CN110942236A - Abnormal user identification method integrating power failure record and electricity utilization data - Google Patents
Abnormal user identification method integrating power failure record and electricity utilization data Download PDFInfo
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Abstract
The invention discloses an abnormal user identification method for comprehensive power failure records and power utilization data. The problems that in the prior art, equipment needs to be additionally installed, cost is high, the data size required by calculation is large, the calculation method is complex, and the calculation flow is long are solved; the method comprises the following steps: s1: preprocessing historical power failure data; inquiring power failure records and user power utilization data; s2: calculating the suspicion degree of abnormal electricity utilization of the user; calculating the abnormal suspicion degree of each power failure of each user according to the power failure records and the power utilization data; s3: calculating the comprehensive suspicion degree of abnormal electricity utilization of the users, and calculating the comprehensive suspicion degree and the suspicion time of the abnormal electricity utilization of each user; s4: and (4) sorting the comprehensive suspicion degree of abnormal electricity utilization of the users, and checking the abnormal users by workers. The method has the advantages that the equipment does not need to be additionally installed, preliminary analysis is carried out through the change of the electric quantity of a user and the line loss of the power distribution area before and after power failure, suspicion in the power failure distribution area is identified, the required data volume is small, the calculation method is simple, and the calculation process is simple, convenient and fast.
Description
Technical Field
The invention relates to the field of power consumption abnormal user identification, in particular to an abnormal user identification method integrating power failure records and power consumption data.
Background
With the continuous expansion of the electric power scale, the normal operation of the electric power plays an important role in the daily life and production of people. The power consumption of the electricity consumers is measured through the intelligent electric meter, but the electricity stealing behavior or the electric meter fault exists widely at present, so that the power consumption of the electricity consumers is not really measured, and the direct economic loss of an electric power enterprise is caused. The electricity stealing behavior or meter faults have the characteristic of high concealment and are difficult to detect, so that the economic loss of power supply enterprises can be continuously caused.
At present, the fault meter or the electricity stealing user is mainly positioned by additionally installing equipment or manually checking. Firstly, a metering device is installed at each outgoing line of the transformer, then the metering device is additionally installed at each branch point, and links with large current loss are identified, so that a fault meter or a power stealing user is positioned. The time cost and the economic cost of installing equipment are high, and the method can only be applied to part of users and transformer areas with high suspicion and is difficult to be widely applied.
For example, a publication number "CN 108022043 a" of "an abnormal electricity consumption behavior recognition method, apparatus, and central server disclosed in chinese patent literature includes collecting user information recorded by a smart meter, summarizing and sorting the collected user information, generating an electricity consumption data set, extracting, in the electricity consumption data set, electricity consumption information of all users in a first preset time period, calculating a standard electricity consumption characteristic value representing an average electricity consumption level of all users, extracting electricity consumption information of an appointed user in a second preset time period, calculating an individual electricity consumption characteristic value representing an electricity consumption level of the appointed user, and comparing the individual electricity consumption characteristic value with the standard electricity consumption characteristic value, if the individual electricity consumption characteristic value of the appointed user is greater than the standard electricity consumption characteristic value, it may be determined that the appointed user has an abnormal electricity consumption behavior. The method has high time cost and economic cost of additionally arranging equipment, and is difficult to be widely applied. And the data volume required by calculation is large, the calculation method is complex, and the calculation process is long.
Disclosure of Invention
The invention mainly solves the problems of high cost of equipment needing to be additionally installed, large data amount required by calculation, complex calculation method and long calculation flow in the prior art; the abnormal user identification method for the comprehensive power failure record and the power utilization data is provided, equipment does not need to be additionally installed, preliminary analysis is carried out through changes of user electric quantity and station area line loss before and after power failure, suspicion in a power failure station area is identified, the required data volume is small, the calculation method is simple, and the calculation process is simple, convenient and fast.
The technical problem of the invention is mainly solved by the following technical scheme:
the invention comprises the following steps:
s1: preprocessing historical power failure data; inquiring power failure records and user power utilization data;
s2: calculating the suspicion degree of abnormal electricity utilization of the user; calculating the abnormal suspicion degree of each power failure of each user according to the power failure records and the power utilization data;
s3: calculating the comprehensive suspicion degree of abnormal electricity utilization of the users, and calculating the comprehensive suspicion degree and the suspicion time of the abnormal electricity utilization of each user;
s4: and (4) sorting the comprehensive suspicion degree of abnormal electricity utilization of the users, and checking the abnormal users by workers.
And calculating the suspicion degree of the abnormal data of the user based on the power failure record and the power utilization data of the user, wherein the power utilization data of the user can reflect whether the user abnormally utilizes power in the power failure process. The power utilization abnormity suspicion degree of the user is calculated by integrating the power failure records and the power utilization data of the user, and the method is novel. Data required by calculation do not need to be acquired by additionally installing equipment, and the judgment of abnormal power consumption behaviors of users does not need to be performed by additionally installing equipment, so that the time cost and the economic cost are saved. Whether the user abnormally uses electricity is checked within the power failure range, the suspicion degree of the abnormal electricity consumption of the user can be accurately calculated, the calculated data volume is greatly reduced, the workload is reduced, the calculation speed is increased, and the working efficiency is improved. The suspicion degree of the comprehensive abnormal power utilization of the user is calculated according to the suspicion degree of the abnormal power utilization of each power failure of each user, the suspicion time of the abnormal power utilization of each user is calculated according to the power failure records, the calculating method is simple, and the calculating process is simple, convenient and fast.
Preferably, the step S1 includes the following steps:
s11: selecting a time interval and a distribution area;
s12: and inquiring effective power failure records and user power utilization data of the distribution room in the time interval.
The effective power failure records and the user power consumption data of the selected distribution area are inquired in the selected time interval, the inquiry range is narrowed, the inquired data volume is reduced, the subsequent data sorting, screening and calculation are facilitated, the pertinence of data screening is enhanced, the useless data and the workload are reduced, and the working efficiency is improved. The data required by calculation is acquired without additionally installing equipment, the required data can be acquired by applying the original equipment, the judgment of the abnormal power consumption behavior of the user is not required by additionally installing equipment, the time cost and the economic cost are saved, the wide application can be realized, and the application limit is reduced.
Preferably, the effective power failure record comprises power failure times, power failure time and line loss rate; the user electricity consumption data comprises the electricity consumption of the user. The effective power failure record is recorded in a non-repeated and actually executed power failure record. And inquiring the power failure times and the power failure time of each user in the effective power failure record, and the power consumption of each user in the selected time interval.
Preferably, the step S2 includes the following steps:
s21: calculating the suspicion degree of abnormal electricity utilization of each user during each power failure;
wherein ,CijSuspicion degree of abnormal electricity consumption, T, of power failure of the ith time for user jiThe ratio of the ith power failure time to the longest day power failure time, PijThe ratio of the power consumption of the user j in the ith power failure to the power consumption of the last day without power failure, LiLine loss rate for the ith blackout, LcIs line lossThe upper limit of rate assessment;
s22: identifying suspected users of electricity utilization abnormality;
suspicion degree C of abnormal electricity consumption of user j in ith power failureijAnd the user j more than or equal to 1 is the suspected user of abnormal electricity utilization of the ith power failure.
And calculating the abnormal electricity consumption suspicion degree of each user in each power failure according to the power failure time and the electricity consumption of the users. Line loss rate assessment upper limit LcThe content was 7%. And calculating the electric quantity variation of each user on the day before and on the day of power failure in all users. If the antenna is damaged normally when power is off, the larger the change is, the larger the suspicion degree is; if the antenna loss is abnormal during power failure, the change is smaller, and the suspicion degree is larger. According to the changes of the electric quantity of the user and the line loss of the transformer area before and after power failure, the suspicion degree of abnormal power utilization of the user can be calculated, and the abnormal power utilization condition of the user can be preliminarily judged.
Preferably, the ratio T of the ith power failure time to the longest day power failure timeiAnd the ratio P of the electricity consumption of the user j in the ith power failure to the electricity consumption of the last day without power failureijObtained from the following equation:
wherein ,|tiI is the ith power failure time length, and T is the longest day power failure time length;
wherein ,AijIs the power consumption, A ', of user j at the ith power failure'ijThe electricity consumption of the last day without power failure before the ith power failure is the user.
Calculating the ratio T of the ith power failure time length to the longest day power failure time lengthiAnd the electricity consumption of the user j in the ith power failure and the electricity consumption of the last day without power failureRatio PijTo calculate the suspicion C of abnormal power consumption of user j at the ith power failureijAnd data support is provided, the calculation mode is simple, and the calculation process is quick.
Preferably, the step S3 includes the following steps:
s31: identifying abnormal electricity utilization time on a power failure day;
wherein ,
the abnormal electricity utilization time interval of the ith power failure day, tiThe power failure time interval of the ith power failure;
s32: calculating the abnormal electricity suspicion time;
wherein ,TTjThe abnormal electricity consumption suspicion time of the user j is obtained;
s33: calculating the comprehensive suspicion degree of abnormal electricity utilization of the user;
wherein ,TCjThe suspicion degree of abnormal electricity utilization of the user j is obtained, and n is the upper limit of the power failure times in the time interval.
Abnormal power usage can cause line loss rates to change, e.g., for a long-term line loss abnormal block, the block has been powered off at 16:00-18:00 a day. If the line loss of the transformer area returns to normal on the same day of power failure, the probability that the transformer area is abnormal within the time range of 16:00-18:00 is higher; if the line loss of the transformer area is still abnormal in the power failure on the day, the probability that the transformer area is abnormal in the time range of 16:00-18:00 is high. Thereby obtaining the abnormal electricity utilization time. The abnormal electricity utilization time of the user in each power failure is collected, and the abnormal electricity utilization time of the user can be obtained. The calculation method is simple, and the calculation is convenient and quick.
Preferably, the maximum daily blackout time T is 720 minutes. According to the statistics of big data, the peak period of electricity utilization of the user is 12 hours, so the longest power failure time per day is 720 minutes. The data selection conforms to the actual situation, the scientificity of calculation is enhanced, and the reliability of the calculation result is higher.
The invention has the beneficial effects that:
1. selecting a time interval and a station area, screening effective power failure data and power utilization data, and calculating the suspicion degree of abnormal power utilization; the amount of data required for calculation is reduced and useless data is avoided. The workload is reduced, and the calculation efficiency is improved.
2. The suspicion degree of the abnormal use of the user is calculated by integrating the power failure record and the power utilization data, the calculation mode is novel and simple, and the calculation process is simple, convenient and quick.
3. And equipment does not need to be additionally arranged for judging the suspicion degree of abnormal electricity utilization, so that the time cost and the economic cost are saved.
Drawings
Fig. 1 is a flowchart of a method for identifying a user with abnormal electricity consumption according to the present invention.
Detailed Description
The technical scheme of the invention is further specifically described by the following embodiments and the accompanying drawings.
Example (b):
an abnormal user identification method integrating power failure records and electricity utilization data is shown in fig. 1, and comprises the following steps:
s1: preprocessing historical power failure data; and inquiring the power failure record and the power utilization data of the user.
S11: a time interval and a station zone are selected.
Selecting a time interval and a station area, and acquiring effective power failure data and power utilization data in a selection range to calculate the suspicion degree of abnormal power utilization; the amount of data required for calculation is reduced and useless data is avoided. The workload is reduced, and the calculation efficiency is improved.
S12: and inquiring effective power failure records and user power utilization data of the distribution room in the time interval.
The effective power failure records comprise power failure times, power failure time and line loss rate; the user electricity consumption data comprises the electricity consumption of the user. The data can be acquired through original equipment without additionally installing equipment, the data is acquired or the power utilization abnormity suspicion degree of a user is judged, the time cost and the economic cost are saved, data support is provided for suspicion degree calculation, and the calculation efficiency is improved.
S2: calculating the suspicion degree of abnormal electricity utilization of the user; and calculating the abnormal suspicion degree of each power failure of each user according to the power failure records and the power utilization data.
S21: and calculating the suspicion degree of abnormal electricity utilization of each user at each power failure.
wherein ,CijSuspicion degree of abnormal electricity consumption, T, of power failure of the ith time for user jiThe ratio of the ith power failure time to the longest day power failure time, PijThe ratio of the power consumption of the user j in the ith power failure to the power consumption of the last day without power failure, LiLine loss rate for the ith blackout, LcAnd the upper limit of the line loss rate is checked.
wherein ,|tiAnd l is the power failure time of the ith time, and T is the power failure time of the longest day. The unit of the power failure time is minutes, and the longest day power failure time T is 720 minutes.
According to the statistics of big data, the peak period of electricity utilization of the user is 12 hours, so the longest power failure time per day is 720 minutes. The data selection conforms to the actual situation, the scientificity of calculation is enhanced, and the reliability of the calculation result is higher.
wherein ,AijIs the power consumption, A ', of user j at the ith power failure'ijThe electricity consumption of the last day without power failure before the ith power failure is the user.
And calculating the abnormal electricity consumption suspicion degree of each user in each power failure according to the power failure time and the electricity consumption of the users. The upper limit L _ c of the line loss rate is 7 percent. And calculating the electric quantity variation of each user on the day before and on the day of power failure in all users. If the antenna is damaged normally when power is off, the larger the change is, the larger the suspicion degree is; if the antenna loss is abnormal during power failure, the change is smaller, and the suspicion degree is larger. According to the changes of the electric quantity of the user and the line loss of the transformer area before and after power failure, the suspicion degree of abnormal power utilization of the user can be calculated, and the abnormal power utilization condition of the user can be preliminarily judged. The calculation mode is novel and simple, and the calculation process is simple, convenient and quick.
S22: and identifying the suspected user with the electricity abnormality.
Suspicion degree C of abnormal electricity consumption of user j in ith power failureijAnd the user j more than or equal to 1 is the suspected user of abnormal electricity utilization of the ith power failure.
The suspicion degree of the electricity consumption abnormality is calculated simply and quickly.
S3: and calculating the comprehensive suspicion degree of abnormal electricity utilization of the users, and calculating the comprehensive suspicion degree and the suspicion time of the abnormal electricity utilization of each user.
S31: and identifying abnormal electricity utilization time on the power failure day.
wherein ,
the abnormal electricity utilization time interval of the ith power failure day, tiThe power failure time interval of the ith power failure.
Abnormal power usage can cause line loss rates to change, e.g., for a long-term line loss abnormal block, the block has been powered off at 16:00-18:00 a day. If the line loss of the transformer area returns to normal on the same day of power failure, the probability that the transformer area is abnormal within the time range of 16:00-18:00 is higher; if the line loss of the transformer area is still abnormal in the power failure on the day, the probability that the transformer area is abnormal in the time range of 16:00-18:00 is high. Thereby obtaining the abnormal electricity utilization time.
S32: and calculating the abnormal electricity suspicion time.
wherein ,TTjAnd electricity is used for the abnormal electricity suspicion time of the user j.
The abnormal electricity utilization time of the user in each power failure is collected, and the abnormal electricity utilization time of the user can be obtained. The calculation method is simple, and the calculation is convenient and quick.
S33: and calculating the comprehensive suspicion degree of abnormal electricity utilization of the user.
wherein ,TCjThe suspicion degree of abnormal electricity utilization of the user j is obtained, and n is the upper limit of the power failure times in the time interval.
And adding the suspicion degrees in all power failures of each user to obtain the comprehensive power utilization abnormity suspicion degree. The algorithm is simple, fast and efficient.
S4: and (4) sorting the comprehensive suspicion degree of abnormal electricity utilization of the users, and checking the abnormal users by workers.
According to the sequencing from high to low of the neutralization suspicion degree of the user, the staff carries out investigation from the beginning of high suspicion degree, and the equipment is additionally arranged for monitoring and accurate positioning, so that the investigation range is reduced, the pertinence is improved, the working efficiency is increased, and the labor cost, the time cost and the economic cost are saved.
The method does not need to additionally install equipment for judging the suspicion degree of abnormal electricity utilization, and saves time cost and economic cost. Selecting a time interval and a station area in the calculation process, screening effective power failure data and power utilization data, and calculating the suspicion degree of abnormal power utilization; the amount of data required for calculation is reduced and useless data is avoided. The workload is reduced, and the calculation efficiency is improved. The suspicion degree of the abnormality of the user is calculated from the comprehensive power failure record and the power utilization data, the calculation mode is novel and simple, and the calculation process is simple, convenient and quick.
Claims (7)
1. A method for identifying abnormal users by integrating power failure records and power utilization data is characterized by comprising the following steps:
s1: preprocessing historical power failure data; inquiring power failure records and user power utilization data;
s2: calculating the suspicion degree of abnormal electricity utilization of the user; calculating the abnormal suspicion degree of each power failure of each user according to the power failure records and the power utilization data;
s3: calculating the comprehensive suspicion degree of abnormal electricity utilization of the users, and calculating the comprehensive suspicion degree and the suspicion time of the abnormal electricity utilization of each user;
s4: and (4) sorting the comprehensive suspicion degree of abnormal electricity utilization of the users, and checking the abnormal users by workers.
2. The method for identifying abnormal users based on integrated blackout records and electricity consumption data as claimed in claim 1, wherein said step S1 comprises the steps of:
s11: selecting a time interval and a distribution area;
s12: and inquiring effective power failure records and user power utilization data of the distribution room in the time interval.
3. The method of claim 2, wherein the effective power outage records include the number of power outages, the time of power outage and the line loss rate; the user electricity consumption data comprises the electricity consumption of the user.
4. The method for identifying abnormal users based on integrated blackout records and electricity consumption data as claimed in claim 1, wherein said step S2 comprises the steps of:
s21: calculating the suspicion degree of abnormal electricity utilization of each user during each power failure;
wherein ,CijSuspicion degree of abnormal electricity consumption, T, of power failure of the ith time for user jiThe ratio of the ith power failure time to the longest day power failure time, PijThe ratio of the power consumption of the user j in the ith power failure to the power consumption of the last day without power failure, LiLine loss rate for the ith blackout, LcThe upper limit of the line loss rate is checked;
s22: identifying suspected users of electricity utilization abnormality;
suspicion degree C of abnormal electricity consumption of user j in ith power failureijAnd the user j more than or equal to 1 is the suspected user of abnormal electricity utilization of the ith power failure.
5. The method as claimed in claim 4, wherein the ratio T of the ith blackout period to the longest daily blackout periodiAnd the ratio P of the electricity consumption of the user j in the ith power failure to the electricity consumption of the last day without power failureijObtained from the following equation:
wherein ,|tiI is the ith power failure time length, and T is the longest day power failure time length;
wherein ,AijIs the power consumption, A ', of user j at the ith power failure'ijThe electricity utilization of the last day without power failure before the ith power failure is realized for the userAmount of the compound (A).
6. The method for identifying abnormal users based on integrated blackout records and electricity consumption data as claimed in claim 4, wherein the step S3 comprises the steps of:
s31: identifying abnormal electricity utilization time on a power failure day;
wherein ,
the abnormal electricity utilization time interval of the ith power failure day, tiThe power failure time interval of the ith power failure;
s32: calculating the abnormal electricity suspicion time;
wherein ,TTjThe abnormal electricity consumption suspicion time of the user j is obtained;
s33: calculating the comprehensive suspicion degree of abnormal electricity utilization of the user;
wherein ,TCjThe suspicion degree of abnormal electricity utilization of the user j is obtained, and n is the upper limit of the power failure times in the time interval.
7. The method of claim 5, wherein said maximum daily blackout duration T is 720 minutes.
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