CN-121980431-A - Low-voltage station area group hidden danger identification and quantification method based on medium-station multivariate data

CN121980431ACN 121980431 ACN121980431 ACN 121980431ACN-121980431-A

Abstract

The invention discloses a low-voltage station group hidden danger identification and quantification method based on multi-element data of a middle station, which comprises the following steps of S1, data acquisition and cleaning, S2, merging of outage events, S3, splitting of multi-user outage events, S4, hidden danger identification and classification, S5, hidden danger related data retention and suspicious user confirmation, S6, node type breaking hidden danger assessment, S7, risk value quantification of classified multi-user hidden dangers, and S8, reinforcement monitoring and active response. The method for identifying and quantifying the group hidden trouble of the low-voltage transformer area based on the multi-element data of the middle transformer area, disclosed by the invention, utilizes multi-system multi-element data, and accurately identifies the group hidden trouble of the low-voltage transformer area and quantifies the risk through the steps of data cleaning, event merging, splitting, hidden trouble classification, risk quantification, strengthening monitoring and the like, thereby realizing active rush repair and improving the reliability of low-voltage power supply and the user satisfaction.

Inventors

ZHENG QING
FENG CHAO
WANG YINFA
SHEN JINMING
SHAO MEICAI
LIN KAIFENG
ZHAO YIFAN
WANG SHENHUA
REN JUNDONG
XU YONGJUN
JIANG SHUO
ZHANG YICHENG
ZHU YI

Assignees

国网浙江省电力有限公司金华供电公司

Dates

Publication Date: 20260505
Application Date: 20260408

Claims (9)

1. The low-voltage station group hidden danger identification and quantification method based on the multi-element data of the middle station is characterized by comprising the following steps of: step S1, collecting relevant data, collecting historical low-voltage user power-down records according to a platform area, and removing the power-down records after power-down records are removed to obtain cleaned power-down records; step S2, sorting the cleaned power-down records in ascending order according to time sequence, merging power failure events according to start-stop time deviation and a threshold value; s3, de-duplication and splitting are carried out on the multi-user power failure event, and each user only appears 1 time in each multi-user power failure event after splitting; step S4, aiming at a plurality of power outage events of a single user, which only occur 1 time, identifying repeated power outage hidden dangers of a user group, and identifying and classifying the hidden danger events according to a power outage range; S5, keeping key measurement point data according to the type of the hidden danger of the switch, and judging a leakage suspicious user; Step S6, calculating a node class break hidden danger value; S7, quantifying risk values of the classified multi-family hidden dangers; And S8, strengthening monitoring and active response.
2. The method for identifying and quantifying hidden dangers of a low-voltage station group based on multi-element data of a middle station according to claim 1, wherein the related data of the step S1 comprises low-voltage station data in a power data middle station, a safety monitoring safety domain, a real-time measurement center, a weather center, a PMS ledger and a power supply service command system, and the low-voltage station data comprises power failure records, measurement data, branch total insurance action data and user repair work order data.
3. The method for identifying and quantifying hidden dangers of a low-voltage transformer area group based on multi-element data of a middle transformer according to claim 2, wherein the relevant power-down records in step S1 comprise power-down records corresponding to transformation work in an electric power safety domain, power-down records corresponding to replacement or business expansion capacity work in a marketing system, power-down records corresponding to power-down events in a total surface terminal, and power-down records corresponding to planned power-down work in a marketing system or a power supply service command system.
4. The method for identifying and quantifying hidden danger of a low-voltage platform area group based on platform multivariate data according to claim 3, wherein in step S2, a power outage start time deviation Spc and a power outage end time deviation Tpc of each power outage record and a previous record are calculated, a threshold Tyz is set, if Spc is less than or equal to Tyz and Tpc is less than or equal to Tyz, the same power outage event is marked, otherwise, the new event is marked, a plurality of power outage event sets in the platform area are generated, and a power outage event SJ is obtained, wherein: power outage start time deviation Spc = current power outage start time minus previous power outage start time; power outage end time deviation tpc=current power outage end time minus previous power outage end time.
5. The method for identifying and quantifying hidden danger of low-voltage transformer area group based on multi-user data of claim 4, wherein in step S3, for a power failure event SJ containing multi-user power failure records, if there are the same users, the event is defined as an event to be split SJM, splitting treatment is performed, each user in each multi-user power failure event SJ occurs only 1 time after splitting, and the splitting treatment is specifically implemented as follows: Step S3.1, traversing the power failure records, counting the occurrence frequency of the power failure times of each user, and taking the times with the highest frequency as the number K of split multi-user power failure events SJ; s3.2, sorting the power-down record sets corresponding to N users with the power-down times equal to K according to the power-down start time ascending sequence, and distributing serial numbers to each record The power-down record of the user 1 is set as And the power down of user N is recorded as Each power failure record comprises power failure starting time and power failure duration; power-down record set for different users corresponding to same serial number X Taking the power-off duration and the power-off starting time in a preset quantile range, and respectively calculating average values as the power-off duration and the power-off starting time of a power-off event SJ corresponding to the sequence number seq; Step S3.3, merging the power failure records with the power failure times not equal to K of the user, comparing each record with the power failure duration and the power failure starting time of each sequence number seq power failure event SJ, if the deviation between the power failure duration and the power failure event SJ of a certain sequence number seq does not exceed a preset value, taking the sequence number seq power failure event SJ into the record closest to the power failure event starting time when the user has a plurality of records meeting the condition, and taking the record closest to the power failure event starting time into the power failure event SJ of the sequence number seq; And S3.4, adding the finally formed serial numbers SJ into the SJ set.
6. The method for identifying and quantifying hidden danger of a low-voltage platform area group based on platform multivariate data according to claim 5, wherein in step S4, a multi-user outage event SJ occurs only 1 time for a single user, if there is a repeated outage with the user group, the hidden danger and the corresponding outage event set YHSJ are defined, the outage scope identification classification is performed on YHSJ, the outage scope identification classification includes multiple access points, single access point, multiple table boxes and single table boxes, and the outage cause identification is performed for multiple access points: Installing a total protection area, judging whether the total protection action of the same area exists in the range of the power failure starting time +/-threshold value, if so, acquiring an action reason, marking the power failure reason of the SJ, marking as a switch hidden danger KGYH, and marking as a node type disconnection hidden danger JDYH if the total protection action does not exist; the general protection area is not installed, the occupation ratio ZB of the user population quantity in the area is influenced by the report description, the power failure duration and the influence, and the load current is lost after power failure, and the current is classified, wherein: The report improve literature describes that the switch or ZB is larger than the threshold value of the number of users in the platform area, and judges that the switch fails, wherein the power failure time is further judged to be the hidden danger of the leakage switch in the first time, the power failure time is judged to be the hidden danger of the overload switch in the second time, the report improve literature describes that the switch or ZB is not switched or smaller than the threshold value of the number of users in the platform area, and the node type hidden danger JDYH is identified.
7. The method for identifying and quantifying the hidden danger of the low-voltage station group based on the multi-element data of the middle station according to claim 6, wherein in the step S5, the station load current TQmaxFH of the station before the overload power failure and the current ZBmaxFH of the station before the overload tripping equipment of the total protection station are reserved for the hidden danger of the overload switch, the zero fire differences LHC of all users in the station of the station before the electric leakage power failure are obtained for the hidden danger of the electric leakage switch, and suspicious users are judged; If the total protection area is the total protection area, the leakage current action value DZZ at the total protection trip time is also required to be obtained, wherein: in the total station protection area, if the users meet DZZ/2< LHC < DZZ, judging the users as suspicious users and raising risk values, otherwise, judging the users as three-phase electric leakage or line electric leakage; And in the non-total station keeping area, returning to the three users with the largest LHC of the previous measuring point before power failure, and if the users with the LHC more than the preset value exist, increasing the risk value.
8. The method for identifying and quantifying hidden danger of a low-voltage area group based on multi-element data of a middle station according to claim 7, wherein in step S6, for node class break hidden danger JDYH, a risk value of node class break hidden danger JDYH is determined by calculating related information, and the method is specifically implemented as follows: Step S6.1, taking a power failure user set of node type disconnection hidden danger JDYH, taking a measurement point voltage average value DY of the user set of one week before power failure, and simultaneously calculating a summary current DL of JDYH; Step S6.2, calculating potential resistance DZ=DLBH/DYBH by calculating voltage change DYBH under the condition of DL severe fluctuation DLBH, taking an event of voltage and current reverse change, and obtaining average resistance PJDZ of all the events; Step S6.3, identifying whether the hidden danger resistance is increased at the moment of high current, in the event of severe current fluctuation, obtaining the maximum value of the current of the measuring point, and calculating the hidden danger resistance DDLDZ, and if DDLDZ is larger than the average resistance PJDZ by a threshold value, increasing the hidden danger risk; S6.4, if the high load moment exists in the first 2 measuring points at the moment of power failure, the power failure moment occupation ratio meeting the condition is larger than a threshold value, and the hidden danger risk value is improved; and S6.5, if the temperature at the power failure moment is positioned in the lowest temperature preset section of the temperature measuring point, the power failure moment occupying ratio meeting the condition is larger than a threshold value, and the hidden danger risk value is improved.
9. The method for identifying and quantifying hidden danger of low-voltage area group based on multi-element data of middle station according to claim 8, wherein the step S8 is specifically implemented as the following steps: step S8.1, the risk value after quantification is larger than a preset value, or the hidden danger corresponding to the 95598 work order and the safety domain rush repair ticket is brought into the enhanced monitoring in the last time in a plurality of power failure events corresponding to the hidden danger, and the monitoring period is 1 month; Step S8.2, monitoring the total protection action situation and the user power failure record through a real-time measurement center, and if a power failure event in a similar user range occurs, pushing information to a corresponding power supply station rush repair responsible person through a short message and an app, wherein the information comprises hidden danger types, common weights, exclusive weights of hidden danger types, switch leakage hidden dangers, switch overload hidden dangers and load reduction amplitude after overload is carried out on a non-total protection area; And S8.3, aiming at overload hidden danger, stopping monitoring when the total protection current value of the corresponding overload switch of the total protection area is larger than ZBmaxFH or the total protection area current is larger than TQmaxFH preset times.

Description

Low-voltage station area group hidden danger identification and quantification method based on medium-station multivariate data Technical Field The invention belongs to the technical field of low-voltage power supply safety monitoring, and particularly relates to a low-voltage station area group hidden danger identification and quantification method based on medium-station multivariate data. Background The low-voltage operation environment is complex, the group hidden trouble is frequent, the power failure range is large, and the low-voltage operation environment is a main reason for influencing the reliability of low-voltage power supply. The group hidden trouble is mainly divided into two types, namely a fault caused by the disconnection of a switch, a fault caused by overload (most of occurrence in summer) and electric leakage causes tripping of the switch, after an operator of a power supply station quickly resumes power transmission, the problem of overload is found out due to the electric leakage, the problem of overload is solved with great difficulty, hidden danger is always continuously existed and repeated power failure is easily caused by group users, and a fault caused by the disconnection of a cold contraction, such as the oxidization of a joint of an RT0 fuse, a line clamp and the like at a tapping box to form high resistance, the thermal expansion and the cold contraction after the current passing are caused, the middle period is represented by the instantaneous power failure after the high load period, the power failure time is short, the user is possibly not felt, but the equipment such as an RT0 fuse, the line clamp and the like is thoroughly burnt out due to long-term development, and the large-area power failure is caused. The data of the two hidden dangers are hidden in the power failure records, the work order records and various measurement data of the low-voltage transformer area of the group users, the hidden dangers are identified through an algorithm, the hidden dangers are quantized and monitored, and the hidden dangers are timely processed to prevent permanent power failure of the group users. In the prior art, although related patents exist in the aspects of oxidization faults and frequent power failure hidden danger identification, a lifting space still exists in the aspects of multi-user hidden danger power failure event splitting, hidden danger feature extraction, risk quantification, active rush repair monitoring and the like, so that an optimized group hidden danger identification and quantification method is needed to improve the reliability of low-voltage power supply and the user satisfaction. Disclosure of Invention The invention mainly aims to provide a low-voltage area group hidden danger identification and quantification method based on middle-stage multi-element data, which utilizes multi-system multi-element data to accurately identify and quantify hidden danger of the low-voltage area group and risk through the steps of data cleaning, event merging, splitting, hidden danger classification, risk quantification, strengthening monitoring and the like, thereby realizing active rush repair and improving low-voltage power supply reliability and user satisfaction. In order to achieve the purpose, the invention discloses a low-voltage station area group hidden danger identification and quantification method based on medium-station multivariate data, which comprises the following steps: step S1, collecting relevant data, collecting historical low-voltage user power-down records according to a platform area, and removing the power-down records after power-down records are removed to obtain cleaned power-down records; step S2, sorting the cleaned power-down records in ascending order according to time sequence, merging power failure events according to start-stop time deviation and a threshold value; s3, de-duplication and splitting are carried out on the multi-user power failure event, and each user only appears 1 time in each multi-user power failure event after splitting; step S4, aiming at a plurality of power outage events of a single user, which only occur 1 time, identifying repeated power outage hidden dangers of a user group, and identifying and classifying the hidden danger events according to a power outage range; S5, keeping key measurement point data according to the type of the hidden danger of the switch, and judging a leakage suspicious user; Step S6, calculating a node class break hidden danger value; S7, quantifying risk values of the classified multi-family hidden dangers; And S8, strengthening monitoring and active response. As a further preferable technical scheme of the above technical scheme, the relevant data in step S1 includes low-voltage station data in the power data center, safety supervision domain, real-time measurement center, weather center, PMS ledger, and power supply service command system, and the low-voltage station data includes power