluster at the i-th moment, represents the distribution eigenvalue at the i-th moment, represents the second fault eigenvalue.

[0101] Step 3, obtain the weather anomaly coefficient of the preset time period based on the weather anomaly degree, weather anomaly duration, and weather anomaly frequency in the weather log of the power distribution network in the preset time period.

[0102] After analyzing the fault conditions of the distribution network in a preset time period, the weather anomalies in the same time period can be analyzed. According to the correlation characteristics between the weather anomalies and the fault conditions of the distribution network, the degree of weather influence can be analyzed, so as to judge the reliability characteristics of the distribution network. Therefore, the weather anomalies in the preset time period are analyzed first. Therefore, the weather anomaly coefficient of the preset time period is obtained according to the weather anomaly degree, weather anomaly duration, and weather anomaly frequency in the weather log of the distribution network. In this embodiment, the abnormal weather in the weather log includes at least one of rainfall weather, strong wind weather, and thunderstorm weather.

[0103] Since rainfall, strong wind, and thunderstorm weather occur relatively frequently and are extremely likely to damage the lines of the distribution network, in the embodiment of the present invention, rainfall, strong wind, and thunderstorm weather are analyzed. In this embodiment, the abnormal weather in the weather log includes rainfall weather, strong wind weather, and thunderstorm weather. Implementers can add other weather types for analysis according to the implementation scenario.

[0104] For any rainfall weather in the weather log, normalize the rainfall amount of the any rainfall weather to obtain a rainfall anomaly degree value; the greater the rainfall amount, the more severe the rainfall weather, and the greater the possibility of causing faults in the distribution network lines. Calculate the product of the reciprocal of the average time interval between the any rainfall weather and the adjacent rainfall weather before and after, the rainfall duration of the any rainfall weather, and the rainfall anomaly degree value to obtain the rainfall anomaly coefficient of the any rainfall weather. Among them, the reciprocal of the average time interval between the any rainfall weather and the adjacent rainfall weather before and after is the weather anomaly frequency corresponding to the any rainfall weather. The smaller the average time interval, the more frequent the rainfall, and the more likely the lines and equipment are to fail; the longer the rainfall duration and the greater the rainfall anomaly degree value, the more severe the rainfall, and the greater the rainfall anomaly coefficient, which means that this rainfall is more likely to cause faults in the lines and equipment of the distribution network. Calculate the sum of the rainfall anomaly coefficients of all rainfall weather in the weather log and perform normalization processing to obtain the weather anomaly coefficient of the rainfall weather type in the preset time period; the greater the rainfall anomaly coefficient of each rainfall in the preset time period, the greater the weather anomaly coefficient of the rainfall weather type, and the more likely the rainfall weather is to cause faults in the distribution network.

[0105] For any gale weather in the weather log that exceeds the preset wind force level, normalize the wind force level of the any gale weather to obtain a gale anomaly degree value. The larger the gale anomaly degree value, the higher the wind force and the easier it is to break the lines of the distribution network. In this embodiment, the preset wind force level is level 4. When the gale exceeds level 4, it means the wind force is relatively large, and the implementer can determine the preset wind force level according to the implementation scenario. Calculate the product of the reciprocal of the average time interval between the any gale weather and the adjacent gale weathers before and after, the gale duration of the any gale weather, and the gale anomaly degree value to obtain the gale anomaly coefficient of the any gale weather. Among them, the reciprocal of the average time interval between the any gale weather and the adjacent gale weathers before and after is the weather anomaly frequency corresponding to the any gale weather. When the gale anomaly coefficient is larger, it means that this gale weather is more likely to damage the lines in the distribution network. Calculate the sum of the gale anomaly coefficients of all gale weathers in the weather log and perform normalization to obtain the weather anomaly coefficient of the gale weather type in the preset time period. When the weather anomaly coefficient of the gale weather type is larger, it means that the gale weather in this preset time period is more likely to cause line and equipment failures in the distribution network.

[0106] For any thunderstorm weather in the weather log that exceeds the preset peak current intensity, normalize the peak current intensity of the any thunderstorm weather to obtain a thunderstorm anomaly degree value. When the thunderstorm anomaly degree value is larger, it means the thunderstorm is more severe and more likely to cause distribution network failures. In this embodiment, the preset peak current intensity is 10 kA. It should be noted that the peak current intensity is a monitoring index for thunderstorm weather, and the specific monitoring process will not be elaborated here. Calculate the product of the reciprocal of the average time interval between the any thunderstorm weather and the adjacent thunderstorm weathers before and after, the thunderstorm duration of the any thunderstorm weather, and the thunderstorm anomaly degree value to obtain the thunderstorm anomaly coefficient of the any thunderstorm weather. Among them, the reciprocal of the average time interval between the any thunderstorm weather and the adjacent thunderstorm weathers before and after is the weather anomaly frequency corresponding to the any thunderstorm weather. When the thunderstorm anomaly coefficient is larger, it means that the thunderstorm weather is likely to cause distribution network failures. Calculate the sum of the thunderstorm anomaly coefficients of all thunderstorm weathers in the weather log and perform normalization to obtain the weather anomaly coefficient of the thunderstorm weather type in the preset time period. When the weather anomaly coefficient of the thunderstorm weather type is larger, it means that the thunderstorm weather in this preset time period is more likely to cause line and equipment failures in the distribution network.

[0107] Step 4, determine the weather influence degree of the distribution network according to the correlation characteristics between the fault evaluation coefficient and the weather anomaly coefficient.

[0108] After obtaining the fault evaluation coefficient of the distribution network and the weather anomaly coefficient of the weather conditions in the preset period, the correlation characteristics between the two can be analyzed to determine whether the distribution network is vulnerable to weather anomalies. In this embodiment, the steps for obtaining the weather influence degree include: constructing a fault evaluation sequence according to the fault evaluation coefficients of the connected preset periods, and constructing a weather anomaly sequence according to the weather anomaly coefficients of the same weather type in the connected preset periods; for example, for the rainfall weather type, only the weather anomaly sequences of the rainfall weather type in different preset periods are constructed. In this embodiment, the sequences are constructed based on 4 connected preset periods, that is, the lengths of the fault evaluation sequence and the weather anomaly sequence are both 4. Calculate the Pearson correlation coefficient between the fault evaluation sequence and the weather anomaly sequence and perform a positive correlation mapping to obtain the weather influence degree of the distribution network; in this embodiment, the positive correlation mapping is within the range of 0 to 1. It should be noted that the Pearson correlation coefficient belongs to the prior art. When the changes of two variables show a positive correlation, the result is closer to 1. When they show a negative correlation, the result is closer to -1. When there is no correlation, the result tends to 0; therefore, when the weather influence degree is greater, it means that the faults of the distribution network are more likely to be affected by abnormal weather, and the reliability of the distribution network is lower.

[0109] Step 5, obtaining the fault outage coefficient of the distribution network based on the number of faults in the historical power consumption status log of the distribution node in the preset period.

[0110] After analyzing the degree of influence of weather on the distribution network, in order to more comprehensively analyze the reliability of the power supply state of the distribution network, the fault outage coefficient of the distribution network can be obtained according to the fault frequency characteristics in the historical power consumption state log of the distribution nodes. In this embodiment, the steps for obtaining the fault outage coefficient include: calculating the ratio of the number of faults of the distribution node within a preset time period to the preset time period to obtain the fault outage rate of the distribution node; the larger the fault outage rate, the more likely it is that the distribution node will experience an interruption in power consumption due to a fault. Calculate the average value of the fault outage rates of all distribution nodes in the distribution network to obtain the average fault outage index; the larger the fault outage rates of more distribution nodes, the more unstable the power supply of the distribution network and the lower the reliability, and the larger the average fault outage index. Calculate the ratio of the fault outage rate of the overhead line to the distribution node to obtain the scaling coefficient. The overhead line refers to the total power transmission line of the distribution network. When a fault occurs in the overhead line, power consumption interruptions will occur in all the distribution nodes as a whole; therefore, the larger the scaling coefficient, the more likely it is that the power consumption interruption of the distribution node is due to a fault in the power transmission line of the distribution network, and the less reliable the distribution network. The smaller the scaling coefficient, the more likely it is that the distribution node has a fault, which is caused by a fault in the line equipment of the distribution node itself. Calculate the product of the scaling coefficient and the average fault outage index to obtain the fault outage rate benchmark of the distribution node; the larger the fault outage rate benchmark, the lower the reliability of the distribution network and the more likely it is to have a fault. Calculate the product of the preset first proportional coefficient and the fault outage rate to obtain the first outage characteristic value; calculate the product of the preset second proportional coefficient and the fault outage rate benchmark to obtain the second outage characteristic value; in this embodiment, the preset first proportional coefficient is 0.6 and the preset second proportional coefficient is 0.4. Calculate the sum of the first outage characteristic value and the second outage characteristic value to obtain the fault outage characteristic value of the distribution node; the larger the fault outage characteristic value, the more likely it is that the distribution node and the corresponding distribution network will have a fault. Calculate the average value of the fault outage characteristic values of all distribution nodes to obtain the fault outage coefficient of the distribution network; the larger the fault outage coefficient, the more likely it is that the distribution network will have a fault and the lower the reliability

[0111] Step 6. Obtain the reliability of the distribution network according to the weather influence degree, the weather anomaly coefficient, and the fault outage coefficient.

[0112] The weather influence degree reflects the correlation characteristics between weather anomalies and distribution network faults. If the faults of the distribution network are more easily affected by weather anomalies, it means that the reliability of the distribution network is lower; the more abnormal the weather and the larger the weather anomaly coefficient, the greater the possibility of abnormal faults in the distribution network and the lower the reliability; the larger the fault outage coefficient, the more serious the power consumption interruption of the distribution nodes in the distribution network due to abnormal distribution network faults. Therefore, the reliability of the distribution network is obtained according to the weather influence degree, the weather anomaly coefficient, and the fault outage coefficient.

[0113] The steps for obtaining the reliability of the distribution network in this embodiment include: calculating the sum of the products of the weather impact degrees and weather anomaly coefficients of all abnormal weather types and performing a negative correlation mapping to obtain the first reliability coefficient; performing a negative correlation mapping on the fault outage coefficient to obtain the second reliability coefficient, and calculating the sum of the first reliability coefficient and the second reliability coefficient to obtain the reliability of the distribution network. When the first reliability coefficient and the second reliability coefficient are smaller, it means that the faults of the distribution network are more easily affected by weather anomalies, the weather anomaly conditions in the area where the distribution network is located are more serious, and the fault conditions of the distribution nodes caused by the distribution network are more serious. The acquisition of the reliability can be expressed as:

[0114]

[0115] In the formula, W represents the reliability of the distribution network, exp() represents the exponential function with the natural constant as the base, F represents the number of abnormal weather types, G f represents the weather impact degree of the f-th weather type, S f represents the weather anomaly coefficient of the f-th weather type, D represents the fault outage coefficient, represents the first reliability coefficient, and exp(-D) represents the second reliability coefficient.

[0116] After obtaining the reliability of the distribution network, the distribution network can be maintained according to the reliability. When the reliability is lower than the preset reliability threshold, it means that the line and equipment status of the distribution network are poor, the power supply is unstable, and maintenance inspections are required.

[0117] It is not difficult to find that this embodiment comprehensively and accurately analyzes the reliability of the distribution network based on the fault degree of the distribution nodes in the distribution network, the weather conditions, and the correlation characteristics between the distribution network faults and weather anomalies, thereby improving the judgment accuracy of the maintenance and operation status of the distribution network.

[0118] The second embodiment of the present invention relates to a distribution network reliability evaluation device, including:

[0119] An acquisition module, configured to acquire the weather log of the distribution network within a preset time period, as well as the coordinates of the distribution nodes and the historical power consumption status log within the preset time period;

[0120] A fault evaluation coefficient module, configured to determine the fault evaluation coefficient of the distribution network within a preset time period based on the fault duration, fault times, and the number and coordinates of the distribution nodes in the historical power consumption status log of the distribution nodes within the preset time period;

[0121] A weather anomaly coefficient module, configured to obtain the weather anomaly coefficient of the preset time period based on the weather anomaly degree, weather anomaly duration, and weather anomaly frequency in the weather log of the distribution network within the preset time period;

[0122] A weather impact degree module, configured to determine the weather impact degree of a distribution network according to the correlation characteristics between the fault evaluation coefficient and the weather anomaly coefficient;

[0123] A fault outage coefficient module, configured to obtain the fault outage coefficient of a distribution network based on the number of faults in the historical power consumption status log of the distribution nodes within a preset time period;

[0124] A reliability evaluation module, configured to obtain the reliability of a distribution network according to the weather impact degree, the weather anomaly coefficient, and the fault outage coefficient.

[0125] The fault evaluation coefficient module includes:

[0126] A first fault characteristic value unit, configured to obtain a first fault characteristic value based on the fault duration and the number of faults in the historical power consumption status log of the distribution nodes within a preset time period;

[0127] A second fault characteristic value unit, configured to obtain a second fault characteristic value according to the number of faults and the coordinates of the distribution nodes;

[0128] A fault evaluation coefficient calculation unit, configured to calculate the sum of the first fault characteristic value and the second fault characteristic value, and use the obtained value as the fault evaluation coefficient of the distribution network within a preset time period.

[0129] The first fault characteristic value unit includes:

[0130] An accumulated fault duration calculation subunit, configured to calculate the total sum of the fault durations of all distribution nodes in the distribution network within a preset time period to obtain the accumulated fault duration of the distribution network;

[0131] A first fault characteristic value calculation subunit, configured to calculate the product of the accumulated fault duration and the number of faults of all distribution nodes within a preset time period, and perform normalization processing to obtain the first fault characteristic value.

[0132] The second fault characteristic value unit includes:

[0133] A clustering subunit, configured to perform clustering according to the number of faults and the coordinates of the distribution nodes to obtain different fault area clustering clusters at the same moment;

[0134] A distance calculation subunit, configured to calculate the average value of the Euclidean distances between the clustering centers of any fault area clustering cluster and other fault area clustering clusters at the same moment to obtain the discrete characteristic value of the any fault area clustering cluster;

[0135] A distribution characteristic value calculation subunit, configured to calculate the sum of the discrete characteristic values of all fault area clustering clusters at the same moment to obtain the distribution characteristic value;

[0136] The second fault eigenvalue calculation sub-unit is configured to calculate the sum of the products of the number of fault area clustering clusters and the distribution eigenvalues at all times, and perform normalization processing to obtain the second fault eigenvalue.

[0137] The abnormal weather in the weather log includes at least one of rainy weather, windy weather, and thunderstorm weather; the weather anomaly coefficient module includes:

[0138] The rainy weather anomaly coefficient acquisition unit is configured to, for any rainy weather in the weather log, normalize the rainfall of the any rainy weather to obtain a rainfall anomaly degree value; calculate the product of the reciprocal of the average time interval between the any rainy weather and the adjacent rainy weather before and after, the rainfall duration of the any rainy weather, and the rainfall anomaly degree value to obtain the rainfall anomaly coefficient of the any rainy weather; calculate the sum of the rainfall anomaly coefficients of all rainy weather in the weather log, and perform normalization processing to obtain the weather anomaly coefficient of the rainy weather type in a preset period; the reciprocal of the average time interval between the any rainy weather and the adjacent rainy weather before and after is the weather anomaly frequency corresponding to the any rainy weather;

[0139] The windy weather anomaly coefficient acquisition unit is configured to, for any windy weather exceeding a preset wind force level in the weather log, normalize the wind force level of the any windy weather to obtain a windy anomaly degree value; calculate the product of the reciprocal of the average time interval between the any windy weather and the adjacent windy weather before and after, the windy duration of the any windy weather, and the windy anomaly degree value to obtain the windy anomaly coefficient of the any windy weather; calculate the sum of the windy anomaly coefficients of all windy weather in the weather log, and perform normalization processing to obtain the weather anomaly coefficient of the windy weather type in a preset period; the reciprocal of the average time interval between the any windy weather and the adjacent windy weather before and after is the weather anomaly frequency corresponding to the any windy weather;

[0140] The thunderstorm weather anomaly coefficient acquisition unit is configured to, for any thunderstorm weather exceeding a preset peak current intensity in the weather log, normalize the peak current intensity of the any thunderstorm weather to obtain a thunderstorm anomaly degree value; calculate the product of the reciprocal of the average time interval between the any thunderstorm weather and the adjacent thunderstorm weather before and after, the thunderstorm duration of the any thunderstorm weather, and the thunderstorm anomaly degree value to obtain the thunderstorm anomaly coefficient of the any thunderstorm weather; calculate the sum of the thunderstorm anomaly coefficients of all thunderstorm weather in the weather log, and perform normalization processing to obtain the weather anomaly coefficient of the thunderstorm weather type in a preset period; the reciprocal of the average time interval between the any thunderstorm weather and the adjacent thunderstorm weather before and after is the weather anomaly frequency corresponding to the any thunderstorm weather.

[0141] The weather impact degree module includes:

[0142] A fault evaluation sequence construction unit, configured to construct a fault evaluation sequence according to the fault evaluation coefficients in connected preset time periods;

[0143] A weather anomaly sequence construction unit, configured to construct a weather anomaly sequence according to the weather anomaly coefficients of the same weather type in connected preset time periods;

[0144] A weather impact degree calculation unit, configured to calculate the Pearson correlation coefficient between the fault evaluation sequence and the weather anomaly sequence and perform a positive correlation mapping to obtain the weather impact degree of the distribution network.

[0145] The fault outage coefficient module includes:

[0146] A fault outage rate calculation unit, configured to calculate the ratio of the number of faults of the distribution node in a preset time period to the preset time period to obtain the fault outage rate of the distribution node;

[0147] An average fault outage index calculation unit, configured to calculate the average value of the fault outage rates of all distribution nodes in the distribution network to obtain an average fault outage index;

[0148] A scaling coefficient calculation unit, configured to calculate the ratio of the fault outage rate of the overhead line to the distribution node to obtain a scaling coefficient;

[0149] A fault outage rate benchmark calculation unit, configured to calculate the product of the scaling coefficient and the average fault outage index to obtain the fault outage rate benchmark of the distribution node;

[0150] A first outage eigenvalue calculation unit, configured to calculate the product of a preset first proportional coefficient and the fault outage rate to obtain a first outage eigenvalue;

[0151] A second outage eigenvalue calculation unit, configured to calculate the product of a preset second proportional coefficient and the fault outage rate benchmark to obtain a second outage eigenvalue;

[0152] A fault outage eigenvalue calculation unit, configured to calculate the sum of the first outage eigenvalue and the second outage eigenvalue to obtain the fault outage eigenvalue of the distribution node;

[0153] A fault outage coefficient calculation unit, configured to calculate the average value of the fault outage eigenvalues of all distribution nodes to obtain the fault outage coefficient of the distribution network.

[0154] The reliability evaluation module includes:

[0155] The first reliability coefficient calculation unit is configured to calculate the sum of the products of the weather impact degrees and the weather anomaly coefficients of all abnormal weather types, and perform a negative correlation mapping to obtain a first reliability coefficient;

[0156] The second reliability coefficient calculation unit is configured to perform a negative correlation mapping on the fault outage coefficient to obtain a second reliability coefficient;

[0157] The reliability calculation unit is configured to calculate the sum of the first reliability coefficient and the second reliability coefficient to obtain the reliability of the distribution network.

[0158] The third embodiment of the present invention relates to an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the steps of the distribution network reliability evaluation method of the first embodiment are implemented.

[0159] The fourth embodiment of the present invention relates to a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the distribution network reliability evaluation method of the first embodiment are implemented.

[0160] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0161] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as the combination of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0162] These computer program instructions can 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, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction method that implements the process inFigure 1 one process or multiple processes and / or blocks Figure 1 functions specified in one block or multiple blocks.

[0163] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or multiple processes and / or blocks.

[0164] As described above, it is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A distribution network reliability assessment method, characterized in that: The following steps are involved: Obtain the weather log of the distribution network within a preset period of time, as well as the coordinates of the distribution nodes and the historical power consumption status log within the preset period of time; Determine a fault evaluation coefficient of the distribution network in a preset period based on the fault duration, the number of faults, and the number and coordinates of faults of the distribution node in the historical power consumption status log of the distribution node in the preset period; Obtaining a weather anomaly coefficient for a preset period of time based on the degree of weather anomaly, weather anomaly duration, and weather anomaly frequency in the weather log of the distribution network within the preset period of time; Determining the weather impact of the distribution network according to the correlation characteristics of the fault evaluation coefficient and the weather anomaly coefficient; Obtaining a failure outage coefficient of the distribution network based on the number of failures in the historical power consumption status log of the distribution node within a preset period of time; The reliability of the distribution network is obtained according to the weather influence degree, weather anomaly coefficient and fault outage coefficient.

2. The distribution network reliability assessment method according to claim 1, characterized in that: The determining of the fault evaluation coefficient of the distribution network in the preset period based on the fault duration, the number of faults, and the number and coordinates of the faults of the distribution node in the historical power consumption status log of the distribution node in the preset period specifically includes: Obtaining a first fault characteristic value based on the fault duration and the number of faults in the historical power consumption status log of the power distribution node within a preset period of time; Obtaining a second fault characteristic value according to the fault quantity and coordinates of the power distribution node; The sum of the first fault characteristic value and the second fault characteristic value is calculated, and the obtained value is used as the fault evaluation coefficient of the distribution network in a preset time period.

3. The distribution network reliability assessment method according to claim 2, characterized in that: The first fault characteristic value is obtained based on the fault duration and the number of faults in the historical power consumption status log of the power distribution node within a preset period of time, specifically: Calculate the sum of the fault durations of all distribution nodes in the distribution network within a preset period of time to obtain the cumulative fault duration of the distribution network; The product of the accumulated fault duration and the number of faults of all power distribution nodes within a preset time period is calculated, and normalized to obtain a first fault characteristic value.

4. The distribution network reliability assessment method according to claim 2, characterized in that: The second fault characteristic value is obtained according to the fault quantity and coordinates of the power distribution node, specifically: Cluster the faults of the distribution nodes according to their number and coordinates to obtain clusters of different fault areas at the same time. Calculate the average value of the Euclidean distance between the cluster centers of any fault region cluster and other fault region clusters at the same time to obtain the discrete characteristic value of the any fault region cluster; Calculate the sum of the discrete eigenvalues ​​of all fault area clusters at the same time to obtain the distribution eigenvalue; The sum of the products of the number of fault region clusters and the distribution characteristic value at all times is calculated and normalized to obtain the second fault characteristic value.

5. The distribution network reliability assessment method according to claim 1, characterized in that: The abnormal weather in the weather log includes at least one of rainy weather, strong wind weather and thunderstorm weather; the weather anomaly coefficient for the preset period of time obtained based on the weather anomaly degree, weather anomaly duration and weather anomaly frequency in the weather log of the distribution network within the preset period of time specifically includes: For any rainfall weather in the weather log, the rainfall amount of any rainfall weather is normalized to obtain a rainfall anomaly degree value; the reciprocal of the average time interval between any rainfall weather and the preceding and following adjacent rainfall weathers, the product of the rainfall duration of any rainfall weather and the rainfall anomaly degree value are calculated to obtain a rainfall anomaly coefficient of any rainfall weather; the sum of the rainfall anomaly coefficients of all rainfall weathers in the weather log is calculated and normalized to obtain a weather anomaly coefficient of the rainfall weather type in a preset time period; the reciprocal of the average time interval between any rainfall weather and the preceding and following adjacent rainfall weathers is the weather anomaly frequency corresponding to any rainfall weather; For any strong wind weather exceeding the preset wind force level in the weather log, the wind force level of any strong wind weather is normalized to obtain a strong wind anomaly degree value; the reciprocal of the average time interval between any one strong wind weather and the adjacent strong wind weather before and after, the product of the strong wind duration of any one strong wind weather and the strong wind anomaly degree value is calculated to obtain a strong wind anomaly coefficient of any one strong wind weather; the sum of the strong wind anomaly coefficients of all strong wind weathers in the weather log is calculated and normalized to obtain a weather anomaly coefficient of the strong wind weather type in the preset time period; the reciprocal of the average time interval between any one strong wind weather and the adjacent strong wind weather before and after is the weather anomaly frequency corresponding to any one strong wind weather; For any thunderstorm in the weather log that exceeds a preset peak current intensity, the peak current intensity of the any thunderstorm is normalized to obtain a thunderstorm anomaly degree value; the reciprocal of the average time interval between the any thunderstorm and the preceding and following adjacent thunderstorms, the product of the thunderstorm duration of the any thunderstorm and the thunderstorm anomaly degree value are calculated to obtain the thunderstorm anomaly coefficient of the any thunderstorm; the sum of the thunderstorm anomaly coefficients of all thunderstorms in the weather log is calculated and normalized to obtain the weather anomaly coefficient of the thunderstorm type in the preset time period; the reciprocal of the average time interval between the any thunderstorm and the preceding and following adjacent thunderstorms is the weather anomaly frequency corresponding to the any thunderstorm.

6. The distribution network reliability assessment method according to claim 1, characterized in that: The step of determining the weather influence of the distribution network according to the correlation characteristics between the fault evaluation coefficient and the weather anomaly coefficient is specifically as follows: A fault evaluation sequence is constructed according to the fault evaluation coefficients of the connected preset time periods, and a weather anomaly sequence is constructed according to the weather anomaly coefficients of the same weather type in the connected preset time periods; The Pearson correlation coefficient of the fault evaluation sequence and the weather anomaly sequence is calculated and positive correlation mapping is performed to obtain the weather influence degree of the distribution network.

7. The distribution network reliability assessment method according to claim 1, characterized in that: The obtaining of the failure outage coefficient of the distribution network based on the number of failures in the historical power consumption status log of the distribution node within a preset period of time specifically includes: Calculating the ratio of the number of failures of the power distribution node within a preset period to the preset period to obtain the failure outage rate of the power distribution node; Calculate the average fault outage rate of all distribution nodes in the distribution network to obtain the average fault outage index; Calculate the ratio of the failure outage rate of the overhead line and the distribution node to obtain the scaling factor; Calculating the product of the scaling factor and the average failure outage index to obtain a failure outage rate benchmark of the power distribution node; Calculating the product of a preset first proportional coefficient and the fault outage rate to obtain a first outage characteristic value; Calculate the product of a preset second proportional coefficient and the fault outage rate benchmark to obtain a second outage characteristic value; Calculating the sum of the first outage characteristic value and the second outage characteristic value to obtain a fault outage characteristic value of the power distribution node; The average value of the fault outage characteristic values ​​of all distribution nodes is calculated to obtain the fault outage coefficient of the distribution network.

8. The distribution network reliability assessment method according to claim 1, characterized in that: The obtaining of the reliability of the distribution network according to the weather impact degree, the weather anomaly coefficient and the fault outage coefficient specifically includes: Calculating the sum of the products of the weather impact degree and the weather anomaly coefficient of all abnormal weather types, and performing negative correlation mapping to obtain a first reliability coefficient; Performing negative correlation mapping on the failure outage coefficient to obtain a second reliability coefficient; The sum of the first reliability coefficient and the second reliability coefficient is calculated to obtain the reliability of the distribution network.

9. A distribution network reliability assessment device, characterized in that: include: An acquisition module is used to acquire the weather log of the distribution network within a preset period of time, as well as the coordinates of the distribution nodes and the historical power consumption status log within the preset period of time; A fault evaluation coefficient module is used to determine the fault evaluation coefficient of the distribution network in a preset period of time based on the fault duration, the number of faults, and the number and coordinates of faults of the distribution node in the historical power consumption status log of the distribution node in the preset period of time; A weather anomaly coefficient module, used to obtain a weather anomaly coefficient for a preset period of time based on the weather anomaly degree, weather anomaly duration and weather anomaly frequency in the weather log of the distribution network within the preset period of time; A weather impact module, used to determine the weather impact of the distribution network according to the correlation characteristics of the fault evaluation coefficient and the weather anomaly coefficient; A fault outage coefficient module, used to obtain a fault outage coefficient of the distribution network based on the number of faults in the historical power consumption status log of the distribution node within a preset time period; The reliability evaluation module is used to obtain the reliability of the distribution network according to the weather impact degree, weather anomaly coefficient and fault outage coefficient.

10. The distribution network reliability assessment device according to claim 9, characterized in that: The fault evaluation coefficient module includes: A first fault characteristic value unit, configured to obtain a first fault characteristic value based on a fault duration and a number of faults in a historical power consumption state log of the power distribution node within a preset period of time; A second fault characteristic value unit is used to obtain a second fault characteristic value according to the number and coordinates of the faults of the distribution node; a fault evaluation coefficient calculation unit is used to calculate the sum of the first fault characteristic value and the second fault characteristic value, and use the obtained value as the fault evaluation coefficient of the distribution network in a preset time period.

11. The distribution network reliability assessment device according to claim 10, characterized in that: The first fault characteristic value unit includes: The accumulated fault duration calculation subunit is used to calculate the sum of the fault durations of all distribution nodes in the distribution network within a preset period of time to obtain the accumulated fault duration of the distribution network; The first fault characteristic value calculation subunit is used to calculate the product of the accumulated fault duration and the number of faults of all distribution nodes within a preset time period, and perform normalization processing to obtain a first fault characteristic value.

12. The distribution network reliability assessment device according to claim 10, characterized in that: The second fault characteristic value unit includes: The clustering subunit is used to cluster the fault quantity and coordinates of the distribution nodes to obtain different fault area clusters at the same time; A distance calculation subunit, used to calculate the average value of the Euclidean distance between any fault region cluster and the cluster centers of other fault region clusters at the same time, to obtain the discrete characteristic value of the any fault region cluster; The distribution characteristic value calculation subunit is used to calculate the sum of the discrete characteristic values ​​of all fault area clusters at the same time to obtain the distribution characteristic value; The second fault characteristic value calculation subunit is used to calculate the sum of the products of the number of fault region clusters and the distribution characteristic value at all times, and perform normalization processing to obtain the second fault characteristic value.

13. The distribution network reliability assessment device according to claim 9, characterized in that: The abnormal weather in the weather log includes at least one of rainy weather, strong wind weather and thunderstorm weather; the weather anomaly coefficient module includes: a rainy weather anomaly coefficient acquisition unit, which is used to normalize the rainfall of any rainy weather in the weather log to obtain a rain anomaly degree value; calculate the reciprocal of the average time interval between any rainy weather and the adjacent rainy weather before and after, the product of the rainfall duration of any rainy weather and the rain anomaly degree value to obtain the rain anomaly coefficient of any rainy weather; calculate the sum of the rain anomaly coefficients of all rain anomaly weathers in the weather log, and perform normalization to obtain the weather anomaly coefficient of the rainy weather type in a preset time period; the reciprocal of the average time interval between any rainy weather and the adjacent rainy weather before and after is the weather anomaly frequency corresponding to the any rainy weather; A strong wind weather anomaly coefficient acquisition unit is used to normalize the wind force level of any strong wind weather in the weather log that exceeds a preset wind force level to obtain a strong wind anomaly degree value; calculate the reciprocal of the average time interval between any one strong wind weather and the adjacent strong wind weather before and after, the product of the strong wind duration of any one strong wind weather and the strong wind anomaly degree value to obtain the strong wind anomaly coefficient of any one strong wind weather; calculate the sum of the strong wind anomaly coefficients of all strong wind weathers in the weather log, and perform normalization processing to obtain the weather anomaly coefficient of the strong wind weather type in the preset time period; the reciprocal of the average time interval between any one strong wind weather and the adjacent strong wind weather before and after is the weather anomaly frequency corresponding to the any one strong wind weather; The thunderstorm weather anomaly coefficient acquisition unit is used to normalize the peak current intensity of any thunderstorm weather exceeding a preset peak current intensity in the weather log to obtain a thunderstorm anomaly degree value; calculate the reciprocal of the average time interval between any thunderstorm weather and the preceding and following adjacent thunderstorm weathers, the product of the thunderstorm duration of any thunderstorm weather and the thunderstorm anomaly degree value to obtain the thunderstorm anomaly coefficient of any thunderstorm weather; calculate the sum of the thunderstorm anomaly coefficients of all thunderstorm weathers in the weather log, and perform normalization processing to obtain the weather anomaly coefficient of the thunderstorm weather type in a preset time period; the reciprocal of the average time interval between any thunderstorm weather and the preceding and following adjacent thunderstorm weathers is the weather anomaly frequency corresponding to the any thunderstorm weather.

14. The distribution network reliability assessment device according to claim 9, characterized in that: The weather impact module includes: a fault evaluation sequence construction unit, which is used to construct a fault evaluation sequence according to the fault evaluation coefficients of the connected preset time periods; a weather anomaly sequence construction unit, which is used to construct a weather anomaly sequence according to the weather anomaly coefficients of the same weather type in the connected preset time periods; The weather impact calculation unit is used to calculate the Pearson correlation coefficient of the fault evaluation sequence and the weather anomaly sequence and perform positive correlation mapping to obtain the weather impact of the distribution network.

15. The distribution network reliability assessment device according to claim 9, characterized in that: The failure outage coefficient module includes: A failure outage rate calculation unit, used to calculate the ratio of the number of failures of the power distribution node within a preset period to the preset period, and obtain the failure outage rate of the power distribution node; The average failure outage index calculation unit is used to calculate the average failure outage rate of all distribution nodes in the distribution network to obtain the average failure outage index; A scaling factor calculation unit is used to calculate the ratio of the fault outage rate of the overhead line and the distribution node to obtain the scaling factor; A fault outage rate benchmark calculation unit, used to calculate the product of the scaling factor and the average fault outage index to obtain a fault outage rate benchmark of the power distribution node; A first outage characteristic value calculation unit, used for calculating the product of a preset first proportional coefficient and the fault outage rate to obtain a first outage characteristic value; A second outage characteristic value calculation unit, used to calculate the product of a preset second proportional coefficient and the fault outage rate reference to obtain a second outage characteristic value; a fault outage characteristic value calculation unit, configured to calculate the sum of the first outage characteristic value and the second outage characteristic value to obtain the fault outage characteristic value of the power distribution node; The fault outage coefficient calculation unit is used to calculate the average value of the fault outage characteristic values ​​of all distribution nodes to obtain the fault outage coefficient of the distribution network.

16. The distribution network reliability assessment device according to claim 9, characterized in that: The reliability assessment module includes: A first reliability coefficient calculation unit, used to calculate the sum of the products of the weather impact degree and the weather anomaly coefficient of all abnormal weather types, and perform negative correlation mapping to obtain a first reliability coefficient; A second reliability coefficient calculation unit, used for performing negative correlation mapping on the failure outage coefficient to obtain a second reliability coefficient; The reliability calculation unit is used to calculate the sum of the first reliability coefficient and the second reliability coefficient to obtain the reliability of the distribution network.

17. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the distribution network reliability assessment method as described in any one of claims 1-8 are implemented.

18. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the distribution network reliability assessment method as claimed in any one of claims 1 to 8 are implemented.

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