CN112184477A - A method of supplementary electricity based on clustering and PQUI identification algorithm - Google Patents

A method of supplementary electricity based on clustering and PQUI identification algorithm Download PDF

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CN112184477A
CN112184477A CN202010866383.6A CN202010866383A CN112184477A CN 112184477 A CN112184477 A CN 112184477A CN 202010866383 A CN202010866383 A CN 202010866383A CN 112184477 A CN112184477 A CN 112184477A
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stealing
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侯素颖
裘炜浩
王建波
金挺超
蔡慧
郁春雷
包锦辉
陈嘉
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State Grid Zhejiang Electric Power Co Ltd
China Jiliang University
Zhejiang Huayun Information Technology Co Ltd
Taizhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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China Jiliang University
Zhejiang Huayun Information Technology Co Ltd
Taizhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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Abstract

本发明公开了一种基于聚类和PQUI识别算法的追补电量方法，涉及电网运维领域。目前，窃电电量不能准确计算，计算的窃电电量与实际的窃电电量相差过多，用户、电力公司之间难以保持公平。本发明先根据用户的负荷数据判断出用户的窃电时间，再根据负荷数据之间的关系确定窃电手法，然后对照窃电行为确定更正系数，最后根据更正系数计算追补电量；窃电天数计算时采用聚类算法和/或PQUI算法。本技术方案采用聚类算法、PQUI识别算法计算对应的窃电量，计算匹配性好，使得准确性高，且采集的数据可以满足要求，实现准确、客观、快速地计算窃电用户的追补电量，使其与实际发生电量相一致，提高用电的公平性。

Figure 202010866383

The invention discloses a method for supplementary electric quantity based on clustering and PQUI identification algorithm, and relates to the field of power grid operation and maintenance. At present, the amount of electricity stolen cannot be accurately calculated, and the calculated amount of electricity stolen is too different from the actual amount of electricity stolen, and it is difficult to maintain fairness between users and power companies. The invention firstly judges the user's electricity stealing time according to the user's load data, then determines the electricity stealing method according to the relationship between the load data, then determines the correction coefficient according to the electricity stealing behavior, and finally calculates the supplementary electricity according to the correction coefficient; The clustering algorithm and/or the PQUI algorithm are used. The technical solution adopts the clustering algorithm and the PQUI identification algorithm to calculate the corresponding stolen electricity, and the calculation matching is good, so that the accuracy is high, and the collected data can meet the requirements, so as to realize the accurate, objective and rapid calculation of the recovery electricity of the electricity stealing users. , so that it is consistent with the actual electricity consumption, and the fairness of electricity consumption is improved.

Figure 202010866383

Description

Clustering and PQUI recognition algorithm-based electric quantity supplementing method
Technical Field
The invention relates to the field of power grid operation and maintenance, in particular to a method for supplementing electric quantity based on clustering and a PQUI recognition algorithm.
Background
The commensurate increase in power production and consumption levels has greatly pushed the development of electricity management technologies, but with the attendant increased problems of electricity theft. The problem of electricity stealing not only brings about the safety problem of electricity utilization, but also leads to the fairness problem among users and the direct economic loss of power supply enterprises. Nowadays, most of the recollection electric quantity is estimated by directly depending on manual analysis, but the method is too flexible to cause insufficient persuasion and is difficult to convince. Therefore, a reasonable method is provided to find out a reasonable electricity stealing interval from the historical load data and correctly calculate the compensation electricity quantity.
The electricity stealing time of the user can be found out more reasonably and accurately by combining various electricity stealing time confirmation methods and the electricity consumption curve of the user. Because the number of users is large, the electricity consumption requirements are different, and electricity stealing methods are various, the electricity stealing time judging method cannot accurately judge the electricity stealing time. Therefore, various methods are required.
After the electricity stealing time is determined, the electricity stealing methods need to be confirmed, due to the fact that the electricity stealing methods are different, correction coefficients of all users are different, electricity stealing electric quantity cannot be calculated accurately, the calculated electricity stealing electric quantity is too much different from the actual electricity stealing electric quantity, and fairness is difficult to maintain between users and power companies.
Disclosure of Invention
The technical problem to be solved and the technical task are to perfect and improve the prior technical scheme and provide a method for supplementing electric quantity based on clustering and a PQUI recognition algorithm so as to achieve the aim of accurately calculating the supplemented electric quantity. Therefore, the invention adopts the following technical scheme.
A cluster and PQUI recognition algorithm-based electric quantity compensation method is implemented by adopting an electric energy metering device remote monitoring and diagnosing system, wherein the electric energy metering device remote monitoring and diagnosing system comprises an electric energy information acquisition system, a data collection and analysis server and a database server; the electricity utilization information acquisition system acquires electricity utilization data and events of power consumers in real time; the data collection and analysis server analyzes and calculates electricity stealing behaviors and corresponding compensation electric quantity according to the acquired data; the database server stores electricity consumption data, events, analysis and judgment thresholds, an electricity stealing behavior feature library and correction coefficients under each electricity stealing behavior, and the data collection and analysis server comprises the following steps when analyzing and calculating the compensation electric quantity:
1) judging whether the electricity stealing amount depends on the time length; if not, calculating the difference between the on-site copy electric quantity and the final settlement electric quantity of the power supply enterprise directly; if yes, entering the next step;
2) calculating the number of electricity stealing days, and judging whether the number of electricity stealing days can be determined; if yes, directly calculating the electricity stealing amount according to the determined days; if not, calculating the electricity stealing amount by preset days; the number of electricity stealing days is calculated by adopting a clustering algorithm and/or a PQUI algorithm;
3) identifying electricity stealing behaviors; acquiring power utilization data of a suspected electricity stealing user; comparing the electricity stealing behavior feature libraries in the corresponding metering modes to match the most probable electricity stealing behaviors;
4) inquiring a database server, comparing with electricity stealing behaviors, judging whether a correction coefficient exists, if so, determining actual daily electric quantity according to the corresponding correction coefficient, and then adding the daily electric quantity within the electricity stealing time to obtain the actual electric quantity; if not, entering the next step;
5) judging whether the daily electric quantity prediction requirement is met; if not, replacing the actual load with the capacity indicated by the charging electric energy meter, then judging the electricity utilization time of one day according to the produced commercial electricity utilization and the domestic electricity utilization of different electricity utilization users, and multiplying the electricity utilization time by the electricity stealing time to obtain electricity stealing amount; if so, predicting the daily electric quantity by using an algorithm, and calculating the electric stealing quantity of the current day of stopping electric stealing by multiplying the daily electric quantity by the electric stealing days;
6) and storing the calculated electricity stealing amount and the calculation basis, and modifying the correction coefficient of the database server when the difference value between the calculated electricity stealing amount and the actual electricity stealing amount exceeds a set threshold value.
As a preferable technical means: when the number of electricity stealing days is calculated in the step 2), the specific steps of adopting a clustering algorithm and/or a PQUI algorithm are as follows;
201) and (3) clustering algorithm: dividing the power consumption into three types, and dividing the power consumption into a label 1 type, a label 2 type, a label 3 type and a label 3 type according to the clustering center of the power consumption; regarding the 3 types of labels, if the time corresponding to the electricity consumption of the 3 types of labels, namely the abnormal time continuously exceeds 15 days, the continuous abnormal time is considered as electricity stealing time;
202) PQUI algorithm: carrying out relevant calculation according to the load of the user and extracting useful features; the three-phase four-wire calculation formula is as follows:
S1＝UaIa+UbIb+UcIc……(1)
Figure BDA0002649874760000021
K＝(S1-S2)/W……(3)
in the formula of Ua，Ub，Uc，Ia，Ib，IcThree-phase voltage and three-phase current respectively; w is the electricity consumption; p and Q are respectively active power and reactive power; s1，S2Is the apparent power;
for normal users, the electricity consumption condition of the users at ordinary times can not be changed too much, and the fluctuation of the voltage and the current can not be too large, so that the K value can not fluctuate too much and can be in a stable range; when a user has an electricity stealing behaviour, the change in the electricity consumption behaviour directly leads to a change in the relationship between the loads, S1，S2The difference between the two values is increased, the electricity consumption W is reduced, and therefore the ratio K is increased; therefore, when the K value is far larger than the K value in normal electricity utilization, the electricity utilization of the day is considered to be abnormal, and if the abnormal K value appears for 15 consecutive days, the user is considered to steal the electricity;
the PQUI method requires electric quantity, voltage, current and power for calculation, so that the PQUI method is not suitable for low-voltage users, and can only calculate special transformer users; clustering only needs to use electric quantity data, so that low-voltage and special transformer users can use the data; therefore, when the user is a low-voltage user, the clustering algorithm is used for judging the electricity stealing time; when the user is a special transformer user, the condition needs to be supplemented; the PQUI recognition algorithm judges whether the power utilization is abnormal or not through K value fluctuation, so that when the fluctuation is small, accurate judgment cannot be carried out; the clustering algorithm directly judges through the continuous time of the lowest class, so that the defect of small fluctuation is avoided, but because of the need of multiple iterations, a large amount of time is needed for calculation; in order to avoid the defects of the two algorithms, a parameter D is added to calculate the fluctuation condition through distance, and then a proper algorithm is selected according to the D; the specific calculation is as follows:
firstly, carrying out normalization processing on data:
Figure BDA0002649874760000022
in the formula, Wi(i-1, 2,3, …, n) is a set of electrical data of a user, WminIs the minimum value in the data, WmaxIs the maximum value in the data;
then, the parameter D is calculated:
D＝|Wi *-mean(W*)|/n……(5)
in the formula, W*Is Wi *Sum of all data in (1), mean (W)*) The average value of the normalized data is obtained, and n is the number of the data;
setting the threshold value to be 0.2, and using a PQUI algorithm when D is more than or equal to 0.2; when D <0.2, a clustering algorithm is used.
Through a large amount of experimental calculation and comparison, the threshold value is 0.2, the calculation result is more accurate, and the calculation result is most matched with the actual electricity stealing time.
As a preferable technical means: the electric energy metering device remote monitoring and diagnosing system also comprises an electricity stealing behavior characteristic library establishing module, and comprises the following steps when the electricity stealing behavior characteristic library is established:
A1) acquiring historical electricity stealing data;
A2) processing historical electricity stealing data; obtaining electricity stealing methods, numbering each electricity stealing method, and classifying; the classification of electricity stealing techniques includes: electricity stealing without meters, disconnection of a voltage loop, poor contact of the voltage loop, voltage division of the voltage loop, open circuit of a current loop, short circuit of the current loop, shunt of the current loop, phase-shifting electricity stealing, change of the internal structure of the electricity meter, damage of the electricity meter by large current or mechanical force and external interference;
A3) establishing a corresponding electricity stealing behavior feature library aiming at three metering modes of high supply and high metering, high supply and low metering and low supply and low metering; wherein:
the data of the electricity stealing behavior characteristic library in the high power supply and high metering mode comprise categories, output electricity stealing methods corresponding to the categories, A phase voltage, C phase voltage, A phase current, C phase current and active power; judging a fault phase and an electricity stealing method according to the phase voltage, the phase current and the active power of each phase when judging electricity stealing behavior;
the data of the electricity stealing behavior characteristic library under the high-power supply and low-metering mode comprise categories, output electricity stealing methods corresponding to the categories, A-phase voltage, B-phase voltage, C-phase voltage, A-phase current, B-phase current, C-phase current and active power; judging a fault phase and an electricity stealing method according to the phase voltage, the phase current and the active power of each phase when judging electricity stealing behavior;
the data of the electricity stealing behavior feature library in the low supply and low metering mode comprise categories, output electricity stealing methods and electric quantity corresponding to the categories; and judging the fault phase and the electricity stealing method according to the electric quantity when judging the electricity stealing behavior.
The technical scheme provides a perfect electricity stealing behavior data characteristic library on the basis of three typical electricity utilization types, namely the electricity stealing behavior characteristic library is established aiming at electricity utilization characteristics of high supply and high metering, high supply and low metering and low supply and low metering. On the basis, a perfect matching mechanism is established, the electricity stealing behavior matching is carried out on the user data actually containing the suspicion of electricity stealing, the possible electricity stealing behavior is identified, technical support is provided for electricity stealing verification, the electricity stealing identification efficiency is effectively improved, and the labor cost is reduced.
As a preferable technical means: the electricity stealing methods are classified into electricity stealing methods without meters and comprising the following steps: the voltage transformer is disconnected with a wire Q1, and is connected across a transformer cross-over Q2, and is connected with a bypass cross-over electric energy meter Q3;
the electricity stealing methods classified as the broken line of the voltage loop include: the method comprises the following steps of loosening a fuse Q4 of the TV, breaking a fuse Q5 in a fuse tube, loosening a connecting terminal Q6 of a voltage loop, breaking a wire core Q7 of a voltage loop wire and loosening a voltage connecting piece Q8 of the electric energy meter;
the classification of electricity stealing methods into poor-contact electricity stealing methods of voltage loops comprises the following steps: a voltage connecting piece Q9 of the electric energy meter is unscrewed, a connecting terminal Q10 of a voltage loop is unscrewed, and a low-voltage fuse Q11 of the TV is unscrewed;
the electricity stealing method classified into voltage division of the voltage loop comprises the following steps: a resistor Q12 is connected in series in a secondary circuit of the TV, a zero line on the wire inlet side of the single-phase meter is broken, and a resistor voltage drop Q13 is connected in series between the outlet wire and the ground;
the electricity stealing methods classified as open-circuit current circuit electricity stealing methods include: loosening a TA secondary outgoing line terminal Q14, artificially manufacturing a poor contact fault Q15 of a connecting terminal in a TA secondary circuit, breaking a wire core Q16 of a current circuit lead, and breaking a zero line and electricity stealing Q17;
the electricity stealing methods classified as short circuit of current loop include: the current terminal Q18 of the short circuit electric energy meter, the terminal row Q19 in the short circuit current loop, and the short circuit TA primary side or secondary side Q20;
the electricity stealing methods classified into current loop shunting include: replacing TA Q21 with different transformation ratios, changing a secondary tap Q22 of a tapped TA and changing the number of primary turns Q23 of a core-through TA;
the electricity stealing methods classified into phase-shift electricity stealing methods include: the phase line and the zero line of the single-phase meter are interchanged, and the ground wire is used as the zero line Q24; exchanging an inlet/outlet line Q25 on the primary side of the TA; exchanging a homonymous terminal Q26 on the secondary side of the TA; exchanging an incoming and outgoing line Q27 of a current terminal of the electric energy meter; exchanging TA to a phase Q28 of a connecting line of the electric energy meter; swapping the polarity Q29 of TV primary or secondary; exchanging the TV to a phase Q30 of a power meter connecting line; phase-shifting Q31 with special inductor or capacitor;
the electricity stealing techniques classified into those for changing the internal structure of the electricity meter include: reducing the number of current coil turns Q32; performing resistance spot welding on the manganin, and cutting off a manganin signal wire Q33; the current sampling loop is connected in parallel and in series with a resistor Q34; replacing the voltage sampling loop grading sampling resistor Q35; a voltage coil series resistor and other electronic elements to divide the voltage Q36; a copper wire hook short circuit Q37; implanting a remote control shunt Q38;
the electricity stealing techniques classified as electricity stealing techniques that damage electricity meters with large current or mechanical force include: burning out the current coil Q39 by overload current, impacting the electric meter Q40 by electric power of short-circuit current, and damaging the electric meter Q41 by mechanical external force;
the electricity stealing techniques classified as external interference include: the high-voltage pulse electricity stealing system comprises a strong magnetic interference electricity stealing Q42, a high-frequency interference electricity stealing Q43, a high-voltage pulse electricity stealing Q44 and a short-circuit metering box inlet and outlet wire Q45.
As a preferable technical means: the characteristic library of electricity stealing behavior under the high-supply and high-metering mode is as follows:
Figure BDA0002649874760000041
Figure BDA0002649874760000051
the electricity stealing behavior characteristic library under the high-power supply and low-metering mode is as follows:
Figure BDA0002649874760000052
Figure BDA0002649874760000061
Figure BDA0002649874760000071
the electricity stealing behavior characteristic library under the low-supply and low-metering mode is as follows:
Figure BDA0002649874760000072
。
as a preferable technical means: the electricity stealing behavior judgment comprises electricity stealing behavior identification in a high supply and low metering mode, electricity stealing behavior identification in a high supply and high metering mode and electricity stealing behavior identification in a low supply and low metering mode;
the electricity stealing behavior identification under the high power supply and low metering mode comprises the following steps:
3101) inputting electricity utilization data of suspected electricity stealing users;
3102) judging A, B, C whether the phase current voltage has a value; if not, determining that the judgment cannot be carried out, and finishing; if yes, entering the next step;
3103) judging whether the three-phase voltage and the three-phase current are both close to 0; if yes, comparing the electricity stealing behavior feature library in the high-power supply and low-metering mode, outputting 9 types of electricity stealing reasons, and ending; if not, entering the next step;
3104) judging whether the three-phase current has one phase or multiple phases close to 0; if yes, comparing the electricity stealing behavior feature library in the high-power supply and low-metering mode, outputting 1 type of electricity stealing reason and an electricity stealing phase with the output current close to 0, and ending; if not, entering the next step;
3105) judging whether the three-phase voltage has one phase or multiple phases close to 0; if yes, comparing the electricity stealing behavior feature library in the high-power supply and low-metering mode, outputting the type 2 electricity stealing reasons and the electricity stealing phase with the output voltage close to 0, and ending; if not, entering the next step;
3106) summoning the split-phase power factor;
3107) judging whether all the three-phase power factors are normal or not; if not, go to step 2110; if yes, entering the next step;
3108) judging whether the three-phase voltages are all larger than or close to 220V; if not, comparing the electricity stealing behavior feature library in the high-power supply and low-metering mode, outputting 4 types of electricity stealing reasons and electricity stealing phases with undersize output voltage, and ending; if yes, entering the next step;
3109) judging whether the three-phase currents are equal, if not, comparing the three-phase currents with the electricity stealing behavior feature library in the high-power supply and low-metering mode, outputting 3 types of electricity stealing reasons and electricity stealing phases with smaller output currents, and ending; if yes, outputting 3 types of electricity stealing reasons and electricity stealing phases with smaller current, and ending;
3110) judging whether the daily electric quantity of the user is 0 or not; if yes, comparing the electricity stealing behavior feature library in the high-power supply and low-metering mode, outputting 6 or 8 types of electricity stealing reasons and electricity stealing phases with abnormal power factors, and ending; if not, entering the next step;
3111) judging whether the three-phase current has one phase or multiple phases smaller than 0; if yes, comparing the electricity stealing behavior feature library in the high-power supply and low-metering mode, outputting 5 types of electricity stealing reasons and electricity stealing phases with negative current, and ending; if not, outputting 7 types of electricity stealing reasons and electricity stealing phases with negative voltage, and ending;
the electricity stealing behavior identification under the high power supply and high metering mode comprises the following steps:
3201) inputting electricity data of suspected electricity stealing users
3202) Judging A, C whether the phase current and voltage have values, if not, judging that the judgment cannot be carried out and finishing;
3203) judging whether A, C phase voltage and current are both close to 0; if yes, comparing the electricity stealing behavior feature library in the high-supply high-count metering mode, outputting 9 types of electricity stealing reasons, and ending; if not, entering the next step;
3204) determining A, C whether the phase voltage has one or more phases approaching 0; if yes, comparing the electricity stealing behavior feature library in the high power supply and high metering mode, outputting 1 type electricity stealing reason and an electricity stealing phase with the voltage close to 0, and ending; if not, entering the next step;
3205) determining A, C whether the phase current has one or more phases that are close to 0; if yes, comparing the electricity stealing behavior feature library in the high power supply and high metering mode, and outputting the type 2 electricity stealing reasons and the electricity stealing phases with the power supply close to 0; if not, entering the next step;
3206) summoning the split-phase power factor;
3207) judging whether A, C-phase power factors are all normal; if not, go to step 2210; if yes, entering the next step;
3208) determining A, C whether the phase voltages are both greater than or near 220V; if not, comparing the electricity stealing behavior feature library in the high power supply and high metering mode, outputting 3 types of electricity stealing reasons and electricity stealing phases with undersize voltage, and ending; if yes, entering the next step;
3209) judging A, C whether the phase currents are equal; if yes, comparing the electricity stealing behavior feature library in the high power supply and high metering mode, outputting 4 types of electricity stealing reasons and A, C two phases of electricity stealing phases, and ending; if not, comparing the electricity stealing behavior feature library in the high power supply and high metering mode, outputting 4 types of electricity stealing reasons and electricity stealing phases with smaller current, and ending;
3210) determining A, C whether the phase voltage is less than 0; if yes, comparing the electricity stealing behavior feature library in the high power supply and high metering mode, outputting 7 types of electricity stealing reasons and electricity stealing phases with the voltage less than 0, and ending; if not, entering the next step;
3211) judging A, C whether the phase current is less than 0; if yes, comparing the electricity stealing behavior feature library in the high power supply and high metering mode, outputting 5 types of electricity stealing reasons and electricity stealing phases with the current less than 0, and ending; if not, entering the next step;
3212) judging whether the daily electric quantity of the user is 0; if yes, comparing the electricity stealing behavior feature library in the high-supply high-metering mode, outputting 6 or 8 types of electricity stealing reasons and an electricity stealing phase AC or AB phase, and ending; if not, comparing the electricity stealing behavior feature library in the high power supply and high metering mode, outputting two phases of 8 types of electricity stealing reasons and electricity stealing phases BC, and ending;
the electricity stealing behavior identification under the low supply and low metering mode comprises the following steps:
3301) inputting electricity data of suspected electricity stealing users
3302) Judging whether the daily electric quantity of the user has a value or not; if not, determining that the judgment cannot be carried out, and finishing; if yes, entering the next step;
3303) judging whether the daily electric quantity of the user is close to 0; if yes, comparing the electricity stealing behavior feature library in the low-supply and low-metering mode, outputting the 1-class electricity stealing reason, and ending; if not, entering the next step;
3304) judging whether the daily electric quantity of the user is negative or not; if yes, comparing the electricity stealing behavior feature library in the low-supply and low-metering mode, outputting the 2 types of electricity stealing reasons, and ending; if not, comparing the electricity stealing behavior feature library in the low-supply and low-metering mode, outputting 3 types of electricity stealing reasons, and ending.
As a preferable technical means: the database server stores the electricity stealing behaviors and correction coefficients under each electricity stealing behavior, wherein the correction coefficients comprise a private-transformer high-supply and high-supply user correction coefficient, a private-transformer high-supply and low-supply user correction coefficient and a low-voltage user correction coefficient;
the user correction factors for the high supply and high supply of the special transformer are shown in the following table:
Figure BDA0002649874760000091
Figure BDA0002649874760000101
Figure BDA0002649874760000111
the private high and low user correction factors are shown in the following table:
Figure BDA0002649874760000112
Figure BDA0002649874760000121
Figure BDA0002649874760000131
the low voltage user correction factor is shown in the following table:
Figure BDA0002649874760000132
。
has the advantages that: the technical scheme solves the defects that the current electric quantity compensation can only be estimated, the accuracy is low, the persuasion is insufficient, and a large amount of manpower is consumed. According to the technical scheme, the clustering algorithm and the PQUI recognition algorithm are adopted to calculate the corresponding electricity stealing amount, the calculation matching is good, the accuracy is high, the acquired data can meet the requirements, the compensation amount of the electricity stealing users can be accurately, objectively and quickly calculated, the compensation amount is consistent with the actually generated electricity, and the electricity utilization fairness is improved.
Drawings
FIG. 1 is a flow chart of the present invention.
Fig. 2 is a line graph of daily electricity usage by a low voltage customer.
FIG. 3 is a graph of the results of the user's power usage through a clustering algorithm.
FIG. 4 is a line graph of daily power usage by a specific power consumer.
FIG. 5 is a proprietary user PQUI result diagram.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the drawings in the specification.
As shown in fig. 1, a cluster and PQUI recognition algorithm-based electric quantity compensation method is implemented by using an electric energy metering device remote monitoring and diagnosing system, wherein the electric energy metering device remote monitoring and diagnosing system comprises an electric energy information acquisition system, a data collection and analysis server and a database server; the electricity utilization information acquisition system acquires electricity utilization data and events of power consumers in real time; the data collection and analysis server analyzes and calculates electricity stealing behaviors and corresponding compensation electric quantity according to the acquired data; the database server stores electricity consumption data, events, analysis and judgment thresholds, an electricity stealing behavior feature library and correction coefficients under each electricity stealing behavior, and the data collection and analysis server comprises the following steps when analyzing and calculating the compensation electric quantity:
1) judging whether the electricity stealing amount depends on the time length; if not, calculating the difference between the on-site copy electric quantity and the final settlement electric quantity of the power supply enterprise directly; if yes, entering the next step;
2) judging whether the number of electricity stealing days can be determined; if yes, directly calculating according to the determined days; if not, calculating by preset days;
3) identifying electricity stealing behaviors; acquiring power utilization data of a suspected electricity stealing user; comparing the electricity stealing behavior feature libraries in the corresponding metering modes to match the most probable electricity stealing behaviors;
4) inquiring a database server, comparing with electricity stealing behaviors, judging whether a correction coefficient exists, if so, determining actual daily electric quantity according to the corresponding correction coefficient, and then adding the daily electric quantity within the electricity stealing time to obtain the actual electric quantity; if not, entering the next step;
5) judging whether the daily electric quantity prediction requirement is met; if not, replacing the actual load with the capacity indicated by the charging electric energy meter, then judging the electricity utilization time of one day according to the produced commercial electricity utilization and the domestic electricity utilization of different electricity utilization users, and multiplying the electricity utilization time by the electricity stealing time to obtain electricity stealing amount; if so, predicting the daily electric quantity by using an algorithm, and calculating the electric stealing quantity of the current day of stopping electric stealing by multiplying the daily electric quantity by the electric stealing days;
6) and storing the calculated electricity stealing amount and the calculation basis, and modifying the correction coefficient of the database server when the difference value between the calculated electricity stealing amount and the actual electricity stealing amount exceeds a set threshold value.
When the number of electricity stealing days is determined in the step 2), a clustering algorithm and/or a PQUI algorithm are/is adopted;
201) when the number of electricity stealing days is calculated in the step 2), the specific steps of adopting a clustering algorithm and/or a PQUI algorithm are as follows;
201) and (3) clustering algorithm: dividing the power consumption into three types, and dividing the power consumption into a label 1 type, a label 2 type, a label 3 type and a label 3 type according to the clustering center of the power consumption; regarding the 3 types of labels, if the time corresponding to the electricity consumption of the 3 types of labels, namely the abnormal time continuously exceeds 15 days, the continuous abnormal time is considered as electricity stealing time;
202) PQUI algorithm: carrying out relevant calculation according to the load of the user and extracting useful features; the three-phase four-wire calculation formula is as follows:
S1＝UaIa+UbIb+UcIc……(1)
Figure BDA0002649874760000141
K＝(S1-S2)/W……(3)
in the formula of Ua，Ub，Uc，Ia，Ib，IcThree-phase voltage and three-phase current respectively; w is the electricity consumption; p and Q are respectively active power and reactive power; s1，S2Is the apparent power;
for normal users, the electricity consumption condition of the users at ordinary times can not be changed too much, and the fluctuation of the voltage and the current can not be too large, so that the K value can not fluctuate too much and can be in a stable range; when a user has an electricity stealing behaviour, the change in the electricity consumption behaviour directly leads to a change in the relationship between the loads, S1，S2Decrease, difference of bothThe value becomes larger, the used amount W decreases, and therefore the ratio K becomes larger; therefore, when the K value is far larger than the K value in normal electricity utilization, the electricity utilization of the day is considered to be abnormal, and if the abnormal K value appears for 15 consecutive days, the user is considered to steal the electricity;
the PQUI method requires electric quantity, voltage, current and power for calculation, so that the PQUI method is not suitable for low-voltage users, and can only calculate special transformer users; clustering only needs to use electric quantity data, so that low-voltage and special transformer users can use the data; therefore, when the user is a low-voltage user, the clustering algorithm is used for judging the electricity stealing time; when the user is a special transformer user, the condition needs to be supplemented; the PQUI recognition algorithm judges whether the power utilization is abnormal or not through K value fluctuation, so that when the fluctuation is small, accurate judgment cannot be carried out; the clustering algorithm directly judges through the continuous time of the lowest class, so that the defect of small fluctuation is avoided, but because of the need of multiple iterations, a large amount of time is needed for calculation; in order to avoid the defects of the two algorithms, a parameter D is added to calculate the fluctuation condition through distance, and then a proper algorithm is selected according to the D; the specific calculation is as follows:
firstly, carrying out normalization processing on data:
Figure BDA0002649874760000151
in the formula, Wi(i-1, 2,3, …, n) is a set of electrical data of a user, WminIs the minimum value in the data, WmaxIs the maximum value in the data;
then, the parameter D is calculated:
D＝|Wi *-mean(W*)|/n……(5)
in the formula, W*Is Wi *Sum of all data in (1), mean (W)*) The average value of the normalized data is obtained, and n is the number of the data;
setting the threshold value to be 0.2, and using a PQUI algorithm when D is more than or equal to 0.2; when D <0.2, a clustering algorithm is used.
Through a large amount of experimental calculation and comparison, the threshold value is 0.2, the calculation result is more accurate, and the calculation result is most matched with the actual electricity stealing time.
The electric energy metering device remote monitoring and diagnosing system also comprises an electricity stealing behavior characteristic library establishing module, and comprises the following steps when the electricity stealing behavior characteristic library is established:
A1) acquiring historical electricity stealing data;
A2) processing historical electricity stealing data; obtaining electricity stealing methods, numbering each electricity stealing method, and classifying; the classification of electricity stealing techniques includes: electricity stealing without meters, disconnection of a voltage loop, poor contact of the voltage loop, voltage division of the voltage loop, open circuit of a current loop, short circuit of the current loop, shunt of the current loop, phase-shifting electricity stealing, change of the internal structure of the electricity meter, damage of the electricity meter by large current or mechanical force and external interference;
A3) establishing a corresponding electricity stealing behavior feature library aiming at three metering modes of high supply and high metering, high supply and low metering and low supply and low metering; wherein:
the data of the electricity stealing behavior characteristic library in the high power supply and high metering mode comprise categories, output electricity stealing methods corresponding to the categories, A phase voltage, C phase voltage, A phase current, C phase current and active power; judging a fault phase and an electricity stealing method according to the phase voltage, the phase current and the active power of each phase when judging electricity stealing behavior;
the data of the electricity stealing behavior characteristic library under the high-power supply and low-metering mode comprise categories, output electricity stealing methods corresponding to the categories, A-phase voltage, B-phase voltage, C-phase voltage, A-phase current, B-phase current, C-phase current and active power; judging a fault phase and an electricity stealing method according to the phase voltage, the phase current and the active power of each phase when judging electricity stealing behavior;
the data of the electricity stealing behavior feature library in the low supply and low metering mode comprise categories, output electricity stealing methods and electric quantity corresponding to the categories; and judging the fault phase and the electricity stealing method according to the electric quantity when judging the electricity stealing behavior.
The electricity stealing methods are classified into electricity stealing methods without meters and comprising the following steps: the voltage transformer is disconnected with a wire Q1, and is connected across a transformer cross-over Q2, and is connected with a bypass cross-over electric energy meter Q3;
the electricity stealing methods classified as the broken line of the voltage loop include: the method comprises the following steps of loosening a fuse Q4 of the TV, breaking a fuse Q5 in a fuse tube, loosening a connecting terminal Q6 of a voltage loop, breaking a wire core Q7 of a voltage loop wire and loosening a voltage connecting piece Q8 of the electric energy meter;
the classification of electricity stealing methods into poor-contact electricity stealing methods of voltage loops comprises the following steps: a voltage connecting piece Q9 of the electric energy meter is unscrewed, a connecting terminal Q10 of a voltage loop is unscrewed, and a low-voltage fuse Q11 of the TV is unscrewed;
the electricity stealing method classified into voltage division of the voltage loop comprises the following steps: a resistor Q12 is connected in series in a secondary circuit of the TV, a zero line on the wire inlet side of the single-phase meter is broken, and a resistor voltage drop Q13 is connected in series between the outlet wire and the ground;
the electricity stealing methods classified as open-circuit current circuit electricity stealing methods include: loosening a TA secondary outgoing line terminal Q14, artificially manufacturing a poor contact fault Q15 of a connecting terminal in a TA secondary circuit, breaking a wire core Q16 of a current circuit lead, and breaking a zero line and electricity stealing Q17;
the electricity stealing methods classified as short circuit of current loop include: the current terminal Q18 of the short circuit electric energy meter, the terminal row Q19 in the short circuit current loop, and the short circuit TA primary side or secondary side Q20;
the electricity stealing methods classified into current loop shunting include: replacing TA Q21 with different transformation ratios, changing a secondary tap Q22 of a tapped TA and changing the number of primary turns Q23 of a core-through TA;
the electricity stealing methods classified into phase-shift electricity stealing methods include: the phase line and the zero line of the single-phase meter are interchanged, and the ground wire is used as the zero line Q24; exchanging an inlet/outlet line Q25 on the primary side of the TA; exchanging a homonymous terminal Q26 on the secondary side of the TA; exchanging an incoming and outgoing line Q27 of a current terminal of the electric energy meter; exchanging TA to a phase Q28 of a connecting line of the electric energy meter; swapping the polarity Q29 of TV primary or secondary; exchanging the TV to a phase Q30 of a power meter connecting line; phase-shifting Q31 with special inductor or capacitor;
the electricity stealing techniques classified into those for changing the internal structure of the electricity meter include: reducing the number of current coil turns Q32; performing resistance spot welding on the manganin, and cutting off a manganin signal wire Q33; the current sampling loop is connected in parallel and in series with a resistor Q34; replacing the voltage sampling loop grading sampling resistor Q35; a voltage coil series resistor and other electronic elements to divide the voltage Q36; a copper wire hook short circuit Q37; implanting a remote control shunt Q38;
the electricity stealing techniques classified as electricity stealing techniques that damage electricity meters with large current or mechanical force include: burning out the current coil Q39 by overload current, impacting the electric meter Q40 by electric power of short-circuit current, and damaging the electric meter Q41 by mechanical external force;
the electricity stealing techniques classified as external interference include: the high-voltage pulse electricity stealing system comprises a strong magnetic interference electricity stealing Q42, a high-frequency interference electricity stealing Q43, a high-voltage pulse electricity stealing Q44 and a short-circuit metering box inlet and outlet wire Q45.
The electricity stealing behavior is expressed in tabular form as:
watch 1 stealing electricity behavior watch
Figure BDA0002649874760000161
Figure BDA0002649874760000171
The characteristic library of electricity stealing behavior under the high-supply and high-metering mode is as follows:
Figure BDA0002649874760000172
Figure BDA0002649874760000181
Figure BDA0002649874760000191
the electricity stealing behavior characteristic library under the high-power supply and low-metering mode is as follows:
Figure BDA0002649874760000192
Figure BDA0002649874760000201
the electricity stealing behavior characteristic library under the low-supply and low-metering mode is as follows:
Figure BDA0002649874760000202
Figure BDA0002649874760000211
the electricity stealing behavior judgment comprises electricity stealing behavior identification in a high supply and low metering mode, electricity stealing behavior identification in a high supply and high metering mode and electricity stealing behavior identification in a low supply and low metering mode;
the electricity stealing behavior identification under the high power supply and low metering mode comprises the following steps:
3101) inputting electricity utilization data of suspected electricity stealing users;
3102) judging A, B, C whether the phase current voltage has a value; if not, determining that the judgment cannot be carried out, and finishing; if yes, entering the next step;
3103) judging whether the three-phase voltage and the three-phase current are both close to 0; if yes, comparing the electricity stealing behavior feature library in the high-power supply and low-metering mode, outputting 9 types of electricity stealing reasons, and ending; if not, entering the next step;
3104) judging whether the three-phase current has one phase or multiple phases close to 0; if yes, comparing the electricity stealing behavior feature library in the high-power supply and low-metering mode, outputting 1 type of electricity stealing reason and an electricity stealing phase with the output current close to 0, and ending; if not, entering the next step;
3105) judging whether the three-phase voltage has one phase or multiple phases close to 0; if yes, comparing the electricity stealing behavior feature library in the high-power supply and low-metering mode, outputting the type 2 electricity stealing reasons and the electricity stealing phase with the output voltage close to 0, and ending; if not, entering the next step;
3106) summoning the split-phase power factor;
3107) judging whether all the three-phase power factors are normal or not; if not, go to step 2110; if yes, entering the next step;
3108) judging whether the three-phase voltages are all larger than or close to 220V; if not, comparing the electricity stealing behavior feature library in the high-power supply and low-metering mode, outputting 4 types of electricity stealing reasons and electricity stealing phases with undersize output voltage, and ending; if yes, entering the next step;
3109) judging whether the three-phase currents are equal, if not, comparing the three-phase currents with the electricity stealing behavior feature library in the high-power supply and low-metering mode, outputting 3 types of electricity stealing reasons and electricity stealing phases with smaller output currents, and ending; if yes, outputting 3 types of electricity stealing reasons and electricity stealing phases with smaller current, and ending;
3110) judging whether the daily electric quantity of the user is 0 or not; if yes, comparing the electricity stealing behavior feature library in the high-power supply and low-metering mode, outputting 6 or 8 types of electricity stealing reasons and electricity stealing phases with abnormal power factors, and ending; if not, entering the next step;
3111) judging whether the three-phase current has one phase or multiple phases smaller than 0; if yes, comparing the electricity stealing behavior feature library in the high-power supply and low-metering mode, outputting 5 types of electricity stealing reasons and electricity stealing phases with negative current, and ending; if not, outputting 7 types of electricity stealing reasons and electricity stealing phases with negative voltage, and ending;
the electricity stealing behavior identification under the high power supply and high metering mode comprises the following steps:
3201) inputting electricity data of suspected electricity stealing users
3202) Judging A, C whether the phase current and voltage have values, if not, judging that the judgment cannot be carried out and finishing;
3203) judging whether A, C phase voltage and current are both close to 0; if yes, comparing the electricity stealing behavior feature library in the high-supply high-count metering mode, outputting 9 types of electricity stealing reasons, and ending; if not, entering the next step;
3204) determining A, C whether the phase voltage has one or more phases approaching 0; if yes, comparing the electricity stealing behavior feature library in the high power supply and high metering mode, outputting 1 type electricity stealing reason and an electricity stealing phase with the voltage close to 0, and ending; if not, entering the next step;
3205) determining A, C whether the phase current has one or more phases that are close to 0; if yes, comparing the electricity stealing behavior feature library in the high power supply and high metering mode, and outputting the type 2 electricity stealing reasons and the electricity stealing phases with the power supply close to 0; if not, entering the next step;
3206) summoning the split-phase power factor;
3207) judging whether A, C-phase power factors are all normal; if not, go to step 2210; if yes, entering the next step;
3208) determining A, C whether the phase voltages are both greater than or near 220V; if not, comparing the electricity stealing behavior feature library in the high power supply and high metering mode, outputting 3 types of electricity stealing reasons and electricity stealing phases with undersize voltage, and ending; if yes, entering the next step;
3209) judging A, C whether the phase currents are equal; if yes, comparing the electricity stealing behavior feature library in the high power supply and high metering mode, outputting 4 types of electricity stealing reasons and A, C two phases of electricity stealing phases, and ending; if not, comparing the electricity stealing behavior feature library in the high power supply and high metering mode, outputting 4 types of electricity stealing reasons and electricity stealing phases with smaller current, and ending;
3210) determining A, C whether the phase voltage is less than 0; if yes, comparing the electricity stealing behavior feature library in the high power supply and high metering mode, outputting 7 types of electricity stealing reasons and electricity stealing phases with the voltage less than 0, and ending; if not, entering the next step;
3211) judging A, C whether the phase current is less than 0; if yes, comparing the electricity stealing behavior feature library in the high power supply and high metering mode, outputting 5 types of electricity stealing reasons and electricity stealing phases with the current less than 0, and ending; if not, entering the next step;
3212) judging whether the daily electric quantity of the user is 0; if yes, comparing the electricity stealing behavior feature library in the high-supply high-metering mode, outputting 6 or 8 types of electricity stealing reasons and an electricity stealing phase AC or AB phase, and ending; if not, comparing the electricity stealing behavior feature library in the high power supply and high metering mode, outputting two phases of 8 types of electricity stealing reasons and electricity stealing phases BC, and ending;
the electricity stealing behavior identification under the low supply and low metering mode comprises the following steps:
3301) inputting electricity data of suspected electricity stealing users
3302) Judging whether the daily electric quantity of the user has a value or not; if not, determining that the judgment cannot be carried out, and finishing; if yes, entering the next step;
3303) judging whether the daily electric quantity of the user is close to 0; if yes, comparing the electricity stealing behavior feature library in the low-supply and low-metering mode, outputting the 1-class electricity stealing reason, and ending; if not, entering the next step;
3304) judging whether the daily electric quantity of the user is negative or not; if yes, comparing the electricity stealing behavior feature library in the low-supply and low-metering mode, outputting the 2 types of electricity stealing reasons, and ending; if not, comparing the electricity stealing behavior feature library in the low-supply and low-metering mode, outputting 3 types of electricity stealing reasons, and ending.
The database server stores the electricity stealing behaviors and correction coefficients under each electricity stealing behavior, wherein the correction coefficients comprise a private-transformer high-supply and high-supply user correction coefficient, a private-transformer high-supply and low-supply user correction coefficient and a low-voltage user correction coefficient;
the user correction factors for the high supply and high supply of the special transformer are shown in the following table 2:
TABLE 2 SPECIFIC CHANGE HIGH SUPPLY-HIGH CALCULATION USER CORRECTION COEFFICIENCY TABLE
Figure BDA0002649874760000221
Figure BDA0002649874760000231
Figure BDA0002649874760000241
The private high-and low-user correction coefficients are shown in table 3:
TABLE 3 correction coefficient tables for exclusive HDR/LF users
Figure BDA0002649874760000242
Figure BDA0002649874760000251
Figure BDA0002649874760000261
The low voltage user correction factor is shown in table 4:
table 4 low voltage user correction coefficient table
Figure BDA0002649874760000262
The technical solution is further illustrated by the following specific examples:
calculation process of certain low-voltage user compensation electric quantity based on clustering algorithm for judging electricity stealing time
As shown in fig. 2, electricity data of a low-voltage user is obtained, a line graph of daily electricity consumption as shown in fig. 2 is generated, and a result schematic diagram after clustering processing as shown in fig. 3 is generated.
The lowest class is numbered 2345789222526353637383940414243444546..... 674675676677678679680681704808810811813 (1 for 2017.01.01; 820 for 2018.12.31)
The analysis shows that: the first over 15 days of low power usage is from time 2017.02.04 and over 60 days, as shown in the box of FIG. 2. The electricity stealing time is directly judged.
As a result: day 2017.02.04 is the time when electricity theft began.
And (3) calculating the compensation electric quantity: considering that the electricity stealing time of the user is 2 months and 4 days in 2017, the electricity quantity of the user is close to 0 during the electricity stealing period, so that the electricity stealing method can be judged to be one of the following methods: the bypass type electric energy meter is additionally connected with a bypass type electric energy meter (Q3), a wiring terminal (Q9) of a unscrewing voltage loop, a voltage connecting piece (Q10) of the unscrewing electric energy meter, a current terminal (Q18) of the short-circuit electric energy meter, a copper wire hook short circuit (Q37) and the like. Therefore, in the electric quantity compensation process, the capacity (rated voltage and rated current) indicated by the rated current value of the charging electric energy meter is determined to replace the actual load (xKW), the electricity stealing days are 717 days (2017.02.04-2019.01.23), the domestic electricity consumption of the residential users is calculated according to 6 hours, and the compensation electric quantity is xKW, 6, 717 4296xKWh
Actual compensation of electric quantity:
the daily electric quantity is: 3.7 × 6 ═ 22.2kwh
And (3) totalizing the compensation electric quantity: 22.2 × 180 ═ 3996kwh
Supplementing the electricity charge: 3996 x 0.538 x 2149.85 yuan
Default electricity usage: (3996 × 0.538) × 3 ═ 6449.54 yuan
Adding additional electric charge: 2149.85+ 6449.54-8599.39 yuan
Second, a calculation process of compensation electric quantity of a specific transformer user for judging electricity stealing time based on PQUI algorithm
Acquiring power consumption data of a certain special transformer user, and generating a line graph of daily power consumption of the certain special transformer user as shown in fig. 4; and as shown in FIG. 5, a result diagram of the user PQUI determination method.
Judging the electricity stealing time: as can be seen from fig. 4, the power consumption is abnormal during the period from 17 th 6 to 3 rd 2016 (as indicated by the box in fig. 4). As can be seen from fig. 5, the ratio increases (as shown in the box of fig. 5) during the same time, and the ratio is far higher than the normal value for 50 consecutive days, so that the power consumption is considered abnormal during this time. The electricity utilization abnormality in the time is considered in both aspects. Thus, electricity stealing time is considered to be 6 months and 17 days to 2016 and 8 months and 3 days.
Identification of electricity stealing skills: as can be seen from fig. 4, since the amount of electricity used is decreased 2/3 during the period of electricity stealing, it is assumed that the user uses a method of reducing or cutting off the current of two phases thereof, which corresponds to Q14-Q20 in table 1 and category 1 in table 3.
Electric quantity compensation: when the two-phase current is reduced, the correction coefficient is 3, so the compensation electric quantity is:
Wpursuing＝WTheft-proof device*3-WTheft-proof device＝178212*3-178212＝296416(W)
Actual compensation results:
the electricity to be measured is 3UI Cos phi T
＝3*225.5*612.9*0.98*1128/1000
458345 degrees
Less electricity metering: 458345 + 161929 degrees.
The method for supplementing electric quantity based on clustering and PQUI recognition algorithm shown in FIG. 1 is a specific embodiment of the present invention, already embodies the essential features and progress of the present invention, and can make equivalent modifications in shape, structure, etc. according to the practical use requirements, and is within the scope of protection of the present invention.

Claims (7)

1.一种基于聚类和PQUI识别算法的追补电量方法，其特征在于：采用电能计量装置远程监测诊断系统实施，所述的电能计量装置远程监测诊断系统包括用电信息采集系统、数据收集和分析服务器、数据库服务器；所述的用电信息采集系统实时采集电力用户的用电数据和事件；数据收集和分析服务器根据获取的数据进行分析计算窃电行为及对应的追补电量；数据库服务器存储用电数据、事件、分析判断阀值、窃电行为特征库及在各个窃电行为下的更正系数，数据收集和分析服务器在分析计算追补电量时包括以下步骤：1. a method for supplementary electric quantity based on clustering and PQUI identification algorithm, it is characterized in that: adopt electric energy metering device remote monitoring and diagnosis system to implement, and described electric energy metering device remote monitoring and diagnosis system comprises electricity consumption information collection system, data collection and Analysis server and database server; the electricity consumption information collection system collects electricity consumption data and events of electricity users in real time; the data collection and analysis server analyzes and calculates electricity stealing behavior and the corresponding supplementary electricity according to the acquired data; Electricity data, events, analysis and judgment thresholds, electricity stealing behavior feature library and correction coefficients under each electricity stealing behavior. The data collection and analysis server includes the following steps when analyzing and calculating the supplementary electricity: 1)判断窃电量是否依赖时长；若不能，则直接以现场抄见电量与供电企业最后结算电量差额计算；若能，则入下一步；1) Determine whether the stolen electricity depends on the duration; if not, then directly calculate the difference between the electricity recorded on site and the electricity finally settled by the power supply company; if so, go to the next step; 2)计算窃电天数，判断是否能确定窃电天数；若能，则直接按所确定的天数计算窃电量；若不能，则以预设的天数计算窃电量；窃电天数计算采用聚类算法和/或PQUI算法；2) Calculate the days of stealing electricity, and judge whether the number of days of stealing electricity can be determined; if yes, directly calculate the number of days of stealing electricity; if not, calculate the number of days of stealing electricity; Class Algorithms and/or PQUI Algorithms; 3)进行窃电行为识别；获取窃电嫌疑用户的用电数据；比对对应计量方式下的窃电行为特征库，匹配出最可能存在的窃电行为；3) Identify the electricity stealing behavior; obtain the electricity consumption data of the suspected users of electricity stealing; compare the electricity stealing behavior feature database under the corresponding metering method, and match the most likely electricity stealing behavior; 4)查询数据库服务器，对照窃电行为，判断是否存在修正系数，若是，则根据对应的更正系数确定实际日电量，然后将窃电时间内的日电量相加得到实际用电量；若否，则进入下一步；4) Query the database server, compare the behavior of stealing electricity, and judge whether there is a correction coefficient. If so, determine the actual daily electricity quantity according to the corresponding correction coefficient, and then add the daily electricity quantity during the electricity stealing time to obtain the actual electricity consumption; if not, then enter the next step; 5)判断是否满足日电量预测要求；若否，则以计费电能表所指的容量代替实际负荷，然后根据不同用电用户按成产经营用电和生活用电判断一天的用电时间，与窃电时间相乘得到窃电量；若是，则用算法预测日电量，通过日电量与窃电天数相乘计算截止窃电当日的窃电量；5) Judge whether the daily electricity forecast requirement is met; if not, replace the actual load with the capacity indicated by the billing electric energy meter, and then judge the electricity consumption time of a day according to the electricity consumption of production and business and the electricity consumption of life by different electricity users. Multiply it with the stealing time to get the stealing power; if so, use the algorithm to predict the daily power, and calculate the stealing power on the end of the day of stealing by multiplying the daily power and the number of days of stealing; 6)存储计算窃电量及计算依据，当计算窃电量与实际窃电量的差值超过设定阈值时，修改数据库服务器的修正系数。6) Store the calculated stolen electricity and the calculation basis, and modify the correction coefficient of the database server when the difference between the calculated stolen electricity and the actual stolen electricity exceeds the set threshold. 2.根据权利要求1所述的一种基于聚类和PQUI识别算法的追补电量方法，其特征在于：在步骤2)计算窃电天数时，采用聚类算法和/或PQUI算法的具体步骤为；2. a kind of recovery power method based on clustering and PQUI identification algorithm according to claim 1, is characterized in that: when step 2) calculates the number of days of stealing electricity, the concrete steps of adopting clustering algorithm and/or PQUI algorithm are: ; 201)聚类算法：将用电量分为三类，根据用电量的聚类中心将用电量从高到低分为标签1类，标签2类，标签3类，标签3类为异常电量；对于标签3类，若标签3类用电量对应的时间，即异常时间出现连续超过15天的情况，则认为该连续的异常时间为窃电时间；201) Clustering algorithm: divide the electricity consumption into three categories, and according to the cluster center of electricity consumption, divide the electricity consumption into label 1, label 2, label 3, and label 3 as abnormal Electricity; for label type 3, if the time corresponding to the electricity consumption of label type 3, that is, the abnormal time exceeds 15 consecutive days, the continuous abnormal time is considered to be the time of electricity stealing; 202)PQUI算法：根据用户的负荷进行相关的计算，提取有用的特征；三相四线计算公式如下：202) PQUI algorithm: perform relevant calculations according to the user's load to extract useful features; the three-phase four-wire calculation formula is as follows: S1＝UaIa+UbIb+UcIc……(1)S 1 =U a I a +U b I b +U c I c …(1)
Figure FDA0002649874750000021
Figure FDA0002649874750000021
 K＝(S1-S2)/W……(3)K=(S 1 -S 2 )/W...(3) 式中，Ua,Ub,Uc,Ia,Ib,Ic分别为三相电压和三相电流；W为用电量；P,Q分别为有功功率和无功功率；S1,S2是视在功率；In the formula, U a , U b , U c , I a , I b , and I c are the three-phase voltage and three-phase current, respectively; W is the electricity consumption; P and Q are the active power and reactive power, respectively; S 1 , S 2 is the apparent power; 对于正常用户而言，其平时用电的情况不会发生过大变化，电压、电流的值波动不会太大，所以K值也不会发生太大的波动，会处在一个平稳的范围；当用户发生窃电行为时，用电行为的改变直接导致负荷之间的关系改变，S1,S2减小，两者的差值变大，用电量W减小，因此比值K会变大；故，当K值远大于正常用电时候的K值时，认为该天用电异常，若是连续15天出现异常K值则认为该用户窃电；For normal users, their usual electricity consumption will not change too much, and the value of voltage and current will not fluctuate too much, so the K value will not fluctuate too much, and it will be in a stable range; When the user steals electricity, the change of electricity consumption directly leads to the change of the relationship between the loads, S 1 and S 2 decrease, the difference between the two increases, and the electricity consumption W decreases, so the ratio K will change. Therefore, when the K value is much larger than the K value during normal electricity consumption, it is considered that the electricity consumption on that day is abnormal, and if the abnormal K value occurs for 15 consecutive days, the user is considered to have stolen electricity; PQUI方法需要电量、电压、电流以及功率来计算，因此不适用于低压用户，只能对专变用户进行计算；聚类只需要使用电量数据，所以低压和专变用户都可以使用；因此当用户为低压用户时，使用聚类算法判断窃电时间；当用户为专变用户时，则需要补充条件；PQUI识别算法通过K值波动来判断用电是否异常，因此当波动较小时，无法进行准确的判断；而聚类算法直接通过最低类的连续时间进行判断，避免了波动太小的弊端，但由于需要多次迭代，需要大量的时间进行计算；为了避免两种算法的弊端，加入了参数D通过距离计算波动情况，再根据D选择合适的算法；具体计算情况如下：The PQUI method requires electricity, voltage, current and power to calculate, so it is not suitable for low-voltage users, and can only be calculated for special-purpose users; clustering only needs to use electricity data, so both low-voltage and special-purpose users can use it; therefore, when the user When it is a low-voltage user, the clustering algorithm is used to judge the time of electricity stealing; when the user is a special-purpose user, additional conditions are required; the PQUI identification algorithm judges whether the electricity consumption is abnormal through the fluctuation of the K value, so when the fluctuation is small, it cannot be accurately carried out. The clustering algorithm directly judges by the continuous time of the lowest class, which avoids the disadvantage of too small fluctuation, but requires a lot of time to calculate due to the need for multiple iterations; in order to avoid the disadvantages of the two algorithms, parameters are added. D calculates the fluctuation situation through the distance, and then selects the appropriate algorithm according to D; the specific calculation situation is as follows: 先将数据进行归一化处理：First normalize the data:
Figure FDA0002649874750000022
Figure FDA0002649874750000022
 式中，Wi(i＝1,2,3,…，n)为用户的一组电量数据，Wmin为数据中的最小值，Wmax为数据中的最大值；In the formula, Wi ( i =1, 2, 3, ..., n) is a set of power data of the user, W min is the minimum value in the data, and W max is the maximum value in the data; 然后计算参数D：Then calculate the parameter D: D＝|Wi *-mean(W*)|/n……(5)D=|W i * -mean(W * )|/n...(5) 式中，W*为Wi *中所有数据的总和，mean(W*)为归一化处理后数据的平均值，n为数据的数量；In the formula, W * is the sum of all data in Wi * , mean(W * ) is the average value of the normalized data, and n is the number of data; 将阈值设为0.2，当D≧0.2时，使用PQUI算法；当D<0.2时，使用聚类算法。The threshold is set to 0.2, when D≧0.2, the PQUI algorithm is used; when D<0.2, the clustering algorithm is used. 3.根据权利要求2所述的一种基于聚类和PQUI识别算法的追补电量方法，其特征在于：电能计量装置远程监测诊断系统还包括窃电行为特征库建立模块，在建立窃电行为特征库时，包括步骤：3. a kind of method for replenishing electric quantity based on clustering and PQUI identification algorithm according to claim 2, it is characterized in that: electric energy metering device remote monitoring and diagnosis system also comprises electricity stealing behavior feature library establishment module, in establishing electricity stealing behavior characteristic When library, include steps: A1)获取历史窃电数据；A1) Obtain historical electricity theft data; A2)对历史窃电数据进行处理；获得窃电手法，给各窃电手法编号，并进行分类；窃电手法分类包括：无表窃电、电压回路断线、电压回路接触不良、电压回路分压、电流回路开路、电流回路短路、电流回路分流、移相窃电、改变电表内部结构、用大电流或机械力损坏电表、外部干扰；A2) Process the historical electricity stealing data; obtain electricity stealing methods, number and classify each electricity stealing method; the classification of electricity stealing methods includes: stealing electricity without meter, disconnection of voltage circuit, poor contact of voltage circuit, breakdown of voltage circuit Voltage, current loop open circuit, current loop short circuit, current loop shunt, phase shift stealing electricity, changing the internal structure of the meter, damaging the meter with large current or mechanical force, external interference; A3)针对高供高计、高供低计、低供低计三种计量方式建立对应的窃电行为特征库；其中：A3) Establish a corresponding feature library of electricity stealing behavior for the three metering methods of high supply and high meter, high supply and low meter, and low supply and low meter; wherein: 高供高计计量方式下窃电行为特征库的数据包括类别、对应于该类别的输出窃电手法、A相电压、C相电压、A相电流、C相电流和有功功率；在判断时窃电行为时根据各相电压、各相电流及有功功率来判断故障相及窃电手法；The data of the power stealing behavior feature database in the high-supply-altitude meter metering mode includes the category, the output power stealing method corresponding to the category, the A-phase voltage, the C-phase voltage, the A-phase current, the C-phase current and the active power; According to the voltage of each phase, the current of each phase and the active power to judge the faulty phase and the method of stealing electricity; 高供低计计量方式下窃电行为特征库的数据包括类别、对应于该类别的输出窃电手法、A相电压、B相电压、C相电压、A相电流、B相电流、C相电流和有功功率；在判断时窃电行为时根据各相电压、各相电流及有功功率来判断故障相及窃电手法；The data of the electricity stealing behavior feature database in the metering mode of high supply and low meter includes categories, output electricity stealing methods corresponding to the category, A-phase voltage, B-phase voltage, C-phase voltage, A-phase current, B-phase current, C-phase current and active power; when judging the behavior of stealing electricity, judge the faulty phase and the method of stealing electricity according to the voltage of each phase, the current of each phase and the active power; 低供低计计量方式下窃电行为特征库的数据包括类别、对应于该类别的输出窃电手法和电量；在判断时窃电行为时根据电量来判断故障相及窃电手法。The data of the electricity stealing behavior feature database in the low-supply and low-meter metering mode includes the category, the output electricity-stealing method and the electric quantity corresponding to the type; when judging the electricity-stealing behavior, the fault phase and the electricity-stealing method are judged according to the electricity quantity. 4.根据权利要求3所述的一种基于聚类和PQUI识别算法的追补电量方法，其特征在于：窃电手法分类为无表窃电的窃电手法包括：电压互感器断线Q1、绕越互感器跨接Q2、加接旁路绕越电能表Q3；4. a kind of recovery power method based on clustering and PQUI identification algorithm according to claim 3, it is characterized in that: the electricity stealing method that steals electricity is classified as the electricity stealing method of meterless electricity stealing comprises: voltage transformer disconnection Q1, winding. The more transformer is connected across Q2, and the bypass is connected across Q3 of the electric energy meter; 窃电手法分类为电压回路断线的窃电手法包括：松开TV的熔断器Q4、弄断熔丝管内的熔丝Q5、松开电压回路的接线端子Q6、弄断电压回路导线的线芯Q7、松开电能表的电压连片Q8；The methods of stealing electricity are classified as voltage circuit disconnection, including: loosening the fuse Q4 of the TV, breaking the fuse Q5 in the fuse tube, loosening the terminal Q6 of the voltage circuit, breaking the core of the voltage circuit wire Q7. Loosen the voltage connection Q8 of the electric energy meter; 窃电手法分类为电压回路接触不良的窃电手法包括：拧松电能表的电压连片Q9、拧松电压回路的接线端子Q10、拧松TV的低压熔丝Q11；The methods of stealing electricity are classified into the methods of stealing electricity with poor contact of the voltage circuit, including: loosening the voltage connecting piece Q9 of the electric energy meter, loosening the terminal Q10 of the voltage circuit, and loosening the low-voltage fuse Q11 of the TV; 窃电手法分类为电压回路分压的窃电手法包括：在TV的二次回路串入电阻Q12、弄断单相表进线侧的零线而在出线至地之间串入电阻降压Q13；The methods of stealing electricity are classified as voltage circuit dividers, including: inserting resistor Q12 in series in the secondary circuit of the TV, breaking the zero line on the incoming side of the single-phase meter, and inserting a resistor Q13 in series between the outgoing line and the ground. ; 窃电手法分类为电流回路开路的窃电手法包括：松开TA二次出线端子Q14、人为制造TA二次回路中接线端子的接触不良故障Q15、弄断电流回路导线的线芯Q16、断零线窃电Q17；The methods of stealing electricity are classified as open current loop, including: loosening the TA secondary outlet terminal Q14, artificially creating a poor contact fault Q15 of the terminal in the TA secondary circuit, breaking the core of the current loop wire Q16, breaking the zero Line stealing Q17; 窃电手法分类为电流回路短路的窃电手法包括：短接电能表的电流端子Q18、短接电流回路中的端子排Q19、短接TA一次或二次侧Q20；Electricity stealing methods classified as current loop short circuit include: short-circuiting the current terminal Q18 of the electric energy meter, short-circuiting the terminal block Q19 in the current loop, and short-circuiting the TA primary or secondary side Q20; 窃电手法分类为电流回路分流的窃电手法包括：更换不同变比的TA Q21、改变抽头式TA的二次抽头Q22、改变穿芯式TA一次侧匝数Q23；Electricity stealing methods are classified into current loop shunting methods including: replacing TA Q21 with different transformation ratios, changing the secondary tap Q22 of the tapped TA, and changing the number of turns Q23 on the primary side of the through-core TA; 窃电手法分类为移相窃电的窃电手法包括：单相表相线和零线互换，同时利用地线作零线Q24；调换TA一次侧的进出线Q25；调换TA二次侧的同名端Q26；调换电能表电流端子的进出线Q27；调换TA至电能表连线的相别Q28；调换TV一次或二次的极性Q29；调换TV至电能表连线的相别Q30；用特殊电感或电容移相Q31；The methods of stealing electricity are classified as phase-shifting stealing electricity, including: swapping the phase line and neutral line of the single-phase meter, and using the ground wire as the neutral line Q24 at the same time; replacing the incoming and outgoing lines Q25 of the primary side of the TA; Terminal Q26 of the same name; change the input and output lines of the current terminal of the electric energy meter Q27; exchange the phase Q28 of the connection between the TA and the electric energy meter; exchange the polarity of the TV once or twice; exchange the phase of the TV to the electric energy meter connection Q30; use Special inductor or capacitor phase-shift Q31; 窃电手法分类为改变电表内部结构的窃电手法包括：减少电流线圈匝数Q32；锰铜电阻点焊，剪开锰铜信号线Q33；电流采样回路并联、串接电阻Q34；更换电压采样回路分级采样电阻Q35；电压线圈串联电阻等电子元件分压Q36；铜线钩短接Q37；植入遥控器分流Q38；The methods of stealing electricity are classified into the methods of stealing electricity by changing the internal structure of the meter, including: reducing the number of turns of the current coil Q32; spot welding of manganese copper resistance, cutting the signal line Q33 of manganese copper; parallel connection of the current sampling circuit and series connection of the resistance Q34; replacement of the voltage sampling circuit Graded sampling resistor Q35; electronic components such as voltage coil series resistance divider Q36; copper wire hook short-circuit Q37; implanted remote control shunt Q38; 窃电手法分类为用大电流或机械力损坏电表的窃电手法包括：用过负荷电流烧坏电流线圈Q39、用短路电流的电动力冲击电表Q40、机械外力损坏电表Q41；The methods of stealing electricity are classified into methods that damage the meter with high current or mechanical force, including: burning the current coil Q39 with overload current, impacting the meter Q40 with short-circuit current, and damaging the meter Q41 with mechanical external force; 窃电手法分类为外部干扰的窃电手法包括：强磁干扰窃电Q42、高频干扰窃电Q43、高压脉冲窃电Q44、短接计量箱进出线Q45。Power stealing methods are classified as external interference power stealing methods including: strong magnetic interference power stealing Q42, high-frequency interference power stealing Q43, high-voltage pulse power stealing Q44, and short-circuit metering box inlet and outlet lines Q45. 5.根据权利要求4所述的一种基于聚类和PQUI识别算法的追补电量方法，其特征在于：5. a kind of supplementary electric quantity method based on clustering and PQUI identification algorithm according to claim 4, is characterized in that: 高供高计计量方式下窃电行为特征库为：The characteristic database of electricity stealing behavior under the high-supply-altitude metering mode is:
Figure FDA0002649874750000041
Figure FDA0002649874750000041
Figure FDA0002649874750000051
Figure FDA0002649874750000051
Figure FDA0002649874750000061
Figure FDA0002649874750000061
 高供低计计量方式下窃电行为特征库为：The characteristic database of electricity stealing behavior under high supply and low metering mode is:
Figure FDA0002649874750000062
Figure FDA0002649874750000062
Figure FDA0002649874750000071
Figure FDA0002649874750000071
Figure FDA0002649874750000081
Figure FDA0002649874750000081
Figure FDA0002649874750000091
Figure FDA0002649874750000091
 低供低计计量方式下窃电行为特征库为：The characteristic database of electricity stealing behavior under low supply and low metering mode is:
Figure FDA0002649874750000092
。
Figure FDA0002649874750000092
. 6.根据权利要求5所述的一种基于聚类和PQUI识别算法的追补电量方法，其特征在于：窃电行为判断包括高供低计计量方式下的窃电行为识别、高供高计计量方式下的窃电行为识别、低供低计计量方式下的窃电行为识别；6. a kind of recovery power method based on clustering and PQUI identification algorithm according to claim 5, it is characterized in that: the electricity stealing behavior judgment comprises the electricity stealing behavior identification under high supply and low meter metering mode, high supply and high meter metering Recognition of electricity stealing behavior under the mode, electricity stealing behavior recognition under the low supply and low meter metering mode; 高供低计计量方式下的窃电行为识别包括步骤：The identification of electricity stealing behavior under the metering mode of high supply and low meter includes steps: 3101)输入疑似窃电用户的用电数据；3101) Input the electricity consumption data of the user suspected of stealing electricity; 3102)判断A、B、C相电流电压是否有值；若否，则认为无法判断，并结束；若是，进入下一步；3102) Determine whether the current and voltage of A, B, and C phases have values; if not, it is considered impossible to judge and end; if so, go to the next step; 3103)判断三相电压、电流是否都接近0；若是，则比对高供低计计量方式下窃电行为特征库，输出9类窃电原因，并结束；若否，进入下一步；3103) Determine whether the three-phase voltage and current are close to 0; if so, compare the power-stealing behavior feature library under the high-supply-low-meter metering mode, output 9 types of power-stealing reasons, and end; if not, go to the next step; 3104)判断三相电流是否有接近0的一相或多相；若是，则比对高供低计计量方式下窃电行为特征库，输出1类窃电原因和输出电流接近0的窃电相，并结束；若否，进入下一步；3104) Determine whether the three-phase current has one or more phases that are close to 0; if so, compare the power stealing behavior feature library under the high-supply-low-meter metering mode, and output the reason for the stealing of type 1 electricity and the phase with the output current close to 0. , and end; if not, go to the next step; 3105)判断三相电压是否有接近0的一相或多相；若是，则比对高供低计计量方式下窃电行为特征库，输出2类窃电原因和输出电压接近0的窃电相，并结束；若否，进入下一步；3105) Determine whether the three-phase voltage has one or more phases that are close to 0; if so, compare the power stealing behavior feature library under the high-supply-low metering metering mode, and output two types of power-stealing reasons and the power-stealing phase with the output voltage close to 0 , and end; if not, go to the next step; 3106)召测分相功率因数；3106) Call and measure split-phase power factor; 3107)判断三相功率因数是否全部正常；若否，则进入步骤2110；若是，则进入下一步；3107) Determine whether the three-phase power factors are all normal; if not, go to step 2110; if so, go to the next step; 3108)判断三相电压是否都大于或接近220V；若否，则比对高供低计计量方式下窃电行为特征库，输出4类窃电原因和输出电压过小的窃电相，并结束；若是，则进入下一步；3108) Determine whether the three-phase voltages are all greater than or close to 220V; if not, compare the power stealing behavior feature library under the high-supply-low metering mode, output 4 types of power-stealing reasons and the power-stealing phase that the output voltage is too small, and end ; if so, go to the next step; 3109)判断三相电流是否相等，若否，则比对高供低计计量方式下窃电行为特征库，输出3类窃电原因和输出电流较小的窃电相，并结束；若是，则输出3类窃电原因和电流较小的窃电相，并结束；3109) Judging whether the three-phase currents are equal, if not, compare the power stealing behavior feature library under the high supply and low meter metering mode, output 3 types of power stealing reasons and the power stealing phase with a smaller output current, and end; if so, then Output 3 types of electricity stealing reasons and the electricity stealing phase with smaller current, and end; 3110)判断用户日电量是否为0；若是，则比对高供低计计量方式下窃电行为特征库，输出6类或8类窃电原因和功率因素异常的窃电相，并结束；若否，则进入下一步；3110) Determine whether the user's daily power is 0; if so, compare the power stealing behavior feature library under the high-supply low-meter metering mode, output 6 or 8 types of electricity stealing reasons and power factor abnormality stealing phases, and end; if No, go to the next step; 3111)判断三相电流是否有小于0的一相或多相；若是，则对比高供低计计量方式下窃电行为特征库，输出5类窃电原因和电流为负的窃电相，并结束；若否，则输出7类窃电原因和电压为负的窃电相，并结束；3111) Determine whether the three-phase current has one or more phases less than 0; if so, compare the power stealing behavior feature library under the high-supply-low metering mode, output 5 types of power-stealing reasons and the power-stealing phases whose current is negative, and End; if not, output 7 types of electricity stealing reasons and the electricity stealing phase with negative voltage, and end; 高供高计计量方式下的窃电行为识别包括步骤：The identification of electricity stealing behavior under the metering mode of high supply and height meter includes steps: 3201)输入疑似窃电用户的用电数据3201) Enter the electricity consumption data of the user suspected of stealing electricity 3202)判断A、C相电流电压是否有值，若否，则认为无法判断并结束；3202) Determine whether the current and voltage of phase A and C have values, if not, it is considered impossible to judge and end; 3203)判断A、C相电压、电流是否都接近0；若是，则比对高供高计计量方式下窃电行为特征库，输出9类窃电原因，并结束；若否，则进入下一步；3203) Determine whether the voltages and currents of phases A and C are close to 0; if so, compare the power stealing behavior feature library under the high-supply-altitude metering mode, output 9 types of power stealing reasons, and end; if not, enter the next step ; 3204)判断A、C相电压是否有接近0的一相或多相；若是，则比对高供高计计量方式下窃电行为特征库，输出1类窃电原因和电压接近0的窃电相，并结束；若否，则进入下一步；3204) Determine whether the A and C-phase voltages have one or more phases close to 0; if so, compare the power stealing behavior feature library under the high-supply-altitude metering mode, and output Type 1 power stealing reasons and power stealing with voltages close to 0 phase, and end; if not, go to the next step; 3205)判断A、C相电流是否有接近0的一相或多相；若是，则比对高供高计计量方式下窃电行为特征库，输出2类窃电原因和电源接近0的窃电相；若否，则进入下一步；3205) Determine whether the A and C-phase currents have one or more phases that are close to 0; if so, compare the power stealing behavior feature library under the high power supply meter metering mode, and output 2 types of power stealing reasons and power stealing close to 0. phase; if not, go to the next step; 3206)召测分相功率因数；3206) Call and measure split-phase power factor; 3207)判断A、C相功率因数是否全部正常；若否，则进入步骤2210；若是，则进入下一步；3207) Determine whether the power factors of A and C phases are all normal; if not, enter step 2210; if so, enter the next step; 3208)判断A、C相电压是否都大于或接近220V；若否，则比对高供高计计量方式下窃电行为特征库，输出3类窃电原因和电压过小的窃电相，并结束；若是，则进入下一步；3208) Determine whether the voltages of phases A and C are both greater than or close to 220V; if not, compare the power stealing behavior feature library under the high-supply-altitude metering mode, and output 3 types of power-stealing reasons and power-stealing phases with too low voltage, and end; if so, go to the next step; 3209)判断A、C相电流是否相等；若是，则比对高供高计计量方式下窃电行为特征库，输出4类窃电原因和A、C两相窃电相，并结束；若否，则比对高供高计计量方式下窃电行为特征库，输出4类窃电原因和电流较小的窃电相，并结束；3209) Determine whether the A and C phase currents are equal; if so, compare the power stealing behavior feature library under the high-supply-altitude metering mode, output 4 types of power theft reasons and the A and C two-phase power stealing phases, and end; if not , then compare the power stealing behavior feature library under the high supply and high meter metering mode, output 4 types of power stealing reasons and the power stealing phase with smaller current, and end; 3210)判断A、C相电压是否存在小于0；若是，则比对高供高计计量方式下窃电行为特征库，输出7类窃电原因和电压小于0的窃电相，并结束；若否，则进入下一步；3210) Determine whether the voltages of phases A and C are less than 0; if so, compare the power stealing behavior feature library under the metering mode of high power supply and height meter, output 7 types of power stealing reasons and power stealing phases whose voltage is less than 0, and end; No, go to the next step; 3211)判断A、C相电流是否存在小于0；若是，则比对高供高计计量方式下窃电行为特征库，输出5类窃电原因和电流小于0的窃电相，并结束；若否，则进入下一步；3211) Judging whether the currents of phases A and C are less than 0; if so, compare the power stealing behavior feature library under the metering mode of high power supply and height meter, output 5 types of power stealing reasons and power stealing phases whose current is less than 0, and end; No, go to the next step; 3212)判断用户日用电量是否为0；若是，则比对高供高计计量方式下窃电行为特征库，输出6类或8类窃电原因和窃电相AC或AB相，并结束；若否，则比对高供高计计量方式下窃电行为特征库，输出8类窃电原因和窃电相BC两相，并结束；3212) Determine whether the daily electricity consumption of the user is 0; if it is, then compare the power stealing behavior feature library under the high supply and high meter metering mode, output 6 or 8 types of electricity stealing reasons and the AC or AB phase of the stealing phase, and end ; If not, compare the power stealing behavior feature library under the high power supply and high meter metering mode, output 8 types of power stealing reasons and two phases of power stealing phase BC, and end; 低供低计计量方式下的窃电行为识别包括步骤：The identification of electricity stealing behavior under the low supply and low meter metering method includes steps: 3301)输入疑似窃电用户的用电数据3301) Enter the electricity consumption data of the user suspected of stealing electricity 3302)判断用户日用电量是否有值；若否，则认为无法判断，并结束；若是，则进入下一步；3302) Determine whether the daily electricity consumption of the user has a value; if not, it is considered that it cannot be determined, and ends; if it is, then enter the next step; 3303)判断用户日用电量是否接近0；若是，则比对低供低计计量方式下窃电行为特征库，输出1类窃电原因，并结束；若否，则进入下一步；3303) Determine whether the daily electricity consumption of the user is close to 0; if so, compare the power-stealing behavior feature library under the low-supply and low-meter metering mode, output the reason for type 1 power-stealing, and end; if not, enter the next step; 3304)判断用户日用电量是否为负；若是，则比对低供低计计量方式下窃电行为特征库，输出2类窃电原因，并结束；若否，则比对低供低计计量方式下窃电行为特征库，输出3类窃电原因，并结束。3304) Determine whether the daily electricity consumption of the user is negative; if so, compare the power stealing behavior feature library under the low-supply and low-meter metering mode, output 2 types of power-stealing reasons, and end; if not, compare the low supply and low meter In the metering mode, the electricity stealing behavior feature library outputs 3 types of electricity stealing reasons, and ends. 7.根据权利要求5所述的一种基于聚类和PQUI识别算法的追补电量方法，其特征在于：数据库服务器存储窃电行为、及各个窃电行为下的更正系数，更正系数包括专变高供高计用户更正系数、专变高供低计用户更正系数、低压用户更正系数；7. a kind of recovery power method based on clustering and PQUI identification algorithm according to claim 5, it is characterized in that: database server stores the correction coefficient under the behavior of stealing electricity and each behavior of stealing electricity, and the correction coefficient comprises a special change high Correction coefficient for high meter users, high correction coefficient for low meter users, correction coefficient for low voltage users; 专变高供高计用户更正系数如下表所示：The special variable height correction coefficient for altimeter users is shown in the following table:
Figure FDA0002649874750000121
Figure FDA0002649874750000121
Figure FDA0002649874750000131
Figure FDA0002649874750000131
Figure FDA0002649874750000141
Figure FDA0002649874750000141
 专变高供低计用户更正系数如下表所示：The correction factor for high-for-low meter users is shown in the following table:
Figure FDA0002649874750000142
Figure FDA0002649874750000142
Figure FDA0002649874750000151
Figure FDA0002649874750000151
 低压用户更正系数如下表所示：The low-voltage user correction factor is shown in the table below:
Figure FDA0002649874750000161
Figure FDA0002649874750000161
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