CN111598385B - Method and system for determining electricity consumption behavior based on fuzzy analytic hierarchy process and comprehensive evaluation - Google Patents
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Abstract
The invention discloses a method for determining electricity consumption behavior based on fuzzy analytic hierarchy process and comprehensive evaluation, which comprises the steps of firstly determining a first-level characteristic index and a second-level characteristic index for describing the electricity consumption behavior of a user, then determining an evaluation set matrix according to a selected index system, calculating a judgment matrix constructed by a fuzzy analytic hierarchy process and a fuzzy comprehensive evaluation method and the fuzzy evaluation matrix to obtain a fuzzy comprehensive evaluation set of the user, and judging whether the electricity stealing suspicion of the user exceeds a reasonable range according to a set threshold value so as to judge the abnormal electricity consumption user. The invention analyzes the electricity consumption behavior of the user by multiple layers and multiple dimensions, and reserves all the needed information to the greatest extent under the condition of comprehensively considering multiple factors; meanwhile, the electricity stealing suspicion of the user is calculated by adopting a method combining a fuzzy analytic hierarchy process and a fuzzy comprehensive evaluation, so that the unilateral performance of the traditional single characteristic weight calculation method is avoided, and the accuracy of the description of different electricity utilization characteristic indexes on the electricity utilization behavior of the user can be accurately measured.
Description
Technical Field
The invention relates to the technical field of power system electricity utilization abnormality detection, in particular to a method and a system for determining electricity utilization behavior based on fuzzy analytic hierarchy process and comprehensive evaluation.
Background
At present, the development of the power industry plays an important role in the economic growth of China and is closely related to the daily life of people. However, the power grid has large power transmission and distribution losses in operation at present, and the losses are divided into technical losses and non-technical losses, and the technical losses are unavoidable. Non-technical losses refer to abnormal power usage by the user, such as electricity theft, etc. According to the related data, electricity theft is one of the main reasons for greatly increasing the comprehensive value of the line loss of the power grid. The harm caused by electricity larceny is huge, so that normal electric power market order is disturbed, and dangerous operation of electricity larceny can cause equipment short circuit, damage electric power lines, change the state of power transmission and distribution equipment and endanger the life safety of surrounding personnel. Conventionally, electricity theft detection mainly depends on dispatching technicians to check electricity by a power supply enterprise, so that the electricity consumption is low, the efficiency is high, and the consumption of manpower and material resources is huge.
Therefore, there is an urgent need for a method capable of efficiently and accurately determining abnormal electrical behavior of a user.
Disclosure of Invention
The invention provides a method and a system for determining electricity consumption behavior based on fuzzy analytic hierarchy process and comprehensive evaluation, which are used for solving the problem of how to determine abnormal electricity consumption behavior of a user.
In order to solve the above problems, according to an aspect of the present invention, there is provided a method of determining an electricity usage behavior based on fuzzy analytic hierarchy process and comprehensive evaluation, the method comprising:
determining a first-level index set and a second-level index set according to the category of the target object, and respectively constructing a first-level index evaluation set matrix and a second-level index evaluation set matrix;
determining the membership degree of the power consumption data corresponding to each secondary index in the secondary index set to the secondary index evaluation set matrix, and determining a fuzzy evaluation matrix of each secondary index according to the membership degree;
determining a secondary index weight set matrix by using a fuzzy analytic hierarchy process based on the secondary index evaluation set matrix;
determining a fuzzy comprehensive evaluation set of each secondary index according to the product of the secondary index weight set matrix and the fuzzy evaluation matrix of each secondary index, and constructing a fuzzy evaluation matrix of the primary index by using the fuzzy comprehensive evaluation set of each secondary index;
determining a first-level index weight set matrix by using a fuzzy analytic hierarchy process based on the first-level index set;
Determining a fuzzy comprehensive evaluation set of the power utilization state according to the product of the first-level index weight set matrix and the fuzzy evaluation matrix of the first-level index;
and determining the occurrence probability of each electricity utilization state in the first-level index evaluation set matrix according to the fuzzy comprehensive evaluation set of the electricity utilization state, and determining the electricity utilization behavior of the target object according to the occurrence probability of each electricity utilization state.
Preferably, the determining the primary index set and the secondary index set according to the category of the target object includes:
when the category of the target object is a single-phase user, the first-level index set includes: at least one of daily electricity, single-phase current and single-phase voltage;
when the first level index set includes a daily electricity amount, the second level index set includes: the cumulative fluctuation rate of the daily electric quantity; when the primary index set includes a single-phase current, the secondary index set includes: a current ramp down rate; when the primary index set includes a single-phase voltage, the secondary index set includes: voltage dip rate;
when the user category of the target object is a three-phase user, the first-level index set includes: at least one of daily active power, three-phase current, three-phase voltage, and power factor; when the primary index set includes daily active power, the secondary index set includes: daily active power fluctuation rate; when the primary index set includes three-phase current, the secondary index set includes: current ramp down rates for phase a, phase B and phase C; when the primary index set includes three-phase voltages, the secondary index set includes: undervoltage percentages of phase a, phase B, and phase C; when the primary index set includes a power factor, the secondary index set includes: power factor dip rate.
Preferably, the constructing a fuzzy evaluation matrix of each secondary index according to membership degree includes:
wherein R is Index i A fuzzy evaluation matrix for the ith secondary index; m is a secondary index evaluation set matrix V= [ V ] 1 ,v 2 ,…,v m ]The number of elements in the list; the evaluation grade of the ith secondary index is v j Is a fuzzy subset matrix R j ＝{r j1 ,r j2 ,…,r js }，r j1 ,r j2 ,…,r js For an evaluation rating of v j Membership range at that time.
Preferably, the determining the secondary index weight set matrix a by using a fuzzy analytic hierarchy process based on the secondary index evaluation set matrix includes:
marking the importance degree between each element in the secondary index evaluation set matrix to obtain a fuzzy judgment matrix;
determining the initial weight of each element according to the fuzzy judgment matrix;
and performing defuzzification processing on the initial weight of each element, performing per unit processing on the weight obtained through the defuzzification processing, and determining a secondary index weight set matrix.
Preferably, the determining the occurrence probability of each power utilization state in the first-level index evaluation set matrix according to the fuzzy comprehensive evaluation set of power utilization states, and determining the power utilization behavior of the target object according to the occurrence probability of each power utilization state includes:
The elements in the fuzzy comprehensive evaluation set of the power utilization state and the power utilization state in the primary index evaluation set are in one-to-one correspondence to determine the occurrence probability of each power utilization state;
comparing the occurrence probability of the preset power utilization state with a preset probability threshold value, and obtaining a comparison result;
if the comparison result indicates that the occurrence probability of the preset power utilization state is smaller than a preset probability threshold, determining that the power utilization behavior of the target object belongs to abnormal power utilization behavior; otherwise, determining that the electricity consumption behavior of the target object belongs to normal electricity consumption behavior.
According to another aspect of the present invention, there is provided a system for determining electricity usage behavior based on fuzzy analytic hierarchy process and comprehensive evaluation, the system comprising:
the index set and index evaluation set matrix determining unit is used for determining a first-level index set and a second-level index set according to the category of the target object and respectively constructing a first-level index evaluation set matrix and a second-level index evaluation set matrix V;
the fuzzy evaluation matrix determining unit of the secondary indexes is used for determining the membership degree of the power utilization data corresponding to each secondary index in the secondary index set to the secondary index evaluation set matrix V and determining a fuzzy evaluation matrix R of each secondary index according to the membership degree;
The secondary index weight set matrix determining unit is used for determining a secondary index weight set matrix A by using a fuzzy analytic hierarchy process based on the secondary index evaluation set matrix V;
the fuzzy evaluation matrix determining unit of the first-level index is used for determining a fuzzy comprehensive evaluation set B of each second-level index according to the product of the weight set matrix A of the second-level index and the fuzzy evaluation matrix R of each second-level index, and constructing a fuzzy evaluation matrix R' of the first-level index by utilizing the fuzzy comprehensive evaluation set B of each second-level index;
the primary index weight set matrix determining unit is used for determining a primary index weight set matrix A' by using a fuzzy analytic hierarchy process based on the primary index set;
the fuzzy comprehensive evaluation set determining unit of the electricity utilization state is used for determining a fuzzy comprehensive evaluation set of the electricity utilization state according to the product of the first-level index weight set matrix and the fuzzy evaluation matrix R' of the first-level index;
and the electricity consumption behavior determining unit is used for determining the occurrence probability of each electricity consumption state in the primary index evaluation set matrix according to the fuzzy comprehensive evaluation set of the electricity consumption state and determining the electricity consumption behavior of the target object according to the occurrence probability of each electricity consumption state.
Preferably, the index set and index evaluation set matrix determining unit determines a primary index set and a secondary index set according to a category of the target object, including:
when the category of the target object is a single-phase user, the first-level index set includes: at least one of daily electricity, single-phase current and single-phase voltage;
when the first level index set includes a daily electricity amount, the second level index set includes: the cumulative fluctuation rate of the daily electric quantity; when the primary index set includes a single-phase current, the secondary index set includes: a current ramp down rate; when the primary index set includes a single-phase voltage, the secondary index set includes: voltage dip rate;
when the user category of the target object is a three-phase user, the first-level index set includes: at least one of daily active power, three-phase current, three-phase voltage, and power factor; when the primary index set includes daily active power, the secondary index set includes: daily active power fluctuation rate; when the primary index set includes three-phase current, the secondary index set includes: current ramp down rates for phase a, phase B and phase C; when the primary index set includes three-phase voltages, the secondary index set includes: undervoltage percentages of phase a, phase B, and phase C; when the primary index set includes a power factor, the secondary index set includes: power factor dip rate.
Preferably, the fuzzy evaluation matrix determining unit of the secondary index constructs a fuzzy evaluation matrix of each secondary index according to membership degree, including:
wherein R is Index i A fuzzy evaluation matrix for the ith secondary index;m is a secondary index evaluation set matrix V= [ V ] 1 ,v 2 ,…,v m ]The number of elements in the list; the evaluation grade of the ith secondary index is v j Is a fuzzy subset matrix R j ＝{r j1 ,r j2 ,…,r js }，r j1 ,r j2 ,…,r js For an evaluation rating of v j Membership range at that time.
Preferably, the secondary index weight set matrix determining unit determines a secondary index weight set matrix a by using a fuzzy analytic hierarchy process based on the secondary index evaluation set matrix, including:
marking the importance degree between each element in the secondary index evaluation set matrix to obtain a fuzzy judgment matrix;
determining the initial weight of each element according to the fuzzy judgment matrix;
and performing defuzzification processing on the initial weight of each element, performing per unit processing on the weight obtained through the defuzzification processing, and determining a secondary index weight set matrix.
Preferably, the electricity consumption behavior determining unit determines occurrence probability of each electricity consumption state in the first-level index evaluation set matrix according to the fuzzy comprehensive evaluation set of the electricity consumption states, and determines the electricity consumption behavior of the target object according to the occurrence probability of each electricity consumption state, including:
The elements in the fuzzy comprehensive evaluation set of the power utilization state and the power utilization state in the primary index evaluation set are in one-to-one correspondence to determine the occurrence probability of each power utilization state;
comparing the occurrence probability of the preset power utilization state with a preset probability threshold value, and obtaining a comparison result;
if the comparison result indicates that the occurrence probability of the preset power utilization state is smaller than a preset probability threshold, determining that the power utilization behavior of the target object belongs to abnormal power utilization behavior; otherwise, determining that the electricity consumption behavior of the target object belongs to normal electricity consumption behavior.
The invention provides a method and a system for determining electricity consumption behavior based on fuzzy analytic hierarchy process and comprehensive evaluation, which comprises the steps of firstly determining a first-level characteristic index and a second-level characteristic index for describing electricity consumption behavior of a user, and determining an evaluation set matrix according to a selected index system; then calculating a judgment matrix and a fuzzy evaluation matrix constructed by a fuzzy analytic hierarchy process and a fuzzy comprehensive evaluation process to obtain a fuzzy comprehensive evaluation set; and finally, judging whether the electricity utilization behavior of the user is abnormal according to the set threshold value. According to the invention, through establishing a primary index system and a secondary index system of the electricity consumption behavior of the user, the electricity consumption behavior of the user can be analyzed by multiple layers and multiple dimensions, and all required information can be reserved to the greatest extent under the condition of comprehensively considering multiple factors; meanwhile, the electricity larceny suspicion degree of the user is calculated by adopting a method combining the fuzzy analytic hierarchy process and the fuzzy comprehensive evaluation, so that the unilateral property of the traditional single characteristic weight calculation method is avoided, the accuracy of different electricity utilization characteristic indexes on the description of the electricity utilization behavior of the user can be accurately measured, and the efficiency and the accuracy of detecting the abnormal electricity utilization behavior are improved.
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Exemplary embodiments of the present invention may be more completely understood in consideration of the following drawings:
FIG. 1 is a flow chart of a method 100 of determining power usage behavior based on fuzzy analytic hierarchy process and comprehensive evaluation in accordance with an embodiment of the present invention;
fig. 2 is a block diagram of exemplary features according to an embodiment of the present invention.
FIG. 3 is a flow chart of determining the power usage behavior of a single-phase user based on fuzzy analytic hierarchy process and comprehensive evaluation in accordance with an embodiment of the present invention;
FIG. 4 is a schematic diagram of a system 400 for determining power usage behavior based on fuzzy analytic hierarchy process and comprehensive evaluation in accordance with an embodiment of the present invention.
Detailed Description
The exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, however, the present invention may be embodied in many different forms and is not limited to the examples described herein, which are provided to fully and completely disclose the present invention and fully convey the scope of the invention to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the accompanying drawings is not intended to be limiting of the invention. In the drawings, like elements/components are referred to by like reference numerals.
Unless otherwise indicated, terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art. In addition, it will be understood that terms defined in commonly used dictionaries should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense.
The intelligent ammeter can accurately record and report the electricity consumption data of the user at high frequency, and lays a key foundation for accurately identifying electricity stealing behaviors. Although the user can still adopt various technical means to manipulate or tamper the reported electric quantity data, the electricity stealing behavior can be identified according to the electricity consumption data of the metering master station. Regardless of how a user steals electricity, the motivation is to always reduce the electricity consumption to get the electricity difference benefit regardless of the peak-to-valley electricity price effect, and the significant reduction of the electricity consumption is often shown on the electricity load curve. The existing electricity stealing detection is based on the premise that a plurality of related indexes are derived and defined except according to electricity load curves, and then clustering or classification analysis is carried out to identify abnormal users with power consumption mutation. By analyzing the electricity stealing means commonly adopted by the user in the electricity stealing process, the electricity stealing characteristics of different electricity stealing means are found to be different. Therefore, relevant characteristic indexes describing the electricity stealing means can be further selected according to different electricity stealing characteristics, so that the electricity stealing behavior of the user is detected through multiple layers and multiple angles. Therefore, the embodiment of the invention provides a method and a system for determining electricity utilization behavior based on fuzzy analytic hierarchy process and comprehensive evaluation.
FIG. 1 is a flow chart of a method 100 for determining power usage behavior based on fuzzy analytic hierarchy process and comprehensive evaluation in accordance with an embodiment of the present invention. As shown in fig. 1, the method for determining the electricity consumption behavior based on fuzzy analytic hierarchy process and comprehensive evaluation provided by the embodiment of the invention can analyze the electricity consumption behavior of the user by multiple layers and multiple dimensions by establishing a first-level index system and a second-level index system of the electricity consumption behavior of the user, and can furthest reserve all required information under the condition of comprehensively considering multiple factors; meanwhile, the electricity larceny suspicion degree of the user is calculated by adopting a method combining the fuzzy analytic hierarchy process and the fuzzy comprehensive evaluation, so that the unilateral property of the traditional single characteristic weight calculation method is avoided, the accuracy of different electricity utilization characteristic indexes on the description of the electricity utilization behavior of the user can be accurately measured, and the efficiency and the accuracy of detecting the abnormal electricity utilization behavior are improved. The method 100 for determining electricity consumption behavior based on fuzzy analytic hierarchy process and comprehensive evaluation provided by the embodiment of the invention starts from step 101, determines a primary index set and a secondary index set according to the category of a target object in step 101, and respectively constructs a primary index evaluation set matrix and a secondary index evaluation set matrix.
Preferably, the determining the primary index set and the secondary index set according to the category of the target object includes:
when the category of the target object is a single-phase user, the first-level index set includes: at least one of daily electricity, single-phase current and single-phase voltage;
when the first level index set includes a daily electricity amount, the second level index set includes: the cumulative fluctuation rate of the daily electric quantity; when the primary index set includes a single-phase current, the secondary index set includes: a current ramp down rate; when the primary index set includes a single-phase voltage, the secondary index set includes: voltage dip rate;
when the user category of the target object is a three-phase user, the first-level index set includes: at least one of daily active power, three-phase current, three-phase voltage, and power factor; when the primary index set includes daily active power, the secondary index set includes: daily active power fluctuation rate; when the primary index set includes three-phase current, the secondary index set includes: current ramp down rates for phase a, phase B and phase C; when the primary index set includes three-phase voltages, the secondary index set includes: undervoltage percentages of phase a, phase B, and phase C; when the primary index set includes a power factor, the secondary index set includes: power factor dip rate.
In the embodiment of the invention, according to the common electricity stealing mode (undervoltage method, undercurrent method, spread method and phase shift method) of the user, the structure capable of describing the typical characteristics of the electricity utilization behaviors of the single-phase user and the three-phase user is determined as shown in fig. 2, the result of dividing the electricity utilization behaviors into the primary index and the secondary index is shown in table 1, and the calculation method of each secondary index is shown in table 2. When the electricity utilization behavior of the target user is determined, a first-level index set and a second-level index set can be determined according to actual requirements, and indexes in the first-level index set and indexes in the second-level index set are in one-to-one correspondence.
TABLE 1 characterization index selection results
Table 2 two-level index calculation method
In the embodiment of the present invention, a two-level index evaluation set matrix v= [ V ] is set 1 ,v 2 ,…,v m ]And according to the selected secondary index set, the elements in the secondary index evaluation set matrix can be subjected to custom setting. For example, a secondary index evaluation set matrix v= [ V ] is determined 1 ,v 2 ,v 3 ]= [ index exceeds the set threshold by more than 1.5 times, index approaches the set threshold, and index is lower than the set threshold by less than 0.5 times]. Setting a first-level index evaluation set matrix V '= [ V ]' 1 ，v′ 2 ，…，v′ s ]And determining elements in the first-level index evaluation set matrix according to the type of the electricity utilization state. For example, if the type of power consumption state includes: if serious electricity stealing, electricity stealing and normal are carried out, a first-level index evaluation set matrix V' = [ V ] is set 1 ，v 2 ，v 3 ]= [ severe electricity larceny, normal]。
In step 102, a membership degree of the power consumption data corresponding to each secondary index in the secondary index set to the secondary index evaluation set matrix is determined, and a fuzzy evaluation matrix of each secondary index is determined according to the membership degree.
Preferably, the constructing a fuzzy evaluation matrix of each secondary index according to membership degree includes:
wherein R is Index i A fuzzy evaluation matrix for the ith secondary index; m is a secondary index evaluation set matrix V= [ V ] 1 ，v 2 ，…，v m ]The number of elements in the list; the evaluation grade of the ith secondary index is v j Is a fuzzy subset matrix R j ＝{r j1 ，r j2 ，...，r js }，r j1 ，r j2 ，...，r js For an evaluation rating of v j Membership range at that time.
In the embodiment of the present invention, a secondary index set u= [ U ] is set 1 ，u 2 ，…，u n ]Wherein u is 1 ，u 2 ，…，u n Respectively representing each secondary index, and the matrix V= [ V ] of the secondary index evaluation set 1 ，v 2 ，…，v m ]Two-level index weight set matrix a= [ a ] 1 ，a 2 ，…，a m ]，a i To evaluate the weighted value of the element in the set matrix V for the ith secondary index, and
one-factor fuzzy evaluation of the ith secondary index as fuzzy subset matrix R on V j ＝{r j1 ，r j2 ，...，r js ) The number of s is determined according to the type of the power utilization state. Accordingly, the fuzzy evaluation matrix R of the ith secondary index can be constructed Index i The method comprises the following steps:
for example, when s=3, the fuzzy evaluation matrix R of the ith secondary index Index i The method comprises the following steps:
wherein r is j1 ，r j2 ，r j3 Representing the secondary index u i Is rated v j Membership range at time, r j1 R is the upper bound, r j3 Is the lower bound.
In an embodiment of the invention, the membership is determined using expert scoring. Determining a secondary index evaluation set matrix V= [ V ] 1 ,v 2 ,v 3 ]= [ index exceeds the set threshold by more than 1.5 times, index approaches the set threshold, and index is lower than the set threshold by less than 0.5 times]. Evaluating a set matrix V= [ V ] for the secondary indexes according to each secondary index 1 ,v 2 ,v 3 ]Quantifying elements in each secondary index evaluation set matrix, determining the membership degree, and forming a fuzzy evaluation matrix R of each secondary index Index i 。
In step 103, a secondary index weight set matrix is determined by using a fuzzy analytic hierarchy process based on the secondary index evaluation set matrix.
Preferably, the determining the secondary index weight set matrix by using a fuzzy analytic hierarchy process based on the secondary index evaluation set matrix includes:
marking the importance degree between each element in the secondary index evaluation set matrix to obtain a fuzzy judgment matrix;
determining the initial weight of each element according to the fuzzy judgment matrix;
and performing defuzzification processing on the initial weight of each element, performing per unit processing on the weight obtained through the defuzzification processing, and determining a secondary index weight set matrix.
In an embodiment of the present invention, the secondary index weight set matrix a is calculated by a fuzzy analytic hierarchy process based on the acceptable level of the secondary index exceeding a set threshold.
First, a fuzzy judgment matrix is constructed, and the importance degree of the three is judged by the index value by comparing the importance degree of the index exceeding the set threshold value by more than 1.5 times, the index approaching the set threshold value and the index falling within 0.5 times. Wherein the relationship between the scale value and the importance is shown in Table 3.
Table 3 correspondence table of scale values and importance
Which are compared by an expert, and the fuzzy numbers (l) are given respectively by taking 3-bit expert scoring as an example 1 ,m 1 ,u 1 )、(l 2 ,m 2 ,u 2 )、(l 3 ,m 3 ,u 3 ) Integrating 3 ambiguities into one
Repeating the above steps until all the comparison results become a fuzzy number. Respectively to C by 3-bit expert 1 The index exceeds the set threshold by more than 1.5 times and C 2 The index approaches to the set threshold value, C 3 Scoring is performed within 0.5 times of the set threshold, and the results are shown in Table 4.
Table 4 expert scoring table
C 1 And C 2 Can be integrated into one fuzzy value by:
therefore C 1 And C 2 Compared with the prior art, the importance degree is as follows: (0.39,0.67,1.00) similarly calculating the comparative blur values among other factors to obtain a blur matrix as:
Then calculate C 1 : index exceeds the set threshold by more than 1.5 times, C 2 : the index is close to the set threshold value, C 3 : the index is lower than the comprehensive weight of the three materials within 0.5 times of the set threshold. According to
Wherein a is ij For fuzzy matrix FCM 1 The elements in (C) are obtained through calculation 1 : index exceeds the set threshold by more than 1.5 times, C 2 : the index is close to the set threshold value, C 3 : the initial weight within 0.5 times of the index lower than the set threshold is: d (D) c1 ＝(0.1509，0.2897，0.5083)，D c2 ＝(0.1690，0.3310，0.5314)，D c3 ＝(0.1368，0.2731，0.5314)，D c4 ＝(0.0658，0.1062，0.2014)。
Then, the pair C is defined by the triangle ambiguity likelihood function 1 : the index exceeds the set threshold1.5 times or more of C 2 : the index is close to the set threshold value, C 3 : and (5) performing defuzzification processing on the initial weight with the index being within 0.5 times of the set threshold. If the fuzzy number M is known 1 (l 1 ，m 1 ，u 1 ) M and M 2 (l 2 ，m 2 ，u 2 )，M 1 ＞M 2 The likelihood of (2) is defined by a triangular blur function:
the likelihood that one ambiguity is greater than the other K ambiguities is defined as:
P(M≥M 1 ，M 2 ，......M k )＝min P(M≥M 1 )，i＝1，2，...k，
in D c1 For example:
P(D c1 ≥D c3 )＝1，
therefore D c1 The method comprises the following steps of:
d(D c1 )＝minV(D c1 ≥D c2 ，D c3 )＝min(0.8913，1，1)＝0.8913，
similarly, obtain D c2 、D c3 The values after deblurring are respectively: 0.8622 and 0.2452.
Finally, D is c1 、D c2 、D c3 Performing per unit treatment on the defuzzified numerical value to obtain a secondary index weight set matrix A which is as follows:
in step 104, a fuzzy comprehensive evaluation set of each secondary index is determined according to the product of the secondary index weight set matrix and the fuzzy evaluation matrix of each secondary index, and the fuzzy comprehensive evaluation set of each secondary index is utilized to construct a fuzzy evaluation matrix of the primary index.
In the embodiment of the present invention, the fuzzy comprehensive evaluation set B for each secondary index can be calculated according to the fuzzy comprehensive evaluation set calculation formula b=a·r. Taking a single-phase user as an example, if the secondary index includes: the cumulative fluctuation rate, the current sudden drop rate and the undervoltage percentage of the daily electric quantity can determine that the fuzzy comprehensive evaluation sets of the secondary indexes are respectively: the accumulated fluctuation rate of the daily electricity quantity, the current sudden drop rate and the undervoltage percentage of the current B; the fuzzy evaluation matrix R' of the first-level index is constructed as follows:
in step 105, a primary index weight set matrix is determined using fuzzy analytic hierarchy process based on the primary index set.
In step 106, a fuzzy comprehensive evaluation set of the electricity utilization state is determined according to the product of the first-level index weight set matrix and the fuzzy evaluation matrix of the first-level index.
In the embodiment of the present invention, a first-order index set matrix U '= [ U ]' 1 ，u′ 2 ，…，u′ n ]Wherein u' 1 ，u′ 2 ，…，u′ n Respectively representing each level of index, and evaluating a set matrix V '= [ V ]' 1 ，v′ 2 ，…，v′ m ]First-order index weight set matrix a '= [ a ]' 1 ，a′ 2 ，…，a′ n ]In a' i Is the weighted value of the ith primary index, and
according to the calculated fuzzy comprehensive evaluation set B daily electricity quantity accumulated fluctuation rate, B current sudden drop rate, B undervoltage percentage and the like of each secondary index, taking a single-phase user as an example, obtaining a fuzzy evaluation matrix R' of the primary index:
In the embodiment of the invention, the types of the electricity utilization states and the elements in the first-level index evaluation set are in one-to-one correspondence. The kinds of electricity consumption states include: if the serious electricity stealing and electricity stealing are normal, a first-level index evaluation set matrix V' = [ V ] is correspondingly constructed 1 ，v 2 ，v 3 ]= [ severe electricity larceny, normal]. According to the importance degree of the primary index for judging the electricity stealing of the user, a primary index weight set matrix A 'is calculated by a fuzzy analytic hierarchy process, and the primary index weight set matrix A' is used for single-phase users: a '= [ a ]' Cumulative fluctuation rate of daily electricity ，a′ Current ramp down rate ，a′ Percentage of under-voltage Ratio of]. And calculating to obtain a fuzzy comprehensive evaluation set B 'of the suspected electricity larceny of the user according to a fuzzy comprehensive evaluation set calculation formula B' =A '·R'.
In step 107, the occurrence probability of each power utilization state in the primary index evaluation set matrix is determined according to the fuzzy comprehensive evaluation set of power utilization states, and the power utilization behavior of the target object is determined according to the occurrence probability of each power utilization state.
Preferably, the determining the occurrence probability of each power utilization state in the first-level index evaluation set matrix according to the fuzzy comprehensive evaluation set of power utilization states, and determining the power utilization behavior of the target object according to the occurrence probability of each power utilization state includes:
The elements in the fuzzy comprehensive evaluation set of the power utilization state and the power utilization state in the primary index evaluation set are in one-to-one correspondence to determine the occurrence probability of each power utilization state;
comparing the occurrence probability of the preset power utilization state with a preset probability threshold value, and obtaining a comparison result;
if the comparison result indicates that the occurrence probability of the preset power utilization state is smaller than a preset probability threshold, determining that the power utilization behavior of the target object belongs to abnormal power utilization behavior; otherwise, determining that the electricity consumption behavior of the target object belongs to normal electricity consumption behavior.
In the present inventionIn an embodiment, taking a single-phase user as an example, the preset power utilization state is "normal", and the preset probability threshold is 0.5. If the first-level index weight set matrix A ' = [ a ' ] is obtained through calculation ' Cumulative fluctuation rate of daily electricity ，a′ Current ramp down rate ，a′ Percentage of under-voltage ]＝[0.45，0.35，0.20]The fuzzy comprehensive evaluation set matrix R' of the electricity utilization state is as follows:
then B '= [0.452,0.255,0.293] is obtained according to the calculation formula B' =a '·r' of the fuzzy comprehensive evaluation set. Therefore, it can be found that the occurrence probability of the electricity consumption state of "serious electricity theft" is 0.452, the occurrence probability of the electricity consumption state of "electricity theft" is 0.255, and the probability of the electricity consumption state of "normal" is 0.293 in the detection range. Because 0.293 is less than 0.5, the electricity consumption behavior of the user is determined to belong to abnormal electricity consumption behavior at the moment, and the electricity stealing suspicion exists.
FIG. 3 is a flow chart of determining the power usage behavior of a single-phase user based on fuzzy analytic hierarchy process and comprehensive evaluation in accordance with an embodiment of the present invention. As shown in fig. 3, the step of determining the electricity usage behavior of the single-phase user based on the fuzzy analytic hierarchy process and the comprehensive evaluation includes:
s1, constructing a primary index system and a secondary index system, and determining a primary index set and a secondary index set;
s2, constructing a secondary index evaluation set matrix V;
s3, quantifying each evaluation index in the V according to the membership degree of the secondary index evaluation set matrix V according to the secondary index, determining the membership degree, and determining a fuzzy evaluation matrix R of each secondary index;
s4, calculating a secondary index weight set matrix A by a fuzzy analytic hierarchy process according to the acceptable degree that the secondary index exceeds a preset threshold;
s5, calculating a fuzzy comprehensive evaluation set B of each secondary index according to a fuzzy comprehensive evaluation set calculation formula B=A.R, and determining a fuzzy evaluation matrix R' of the primary index by using the fuzzy comprehensive evaluation set B of each secondary index;
s6, constructing a first-level index evaluation set matrix V ', and calculating a first-level index weight matrix A';
s8, calculating a fuzzy comprehensive evaluation set of each power utilization state according to a fuzzy comprehensive evaluation set calculation formula B=A.R;
And S9, judging whether the electricity utilization behavior of the user exceeds a reasonable range according to a preset probability threshold value, and determining the electricity stealing user.
The following specifically exemplifies embodiments of the present invention
Taking the electricity consumption condition of a single-phase user on 7 days a week as an example, calculating the cumulative fluctuation rate, the current sudden drop rate and the under-voltage percentage mean value of the daily electricity consumption in 7 days, further obtaining the occurrence probability of the electricity consumption state of the user through a fuzzy analytic hierarchy process and a fuzzy comprehensive evaluation calculation, and finally judging whether the electricity stealing suspicion degree of the user exceeds a reasonable range according to a threshold value. The index data within 7 days are shown in table 5.
Table 5 single-phase user 7-day index data table
According to the 7-day average pair evaluation set matrix V= [ V ] of the secondary index daily electricity consumption cumulative fluctuation rate, the current sudden drop rate and the undervoltage percentage 1 ,v 2 ,v 3 ]= [ index exceeds the set threshold by more than 1.5 times, index approaches the set threshold, and index is lower than the set threshold by less than 0.5 times]Quantifying each rating index, determining the membership degree, and forming a fuzzy evaluation matrix R:
according to the acceptable degree that the secondary index exceeds the set threshold, calculating a secondary index weight set matrix A by a fuzzy analytic hierarchy process, firstly, constructing a fuzzy judgment matrix, and respectively aiming at C by 3-bit experts 1 The index exceeds the set threshold by more than 1.5 times and C 2 The index approaches to the set threshold value, C 3 Scoring is performed within 0.5 times of the set threshold, and the results are shown in Table 6.
Table 6 expert scoring table
The two-level index weight set matrix A= [0.418,0.309,0.272 ] is obtained through calculating a fuzzy matrix, defuzzifying and per unit process]From B Cumulative fluctuation rate of daily electricity ＝A*R Cumulative fluctuation rate of daily electricity 、B Current ramp down rate ＝A*R Current ramp down rate 、B Percentage of under-voltage ＝A*R Percentage of under-voltage The fuzzy comprehensive evaluation set of each secondary index is obtained by calculation:
B cumulative fluctuation rate of daily electricity ＝[0.515,0.401,0.285]，
B Current ramp down rate ＝[0.416，0.315，0.227]，
B Percentage of under-voltage ＝[0.405，0.243，0.169]，
Therefore, the fuzzy evaluation matrix of the first-level index is as follows:
determining a first-level index weight set matrix A 'by a fuzzy analytic hierarchy process, wherein the first-level index weight set matrix A' comprises the following components for single-phase users: a '= [ a ]' Cumulative fluctuation rate of daily electricity ，a′ Current ramp down rate ，a′ Percentage of under-voltage Ratio of]＝[0.512，0.387，0.101]The user B is calculated by a fuzzy comprehensive evaluation set calculation formula B=A '. R'＝[0.466，0.352，0.251]. It is explained that the probability of occurrence of the "serious power theft" event by the user is 0.466, the probability of occurrence of the "power theft" event is 0.352, and the probability of occurrence of the "normal" event is 0.251 in the detection range. If the preset probability threshold of "normal" is set to 0.5, the user is informed that the electricity theft suspicion exists because 0.251 is less than 0.5, and the user should be taken as a key inspection object.
Conventionally, electricity theft detection mainly depends on dispatching technicians to check electricity by a power supply enterprise, so that the electricity consumption is low, the efficiency is high, and the consumption of manpower and material resources is huge. The intelligent ammeter can accurately record and report the electricity consumption data of the user at high frequency, and lays a key foundation for accurately identifying electricity stealing behaviors. Although the user can still adopt various technical means to manipulate or tamper the reported electric quantity data, the electricity stealing behavior can be identified according to the electricity consumption data of the metering master station. According to the invention, through analyzing the electricity stealing means commonly adopted by the user in the electricity stealing process, the electricity stealing characteristics of different electricity stealing means are different, and the related characteristic indexes for describing the electricity stealing means can be further selected according to the different electricity stealing characteristics, so that the user can be subjected to multi-level and multi-angle detection of electricity stealing behaviors.
The electricity larceny suspicion prediction method based on analytic hierarchy process and fuzzy comprehensive evaluation selects characteristic indexes for single-phase users and three-phase users respectively, and the constructed fuzzy comprehensive evaluation set fuses a plurality of characteristic index data of the users and is used for analyzing the electricity larceny risk of the users, and the electricity larceny suspicion prediction method is favorable for improving the electricity larceny recognition accuracy by setting a proper threshold value and has the following outstanding advantages: (1) Constructing a first-level and second-level characteristic index framework of a user from four dimensions of electricity consumption, current, voltage and power factor, and detecting electricity stealing of the user through multiple dimensions and multiple layers; (2) The method combining the analytic hierarchy process and the fuzzy comprehensive evaluation is adopted to quantify the electricity stealing risk of the user, so that the unilateral performance of the traditional single characteristic weight calculation method is avoided, and the accuracy of the description of different electricity utilization characteristic indexes on the electricity utilization behavior of the user can be accurately measured.
FIG. 4 is a schematic diagram of a system 400 for determining power usage behavior based on fuzzy analytic hierarchy process and comprehensive evaluation in accordance with an embodiment of the present invention. As shown in fig. 4, a system 400 for determining electricity consumption behavior based on fuzzy analytic hierarchy process and comprehensive evaluation according to an embodiment of the present invention includes: an index set and index evaluation set matrix determination unit 401, a fuzzy evaluation matrix determination unit 402 of secondary indexes, a secondary index weight set matrix determination unit 403, a fuzzy evaluation matrix determination unit 404 of primary indexes, a primary index weight set matrix determination unit 405, a fuzzy comprehensive evaluation set determination unit 406 of electricity consumption states, and an electricity consumption behavior determination unit 407.
Preferably, the index set and index evaluation set matrix determining unit 401 is configured to determine a primary index set and a secondary index set according to the category of the target object, and construct a primary index evaluation set matrix and a secondary index evaluation set matrix respectively.
Preferably, the index set and index evaluation set matrix determining unit 401 determines a primary index set and a secondary index set according to a category of the target object, including:
when the category of the target object is a single-phase user, the first-level index set includes: at least one of daily electricity, single-phase current and single-phase voltage;
When the first level index set includes a daily electricity amount, the second level index set includes: the cumulative fluctuation rate of the daily electric quantity; when the primary index set includes a single-phase current, the secondary index set includes: a current ramp down rate; when the primary index set includes a single-phase voltage, the secondary index set includes: voltage dip rate;
when the user category of the target object is a three-phase user, the first-level index set includes: at least one of daily active power, three-phase current, three-phase voltage, and power factor; when the primary index set includes daily active power, the secondary index set includes: daily active power fluctuation rate; when the primary index set includes three-phase current, the secondary index set includes: current ramp down rates for phase a, phase B and phase C; when the primary index set includes three-phase voltages, the secondary index set includes: undervoltage percentages of phase a, phase B, and phase C; when the primary index set includes a power factor, the secondary index set includes: power factor dip rate.
Preferably, the fuzzy evaluation matrix determining unit 402 of the secondary index is configured to determine a membership degree of the power consumption data corresponding to each secondary index in the secondary index set to the secondary index evaluation set matrix, and determine the fuzzy evaluation matrix of each secondary index according to the membership degree.
Preferably, the fuzzy evaluation matrix determining unit 402 for the secondary index constructs a fuzzy evaluation matrix for each secondary index according to membership degrees, including:
wherein R is Index i A fuzzy evaluation matrix for the ith secondary index; m is a secondary index evaluation set matrix V= [ V ] 1 ，v 2 ，…，v m ]The number of elements in the list; the evaluation grade of the ith secondary index is v j Is a fuzzy subset matrix R j ＝{r j1 ，r j2 ，...，r js }，r j1 ，r j2 ，...，r js For an evaluation rating of v j Membership range at that time.
Preferably, the secondary index weight set matrix determining unit 403 is configured to determine a secondary index weight set matrix by using a fuzzy analytic hierarchy process based on the secondary index evaluation set matrix.
Preferably, the secondary index weight set matrix determining unit 403 determines a secondary index weight set matrix by using a fuzzy analytic hierarchy process based on the secondary index evaluation set matrix, including:
marking the importance degree between each element in the secondary index evaluation set matrix to obtain a fuzzy judgment matrix;
determining the initial weight of each element according to the fuzzy judgment matrix;
and performing defuzzification processing on the initial weight of each element, performing per unit processing on the weight obtained through the defuzzification processing, and determining a secondary index weight set matrix.
Preferably, the primary index fuzzy evaluation matrix determining unit 404 is configured to determine a fuzzy comprehensive evaluation set of each secondary index according to a product of the secondary index weight set matrix and the fuzzy evaluation matrix of each secondary index, and construct the fuzzy evaluation matrix of the primary index by using the fuzzy comprehensive evaluation set of each secondary index.
Preferably, the primary index weight set matrix determining unit 405 is configured to determine a primary index weight set matrix by using a fuzzy analytic hierarchy process based on the primary index set.
Preferably, the fuzzy comprehensive evaluation set determining unit 406 of the electricity utilization state is configured to determine a fuzzy comprehensive evaluation set of the electricity utilization state according to a product of the first-level index weight set matrix and the first-level index fuzzy evaluation matrix.
Preferably, the electricity consumption behavior determining unit 407 is configured to determine, according to the fuzzy comprehensive evaluation set of electricity consumption states, occurrence probability of each electricity consumption state in the first-level index evaluation set matrix, and determine, according to the occurrence probability of each electricity consumption state, electricity consumption behavior of the target object.
Preferably, the electricity consumption behavior determining unit 407 determines, according to the fuzzy comprehensive evaluation set of electricity consumption states, occurrence probability of each electricity consumption state in the first-level index evaluation set matrix, and determines, according to the occurrence probability of each electricity consumption state, electricity consumption behavior of the target object, including:
The elements in the fuzzy comprehensive evaluation set of the power utilization state and the power utilization state in the primary index evaluation set are in one-to-one correspondence to determine the occurrence probability of each power utilization state;
comparing the occurrence probability of the preset power utilization state with a preset probability threshold value, and obtaining a comparison result;
if the comparison result indicates that the occurrence probability of the preset power utilization state is smaller than a preset probability threshold, determining that the power utilization behavior of the target object belongs to abnormal power utilization behavior; otherwise, determining that the electricity consumption behavior of the target object belongs to normal electricity consumption behavior.
The system 400 for determining electricity usage behavior based on fuzzy analytic hierarchy process and comprehensive evaluation of the embodiment of the present invention corresponds to the method 100 for determining electricity usage behavior based on fuzzy analytic hierarchy process and comprehensive evaluation of another embodiment of the present invention, and will not be described herein.
The invention has been described with reference to a few embodiments. However, as is well known to those skilled in the art, other embodiments than the above disclosed invention are equally possible within the scope of the invention, as defined by the appended patent claims.
Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the technical field, unless explicitly defined otherwise therein. All references to "a/an/the [ means, component, etc. ]" are to be interpreted openly as referring to at least one instance of said means, component, etc., unless explicitly stated otherwise. The steps of any method disclosed herein do not have to be performed in the exact order disclosed, unless explicitly stated.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (8)
1. A method for determining electricity consumption behavior based on fuzzy analytic hierarchy process and comprehensive evaluation, the method comprising:
determining a first-level index set and a second-level index set according to the category of the target object, and respectively constructing a first-level index evaluation set matrix and a second-level index evaluation set matrix;
determining the membership degree of the power consumption data corresponding to each secondary index in the secondary index set to the secondary index evaluation set matrix, and determining a fuzzy evaluation matrix of each secondary index according to the membership degree;
determining a secondary index weight set matrix by using a fuzzy analytic hierarchy process based on the secondary index evaluation set matrix;
determining a fuzzy comprehensive evaluation set of each secondary index according to the product of the secondary index weight set matrix and the fuzzy evaluation matrix of each secondary index, and constructing a fuzzy evaluation matrix of the primary index by using the fuzzy comprehensive evaluation set of each secondary index;
determining a first-level index weight set matrix by using a fuzzy analytic hierarchy process based on the first-level index set;
determining a fuzzy comprehensive evaluation set of the power utilization state according to the product of the first-level index weight set matrix and the fuzzy evaluation matrix of the first-level index;
determining the occurrence probability of each power utilization state in the first-level index evaluation set matrix according to the fuzzy comprehensive evaluation set of the power utilization state, and determining the power utilization behavior of the target object according to the occurrence probability of each power utilization state;
The determining a primary index set and a secondary index set according to the category of the target object comprises the following steps:
when the category of the target object is a single-phase user, the first-level index set includes: at least one of daily electricity, single-phase current and single-phase voltage;
when the first level index set includes a daily electricity amount, the second level index set includes: the cumulative fluctuation rate of the daily electric quantity; when the primary index set includes a single-phase current, the secondary index set includes: a current ramp down rate; when the primary index set includes a single-phase voltage, the secondary index set includes: voltage dip rate;
when the user category of the target object is a three-phase user, the first-level index set includes: at least one of daily active power, three-phase current, three-phase voltage, and power factor; when the primary index set includes daily active power, the secondary index set includes: daily active power fluctuation rate; when the primary index set includes three-phase current, the secondary index set includes: current ramp down rates for phase a, phase B and phase C; when the primary index set includes three-phase voltages, the secondary index set includes: undervoltage percentages of phase a, phase B, and phase C; when the primary index set includes a power factor, the secondary index set includes: power factor dip rate.
2. The method of claim 1, wherein constructing a fuzzy evaluation matrix for each secondary index based on the membership degree comprises:
wherein R is Index i A fuzzy evaluation matrix for the ith secondary index; m is a secondary index evaluation set matrix V= [ V ] 1 ,v 2 ,…,v m ]The number of elements in the list; the evaluation grade of the ith secondary index is v j Is a fuzzy subset matrix R j ＝{r j1 ,r j2 ,…,r js }，r j1 ,r j2 ,…,r js For an evaluation rating of v j Membership range at that time.
3. The method of claim 1, wherein determining a secondary index weight set matrix a using fuzzy analytic hierarchy process based on the secondary index evaluation set matrix comprises:
marking the importance degree between each element in the secondary index evaluation set matrix to obtain a fuzzy judgment matrix;
determining the initial weight of each element according to the fuzzy judgment matrix;
and performing defuzzification processing on the initial weight of each element, performing per unit processing on the weight obtained through the defuzzification processing, and determining a secondary index weight set matrix.
4. The method of claim 1, wherein determining the occurrence probability of each power utilization state in the primary index evaluation set matrix according to the fuzzy comprehensive evaluation set of power utilization states, and determining the power utilization behavior of the target object according to the occurrence probability of each power utilization state, comprises:
The elements in the fuzzy comprehensive evaluation set of the power utilization state and the power utilization state in the primary index evaluation set are in one-to-one correspondence to determine the occurrence probability of each power utilization state;
comparing the occurrence probability of the preset power utilization state with a preset probability threshold value, and obtaining a comparison result;
if the comparison result indicates that the occurrence probability of the preset power utilization state is smaller than a preset probability threshold, determining that the power utilization behavior of the target object belongs to abnormal power utilization behavior; otherwise, determining that the electricity consumption behavior of the target object belongs to normal electricity consumption behavior.
5. A system for determining electricity usage behavior based on fuzzy analytic hierarchy process and comprehensive evaluation, the system comprising:
the index set and index evaluation set matrix determining unit is used for determining a first-level index set and a second-level index set according to the category of the target object and respectively constructing a first-level index evaluation set matrix and a second-level index evaluation set matrix;
the fuzzy evaluation matrix determining unit of the secondary indexes is used for determining the membership degree of the power utilization data corresponding to each secondary index in the secondary index set to the secondary index evaluation set matrix and determining the fuzzy evaluation matrix of each secondary index according to the membership degree;
The secondary index weight set matrix determining unit is used for determining a secondary index weight set matrix by using a fuzzy analytic hierarchy process based on the secondary index evaluation set matrix;
the fuzzy evaluation matrix determining unit of the first-level index is used for determining a fuzzy comprehensive evaluation set of each second-level index according to the product of the weight set matrix of the second-level index and the fuzzy evaluation matrix of each second-level index, and constructing a fuzzy evaluation matrix of the first-level index by utilizing the fuzzy comprehensive evaluation set of each second-level index;
the primary index weight set matrix determining unit is used for determining a primary index weight set matrix by using a fuzzy analytic hierarchy process based on the primary index set;
the fuzzy comprehensive evaluation set determining unit is used for determining a fuzzy comprehensive evaluation set of the electricity utilization state according to the product of the first-level index weight set matrix and the first-level index fuzzy evaluation matrix;
the power consumption behavior determining unit is used for determining the occurrence probability of each power consumption state in the primary index evaluation set matrix according to the fuzzy comprehensive evaluation set of the power consumption state and determining the power consumption behavior of the target object according to the occurrence probability of each power consumption state;
The index set and index evaluation set matrix determining unit determines a first-level index set and a second-level index set according to the category of the target object, and includes:
when the category of the target object is a single-phase user, the first-level index set includes: at least one of daily electricity, single-phase current and single-phase voltage;
when the first level index set includes a daily electricity amount, the second level index set includes: the cumulative fluctuation rate of the daily electric quantity; when the primary index set includes a single-phase current, the secondary index set includes: a current ramp down rate; when the primary index set includes a single-phase voltage, the secondary index set includes: voltage dip rate;
when the user category of the target object is a three-phase user, the first-level index set includes: at least one of daily active power, three-phase current, three-phase voltage, and power factor; when the primary index set includes daily active power, the secondary index set includes: daily active power fluctuation rate; when the primary index set includes three-phase current, the secondary index set includes: current ramp down rates for phase a, phase B and phase C; when the primary index set includes three-phase voltages, the secondary index set includes: undervoltage percentages of phase a, phase B, and phase C; when the primary index set includes a power factor, the secondary index set includes: power factor dip rate.
6. The system according to claim 5, wherein the fuzzy evaluation matrix determining unit of the secondary index constructs a fuzzy evaluation matrix of each secondary index according to membership degree, comprising:
wherein R is Index i A fuzzy evaluation matrix for the ith secondary index; m is a secondary index evaluation set matrix V= [ V ] 1 ,v 2 ,…,v m ]The number of elements in the list; the evaluation grade of the ith secondary index is v j Is a fuzzy subset matrix R j ＝{r j1 ,r j2 ,…,r js }，r j1 ,r j2 ,…,r js For an evaluation rating of v j Membership range at that time.
7. The system according to claim 5, wherein the secondary index weight set matrix determining unit determines a secondary index weight set matrix using a fuzzy analytic hierarchy process based on the secondary index evaluation set matrix, comprising:
marking the importance degree between each element in the secondary index evaluation set matrix to obtain a fuzzy judgment matrix;
determining the initial weight of each element according to the fuzzy judgment matrix;
and performing defuzzification processing on the initial weight of each element, performing per unit processing on the weight obtained through the defuzzification processing, and determining a secondary index weight set matrix.
8. The system according to claim 5, wherein the electricity usage behavior determination unit determines occurrence probability of each electricity usage state in the primary index evaluation set matrix from the fuzzy comprehensive evaluation set of electricity usage states, and determines the electricity usage behavior of the target object from the occurrence probability of each electricity usage state, including:
The elements in the fuzzy comprehensive evaluation set of the power utilization state and the power utilization state in the primary index evaluation set are in one-to-one correspondence to determine the occurrence probability of each power utilization state;
comparing the occurrence probability of the preset power utilization state with a preset probability threshold value, and obtaining a comparison result;
if the comparison result indicates that the occurrence probability of the preset power utilization state is smaller than a preset probability threshold, determining that the power utilization behavior of the target object belongs to abnormal power utilization behavior; otherwise, determining that the electricity consumption behavior of the target object belongs to normal electricity consumption behavior.
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