A user portrait-based power distribution network operation and maintenance intelligent management system and method

Abstract

The application relates to the technical field of distribution network management, in particular to a distribution network operation and maintenance intelligent management system and method based on user portraits, which comprises a distribution network fault area analysis module. The distribution network fault area analysis module monitors the power consumption of each numbered user in a distribution network monitoring area in real time, combines the power user portrait of the corresponding numbered user, judges whether a distribution network fault exists in the distribution network monitoring area, and locks the distribution network fault area in the case that a distribution network fault exists. According to the acquired power user portrait and the actual power distribution condition, the distribution network monitoring area can be accurately identified, the distribution fault area in the distribution network monitoring area is screened according to the operation data of each power distribution line and power distribution equipment in the distribution fault area, the power distribution fault is ensured to be quickly repaired, the operation and maintenance target of the distribution network is clear, the operation and maintenance time of the distribution network is shortened, and the operation and maintenance efficiency of the distribution network is improved.

Description

Technical Field

[0001] This invention relates to the field of distribution network management technology, specifically to an intelligent distribution network operation and maintenance management system and method based on user profiles. Background Technology

[0002] With the continuous development of technology and the widespread application of intelligent devices, people's electricity demand is increasing, and the number of power grid users is growing day by day, making the power grid structure more complex, which in turn increases the pressure on power grid distribution.

[0003] The Internet of Things (IoT) for power is a new type of power network operation model resulting from the deep integration of traditional industrial technology and IoT technology. By endowing distribution network equipment with sensitive and accurate sensing capabilities and interconnection, interoperability and interoperability functions between equipment, it constructs a highly flexible and distributed intelligent collaborative distribution network system based on software definition. This enables comprehensive perception, data fusion and intelligent application of the distribution network, meets the needs of lean management of the distribution network, supports the rapid development of the energy internet, and is the operation mode and manifestation of the distribution network in the next generation of power systems.

[0004] Existing intelligent management systems for distribution network operation and maintenance cannot accurately identify distribution faults within the monitoring area. They typically rely on manual feedback (reporting power outages or unstable power supply) and screening methods for operation and maintenance management. This approach has significant drawbacks: the screening method lacks specificity, the operation and maintenance time is long, and the operation and maintenance efficiency is low. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent management system and method for power distribution network operation and maintenance based on user profiles, so as to solve the problems mentioned in the background art.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: an intelligent management method for distribution network operation and maintenance based on user profiles, the method comprising the following steps: S1. Number the users in the distribution network monitoring area, and collect the electricity consumption data feature set corresponding to each numbered user in the distribution network monitoring area in different time node intervals. The interval length corresponding to each time node interval is the same and is T preset in the database. S2. Based on the electricity consumption data feature set of each numbered user in the historical data for each time node interval, analyze the electricity user profile of the corresponding numbered user. S3. Monitor the electricity consumption of each numbered user in the distribution network monitoring area in real time, and determine whether there is a distribution network fault in the distribution network monitoring area by combining the power user profile of the corresponding numbered user. If there is a distribution network fault, locate the distribution network fault area. Determining whether there is a distribution network fault within the distribution network monitoring area includes: when If the condition is met, it is determined that there is no distribution network fault within the distribution network monitoring area. when If a fault occurs within the distribution network monitoring area, it is determined that a distribution network fault exists. The distribution network fault area is the sum of the distribution network areas corresponding to each element within Ur. Ur represents the intersection of the sets of upstream power distribution equipment and power distribution line sections corresponding to each marked user within the power distribution network monitoring area, and the marked user is a user with abnormal power consumption status. The UR represents the union of the sets of upstream power distribution equipment and power distribution lines corresponding to each unmarked user within the power distribution network monitoring area. The unmarked user is a user with normal power consumption status.

[0007] S4. Obtain the usage data of each distribution section line and the historical operating data of each upstream power distribution equipment within the distribution network fault area, and calculate the abnormal state risk value corresponding to each distribution section line and each upstream power distribution equipment within the distribution network fault area. The power distribution section lines refer to the lines within a pre-defined area among all the road segments through which electrical energy is transmitted during the power distribution process to users. The upstream power distribution equipment refers to the power distribution equipment that the power is transmitted through during the power distribution process to the user; S5. Based on the abnormal status risk values ​​of each distribution section line and each upstream distribution equipment in the distribution network fault area, generate the distribution network maintenance sequence and feed it back to the administrator for early warning.

[0008] Furthermore, in step S1, when numbering users within the distribution network monitoring area, the number of the i-th user within the distribution network monitoring area is denoted as Ai. Each user corresponds to a set of electricity consumption data features for a given time interval, and this set of electricity consumption data features contains all the user's electricity consumption feature data within the corresponding time interval. The number of the time interval to which the current time belongs is denoted as B0, and the number of the j-th time interval preceding B0 is denoted as Bj. Let CAi be the set of electricity consumption data features of user Ai within the time interval corresponding to time node Bj. Bj , The CAi Bj ={D1Ai Bj D2Ai Bj D3Ai Bj D4Ai Bj D5Ai Bj}, Among them, D1Ai Bj This represents the maximum instantaneous power consumption of user Ai within the time interval corresponding to time node Bj. D2Ai Bj This represents the minimum instantaneous power consumption of user Ai within the time interval corresponding to time node Bj. D3Ai Bj This represents the average power consumption of user Ai within the time interval corresponding to time node Bj. D4Ai Bj This indicates the time period within the day for user Ai at the time node corresponding to Bj. A day consists of 24 time periods, each corresponding to one hour. D5Ai Bj This represents the average temperature of the corresponding distribution network monitoring area for user Ai within the time interval corresponding to time node Bj. The CAi Bj Each element in the table represents the electricity consumption characteristics of user Ai within the time interval corresponding to time node Bj.

[0009] Furthermore, the method for analyzing the electricity user profile of the corresponding numbered user in S2 includes the following steps: S21. Obtain the set of electricity consumption data features for each user ID in the historical data for each time interval. S22. Obtain the first electricity consumption feature data pair (XAi, YAi, ZAi) corresponding to each electricity consumption data feature set of the user with number Ai. Where XAi represents the data corresponding to the fifth element in the phase application power data feature set, YAi represents the data corresponding to the fourth element in the phase application power data feature set, and ZAi represents the data corresponding to the third element in the phase application power data feature set. S23. Perform clustering processing on each pair of first electricity consumption characteristic data corresponding to user number Ai to obtain different cluster sets corresponding to user number Ai, and obtain the equivalent electricity consumption characteristic data pairs corresponding to each cluster set. In each cluster set, the first value within each pair of first electricity consumption characteristic data corresponding to each element is equal, and the second value within each pair of first electricity consumption characteristic data corresponding to each element in each cluster set is also equal. The nth value in each equivalent electricity consumption feature data pair is the mean of the nth values ​​in the first electricity consumption feature data pair corresponding to each element in the corresponding cluster set, where n is 1, 2, or 3. S24. Construct a spatial rectangular coordinate system with o as the origin, the average temperature of the corresponding distribution network monitoring area within a time node interval as the x-axis, the number of time periods in a day as the y-axis, and the average power consumption within a time node interval as the z-axis, where 0 < y ≤ 24. S25. Mark the equivalent electricity consumption characteristic data pairs corresponding to user number Ai at the corresponding coordinate points in a spatial rectangular coordinate system. Connect the marked points with the same y-value in ascending order of x-axis coordinate value to obtain a line graph formed by the marked points with the same y-axis. Let Gi be the function corresponding to the line graph formed by the marked points with y-axis coordinate y1. y1 (x), y1 are integers in the interval (0, 24], Gi y1 (x) is a piecewise function; S26. Obtain the electricity user profile HX of user number Ai. Ai , HX Ai ={P D1Ai P D2Ai , {Gi y1 (x)|y1∈(0,24]}}, Among them, P D1Ai This represents the average of the maximum instantaneous power consumption for user Ai within each time interval in the historical data. P D2Ai This represents the average of the minimum instantaneous power consumption for each time interval corresponding to user Ai in the historical data. {Gi y1 (x)|y1∈(0,24]} represents the relationship function between the average power consumption and the average temperature of a user with ID Ai in the historical data during the y1th time period of a day, when y1 has different values.

[0010] In the process of analyzing the electricity user profile of the corresponding numbered user in this invention, {Gi} is obtained. y1 (x)|y1∈(0,24]} is used to analyze the changes in user power consumption due to time period and temperature, while P D1Ai With P D2Ai This reflects, to some extent, a fluctuation range of user power consumption. This range can serve as a reference for predicting power consumption at different times and temperatures in subsequent steps, providing a data basis for determining whether there are distribution network faults in the distribution network monitoring area in subsequent steps.

[0011] Furthermore, the method for determining whether a distribution network fault exists within the distribution network monitoring area in step S3 includes the following steps: S301. Real-time monitoring of the electricity consumption of each numbered user in the distribution network monitoring area at the current time. The electricity consumption of user numbered Ai at the current time includes power consumption Dd1, time period Dd2, and average temperature Dd3 of the corresponding distribution network monitoring area. S302. Obtain the electricity user profile HX of user number Ai. Ai ={P D1Ai P D2Ai , {Gi y1 (x)|y1∈(0,24]}}; S303. Determine whether the current power consumption status of user number Ai is abnormal. When|Gi Dd2 (Dd3)-Dd1| / (P D1Ai -P D2Ai If ) ≥ g, then the current power consumption status of user Ai is determined to be abnormal, and user Ai is marked, Gi Dd2 (Dd3) represents Gi when y1 equals Dd2 and x equals Dd3. y1 The value corresponding to (x), P D1Ai -P D2Ai This represents the maximum deviation value of the instantaneous power consumption of user Ai, where g represents the preset deviation coefficient threshold in the database and is a fixed constant. When|Gi Dd2 (Dd3)-Dd1| / (P D1Ai -P D2Ai If ) < g, then the current power consumption status of user Ai is determined to be normal; S304. Obtain the set of upstream power distribution equipment and power distribution section lines corresponding to each user in S303. Denote the set of upstream power distribution equipment and power distribution section lines corresponding to the user with number Ai as UAi. Each element in UAi corresponds to an upstream power distribution equipment or power distribution section line of a user with number Ai. S305. Determine whether there is a distribution network fault within the distribution network monitoring area.

[0012] In the process of determining whether there is a distribution network fault in the distribution network monitoring area, the present invention calculates Ur∩UR. This is because the power distribution process is regional. When the target analysis node corresponding to the distribution section line or upstream power distribution equipment fails, it will affect all users subsequently connected to the target analysis node. Then, it is determined whether the intersection of Ur and UR is an empty set ∅. This enables the screening of target analysis nodes and further accurately locates the distribution network fault area.

[0013] Furthermore, the method for calculating the abnormal state risk values ​​corresponding to each distribution section line and each upstream distribution equipment within the distribution network fault area in S4 includes the following steps: S41. Obtain the usage data of each distribution section line and the historical operation data of each upstream power distribution equipment within the fault area of ​​the distribution network. Each distribution section line or each upstream distribution device within the distribution network fault area is treated as a target analysis node, and the usage data of each distribution section line or the historical operating data of each upstream distribution device within the distribution network fault area is used as the analysis data corresponding to the target analysis node. The analysis data corresponding to the target analysis node includes standard passage data threshold, standard usage duration threshold, and actual passage data corresponding to different usage durations. The actual passage data corresponding to different usage durations in the analysis data of the target analysis node are actually acquired in real time by sensors. The standard passage data threshold and standard usage duration threshold in the analysis data of the target analysis node are obtained by querying preset forms in the database. S42. Calculate the abnormal state risk value corresponding to each target analysis node within the distribution network fault area. Let the abnormal state risk value corresponding to the m-th target analysis node within the distribution network fault area be denoted as Wm, where Wm = max{W1m, W2m}. The max{W1m, W2m} represents the maximum value among W1m and W2m. , , Where tfm represents the actual maximum usage time corresponding to the m-th target analysis node within the distribution network fault area, and hsm tf This represents the actual access data of the m-th target analysis node within the distribution network fault area during the actual usage time tf, where hbm represents the standard access data threshold corresponding to the m-th target analysis node within the distribution network fault area, and tfb represents the access data threshold. m This represents the standard usage time threshold corresponding to the m-th target analysis node within the distribution network fault area.

[0014] When calculating the abnormal state risk value (corresponding to a time period) of each distribution section line and each upstream power distribution equipment within the fault area of ​​the distribution network, this invention takes into account that the target analysis nodes (distribution section lines or upstream power distribution equipment) all have a life cycle (lifespan and overall usage). W1m reflects the assessment of the abnormal state of the target analysis node from the perspective of lifespan, while W2m assesses the abnormal state from the perspective of the overall usage of the target analysis node. Both can reflect the abnormal state of the corresponding target analysis node to a certain extent. Obtaining Wm is for the purpose of comprehensive analysis of W1m and W2m, to calibrate the assessment results of the abnormal state of the corresponding target analysis node, and to ensure the accuracy of the assessment results.

[0015] Furthermore, when generating the distribution network maintenance sequence in S5, the Ur corresponding to the distribution network fault area and the corresponding abnormal state risk value of each element in Ur corresponding to the distribution section line or upstream distribution equipment are obtained. The positions of each element in Ur are calibrated in descending order of abnormal state risk value. The element with the larger abnormal state risk value is placed in front of the element with the smaller abnormal state risk value. The positions of elements in Ur with the same abnormal state risk value remain unchanged. The calibrated set is used as the distribution network maintenance sequence.

[0016] A power distribution network operation and maintenance intelligent management system based on user profiles, the system comprising the following modules: The electricity consumption data feature set acquisition module assigns a number to users within the distribution network monitoring area and collects the electricity consumption data feature set corresponding to each numbered user within the distribution network monitoring area at different time intervals. The power user profile building module analyzes the power user profile of the corresponding numbered user based on the set of electricity consumption data characteristics of each numbered user in the historical data at each time node interval. The distribution network fault area analysis module monitors the electricity consumption of each numbered user within the distribution network monitoring area in real time. Based on the electricity user profile of the corresponding numbered user, it determines whether there is a distribution network fault within the distribution network monitoring area, and if there is a distribution network fault, it locks down the distribution network fault area. An abnormal state risk analysis module obtains the usage data of each distribution section line and the historical operation data of each upstream power distribution equipment in the distribution network fault area, and calculates the abnormal state risk value corresponding to each distribution section line and each upstream power distribution equipment in the distribution network fault area respectively. The distribution network maintenance sequence feedback and early warning module generates a distribution network maintenance sequence based on the abnormal state risk values ​​corresponding to each distribution section line and each upstream distribution equipment within the distribution network fault area, and feeds the distribution network maintenance sequence back to the administrator for early warning.

[0017] Furthermore, in the electricity consumption data feature set acquisition module, the interval length corresponding to each time node interval is the same and is T preset in the database. The number of the i-th user in the distribution network monitoring area is denoted as Ai. Each user corresponds to an electricity consumption data feature set in one time node interval, and the electricity consumption data feature set contains the user's various electricity consumption feature data in the corresponding time node interval. The number of the time node interval to which the current time belongs is denoted as B0, the number corresponding to the j-th time node interval before B0 is denoted as Bj, and the electricity consumption data feature set of the user with number Ai in the time node interval corresponding to Bj is denoted as CAi. BjThe CAi Bj Each element in the data represents the electricity consumption characteristics of user Ai within the time interval corresponding to time node Bj, where CAi is... Bj ={D1Ai Bj D2Ai Bj D3Ai Bj D4Ai Bj D5Ai Bj}, Among them, D1Ai Bj This represents the maximum instantaneous power consumption of user Ai within the time interval corresponding to time node Bj. D2Ai Bj This represents the minimum instantaneous power consumption of user Ai within the time interval corresponding to time node Bj. D3Ai Bj This represents the average power consumption of user Ai within the time interval corresponding to time node Bj. D4Ai Bj This indicates the time period within the day for user Ai at the time node corresponding to Bj. A day consists of 24 time periods, each corresponding to one hour. D5Ai Bj This represents the average temperature of the corresponding distribution network monitoring area for user number Ai within the time interval corresponding to time node Bj.

[0018] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention can accurately identify the distribution fault area in the distribution network monitoring area based on the obtained power user profile and the actual power distribution situation, and assist managers in screening the distribution fault area based on the operation data of each distribution line and distribution equipment in the distribution fault area, so as to ensure rapid repair of distribution faults, clarify the distribution network operation and maintenance goals, shorten the distribution network operation and maintenance time, and improve the distribution network operation and maintenance efficiency. Attached Figure Description

[0019] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart illustrating an intelligent management method for power distribution network operation and maintenance based on user profiles, according to the present invention. Figure 2 This is a schematic diagram of the structure of an intelligent distribution network operation and maintenance management system based on user profiles according to the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Please see Figure 1 This invention provides a technical solution: an intelligent management method for distribution network operation and maintenance based on user profiles, the method comprising the following steps: S1. Number the users in the distribution network monitoring area, and collect the electricity consumption data feature set corresponding to each numbered user in the distribution network monitoring area in different time node intervals. The interval length corresponding to each time node interval is the same and is T preset in the database. In step S1, when assigning numbers to users within the distribution network monitoring area, the number of the i-th user within the distribution network monitoring area is denoted as Ai. Each user corresponds to a set of electricity consumption data features for a given time interval, and this set of electricity consumption data features contains all the user's electricity consumption feature data within the corresponding time interval. The number of the time interval to which the current time belongs is denoted as B0, and the number of the j-th time interval preceding B0 is denoted as Bj. Let CAi be the set of electricity consumption data features of user Ai within the time interval corresponding to time node Bj. Bj , The CAi Bj ={D1Ai Bj D2Ai Bj D3Ai Bj D4Ai Bj D5Ai Bj}, Among them, D1Ai Bj This represents the maximum instantaneous power consumption of user Ai within the time interval corresponding to time node Bj. D2Ai Bj This represents the minimum instantaneous power consumption of user Ai within the time interval corresponding to time node Bj. D3Ai Bj This represents the average power consumption of user Ai within the time interval corresponding to time node Bj. D4Ai Bj This indicates the time period within the day for user Ai at the time node corresponding to Bj. A day consists of 24 time periods, each corresponding to one hour. D5Ai BjThis represents the average temperature of the corresponding distribution network monitoring area for user Ai within the time interval corresponding to time node Bj. The CAi Bj Each element in the table represents the electricity consumption characteristics of user Ai within the time interval corresponding to time node Bj.

[0022] S2. Based on the electricity consumption data feature set of each numbered user in the historical data for each time node interval, analyze the electricity user profile of the corresponding numbered user. The method for analyzing the electricity user profile of the corresponding numbered user in S2 includes the following steps: S21. Obtain the set of electricity consumption data features for each user ID in the historical data for each time interval. S22. Obtain the first electricity consumption feature data pair (XAi, YAi, ZAi) corresponding to each electricity consumption data feature set of the user with number Ai. Where XAi represents the data corresponding to the fifth element in the phase application power data feature set, YAi represents the data corresponding to the fourth element in the phase application power data feature set, and ZAi represents the data corresponding to the third element in the phase application power data feature set. S23. Perform clustering processing on each pair of first electricity consumption characteristic data corresponding to user number Ai to obtain different cluster sets corresponding to user number Ai, and obtain the equivalent electricity consumption characteristic data pairs corresponding to each cluster set. In each cluster set, the first value within each pair of first electricity consumption characteristic data corresponding to each element is equal, and the second value within each pair of first electricity consumption characteristic data corresponding to each element in each cluster set is also equal. The nth value in each equivalent electricity consumption feature data pair is the mean of the nth values ​​in the first electricity consumption feature data pair corresponding to each element in the corresponding cluster set, where n is 1, 2, or 3. In this embodiment, the value of n is 1, 2, or 3 because there are only three data points in the equivalent electricity consumption characteristic data pair. S24. Construct a spatial rectangular coordinate system with o as the origin, the average temperature of the corresponding distribution network monitoring area within a time node interval as the x-axis, the number of time periods in a day as the y-axis, and the average power consumption within a time node interval as the z-axis, where 0 < y ≤ 24. S25. Mark the equivalent electricity consumption characteristic data pairs corresponding to user number Ai at the corresponding coordinate points in a spatial rectangular coordinate system. Connect the marked points with the same y-value in ascending order of x-axis coordinate value to obtain a line graph formed by the marked points with the same y-axis. Let Gi be the function corresponding to the line graph formed by the marked points with y-axis coordinate y1. y1 (x), y1 are integers in the interval (0, 24], Gi y1 (x) is a piecewise function; S26. Obtain the electricity user profile HX of user number Ai. Ai , HX Ai ={P D1Ai P D2Ai , {Gi y1 (x)|y1∈(0,24]}}, In this embodiment, y1 is an integer. Since there are 24 integers in (0, 24), the electricity user profile HX of user number Ai is obtained. Ai Gi in y1 (x) exists in 24 forms, and each Gi... y1 (x) may correspond to different functions; Among them, P D1Ai This represents the average of the maximum instantaneous power consumption for user Ai within each time interval in the historical data. P D2Ai This represents the average of the minimum instantaneous power consumption for each time interval corresponding to user Ai in the historical data. {Gi y1 (x)|y1∈(0,24]} represents the relationship function between the average power consumption and the average temperature of a user with ID Ai in the historical data during the y1th time period of a day, when y1 has different values.

[0023] S3. Monitor the electricity consumption of each numbered user in the distribution network monitoring area in real time, and determine whether there is a distribution network fault in the distribution network monitoring area by combining the power user profile of the corresponding numbered user. If there is a distribution network fault, locate the distribution network fault area. The method for determining whether a distribution network fault exists within the distribution network monitoring area in S3 includes the following steps: S301. Real-time monitoring of the electricity consumption of each numbered user in the distribution network monitoring area at the current time. The electricity consumption of user numbered Ai at the current time includes power consumption Dd1, time period Dd2, and average temperature Dd3 of the corresponding distribution network monitoring area. S302. Obtain the electricity user profile HX of user number Ai. Ai ={P D1Ai P D2Ai , {Gi y1 (x)|y1∈(0,24]}}; S303. Determine whether the current power consumption status of user number Ai is abnormal. When|Gi Dd2 (Dd3)-Dd1| / (P D1Ai -P D2Ai If ) ≥ g, then the current power consumption status of user Ai is determined to be abnormal, and user Ai is marked, Gi Dd2 (Dd3) represents Gi when y1 equals Dd2 and x equals Dd3. y1 The value corresponding to (x), P D1Ai -P D2Ai This represents the maximum deviation value of the instantaneous power consumption of user Ai, where g represents the preset deviation coefficient threshold in the database and is a fixed constant. When|Gi Dd2 (Dd3)-Dd1| / (P D1Ai -P D2Ai If ) < g, then the current power consumption status of user Ai is determined to be normal; S304. Obtain the set of upstream power distribution equipment and power distribution section lines corresponding to each user in S303. Denote the set of upstream power distribution equipment and power distribution section lines corresponding to the user with number Ai as UAi. Each element in UAi corresponds to an upstream power distribution equipment or power distribution section line of a user with number Ai. S305. Determine whether there is a distribution network fault within the distribution network monitoring area. when If the condition is met, it is determined that there is no distribution network fault within the distribution network monitoring area. when If a fault occurs within the distribution network monitoring area, it is determined that a distribution network fault exists. The distribution network fault area is the sum of the distribution network areas corresponding to each element within Ur. Ur represents the intersection of the sets of upstream power distribution equipment and power distribution line sections corresponding to each marked user within the power distribution network monitoring area, and the marked user is a user with abnormal power consumption status. The UR represents the union of the sets of upstream power distribution equipment and power distribution lines corresponding to each unmarked user within the power distribution network monitoring area. The unmarked user is a user with normal power consumption status.

[0024] In this embodiment, if there are two marked users, A and B, in the power distribution network monitoring area, If the set corresponding to A is {L1, L2, L3}, where L1, L2 and L3 are all target analysis nodes corresponding to A, and one target analysis node corresponds to a power distribution section line or an upstream power distribution device; If the set corresponding to B is {L4, L2, L3}, where L4, L2, and L3 are the target analysis nodes corresponding to B. Then Ur={L1, L2, L3}∩{L4, L2, L3}={L2, L3}; If there are two unmarked users, C and D, in the distribution network monitoring area, If the set corresponding to C is {L4, L5, L6}, where L4, L5, and L6 are all target analysis nodes corresponding to C; If the set corresponding to Ding is {L4, L5, L7}, where L4, L5, and L7 are the target analysis nodes corresponding to Ding. Then UR={L4, L5, L6}∪{L4, L5, L7}={L4, L5, L6, L7}; Since {L2, L3}∩{L4, L5, L6, L7}=∅, it is determined that there is a distribution network fault in the distribution network monitoring area. The distribution network fault area is the sum of the distribution network areas corresponding to L2 and L3 within {L2, L3}. S4. Obtain the usage data of each distribution section line and the historical operating data of each upstream power distribution equipment within the distribution network fault area, and calculate the abnormal state risk value corresponding to each distribution section line and each upstream power distribution equipment within the distribution network fault area. The power distribution section lines refer to the lines within a pre-defined area among all the road segments through which electrical energy is transmitted during the power distribution process to users. The upstream power distribution equipment refers to the power distribution equipment that the power is transmitted through during the power distribution process to the user; The method for calculating the abnormal state risk value corresponding to each distribution section line and each upstream distribution equipment within the distribution network fault area in S4 includes the following steps: S41. Obtain the usage data of each distribution section line and the historical operation data of each upstream power distribution equipment within the fault area of ​​the distribution network. Each distribution section line or each upstream distribution device within the distribution network fault area is treated as a target analysis node, and the usage data of each distribution section line or the historical operating data of each upstream distribution device within the distribution network fault area is used as the analysis data corresponding to the target analysis node. The analysis data corresponding to the target analysis node includes standard passage data threshold, standard usage duration threshold, and actual passage data corresponding to different usage durations. In this embodiment, both the standard passage data threshold and the actual passage data are power distribution current; The actual passage data corresponding to different usage durations in the analysis data of the target analysis node are actually acquired in real time by sensors. The standard passage data threshold and standard usage duration threshold in the analysis data of the target analysis node are obtained by querying preset forms in the database. S42. Calculate the abnormal state risk value corresponding to each target analysis node within the distribution network fault area. Let the abnormal state risk value corresponding to the m-th target analysis node within the distribution network fault area be denoted as Wm, where Wm = max{W1m, W2m}. The max{W1m, W2m} represents the maximum value among W1m and W2m. , , Where tfm represents the actual maximum usage time corresponding to the m-th target analysis node within the distribution network fault area, and hsm tf This represents the actual access data of the m-th target analysis node within the distribution network fault area during the actual usage time tf, where hbm represents the standard access data threshold corresponding to the m-th target analysis node within the distribution network fault area, and tfb represents the access data threshold. m This represents the standard usage time threshold corresponding to the m-th target analysis node within the distribution network fault area.

[0025] S5. Based on the abnormal status risk values ​​of each distribution section line and each upstream distribution equipment in the distribution network fault area, generate the distribution network maintenance sequence and feed it back to the administrator for early warning.

[0026] When generating the distribution network maintenance sequence in S5, the Ur corresponding to the distribution network fault area and the corresponding abnormal state risk value of each element in Ur corresponding to the distribution section line or upstream distribution equipment are obtained. The positions of each element in Ur are calibrated in descending order of abnormal state risk value. The element with the larger abnormal state risk value is placed in front of the element with the smaller abnormal state risk value. The positions of elements with the same abnormal state risk value in Ur remain unchanged. The calibrated set is used as the distribution network maintenance sequence.

[0027] like Figure 2 As shown, a power distribution network operation and maintenance intelligent management system based on user profiles is disclosed. The system includes the following modules: The electricity consumption data feature set acquisition module assigns a number to users within the distribution network monitoring area and collects the electricity consumption data feature set corresponding to each numbered user within the distribution network monitoring area at different time intervals. The power user profile building module analyzes the power user profile of the corresponding numbered user based on the set of electricity consumption data characteristics of each numbered user in the historical data at each time node interval. The distribution network fault area analysis module monitors the electricity consumption of each numbered user within the distribution network monitoring area in real time. Based on the electricity user profile of the corresponding numbered user, it determines whether there is a distribution network fault within the distribution network monitoring area, and if there is a distribution network fault, it locks down the distribution network fault area. An abnormal state risk analysis module obtains the usage data of each distribution section line and the historical operation data of each upstream power distribution equipment in the distribution network fault area, and calculates the abnormal state risk value corresponding to each distribution section line and each upstream power distribution equipment in the distribution network fault area respectively. The distribution network maintenance sequence feedback and early warning module generates a distribution network maintenance sequence based on the abnormal state risk values ​​corresponding to each distribution section line and each upstream distribution equipment within the distribution network fault area, and feeds the distribution network maintenance sequence back to the administrator for early warning.

[0028] In the electricity consumption data feature set acquisition module, each time node interval has the same interval length, which is T preset in the database. The number of the i-th user in the distribution network monitoring area is denoted as Ai. Each user corresponds to an electricity consumption data feature set in one time node interval, and this electricity consumption data feature set contains the user's various electricity consumption feature data in the corresponding time node interval. The number of the time node interval to which the current time belongs is denoted as B0, the number corresponding to the j-th time node interval before B0 is denoted as Bj, and the electricity consumption data feature set of the user with number Ai in the time node interval corresponding to Bj is denoted as CAi. Bj The CAi Bj Each element in the data represents the electricity consumption characteristics of user Ai within the time interval corresponding to time node Bj, where CAi is... Bj ={D1Ai Bj D2Ai Bj D3Ai Bj D4Ai Bj D5Ai Bj}, Among them, D1Ai Bj This represents the maximum instantaneous power consumption of user Ai within the time interval corresponding to time node Bj. D2Ai Bj This represents the minimum instantaneous power consumption of user Ai within the time interval corresponding to time node Bj. D3Ai BjThis represents the average power consumption of user Ai within the time interval corresponding to time node Bj. D4Ai Bj This indicates the time period within the day for user Ai at the time node corresponding to Bj. A day consists of 24 time periods, each corresponding to one hour. D5Ai Bj This represents the average temperature of the corresponding distribution network monitoring area for user number Ai within the time interval corresponding to time node Bj.

[0029] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0030] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent management of power distribution network operation and maintenance based on user profiles, characterized in that, The method includes the following steps: S1. Number the users in the distribution network monitoring area, and collect the electricity consumption data feature set corresponding to each numbered user in the distribution network monitoring area at different time node intervals. The interval length corresponding to each time node interval is the same and is T preset in the database. S2. Based on the electricity consumption data feature set of each numbered user in the historical data for each time node interval, analyze the electricity user profile of the corresponding numbered user. S3. Monitor the electricity consumption of each numbered user in the distribution network monitoring area in real time, and determine whether there is a distribution network fault in the distribution network monitoring area by combining the power user profile of the corresponding numbered user. If there is a distribution network fault, locate the distribution network fault area. Determining whether there is a distribution network fault within the distribution network monitoring area includes: when If the condition is met, it is determined that there is no distribution network fault within the distribution network monitoring area. when If a fault occurs within the distribution network monitoring area, it is determined that a distribution network fault exists. The distribution network fault area is the sum of the distribution network areas corresponding to each element within Ur. Ur represents the intersection of the sets of upstream power distribution equipment and power distribution line sections corresponding to each marked user within the power distribution network monitoring area, and the marked user is a user with abnormal power consumption status. The UR represents the union of the sets of upstream power distribution equipment and power distribution section lines corresponding to each unmarked user within the power distribution network monitoring area, where the unmarked user is a user with normal power consumption status. S4. Obtain the usage data of each distribution section line and the historical operating data of each upstream power distribution equipment within the distribution network fault area, and calculate the abnormal state risk value corresponding to each distribution section line and each upstream power distribution equipment within the distribution network fault area. The power distribution section lines refer to the lines within a pre-defined area among all the road segments through which electrical energy is transmitted during the power distribution process to users. The upstream power distribution equipment refers to the power distribution equipment that the power is transmitted through during the power distribution process to the user; S5. Based on the abnormal status risk values ​​of each distribution section line and each upstream distribution equipment in the distribution network fault area, generate the distribution network maintenance sequence and feed it back to the administrator for early warning.

2. The intelligent management method for distribution network operation and maintenance based on user profiles according to claim 1, characterized in that: In step S1, when assigning numbers to users within the distribution network monitoring area, the number of the i-th user within the distribution network monitoring area is denoted as Ai. Each user corresponds to a set of electricity consumption data features for a given time interval, and this set of electricity consumption data features contains all the user's electricity consumption feature data within the corresponding time interval. The number of the time interval to which the current time belongs is denoted as B0, and the number of the j-th time interval preceding B0 is denoted as Bj. Let CAi be the set of electricity consumption data features of user Ai within the time interval corresponding to time node Bj. Bj , Stated CAi Bj ={D1Ai Bj , D2Ai Bj , D3Ai Bj , D4Ai Bj , D5Ai Bj }, Among them, D1Ai Bj This represents the maximum instantaneous power consumption of user Ai within the time interval corresponding to time node Bj. D2Ai Bj This represents the minimum instantaneous power consumption of user Ai within the time interval corresponding to time node Bj. D3Ai Bj This represents the average power consumption of user Ai within the time interval corresponding to time node Bj. D4Ai Bj This indicates the time period within the day for user Ai at the time node corresponding to Bj. A day consists of 24 time periods, each corresponding to one hour. D5Ai Bj This represents the average temperature of the corresponding distribution network monitoring area for user Ai within the time interval corresponding to time node Bj. The CAi Bj Each element in the table represents the electricity consumption characteristics of user Ai within the time interval corresponding to time node Bj.

3. The intelligent management method for distribution network operation and maintenance based on user profiles according to claim 2, characterized in that: The method for analyzing the electricity user profile of the corresponding numbered user in S2 includes the following steps: S21. Obtain the set of electricity consumption data features for each user ID in the historical data for each time interval. S22. Obtain the first electricity consumption feature data pair (XAi, YAi, ZAi) corresponding to each electricity consumption data feature set of the user with number Ai. Where XAi represents the data corresponding to the fifth element in the phase application power data feature set, YAi represents the data corresponding to the fourth element in the phase application power data feature set, and ZAi represents the data corresponding to the third element in the phase application power data feature set. S23. Perform clustering processing on each pair of first electricity consumption characteristic data corresponding to user number Ai to obtain different cluster sets corresponding to user number Ai, and obtain the equivalent electricity consumption characteristic data pairs corresponding to each cluster set. In each cluster set, the first value within each pair of first electricity consumption characteristic data corresponding to each element is equal, and the second value within each pair of first electricity consumption characteristic data corresponding to each element in each cluster set is also equal. The nth value in each equivalent electricity consumption feature data pair is the mean of the nth values ​​in the first electricity consumption feature data pair corresponding to each element in the corresponding cluster set, where n is 1, 2, or 3. S24. Construct a spatial rectangular coordinate system with o as the origin, the average temperature of the corresponding distribution network monitoring area within a time node interval as the x-axis, the number of time periods in a day as the y-axis, and the average power consumption within a time node interval as the z-axis, where 0 < y ≤ 24. S25. Mark the equivalent electricity consumption characteristic data pairs corresponding to user number Ai at the corresponding coordinate points in a spatial rectangular coordinate system. Connect the marked points with the same y-value in ascending order of x-axis coordinate value to obtain a line graph formed by the marked points with the same y-axis. Let Gi be the function corresponding to the line graph formed by the marked points with y-axis coordinate y1. y1 (x), y1 are integers in the interval (0, 24], Gi y1 (x) is a piecewise function; S26. Obtain the electricity user profile HX of user number Ai. Ai , HX Ai ={P D1Ai P D2Ai {Gi y1 (x)|y1∈(0,24]}}, Among them, P D1Ai This represents the average of the maximum instantaneous power consumption for user Ai within each time interval in the historical data. P D2Ai This represents the average of the minimum instantaneous power consumption for each time interval corresponding to user Ai in the historical data. {Gi y1 (x)|y1∈(0,24]} represents the relationship function between the average power consumption and the average temperature of a user with ID Ai in the historical data during the y1th time period of a day, when y1 has different values.

4. The intelligent management method for distribution network operation and maintenance based on user profiles according to claim 3, characterized in that: The method for determining whether a distribution network fault exists within the distribution network monitoring area in S3 includes the following steps: S301. Real-time monitoring of the electricity consumption of each numbered user in the distribution network monitoring area at the current time. The electricity consumption of user numbered Ai at the current time includes power consumption Dd1, time period Dd2, and average temperature Dd3 of the corresponding distribution network monitoring area. S302. Obtain the electricity user profile HX of user number Ai. Ai ={P D1Ai P D2Ai , {Gi y1 (x)|y1∈(0,24]}}; S303. Determine whether the current power consumption status of user number Ai is abnormal. When|Gi Dd2 (Dd3)-Dd1| / (P D1Ai -P D2Ai If ) ≥ g, then the current power consumption status of user Ai is determined to be abnormal, and user Ai is marked, Gi Dd2 (Dd3) represents Gi when y1 equals Dd2 and x equals Dd3. y1 The value corresponding to (x), P D1Ai -P D2Ai This represents the maximum deviation value of the instantaneous power consumption of user Ai, where g represents the preset deviation coefficient threshold in the database and is a fixed constant. When|Gi Dd2 (Dd3)-Dd1| / (P D1Ai -P D2Ai If ) < g, then the current power consumption status of user Ai is determined to be normal; S304. Obtain the set of upstream power distribution equipment and power distribution section lines corresponding to each user in S303. Denote the set of upstream power distribution equipment and power distribution section lines corresponding to the user with number Ai as UAi. Each element in UAi corresponds to an upstream power distribution equipment or power distribution section line of a user with number Ai. S305. Determine whether there is a distribution network fault within the distribution network monitoring area.

5. The intelligent management method for distribution network operation and maintenance based on user profiles according to claim 4, characterized in that: The method for calculating the abnormal state risk value corresponding to each distribution section line and each upstream distribution equipment within the distribution network fault area in S4 includes the following steps: S41. Obtain the usage data of each distribution section line and the historical operation data of each upstream power distribution equipment within the fault area of ​​the distribution network. Each distribution section line or each upstream distribution device within the distribution network fault area is treated as a target analysis node, and the usage data of each distribution section line or the historical operating data of each upstream distribution device within the distribution network fault area is used as the analysis data corresponding to the target analysis node. The analysis data corresponding to the target analysis node includes standard passage data threshold, standard usage time threshold, and actual passage data corresponding to different usage times. The standard passage data threshold and the actual passage data are both distribution current. The actual passage data corresponding to different usage durations in the analysis data of the target analysis node are actually acquired in real time by sensors. The standard passage data threshold and standard usage duration threshold in the analysis data of the target analysis node are obtained by querying preset forms in the database. S42. Calculate the abnormal state risk value corresponding to each target analysis node within the distribution network fault area. Let the abnormal state risk value corresponding to the m-th target analysis node within the distribution network fault area be denoted as Wm, where Wm = max{W1m, W2m}. The max{W1m, W2m} represents the maximum value among W1m and W2m. ， ， Where tfm represents the actual maximum usage time corresponding to the m-th target analysis node within the distribution network fault area, and hsm tf This represents the actual access data of the m-th target analysis node within the distribution network fault area during the actual usage time tf, where hbm represents the standard access data threshold corresponding to the m-th target analysis node within the distribution network fault area, and tfb represents the access data threshold. m This represents the standard usage time threshold corresponding to the m-th target analysis node within the distribution network fault area.

6. The intelligent management method for distribution network operation and maintenance based on user profiles according to claim 4, characterized in that: When generating the distribution network maintenance sequence in S5, the Ur corresponding to the distribution network fault area and the corresponding abnormal state risk value of each element in Ur corresponding to the distribution section line or upstream distribution equipment are obtained. The positions of each element in Ur are calibrated in descending order of abnormal state risk value. The element with the larger abnormal state risk value is placed in front of the element with the smaller abnormal state risk value. The positions of elements with the same abnormal state risk value in Ur remain unchanged. The calibrated set is used as the distribution network maintenance sequence.

7. A power distribution network operation and maintenance intelligent management system based on user profiles, characterized in that, The system includes the following modules: The electricity consumption data feature set acquisition module assigns a number to users within the distribution network monitoring area and collects the electricity consumption data feature set corresponding to each numbered user within the distribution network monitoring area at different time intervals. The power user profile building module analyzes the power user profile of the corresponding numbered user based on the set of electricity consumption data characteristics of each numbered user in the historical data at each time node interval. The distribution network fault area analysis module monitors the electricity consumption of each numbered user within the distribution network monitoring area in real time. Based on the electricity user profile of the corresponding numbered user, it determines whether there is a distribution network fault within the distribution network monitoring area, and if there is a distribution network fault, it locks down the distribution network fault area. Determining whether there is a distribution network fault within the distribution network monitoring area includes: when If the condition is met, it is determined that there is no distribution network fault within the distribution network monitoring area. when If a fault occurs within the distribution network monitoring area, it is determined that a distribution network fault exists. The distribution network fault area is the sum of the distribution network areas corresponding to each element within Ur. Ur represents the intersection of the sets of upstream power distribution equipment and power distribution line sections corresponding to each marked user within the power distribution network monitoring area, and the marked user is a user with abnormal power consumption status. The UR represents the union of the sets of upstream power distribution equipment and power distribution section lines corresponding to each unmarked user within the power distribution network monitoring area, where the unmarked user is a user with normal power consumption status. An abnormal state risk analysis module obtains the usage data of each distribution section line and the historical operation data of each upstream power distribution equipment in the distribution network fault area, and calculates the abnormal state risk value corresponding to each distribution section line and each upstream power distribution equipment in the distribution network fault area respectively. The distribution network maintenance sequence feedback and early warning module generates a distribution network maintenance sequence based on the abnormal state risk values ​​corresponding to each distribution section line and each upstream distribution equipment within the distribution network fault area, and feeds the distribution network maintenance sequence back to the administrator for early warning.

8. The intelligent management system for distribution network operation and maintenance based on user profiles according to claim 7, characterized in that: In the electricity consumption data feature set acquisition module, each time node interval has the same interval length, which is T preset in the database. The number of the i-th user in the distribution network monitoring area is denoted as Ai. Each user corresponds to an electricity consumption data feature set in one time node interval, and this electricity consumption data feature set contains the user's various electricity consumption feature data in the corresponding time node interval. The number of the time node interval to which the current time belongs is denoted as B0, the number corresponding to the j-th time node interval before B0 is denoted as Bj, and the electricity consumption data feature set of the user with number Ai in the time node interval corresponding to Bj is denoted as CAi. Bj The CAi Bj Each element in the data represents the electricity consumption characteristics of user Ai within the time interval corresponding to time node Bj, where CAi is... Bj ={D1Ai Bj D2Ai Bj D3Ai Bj D4Ai Bj D5Ai Bj }, Among them, D1Ai Bj This represents the maximum instantaneous power consumption of user Ai within the time interval corresponding to time node Bj. D2Ai Bj This represents the minimum instantaneous power consumption of user Ai within the time interval corresponding to time node Bj. D3Ai Bj This represents the average power consumption of user Ai within the time interval corresponding to time node Bj. D4Ai Bj This indicates the time period within the day for user Ai at the time node corresponding to Bj. A day consists of 24 time periods, each corresponding to one hour. D5Ai Bj This represents the average temperature of the corresponding distribution network monitoring area for user number Ai within the time interval corresponding to time node Bj.

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