Latent hazard identification method for branch line nodes based on dead zone crossing change

Abstract

The present application discloses a method for identifying potential hidden dangers of branch line nodes based on dead zone crossing changes, which is applied to the technical field of power grid hidden danger identification. The method includes the following steps: obtaining voltage and current measurement data; obtaining a dead zone crossing change set based on the comparison result between the current change in the voltage and current measurement data and the dynamic dead zone threshold; obtaining a first positioning result based on the voltage fluctuation correlation and the correlation coefficient of the dead zone crossing change set; 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 sequence; obtaining a risk location according to the second positioning result, obtaining a hidden danger risk value by combining a preset risk dimension, obtaining a patrol path and a patrol time period, and obtaining the location of potential hidden dangers based on the patrol path and the patrol time period. The present application optimizes data through dead zone crossing changes, reduces the amount of data for subsequent overall comparison, and improves the accuracy of hidden danger identification by using the mutual verification of current fluctuations and voltage fluctuations.

Description

Technical Field

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

[0002] During the operation of low-voltage power distribution areas, various nodes on branch lines are prone to potential hidden dangers such as large current heating, oxidation, and loosening. These usually occur at wire clamps, intermediate joints of lines, and various terminal connection points. As the node resistance increases, users often experience a rapid voltage drop due to resistance voltage drop after power consumption, and the heating leads to further oxidation. In the long run, it causes frequent power outages and the inability to use some electrical appliances, seriously affecting normal power consumption, and even triggering the burning of joints or even fires, which affects safety and production.

[0003] In related technologies, the voltage drop method from the transformer to the user is usually used to identify abnormal increases in resistance. However, due to the lack of identification of line parameters and user phase types in the existing low-voltage account books, and the low-voltage data only includes user meter and substation total meter data, without low-voltage line measurement data, it is impossible to identify in 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 obtaining risk characteristic information of each bus and / or branch line in the distribution network automation monitoring network in real time according to the risk stress mechanism; extracting risk factors in the risk characteristic information, and determining the risk level of the risk characteristic information according to the risk factors combined with the risk value degree; 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 and has low efficiency.

[0005] Patent "Method, Device, Equipment and Storage Medium for Identifying Hidden Danger Signals in Transmission Lines", Publication Number: CN119397306A, Publication Date: February 7, 2025, specifically discloses: obtaining the discharge current signal to be identified of the transmission line by using a signal acquisition terminal; the signal acquisition terminals are distributed on the transmission line according to a preset interval; obtaining key characteristic parameters based on the discharge current signal to be identified, and obtaining a multi-dimensional feature vector based on the key characteristic parameters; obtaining the distances between the multi-dimensional feature vector and each initial clustering center; the initial clustering centers are obtained through clustering analysis of historical fault hidden danger discharge current signals; taking the initial clustering center corresponding to the minimum distance among the distances as the target initial clustering center, obtaining the hidden danger type corresponding to the target initial clustering center, and determining the hidden danger type as the hidden danger identification result of the discharge current signal to be identified. This solution uses clustering for hidden danger identification. Although it improves the efficiency of hidden danger identification, the clustering algorithm is prone to the problem that the clustering points deviating from the center are ignored, and the accuracy is relatively low. Summary of the Invention

[0006] In view of the problem in the prior art that it is impossible to balance the accuracy and efficiency of identifying latent hidden dangers in the power grid, the present application provides a method for identifying latent hidden dangers at branch line nodes based on the change across the dead zone. By using the negative correlation between current and voltage caused by latent hidden dangers to construct a set of changes across the dead zone, users with possible hidden dangers are preliminarily screened, reducing the amount of data for subsequent user comparison and improving the identification efficiency. At the same time, the similarity of voltage fluctuations between users is used for secondary verification, and the location of latent hidden dangers is further located based on the correlation of branch lines between users. The moment of sudden drop is used to match the inspection time, ensuring that the actual power consumption situation at the moment of sudden drop is met during the inspection, improving the accuracy of the inspection, and improving the efficiency of latent hidden danger investigation on the basis of ensuring the accuracy of latent hidden danger identification.

[0007] To achieve the above technical objectives, a technical solution provided by the present application is a method for identifying latent hidden dangers at branch line nodes based on the change across the dead zone, including the following steps: when the user transformer and the substation transformer satisfy the preset voltage relationship, obtaining voltage and current measurement data; obtaining a set of changes across the dead zone based on the comparison result between the current change in the voltage and current measurement data and the dynamic dead zone threshold; obtaining a first positioning result based on the current-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 sequence 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; obtaining an inspection path and an inspection time period according to the hidden danger risk value and the voltage sudden drop time period, obtaining an inspection result based on the inspection path and the inspection time period, and obtaining the location of latent hidden dangers based on the inspection result.

[0008] Further, the obtaining of the over-deadzone change set based on the comparison result between the current change in the voltage-current measurement data and the dynamic deadzone threshold includes: iteratively executing multiple comparison batches according to the voltage-current measurement data and the dynamic deadzone threshold, wherein, in each comparison batch, the following processing is performed: sequentially obtaining the current change values according to the reference data of the current comparison batch and the set of measurement points of the data to be compared; when the current change value is greater than the dynamic deadzone threshold, terminating the current comparison batch, incorporating the latest compared measurement point data into the over-deadzone change set, and using it as the reference data for the next comparison batch; incorporating the remaining uncompared measurement point data into the set of measurement points of the data to be compared for the next comparison batch.

[0009] Further, after obtaining the voltage-current measurement data, the following is performed: if the measured point current data in the voltage-current measurement data does not exceed the small current threshold, the measured point current data is updated according to the current ignore value.

[0010] Further, the obtaining of the first positioning result based on the voltage fluctuation correlation and the correlation coefficient of the over-deadzone change set includes: obtaining the correlation threshold based on the voltage fluctuation correlation; calculating the voltage-current correlation coefficient according to the test point voltage data and the test point current data in the over-deadzone change set; if the voltage-current correlation coefficient is less than the correlation threshold, incorporating the user into the first positioning result.

[0011] Further, the obtaining of the voltage dip event in the preset time period according to the first positioning result and the matching of the second positioning result associated with the fluctuation time sequence according to the voltage dip moment of the voltage dip event includes: obtaining the voltage dip events of all users in the first positioning result in the preset time period; obtaining the voltage dip correlation data of adjacent continuous time segments according to the voltage dip moment of the voltage dip event; obtaining the second positioning result according to the matching result between the voltage dip correlation data and the voltage data of the remaining users in the corresponding substation area.

[0012] Further, the obtaining of the second positioning result according to the matching result between the voltage dip correlation data and the voltage-current data of the remaining users in the corresponding substation area includes: matching the voltage dip correlation data with the voltage data of the remaining users in the corresponding substation area according to the time sequence, and calculating the voltage difference of the measured point voltages at the same time sequence; if the voltage difference is less than or equal to the preset voltage difference threshold, marking the measured point as a similar measured point, and if the voltage difference is greater than the preset voltage difference threshold, marking the measured point as a dissimilar measured point; if the proportion of the similar measured points is greater than the proportion threshold, it is considered that the voltage-current data of the user matches the voltage dip correlation data, and the user is incorporated into the second positioning result.

[0013] Further, obtaining the risk location 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 location; 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 location.

[0014] Further, the preset risk dimensions include: voltage-current correlation coefficient, voltage difference at the measurement point before and after the voltage dip event, theoretical resistance, theoretical resistance constant relationship, theoretical resistance increase rate, number of voltage dip events, power outage record, minimum voltage dip value, and theoretical power; after obtaining the risk location according to the second positioning result, execute: based on the comparison results of the voltage-current correlation coefficient, theoretical resistance constant relationship, and theoretical power in the preset risk dimensions with the preset end threshold, determine whether the user belongs to an end user. If so, output a line renovation prompt. If not, obtain the hidden danger risk value based on the risk location and the preset risk dimensions.

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

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

[0017] Advantages of this application: 1. Through the optimized processing of the user voltage and current measurement data after the voltage dip occurs, the current change across the dead zone is obtained. The current change across the dead zone shows the abnormal current change of the users with voltage dips under the condition of excluding other voltage jitter interferences. The abnormal current change is used as the judgment basis for whether there may be latent hidden dangers on the corresponding lines of these users. And according to whether there are other users with similar dip situations to this user within the preset time period, judge the branch line where the latent hidden danger exists, and use this as the risk location. According to the preset risk dimensions, count the hidden danger risk values of all risk locations, so as to facilitate the subsequent planning of the inspection scope, ensure the inspection efficiency and timeliness. At the same time, obtain the inspection time period according to the occurrence moment of the user dip event, which is convenient for using tools such as infrared thermometry for fault location that cannot be identified by the naked eye, avoiding ineffective inspections or affecting the user's power consumption. Determine the latent hidden danger location according to the inspection results. On the basis of ensuring the accuracy of identifying the latent hidden danger location, effectively improve the inspection pertinence and accuracy, and improve the user's power consumption experience.

[0018] 2. Obtain the voltage and current data of the remaining users in the corresponding adjacent continuous time periods in the substation area corresponding to the user. Screen whether there are co-dropping 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. Further investigate the users with abnormal voltage fluctuations through the similarity of voltage fluctuations among users in the substation area, realizing secondary investigation, improving the accuracy of investigation while narrowing the possible range of latent hidden dangers, and improving the accuracy and efficiency of subsequent inspection path planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic flow chart of the method for identifying latent hidden dangers of branch line nodes based on the change across the dead zone of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] To make the objectives, technical solutions, and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only the best embodiments of the present application, only used to explain the present application, and do not limit the protection scope of the present application. All other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the protection scope of the present application.

[0021] As Figure 1 shown, as Embodiment 1 of the present application, the method for identifying latent hidden dangers of branch line nodes based on the change across the dead zone includes the following steps:

[0022] When the user transformer and the substation area transformer meet the preset voltage relationship, obtain the voltage and current measurement data;

[0023] Obtain the set of changes across the dead zone based on the comparison result between the current change in the voltage and current measurement data and the dynamic dead zone threshold;

[0024] Obtain the first positioning result based on the voltage fluctuation correlation and the correlation coefficient of the set of changes across the dead zone;

[0025] Obtain the sudden drop event in the preset time period according to the first positioning result, and match the second positioning result associated with the fluctuation time sequence according to the sudden drop moment of the sudden drop event;

[0026] Obtain the risk location according to the second positioning result, and obtain the hidden danger risk value based on the risk location and the preset risk dimension;

[0027] Obtain the inspection path and inspection time period according to the hidden danger risk value and the voltage sudden drop time period, obtain the inspection result based on the inspection path and inspection time period, and obtain the latent hidden danger location based on the inspection result.

[0028] In this embodiment, when the user transformer and the substation area transformer satisfy the preset voltage relationship, it is considered that a voltage sag event has occurred to the user. Through the optimized processing of the measured voltage and current data of the user after the voltage sag occurs, the change of current crossing the dead zone is obtained, and the change of current crossing the dead zone is used to show the abnormal current change of the user with voltage sag under the condition of exceeding other voltage jitter interferences. The abnormal current change is used as the judgment basis for 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 situations to this user within the preset time period, the branch line where the potential hidden danger exists is judged, and this is used as the risk position. The hidden danger risk values of all risk positions are statistically calculated according to the preset risk dimensions, so as to facilitate the subsequent planning of the inspection scope, ensure the inspection efficiency and timeliness. At the same time, the inspection time period is obtained according to the occurrence time of the user sag event, so as to avoid ineffective inspections or impacts on the user's electricity consumption. The potential hidden danger position is determined according to the inspection result. On the basis of ensuring the accuracy of identifying the potential hidden danger position, the inspection pertinence and accuracy are effectively improved, and the user's electricity consumption experience is improved.

[0029] Among them, when there is a voltage difference between the user and the substation area transformer, 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 from the substation area transformer, the measured voltage and current data of this user are obtained.

[0030] Specifically, the method for identifying potential hidden dangers of branch line nodes based on the change of crossing the dead zone further includes:

[0031] Obtain the voltage drop of the measurement point in the continuous time series of the user and the difference between the voltage of the measurement point and the lowest phase voltage of the main meter;

[0032] If the voltage drop of the measurement point in the continuous time series of the user is greater than the preset sag threshold and the difference between the voltage of the measurement point and the lowest phase voltage of the main meter is greater than the preset sag threshold, it is considered that the user transformer and the substation area transformer satisfy the preset voltage relationship.

[0033] When the voltage drop between the two measurement points of the user is greater than the preset sag threshold and the voltage difference between the voltage of the latter measurement point and the lowest phase voltage of the main meter is greater than the preset sag threshold, it is considered that the user transformer and the substation area transformer satisfy the preset voltage relationship, that is, a voltage sag has occurred to this user. At this time, in response to the voltage sag, the measured voltage and current data of this user after the sag occurs are 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 occurs are obtained. It can be understood that the preset sag threshold and the number of voltage and current data acquisition points can be set according to the actual situation to adapt to different user natures.

[0034] Obtaining the set of changes of crossing the dead zone based on the comparison result between the current change and the dynamic dead zone threshold in the measured voltage and current data includes:

[0035] Execute multiple comparison batches iteratively based on voltage and current measurement data and the dynamic dead zone threshold. Among them, the following processing is performed in each comparison batch:

[0036] Obtain the current change value sequentially according to the measurement point order based on the reference data of the current comparison batch and the set of measurement points to be compared.

[0037] When the current change value is greater than the dynamic dead zone threshold, terminate the current comparison batch, incorporate the latest compared measurement point data into the set of changes across the dead zone, and use it as the reference data for the next comparison batch.

[0038] Incorporate the remaining uncompared measurement point data into the set of measurement points to be compared in the next comparison batch.

[0039] In this embodiment, use the first measurement point data in the voltage and current measurement data as the reference data for the first comparison batch. Use all measurement point data except the first measurement point data as the set of measurement points to be compared in the first comparison batch. Calculate the current difference between the first measurement point data and each measurement point data to be compared sequentially according to the measurement point order as the current change value. At the same time, compare the size of the current change value with the dynamic dead zone threshold until the current change value is greater than the dynamic dead zone threshold. Stop calculating the current change value. Incorporate the latest compared measurement point data into the set of changes across the dead zone, and use this latest compared measurement point data as the reference data for the next comparison batch. The remaining uncompared measurement point data is used as the set of measurement points to be compared in the next comparison batch, and perform loop calculations until the set of measurement points to be compared is an empty set. It can be understood that when the current change value has been calculated for any measurement point data and the calculated current change value has been compared with the dynamic dead zone threshold, it is considered that the measurement point data has been converted from the measurement point data to be compared to the compared measurement point data.

[0040] For example, if there is a first measurement point C1 in the voltage and current measurement data, incorporate C1 into the set of changes across the dead zone. Use the current I1 of C1 as the reference data. Search backward according to the measurement point time sequence. If the current change value of the current I2 of the measurement point C2 compared to I1 is not greater than the dynamic dead zone threshold, then retrieve the current I3 of the measurement point C3 for comparison until the current change value of the current of any measurement point compared to I1 is greater than the dynamic dead zone threshold. Use the current of this measurement point as the reference data for a new search and continue to search backward.

[0041] In this embodiment, the current change value is:

[0042] ;

[0043] Among them, represents the current change value, represents the current of the nth measurement point, Represents reference data.

[0044] In this embodiment, the dynamic dead-zone threshold is 30%. Due to factors such as the voltage floating with the change of the voltage at the primary end of the transformer, it is inevitable that the voltage cannot always remain unchanged. Therefore, a dynamic dead-zone threshold is set. The voltage-current correlation coefficient is calculated at the moment when the current change amplitude is large, and the slight current changes caused by the interference of other factors are excluded to avoid misidentifying potential hidden dangers. That is, when it is the case, it is included in the set of changes across the dead zone for calculating the current-voltage correlation coefficient. When it is not the case, it is not included in the set of changes across the dead zone and the current-voltage correlation coefficient is not calculated.

[0045] In order to exclude the situation where the current fluctuation of small-load users exceeds the change value under the small-load state but actually does not cause a voltage drop change and is misjudged due to voltage fluctuation, the current change value is obtained by the method of iterative update of reference data, so as to adapt to the electricity consumption of small-load users at different times, avoid misidentifying the current change caused by electricity consumption change as a potential hidden danger, and improve the accuracy of identifying potential hidden dangers.

[0046] In actual situations, the dynamic dead-zone threshold can be set according to the voltage floating situation and environmental impact factors of the actual substation area to improve the adaptability of the dynamic dead-zone threshold and thus improve the accuracy of finally identifying potential hidden dangers.

[0047] During the application process, there may be a situation where the current at a certain measurement point is less than the small-current threshold, that is, a small current caused by zero drift. Zero drift represents Zero Drift, that 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 fluctuation, resulting in the measured value fluctuating near zero. Even when the actual current is zero, the measured value may show a small current value. At this time, after obtaining the voltage-current measurement data, execute:

[0048] If the measured current data in the voltage-current measurement data does not exceed the small-current threshold, update the measured current data of this measurement point according to the current neglect value.

[0049] The voltage and current measurement data includes 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. At this time, the measurement point data is replaced with the current ignore value. Thus, the fluctuations caused by zero drift are uniformly processed as the current ignore value. During the subsequent comparison with the dynamic dead zone threshold, the current ignore value is ignored, so that noise can be effectively filtered out, avoiding misclassifying noise as the effective load and reducing the impact of equipment noise on the identification of potential hazards. In this embodiment, the small current threshold is 0.05A, and the current ignore value is 0.05A. It can be understood that in other embodiments, the small current threshold and the current ignore value can also be set according to the zero drift situation that may occur in actual power equipment.

[0050] In some other cases, after obtaining the voltage and current measurement data, the following is performed:

[0051] If the current data of the measurement point in the voltage and current measurement data does not exceed the small current threshold, the current ignore value is obtained according to the dynamic dead zone threshold, and the current data of the measurement point is updated with the current ignore value.

[0052] In this case, the current ignore value is set according to the current dynamic dead zone threshold to ensure that the voltage data and current data of the measurement point with too small current data of the measurement point will not be included in the cross-dead zone change set in subsequent calculations, ensuring that the data in the cross-dead zone change set belongs to the data with abnormal current change amplitude and improving the identification accuracy of potential hazards.

[0053] In this embodiment, the error data in the voltage and current measurement data is optimized by setting a preset compensation current, so that while avoiding affecting the subsequent identification of potential hazards, the current fluctuation anomalies that may occur at the moment of zero drift can also be retained, avoiding missing the current fluctuation anomalies caused by direct screening of equipment noise, and further improving the current anomaly identification accuracy by data optimization.

[0054] Obtaining the first positioning result based on the voltage fluctuation correlation and the correlation coefficient of the cross-dead zone change set includes:

[0055] Obtaining the correlation threshold based on the voltage fluctuation correlation;

[0056] Calculating the voltage-current correlation coefficient according to the test point voltage data and test point current data in the cross-dead zone change set;

[0057] If the voltage-current correlation coefficient is less than the correlation threshold, the user is included in the first positioning result.

[0058] In the power system, contact parts such as wire clamps, line joints, and terminal lap joints may have an abnormal increase in contact resistance due to oxidation or loosening, forming local heating points, which may cause equipment failures or even fires. According to the voltage division effect, when the contact resistance increases abnormally, an additional voltage drop will occur when the current passes through the contact resistance during user power consumption, resulting in a decrease in the user-side voltage. The current and voltage show an inverse correlation. When the user is not using electricity or the current remains stable, the irregular voltage fluctuations caused by grid fluctuations or load changes are not included in the calculation of the voltage-current correlation. In the previous process, by determining the change across the dead zone of the current, the measurement point data with a current change exceeding the threshold is included in the set of changes across the dead zone, thereby excluding the influence of voltage fluctuations when the current has no obvious change on the correlation calculation. During this process, the correlation coefficient of the change across the dead zone of the current, that is, the voltage-current correlation coefficient, is introduced to further distinguish normal voltage fluctuations from abnormal voltage division caused by potential hazards. The correlation threshold can be set based on expert experience based on the voltage fluctuation correlation. For example, in this embodiment, the correlation threshold is -0.3.

[0059] Specifically, the voltage-current correlation coefficient is used to measure the strength and direction of the linear relationship between voltage and current, and its range is [-1, 1]. When the voltage-current correlation coefficient is negative, it indicates that the voltage and current are negatively correlated. When the voltage-current correlation coefficient is positive, it indicates that the voltage and current are positively correlated. When the voltage-current correlation coefficient is 0, it indicates that there is no linear correlation between voltage and current. In this embodiment, based on the research experience of voltage fluctuation correlation, the correlation threshold is -0.3, which can not only screen out users with a strong negative correlation between voltage and current, but also avoid interference in judgment caused by data errors at some moments. Users with a strong negative correlation as a whole are included in the first positioning result and defined as sudden drop users with a negative voltage-current correlation to ensure the identification accuracy, and the influence range of potential hazards is obtained according to the first positioning result, which is convenient for further identification of potential hazards in the future.

[0060] Specifically, calculate the voltage-current correlation coefficient according to the test point voltage data and test point current data in the set of changes across the dead zone, and use the corr() function of python to obtain the normalized value of the voltage-current correlation coefficient. Compare the normalized value of the voltage-current correlation coefficient with the correlation threshold. When the normalized value of the voltage-current correlation coefficient is less than the correlation threshold, include this user in the first positioning result.

[0061] In this embodiment, according to the first positioning result, obtain the sudden drop events during the preset time period. The second positioning result matching the fluctuation time sequence according to the sudden drop moment of the sudden drop event includes:

[0062] Obtain the sudden drop events of all users in the first positioning result during the preset time period;

[0063] Obtain the sudden-drop correlation data of adjacent continuous time series segments according to the sudden-drop moment of the sudden-drop event;

[0064] Obtain the second positioning result according to the matching result between the sudden-drop correlation data and the voltage data of the remaining users in the corresponding substation area.

[0065] For any user in the first positioning result, obtain the sudden-drop events in the preset time period, and obtain the sudden-drop correlation data according to the measurement point data included in the adjacent continuous time periods of the sudden-drop events. According to the substation area corresponding to the user, obtain the voltage data of the remaining users in the corresponding substation area in the corresponding adjacent continuous time periods. Check whether there are users with similar voltage fluctuations and co-drop according to the sudden-drop correlation data and the voltage data, and use this as the second positioning result. Further investigate the users with abnormal voltage fluctuations through the similarity of voltage fluctuations among users in the substation area, realizing secondary investigation, improving the accuracy of investigation while narrowing the possible range of latent hidden dangers, and improving the accuracy and efficiency of subsequent inspection path planning. It can be understood that the preset time period and the adjacent continuous time series segments can be set according to the actual situation.

[0066] In this embodiment, obtain all the sudden-drop events of the users in the first positioning result within the recent X days, denoted as SJ1 - SJn. For the pre-measurement point moment of each sudden-drop event Obtain the corresponding pre-measurement point voltage , for the post-measurement point moment of each sudden-drop event Obtain the corresponding post-measurement point voltage . Obtain the voltage difference between the voltage of the remaining users TJYH in the substation area at 、 moment and the voltage at 、 . Take the remaining users TJYH with similar voltage fluctuations as co-drop users and include them in the second positioning result.

[0067] Specifically, obtaining the second positioning result according to the matching result between the sudden-drop correlation data and the voltage data of the remaining users in the corresponding substation area includes:

[0068] Match the sudden-drop correlation data with the voltage data of the remaining users in the corresponding substation area according to the time sequence, and calculate the voltage difference of the measurement point voltages at the same time sequence;

[0069] If the voltage difference is less than or equal to the preset voltage difference threshold, mark this measurement point as a similar measurement point; if the voltage difference is greater than the preset voltage difference threshold, mark this measurement point as a dissimilar measurement point;

[0070] 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 correlation data, and this user is included in the second positioning result.

[0071] Users in the same transformer substation area may simultaneously experience abnormal voltage fluctuations due to common factors in the power grid. Therefore, when a sudden voltage drop event occurs to any user, if the voltage changes of the remaining users in the same transformer substation area are similar, it indicates that there may be a common voltage fluctuation source, that is, the latent hazard location is on the common line between users, thus realizing further latent hazard location.

[0072] In this embodiment, the preset voltage difference threshold is 3V, and the proportion threshold is 90%. If the measured voltages of the remaining users TJYH in the same transformer substation area at 、 moments satisfy:

[0073] and ;

[0074] then record this measurement point as a similar measurement point and calculate:

[0075] ;

[0076] where represents the measured voltage at the measurement point at moment, represents the measured voltage at the measurement point at moment, represents the number of similar measurement points, represents the number of dissimilar measurement points, represents the voltage fluctuation similarity between the voltage data of the remaining users and the sudden voltage drop user.

[0077] When a certain user has , it is considered that this user belongs to the same voltage drop user and is included in the second positioning result.

[0078] In this embodiment, by correlating the spatio-temporal data between different users and performing mutual verification between users, the error of single-user data can be effectively filtered, the robustness of the result can be enhanced, false alarms or problem omissions can be reduced, and the accuracy of latent hazard identification can be improved. At the same time, there is no need to check each user one by one. The abnormal current change of a single user is obtained through the change judgment across the dead zone, and the abnormal voltage change of the entire transformer substation area is macroscopically obtained through the group voltage synchronism, reducing the amount of calculation data for the overall judgment and improving the efficiency.

[0079] It can be understood that in practical applications, more suitable voltage difference thresholds and proportion thresholds can be set according to the load characteristics of the transformer substation area, such as the load characteristics of industrial areas and residential areas.

[0080] Obtaining the risk location according to the second positioning result includes:

[0081] If the second positioning result is an empty set, then use the rear side of the single-user branch as the risk location;

[0082] If the second positioning result is not an empty set, an overlapping user branch is obtained based on the first positioning result and the second positioning result, and the overlapping user branch is used as the risk location.

[0083] When there is a second positioning result, that is, there is a certain user with the same voltage fluctuation as the sudden-drop users in the first positioning result. At this time, the potential hidden danger causing 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 location of the potential hidden danger is behind the single-user branch, such as the meter joint, the terminal inside the meter box, the intermediate joint of the line, or the piercing clamp of the household connection line. If the second positioning result is not an empty set, through topological positioning, it is on the multi-user branch common to the sudden-drop users and the co-drop users, such as the multi-user branch clamp, the intermediate joint of the line between the multi-user branch and the first sudden-drop / co-drop user branch.

[0084] Furthermore, after determining the risk location, a hidden danger risk value is obtained according to the risk location and the preset risk dimensions. Among them, the preset risk dimensions include:

[0085] Voltage-current correlation coefficient, voltage difference at the measurement point before and after the sudden-drop event, theoretical resistance, theoretical resistance constant relationship, theoretical resistance increase rate, number of sudden-drop events, power outage record, minimum value of voltage sudden-drop, and theoretical power.

[0086] The voltage-current correlation coefficient shows the abnormal size of the contact resistance. The greater the abnormal increase value of the contact resistance, the greater the hidden danger and the higher the priority for investigation.

[0087] The voltage difference at the measurement point before and after the sudden-drop event shows the stability of the grid voltage. The greater the voltage difference, the greater the problem of voltage fluctuation or instability in the grid and the higher the priority for investigation.

[0088] The theoretical resistance is calculated based on the ratio of the current voltage difference to the current difference to reflect the abnormal change value of the current grid equipment resistance. The greater the abnormal change value, the higher the priority for investigation.

[0089] The theoretical resistance constant relationship is calculated based on the fluctuation degree of the ratio of the voltage difference to the 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 and the higher the priority for investigation.

[0090] The theoretical resistance increase rate is obtained based on the change trend of the ratio of the voltage difference to the current difference at each time sequence to reflect the degree of aggravation of the potential hidden danger. The greater the theoretical resistance increase rate, the higher the priority for investigation.

[0091] The number of sudden-drop events shows the frequency of the user being affected by the unstable grid voltage. The more sudden-drop events, the higher the instability and the higher the priority for investigation.

[0092] The power outage record shows the number of power outages and the duration for users. Once a power outage occurs, it indicates that potential hidden trouble problems are serious and have a higher priority for investigation.

[0093] The minimum value of voltage sag represents the lowest voltage reached during the sag event, and the minimum value of low voltage reflects the extreme situation of the grid voltage. The smaller the minimum value of voltage sag, the higher the priority for investigation.

[0094] The theoretical power is calculated based on the product of the voltage difference and the current. Taking it as a reverse index, the greater the power, the lower the risk value and the lower the priority for investigation.

[0095] Corresponding weights are assigned according to the importance of each dimension. For example, the correlation coefficient of voltage and current is used as the main important weight, and the power outage record is used as the secondary important weight. The weight coefficients of each dimension are assigned in turn according to expert experience to calculate the hidden trouble risk value of this risk location. By comprehensively considering multiple dimensions, the risk value of potential hidden troubles can be evaluated more comprehensively, the accuracy of risk assessment can be improved, which is convenient for the subsequent planning of inspection paths, and ensures that potential hidden troubles with higher risks have a higher priority for investigation.

[0096] In some other embodiments, the weight coefficients of each dimension can also be calculated through historical grid data, and the weight coefficients of each dimension are assigned according to the proportion of each dimension when historical power consumption faults occur, so as to improve the accuracy of comprehensive evaluation.

[0097] At this time, the hidden trouble risk value R is:

[0098] ;

[0099] Among them, 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, represents the voltage difference at the measurement point before and after the sag event; represents the weight coefficient of the theoretical resistance, represents the theoretical resistance; represents the weight coefficient of the theoretical resistance constant relationship, represents the theoretical resistance constant relationship;

[0100] represents the weight coefficient of the theoretical resistance increase amplitude, represents the theoretical resistance increase amplitude; represents the weight coefficient of the number of sag events, represents the number of sag events; represents the weight coefficient of the power outage record, represents the power outage record; represents the weight coefficient of the minimum value of voltage sag, Represents the minimum value of voltage sag; Represents the theoretical power weight coefficient, Represents the theoretical power. Among them, 。

[0101] In this embodiment, considering the situation that the power supply radius of some power supply areas is too long, the wire diameter is too thin and the account is not fine, for the voltage difference Current > 200w situation, risk impairment is carried out, that is, if the power of 200w is concentrated on a certain joint or the oxidation point or loosening point of the wire clamp, the insulating shell at this point has generally been burned out. Therefore, the fluctuation factor is generally the impedance loss of the long line. Therefore, the theoretical power is used as the reverse index for impairment treatment to avoid misidentification of potential hidden dangers caused by the impedance loss of the long line.

[0102] In some other embodiments, the method for identifying potential hidden dangers of branch line nodes based on crossing the dead zone further includes: after obtaining the risk position according to the second positioning result, execute:

[0103] Based on the voltage-current correlation coefficient, the theoretical resistance constant relationship, and the comparison result of the theoretical power and the preset end threshold in the preset risk dimension, determine whether the user belongs to the end user. If so, output a line transformation prompt. If not, obtain the hidden danger risk value based on the risk position and the preset risk dimension.

[0104] Since there may be situations where the power supply radius and line parameters cannot meet the user's needs, the voltage-current correlation coefficient, the theoretical resistance constant relationship, and the theoretical power of the user are calculated and compared with the preset end threshold, so as to determine whether the abnormal current change is caused by head oxidation and contact problems due to the mismatch between the power supply radius and line parameters, and further improve the accuracy of identifying the source of abnormal power consumption of users.

[0105] At this time, the preset end threshold includes at least the resistance constant end threshold, the voltage-current correlation coefficient end threshold, and the theoretical power end threshold. In this embodiment, the resistance constant end threshold is 0.8, the voltage-current correlation coefficient end threshold is -0.8, and the theoretical power end threshold is 200W. If the voltage-current correlation coefficient is less than the voltage-current correlation coefficient end threshold, the theoretical resistance constant relationship is greater than the resistance constant end threshold, and the theoretical power is greater than the theoretical power end threshold at the same time, then this user belongs to the end user.

[0106] 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-current correlation coefficient shows a strong negative correlation (less than -0.8), and the theoretical power is greater than the theoretical power end threshold (200W) at the same time, then this user is defined as the end user, that is, the power supply radius and line parameters are not matched, and it is prompted that such users are non-node problem users and the line needs to be transformed.

[0107] Obtaining the inspection path and inspection time period based on the hidden danger risk value and the voltage sag period includes:

[0108] Obtaining the voltage sag period corresponding to the hidden danger risk value according to the sag moment of the sag event;

[0109] Obtaining the inspection priority and inspection time period of the hidden danger points according to the voltage sag period and the hidden danger risk value, and obtaining the inspection path according to the inspection priority and inspection time period.

[0110] In this embodiment, the possible time point of the sag occurrence, that is, the voltage sag period, is obtained according to the moment when the user sag event occurs, and the inspection path is planned according to the voltage sag period and the inspection priority of the hidden danger points. For example, in the infrared temperature measurement inspection, it is necessary to conduct inspections at night. The voltage sag period of user A is 19:00 - 20:00, and the voltage sag period of user B is 19:30 - 21:00. If the hidden danger risk values of user A and user B are the same, the inspection path is planned according to inspecting the risk location of user A at 19:00 and the risk location of user B at 20:00. In the case where the hidden danger risk values are different, considering the hidden danger risk value and the compliance of the time period comprehensively, the risk location of the user with the nearest voltage sag period and the highest hidden danger risk value is used as the position point for earlier inspection as much as possible. It can be understood that the latent hidden danger identification carried out in this application belongs to the situation when there is still not much impact on the user's power consumption. When it has already affected the user's power consumption, such as when the hidden danger has caused a power outage for the user, it belongs to the scope of direct on-site investigation.

[0111] Furthermore, obtaining the inspection result according to the inspection path and inspection time period, and according to the inspection result, such as obvious heating and oxidation traces on the equipment appearance (terminal discoloration, connector black-green oxidation, cable outer insulation melting, cable bulging, etc.), abnormal heating points are found by infrared temperature measurement at high load moments, and the latent hidden danger location is confirmed. While improving the inspection efficiency, the accuracy of the inspection points is ensured.

[0112] As the second embodiment of this application, for the latent hidden danger identification method of the branch line node based on the change across the dead zone, it further includes:

[0113] Obtaining the 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.

[0114] In this embodiment, due to the differences in the user's power consumption behavior habits, fixed dead zone thresholds may cause mismatches in the screening of changes across the dead zone. Therefore, the dynamic dead zone threshold is set according to the average value of the current of the corresponding user within a preset period, so as to ensure that the dynamic dead zone threshold adapts to the actual user habits of each user.

[0115] Specifically, obtaining the user current data within a preset period and calculating the average current, and obtaining the dynamic dead zone threshold according to the average current includes:

[0116] Obtain the user current data within a preset period, and select a preset number of large current data according to the current magnitude;

[0117] Obtain the dynamic dead zone threshold according to the average current of the large current data and the fluctuation coefficient.

[0118] In this embodiment, the preset period is 1 week, the preset number is 10, and the fluctuation coefficient is 0.1. Select the largest 10 current data of the user within 1 week, calculate the average current, and obtain the dynamic dead zone threshold according to the product of the average current and the fluctuation coefficient. Calculate the dynamic dead zone threshold through the maximum load condition when the user uses electricity, avoid misjudgment caused by different user loads, and improve the accuracy of the dynamic dead zone threshold.

[0119] In some other cases, obtaining the set of dead zone crossing changes based on the comparison result between the current change and the dynamic dead zone threshold in the voltage-current measurement data includes:

[0120] Select the threshold change coefficient and the initial dead zone threshold according to the preset dead zone threshold range, and obtain the set of dynamic dead zone thresholds according to the initial dead zone threshold and the threshold change coefficient;

[0121] Obtain the comparison result between the current change and the dynamic dead zone threshold in turn according to the dynamic dead zone threshold in the set of dynamic dead zone thresholds until the comparison results of consecutive dynamic dead zone thresholds are the same, and obtain the set of dead zone crossing changes according to the same comparison result.

[0122] In this case, according to the characteristics of the distribution area, such as the influence of the user's electricity load fluctuation on the current, obtain the corresponding threshold change coefficient and the initial dead zone threshold. Use the initial dead zone threshold as the first dynamic dead zone threshold, obtain the first comparison result, and update the initial dead zone threshold according to the threshold change coefficient. Use the dead zone threshold after the first update as the second dynamic dead zone threshold, obtain the second comparison result. If the first comparison result and the second comparison result are the same, end the dynamic update of the dead zone threshold, and obtain the set of dead zone crossing changes according to the current comparison result. If they are not the same, continue the dynamic update of the dead zone threshold until the comparison result is no longer updated, so as to ensure the accuracy of the final set of dead zone crossing changes. The preset dead zone threshold range can be 10%-50%.

[0123] In this embodiment, since it may be interfered by voltage fluctuations, such as the influence of distributed photovoltaic power generation, the change of the voltage of the upper-level power supply, and the three-phase imbalance on the voltage, the influence of distributed photovoltaic power generation, the change of the voltage of the upper-level power supply, and the three-phase imbalance on the voltage is quantified for fluctuation, and the correlation threshold is set according to the actual influence at different times. At this time, obtaining the correlation threshold based on the voltage fluctuation correlation includes:

[0124] Obtain historical power grid data, and obtain the influence relationship of the main meter voltage fluctuation according to the correlation between the distributed photovoltaic data, the upstream power supply voltage data, the three-phase unbalance data and the main meter voltage fluctuation in the historical power grid data;

[0125] According to the main meter voltage fluctuation and the latent hazard data in the historical power grid data, obtain the correlation between the fluctuation and the hazard;

[0126] Obtain the voltage fluctuation correlation according to the influence relationship of the main meter voltage fluctuation and the correlation between the fluctuation and the hazard;

[0127] Obtain the distributed photovoltaic data, the upstream power supply voltage data, and the three-phase unbalance data in the current power grid data, and obtain the correlation threshold according to the voltage fluctuation correlation and the distributed photovoltaic data, the upstream power supply voltage data, and the three-phase unbalance data in the current power grid data.

[0128] In this embodiment, according to the influence of the distributed photovoltaic data, the upstream power supply voltage data, and the three-phase unbalance data on the main meter voltage fluctuation, and the correlation between the main meter voltage fluctuation and the latent hazard data in the historical data, obtain the correlation threshold to ensure the adaptability of the correlation threshold. The latent hazard data at least includes the voltage and current data when there are latent hazards. Correlate the standard deviation of the historical main meter voltage fluctuation with the minimum value of the voltage and current correlation coefficient in the latent hazard data. When the main meter voltage fluctuates, judge whether there are other influencing factors according to the standard deviation, and adjust the correlation threshold. For example, when the standard deviation of the voltage fluctuation is large, reduce the correlation threshold, and calculate the reduction amplitude according to the historical matching situation. The adjusted correlation threshold can be obtained specifically in the following way:

[0129] 1. Obtain the correlation A1 between the change of the distributed photovoltaic data and the main meter voltage fluctuation, the correlation A2 between the change of the upstream power supply voltage data and the main meter voltage fluctuation, and the correlation A3 between the change of the three-phase unbalance data and the main meter voltage fluctuation according to the change values of the distributed photovoltaic data, the upstream power supply voltage data, and the three-phase unbalance data in the historical power grid data and the corresponding main meter voltage fluctuation in the historical data. Obtain the distributed photovoltaic data a1, the upstream power supply voltage data a2, and the three-phase unbalance data a3 according to the current power grid data, and calculate the main meter voltage fluctuation value caused by the current distributed photovoltaic data, the upstream power supply voltage data, and the three-phase unbalance data:

[0130] ;

[0131] Among them, represents the main meter voltage fluctuation value caused by the current distributed photovoltaic data, the upstream power supply voltage data, and the three-phase unbalance data;

[0132] 2. Calculate the total meter voltage standard deviation based on the total meter voltage fluctuation value and the average total meter voltage fluctuation calculated in step 1:

[0133] ;

[0134] Where, represents the total meter voltage standard deviation, represents the total number of total meter voltage fluctuation values, represents the first total meter voltage fluctuation value, represents the (i - 1)-th total meter voltage fluctuation value, represents the average total meter voltage fluctuation;

[0135] 3. Calculate the voltage - current correlation coefficient in the latent hazard data based on the voltage and current data (current measurement data when the latent hazard is confirmed) at the time of latent hazard in the historical power grid data:

[0136] ;

[0137] Where, represents the voltage - current correlation coefficient, represents the average voltage at the time of latent hazard, represents the average current at the time of latent hazard, represents the total number of sampling points at the time of latent hazard, represents the current measurement data at the t - th sampling point, represents the voltage measurement data at the t - th sampling point;

[0138] 4. Obtain the set of voltage - current correlation coefficients when the total meter voltage standard deviation is the same according to the voltage - current correlation coefficient calculated in step 3, and construct the correlation B1 between fluctuation and hazard by taking the minimum voltage - current correlation coefficient in the set and this total meter voltage standard deviation, that is:

[0139] ;

[0140] Where, represents the j - th total meter voltage standard deviation, represents all voltage - current correlation coefficients corresponding to the j - th total meter voltage standard deviation, represents the mapping relationship;

[0141] 5. Obtain the correlation threshold matching the total meter voltage standard deviation according to the total meter voltage standard deviation calculated in step 2 and the mapping relationship in B1 calculated in step 4:

[0142] ;

[0143] Where, represents the correlation threshold represents the mapping function. According to the mapping function, the corresponding correlation threshold is retrieved in real time based on the correlation B1 between fluctuations and potential hazards.

[0144] In some other cases, the method for identifying potential hazards at branch line nodes based on the change across the dead zone further includes:

[0145] When the correlation threshold changes, the dynamic dead zone threshold is adjusted synchronously according to the threshold change coefficient.

[0146] When the standard deviation of voltage fluctuations is large, it indicates that the voltage data is greatly affected by other factors such as the integration of distributed photovoltaic power generation. Therefore, the correlation threshold is reduced, and the dynamic dead zone threshold is increased synchronously to improve the accuracy of excluding other factors, thereby improving the accuracy of subsequent potential hazard identification.

[0147] This application determines the measurement points to be compared through the change across the dead zone of current. Through its inverse correlation, it accurately discovers potential hazards such as oxidation and looseness at wire clamps, intermediate joints of lines, and various terminal connection points. The location of the hazards is determined through voltage dips and simultaneous dips of users, which facilitates maintenance personnel to discover potential hazard problems in advance through visual inspection and infrared inspection, improving the accuracy of potential hazard identification and location. At the same time, the change across the dead zone is used to screen the user's electricity consumption data once, reducing the amount of data for subsequent overall macroscopic comparison and improving efficiency.

[0148] The above-described specific implementation manner is a preferred implementation manner of the method for identifying potential hazards at branch line nodes based on the change across the dead zone of this application. It does not limit the specific implementation scope of this application. The scope of this application includes but is not limited to this specific implementation manner. Any equivalent changes made according to 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; Obtain the inspection path and inspection period according to the hidden danger risk value and the voltage sag period, obtain the inspection result based on the inspection path and inspection period, and obtain the potential hidden danger location based on the inspection result; The method further includes: obtaining a correlation threshold based on the voltage fluctuation correlation; calculating a 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, including the user in the first positioning result; Obtain the sudden drop events of all users in the first positioning result in the preset time period; obtain the sudden drop associated data of the adjacent continuous time series according to the sudden drop moment of the sudden drop event; match the sudden drop associated data with the voltage data of other users in the corresponding substation according to the time series, and calculate the voltage difference of the measurement point voltage under the same time series; if the voltage difference is less than or equal to the preset voltage difference threshold, then the measurement point is recorded as a similar measurement point; if the voltage difference is greater than the preset voltage difference threshold, then the measurement point is recorded as a dissimilar measurement point; if the proportion of similar measurement points is greater than the proportion threshold, then it is considered that the voltage and current data of the user match the sudden drop associated data, and the user is included in the second positioning result; If the second positioning result is an empty set, the rear side of the single user branch is taken as the risk position; 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 taken as the risk position.

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 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.

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 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.

6. 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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