CN110942236B - Abnormal user identification method for comprehensive power failure record and power consumption data - Google Patents
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Abstract
The invention discloses an abnormal user identification method for comprehensive power failure record and power consumption data. In order to solve the problems of high cost of equipment to be added, large data volume required by calculation, complex calculation method and long calculation flow in the prior art; the invention comprises the following steps: s1: preprocessing historical power failure data; inquiring power failure records and user power consumption data; s2: calculating the suspicion degree of abnormal electricity consumption of a user; calculating the abnormal suspicion degree of each power failure of each user according to the power failure record and the power consumption data; s3: calculating the comprehensive suspicion degree of abnormal electricity consumption of users, and calculating the comprehensive suspicion degree and the suspicion time of abnormal electricity consumption of each user; s4: and (5) sequencing the comprehensive suspicion degree of abnormal electricity consumption of users, and checking abnormal users by staff. And the device is not required to be additionally arranged, the preliminary analysis is carried out through the change of the electric quantity of the user and the line loss of the station area before and after the power failure, the suspicion in the station stopping area is identified, the required data quantity is small, the calculation method is simple, and the calculation flow is simple, convenient and quick.
Description
Technical Field
The invention relates to the field of abnormal user identification of electricity consumption, in particular to an abnormal user identification method for comprehensively recording power failure and using electricity data.
Background
With the continuous expansion of the power scale, the normal operation of the power plays a vital role in daily life and production of people. The electricity consumption of the electricity consumer is metered through the intelligent ammeter, but at present, electricity stealing behaviors or ammeter faults widely exist, so that the electricity consumption of the electricity consumer is not truly metered, and 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 perceive, so that the economic loss of a power supply enterprise is continuously caused.
At present, fault meters or electricity stealing users are positioned mainly through additional equipment or manual investigation. Firstly, installing a metering device at each outgoing line of a transformer, then installing the metering device at each branch point, and identifying a link with larger current loss so as to position a fault meter or a power stealing user. The time cost and the economic cost of the equipment are high, and the equipment can only be applied to partial users and areas with high suspicion and is difficult to widely apply.
For example, an identification method, an apparatus and a central server for abnormal electricity consumption behavior are disclosed in chinese patent literature, and the publication number "CN 108022043a" includes collecting user information recorded by a smart meter, collecting and sorting the collected user information to generate an electricity consumption data set, extracting the electricity consumption information of all users in a first preset time period in the electricity consumption data set, calculating standard electricity consumption characteristic values representing average electricity consumption levels of all users, extracting the electricity consumption information of a designated user in a second preset time period, calculating individual electricity consumption characteristic values representing the electricity consumption levels of the designated user, and comparing the individual electricity consumption characteristic values with the standard electricity consumption characteristic values, and if the individual electricity consumption characteristic values of the designated user are greater than the standard electricity consumption characteristic values, determining that the designated user has abnormal electricity consumption behavior. The method has high time cost and economic cost of equipment installation and is difficult to widely apply. And the data volume required by calculation is large, the calculation method is complex, and the calculation flow is long.
Disclosure of Invention
The invention mainly solves the problems of high cost of equipment to be added, large data volume required by calculation, complex calculation method and long calculation flow in the prior art; the abnormal user identification method for integrating the power outage record and the power consumption data is provided, equipment is not required to be added, preliminary analysis is carried out through the change of the electric quantity of the user and the line loss of the station area before and after the power outage, suspicion in the station area is identified, the required data quantity is small, the calculation method is simple, and the calculation flow is simple, convenient and rapid.
The technical problems of the invention are mainly solved by the following technical proposal:
the invention comprises the following steps:
s1: preprocessing historical power failure data; inquiring power failure records and user power consumption data;
s2: calculating the suspicion degree of abnormal electricity consumption of a user; calculating the abnormal suspicion degree of each power failure of each user according to the power failure record and the power consumption data;
s3: calculating the comprehensive suspicion degree of abnormal electricity consumption of users, and calculating the comprehensive suspicion degree and the suspicion time of abnormal electricity consumption of each user;
s4: and (5) sequencing the comprehensive suspicion degree of abnormal electricity consumption of users, and checking abnormal users by staff.
And calculating suspicion of 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 is abnormal in power failure process. And the electricity consumption abnormality suspicion of the user is calculated by combining the power failure record and the electricity consumption data of the user, and the mode is novel. The data required by calculation is not required to be acquired through adding equipment, and the judgment of the abnormal electricity consumption behavior of the user is not required to be realized through adding equipment, so that the time cost and the economic cost are saved. Whether the abnormal electricity consumption of the user is detected in the power failure range can be accurately calculated, the calculated data size is greatly reduced, the work load is lightened, the calculation speed is increased, and the work efficiency is improved. The suspicion of the abnormal electricity consumption of each user is calculated from the suspicion of the abnormal electricity consumption of each user, the suspicion of the abnormal electricity consumption of each user is calculated from the power outage record, the calculation method is simple, and the calculation flow is simple, convenient and quick.
Preferably, the step S1 includes the steps of:
s11: selecting a time interval and a station area;
s12: and inquiring effective power failure records and user power consumption data of the station area in the time interval.
The effective power failure record and the user power consumption data of the selected area are inquired in the selected time interval, the inquiry range is reduced, the data quantity of inquiry is reduced, the data arrangement, screening and calculation after the inquiry are convenient, 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 not required to be acquired by adding equipment, the original equipment can be used for acquiring the required data, and the abnormal electricity consumption behavior of the user is not required to be judged by adding equipment, so that 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 comprise the electricity consumption of the user. The effective power outage record is a power outage record which is recorded in a non-repeated record and is actually executed. 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 a selected time interval.
Preferably, the step S2 includes the steps of:
s21: calculating the suspicion of abnormal electricity consumption of each user in each power failure;
wherein ,Cij For the suspicion of abnormal electricity consumption of the user j in the ith power failure, T i The ratio of the ith power failure time length to the longest power failure time length, P ij For the ratio of the electricity consumption of the user j in the ith power failure to the electricity consumption of the last non-power failure day before, L i Line loss rate of ith power failure, L c An upper limit is checked for line loss rate;
s22: identifying suspected users with abnormal electricity consumption;
abnormal electricity utilization suspicion degree C of ith power failure of user j ij And the user j which is not less than 1 is an abnormal electricity utilization suspected user who fails at the ith time.
And calculating the suspicion of abnormal electricity consumption of each user in each outage according to the outage time and the electricity consumption of the user. Line loss rate assessment upper limit L c 7%. In all usesAnd in the household, calculating the electric quantity change quantity of each user on the day before and the day of the power failure. If the antenna loss is normal in the power failure, the greater the change is, the greater the suspicion is; if the antenna loss is abnormal in the power failure, the smaller the change is, the greater the suspicion is. The abnormal electricity utilization suspicion degree of the user can be calculated according to the electricity quantity of the user and the change of the line loss of the station area before and after the power failure, and the abnormal electricity utilization condition of the user can be primarily judged.
Preferably, the ratio T of the ith power failure time to the longest day power failure time i And the ratio P of the electricity consumption of the user j in the ith power failure to the electricity consumption of the last non-power failure day before ij The method is obtained by the following formula:
wherein ,|ti I is the ith power failure time length, and T is the longest day power failure time length;
wherein ,Aij A 'is the electricity consumption of the user j in the ith power failure' ij The power consumption of the user on the last uninterrupted day before the ith outage is obtained.
Calculating the ratio T of the ith power failure time length to the longest day power failure time length i And the ratio P of the electricity consumption of the user j in the ith power failure to the electricity consumption of the last non-power failure day before ij To calculate the suspicion C of abnormal electricity consumption of the user j in the ith power failure ij The data support is provided, the calculation mode is simple, and the calculation flow is rapid.
Preferably, the step S3 includes the steps of:
s31: identifying abnormal electricity utilization time of a power failure day;
wherein ,
an abnormal electricity utilization time interval of the ith power failure day, t i The power failure time interval is the ith power failure;
s32: calculating abnormal electricity suspicion time;
wherein ,TTj The abnormal electricity utilization suspicion time of the user j;
s33: calculating the comprehensive suspicion of abnormal electricity consumption of a user;
wherein ,TCj And (3) the comprehensive suspicion of abnormal electricity consumption of the user j is obtained, and n is the upper limit of the number of times of power failure in the time interval.
Abnormal power usage may cause a change in the line loss rate, e.g., for a region with long term line loss anomalies, which has a power outage at 16:00-18:00 a day. If the line loss of the station area is recovered to be normal on the same day of power failure, the probability that the station area is abnormal in the time range of 16:00-18:00 is larger; if the area is abnormal when the solar line loss is still in the power failure, the area has a larger probability of being abnormal outside the time range of 16:00-18:00. Thus, the abnormal electricity utilization time is obtained. And the abnormal electricity utilization time of each power failure of the user is obtained by combining the abnormal electricity utilization time of each power failure of the user. The calculation method is simple and convenient and quick.
Preferably, the longest daily power failure duration T is 720 minutes. According to big data statistics, the peak period of electricity consumption of the user is 12 hours, so the longest power failure time per day is 720 minutes. The selection of the data accords with the actual situation, the scientificity of calculation is enhanced, and the reliability of a calculation result is higher.
The beneficial effects of the invention are as follows:
1. selecting a time interval and a station area, screening effective power failure data and power consumption data, and calculating abnormal power consumption suspicion; 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. And the suspicion degree of the abnormal use of the user is calculated by combining the power failure record and the power consumption data, the calculation mode is novel and simple, and the calculation flow is simple and quick.
3. The judgment of the suspicion degree of abnormal electricity does not need to be carried out by adding equipment, so that the time cost and the economic cost are saved.
Drawings
FIG. 1 is a flow chart of a method for identifying abnormal electricity consumption users according to the present invention.
Detailed Description
The technical scheme of the invention is further specifically described below through examples and with reference to the accompanying drawings.
Examples:
an abnormal user identification method for integrating power failure record and power consumption 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 user power consumption data.
S11: a time interval and a zone are selected.
Selecting a time interval and a station area, acquiring effective power failure data and power utilization data in a selection range, and calculating abnormal power utilization suspicion; 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 consumption data of the station area in the time interval.
The effective power failure record comprises power failure times, power failure time and line loss rate; the user electricity consumption data comprise the electricity consumption of the user. The data acquisition can be achieved through original equipment, additional equipment is not needed, the data are acquired or the electricity utilization abnormality 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 consumption of a user; and calculating the abnormal suspicion degree of each power outage of each user according to the power outage record and the power consumption data.
S21: and calculating the suspicion of abnormal electricity consumption of each user in each power failure.
wherein ,Cij For the suspicion of abnormal electricity consumption of the user j in the ith power failure, T i The ratio of the ith power failure time length to the longest power failure time length, P ij For the ratio of the electricity consumption of the user j in the ith power failure to the electricity consumption of the last non-power failure day before, L i Line loss rate of ith power failure, L c And (5) checking the upper limit for the line loss rate.
wherein ,|ti And I is the ith power failure time length, and T is the longest day power failure time length. The unit of the power failure duration is minutes, and the longest daily power failure duration T is 720 minutes.
According to big data statistics, the peak period of electricity consumption of the user is 12 hours, so the longest power failure time per day is 720 minutes. The selection of the data accords with the actual situation, the scientificity of calculation is enhanced, and the reliability of a calculation result is higher.
wherein ,Aij A 'is the electricity consumption of the user j in the ith power failure' ij The power consumption of the user on the last uninterrupted day before the ith outage is obtained.
And calculating the suspicion of abnormal electricity consumption of each user in each outage according to the outage time and the electricity consumption of the user. The upper limit l_c of line loss rate check is 7%. And calculating the power change quantity of each user on the day before and the day of power failure among all users. If the antenna loss is normal in the power failure, the greater the change is, the greater the suspicion is; if the antenna loss is abnormal in the power failure, the smaller the change is, the greater the suspicion is. The abnormal electricity utilization suspicion degree of the user can be calculated according to the electricity quantity of the user and the change of the line loss of the station area before and after the power failure, and the abnormal electricity utilization condition of the user can be primarily judged. The calculation mode is novel and simple, and the calculation flow is simple, convenient and quick.
S22: and identifying suspected users with abnormal electricity consumption.
Abnormal electricity utilization suspicion degree C of ith power failure of user j ij And the user j which is not less than 1 is an abnormal electricity utilization suspected user who fails at the ith time.
The electricity consumption abnormality suspicion is simple and quick to calculate.
S3: and calculating the comprehensive suspicion degree of abnormal electricity consumption of the users, and calculating the comprehensive suspicion degree and the suspicion time of abnormal electricity consumption of each user.
S31: and identifying abnormal electricity utilization time of the power failure day.
wherein ,
an abnormal electricity utilization time interval of the ith power failure day, t i The power failure time interval is the ith power failure.
Abnormal power usage may cause a change in the line loss rate, e.g., for a region with long term line loss anomalies, which has a power outage at 16:00-18:00 a day. If the line loss of the station area is recovered to be normal on the same day of power failure, the probability that the station area is abnormal in the time range of 16:00-18:00 is larger; if the area is abnormal when the solar line loss is still in the power failure, the area has a larger probability of being abnormal outside the time range of 16:00-18:00. Thus, the abnormal electricity utilization time is obtained.
S32: and calculating abnormal electricity suspicion time.
wherein ,TTj And (5) the abnormal electricity utilization suspicion time of the user j.
And the abnormal electricity utilization time of each power failure of the user is obtained by combining the abnormal electricity utilization time of each power failure of the user. The calculation method is simple and convenient and quick.
S33: and (5) calculating the comprehensive suspicion of abnormal electricity consumption of the user.
wherein ,TCj And (3) the comprehensive suspicion of abnormal electricity consumption of the user j is obtained, and n is the upper limit of the number of times of power failure in the time interval.
And adding suspicions of all the power failures of each user to obtain the comprehensive electricity utilization abnormality suspicion. The algorithm is simple, quick and high in efficiency.
S4: and (5) sequencing the comprehensive suspicion degree of abnormal electricity consumption of users, and checking abnormal users by staff.
According to the sorting of users from high to low in neutralization suspicion, workers begin to check from high in suspicion, and the equipment is added for monitoring and accurate positioning, so that the checking 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 and the device for judging the suspicion of abnormal electricity consumption do not need additional equipment, and save time cost and economic cost. Selecting a time interval and a station area in the calculation process, screening effective power failure data and power consumption data, and calculating the suspicion of abnormal power consumption; the amount of data required for calculation is reduced and useless data is avoided. The workload is reduced, and the calculation efficiency is improved. And the suspicion degree of the abnormal use of the user is calculated from the comprehensive power failure record and the power consumption data, the calculation mode is novel and simple, and the calculation flow is simple and quick.
Claims (5)
1. The abnormal user identification method for integrating the power failure record and the power consumption data is characterized by comprising the following steps:
s1: preprocessing historical power failure data; inquiring power failure records and user power consumption data;
s2: calculating the suspicion degree of abnormal electricity consumption of a user; calculating the abnormal suspicion degree of each power failure of each user according to the power failure record and the power consumption data;
the step S2 comprises the following steps:
s21: calculating the suspicion of abnormal electricity consumption of each user in each power failure;
wherein ,
for the suspicion of abnormal electricity consumption of user j in the ith power failure, < >>
The ratio of the ith power failure time to the longest power failure time is%>
For the ratio of the power consumption of user j in the ith power failure to the power consumption of the last non-power failure day before,/>
For the line loss rate of the ith power failure, < >>
An upper limit is checked for line loss rate;
s22: identifying suspected users with abnormal electricity consumption;
suspicion of abnormal electricity consumption of user j in ith power failure
The user j of the (1) is the suspected user of abnormal electricity consumption of the ith power failure;
s3: calculating the comprehensive suspicion degree of abnormal electricity consumption of users, and calculating the comprehensive suspicion degree and the suspicion time of abnormal electricity consumption of each user;
the step S3 comprises the following steps:
s31: identifying abnormal electricity utilization time of a power failure day;
wherein ,
for the abnormal power utilization time interval of the ith power failure day, < > in->
The power failure time interval is the ith power failure;
s32: calculating abnormal electricity suspicion time;
wherein ,
the abnormal electricity utilization suspicion time of the user j;
s33: calculating the comprehensive suspicion of abnormal electricity consumption of a user;
wherein ,
the comprehensive suspicion of abnormal electricity consumption of the user j is given, and n is the upper limit of the power failure times in the time interval;
s4: and (5) sequencing the comprehensive suspicion degree of abnormal electricity consumption of users, and checking abnormal users by staff.
2. The method for identifying abnormal users by integrating power outage record and power utilization data according to claim 1, wherein said step S1 comprises the steps of:
s11: selecting a time interval and a station area;
s12: and inquiring effective power failure records and user power consumption data of the station area in the time interval.
3. The method for identifying abnormal users by integrating power outage records and power consumption data according to claim 2, wherein the effective power outage records comprise power outage times, power outage time and line loss rate; the user electricity consumption data comprise the electricity consumption of the user.
4. The method for identifying abnormal users by integrating power outage record and power utilization data according to claim 1, wherein the ratio of the ith power outage duration to the longest daily power outage duration is as follows
And the ratio of the power consumption of the user j at the ith power outage to the power consumption of the last non-power outage day before +>
The method is obtained by the following formula:
wherein ,
the power failure time length of the ith time is the power failure time length of the longest day, and T is the power failure time length of the longest day;
wherein ,
for the power consumption of user j at the ith power failure, < >>
The power consumption of the user on the last uninterrupted day before the ith outage is obtained.
5. The method for identifying abnormal users by integrating power outage record and power utilization data according to claim 4, wherein the longest power outage duration T is 720 minutes.
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