Branch line node latent hidden danger identification method based on over-dead-zone change

Abstract

The invention discloses a branch line node latent hidden danger identification method based on dead zone change, which is applied to the technical field of power grid hidden danger identification, and comprises the following steps: obtaining voltage and current measurement data; obtaining a dead-zone-crossing change set based on a comparison result of current change in the voltage and current measurement data and a dynamic dead-zone threshold value; obtaining a first positioning result based on the voltage fluctuation correlation and a correlation coefficient of the over-dead-zone change set; acquiring a sudden drop event in a preset time period according to the first positioning result, and matching a second positioning result associated with the fluctuation time sequence; and obtaining a risk position according to the second positioning result, obtaining a hidden danger risk value in combination with a preset risk dimension, obtaining an inspection tour path and an inspection tour time period, and obtaining a latent hidden danger position according to the inspection tour path and the inspection tour time period. According to the method, data optimization is carried out through over-dead-zone change, the data volume of subsequent overall comparison is reduced, and meanwhile, the hidden danger recognition accuracy is improved through mutual verification of current fluctuation and voltage fluctuation.

Description

Technical Field

[0001] The present application relates to the technical field of power grid hidden danger identification, and in particular to a method for identifying potential hidden dangers of branch line nodes based on dead zone changes. Background Art

[0002] During the operation of the low-voltage substation, various nodes of the branch lines will inevitably have potential hidden dangers such as large current heating, oxidation and loosening. These usually occur at line clamps, intermediate joints of the lines, and various terminal overlap points. As the node resistance increases, users often experience a rapid drop in voltage due to resistance drop after using electricity, and heat leads to further oxidation. Long-term development leads to frequent power outages and the unusability of some electrical appliances, which seriously affects normal electricity use and may even cause joint burning or even fire, affecting safety and production.

[0003] In related technologies, the transformer-to-user voltage drop method is usually used to identify abnormal increases in resistance. However, since the existing low-voltage ledger lacks line parameters, user phase identification and other identifications, the low-voltage data only includes user meters and substation total meter data, and there is no low-voltage line measurement data, which makes it impossible to identify most substations, making it difficult to detect potential hidden dangers.

[0004] The patent "Line Insulation Hidden Danger Monitoring Method and System Based on Multi-Parameter Analysis", publication number: CN119107075A, publication date: December 10, 2024, specifically discloses the real-time acquisition of risk characteristic information of each busbar or / and branch line in the distribution network automation monitoring network according to the risk stress mechanism; extracting the risk factor in the risk characteristic information, and determining the risk level of the risk characteristic information according to the risk factor combined with the risk value; determining the hidden danger point according to the risk level and its corresponding positioning strategy, and formulating corresponding hidden danger disposal measures. Although this solution improves the accuracy of hidden danger monitoring, it requires overall data for calculation, which is inefficient.

[0005] The patent "Method, device, equipment and storage medium for identifying hidden danger signals of transmission lines", publication number: CN119397306A, publication date: February 7, 2025, specifically discloses: using a signal acquisition terminal to obtain the discharge current signal to be identified of the transmission line; the signal acquisition terminal is distributed and arranged on the transmission line according to preset intervals; key feature parameters are obtained according to the discharge current signal to be identified, and a multidimensional feature vector is obtained according to the key feature parameters; the distance between the multidimensional feature vector and each initial clustering center is obtained; the initial clustering center is obtained based on the clustering analysis of the discharge current signal of the historical fault hidden danger; the initial clustering center corresponding to the minimum distance among the distances is used as the target initial clustering center, the hidden danger type corresponding to the target initial clustering center is obtained, and the hidden danger type is determined as the hidden danger identification result of the discharge current signal to be identified. This scheme uses clustering for hidden danger identification. Although it improves the efficiency of hidden danger identification, the clustering algorithm is prone to the problem of clustering points that are off-center being ignored, and the accuracy is low. Summary of the invention

[0006] This application aims to solve the problem that the existing technology cannot take into account both the accuracy and efficiency of identifying potential hidden dangers in the power grid. It provides a method for identifying potential hidden dangers in branch line nodes based on dead zone changes. It uses the negative correlation between current and voltage caused by potential hidden dangers to construct a set of dead zone changes, preliminarily screens users with possible hidden dangers, reduces the amount of data for subsequent user comparisons, and improves identification efficiency. At the same time, it uses the similarity of voltage fluctuations between users for secondary verification, and further locates the location of potential hidden dangers based on the correlation between branches between users. It matches the patrol time with the time of sudden drop to ensure that the patrol is consistent with the actual power consumption at the time of sudden drop, improves the accuracy of patrol, and improves the efficiency of latent hidden danger investigation on the basis of ensuring the accuracy of latent hidden danger identification.

[0007] To achieve the above-mentioned technical objectives, a technical solution provided in the present application is a method for identifying potential hidden dangers at branch line nodes based on cross-dead zone changes, comprising the following steps: when the user transformer and the substation transformer meet a preset voltage relationship, obtaining voltage and current measurement data; obtaining a cross-dead zone change set based on the comparison result of the current change in the voltage and current measurement data with the dynamic dead zone threshold; obtaining a first positioning result based on the current and voltage fluctuation correlation coefficient; obtaining a sudden drop event in a preset time period according to the first positioning result, and matching a second positioning result associated with the fluctuation time series according to the sudden drop moment of the sudden drop event; obtaining a risk position according to the second positioning result, and obtaining a hidden danger risk value based on the risk position and the preset risk dimension; obtaining a patrol path and patrol time period according to the hidden danger risk value and the voltage sudden drop time period, obtaining a patrol result based on the patrol path and patrol time period, and obtaining the position of the potential hidden danger based on the patrol result.

[0008] Furthermore, the method of obtaining the dead zone change set based on the comparison result of the current change in the voltage and current measurement data with the dynamic dead zone threshold includes: iteratively executing multiple comparison batches according to the voltage and current measurement data and the dynamic dead zone threshold, wherein the following processing is performed in each comparison batch: the current change value is obtained in sequence according to the measurement point order based on the benchmark data of the current comparison batch and the measurement point data set to be compared; when the current change value is greater than the dynamic dead zone threshold, the current comparison batch is terminated, and the latest compared measurement point data is included in the dead zone change set and used as the benchmark data for the next comparison batch; and the remaining uncompared measurement point data is included in the measurement point data set to be compared of the next comparison batch.

[0009] Further, after the voltage and current measurement data are obtained, the following steps are performed: if the current data of the measurement point in the voltage and current measurement data does not exceed the small current threshold, the current data of the measurement point is updated according to the current neglect value.

[0010] Furthermore, the method of obtaining the first positioning result based on the voltage fluctuation correlation and the correlation coefficient of the cross-dead zone change set includes: obtaining a correlation threshold based on the voltage fluctuation correlation; calculating the voltage-current correlation coefficient based on the test point voltage data and the test point current data in the cross-dead zone change set; if the voltage-current correlation coefficient is less than the correlation threshold, including the user in the first positioning result.

[0011] Furthermore, the method of obtaining the sudden drop events in a preset time period according to the first positioning result and matching the second positioning result associated with the fluctuation time series according to the sudden drop moment of the sudden drop event includes: obtaining the sudden drop events of all users in the first positioning result in the preset time period; obtaining the sudden drop associated data of adjacent continuous time series segments according to the sudden drop moment of the sudden drop event; and obtaining the second positioning result according to the matching result of the sudden drop associated data with the voltage data of other users in the corresponding substation.

[0012] Furthermore, the method of obtaining a second positioning result based on the matching result of the sag associated data with the voltage and current data of other users in the corresponding substation includes: calculating the voltage difference of the measurement point voltage under the same timing according to the timing matching of the sag associated data with the voltage data of other users in the corresponding substation; if the voltage difference is less than or equal to a preset voltage difference threshold, the measurement point is recorded as a similar measurement point; if the voltage difference is greater than the preset voltage difference threshold, the measurement point is recorded as a dissimilar measurement point; if the proportion of similar measurement points is greater than the proportion threshold, it is considered that the voltage and current data of the user match the sag associated data, and the user is included in the second positioning result.

[0013] Furthermore, obtaining the risk position according to the second positioning result includes: if the second positioning result is an empty set, taking the rear side of the single user branch as the risk position; if the second positioning result is not an empty set, obtaining the overlapping user branch according to the first positioning result and the second positioning result, and taking the overlapping user branch as the risk position.

[0014] Furthermore, the preset risk dimensions include: voltage-current correlation coefficient, voltage difference between measuring points before and after a sag event, theoretical resistance, theoretical resistance constant relationship, theoretical resistance increase, number of sag events, power outage records, minimum voltage sag value, and theoretical power; after obtaining the risk position according to the second positioning result, execute: based on the comparison results of the voltage-current correlation coefficient, theoretical resistance constant relationship, theoretical power and preset terminal threshold in the preset risk dimension, determine whether the user belongs to an end user; if so, output a line modification prompt; if not, obtain a hidden danger risk value based on the risk position and the preset risk dimension.

[0015] Furthermore, the method of obtaining the inspection path and the inspection period according to the hidden danger risk value and the voltage sag period includes: obtaining the voltage sag period corresponding to the hidden danger risk value according to the sag moment of the sag event; obtaining the inspection priority and the inspection period of the hidden danger point according to the voltage sag period and the hidden danger risk value, and obtaining the inspection path according to the inspection priority and the inspection period.

[0016] Furthermore, it also includes: obtaining user current data within a preset period, selecting a preset number of large current data according to the current size; and obtaining a dynamic dead zone threshold according to the current average value and fluctuation coefficient of the large current data.

[0017] The beneficial effects of the present application are as follows: 1. By optimizing the user's voltage and current measurement data after a voltage sag occurs, the current dead zone change is obtained, and the current dead zone change is used to show the abnormal current change of the user with a voltage sag when the other voltage jitter interference is excluded. The abnormal current change is used as the basis for judging whether there may be potential hidden dangers on the corresponding line of the user. And according to whether there are other users with similar sag conditions as the user in the preset time period, the branch line where the potential hidden danger exists is judged, and this is used as the risk position. According to the preset risk dimension, the hidden danger risk value of all risk positions is counted, so as to facilitate the subsequent patrol range planning, ensure the patrol efficiency and patrol timeliness. At the same time, the patrol period is obtained according to the time when the user's sudden drop event occurs, which is convenient for using infrared temperature measurement and other tools to locate faults that cannot be identified by the naked eye, avoid invalid patrols or electricity impact on users, and determine the location of potential hidden dangers according to the patrol results. On the basis of ensuring the accuracy of the identification of the location of potential hidden dangers, the patrol targeting and accuracy are effectively improved, and the user's electricity experience is improved.

[0018] 2. According to the substation corresponding to the user, obtain the voltage and current data of the other users in the substation in the corresponding adjacent continuous time periods, and screen whether there are users with similar fluctuations based on the sudden drop correlation data and the voltage and current data, and use this as the second positioning result. Through the similarity of voltage fluctuations between users in the substation, further check for users with abnormal voltage fluctuations, realize secondary investigation, improve the accuracy of the investigation, reduce the scope of potential hidden dangers, and improve the accuracy and efficiency of subsequent patrol route planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a flow chart of a method for identifying potential hazards at branch line nodes based on dead zone changes in this application. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of the present application more clear, the present application is further described in detail below in conjunction with the drawings and examples. It should be understood that the specific implementation method described here is only an optimal embodiment of the present application, which is only used to explain the present application and does not limit the scope of protection of the present application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0021] like Figure 1 As shown, as the first embodiment of the present application, the method for identifying potential hidden dangers of branch line nodes based on the change of dead zone includes the following steps: When the user transformer and the substation transformer meet the preset voltage relationship, the voltage and current measurement data are obtained; Obtaining a dead zone change set based on a comparison result of a current change in the voltage and current measurement data with a dynamic dead zone threshold; Acquire a first positioning result based on the voltage fluctuation correlation and the correlation coefficient of the dead zone change set; Acquire a sudden drop event in a preset time period according to the first positioning result, and match a second positioning result associated with the fluctuation time series according to the sudden drop moment of the sudden drop event; Obtaining a risk location according to the second positioning result, and obtaining a hidden danger risk value based on the risk location and a preset risk dimension; According to the hidden danger risk value and the voltage sag period, the inspection path and inspection period are obtained, the inspection result is obtained based on the inspection path and inspection period, and the potential hidden danger position is obtained based on the inspection result.

[0022] In this embodiment, when the user transformer and the transformer in the substation meet the preset voltage relationship, it is considered that a voltage sag event has occurred in the user. By optimizing the user voltage and current measurement data after the voltage sag occurs, the current dead zone change is obtained. The current dead zone change shows the abnormal current change of the user with voltage sag when exceeding the interference of other voltage jitters. The abnormal current change is used as the basis for judging whether there may be potential hidden dangers on the corresponding line of the user. And according to whether there are other users with similar sag conditions as the user in the preset time period, the branch line where the potential hidden danger exists is judged, and this is used as the risk position. According to the preset risk dimension, the hidden danger risk value of all risk positions is counted, so as to facilitate the subsequent patrol range planning, ensure the patrol efficiency and patrol timeliness. At the same time, the patrol period is obtained according to the time when the user's sag event occurs, to avoid invalid patrols or the impact on the user's electricity consumption, and the potential hidden danger position is determined according to the patrol results. On the basis of ensuring the accuracy of the identification of the potential hidden danger position, the patrol targeting and accuracy are effectively improved, and the user's electricity consumption experience is improved.

[0023] When there is a voltage difference between the user and the transformer in the substation, it is considered that the user has a voltage sag. At this time, in response to the voltage sag of the user with a voltage difference between the user and the transformer in the substation, the voltage and current measurement data of the user are obtained.

[0024] Specifically, the method for identifying potential hidden dangers of branch line nodes based on the dead zone change also includes: Obtain the voltage drop at the user's continuous time series measurement point, and the difference between the voltage at the measurement point and the lowest phase voltage of the total meter; If the voltage drop at the user's continuous measurement point is greater than the preset sag threshold and the difference between the measurement point voltage and the total meter's lowest phase voltage is greater than the preset sag threshold, it is considered that the user's transformer and the substation transformer meet the preset voltage relationship.

[0025] When the voltage drop between the two measurement points before and after the user is greater than the preset sag threshold and the voltage difference between the voltage at the rear measurement point and the lowest phase voltage of the total meter is greater than the preset sag threshold, it is considered that the user transformer and the substation transformer meet the preset voltage relationship, that is, the user has a voltage sag. At this time, in response to the voltage sag, the voltage and current measurement data of the user after the sag is obtained. In this embodiment, the preset sag threshold is 5V. In response to the voltage sag, the voltage measurement data and current measurement data of the user at 96 time points after the sag are obtained. It can be understood that the preset sag threshold and the number of voltage and current data collection points can be set according to actual conditions to adapt to the nature of different users.

[0026] The dead zone change set is obtained based on the comparison result of the current change in the voltage and current measurement data with the dynamic dead zone threshold, including: Multiple comparison batches are iteratively performed based on the voltage and current measurement data and the dynamic dead zone threshold, wherein the following processing is performed in each comparison batch: The current change value is obtained in sequence according to the benchmark data of the current comparison batch and the data set of the measurement points to be compared in the measurement point order; When the current change value is greater than the dynamic dead zone threshold, the current comparison batch is terminated, and the latest compared measurement point data is included in the dead zone change set and used as the benchmark data for the next comparison batch; The remaining unmatched measurement point data are included in the measurement point data set to be compared in the next comparison batch.

[0027] In this embodiment, the first measurement point data in the voltage and current measurement data is used as the reference data of the first comparison batch, and all measurement point data except the first measurement point data are used as the measurement point data set to be compared in the first comparison batch. The current difference between the first measurement point data and each measurement point data to be compared is calculated in the measurement point order as the current change value, and the current change value is compared with the dynamic dead zone threshold until the current change value is greater than the dynamic dead zone threshold, and the calculation of the current change value is stopped, and the latest compared measurement point data is included in the dead zone change set, and the latest compared measurement point data is used as the reference data of the next comparison batch, and the remaining uncompared measurement point data is used as the measurement point data set to be compared in the next comparison batch, and the calculation is performed cyclically until the measurement point data set to be compared is an empty set. It can be understood that when any measurement point data and the reference data perform the current change value calculation and the calculated current change value has been compared with the dynamic dead zone threshold, it is considered that the measurement point data is converted from the measurement point data to be compared to the compared measurement point data.

[0028] If there is a first measurement point C1 in the voltage and current measurement data, C1 is included in the dead zone change set, and the current I1 of C1 is used as the reference data. The search is carried out backward according to the measurement point timing. If the current I2 of the measurement point C2 is not greater than the current change value of I1 than the dynamic dead zone threshold, the current I3 of the measurement point C3 is retrieved for comparison. Until the current change value of any measurement point is greater than the dynamic dead zone threshold compared to I1, the current of the measurement point is used as the reference data for a new search, and the search is continued.

[0029] In this embodiment, the current change value is: ; in, Indicates the current change value, represents the current at the nth measurement point, Indicates benchmark data.

[0030] In this embodiment, the dynamic dead zone threshold is 30%. Since the voltage will fluctuate with the voltage at the transformer head end, the voltage cannot always remain unchanged. Therefore, the dynamic dead zone threshold is set to calculate the voltage-current correlation coefficient when the current changes greatly, and to exclude the slight current changes caused by other factors, so as to avoid misidentification of potential hidden dangers. When the dead zone variation set is included, the current and voltage correlation coefficients are calculated. When , the dead zone change set is not included, and the current-voltage correlation coefficient calculation is not performed.

[0031] In order to eliminate the situation where the current fluctuation of small-load users exceeds the change value but does not actually cause the voltage drop change, and is affected by the voltage fluctuation and is incorrectly judged, the current change value is obtained by iteratively updating the benchmark data, so as to adapt to the electricity consumption of small-load users in different time periods, avoid the current change caused by the change in electricity consumption from being mistakenly identified as a potential hidden danger, and improve the accuracy of identifying potential hidden dangers.

[0032] In actual situations, the dynamic dead zone threshold can be set according to the voltage fluctuation conditions and environmental factors of the actual substation, and the adaptability of the dynamic dead zone threshold can be improved to improve the accuracy of the final latent hidden danger identification.

[0033] During the application process, there may be a point where the current is less than the small current threshold, which is a small current caused by zero drift. Zero drift refers to zero drift, which is the output offset of the measuring instrument when there is no input signal. Zero drift is usually caused by factors such as temperature change, component aging, or power supply fluctuations, causing the measured value to fluctuate around the zero point. Even if the actual current is zero, the measured value may show a small current value. At this time, after obtaining the voltage and current measurement data, execute: If the current data of the measurement point in the voltage and current measurement data does not exceed the small current threshold, the current data of the measurement point is updated according to the current neglect value.

[0034] The voltage and current measurement data include the voltage data of the measurement point and the current data of the measurement point. When the current data of the measurement point is less than the small current threshold, it is considered that the current data of the measurement point is caused by zero drift, and the current ignore value is used to replace the measurement point data. Thereby, the fluctuations caused by zero drift are uniformly treated as current ignore values, and in the subsequent comparison process with the dynamic dead zone threshold, the current ignore value is ignored, so that the noise can be effectively filtered out, avoiding the mistaken inclusion of noise as an effective load, and reducing the impact of equipment noise on the identification of potential hidden dangers. In this embodiment, the small current threshold is 0.05A, and the current ignore value is 0.05A. It can be understood that in the remaining embodiments, the small current threshold and the current ignore value can also be set according to the zero drift that may occur in the actual power equipment.

[0035] In other cases, after acquiring the voltage and current measurement data, perform: If the current data of the measurement point in the voltage and current measurement data does not exceed the small current threshold, the current neglect value is obtained according to the dynamic dead zone threshold, and the current data of the measurement point is updated with the current neglect value.

[0036] In this case, the current ignore value is set according to the current dynamic dead zone threshold to ensure that the measurement point voltage data and measurement point current data with too small current data will not be included in the dead zone change set in subsequent calculations, and to ensure that the data in the dead zone change set are data with abnormal current change amplitude, thereby improving the accuracy of identifying potential hidden dangers.

[0037] In this embodiment, the error data in the voltage and current measurement data is optimized by setting a preset compensation current, thereby avoiding affecting the subsequent identification of potential hidden dangers while retaining the current fluctuation anomaly that may occur when zero drift occurs, avoiding direct screening of the current fluctuation anomaly caused by equipment noise, and using data optimization to further improve the accuracy of current anomaly identification.

[0038] Acquiring a first positioning result based on the voltage fluctuation correlation and the correlation coefficient of the dead zone change set includes: Obtaining a correlation threshold based on voltage fluctuation correlation; Calculate the voltage-current correlation coefficient based on the test point voltage data and the test point current data in the dead zone variation set; If the voltage-current correlation coefficient is less than the correlation threshold, the user is included in the first positioning result.

[0039] In the power system, the contact resistance of the contact parts such as the wire clamp, line joint, and terminal overlap point increases abnormally due to oxidation or looseness, forming local heating points, which may cause equipment failure or even fire. According to the voltage-dividing effect, when the contact resistance increases abnormally, the current will generate additional voltage drop through the contact resistance when the user uses electricity, resulting in a voltage drop at the user end, and the current and voltage are inversely correlated. When the user does not use electricity or the current remains stable, the voltage fluctuates irregularly due to grid fluctuations or load changes and is not included in the voltage-current correlation calculation. In the previous process, the current changes in the dead zone, and the measurement point data whose current changes exceed the threshold are included in the dead zone change set, thereby eliminating the influence of voltage fluctuations on the correlation calculation when the current does not change significantly. In this process, the correlation coefficient of the current changes in the dead zone, that is, the voltage-current correlation coefficient, is introduced to further distinguish between normal voltage fluctuations and abnormal voltage division caused by hidden dangers. The correlation threshold can be set based on the voltage fluctuation correlation according to expert experience. For example, in this embodiment, the correlation threshold is -0.3.

[0040] Specifically, the voltage-current correlation coefficient is used to measure the strength and direction of the linear relationship between voltage and current, and the range is [-1,1]. When the voltage-current correlation coefficient is expressed as a negative number, it means that the voltage and current are negatively correlated. When the voltage-current correlation coefficient is expressed as a positive number, it means that the voltage and current are positively correlated. When the voltage-current correlation coefficient is expressed as 0, it means that the voltage and current are not linearly correlated. In this embodiment, the correlation threshold value of -0.3 is obtained through research experience on the correlation of voltage fluctuations. It can not only screen out users with a strong negative correlation between voltage and current, but also avoid judgment interference caused by data errors at some moments. Users with a strong negative correlation as a whole are included in the first positioning result, and are defined as users with a sudden drop in voltage and current negative correlation, to ensure recognition accuracy, and obtain the impact range of potential hidden dangers based on the first positioning result, so as to facilitate further identification of potential hidden dangers in the future.

[0041] Specifically, the voltage-current correlation coefficient is calculated according to the test point voltage data and the test point current data in the dead zone change set, and the normalized value of the voltage-current correlation coefficient is obtained using the corr() function of Python. The normalized value of the voltage-current correlation coefficient is compared with the correlation threshold. When the normalized value of the voltage-current correlation coefficient is less than the correlation threshold, the user is included in the first positioning result.

[0042] In this embodiment, a sudden drop event in a preset period is obtained according to the first positioning result, and a second positioning result associated with matching the fluctuation time series according to the sudden drop moment of the sudden drop event includes: Obtaining sudden drop events of all users in the first positioning result in a preset time period; Acquire the sudden drop correlation data of adjacent continuous time series segments according to the sudden drop moment of the sudden drop event; The second positioning result is obtained according to the matching result of the sudden drop associated data and the voltage data of other users in the corresponding substation area.

[0043] For any user in the first positioning result, obtain the sudden drop event in the preset time period, and obtain the sudden drop associated data based on the measurement point data contained in the adjacent continuous time period of the sudden drop event. According to the substation corresponding to the user, obtain the voltage data of the remaining users in the substation in the corresponding adjacent continuous time period, and screen whether there are users with similar voltage fluctuations based on the sudden drop associated data and voltage data, and use this as the second positioning result. Through the similarity of voltage fluctuations between users in the substation, further check for users with abnormal voltage fluctuations, realize secondary investigation, improve the accuracy of the investigation, reduce the scope of potential hidden dangers, and improve the accuracy and efficiency of subsequent patrol route planning. It can be understood that the preset time period and adjacent continuous time segments can be set according to actual conditions.

[0044] In this embodiment, all sudden drop events of the user in the past X days in the first positioning result are obtained, recorded as SJ1-SJn, and the previous measurement point time of each sudden drop event is Get the corresponding front measurement point voltage , for each post-sag event, the time at which Get the corresponding post-measurement point voltage . Get the other users TJYH in the area , The voltage at the moment , The voltage difference is calculated, and the other user TJYH with similar voltage fluctuation is taken as the same drop user and included in the second positioning result.

[0045] Specifically, obtaining the second positioning result according to the matching result of the sudden drop associated data and the voltage data of other users in the corresponding substation area includes: According to the time sequence matching sag associated data and the voltage data of other users in the corresponding substation, the voltage difference of the voltage at the measuring point under the same time sequence is calculated; If the voltage difference is less than or equal to the preset voltage difference threshold, the measurement point is recorded as a similar measurement point; if the voltage difference is greater than the preset voltage difference threshold, the measurement point is recorded as a dissimilar measurement point; If the proportion of similar measurement points is greater than the proportion threshold, it is considered that the user voltage data matches the sudden drop associated data, and the user is included in the second positioning result.

[0046] Users in the same substation may experience abnormal voltage fluctuations at the same time due to common factors of the power grid. Therefore, when a sudden drop occurs to any user, if the voltage changes of other users in the same substation are similar, it means that there may be a common source of voltage fluctuations, that is, the potential hidden danger is located on the common line between users, thereby achieving further location of the potential hidden danger.

[0047] In this embodiment, the preset voltage difference threshold is 3V and the ratio threshold is 90%. , The voltage at the measuring point at the moment satisfies: and ; Then record the measurement point as a similar measurement point and calculate: ; in, express The voltage at the measuring point at the time, express The voltage at the measuring point at the time, represents the number of similar measurement points, represents the number of dissimilar measurement points, Indicates the similarity of voltage fluctuation between the voltage data of other users and the sudden drop users.

[0048] When a user exists , the user is considered to be a same-drop user and is included in the second positioning result.

[0049] In this embodiment, the spatiotemporal data of different users are associated and mutual verification is performed between users, which can effectively filter out errors in the data of a single user, enhance the robustness of the results, reduce false alarms or omissions of problems, and improve the accuracy of identifying potential hidden dangers. At the same time, there is no need to check each user one by one. The abnormal current change of a single user can be obtained by judging the change across the dead zone, and the abnormal voltage change of the entire substation can be obtained at a macro level through the group voltage synchronization, thereby reducing the amount of calculation data for the overall judgment and improving efficiency.

[0050] It is understandable that, in practical applications, a more suitable voltage difference threshold and ratio threshold may be set according to the load characteristics of the substation area, such as the load characteristics of an industrial area or a residential area.

[0051] Obtaining the risk location based on the second positioning result includes: If the second positioning result is an empty set, the rear side of the single-user branch is taken as the risk location; If the second positioning result is not an empty set, the overlapping user branch is obtained according to the first positioning result and the second positioning result, and the overlapping user branch is used as the risk position.

[0052] When there is a second positioning result, that is, there is a user who has the same voltage fluctuation as the sudden drop user in the first positioning result, then the potential hidden danger that causes the fluctuation is more likely to exist on the common branch line of the two. Therefore, if the second positioning result is an empty set, the potential hidden danger is located at the back of the single-user branch, such as the meter joint, the terminal in the meter box, the middle joint of the line, or the piercing wire clamp of the household connection line. If the second positioning result is not an empty set, the topology is used to locate the multi-user branch common to the sudden drop user and the same drop user, such as the multi-user branch clamp, the middle joint of the line between the multi-user branch and the first sudden drop / same drop user branch.

[0053] Then, after the risk location is determined, the hidden danger risk value is obtained according to the risk location and the preset risk dimension. The preset risk dimension includes: Voltage-current correlation coefficient, voltage difference between measurement points before and after a sag event, theoretical resistance, theoretical resistance constant relationship, theoretical resistance increase, number of sag events, power outage records, minimum voltage sag value, and theoretical power.

[0054] The voltage-current correlation coefficient shows the abnormal size of the contact resistance. The greater the abnormal increase in the contact resistance, the greater the hidden danger and the higher the priority for troubleshooting.

[0055] The voltage difference between the measurement points before and after the sudden drop event shows the stability of the grid voltage. The larger the voltage difference, the greater the voltage fluctuation or instability problem in the grid, and the higher the troubleshooting priority.

[0056] The theoretical resistance is calculated based on the ratio of the current voltage difference to the current current difference to reflect the abnormal change value of the resistance of the current power grid equipment. The larger the abnormal change value, the higher the troubleshooting priority.

[0057] The theoretical resistance constant relationship is calculated based on the fluctuation degree of the ratio of voltage difference to current difference at each time sequence to reflect the constancy of the theoretical resistance. The greater the fluctuation degree, the more unstable the theoretical resistance is and the higher the troubleshooting priority is.

[0058] The theoretical resistance increase is obtained based on the changing trend of the ratio of the voltage difference to the current difference at each time sequence to reflect the severity of the latent hidden dangers. The larger the theoretical resistance increase, the higher the troubleshooting priority.

[0059] The number of sag events shows how often users are affected by grid voltage instability. The greater the number of sag events, the higher the instability and the higher the troubleshooting priority.

[0060] The power outage record shows the number and duration of power outages for users. Once a power outage occurs, it means that the potential hidden dangers are serious and have a higher priority for investigation.

[0061] The minimum voltage sag value indicates the lowest value to which the voltage drops during a sag event. The minimum low voltage value reflects the extreme situation of the grid voltage. The smaller the minimum voltage sag value, the higher the troubleshooting priority.

[0062] The theoretical power is calculated based on the product of the voltage difference and the current. It is used as a reverse indicator. The greater the power, the lower the risk value and the lower the troubleshooting priority.

[0063] According to the importance of each dimension, the corresponding weight is assigned, such as the voltage and current correlation coefficient as the main important weight, and the power outage record as the secondary important weight. According to the experience of experts, the weight coefficients of each dimension are assigned in turn to calculate the risk value of the hidden dangers at the risk location. By comprehensively considering multiple dimensions, the risk value of potential hidden dangers can be more comprehensively evaluated, the accuracy of risk assessment can be improved, and the planning of subsequent patrol routes can be facilitated to ensure that potential hidden dangers with higher risks have higher priority for investigation.

[0064] In other embodiments, the weight coefficient of each dimension may be calculated through historical power grid data, and the weight coefficient of each dimension may be allocated according to the proportion of each dimension when historical power failures occur, thereby improving the accuracy of the comprehensive evaluation.

[0065] At this time, the hidden danger risk value R is: ; in, represents the weight coefficient of the voltage-current correlation coefficient, represents the voltage-current correlation coefficient; Represents the weight coefficient of the voltage difference at the measurement point before and after the sag event, Indicates the voltage difference between the measurement points before and after the sag event; represents the theoretical resistance weight coefficient, represents theoretical resistance; Represents the weight coefficient of the theoretical resistance constant relationship, Indicates the theoretical resistance constant relationship; Represents the theoretical resistance amplification weight coefficient, Indicates the theoretical resistance increase; represents the weight coefficient of the number of sudden drop events, Indicates the number of sag events; represents the power outage record weight coefficient, Indicates power outage record; Indicates the weight coefficient of the minimum voltage sag, Indicates the minimum value of voltage sag; represents the theoretical power weight coefficient, represents the theoretical power. .

[0066] In this embodiment, considering that the power supply radius of some areas is too long, the wire diameter is too thin, and the ledger is not precise, the voltage difference Risk impairment is performed when the current is >200w. That is, if the power of 200w is concentrated on an oxidation point or loose point of a certain joint or wire clamp, the insulating shell of this point is generally burned. Therefore, the fluctuation factor is generally the impedance loss of the long line. Therefore, the theoretical power is used as the reverse indicator for impairment processing to avoid misidentification of potential risks caused by long line impedance loss.

[0067] In some other embodiments, the method for identifying potential hidden dangers of branch line nodes based on the dead zone change further includes: after obtaining the risk position according to the second positioning result, executing: Based on the comparison results of the voltage-current correlation coefficient, theoretical resistance constant relationship, theoretical power and preset terminal threshold in the preset risk dimension, it is judged whether the user is an end user. If so, a line modification prompt is output; if not, the hidden danger risk value is obtained based on the risk location and the preset risk dimension.

[0068] Since the power supply radius and line parameters may not meet user needs, the user's voltage-current correlation coefficient, theoretical resistance constant relationship and theoretical power are calculated and compared with the preset terminal threshold to determine whether the abnormal current change is caused by head oxidation and contact problems caused by the mismatch between the power supply radius and line parameters, further improving the accuracy of identifying the source of abnormal power consumption of users.

[0069] At this time, the preset terminal threshold at least includes a constant resistance terminal threshold, a voltage-current correlation coefficient terminal threshold, and a theoretical power terminal threshold. In this embodiment, the constant resistance terminal threshold is 0.8, the voltage-current correlation coefficient terminal threshold is -0.8, and the theoretical power terminal threshold is 200W. If the voltage-current correlation coefficient is less than the voltage-current correlation coefficient terminal threshold, the theoretical constant resistance is greater than the constant resistance terminal threshold, and the theoretical power is greater than the theoretical power terminal threshold, then the user is an end user.

[0070] In the embodiment of the present application, if the theoretical resistance constant relationship is greater than the resistance constant end threshold (the standard deviation normalized value is greater than 0.8), the voltage and current correlation coefficients show a strong negative correlation (less than -0.8), and the theoretical power is greater than the theoretical power end threshold (200W), then the user is defined as an end user, that is, the power supply radius and line parameters are not compatible, indicating that such users are non-node problem users and the lines need to be modified.

[0071] Obtaining the inspection path and inspection period based on the hidden danger risk value and voltage sag period includes: Obtain the voltage sag period corresponding to the hidden danger risk value according to the sag moment of the sag event; The inspection priority and inspection period of the hidden danger point are obtained according to the voltage sag period and the hidden danger risk value, and the inspection path is obtained according to the inspection priority and inspection period.

[0072] In this embodiment, the possible time point of the sudden drop, i.e., the voltage drop period, is obtained according to the moment when the user's sudden drop event occurs, and the patrol path planning is performed according to the voltage drop period and the patrol priority of the hidden danger point. For example, in the infrared temperature measurement patrol, it is necessary to patrol at night. The voltage drop period of user A is 19:00-20:00, and the voltage drop period of user B is 19:30-21:00. The hidden danger risk values ​​of users A and B are the same. Then, the patrol path planning is performed according to the risk position of user A patrolled at 19:00 and the risk position of user B patrolled at 20:00. In the case where the hidden danger risk values ​​are different, the hidden danger risk values ​​and the time period compliance are comprehensively considered, and the user risk position of the most recent voltage drop period and the highest hidden danger risk value is used as the position point for earlier patrol as much as possible. It can be understood that the latent hidden danger identification performed by this application is carried out when the user's electricity consumption is still not affected too much, and when it has affected the user's electricity consumption, such as when the hidden danger has caused the user to lose power, it belongs to the scope of direct on-site investigation.

[0073] Then, the inspection results are obtained based on the inspection path and inspection period. According to the inspection results, if there are obvious signs of heat and oxidation on the appearance of the equipment (terminal discoloration, black-green oxidation of the connector, melting of the cable outer insulation, cable bulging, etc.), infrared temperature measurement at high load times can find abnormal heat points and confirm the location of potential hidden dangers. This improves the inspection efficiency while ensuring the accuracy of the inspection points.

[0074] As a second embodiment of the present application, the method for identifying potential hidden dangers of branch line nodes based on the change of dead zone also includes: The user current data within a preset period is obtained to calculate the current average value, and the dynamic dead zone threshold is obtained according to the current average value.

[0075] In this embodiment, due to the differences in users' electricity consumption habits, a fixed dead zone threshold may cause incompatibility in the dead zone change screening. Therefore, the dynamic dead zone threshold is set according to the average current of the corresponding user in a preset period, thereby ensuring that the dynamic dead zone threshold adapts to the actual user habits of each user.

[0076] Specifically, obtaining user current data within a preset period to calculate the current average value, and obtaining the dynamic dead zone threshold according to the current average value includes: Obtain user current data within a preset period, and select a preset number of large current data according to the current size; The dynamic dead zone threshold is obtained according to the current average value and fluctuation coefficient of the large current data.

[0077] In this embodiment, the preset period is 1 week, the preset number is 10, and the fluctuation coefficient is 0.1. The 10 largest current data of the user within 1 week are selected, the current average value is calculated, and the dynamic dead zone threshold is obtained according to the product of the current average value and the fluctuation coefficient. The dynamic dead zone threshold is calculated according to the maximum load of the user when using electricity, so as to avoid misjudgment caused by different user loads and improve the accuracy of the dynamic dead zone threshold.

[0078] In other cases, obtaining the dead zone change set based on the comparison result of the current change in the voltage and current measurement data with the dynamic dead zone threshold value includes: Selecting a threshold variation coefficient and an initial dead zone threshold according to a preset dead zone threshold range, and obtaining a dynamic dead zone threshold set according to the initial dead zone threshold and the threshold variation coefficient; The comparison results of the current change and the dynamic dead zone threshold are obtained in sequence according to the dynamic dead zone threshold in the dynamic dead zone threshold set, until the comparison results of consecutive dynamic dead zone thresholds are the same, and the dead zone change set is obtained according to the same comparison results.

[0079] In this case, the corresponding threshold variation coefficient and the initial dead zone threshold are obtained according to the characteristics of the substation, such as the impact of the user's power load fluctuation on the current, and the initial dead zone threshold is used as the first dynamic dead zone threshold to obtain the first comparison result, and the initial dead zone threshold is updated according to the threshold variation coefficient, and the dead zone threshold after the first update is used as the second dynamic dead zone threshold to obtain the second comparison result. If the first comparison result and the second comparison result are consistent, the dynamic update of the dead zone threshold is terminated, and the dead zone change set is obtained with the current comparison result. If they are inconsistent, the dynamic update of the dead zone threshold continues until the comparison result is no longer updated, thereby ensuring the accuracy of the final dead zone change set. The preset dead zone threshold range can be 10%-50%.

[0080] In this embodiment, since it may be disturbed by voltage fluctuations, such as the impact of distributed photovoltaic power generation, upper power supply voltage changes and three-phase imbalance on voltage, the impact of distributed photovoltaic power generation, upper power supply voltage changes and three-phase imbalance on voltage is quantified, and the correlation threshold is set according to the actual impact at different times. At this time, obtaining the correlation threshold based on the voltage fluctuation correlation includes: Obtain historical power grid data, and obtain the influence relationship of total meter voltage fluctuation based on the correlation between distributed photovoltaic data, upper power supply voltage data, three-phase unbalanced data and total meter voltage fluctuation in the historical power grid data; Obtain the correlation between fluctuation and hidden dangers based on the voltage fluctuation of the total meter and the potential hidden danger data in the historical power grid data; Obtain the voltage fluctuation correlation based on the influence relationship of the total meter voltage fluctuation and the correlation between the fluctuation and hidden dangers; Obtain distributed photovoltaic data, upper power supply voltage data, and three-phase imbalance data in the current power grid data, and obtain a correlation threshold according to the voltage fluctuation correlation and the distributed photovoltaic data, upper power supply voltage data, and three-phase imbalance data in the current power grid data.

[0081] In this embodiment, the correlation threshold is obtained based on the influence of distributed photovoltaic data, upper power supply voltage data, and three-phase imbalance data on the total meter voltage fluctuation, and the correlation between the influence of the total meter voltage fluctuation in historical data and the potential hidden danger data, to ensure the adaptability of the correlation threshold. The potential hidden danger data at least includes voltage and current data when there are potential hidden dangers. According to the standard deviation of the historical total meter voltage fluctuation and the minimum value of the voltage and current correlation coefficient in the potential hidden danger data, when the total meter voltage fluctuates, it is determined whether there are other influencing factors based on the standard deviation, and the correlation threshold is adjusted. For example, when the standard deviation of the voltage fluctuation is large, the correlation threshold is lowered, and the reduction range is calculated based on the historical matching situation. Specifically, the adjusted correlation threshold can be obtained according to the following method: 1. According to the distributed photovoltaic data, the upper power supply voltage data, the three-phase unbalanced data change values ​​in the historical power grid data and the corresponding total meter voltage fluctuations in the historical data, the correlation A1 between the distributed photovoltaic data change and the total meter voltage fluctuation, the correlation A2 between the upper power supply voltage data change and the total meter voltage fluctuation, and the correlation A3 between the three-phase unbalanced data change and the total meter voltage fluctuation are obtained. According to the current power grid data, the distributed photovoltaic data a1, the upper power supply voltage data a2, and the three-phase unbalanced data a3 are obtained, and the total meter voltage fluctuation value caused by the current distributed photovoltaic data, the upper power supply voltage data, and the three-phase unbalanced data is calculated: ; in, Indicates the total meter voltage fluctuation value caused by the current distributed photovoltaic data, upper power supply voltage data, and three-phase unbalanced data; 2. Calculate the total meter voltage standard deviation based on the total meter voltage fluctuation value calculated in step 1 and the total meter voltage fluctuation average value: ; in, represents the standard deviation of the total meter voltage, Indicates the total voltage fluctuation value of the total meter. Indicates the voltage fluctuation value of the first total meter. Indicates the voltage fluctuation value of the i-1th total meter, Indicates the average value of total meter voltage fluctuation; 3. Calculate the voltage and current correlation coefficient in the potential hidden danger data based on the voltage and current data at the time when the potential hidden danger exists in the historical power grid data (the current measurement data when the potential hidden danger is confirmed to exist): ; in, represents the voltage-current correlation coefficient, Indicates the average voltage at the time of potential hidden danger. Indicates the average current value at the time of potential hidden danger. Indicates the total number of sampling points at the time of potential hidden dangers, represents the current measurement data of the tth sampling point, Represents the voltage measurement data of the tth sampling point; 4. According to the voltage-current correlation coefficient calculated in step 3, obtain the voltage-current correlation coefficient set when the total meter voltage standard deviation is the same, and take the minimum voltage-current correlation coefficient in the set and the total meter voltage standard deviation to construct the correlation B1 between fluctuation and hidden danger, that is: ; in, represents the standard deviation of the jth total meter voltage, represents all voltage and current correlation coefficients corresponding to the jth total meter voltage standard deviation, Indicates a mapping relationship; 5. According to the total meter voltage standard deviation calculated in step 2 and the mapping relationship in B1 calculated in step 4, obtain the correlation threshold matching the total meter voltage standard deviation: ; in, represents the correlation threshold, Represents a mapping function. According to the mapping function, the corresponding correlation threshold is retrieved in real time according to the correlation B1 between the fluctuation and the hidden danger.

[0082] In other cases, the method for identifying potential hidden dangers of branch line nodes based on the change of dead zone also includes: When the correlation threshold changes, the dynamic dead zone threshold is synchronously adjusted according to the threshold change coefficient.

[0083] When the standard deviation of voltage fluctuation is large, it means that the voltage data is greatly affected by other factors such as the inclusion of distributed photovoltaic power generation. Therefore, the correlation threshold is lowered, and the dynamic dead zone threshold is increased simultaneously to improve the accuracy of excluding other factors, thereby improving the accuracy of subsequent potential hidden danger identification.

[0084] The present application determines the measurement points required for comparison through the change of current across the dead zone, and accurately discovers users with potential hidden dangers of oxidation and looseness at wire clamps, intermediate joints of lines, and various terminal overlap points through its inverse correlation. The hidden dangers are located through voltage sags and voltage drops, making it convenient for maintenance personnel to discover potential hidden dangers in advance through appearance and infrared inspections, thereby improving the accuracy of identifying and locating potential hidden dangers. At the same time, the user's electricity consumption data is screened once through the change across the dead zone, reducing the amount of data for subsequent overall macro comparisons and improving efficiency.

[0085] The specific implementation method described above is a preferred implementation method of the method for identifying potential hidden dangers of branch line nodes based on changes across dead zones in this application, and is not intended to limit the specific implementation scope of this application. The scope of this application includes but is not limited to this specific implementation method. All equivalent changes made in accordance with the shape and structure of this application are within the protection scope of this application.

Claims

1. A method for identifying potential hidden dangers of branch line nodes based on dead zone changes, characterized by: The steps include: When the user transformer and the substation transformer meet the preset voltage relationship, the voltage and current measurement data are obtained; Obtaining a dead zone change set based on a comparison result of a current change in the voltage and current measurement data with a dynamic dead zone threshold; Acquire a first positioning result based on the voltage fluctuation correlation and the correlation coefficient of the dead zone change set; Acquire a sudden drop event in a preset time period according to the first positioning result, and match a second positioning result associated with the fluctuation time series according to the sudden drop moment of the sudden drop event; Obtaining a risk location according to the second positioning result, and obtaining a hidden danger risk value based on the risk location and a preset risk dimension; According to the hidden danger risk value and the voltage sag period, the inspection path and inspection period are obtained, the inspection result is obtained based on the inspection path and inspection period, and the potential hidden danger position is obtained based on the inspection result.

2. The method for identifying potential hidden dangers of branch line nodes based on dead zone changes according to claim 1, characterized in that: The step of obtaining a dead zone change set based on a comparison result of a current change in the voltage and current measurement data with a dynamic dead zone threshold comprises: Multiple comparison batches are iteratively performed based on the voltage and current measurement data and the dynamic dead zone threshold, wherein the following processing is performed in each comparison batch: The current change value is obtained in sequence according to the benchmark data of the current comparison batch and the data set of the measurement points to be compared in the measurement point order; When the current change value is greater than the dynamic dead zone threshold, the current comparison batch is terminated, and the latest compared measurement point data is included in the dead zone change set and used as the benchmark data for the next comparison batch; The remaining unmatched measurement point data are included in the measurement point data set to be compared in the next comparison batch.

3. The method for identifying potential hidden dangers of branch line nodes based on dead zone changes according to claim 1, characterized in that: After obtaining the voltage and current measurement data, execute: If the current data of the measurement point in the voltage and current measurement data does not exceed the small current threshold, the current data of the measurement point is updated according to the current neglect value.

4. The method for identifying potential hidden dangers of branch line nodes based on dead zone changes according to claim 1, characterized in that: The obtaining of the first positioning result based on the voltage fluctuation correlation and the correlation coefficient of the dead zone change set includes: Obtaining a correlation threshold based on voltage fluctuation correlation; Calculate the voltage-current correlation coefficient based on the test point voltage data and the test point current data in the dead zone variation set; If the voltage-current correlation coefficient is less than the correlation threshold, the user is included in the first positioning result.

5. The method for identifying potential hidden dangers of branch line nodes based on dead zone changes according to claim 1, characterized in that: The method of acquiring a sudden drop event in a preset period according to the first positioning result and matching a second positioning result associated with the fluctuation time series according to the sudden drop moment of the sudden drop event includes: Obtaining sudden drop events of all users in the first positioning result in a preset time period; Acquire the sudden drop correlation data of adjacent continuous time series segments according to the sudden drop moment of the sudden drop event; The second positioning result is obtained according to the matching result of the sudden drop associated data and the voltage data of other users in the corresponding substation area.

6. The method for identifying potential hidden dangers of branch line nodes based on dead zone changes according to claim 5, characterized in that: The obtaining of the second positioning result according to the matching result between the sudden drop associated data and the voltage data of other users in the corresponding substation area comprises: According to the time sequence matching sag associated data and the voltage data of other users in the corresponding substation, the voltage difference of the voltage at the measuring point under the same time sequence is calculated; If the voltage difference is less than or equal to the preset voltage difference threshold, the measurement point is recorded as a similar measurement point; if the voltage difference is greater than the preset voltage difference threshold, the measurement point is recorded as a dissimilar measurement point; If the proportion of similar measurement points is greater than the proportion threshold, it is considered that the user voltage data matches the sudden drop associated data, and the user is included in the second positioning result.

7. The method for identifying potential hidden dangers of branch line nodes based on dead zone changes according to claim 1, characterized in that: The acquiring the risk position according to the second positioning result comprises: If the second positioning result is an empty set, the rear side of the single-user branch is taken as the risk location; If the second positioning result is not an empty set, the overlapping user branch is obtained according to the first positioning result and the second positioning result, and the overlapping user branch is used as the risk position.

8. The method for identifying potential hidden dangers of branch line nodes based on dead zone changes according to claim 1, characterized in that: The preset risk dimensions include: Voltage-current correlation coefficient, voltage difference between measurement points before and after a sag event, theoretical resistance, theoretical resistance constant relationship, theoretical resistance increase, number of sag events, power outage records, minimum voltage sag value, and theoretical power; After obtaining the risk position according to the second positioning result, executing: Based on the comparison results of the voltage-current correlation coefficient, theoretical resistance constant relationship, theoretical power and preset terminal threshold in the preset risk dimension, it is judged whether the user is an end user. If so, a line modification prompt is output; if not, the hidden danger risk value is obtained based on the risk location and the preset risk dimension.

9. The method for identifying potential hidden dangers of branch line nodes based on dead zone changes according to claim 1, characterized in that: The obtaining of the patrol path and patrol period according to the hidden danger risk value and the voltage sag period includes: Obtain the voltage sag period corresponding to the hidden danger risk value according to the sag moment of the sag event; The inspection priority and inspection period of the hidden danger point are obtained according to the voltage sag period and the hidden danger risk value, and the inspection path is obtained according to the inspection priority and inspection period.

10. The method for identifying potential hidden dangers of branch line nodes based on dead zone changes according to claim 1, characterized in that: Also includes: Obtain user current data within a preset period, and select a preset number of large current data according to the current size; The dynamic dead zone threshold is obtained according to the current average value and fluctuation coefficient of the large current data.

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