Hidden danger identification method and system for low-voltage power distribution areas based on deterioration risk analysis

Abstract

The present invention discloses a method and system for hidden danger identification in low-voltage power distribution areas based on degradation risk analysis, which relates to the technical field of data processing and includes: constructing an initial data set according to measurement data; constructing an abnormal data set according to momentary drop event criteria in combination with the initial data set; determining abnormal conditions at the front end of the meter and their corresponding front-end abnormal degrees according to the joint melting identification algorithm; determining abnormal conditions at the end of the meter and their corresponding end abnormal degrees according to the end abnormal identification algorithm; determining the front-end risk weight and the end risk weight through a linear regression model; determining the front-end hidden danger risk value based on the abnormal conditions at the front end of the meter, the front-end abnormal degree, and the front-end risk weight or / and determining the end hidden danger risk value based on the abnormal conditions at the end of the meter, the end risk weight, and the end abnormal degree; determining the corresponding hidden danger emergency strategy for the power distribution area according to the front-end hidden danger risk value or / and the end hidden danger risk value. The solution significantly improves the accuracy and efficiency of hidden danger identification in low-voltage power distribution areas.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and specifically, to a method and system for identifying hidden dangers in low-voltage power distribution areas based on deterioration risk analysis. Background Art

[0002] During the operation of the power system, the accurate identification of hidden dangers at the front end and the end of the low-voltage power distribution area meters is a key link to ensure power supply reliability. The existing technologies mainly rely on manual regular inspections or threshold alarm mechanisms based on single parameters, and there are core defects such as multi-dimensional data fusion faulting and insufficient decoupling of abnormal features. Specifically, in terms of data collection, traditional methods only use current and voltage sampling at discrete time points, and fail to construct a dynamic data screening mechanism by combining transient event characteristics, resulting in abnormal data being submerged in steady-state noise; in terms of hidden danger identification, existing algorithms mostly adopt a single-dimensional judgment criterion with a fixed threshold, neither establishing a quantitative analysis model for the melting or oxidation characteristics of meter joints, nor having an associated analysis of abnormal fluctuations in the end load, and being unable to distinguish between similar voltage dips caused by front-end contact deterioration and end-user electricity consumption; in terms of risk assessment, current methods generally adopt a static weight allocation strategy, ignoring the dynamic impact of different abnormal types on the equipment deterioration path, resulting in a significant deviation between the risk level assessment and the actual evolution trend of hidden dangers. The traditional system fails to construct a two-dimensional evaluation system for the melting risk of front-end joints and abnormal end impedance, resulting in the inability to accurately locate the hidden danger source point when formulating maintenance strategies, and often adopting the method of repairing after the hidden danger evolves into a fault causing user power outage, seriously affecting power supply continuity.

[0003] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present application, and thus it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0004] The object of the present invention is to solve the problems in the prior art that rely on manual inspections or simple analysis of single data sources, resulting in inaccurate and inefficient identification of hidden dangers in low-voltage power distribution areas, and propose a method and system for identifying hidden dangers in low-voltage power distribution areas based on deterioration risk analysis; through transient drop event criterion screening, a two-path abnormal identification algorithm (joint melting identification / end abnormality identification) and a dynamic regression model, accurate distinction between front-end contact deterioration and end-load abnormality of meters, quantitative calibration of hidden danger risk levels, and directional positioning of hidden danger source points are achieved, significantly improving the accuracy and efficiency of hidden danger identification in low-voltage power distribution areas, and providing a reliable data basis for the prediction of hidden dangers in low-voltage power distribution areas and the formulation of response strategies.

[0005] In a first aspect, a technical solution provided in an embodiment of the present invention is a method for identifying hidden dangers in a low-voltage power distribution area based on deterioration risk analysis, including the following steps:

[0006] Obtain the measurement data of the low-voltage power distribution area according to the sampling period in sequence to construct an initial data set; screen the initial data set according to the momentary drop event criterion in sequence to construct an abnormal data set;

[0007] Analyze the abnormal data set according to the joint melting recognition algorithm to determine the abnormality at the front end of the meter and its corresponding front-end abnormality degree; when the triggering condition is met, analyze the abnormal data set according to the end-end abnormality recognition algorithm to determine the end-end abnormality of the meter and its corresponding end-end abnormality degree;

[0008] Determine the front-end risk weight corresponding to the front-end abnormality of the meter and the end-end risk weight corresponding to the end-end abnormality of the meter through the linear regression model combined with the abnormal situation data corresponding to the front-end abnormality degree and the end-end abnormality degree;

[0009] Determine the front-end hidden danger risk value according to the front-end abnormality of the meter, the front-end abnormality degree and the front-end risk weight or / and the end-end hidden danger risk value determined according to the end-end abnormality of the meter, the end-end risk weight and the end-end abnormality degree;

[0010] Determine the hidden danger emergency strategy corresponding to the power distribution area according to the front-end hidden danger risk value or / and the end-end hidden danger risk value.

[0011] Preferably, the measurement data includes the main meter voltage, the user voltage, and the user current.

[0012] Preferably, the momentary drop event criterion includes:

[0013] Criterion A1: Extract data using a sliding window with a width of 2 measurement points, and the voltage drop between the two measurement points within the window is greater than 5V;

[0014] Criterion A2: The absolute value of the voltage difference between the main meter voltage and the user voltage is greater than 5V;

[0015] Criterion A3: The current at the voltage low point is greater than 0.1A, and (the current at the voltage low point - the current at the voltage high point) / the current at the voltage high point > 50%, and (the current at the voltage low point - the current at the voltage high point) > the average value of the maximum 5 currents at the same measurement point within the unit time 10%;

[0016] Criterion A4: If the three-phase user voltages drop instantaneously at the same time, the momentary drop event is excluded and classified as an end-user;

[0017] Criterion A5: Obtain the current at the moment when the voltage of the momentary drop event instantaneously drops to the low voltage. If the current disappears within 12 measurement moments at the same measurement point after the momentary drop, or the current at the moment of the start of the momentary drop is less than 20% of the average value of the current at the voltage low point, or the current is less than 0.1A and the voltage does not recover to the high point voltage, the momentary drop event is excluded;

[0018] Criterion A6: The user voltage difference Current > 1000w, momentary dip events excluded;

[0019] If the above criteria are all met, a momentary dip event is established;

[0020] Among them, a momentary dip event includes voltage and current data measured before and after. The voltage and current data include current at voltage low point, voltage at voltage low point, current at voltage high point, and voltage at voltage high point.

[0021] Preferably, according to the joint melting recognition algorithm, analyze the abnormal data set to determine the abnormality at the front end of the meter and its corresponding front-end abnormality degree, including the following steps:

[0022] Extract the measurement data in the abnormal data set and calculate to obtain the front-end characteristic data. Analyze and compare the front-end characteristic data through the joint melting recognition algorithm to determine the abnormality at the front end of the meter; determine the front-end abnormality degree according to the abnormal situation corresponding to the abnormality at the front end of the meter; among them, the front-end characteristic data at least includes the momentary voltage difference at each measurement point, the voltage-current anti-correlation coefficient, the average current, and the average value of the momentary voltage high point.

[0023] Preferably, analyze and compare the front-end characteristic data through the joint melting recognition algorithm to determine the abnormality at the front end of the meter, including the following steps:

[0024] When the momentary voltage difference per time Current > 5W, and the voltage-current anti-correlation coefficient within 7 days before melting judgment is less than -0.5, and the single current continuously exceeds 80% of the current at the voltage low point of the momentary dip, and the voltage recovers to the voltage high point before the momentary dip, then it is judged that there is a melting abnormality in the front end of the meter; 80%, and the voltage recovers to the voltage high point before the momentary dip, then it is judged that there is a melting abnormality in the front end of the meter;

[0025] After a single momentary dip ends, if within the range of n sampling points (96 can be taken), there are m (3 can be taken) or more sampling moments corresponding to currents greater than the average current of all voltage low points of the momentary dip within unit time 120%, and at the same time the voltage is greater than the average value of the momentary voltage high point, then it is judged that there is a melting abnormality in the front end of the meter.

[0026] Preferably, determine the front-end abnormality degree according to the abnormal situation corresponding to the abnormality at the front end of the meter, including the following steps:

[0027] Extract the front-end risk factors corresponding to abnormal situations, and accumulate according to the weight coefficients corresponding to the data of each dimension in the front-end risk factors to determine the front-end abnormality degree; among them, the weight coefficients of the data of each dimension in the front-end risk factors are dynamically adjusted according to the electricity consumption of actual users and the voltage fluctuation conditions of the power grid substation area; the data of each dimension of the front-end risk factors include: instantaneous voltage drop difference, low voltage, theoretical resistance, theoretical resistance change trend, theoretical resistance standard deviation, instantaneous drop times, synchronous power outage events, synchronous daily line loss sudden increase events, voltage-current inverse correlation coefficient, theoretical power, instantaneous drop event occurrence time period, joint melting events;

[0028] Among them, determining the front-end abnormality degree according to the front-end risk factors includes the following steps:

[0029] The weight coefficient w1 corresponding to the instantaneous voltage drop difference = the average value of the voltage differences of all instantaneous drop events of the current user within 7 days - 5, and the upper limit of the set weight coefficient w1 is 20. It can be seen that all weights and upper limits can be changed according to specific events. Among them, when the instantaneous voltage drop difference is the high point voltage of the instantaneous drop event - the low point voltage of the instantaneous drop event;

[0030] The weight coefficient w2 corresponding to low voltage = 205v - the average value of the low point voltages of the lowest 5 instantaneous drop events corresponding to the voltage low points in the instantaneous drop events of the current user within 7 days; the upper limit of the set weight coefficient w2 is 20;

[0031] The weight coefficient w3 corresponding to the theoretical resistance = 10 The average value of the voltage differences of the instantaneous drop events of the current user within 7 days / the average value of the current differences of the instantaneous drop events, and the upper limit of the set weight coefficient w3 is 40. Among them, the voltage difference is the high point voltage of the instantaneous drop event - the low point voltage of the instantaneous drop event, and the current difference is the current at the low point voltage of the instantaneous drop event - the current at the high point voltage of the instantaneous drop event;

[0032] The weight coefficient w4 corresponding to the theoretical resistance change trend = (resistance change rate - 1) ×20; among them, the resistance change rate = the average value of the theoretical resistances of the last 3 instantaneous drop events of the current user within 7 days / the average value of the theoretical resistances of the first 3 instantaneous drop events;

[0033] The weight coefficient corresponding to the theoretical resistance standard deviation , where is the average resistance, is the maximum resistance, is the minimum resistance, N is the total number of instantaneous drop events, is the resistance of the nth instantaneous drop event;

[0034] The weight coefficient w6 corresponding to the instantaneous drop times on the same day = (the number of instantaneous drop events of the current user on the same day - 1) ×10; the upper limit of the set weight coefficient w6 is 20;

[0035] The weight coefficient w7 corresponding to the number of instant voltage drops in seven days = (the number of instant voltage drop events of the current user in seven days - 2) 5; The upper limit of the weight coefficient w7 is set to 40;

[0036] The weight coefficient w8 corresponding to the simultaneous power outage event = the number of power outages of the current user within one month after the instant voltage drop event 10; The upper limit of the weight coefficient w8 is set to 40;

[0037] The weight coefficient corresponding to the voltage - current inverse correlation coefficient ;

[0038] The weight coefficient w10 corresponding to the theoretical power = (the average value of the theoretical power - 300) / 10; where the weight coefficient w10 is rounded down, and the upper limit of the reduction value of the weight coefficient w10 is set to - 20; The theoretical power = the voltage drop △U The current value at the voltage low point;

[0039] The weight coefficient w11 corresponding to the time period when the instant voltage drop event occurs = if the proportion of the time stamps of the current user's weekly instant voltage drop events between 17:00 - 20:00 is greater than 60%, it is reduced by - 10 to - 30 according to the proportion, where the proportion of 60% is reduced by - 10, the proportion of 80% is reduced by - 20, and the proportion of 100% is reduced by - 30;

[0040] The weight coefficient w11 corresponding to the connector melting event = 60; The weight coefficient w12 of the synchronous daily line loss sudden increase: If the total risk value of the user is greater than 100, and there is a daily line loss sudden increase event within one month in the user's sub - station area, on the day when the synchronous daily line loss suddenly increases, if the total risk value sum(w1~w11) of the user has an increase of more than 40, w12 = 20. Specifically, the line loss sudden increase event includes at least a line loss sudden increase exceeding 2%.

[0041] Preferably, when the trigger condition is met, the meter end anomaly and its corresponding end anomaly degree are determined by analyzing the anomaly data set according to the end - end anomaly recognition algorithm; The following steps are included:

[0042] End - user identification is carried out within the range of users whose front - end hidden - danger risk is greater than the set threshold H, the measurement data in the anomaly data set is extracted and the end - end characteristic data is calculated and obtained. The meter end anomaly is determined by comparing and analyzing the end - end characteristic data through the end - end anomaly recognition algorithm; The end - end anomaly degree is determined according to the anomaly situation corresponding to the meter end anomaly; Among them, the end - end characteristic data includes at least the constant standard deviation of resistance, the theoretical voltage drop power, the voltage - current inverse correlation coefficient, the voltage - same - drop users, and the theoretical resistance of each measurement point.

[0043] Preferably, the terminal anomaly recognition algorithm is used to compare and analyze the terminal feature data to determine the anomaly at the meter terminal, including the following steps:

[0044] When the constant standard deviation of the user resistance < 0.3, it is included in the analysis of the anomaly at the meter terminal;

[0045] When the anti-correlation coefficient range of the user voltage and current is -0.7 to -1, it is included in the analysis of the anomaly at the meter terminal;

[0046] When the single-phase theoretical power range of the user momentary dip event is greater than 300w, it is included in the analysis of the anomaly at the meter terminal; among them, when a momentary dip event occurs for a certain user in the same transformer area, and the voltage fluctuations of other users are close to that of this user, these users are judged as co-dropping users;

[0047] When more than 2 phases of a three-phase user show front-end anomalies, and |maximum theoretical resistance - minimum theoretical resistance| / maximum theoretical resistance < 0.3 for the phase with momentary dip, it is included in the analysis of the anomaly at the meter terminal.

[0048] Preferably, the degree of terminal anomaly is determined according to the anomaly situation corresponding to the anomaly at the meter terminal, including the following steps:

[0049] From the list where the front-end anomaly risk value of the meter is greater than the set threshold H, extract the terminal risk factors corresponding to the anomaly situation, and sum according to the weight coefficients corresponding to each dimension data in the terminal risk factors to determine the degree of terminal anomaly; among them, the weight coefficients of each dimension data in the terminal risk factors are dynamically adjusted according to the actual electricity consumption of users and the voltage fluctuations in the transformer area; each dimension data in the terminal risk factors includes: constant standard deviation of resistance, theoretical voltage drop power, anti-correlation coefficient of voltage and current, front-end anomaly degree, theoretical resistance, and user co-dropping event;

[0050] Among them, determining the degree of terminal anomaly according to the terminal risk factors includes the following steps:

[0051] B1. For the weight coefficient h1 corresponding to the constant standard deviation of resistance, when 0.3 > constant standard deviation of resistance > 0, the weight coefficient h1 is linearly distributed in the range of 40 to 10;

[0052] B2. For the weight coefficient h2 corresponding to the anti-correlation coefficient of voltage and current, when -0.7 > anti-correlation coefficient of voltage and current > -1, the weight coefficient h2 is linearly distributed in the range of 10 to 40;

[0053] B3. For the weight coefficient h3 corresponding to the theoretical voltage drop power, when 300w < theoretical voltage drop power < 600w, the weight coefficient h3 is linearly distributed in the range of 10 to 40, and when the theoretical voltage drop power ≥ 600w, h3 is fixed at 40;

[0054] B4. For the weight coefficient h4 corresponding to the front-end anomaly degree, when there is a front-end anomaly situation where more than 2 out of 3 three-phase users have momentary drop events, and the difference between the maximum value and the minimum value of the corresponding front-end anomaly degree is within the set threshold of 40, and corresponding to the momentary drop , the weight coefficient h4 is set to 20;

[0055] B5. For the weight coefficient h5 corresponding to the user co-drop event, when it is judged as multi-user co-drop; the corresponding weight coefficient h5 is set to 20;

[0056] On the basis that the front-end hidden danger risk value is greater than the set threshold H, based on the analysis dimensions B1 - B5, calculate the end hidden danger risk value. When the end hidden danger risk value is greater than 40, it is determined that there is an end hidden danger.

[0057] Preferably, the training process of the linear regression model is as follows:

[0058] Obtain the front-end risk-causing factors corresponding to the front-end anomaly degree as the input of the linear regression model, train the linear regression model with the maximum front-end hidden danger risk value as the optimization target, and obtain the front-end risk weights of each front-end risk-causing factor; when the front-end hidden danger risk value is greater than the set threshold H, use the end samples and pre-metering fault samples classified by on-site judgment in the front-end risk-causing sample list as the input of the linear regression model, and train the linear regression model with the maximum end hidden danger risk value as the optimization target to obtain the end risk weights of each end risk-causing factor;

[0059] Among them, the front-end risk-causing factors include multi-dimensional data, specifically including: momentary drop voltage difference, low voltage, theoretical resistance, theoretical resistance change trend, theoretical resistance standard deviation, momentary drop times, synchronous power outage event, synchronous daily line loss sudden increase event, voltage-current anti-correlation coefficient, theoretical power, momentary drop event occurrence time period, joint melting event;

[0060] The end risk-causing factors include multi-dimensional data, specifically including: resistance constant standard deviation, theoretical voltage drop power, voltage-current anti-correlation coefficient, front-end anomaly degree, theoretical resistance, and user co-drop event.

[0061] In the second aspect, a technical solution provided in the embodiments of the present invention is also a low-voltage substation area hidden danger identification system, including:

[0062] The first construction module: sequentially obtain the measurement data of the low-voltage substation area according to the sampling period to construct an initial data set;

[0063] The second construction module: sequentially screen the initial data set according to the momentary drop event criterion to construct an abnormal data set;

[0064] The first analysis module: Determine the front-end anomaly of the meter and its corresponding front-end anomaly degree according to analysis dimensions such as the connector melting recognition algorithm analysis based on the voltage dip event;

[0065] The second analysis module: On the basis that the front-end hidden danger risk value is greater than the set threshold H, determine the end anomaly of the meter and its corresponding end anomaly degree according to the end anomaly recognition algorithm analysis of the anomaly data set;

[0066] The calculation module: Determine the front-end risk weight corresponding to the front-end anomaly of the meter and the end risk weight corresponding to the end anomaly of the meter through the analytic hierarchy process;

[0067] The first fusion module: Determine the front-end hidden danger risk value according to the front-end anomaly of the meter, the front-end anomaly degree, and the front-end risk weight; The second fusion module: Determine the end hidden danger risk value according to the end anomaly of the meter, the end risk weight, and the end anomaly degree;

[0068] The execution module: Determine the corresponding hidden danger emergency strategy for the power distribution area according to the front-end hidden danger risk value or / and the end hidden danger risk value.

[0069] In a third aspect, a technical solution provided in an embodiment of the present invention is an electronic device, including a memory and a processor. A computer program is stored in the memory. When the processor calls the computer program in the memory, the steps of the method for identifying hidden dangers in a low-voltage power distribution area based on degradation risk analysis are implemented.

[0070] In a fourth aspect, a technical solution provided in an embodiment of the present invention is a storage medium. A computer-executable instruction is stored in the storage medium. When the computer-executable instruction is loaded and executed by a processor, the steps of the method for identifying hidden dangers in a low-voltage power distribution area based on degradation risk analysis are implemented.

[0071] Advantages of the present invention:

[0072] Based on the momentary voltage dip event criterion, this application analyzes the obtained data of the low-voltage power distribution area to construct an abnormal data set. By using the joint melting recognition algorithm to quantify the abnormal degree of the front-end contact resistance for the key degradation characteristics (including front-end characteristic data and end-end characteristic data) in the abnormal data set, and combining with the end-end abnormal recognition algorithm to analyze the load fluctuation characteristics, the front-end / end-end fault modes are separated. Then, a linear regression model is used to correlate the abnormal degrees of the front-end / end-end with the historical degradation data, dynamically allocate the risk weights of each risk factor, and further calculate the front-end hidden danger risk value and the end-end hidden danger risk value respectively according to the abnormal degree, abnormal type and the corresponding risk weights, so as to construct a two-dimensional evaluation system, solve the technical problems of insufficient decoupling of abnormal characteristics (i.e., inability to distinguish front-end contact degradation from end-end overload), static risk weights (i.e., ignoring the dynamic evolution of hidden dangers), and fuzzy hidden danger positioning (i.e., relying on manual investigation) in the hidden danger identification of low-voltage power distribution areas, significantly improve the accuracy and efficiency of hidden danger identification, and provide a reliable data basis for the prediction of hidden dangers and the formulation of response strategies in low-voltage power distribution areas.

[0073] The above-mentioned summary of the invention content is only an overview of the technical solution of the present invention. In order to be able to understand the technical means of the present invention more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present invention more obvious and understandable, the following specifically illustrates the specific embodiments of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] By reading the detailed description of the non-limiting embodiments with reference to the following drawings, other features, purposes and advantages of the present invention will become more obvious. The drawings are only for the purpose of showing the preferred embodiments and are not considered as a limitation of the present invention. And throughout the drawings, the same reference numerals are used to represent the same components.

[0075] Figure 1 It is a flowchart of the method for identifying hidden dangers in low-voltage power distribution areas based on degradation risk analysis of the present invention.

[0076] Figure 2 It is a schematic structural diagram of a system for identifying hidden dangers in low-voltage power distribution areas of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0077] In order to make the purpose, technical solution and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described here are only the best embodiments of the present invention, which are only used to explain the present invention and do not limit the protection scope of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the protection scope of the present invention.

[0078] Before discussing the exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts depict the operations (or steps) as sequential processes, many of the operations (or steps) can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operations are completed, but it can also have additional steps not included in the drawings; the process can correspond to a method, function, procedure, subroutine, subprogram, and so on.

[0079] Embodiment 1: As Figure 1 shown, a hidden danger identification method for low-voltage power distribution areas based on deterioration risk analysis includes the following steps:

[0080] Obtain the measurement data of the low-voltage power distribution area in sequence according to the sampling period to construct an initial data set; screen the initial data set in sequence according to the momentary drop event criterion to construct an abnormal data set.

[0081] It can be understood that due to the huge amount of data of power users, a data acquisition period can be set. For example, in this embodiment, the sampling period can be set to 15 minutes. For each user, there are 96 measurement time points per day, and the measurement data corresponding to each user end in the low-voltage power distribution area is collected respectively. Among them, the measurement data can be the main meter voltage, user voltage, and user current; the momentary drop event includes the voltage and current data of two measurements before and after, and the voltage and current data include the current at the voltage low point, the voltage at the voltage low point, the current at the voltage high point, and the voltage at the voltage high point; by participating in subsequent data analysis operations with the original measurement data, derivative data such as the user voltage difference and current average value involved in the momentary drop event criterion can be obtained; through the momentary drop event criterion, abnormal events in the low-voltage power distribution area can be initially screened out, facilitating focusing on key deterioration data for abnormal events to improve the efficiency and accuracy of abnormal analysis in the low-voltage power distribution area.

[0082] As an alternative embodiment, the momentary drop event criterion includes:

[0083] Criterion A1: Extract data using a sliding window with a width of 2 measurement points, and the voltage drop within the window between two measurement points is greater than 5V;

[0084] Criterion A2: The absolute value of the voltage difference between the main meter voltage and the user voltage is greater than 5V;

[0085] Criterion A3: The current at the voltage low point is greater than 0.1A, and (current at the voltage low point - current at the voltage high point) / current at the voltage high point > 50% (current at the voltage low point - current at the voltage high point) > the average value of the largest 5 currents at the same measurement point within the unit time 10%;

[0086] Criterion A4: If the three-phase user voltage drops instantaneously at the same time, the instantaneous drop event is excluded and classified as an end-user;

[0087] Criterion A5: Obtain the current at the moment when the voltage of the instantaneous drop event drops to the low voltage. If the current disappears within 12 measurement moments at the same measurement point after the instantaneous drop, or the current at the start of the instantaneous drop is less than 20% of the average current at the voltage low point, or the current is less than 0.1 A and the voltage has not recovered to the high point voltage, the instantaneous drop event is excluded;

[0088] Criterion A6: User voltage difference Current > 1000w. Due to a single-point fault being unable to withstand excessive power, the instantaneous drop event is excluded;

[0089] If the above criteria are all met, the instantaneous drop event is established.

[0090] It can be understood that by using a 2-measurement-point sliding window to capture the voltage mutation between two measurement points in the adjacent 15 minutes, and combining with a 5V threshold to dynamically filter short-term voltage fluctuations, the sensitivity of the data captured for the instantaneous drop event is ensured. Detecting the disappearance or sudden drop of current within 12 measurement points effectively excludes pseudo-instantaneous drop events caused by instantaneous poor contact or measurement errors, improving the data quality of the abnormal data set; by excluding high-energy instantaneous drop events (such as interference from end-users with high loads at the end of the line) through "user voltage difference × current > 1000W", potential hazards of overheating joints or sudden changes in end impedance with high risks can be focused on. Through the above criteria, the efficiency of the preliminary all-round investigation of potential hazards is significantly improved.

[0091] According to the joint melting recognition algorithm, analyze the abnormal data set to determine the abnormality at the front end of the meter and its corresponding front-end abnormality degree; when the trigger condition is met, according to the end-end abnormality recognition algorithm, analyze the abnormal data set to determine the abnormality at the end of the meter and its corresponding end-end abnormality degree.

[0092] As an optional embodiment, analyzing the abnormal data set according to the joint melting recognition algorithm to determine the abnormality at the front end of the meter and its corresponding front-end abnormality degree includes the following steps:

[0093] Extract the measurement data in the abnormal data set and calculate to obtain the front-end characteristic data. Through the joint melting recognition algorithm, compare and analyze the front-end characteristic data to determine the abnormality at the front end of the meter; determine the front-end abnormality degree according to the abnormal situation corresponding to the abnormality at the front end of the meter; wherein, the front-end characteristic data at least includes the instantaneous drop voltage difference, voltage-current anti-correlation coefficient, average current, and average value of the high point of the instantaneous drop voltage at each measurement point.

[0094] In this embodiment, extracting the measurement data in the abnormal data set and calculating to obtain the front-end characteristic data representing the front-end abnormality can focus on the key deteriorating data, thereby improving the efficiency of data processing. Then, through the joint melting recognition algorithm, identify whether there is a melting abnormality from multiple dimensions, improving the reliability of the recognition result.

[0095] As an alternative embodiment, the front-end feature data is compared and analyzed through the joint melting recognition algorithm to determine the abnormality at the front end of the meter, including the following steps:

[0096] When the single instantaneous voltage drop difference Current > 5W, and the voltage-current anti-correlation coefficient in the 7 days before melting judgment is less than -0.5, and the single current duration is greater than 80% of the current at the low point of the instantaneous voltage drop, and the voltage recovers to the high voltage point before the instantaneous voltage drop, then it is judged that there is a melting abnormality at the front end of the meter;

[0097] When after a single instantaneous voltage drop ends, there are m (m can be taken as 3) or more sampling moments corresponding to the current greater than the average current of all low points of the instantaneous voltage drop within the unit time within the range of n sampling points (n can be taken as 96) by the user, and at the same time the voltage is greater than the average value of the high points of the instantaneous voltage drop, then it is judged that there is a melting abnormality at the front end of the meter.

[0098]

[0099] In this embodiment, considering that copper and aluminum will reach the metal softening point and melting point under the condition of temperature rise, and after reaching, due to the actual situation that the contact area of the joint will temporarily increase (but will intensify oxidation and even fall off after cooling), combined with the metal melting characteristics, the above joint melting recognition scheme is designed, which can comprehensively analyze whether there is a melting situation for the current user, so as to achieve early warning.

[0099] As an alternative embodiment, the front-end abnormality degree is determined according to the abnormal situation corresponding to the front end of the meter, including the following steps:

[0100] Extract the front-end risk factors corresponding to the abnormal situation, and determine the front-end abnormality degree by accumulating the weight coefficients corresponding to the data of each dimension in the front-end risk factors; among them, the weight coefficients of the data of each dimension in the front-end risk factors are dynamically adjusted according to the actual electricity consumption of the user and the voltage fluctuation situation of the substation area; among them, the data of each dimension of the front-end risk factors include: instantaneous voltage drop difference, low voltage, theoretical resistance, theoretical resistance change trend, theoretical resistance standard deviation, instantaneous drop times, synchronous power outage events, synchronous daily line loss sudden increase events, voltage-current anti-correlation coefficient, theoretical power, instantaneous drop event occurrence time period, joint melting event.

[0101] In this embodiment, since the abnormal situations leading to the front-end abnormality are relatively complex, the original data is obtained by classifying the front-end risk factors corresponding to the abnormal situations, and by analyzing each kind of original data separately, and comprehensively judging the analysis results to further determine the front-end abnormality degree.

[0102] Furthermore, determining the front-end abnormality degree according to the front-end risk factors includes the following steps:

[0103] The weight coefficient w1 corresponding to the instantaneous voltage drop = the average value of the voltage differences of all instantaneous voltage drop events of the current user within 7 days - 5. Since the greater the voltage difference, the worse the user's electricity experience and the higher the maintenance priority, the upper limit of the weight coefficient w1 is set to 20. The upper limit of the weight is set to prevent generalization in extreme cases. Among them, the instantaneous voltage drop is the high point voltage of the instantaneous voltage drop event - the low point voltage of the instantaneous voltage drop event.

[0104] The weight coefficient w2 corresponding to the low voltage = 205v - the average value of the low point voltages of the 5 lowest low point voltage instantaneous voltage drop events of the current user within 7 days; since the lower the low point voltage, the worse the user's electricity experience, and to prevent generalization in extreme cases, the upper limit of the weight coefficient w2 is set to 20.

[0105] The weight coefficient w3 corresponding to the theoretical resistance = 10 The average value of the voltage differences of the instantaneous voltage drop events of the current user within 7 days / the average value of the current differences of the instantaneous voltage drop events. The upper limit of the weight coefficient w3 is set to 40. Among them, the voltage difference is the high point voltage of the instantaneous voltage drop event - the low point voltage of the instantaneous voltage drop event, and the current difference is the current at the low point voltage of the instantaneous voltage drop event - the current at the high point voltage of the instantaneous voltage drop event. It can be understood that the copper-aluminum oxidation resistance is between 0.1 ohm and 50 ohms, and the upper limit is set to prevent generalization in extreme cases.

[0106] The weight coefficient w4 corresponding to the theoretical resistance change trend = (resistance change rate - 1) 20; among them, the resistance change rate = the average value of the theoretical resistances of the last 3 instantaneous voltage drop events of the current user within 7 days / the average value of the theoretical resistances of the first 3 instantaneous voltage drop events; it can be understood that as the resistance increases, the power value of the same user's current causing a fault and burning increases.

[0107] The weight coefficient corresponding to the theoretical resistance standard deviation , where, is the average resistance, is the maximum resistance, is the minimum resistance, N is the total number of instantaneous voltage drop events, is the resistance of the nth instantaneous voltage drop event; it can be understood that the smaller the theoretical resistance standard deviation, the greater the possibility of a high-resistance contact point appearing on the outgoing line. However, when it is less than the threshold (0.3), the resistance is surprisingly constant, approaching the impedance caused by fixed line parameters.

[0108] The weight coefficient w6 corresponding to the number of instantaneous voltage drops on the same day = (the number of instantaneous voltage drop events of the current user on the same day - 1) 10; the upper limit of the weight coefficient w6 is set to 20; it can be understood that the more the number of instantaneous voltage drops, the more frequent the voltage drop.

[0109] The weight coefficient w7 corresponding to the number of instantaneous voltage drops in seven days = (the number of instantaneous voltage drop events of the current user in seven days - 2) 5; Set the upper limit of the weight coefficient w7 to 40; It can be understood that the more the number of momentary drops, the more frequent the voltage drop.

[0110] The weight coefficient w8 corresponding to the synchronous power outage event = the number of power outages of the current user within one month after the momentary drop event 10; Set the upper limit of the weight coefficient w8 to 40; It can be understood that the more the number of synchronous power outages, the higher the possibility of the user having a high resistance problem.

[0111] The weight coefficient corresponding to the voltage-current inverse correlation coefficient ; It can be understood that the weight coefficient w9 corresponding to the voltage-current inverse correlation coefficient is calculated using the corr correlation coefficient function (for example, it can be based on the python compilation environment). The input is the time series of voltage and current, which is optimized through the sequence of current crossing the dead zone. The output range is between -1 and 1. For the case where the weight coefficient is greater than -0.8 but less than -0.2, the risk value is calculated to obtain the dynamic weight coefficient, avoiding the problem of inaccurate hidden danger identification caused by ignoring the dynamic evolution of hidden dangers.

[0112] The weight coefficient w10 corresponding to the theoretical power = (the average value of the theoretical power - 300) / 10; where the weight coefficient w10 is rounded down, and the upper limit of the reduction value of the weight coefficient w10 is set to -20; The theoretical power = the voltage drop △U The current value at the voltage low point; It can be understood that due to the voltage at the head end of the transformer area or the three-phase imbalance, the voltage will also decrease at the moment of small current. Therefore, it is necessary to reduce the risk of such users to adapt to the specific working conditions.

[0113] The weight coefficient w11 corresponding to the time period when the momentary drop event occurs = when the proportion of the time stamps of the current user's weekly momentary drop events between 17:00 and 20:00 is greater than 60%, it is reduced by -10 to -30 according to the proportion, where the proportion of 60% is reduced by -10, the proportion of 80% is reduced by -20, and the proportion of 100% is reduced by -30; It can be understood that due to the oxidation and heating of a single node being unable to support high power, the possibility of hidden dangers at the end increases as the theoretical voltage drop power increases. It can be understood that since this period is the peak period of evening electricity consumption, the normal voltage drop of the line increases due to the simultaneous electricity consumption of the users in the transformer area, which may be miscounted into the momentary drop event. A too high proportion means that the user will only experience voltage drop during the high load time of synchronous electricity consumption in the evening. Such problems are caused by synchronous large load electricity consumption and are not latent defect faults. Risk reduction processing is carried out. Especially note that when the computing power is sufficient, the situation of a large number of residential users can be comprehensively considered for inclusion in the calculation.

[0114] The weight coefficient w11 corresponding to the connector melting event = 60; specifically, see the case where it is identified from multiple dimensions through the connector melting recognition algorithm whether the user has melting anomalies.

[0115] Synchronous daily line loss sudden increase weight coefficient w12: If the total risk value of the user is greater than 100, and there is a daily line loss sudden increase event within 1 month in the user's substation area, on the day of the synchronous daily line loss sudden increase, if the total risk value sum (w1~w11) of the user has an increase of more than 40, w12 = 20. Specifically, the line loss sudden increase event at least includes a line loss sudden increase exceeding 2%.

[0116] As an alternative embodiment, when the trigger condition is met, analyze the abnormal data set according to the end anomaly recognition algorithm to determine the meter end anomaly and its corresponding end anomaly degree; the steps are as follows:

[0117] Among the users whose front-end hidden danger risk value is determined to be greater than 80, end-user identification is required. Extract the measurement data in the abnormal data set and calculate to obtain the end characteristic data. Compare and analyze the end characteristic data through the end anomaly recognition algorithm to determine the meter end anomaly; determine the end anomaly degree according to the abnormal situation corresponding to the meter end anomaly; among them, the end characteristic data at least includes the constant standard deviation of resistance, theoretical voltage drop power, voltage-current anti-correlation coefficient, voltage-drop users, and theoretical resistance of each measurement point.

[0118] In this embodiment, whether to perform end anomaly situation discrimination is triggered according to the range of the front-end hidden danger risk value. By analyzing the end characteristics of users with high risks, the computing resources involved in the operation can be reduced. By comprehensively analyzing the multi-dimensional end characteristic data, the problem of high missed detection rate of end hidden dangers caused by single-dimensional judgment can be avoided.

[0119] Preferably, compare and analyze the end characteristic data through the end anomaly recognition algorithm to determine the meter end anomaly; the steps are as follows:

[0120] When the constant standard deviation of the user's resistance < 0.3, it is included in the analysis of the meter end anomaly;

[0121] When the range of the voltage-current anti-correlation coefficient of the user is -0.7~-1, it is included in the analysis of the meter end anomaly; it can be understood that compared with latent faults such as joints and cable bulges, the end impedance increase caused by line parameters shows a stronger voltage-current anti-correlation;

[0122] When the single-phase theoretical power range of the user's voltage dip event is greater than 300w, it is included in the analysis of the meter end anomaly; among them, when a voltage dip event occurs for a certain user in the same substation area, and the voltage fluctuations of other users are close to that user, these users are judged as voltage-drop users; in on-site case analysis, the theoretical voltage drop power above 300w is generally for end users;

[0123] When more than two phases of a three-phase user have front-end anomalies, and the |maximum theoretical resistance - minimum theoretical resistance| / maximum theoretical resistance of the phase with momentary voltage drop is less than 0.3, it is included in the analysis of anomalies at the end of the meter; it can be understood that compared with latent faults such as joint and cable bulging, the excessive end impedance caused by line parameters shows a stronger voltage-current inverse correlation.

[0124] In this embodiment, when the substation area topology is not perfect (since the power PMS system is deployed in the intranet, newly installed users cannot enter data on the mobile Internet. After drawing on-site and supplementing it later, the supplementing personnel are often different from the on-site construction personnel, resulting in inaccurate topology data), the resistance of joint oxidation, cable bulging, and clamp problems will not remain constant due to external wind blowing and shaking, temperature, thermal expansion and contraction, heat softening, melting, etc., but only relatively constant. However, the line impedance caused by line parameters and power supply radius at the end of the line is relatively stable within a certain season. By analyzing multi-dimensional feature data through the above-mentioned end anomaly recognition algorithm to identify end high-resistance problems, it is possible to distinguish urgent joint and cable heating faults, and plans can be made before the peak electricity consumption period for end high-resistance problems, making on-site identification and defect elimination more planned.

[0125] Preferably, determining the degree of end anomaly according to the anomaly situation corresponding to the end of the meter includes the following steps:

[0126] From the list where the front-end anomaly risk value of the meter is greater than the set threshold H, extract the end risk factors corresponding to the anomaly situation, and sum according to the weight coefficients corresponding to each dimension data in the end risk factors to determine the degree of end anomaly; among them, the weight coefficients of each dimension data in the end risk factors are dynamically adjusted according to the electricity consumption of actual users and the voltage fluctuation situation of the substation area; each dimension data in the end risk factors includes the standard deviation of resistance constancy, theoretical voltage drop power, voltage-current inverse correlation coefficient, front-end anomaly degree, theoretical resistance, and user co-drop event.

[0127] In this embodiment, by combining the end risk factors to describe and dynamically evaluate the end anomaly, through the coupling analysis and dynamic weight adaptation of multi-dimensional risk factors, the accurate quantitative grading and evolution trend prediction of end hidden dangers are realized, significantly improving the recognition efficiency of end hidden dangers.

[0128] As an alternative embodiment, determining the degree of end anomaly according to the end risk factors includes the following steps:

[0129] B1. For the weight coefficient h1 corresponding to the standard deviation of resistance constancy, when 0.3 > the standard deviation of resistance constancy > 0, the weight coefficient h1 is linearly distributed numerically between 40 and 10.

[0130] B2. There is a weight coefficient h2 corresponding to the voltage-current inverse correlation coefficient. When -0.7 > voltage-current inverse correlation coefficient > -1, the weight coefficient h2 is linearly distributed in the range of 10 to 40.

[0131] B3. There is a weight coefficient h3 corresponding to the theoretical voltage drop power. When 300w < theoretical voltage drop power < 600w, the weight coefficient h3 is linearly distributed in the range of 10 to 40. When the theoretical voltage drop power is greater than or equal to 600w, the weight coefficient is fixed at 40.

[0132] B4. There is a weight coefficient h4 corresponding to the front-end abnormality degree. For three-phase users, when there are more than 2 phases with momentary drop events, and the difference between the maximum and minimum values of the corresponding front-end abnormality degree is within the set threshold of 40, and there is a corresponding momentary drop , the weight coefficient h4 is set to 20.

[0133] B5. There is a weight coefficient h5 corresponding to the user co-drop event. When it is judged as multi-user co-drop, the corresponding weight coefficient h5 is set to 20.

[0134] It can be understood that on the basis that the front-end hidden danger risk value is greater than the set threshold H, based on the analysis dimensions B1 - B5, the end hidden danger risk value is calculated. When the end hidden danger risk value is greater than the set threshold Z, for example, the threshold = 40, it is determined that there is an end hidden danger. By comprehensively analyzing multi-dimensional data, the reliability of the analysis results can be improved.

[0135] Based on the abnormal situation data corresponding to the front-end abnormality degree and the end abnormality degree, the front-end risk weight corresponding to the front-end abnormality of the meter and the end risk weight corresponding to the end abnormality of the meter are determined through a linear regression model.

[0136] Specifically, the training process of the linear regression model is as follows:

[0137] Obtain the front-end risk factors corresponding to the front-end abnormality degree as the input of the linear regression model, and train the linear regression model with the maximum front-end hidden danger risk value as the optimization target to obtain the front-end risk weight of each front-end risk factor; when the front-end hidden danger risk value is greater than the set threshold H (for example, the threshold H = 80), use the end samples and pre-meter fault samples after on-site judgment and classification in the front-end risk sample list (including all end risk factors of each sample) as the input of the linear regression model, and train the linear regression model with the maximum end hidden danger risk value as the optimization target to obtain the end risk weight of each end risk factor;

[0138] Among them, the front-end risk factors include multi-dimensional data, specifically including: instantaneous voltage drop difference, low voltage, theoretical resistance, theoretical resistance change trend, theoretical resistance standard deviation, number of instantaneous drops, synchronous power outage events, synchronous daily line loss sudden increase events, voltage-current inverse correlation coefficient, theoretical power, time period when the instantaneous drop event occurs, and connector melting event;

[0139] The end risk factors include multi-dimensional data, specifically including: resistance constant standard deviation, theoretical voltage drop power, voltage-current inverse correlation coefficient, front-end anomaly degree, theoretical resistance, and user co-drop event.

[0140] In this embodiment, the goal of pre-metering fault training is front-end problems (including end problems), that is, the front-end hidden danger risk is the greatest; after the front-end risk weights of the front-end risk factors corresponding to the pre-metering fault are trained, according to the on-site investigation of the pre-metering fault, collect the end conditions in the pre-metering fault data set, classify the pre-metering fault data set samples into pre-metering faults and end faults, and use the classified pre-metering fault data set samples combined with the end risk factors corresponding to the end anomaly degree to train the linear regression model of the end to obtain the end risk weights of the end risk factors. After training, in the list where the pre-metering fault risk value is greater than the set threshold H (for example, threshold H = 80), use the trained end identification model to identify the hidden end users among the high-risk users (calculate the risk value by combining the end risk weights with 5 dimensions of end risk factors, and when the risk value is greater than the set threshold, determine the hidden end users among the high-risk users). Through 12 front-end risk factors such as the number of instantaneous drops and the theoretical resistance change trend, quantify the dynamic evolution law of hidden dangers such as connector oxidation and loosening, make the front-end risk value calculation strongly correlated with the actual deterioration speed, and improve the accuracy of front-end hidden danger identification. When the front-end hidden danger risk > 80, combine 5 end risk factors such as user co-drop event and resistance constant standard deviation to dynamically correct the end weight distribution and avoid the problem that the front-end high risk covers up the end hidden danger.

[0141] Determine the front-end hidden danger risk value based on the front-end anomaly of the meter, the front-end anomaly degree, and the front-end risk weight and / or the end hidden danger risk value determined based on the end anomaly of the meter, the end risk weight, and the end anomaly degree.

[0142] It can be understood that the front-end hidden danger risk value can be determined by weighted summation through the front-end anomaly of the meter, the front-end anomaly degree, and the front-end risk weight; similarly, the end hidden danger risk value determined by weighted summation through the end anomaly of the meter, the end risk weight, and the end anomaly degree.

[0143] Determine the hidden danger emergency strategy corresponding to the transformer substation area according to the front-end hidden danger risk value and / or the end hidden danger risk value.

[0144] It is understandable that for the pre-metering faults (front-end hidden dangers), emergency defect elimination is required according to the risk value, and for the end of the long power supply radius (end hidden dangers), defect elimination is required before the arrival of high-load periods such as summer peak load or winter peak load. It should be noted that the defect elimination measures are only related to the type of hidden danger and have nothing to do with the magnitude of the risk value.

[0145] Embodiment 2: Another technical solution provided in the embodiments of the present invention is a hidden danger identification system for low-voltage power distribution areas, as Figure 2 shown, including:

[0146] The first construction module 101: sequentially obtains the measurement data of the low-voltage power distribution area according to the sampling period to construct an initial data set;

[0147] The second construction module 102: sequentially screens the initial data set according to the momentary drop event criterion to construct an abnormal data set;

[0148] The first analysis module 103: analyzes the joint melting recognition algorithm according to the momentary drop event and other analysis dimensions to determine the abnormality at the front end of the meter and its corresponding front-end abnormality degree;

[0149] The second analysis module 104: on the basis that the front-end hidden danger risk value is greater than the set threshold H, analyzes the abnormal data set according to the end-end abnormality recognition algorithm to determine the abnormality at the end of the meter and its corresponding end-end abnormality degree;

[0150] The calculation module 105: determines the front-end risk weight corresponding to the front-end abnormality of the meter and the end-end risk weight corresponding to the end-end abnormality of the meter through the analytic hierarchy process;

[0151] The first fusion module 106: determines the front-end hidden danger risk value according to the front-end abnormality of the meter, the front-end abnormality degree, and the front-end risk weight; the second fusion module 107: determines the end-end hidden danger risk value according to the end-end abnormality of the meter, the end-end risk weight, and the end-end abnormality degree;

[0152] The execution module 108: determines the hidden danger emergency strategy corresponding to the power distribution area according to the front-end hidden danger risk value or / and the end-end hidden danger risk value.

[0153] This embodiment has at least the following substantial technical effects: By using the instantaneous drop event criterion to analyze the obtained data of the low-voltage power distribution area and construct an abnormal data set, this embodiment quantifies the abnormal degree of the front-end contact resistance through the joint melting recognition algorithm for the key deterioration features (including front-end feature data and end feature data) in the abnormal data set, analyzes the load fluctuation characteristics by combining the end abnormal recognition algorithm, and separates the front-end / end fault modes; and uses a linear regression model to associate the abnormal degrees of the front-end / end with the historical deterioration data, dynamically assigns the risk weights of each risk factor, and then calculates the front-end hidden danger risk value and the end hidden danger risk value respectively according to the abnormal degree, abnormal type and the corresponding risk weights, constructs a two-dimensional evaluation system, solves the technical problems of insufficient decoupling of abnormal features (i.e., inability to distinguish front-end contact deterioration from end overload), static risk weights (i.e., ignoring the dynamic evolution of hidden dangers), and fuzzy hidden danger location (i.e., relying on manual inspection) in the hidden danger identification of low-voltage power distribution areas, significantly improves the accuracy and efficiency of hidden danger identification, and provides a reliable data basis for the prediction of hidden dangers and the formulation of response strategies in low-voltage power distribution areas.

[0154] Embodiment 3: In a third aspect, a technical solution provided in an embodiment of the present invention is an electronic device, including a memory and a processor. A computer program is stored in the memory, and when the processor calls the computer program in the memory, the steps of the method for identifying hidden dangers in a low-voltage power distribution area based on deterioration risk analysis are implemented.

[0155] Embodiment 4: In a fourth aspect, a technical solution provided in an embodiment of the present invention is a storage medium. A computer-executable instruction is stored in the storage medium, and when the computer-executable instruction is loaded and executed by a processor, the steps of the method for identifying hidden dangers in a low-voltage power distribution area based on deterioration risk analysis are implemented.

[0156] Through the description of the above embodiments, those skilled in the art can understand that for the convenience and simplicity of description, only the above division of each functional module is used as an example. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of a specific device is divided into different functional modules to complete all or part of the functions described above.

[0157] In the embodiments provided in the present application, it should be understood that the disclosed structures and methods can be implemented in other ways. For example, the embodiments of the structures described above are merely illustrative. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another structure, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections between each other can be through some interfaces, and the indirect couplings or communication connections of structures or units can be in electrical, mechanical or other forms.

[0158] The units described as separate components may or may not be physically separated. The components displayed as units may be one physical unit or multiple physical units, that is, they can be located in one place, or they can be distributed to multiple different places. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0159] In addition, each functional unit in the embodiments of the present application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0160] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiments of the present application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions for causing a device (which can be a single-chip microcomputer, a chip, etc.) or a processor to execute all or part of the steps of the methods of the various embodiments of the present application. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read only memory (ROM), random access memory (RAM), magnetic disks or optical discs and other various media that can store program codes.

[0161] The above-mentioned specific implementation manners are the preferred implementation manners of the method and system for identifying hidden dangers in low-voltage power distribution areas based on degradation risk analysis of the present invention, and do not limit the specific scope of the present invention hereby. The scope of the present invention includes but is not limited to this specific implementation manner. All equivalent changes made according to the shape and structure of the present invention are within the protection scope of the present invention.

Claims

1. A method for identifying hidden dangers in low-voltage areas based on degradation risk analysis, characterized by: The steps include: Acquire the measured data of the low-voltage area in sequence according to the sampling period to construct the initial data set; filter the initial data set in sequence according to the instantaneous drop event judgment criteria to construct the abnormal data set; The abnormal data set is analyzed according to the joint melting recognition algorithm to determine the front-end abnormality of the meter and its corresponding front-end abnormality degree; when the trigger condition is met, the abnormal data set is analyzed according to the terminal abnormality recognition algorithm to determine the terminal abnormality of the meter and its corresponding terminal abnormality degree; The front-end risk weight corresponding to the front-end abnormality of the meter and the terminal risk weight corresponding to the terminal abnormality of the meter are determined by combining the abnormal situation data corresponding to the front-end abnormality degree and the terminal abnormality degree through a linear regression model; Determine the front-end hidden danger risk value based on the front-end abnormality of the meter, the degree of the front-end abnormality and the front-end risk weight, or / and determine the terminal hidden danger risk value based on the terminal abnormality of the meter, the terminal risk weight and the degree of the terminal abnormality; Determine the hidden danger emergency strategy corresponding to the substation area according to the front-end hidden danger risk value and / or the terminal hidden danger risk value; Analyzing the abnormal data set according to the joint melting identification algorithm to determine the front-end abnormality of the meter and its corresponding front-end abnormality degree includes the following steps: Extract the measurement data in the abnormal data set and calculate the front-end feature data. Use the joint melting recognition algorithm to compare and analyze the front-end feature data to determine the front-end abnormality of the meter. Determine the degree of front-end abnormality according to the abnormal situation corresponding to the front-end abnormality of the meter; wherein the front-end characteristic data at least includes the instantaneous voltage drop difference, the voltage-current anti-correlation coefficient, the current average value and the instantaneous voltage drop high point average value of each measuring point; When the trigger condition is met, the abnormal data set is analyzed according to the terminal abnormality recognition algorithm to determine the terminal abnormality of the meter and its corresponding terminal abnormality degree; the steps include: End user identification is performed within the user range where the risk of hidden dangers at the front end is determined to be greater than the set threshold H, the measurement data in the abnormal data set is extracted and the terminal feature data is calculated and obtained, and the terminal abnormality of the meter is determined by comparing and analyzing the terminal feature data through the terminal abnormality identification algorithm; the degree of the terminal abnormality is determined according to the abnormal situation corresponding to the meter terminal abnormality; wherein the terminal feature data at least includes the constant standard deviation of resistance, theoretical voltage drop power, voltage-current anti-correlation coefficient, voltage drop users and theoretical resistance of each measurement point.

2. The method for identifying hidden dangers in low voltage substations based on degradation risk analysis according to claim 1 is characterized in that: The measured data include total meter voltage, user voltage, and user current.

3. The method for identifying hidden dangers in low voltage substations based on degradation risk analysis according to claim 1 or 2, characterized in that: The instantaneous drop event criterion includes: Criterion A1: Use a sliding window with a width of 2 measuring points to extract data, and the voltage drop between the two measuring points in the window is greater than 5V; Criterion A2: The absolute value of the voltage difference between the total meter voltage and the user voltage is greater than 5V; Criteria A3: The current at the low voltage point is greater than 0.1A, and (current at the low voltage point - current at the high voltage point) / current at the high voltage point > 50%, and (current at the low voltage point - current at the high voltage point) > the average value of the maximum 5 currents at the same measurement point in unit time * 10%; Criteria A4: If the voltage of three-phase users drops simultaneously, the voltage drop event is excluded and classified as the end user; Criteria A5: Obtain the current when the voltage drops to a low voltage in a transient sag event. If the current disappears within 12 measurement moments at the same measurement point after the transient sag, or the current at the start of the transient sag is less than 20% of the average current at the low voltage point, or the current is less than 0.1A and the voltage has not recovered to the high voltage point, the transient sag event is ruled out; Criteria A6: User voltage difference*current>1000w, instantaneous drop event is excluded; If the above criteria are met at the same time, the instantaneous drop event is established; The instantaneous drop event includes the voltage and current data of the two measurements before and after, and the voltage and current data include the current at the low voltage point, the voltage at the low voltage point, the current at the high voltage point, and the voltage at the high voltage point.

4. The method for identifying hidden dangers in low voltage substations based on degradation risk analysis according to claim 1 is characterized in that: The front-end feature data is compared and analyzed by the joint melting identification algorithm to determine the front-end abnormality of the meter, including the following steps: When the single instantaneous voltage drop difference*current>5W, and the voltage-current anti-correlation coefficient 7 days before the melting judgment is less than -0.5, and the single current is continuously greater than the current*80% of the instantaneous voltage low point, and the voltage recovers to the voltage high point before the instantaneous drop, it is judged that the front end of the meter is abnormal and there is a melting abnormality; When a single instantaneous sag ends, if there are m or more sampling moments within the range of n sampling points where the current is greater than the average current of all instantaneous sag voltage low points per unit time * 120%, and the voltage is greater than the average current of instantaneous sag voltage high points, then it is judged that there is a melting abnormality at the front end of the meter.

5. The method for identifying hidden dangers in low voltage substations based on degradation risk analysis according to claim 1 is characterized in that: Determining the degree of front-end abnormality according to the abnormal situation corresponding to the front-end abnormality of the meter includes the following steps: The front-end risk factors corresponding to the abnormal situation are extracted, and the degree of front-end abnormality is determined by accumulating the weight coefficients corresponding to the data of each dimension in the front-end risk factors; among which, the weight coefficients of the data of each dimension in the front-end risk factors are dynamically adjusted according to the user's electricity consumption and the voltage fluctuation of the substation; the data of each dimension of the front-end risk factors include: instantaneous sag voltage difference, low voltage, theoretical resistance, theoretical power group change trend, theoretical power group standard deviation, number of instantaneous sags, synchronous power outage events, synchronous daily line loss sudden increase events, voltage and current anti-correlation coefficient, theoretical power, instantaneous sag event occurrence time period, and joint melting events.

6. The method for identifying hidden dangers in low voltage substations based on degradation risk analysis according to claim 1 is characterized in that: The terminal abnormality recognition algorithm is used to compare and analyze the terminal characteristic data to determine the terminal abnormality of the meter; The steps include: When the user resistance constant standard deviation is <0.3, it is included in the meter terminal abnormal analysis; When the user voltage and current anti-correlation coefficient ranges from -0.7 to -1, it is included in the abnormal analysis of the meter terminal; When the single-phase theoretical power range of the user's instantaneous drop event is greater than 300w, it is included in the abnormal analysis of the meter terminal; among them, when a user in the same area has an instantaneous drop event, and the voltage fluctuations of other users are close to that of the user, these users are judged to be the same drop users; When more than two phases of a three-phase user have front-end abnormalities, and the instantaneous drop phase has |maximum theoretical resistance - minimum theoretical resistance| / maximum theoretical resistance < 0.3, it will be included in the meter terminal abnormality analysis.

7. The method for identifying hidden dangers in low voltage substations based on degradation risk analysis according to claim 1 is characterized in that: Determining the degree of terminal abnormality according to the abnormal situation corresponding to the terminal abnormality of the meter includes the following steps: In the list of meters whose front-end abnormal risk values ​​are greater than the set threshold value H, the terminal risk factor corresponding to the abnormal situation is extracted, and the terminal abnormality degree is determined by summing up the weight coefficients corresponding to the various dimensional data in the terminal risk factor; among which, the weight coefficients of the various dimensional data in the terminal risk factor are dynamically adjusted according to the actual power consumption of the user and the voltage fluctuation of the substation; the various dimensional data in the terminal risk factor include: constant standard deviation of resistance, theoretical voltage drop power, voltage-current anti-correlation coefficient, front-end abnormality degree, theoretical resistance and user same-drop events.

8. The method for identifying hidden dangers in low voltage substations based on degradation risk analysis according to claim 1 is characterized in that: The training process of the linear regression model is: The front-end risk factor corresponding to the front-end abnormality degree is obtained as the input of the linear regression model, and the linear regression model is trained with the maximum front-end hidden danger risk value as the optimization target to obtain the front-end risk weight of each front-end risk factor; When the front-end hidden danger risk value is greater than the set threshold H, the terminal samples and pre-table fault samples in the front-end risk sample list that have been classified by on-site judgment are used as the input of the linear regression model, and the maximum terminal hidden danger risk value is used as the optimization target to train the linear regression model to obtain the terminal risk weight of each terminal risk factor; Among them, the front-end risk factors include data of multiple dimensions, including: instantaneous voltage drop difference, low voltage, theoretical resistance, theoretical power group change trend, theoretical power group standard deviation, instantaneous drop times, synchronous power outage events, synchronous daily line loss sudden increase events, voltage and current anti-correlation coefficient, theoretical power, instantaneous drop event time period, and joint melting events; The terminal risk factors include data in multiple dimensions, including: constant standard deviation of resistance, theoretical voltage drop power, voltage-current anti-correlation coefficient, front-end abnormality, theoretical resistance, and user same-drop events.

9. A low voltage area hidden danger identification system, applicable to the low voltage area hidden danger identification method based on degradation risk analysis as claimed in any one of claims 1 to 8, characterized in that: include: The first construction module: sequentially obtains the measurement data of the low-voltage area according to the sampling period to construct an initial data set; The second construction module: according to the instantaneous drop event judgment criteria, the initial data set is screened in turn to construct an abnormal data set; The first analysis module: according to the instantaneous drop event, the joint melting identification algorithm analysis and other analysis dimensions are performed to determine the front-end abnormality of the meter and its corresponding front-end abnormality degree; The second analysis module: on the basis that the risk of hidden dangers at the front end is greater than the set threshold value H, the abnormal data set is analyzed according to the terminal abnormality recognition algorithm to determine the terminal abnormality of the meter and its corresponding terminal abnormality degree; Calculation module: Determine the front-end risk weight corresponding to the front-end abnormality of the meter and the terminal risk weight corresponding to the terminal abnormality of the meter through the hierarchical analysis method; The first fusion module: determines the front-end hidden danger risk value according to the front-end abnormality of the meter, the degree of the front-end abnormality and the front-end risk weight; The second fusion module: the terminal hidden danger risk value is determined according to the terminal abnormality of the meter, the terminal risk weight, and the degree of terminal abnormality; Execution module: Determine the hidden danger emergency strategy corresponding to the substation according to the front-end hidden danger risk value and / or the terminal hidden danger risk value.

10. An electronic device, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program, and when the processor calls the computer program in the memory, the steps of the low-voltage substation hidden danger identification method based on degradation risk analysis as described in any one of claims 1 to 8 are implemented.

11. A storage medium, characterized in that: The storage medium stores computer executable instructions, which, when loaded and executed by a processor, implement the steps of a method for identifying hidden dangers in a low-voltage substation based on degradation risk analysis as described in any one of claims 1 to 8.

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