CN111817299B - Fuzzy reasoning-based intelligent identification method for line loss rate abnormal cause of power distribution station - Google Patents

Fuzzy reasoning-based intelligent identification method for line loss rate abnormal cause of power distribution station Download PDF

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CN111817299B
CN111817299B CN202010708377.8A CN202010708377A CN111817299B CN 111817299 B CN111817299 B CN 111817299B CN 202010708377 A CN202010708377 A CN 202010708377A CN 111817299 B CN111817299 B CN 111817299B
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CN111817299A (en
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陈光宇
吴文龙
张仰飞
郝思鹏
刘海涛
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Nanjing Institute of Technology
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

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Abstract

The invention discloses a fuzzy reasoning-based intelligent identification method for abnormal cause of line loss rate of a power distribution area, which comprises the following steps: s1, acquiring the line loss rate data of the transformer area; acquiring a time period when the line loss rate of the transformer area is abnormal; s2, establishing a membership function in a fuzzy expert library according to historical data of the line loss rate of the transformer area; s3, judging the line loss rate of the abnormal time period in the step S1 by using a membership function; s4, analyzing according to the judgment result of the step S3: if the abnormal line loss rate is determined to be the negative line loss rate, the step S5 is executed; if the abnormal line loss rate is judged to be the high line loss rate, the step S6 is executed; if the line loss rate is judged to be normal, the output line loss rate is normal and an error is reported; s5, classifying the negative line loss rate, judging the abnormal reason, and turning to the step S7; s6, judging the abnormal reason through a line loss rate estimation formula and a Pearson coefficient, and turning to the step S7; and S7, sorting and analyzing the obtained reasons and outputting the reasons. The invention improves the accuracy and speed of the line loss rate abnormity judgment.

Description

Fuzzy reasoning-based intelligent identification method for line loss rate abnormal cause of power distribution station
Technical Field
The invention belongs to the technical field of line loss rate abnormity judgment, and particularly relates to a fuzzy reasoning-based intelligent identification method for abnormal cause of line loss rate of a power distribution area.
Background
The effective reduction of the power loss is a long-term target in the line loss management work of the power enterprises, and the line loss management is the key point in the operation management of the power enterprises. The analysis of the line loss abnormity is the core of the line loss management work, and the analysis of the line loss data is used for timely grasping the operation conditions of all parts in the power grid and finding out corresponding problems, so that the abnormity reason can be timely found out, the faults of the power grid can be eliminated, and the overall management level of a power supply enterprise can be effectively improved. The abnormal analysis of the line loss of the transformer area plays an important role in the aspects of saving energy, reducing power loss, alleviating load tension, improving the economic operation benefit of a power grid and the like. And the abnormal analysis of the line loss can help the staff to more accurately master the running condition of the power grid, and the reason of the abnormal line loss rate can be found in the main process of analyzing and forming line loss data, and finally, a related line loss management method is found.
The abnormal analysis work of the line loss of the transformer area can help personnel to accurately analyze corresponding line loss data, so that a scientific basis is provided for line loss management, and finally, the abnormal position of the line loss of the transformer area is found in time. Because the electric quantity loss in the electric energy transmission process of the power grid cannot be avoided, the line loss is caused by a plurality of reasons, the line loss is complicated in change and cannot be determined in time, and the existing line loss rate abnormity judgment method has some defects in the aspects of efficiency, accuracy and the like.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides an intelligent identification method of abnormal cause of line loss rate of a power distribution area based on fuzzy reasoning, which establishes a membership function in a fuzzy rule on the basis of acquiring line loss rate data of the area; judging the line loss rate by using a membership function; establishing a fuzzy inference engine in a fuzzy expert database according to data analysis and expert experience; acquiring station area line loss rate data in abnormal time periods, and analyzing the reason of the abnormal line loss rate through a fuzzy inference machine; thereby obtaining the reason of the abnormal line loss rate.
In order to achieve the purpose, the invention adopts the following technical scheme:
the intelligent identification method for the abnormal cause of the line loss rate of the power distribution station area based on the fuzzy reasoning comprises the following steps:
s1, acquiring the line loss rate data of the transformer area; acquiring a time period when the line loss rate of the transformer area is abnormal;
s2, establishing a membership function in a fuzzy expert library according to historical data of the line loss rate of the transformer area;
s3, judging the line loss rate of the abnormal time period in the step S1 by using a membership function;
s4, analyzing according to the judgment result of the step S3: if the abnormal line loss rate is determined to be the negative line loss rate, the step S5 is executed; if the abnormal line loss rate is judged to be the high line loss rate, the step S6 is executed; if the line loss rate is judged to be normal, the output line loss rate is normal and an error is reported;
s5, classifying the negative line loss rate, judging the abnormal reason, and turning to the step S7;
s6, judging the abnormal reason through a line loss rate estimation formula and a Pearson coefficient, and turning to the step S7;
and S7, sorting and analyzing the obtained reasons and outputting the reasons.
In order to optimize the technical scheme, the specific measures adopted further comprise:
further, in step S2, the step of establishing a membership function curve in the fuzzy expert database includes the following steps:
s21, firstly, inputting historical data of the line loss rate of the transformer area, and selecting a trapezoid as a curve shape of a membership function; processing historical data of the line loss rate of the transformer area, clustering the line loss of the transformer area, and finding out a clustering center of the normal line loss rate; determining the national standard of the normal line loss rate of the transformer area corresponding to the voltage grade; establishing a preliminary line loss rate membership function;
s22, acquiring the line loss rate data of the transformer area, and performing cluster analysis on historical data of the line loss rate of the transformer area;
s23, establishing a mathematical expression of the membership function curve based on the result obtained in the clustering algorithm as follows:
Figure BDA0002594159290000021
Figure BDA0002594159290000022
Figure BDA0002594159290000023
wherein, mufIs the membership degree of the negative line loss; mu.szThe membership degree of the normal line loss rate; mu.sgThe membership degree of the high linear loss rate; x is the line loss rate;
ΔP％zis the clustering center of normal line loss rate; delta P%aRepresenting the corresponding line loss rate of the point with the membership degree of 1 in the membership degree function of the normal line loss rate; delta P%bGenerally is the national standard of the line loss rate, but when the percentage is delta P%zFar less than the national standard, delta P%bIs equal to delta P%y。
Further, step S22 is specifically: clustering analysis is carried out on the historical data of the line loss rate of the transformer area by using the sps software, and the clustering algorithm is a K-means algorithm;
firstly, inputting station area line loss rate data in a sps software, selecting a k-means clustering algorithm, moving a station area line loss rate variable into a variable frame, determining a clustering number N, and selecting iteration and classification by the method; iteration is carried out to obtain N clustering centers, the number of cases of different clustering centers is compared, and the clustering center with the largest number of cases in each cluster is selected as the clustering center with the normal line loss rate; finally, outputting a clustering result to obtain a clustering center delta P percent of the normal line loss ratezAnd abnormal line loss rate delta P%y。
Further, in step S3, the step of determining the line loss rate using the membership function is as follows:
s31, deriving the station area line loss rate data, and calculating the average value delta P% of the station area line loss rate in the abnormal time period, wherein the formula is as follows:
Figure BDA0002594159290000031
wherein, the Delta P percent is the average value of the station area line loss rate in the abnormal time period, and the Delta P percentiThe line loss rate value of the ith day in the abnormal time period is N, and N is the number of days in the abnormal time period;
s32, if the mean value delta P% of the line loss rate corresponds to mu in the membership functionf＜μzAnd mug＜μzJudging that the line loss rate delta P% is a normal line loss rate;
if it corresponds to mu in the membership functionfIf the line loss rate is more than 0, the line loss rate delta P% is judged to be differentOften, a negative line loss rate;
if it has a high rate of membership mu in the membership functiong＞μzIf the line loss rate Δ P% is abnormal, the line loss rate is determined to be a high line loss rate.
Further, when the abnormal line loss rate is the negative line loss rate in step S5, the method for classifying the negative line loss rate and determining the cause of the abnormality includes the steps of:
s51, judging the negative line loss rate value when the line loss rate in the abnormal time interval of the transformer area is a negative value: if the line loss rate | Δ P% | > | Δ P% during abnormal time periodfb|，ΔP％fbIf the negative line loss rate is determined, the negative line loss rate value in the time interval is large, and the process proceeds to step S52; if the line loss rate | delta P% | < | delta P% in abnormal time periodfbIf yes, the negative line loss rate value in the time interval is small, and the step S53 is carried out;
s52, if the negative line loss rate value is large in the abnormal time interval of the transformer area, the reason that the line loss rate of the transformer area is abnormal can be judged to be power supply quantity abnormality or the adopted system value is abnormal, and the step S7 is carried out;
s53, when the negative line loss rate value in the abnormal time interval of the transformer area is small, the judgment of the abnormal reason of the line loss rate of the transformer area comprises the following steps:
firstly, checking whether the corresponding relation between the electric power marketing system and the public transformer is normal, if the corresponding relation is wrong, correcting the relation between a user and the transformer in the marketing system, and judging the reason of abnormal line loss rate of a station area;
if the corresponding relation is correct, the next judgment is carried out: deriving the electricity consumption of the users in the distribution area, and calculating the line loss rate according to an estimation formula, wherein the estimation formula is as follows:
Figure BDA0002594159290000032
wherein, is delta P%g1Is an estimated value of the line loss rate; pkSupplying power; ptThe electricity consumption is used;
using membership functions muz(x) Judging the calculated line loss rate delta P%g1Whether the line loss rate is normal or not is judged, and if the line loss rate is delta P percentg1If the current line loss rate is abnormal, judging that the reason of the abnormal line loss rate is that the user file is not updated timely or fault data is not completed properly in the power supply range, otherwise, judging that the user power consumption file data is normal, and entering the next judgment; then checking whether the field terminal is normal, if the field terminal is abnormal, judging that the abnormal reason is the abnormal situation of the field terminal, carrying out terminal processing until the field terminal is normal, and entering the next judgment; and then judging whether the multiplying power of the current transformer is consistent with that of the system, if not, judging that the abnormal reason comprises the problems of unreasonable transformer configuration or transformer change, and estimating the line loss rate according to the actual multiplying power on site.
Further, in step S53, the estimation formula for estimating the line loss rate with the actual magnification in the field is as follows:
Figure BDA0002594159290000041
wherein, L 'is the estimated line loss rate, and c' is the multiplying power of the on-site current transformer; c is the multiplying power of the system current transformer; pkSupplying power; ptThe electricity consumption is used;
using membership functions muz(x) Judging whether the estimated line loss rate L' is in a normal range, wherein the specific steps are shown in step S3, if the line loss rate is normal, correcting the multiplying power of the current transformer, and judging whether the abnormal reason comprises the fact that the actual multiplying power of the current transformer is inconsistent with the multiplying power of the current transformer in the system; then, whether the on-site transformer corresponds to the meter or not is judged, if the on-site transformer does not correspond to the meter, the corresponding relation between the transformer and the meter is wrong, and the corresponding relation between a user and the transformer needs to be modified; continuously checking whether the electric energy meter is normal or not, and if the electric energy meter is abnormal, maintaining the electric energy meter; then checking whether the load is normal, if the three-phase load is unbalanced, judging that the reason of the line loss rate abnormality comprises that the secondary load is large, and adjusting the load; finally, the cause of the abnormal line loss rate is summarized and outputted, and the process proceeds to step S7.
Further, in step S6, when the abnormal line loss rate is the high line loss rate, the method for determining the cause of the abnormality by using the line loss rate estimation formula and the pearson coefficient includes the following steps:
s61, preliminarily analyzing the reason of the high line loss rate abnormity, deriving the total line loss rate of the transformer area, analyzing the power factor of the transformer area, judging whether the power factor of the transformer area is normal or not, if the power factor of the transformer area is less than 0.9, the power factor of the transformer area is abnormal, and the reason of the abnormality is that the reactive power of the transformer area is overlarge; otherwise, the power factor of the transformer area is normal, and the step S62 is carried out;
s62, specifically analyzing the reason for the high-line-loss rate abnormality: firstly, whether the duration time of the high line loss rate is long or not and whether the time length T of the abnormal line loss rate is greater than T or not are judgedz，TzTypically 15 in days; if T ≧ TzIf the high line loss rate is judged to be long in duration, the line loss rate of the transformer area is judged to be high for a long time, the line loss rate is abnormal because of the three-phase load imbalance of the transformer, otherwise, the duration of the abnormal condition of the high line loss rate is judged to be short, and the step S63 is entered;
s63, judging whether the line loss rate in a short time changes greatly, firstly, deriving all line loss rate historical data of the station area, arbitrarily taking the time with N durations consistent with the time interval of abnormal line loss rate, and calculating the variance sigma of the line loss rate in the corresponding time2 zTaking the mean value X, and calculating the variance as follows:
Figure BDA0002594159290000051
sigma is the total variance, X is the line loss rate variable, mu is the line loss rate mean value, and N is the number of the taken line loss rate time periods;
obtaining the variance of the abnormal line loss rate by using a variance calculation formula
Figure BDA0002594159290000053
If it is not
Figure BDA0002594159290000054
Judging that the line loss rate of the transformer area changes greatly in a short time, and entering a step S64 for specific analysis, otherwise, entering a step S65;
s64, under the condition that the line loss rate of the transformer area changes greatly in a short time, analyzing the reason of the abnormal line loss rate, and the method comprises the following steps: firstly, the electricity consumption of the station area users is exported, the line loss is calculated according to an estimation formula,
Figure BDA0002594159290000052
wherein, is delta P%g2Is an estimated value of the line loss rate; pkSupplying power; ptIs the amount of electricity used.
Judging the calculated line loss rate delta P percent by using the membership functiong2Whether the power loss rate is normal or not is determined as shown in step S3, if the power loss rate is not within the normal range, it can be determined that the cause of the abnormal power loss rate includes temporary emergency power consumption not counted in the distribution room, meter reading is not in place, or a power stealing problem exists, and the user profile needs to be updated; then checking the condition of the field terminal; then, whether the current transformer works normally or not is confirmed, and maintenance is carried out; judging the states of the public transformer summary table and the user meter, and carrying out appropriate maintenance; then checking the state of the user load, and if the user load is abnormal, carrying out corresponding adjustment; finally, summarizing the reasons which may cause the abnormal line loss rate, and going to step S7;
s65, under the condition that the line loss rate of the transformer area is overlarge, analyzing the reason of the abnormal line loss rate, wherein the method comprises the following steps:
firstly, exporting the power consumption of a distribution area and a user, carrying out line loss calculation according to an estimation formula, and analyzing whether the power consumption of the user is related to the abnormal line loss rate; judging the relation between the power consumption of the user and the line loss rate of the transformer area by using the Pearson correlation coefficient;
the larger the absolute value of the Pearson coefficient is, the stronger the correlation between the line loss rate of the transformer area and the power consumption of the user is, if the Pearson coefficient reaches a strong correlation range, the abnormal line loss rate is related to the user, and the electricity stealing and leakage of the user exist or the electric energy meter of the user fails; otherwise, entering the next analysis; then, deriving a corresponding relation between marketing and a public transformer, checking whether the corresponding relation between the marketing and the public transformer is normal, if the corresponding relation between the marketing and the public transformer is wrong, correcting the relation between a user and the public transformer, and judging that the reason of abnormal line loss rate of the transformer area is the wrong relation between the public transformer and the public transformer; and then deriving the power consumption of the station area users, and calculating the line loss according to an estimation formula, wherein the estimation formula is as follows:
Figure BDA0002594159290000061
wherein, is delta P%g3Is an estimated value of the line loss rate; pkSupplying power; ptThe electricity consumption is used;
judging the calculated line loss rateΔP％g3Whether the line loss rate is normal or not is judged, as shown in step S3, if the line loss rate is not within the normal range, the reason that the line loss rate is abnormal is judged to be a problem of household meter acquisition, the available power and electricity are not counted in the station area, and the acquisition equipment for acquiring the missing meter is modified; then checking whether the field terminal is normal, if the field terminal is abnormal, carrying out terminal processing until the field terminal is normal; and then analyzing whether the multiplying power of the current transformer is consistent with that of the system, if the multiplying power of the current transformer is inconsistent with that of the system, judging that the multiplying power of the current transformer is wrong, and estimating the line loss by using the actual multiplying power on site, wherein the mathematical expression of the estimation formula is as follows:
Figure BDA0002594159290000062
wherein, L 'is the estimated line loss rate, and c' is the multiplying power of the on-site current transformer; c is the multiplying power of the system current transformer; pkSupplying power; ptIs the amount of electricity used.
Using membership functions muz(x) Judging whether the estimated line loss rate L' is in a normal range, wherein the specific steps are shown as step S3, and if the line loss rate is normal, modifying the multiplying power of the current transformer; then, whether the on-site transformer corresponds to the meter or not is judged, if the on-site transformer does not correspond to the meter, the corresponding relation between the transformer and the meter is wrong, and the corresponding relation between a user and the transformer needs to be modified; then, the system is closedPerforming combined maintenance; finally, the cause of the line loss rate abnormality is summarized and outputted, and the process proceeds to step S7.
Further, the mathematical expression of the pearson coefficient r is as follows:
Figure BDA0002594159290000063
where N is the sample size and X, Y is the value of the user power consumption and the station area line loss rate, respectively.
The invention has the beneficial effects that: the line loss rate ambiguity is considered, the line loss rate data of the distribution room are obtained, and a membership function in the fuzzy rule is established according to historical data in the line loss rate data; judging the line loss rate by using a fuzzy rule; establishing a platform area line loss rate standard library based on the historical data clustering of the platform area line loss rate; the problem that a large amount of line loss rate data needs to be processed is solved; and acquiring the line loss rate data of the station area in the abnormal time period, analyzing abnormal conditions of different line loss rates on the basis of a fuzzy rule, and judging corresponding reasons. The fuzzy expert database system is established, inaccurate line loss rate data can be used for reasoning, the fuzzy technology is adopted to judge the reason of the abnormal line loss rate of the transformer area, and the accuracy and the speed of judging the abnormal line loss rate are improved.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a flow chart of membership function for obtaining line loss rate according to the present invention.
FIG. 3 is a graph of membership functions for the line loss rate of the present invention.
Fig. 4 is a flowchart of the present invention for determining an abnormal line loss rate.
Detailed Description
The present invention will now be described in further detail with reference to the accompanying drawings.
It should be noted that the terms "upper", "lower", "left", "right", "front", "back", etc. used in the present invention are for clarity of description only, and are not intended to limit the scope of the present invention, and the relative relationship between the terms and the terms is not limited by the technical contents of the essential changes.
As shown in fig. 1-3, the present invention provides a fuzzy inference based intelligent identification method for abnormal cause of line loss rate in a power distribution area, comprising the following steps:
s1, acquiring the line loss rate data of the transformer area; and acquiring the time period when the line loss rate of the transformer area is abnormal.
And S2, establishing a membership function in the fuzzy expert library according to the historical data of the line loss rate of the transformer area. In step S2, the step of establishing the membership function curve in the fuzzy expert database is as follows:
s21, firstly, inputting historical data of the line loss rate of the transformer area, and preparing for the subsequent data processing; the trapezoid is selected as the curve shape of the membership function, and based on the consideration of artificial experience, the trapezoid is selected as the curve shape of the membership function in the invention. Processing historical data of the line loss rate of the transformer area, clustering the line loss of the transformer area by using a k-means clustering method, and finding out a clustering center of the normal line loss rate; then determining the national standard of the normal line loss rate of the transformer area corresponding to the voltage class, namely an industry management power distribution line loss standard and a rural power grid transformed power distribution line loss rate standard, and taking the line loss rate smaller than zero as negative line loss; and finally, according to the preliminary line loss rate membership function of the data.
S22, obtaining the line loss rate data of the transformer area, and performing cluster analysis on the historical data of the line loss rate of the transformer area by using the sps software; the clustering algorithm is a K-means algorithm and comprises the following steps:
firstly, inputting platform area line loss rate data in a sps software, then selecting a k-means clustering algorithm (k-means clustering algorithm) for analyzing classification in a column, moving a platform area line loss rate variable into a variable frame, determining the clustering number N, generally 3, and selecting iteration and classification by the method; iteration is carried out to obtain N clustering centers, the number of cases of different clustering centers is compared, and the clustering center with the largest number of cases in each cluster is selected as the clustering center with the normal line loss rate; finally, outputting a clustering result to obtain a clustering center delta P percent of the normal line loss ratezAnd abnormal line loss rate delta P%y，ΔP％yIs n times of delta P%zConsidering the size and abnormality of the line loss rate dataWhen the line loss rate is large, n is 2.
S23, establishing a mathematical expression of the membership function curve based on the result obtained in the clustering algorithm as follows:
Figure BDA0002594159290000081
Figure BDA0002594159290000082
Figure BDA0002594159290000083
wherein, mufIs the membership degree of the negative line loss; mu.szThe membership degree of the normal line loss rate; mu.sgThe membership degree of the high linear loss rate; x is the line loss rate;
ΔP％zis the clustering center of normal line loss rate; delta P%aThe corresponding line loss rate of the point with the membership degree of 1 in the membership degree function representing the normal line loss rate takes the value of delta P percentzI.e. the clustering center of the normal line loss rate; delta P%bGenerally is the national standard of the line loss rate, but when the percentage is delta P%zFar less than the national standard, delta P%bIs equal to delta P%y。
The industry management distribution line loss standard and the rural power grid transformed distribution line loss rate standard are 10% of 10kV and 12% of 380/220V.
S3, judging the line loss rate of the abnormal time period in the step S1 by using a membership function, wherein the steps are as follows:
s31, deriving the station area line loss rate data, and calculating the average value delta P% of the station area line loss rate in the abnormal time period, wherein the formula is as follows:
Figure BDA0002594159290000084
wherein, the delta P% is the average value of the line loss rate of the station area in the abnormal time period，ΔP％iThe line loss rate value of the ith day in the abnormal time period is N, and N is the number of days in the abnormal time period;
s32, if the mean value delta P% of the line loss rate corresponds to mu in the membership functionf＜μzAnd mug＜μzJudging that the line loss rate delta P% is a normal line loss rate;
if the mean value delta P% of the line loss rate corresponds to mu in the membership functionfIf the line loss rate is more than 0, judging that the line loss rate delta P% is an abnormal line loss rate, and the line loss rate is a negative line loss rate;
high line loss rate membership mu if the mean value of the line loss rate delta P% in the membership functiong＞μzIf the line loss rate is abnormal, the line loss rate is judged to be high.
S4, analyzing according to the classification result according to the line loss rate: if the abnormal line loss rate is determined to be the negative line loss rate, the step S5 is executed; if the abnormal line loss rate is judged to be the high line loss rate, the step S6 is executed; if the line loss rate is judged to be normal, the output line loss rate is normal and error is reported without analysis.
S5, classifying the negative line loss rate, judging the abnormal reason, and going to step S7. When the abnormal line loss rate is the negative line loss rate, classifying the negative line loss rate according to expert experience and judging the abnormal reason, wherein the method comprises the following steps:
s51, judging the value of the negative line loss rate according to expert experience, wherein the line loss rate in the abnormal time period of the transformer area is a negative value: if the line loss rate | Δ P% | > | Δ P% during abnormal time periodfb|，ΔP％fbIf the value of the negative line loss rate is determined to be-10%, the value of the negative line loss rate is large in the time interval, and the step S52 is executed; if the line loss rate | delta P% | < | delta P% in abnormal time periodfbIf yes, the negative line loss rate value in the time interval is small, and the step S53 is carried out;
s52, judging whether the line loss rate of the station area is abnormal due to abnormal power supply amount or abnormal values of a collecting system (values collected by the collecting system are not correct) if the negative line loss rate value of the station area in the abnormal time period is large, and turning to the step S7;
s53, when the negative line loss rate value in the abnormal time interval of the transformer area is small, the judgment of the abnormal reason of the line loss rate of the transformer area comprises the following steps:
firstly, exporting a corresponding relation between an electric power marketing system (used for recording related data of users in an electric power network) and a public transformer, checking whether the corresponding relation between the electric power marketing system and the public transformer is normal or not, if the corresponding relation is wrong, correcting the relation between the users and the transformer in the marketing system, and judging the reason of abnormal line loss rate of a station area, otherwise, entering the next step of judgment;
and then deriving the electricity consumption of the station area users, and calculating the line loss rate according to an estimation formula, wherein the estimation formula is as follows:
Figure BDA0002594159290000091
wherein, is delta P%glIs an estimated value of the line loss rate; pkSupplying power; ptThe electricity consumption is used;
using membership functions muz(x) Judging the calculated line loss rate delta P%g1The specific steps of determining whether the line loss rate is normal are shown in step S3. If the line loss rate is judged by the membership function, the line loss rate is delta P percentg1If the current line loss rate is abnormal, judging that the reason of the abnormal line loss rate is that the user file is not updated timely or fault data is not completed properly in the power supply range, otherwise, judging that the user power consumption file data is normal, and entering the next judgment; then checking whether the field terminal is normal, if the field terminal is abnormal, judging that the abnormal reason is the abnormal situation of the field terminal, carrying out terminal processing until the field terminal is normal, and entering the next judgment; and then judging whether the multiplying power of a current Transformer (TA) is consistent with that of the system, if the multiplying power of the TA is inconsistent with that of the system, judging whether the abnormal reason comprises the problem of unreasonable transformer configuration or transformer change, and estimating the line loss rate according to the actual multiplying power on site, wherein the mathematical expression of the estimation formula is as follows:
Figure BDA0002594159290000101
wherein L 'is the estimated line loss rate, and c' is the field currentSensor multiplying power; c is the multiplying power of the system current transformer; pkSupplying power; ptIs the amount of electricity used.
Using membership functions muz(x) Judging whether the estimated line loss rate L' is in a normal range, wherein the specific steps are shown in step S3, if the line loss rate is normal, correcting the multiplying power of the current transformer, and judging whether the abnormal reason comprises the fact that the actual multiplying power of the current transformer is inconsistent with the multiplying power of the current transformer in the system; then, whether the on-site transformer corresponds to the meter or not is judged, if the on-site transformer does not correspond to the meter, the corresponding relation between the transformer and the meter is wrong, and the corresponding relation between a user and the transformer needs to be modified; continuously checking whether the electric energy meter is normal or not, and if the electric energy meter is abnormal, maintaining the electric energy meter; then checking whether the load is normal, if the three-phase load is unbalanced, judging that the reason of the line loss rate abnormality comprises that the secondary load is large, and adjusting the load; finally, the cause of the abnormal line loss rate is summarized and outputted, and the process proceeds to step S7.
S6, judging the abnormal reason through a line loss rate estimation formula and a Pearson coefficient, and turning to the step S7;
when the abnormal line loss rate is high, judging the abnormal reason by a line loss rate estimation formula and a Pearson coefficient, and comprising the following steps:
s61, preliminarily analyzing the reason for the high-line-loss-rate abnormity, deriving the line loss rate of the transformer area, including all line loss rate data and line loss rate data in an abnormal time period, analyzing the power factor of the transformer area, judging whether the power factor of the transformer area is normal or not, if the power factor of the transformer area is less than 0.9, judging that the power factor of the transformer area is abnormal, and if the reason for the abnormity is that the reactive power of the transformer area is overlarge; otherwise, the power factor of the transformer area is normal, and the step S62 is carried out;
s62, specifically analyzing the reason for the high-line-loss rate abnormality: firstly, whether the duration time of the high line loss rate is long or not and whether the time length T of the abnormal line loss rate is greater than T or not are judgedz，TzTypically 15 in days; if T ≧ TzIf the high-line-loss-rate duration is judged to be long, the line loss rate of the transformer area is judged to be high for a long time, the line loss rate is abnormal because the three-phase load of the transformer is unbalanced, otherwise, the duration of the high-line-loss-rate abnormal condition is judgedThe duration is short, and the process goes to step S63;
s63, judging whether the line loss rate in a short time changes greatly, firstly, deriving all line loss rate historical data of the station area, arbitrarily taking the time with N durations consistent with the time interval of abnormal line loss rate, and calculating the variance sigma of the line loss rate in the corresponding time2 zTaking the mean value X, and calculating the variance as follows:
Figure BDA0002594159290000102
sigma is the total variance, X is the line loss rate variable, mu is the line loss rate mean value, and N is the number of the taken line loss rate time periods.
Obtaining the variance of the abnormal line loss rate by using a variance calculation formula
Figure BDA0002594159290000113
If it is not
Figure BDA0002594159290000114
Judging that the line loss rate of the transformer area changes greatly in a short time, and entering a step S64 for specific analysis, otherwise, entering a step S65;
s64, under the condition that the line loss rate of the transformer area changes greatly in a short time, analyzing the reason of the abnormal line loss rate, and the method comprises the following steps: firstly, the electricity consumption of the station area users is exported, the line loss is calculated according to an estimation formula,
Figure BDA0002594159290000111
wherein, is delta P%g2Is an estimated value of the line loss rate; pkSupplying power; ptIs the amount of electricity used.
Judging the calculated line loss rate delta P percent by using the membership functiong2Whether the line loss rate is normal or not is determined as shown in step S3, if the line loss rate is not within the normal range, it can be determined that the reason for the abnormal line loss rate includes temporary emergency power consumption not counted in the distribution room, meter reading not in place, or electricity stealing problem, and the user needs to be updatedAnd (7) recording. Then checking the situation of the field terminal, and if the situation is abnormal, processing the terminal; then, whether the current transformer works normally is determined, if the current transformer works normally, the reason that the line loss rate is abnormal is TA wiring error and fault, TA rotation or TA configuration during load rise is unreasonable, metering misalignment is caused, and maintenance is carried out; judging the states of the public transformer substation summary table and the user meter, if the public transformer substation summary table and the user meter are abnormal, the reason of the abnormality is that the meter is in wrong wiring or fails, and the meter needs to be maintained; then checking the state of the user load, wherein the abnormal reason is that the three-phase load of a user power supply circuit in the transformer area is unbalanced, and the load needs to be adjusted; finally, summarizing the reasons which may cause the abnormal line loss rate, and going to step S7;
s65, under the condition that the line loss rate of the transformer area is overlarge, analyzing the reason of the abnormal line loss rate, wherein the method comprises the following steps:
firstly, exporting the power consumption of a distribution area and a user, carrying out line loss calculation according to an estimation formula, and analyzing whether the power consumption of the user is related to the abnormal line loss rate; and then, judging the relation between the power consumption of the user and the line loss rate of the transformer area by using a Pearson correlation coefficient, wherein the Pearson coefficient r has the following mathematical expression:
Figure BDA0002594159290000112
where N is the sample size, X, Y is the values of the user power consumption and the station area line loss rate, respectively, and r is the pearson coefficient.
The larger the absolute value of the Pearson coefficient is, the stronger the correlation between the line loss rate of the transformer area and the power consumption of the user is, if the Pearson coefficient reaches a strong correlation range, the abnormal line loss rate is related to the user, and the electricity stealing and leakage of the user exist or the electric energy meter of the user fails; otherwise, entering the next analysis; then, deriving a corresponding relation between marketing and a public transformer, checking whether the corresponding relation between the marketing and the public transformer is normal or not, if the corresponding relation between the marketing and the public transformer is wrong, correcting the relation between a user and the public transformer, and judging that the reason for the abnormal line loss rate of the transformer area is the wrong relation between the public transformer and the public transformer; and then deriving the power consumption of the station area users, and calculating the line loss according to an estimation formula, wherein the estimation formula is as follows:
Figure BDA0002594159290000121
wherein, is delta P%g3Is an estimated value of the line loss rate; pkSupplying power; ptThe electricity consumption is used;
judging the calculated line loss rate delta P%g3Whether the line loss rate is normal or not is judged, as shown in step S3, if the line loss rate is not within the normal range, the reason that the line loss rate is abnormal is judged to be a problem of household meter acquisition, the available power and electricity are not counted in the station area, and the acquisition equipment for acquiring the missing meter is modified; then checking whether the field terminal is normal, if the field terminal is abnormal, carrying out terminal processing until the field terminal is normal; and then analyzing whether the multiplying power of the current transformer is consistent with that of the system, if the multiplying power of the current transformer is inconsistent with that of the system, judging that the multiplying power of the current transformer is wrong, and estimating the line loss by using the actual multiplying power on site, wherein the mathematical expression of the estimation formula is as follows:
Figure BDA0002594159290000122
wherein, L 'is the estimated line loss rate, and c' is the multiplying power of the on-site current transformer; c is the multiplying power of the system current transformer; pkSupplying power; ptIs the amount of electricity used.
Using membership functions muz(x) Judging whether the estimated line loss rate L' is in a normal range, wherein the specific steps are shown as step S3, and if the line loss rate is normal, modifying the multiplying power of the current transformer; then, whether the on-site transformer corresponds to the meter or not is judged, if the on-site transformer does not correspond to the meter, the corresponding relation between the transformer and the meter is wrong, and the corresponding relation between a user and the transformer needs to be modified; then carrying out system association maintenance; finally, the cause of the line loss rate abnormality is summarized and outputted, and the process proceeds to step S7.
And S7, sorting and analyzing the obtained reasons and outputting the reasons.
One specific embodiment of the method for judging the line loss rate abnormality of the distribution room, to which the present invention is applied, is as follows:
given that the line loss rate data and the power consumption data of each user in the Fujian Quzhou West Tower public transformer 04 area from 1 month in 2018 to 2 months in 2019 are recorded in units of days, the data of the line loss rate and the power consumption data of each user in 1 month in 2019 and February in Table I are shown as follows:
watch 1
Figure BDA0002594159290000123
Figure BDA0002594159290000131
Figure BDA0002594159290000141
Figure BDA0002594159290000151
Firstly, introducing 2018 station area line loss rate data, carrying out clustering analysis on the station area line loss rate by using a k-means clustering method, determining that the number of clusters is 3, and clustering by using a sps software to obtain three clustering centers as shown in a table two:
watch two
Figure BDA0002594159290000152
The table intermediate data is data of three cluster centers of line loss rates, for example, the line loss rate of the cluster center numbered 1 is 2.08%, and there are 373 days in the station area line loss rate data.
The clustering center with the number of 1 can be judged to be the clustering center with the normal line loss rate, namely delta P percent according to the number of caseszIs 2.08%, and Δ P%y4.16%, due toThe table area is 10kv table area, the obtained line loss rate standard is 10%, and because 10% is far more than 4.16%, the error is reduced according to the content of delta P%zAnd. delta. P%bA membership function is established with respect to the line loss rate.
And (3) importing relevant data of the station area in the line loss rate abnormal period of January, wherein the abnormal line loss rate data is shown in a table III:
watch III
Figure BDA0002594159290000153
The data in the middle of the table are the time of the line loss rate abnormality and the corresponding line loss rate, for example, the line loss rate is abnormal in 1 month and 7 days in 2019, the abnormal line loss rate is 5.42%, the line loss rate is abnormal in 1 month and 8 days in 2019, the abnormal line loss rate is 3.7%, and so on.
And (3) rapidly judging the abnormal condition of the line loss rate of the platform area in the 1 month in 2019 by using the membership function, and judging that the abnormal line loss rate in the january is high line loss. And then, carrying out rule matching, and judging that the conditions of line loss rate abnormity belong to the conditions of ultrahigh line loss rate of the transformer area according to the membership function, so that the method directly enters an abnormity reason judgment step of the conditions of ultrahigh line loss rate of the transformer area:
firstly, importing user electricity consumption data, and judging whether the user electricity consumption data is missing or not, wherein the user electricity consumption data is not missing; then, the relationship between the electricity consumption of the user and the line loss rate of the distribution room is judged by utilizing the Pearson correlation coefficient,
Figure BDA0002594159290000154
wherein N is the sample size and is 15; x is the electricity consumption of A user, and Y is the line loss rate of the transformer area.
In the same way, when X is the electricity consumption of the B user, the Pearson correlation coefficient is-0.216; and the Pearson correlation coefficient is-0.08 when X is the electricity consumption of the C user.
Through calculation, the line loss rate of the transformer area is more than 0.8 and is strongly related to the Pearson coefficient of the user A between 7 days in 1 month and 21 days in 1 month, and the line loss rate abnormality in the period can be judged to be related to the user due to the fact that electricity stealing and leakage exist in the user or the electric energy meter of the user fails.
And importing relevant data of the abnormal line loss rate period of the station district in february, wherein the abnormal line loss rate data is shown in a table four:
watch four
Figure BDA0002594159290000161
The data in the middle of the table are the time of the line loss rate abnormality and the corresponding line loss rate, for example, the line loss rate abnormality of 14 days 2 and 14 months in 2019, the abnormal line loss rate is 14.63%, and so on.
And then, performing rule matching, and judging that the abnormal conditions of the line loss rate belong to the ultrahigh conditions of the line loss rate of the transformer area according to the membership function, so that the step of judging the abnormal reason directly enters the step of judging the abnormal conditions of the ultrahigh conditions of the line loss rate of the transformer area.
Firstly, importing user electricity consumption data, judging whether the user electricity consumption data is missing or not, finding that the electricity consumption of the user A is 0 in the period from 14 days in 2 months to 15 days in month, and judging that the reason of the abnormal line loss rate in the period is the problem of collecting a household meter or the fact that the useful electricity quantity is not counted in a station area.
Finally, the result can be output: the line loss rate of the transformer area is abnormal between 1 month and 2 months in 2019, wherein the line loss rate is abnormal between 7 days and 10 days in 1 month and 14 days to 18 days in 1 month in 2019, the reason that the line loss rate is abnormal between 21 days in 1 month is that electricity stealing and electricity leakage exist in the user A or the electric energy meter of the user fails, and the reason that the line loss rate is abnormal between 14 days and 15 days in 2 months in 2019 is that the user meter acquisition of the user A fails or the electricity consumption of the user is not counted in the transformer area.
In conclusion, the line loss rate ambiguity is considered, the line loss rate data of the distribution room are obtained, and the membership function in the fuzzy rule is established according to the historical data; judging the line loss rate by using a fuzzy rule; the problem that a large amount of line loss rate data needs to be processed is solved; and acquiring the line loss rate data of the station area in the abnormal time period, analyzing abnormal conditions of different line loss rates on the basis of a fuzzy rule, and judging corresponding reasons. The fuzzy expert database system is established, inaccurate line loss rate data can be used for reasoning, the fuzzy technology is adopted to judge the reason of the abnormal line loss rate of the transformer area, and the accuracy and the speed of judging the abnormal line loss rate are improved.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above-mentioned embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may be made by those skilled in the art without departing from the principle of the invention.

Claims (7)

1. The intelligent identification method for the abnormal cause of the line loss rate of the power distribution station area based on the fuzzy reasoning is characterized by comprising the following steps of:
s1, acquiring the line loss rate data of the transformer area; acquiring a time period when the line loss rate of the transformer area is abnormal;
s2, establishing a membership function in a fuzzy expert library according to historical data of the line loss rate of the transformer area; the method comprises the following steps:
s21, firstly, inputting historical data of the line loss rate of the transformer area, and selecting a trapezoid as a curve shape of a membership function; processing historical data of the line loss rate of the transformer area, clustering the line loss of the transformer area, and finding out a clustering center of the normal line loss rate; determining the national standard of the normal line loss rate of the transformer area corresponding to the voltage grade; establishing a preliminary line loss rate membership function;
s22, acquiring the line loss rate data of the transformer area, and performing cluster analysis on historical data of the line loss rate of the transformer area;
s23, establishing a mathematical expression of the membership function curve based on the result obtained in the clustering algorithm as follows:
Figure FDA0002882286540000011
Figure FDA0002882286540000012
Figure FDA0002882286540000013
wherein, mufIs the membership degree of the negative line loss rate; mu.szThe membership degree of the normal line loss rate; mu.sgThe membership degree of the high linear loss rate; x is the line loss rate;
ΔP％zis the clustering center of normal line loss rate; delta P%aRepresenting the corresponding line loss rate of the point with the membership degree of 1 in the membership degree function of the normal line loss rate;ΔP％bis the national standard of the line loss rate, but whenΔP％zIs far less than the national standard,ΔP％bis equal toΔP％y，ΔP％zThe cluster center representing the normal line loss rate,ΔP％yrepresenting an abnormal line loss rate;
s3, judging the line loss rate of the abnormal time period in the step S1 by using a membership function;
s4, analyzing according to the judgment result of the step S3: if the abnormal line loss rate is determined to be the negative line loss rate, the step S5 is executed; if the abnormal line loss rate is judged to be the high line loss rate, the step S6 is executed; if the line loss rate is judged to be normal, the output line loss rate is normal and an error is reported;
s5, classifying the negative line loss rate, judging the abnormal reason, and turning to the step S7;
s6, judging the abnormal reason through a line loss rate estimation formula and a Pearson coefficient, and turning to the step S7;
and S7, sorting and analyzing the obtained reasons and outputting the reasons.
2. The method of claim 1, wherein the step S22 specifically comprises: clustering analysis is carried out on the historical data of the line loss rate of the transformer area by using the sps software, and the clustering algorithm is a K-means algorithm;
firstly, inputting the line loss rate data of a distribution area in the sps software, and selecting a k-means clustering algorithmShifting the variation of the line loss rate of the transformer area into a variable frame, determining the clustering number N, and selecting iteration and classification by the method; iteration is carried out to obtain N clustering centers, the number of cases of different clustering centers is compared, and the clustering center with the largest number of cases in each cluster is selected as the clustering center with the normal line loss rate; finally, outputting the clustering result to obtain the clustering center of the normal line loss rateΔP％zAnd abnormal line loss rateΔP％y。
3. The method for intelligently identifying the cause of the abnormal line loss rate of the distribution room as claimed in claim 1, wherein in step S3, the step of judging the line loss rate by using the membership function comprises the following steps:
s31, deriving the station area line loss rate data, and calculating the average value of the station area line loss rate in the abnormal time periodΔP%, the formula is as follows:
Figure FDA0002882286540000021
wherein the content of the first and second substances,Δp% is the average of the station area line loss rate in the abnormal period,ΔP％ithe line loss rate value of the ith day in the abnormal time period is N, and N is the number of days in the abnormal time period;
s32 average value of line loss rateΔP% corresponding μ in membership functionf＜μzAnd mug＜μzThen determine the line loss rateΔP% is the normal line loss rate;
if it corresponds to mu in the membership functionfIf the line loss rate is more than 0, the line loss rate is judged at the momentΔP% abnormal, negative line loss rate;
if it has a high rate of membership mu in the membership functiong＞μzThen determine the line loss rateΔP% abnormal, high line loss rate.
4. The method as claimed in claim 3, wherein in step S5, when the abnormal line loss rate is negative, classifying the negative line loss rate and determining the cause of the abnormality comprises the following steps:
s51, judging the negative line loss rate value when the line loss rate in the abnormal time interval of the transformer area is a negative value: line loss rate if abnormal periodΔP％|＞|ΔP％fb|，ΔP％fbIf the negative line loss rate is determined, the negative line loss rate value in the time interval is large, and the process proceeds to step S52; line loss rate if abnormal periodΔP％|＜|ΔP％fbIf yes, the negative line loss rate value in the time interval is small, and the step S53 is carried out;
s52, if the negative line loss rate value is large in the abnormal time interval of the transformer area, the reason that the line loss rate of the transformer area is abnormal can be judged to be power supply quantity abnormality or the adopted system value is abnormal, and the step S7 is carried out;
s53, when the negative line loss rate value in the abnormal time interval of the transformer area is small, the judgment of the abnormal reason of the line loss rate of the transformer area comprises the following steps:
firstly, checking whether the corresponding relation between the electric power marketing system and the public transformer is normal, if the corresponding relation is wrong, correcting the relation between a user and the transformer in the marketing system, and judging the reason of abnormal line loss rate of a station area;
if the corresponding relation is correct, the next judgment is carried out: deriving the electricity consumption of the users in the distribution area, and calculating the line loss rate according to an estimation formula, wherein the estimation formula is as follows:
Figure FDA0002882286540000031
wherein the content of the first and second substances,ΔP％g1is an estimated value of the line loss rate; pkSupplying power; ptThe electricity consumption is used;
using membership functions muz(x) Judging the calculated line loss rateΔP％g1Whether the line loss rate is normal or not, and if the line loss rate is normalΔP％g1If the current line loss rate is abnormal, judging that the reason of the abnormal line loss rate is that the user file is not updated timely or fault data is not completed properly in the power supply range, otherwise, judging that the user power consumption file data is normal, and entering the next judgment; then checkingWhether the field terminal is normal or not is judged, if the field terminal is abnormal, the abnormal reason is judged to be the abnormal situation of the field terminal, the terminal processing is carried out until the field terminal is normal, and the next judgment is carried out; and then judging whether the multiplying power of the current transformer is consistent with that of the system, if not, judging that the abnormal reason comprises the problems of unreasonable transformer configuration or transformer change, and estimating the line loss rate according to the actual multiplying power on site.
5. The method as claimed in claim 4, wherein in step S53, the formula for estimating the line loss rate with the actual magnification in the field is as follows:
Figure FDA0002882286540000041
wherein, L 'is the estimated line loss rate, and c' is the multiplying power of the on-site current transformer; c is the multiplying power of the system current transformer; pkSupplying power; ptThe electricity consumption is used;
using membership functions muz(x) Judging whether the estimated line loss rate L' is in a normal range, wherein the specific steps are shown in step S3, if the line loss rate is normal, correcting the multiplying power of the current transformer, and judging whether the abnormal reason comprises the fact that the actual multiplying power of the current transformer is inconsistent with the multiplying power of the current transformer in the system; then, whether the on-site transformer corresponds to the meter or not is judged, if the on-site transformer does not correspond to the meter, the corresponding relation between the transformer and the meter is wrong, and the corresponding relation between a user and the transformer needs to be modified; continuously checking whether the electric energy meter is normal or not, and if the electric energy meter is abnormal, maintaining the electric energy meter; then checking whether the load is normal, if the three-phase load is unbalanced, judging that the reason of the line loss rate abnormality comprises that the secondary load is large, and adjusting the load; finally, the cause of the abnormal line loss rate is summarized and outputted, and the process proceeds to step S7.
6. The method as claimed in claim 4, wherein in step S6, when the abnormal line loss rate is a high line loss rate, the method for determining the cause of the abnormality by using a line loss rate estimation formula and a Pearson coefficient includes the following steps:
s61, preliminarily analyzing the reason of the high line loss rate abnormity, deriving the total line loss rate of the transformer area, analyzing the power factor of the transformer area, judging whether the power factor of the transformer area is normal or not, if the power factor of the transformer area is less than 0.9, the power factor of the transformer area is abnormal, and the reason of the abnormality is that the reactive power of the transformer area is overlarge; otherwise, the power factor of the transformer area is normal, and the step S62 is carried out;
s62, specifically analyzing the reason for the high-line-loss rate abnormality: firstly, whether the duration time of the high line loss rate is long or not and whether the time length T of the abnormal line loss rate is greater than T or not are judgedz(ii) a If T ≧ TzIf the high line loss rate is judged to be long in duration, the line loss rate of the transformer area is judged to be high for a long time, the line loss rate is abnormal because of the three-phase load imbalance of the transformer, otherwise, the duration of the abnormal condition of the high line loss rate is judged to be short, and the step S63 is entered;
s63, judging whether the line loss rate in a short time changes greatly, firstly, deriving all line loss rate historical data of the station area, arbitrarily taking the time with N durations consistent with the time interval of abnormal line loss rate, and calculating the variance sigma of the line loss rate in the corresponding time2 zTaking the mean value X, and calculating the variance as follows:
Figure FDA0002882286540000042
sigma is the total variance, X is the line loss rate variable, mu is the line loss rate mean value, and N is the number of the taken line loss rate time periods;
obtaining the variance of the abnormal line loss rate by using a variance calculation formula
Figure FDA0002882286540000051
If it is not
Figure FDA0002882286540000052
Judging that the line loss rate of the transformer area changes greatly in a short time, and turning to the step S64 for specific analysis, otherwise, turning to the step S65;
s64, under the condition that the line loss rate of the transformer area changes greatly in a short time, analyzing the reason of the abnormal line loss rate, and the method comprises the following steps: firstly, the electricity consumption of the station area users is exported, the line loss is calculated according to an estimation formula,
Figure FDA0002882286540000053
wherein the content of the first and second substances,ΔP％g2is an estimated value of the line loss rate; pkSupplying power; ptThe electricity consumption is used;
judging and calculating the line loss rate by utilizing the membership functionΔP％g2Whether the power loss rate is normal or not is determined as shown in step S3, if the power loss rate is not within the normal range, it can be determined that the cause of the abnormal power loss rate includes temporary emergency power consumption not counted in the distribution room, meter reading is not in place, or a power stealing problem exists, and the user profile needs to be updated; then checking the condition of the field terminal; then, whether the current transformer works normally or not is confirmed, and maintenance is carried out; judging the states of the public transformer summary table and the user meter, and carrying out appropriate maintenance; then checking the state of the user load, and if the user load is abnormal, carrying out corresponding adjustment; finally, summarizing the reasons which may cause the abnormal line loss rate, and going to step S7;
s65, under the condition that the line loss rate of the transformer area is overlarge, analyzing the reason of the abnormal line loss rate, wherein the method comprises the following steps:
firstly, exporting the power consumption of a distribution area and a user, carrying out line loss calculation according to an estimation formula, and analyzing whether the power consumption of the user is related to the abnormal line loss rate; judging the relation between the power consumption of the user and the line loss rate of the transformer area by using the Pearson correlation coefficient;
the larger the absolute value of the Pearson coefficient is, the stronger the correlation between the line loss rate of the transformer area and the power consumption of the user is, if the Pearson coefficient reaches a strong correlation range, the abnormal line loss rate is related to the user, and the electricity stealing and leakage of the user exist or the electric energy meter of the user fails; otherwise, entering the next analysis; then, deriving a corresponding relation between marketing and a public transformer, checking whether the corresponding relation between the marketing and the public transformer is normal, if the corresponding relation between the marketing and the public transformer is wrong, correcting the relation between a user and the public transformer, and judging that the reason of abnormal line loss rate of the transformer area is the wrong relation between the public transformer and the public transformer; and then deriving the power consumption of the station area users, and calculating the line loss according to an estimation formula, wherein the estimation formula is as follows:
Figure FDA0002882286540000054
wherein the content of the first and second substances,ΔP％g3is an estimated value of the line loss rate; pkSupplying power; ptThe electricity consumption is used;
judging the calculated line loss rateΔP％g3Whether the line loss rate is normal or not is judged, as shown in step S3, if the line loss rate is not within the normal range, the reason that the line loss rate is abnormal is judged to be a problem of household meter acquisition, the available power and electricity are not counted in the station area, and the acquisition equipment for acquiring the missing meter is modified; then checking whether the field terminal is normal, if the field terminal is abnormal, carrying out terminal processing until the field terminal is normal; and then analyzing whether the multiplying power of the current transformer is consistent with that of the system, if the multiplying power of the current transformer is inconsistent with that of the system, judging that the multiplying power of the current transformer is wrong, and estimating the line loss by using the actual multiplying power on site, wherein the mathematical expression of the estimation formula is as follows:
Figure FDA0002882286540000061
wherein, L 'is the estimated line loss rate, and c' is the multiplying power of the on-site current transformer; c is the multiplying power of the system current transformer; pkSupplying power; ptThe electricity consumption is used;
using membership functions muz(x) Judging whether the estimated line loss rate L' is in a normal range, wherein the specific steps are shown as step S3, and if the line loss rate is normal, modifying the multiplying power of the current transformer; then, whether the on-site transformer is corresponding to the meter or not is judged, if the on-site transformer is not corresponding to the meter, the transformer and the meter are indicatedThe corresponding relation is calculated to be wrong, and the corresponding relation between a user and the transformer needs to be modified; then carrying out system association maintenance; finally, the cause of the line loss rate abnormality is summarized and outputted, and the process proceeds to step S7.
7. The method as claimed in claim 6, wherein the Pearson coefficient r is expressed as follows:
Figure FDA0002882286540000062
where N is the sample size and X, Y is the value of the user power consumption and the station area line loss rate, respectively.
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