CN112016819A - Low-voltage transformer area electric energy quality comprehensive evaluation method - Google Patents

Low-voltage transformer area electric energy quality comprehensive evaluation method Download PDF

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CN112016819A
CN112016819A CN202010825488.7A CN202010825488A CN112016819A CN 112016819 A CN112016819 A CN 112016819A CN 202010825488 A CN202010825488 A CN 202010825488A CN 112016819 A CN112016819 A CN 112016819A
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杨爱纲
浦河海
杨国才
马海阿古
郑光权
胡敏
高彦林
沙石印
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Lijiang Power Supply Bureau of Yunnan Power Grid Co Ltd)
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Abstract

The invention provides a comprehensive evaluation method for the electric energy quality of a low-voltage transformer area. Establishing a primary power quality index set and a secondary power quality index set; further constructing a judgment matrix, determining the subjective weight of the selected index by applying a scale expansion method, determining the objective weight of the selected index by a variable weight theory, and further determining the comprehensive weight of the selected index; after normalization processing is carried out on the monitoring values of the selected indexes, positive and negative ideal solution sequences of the selected indexes are calculated, the resolution coefficient and the positive and negative correlation coefficients of a gray correlation method are further calculated, index aggregation is carried out by using a logarithm method based on a barrel theory, the positive and negative gray correlation degrees with weights are further calculated, the positive and negative Euclidean distances are calculated, the positive and negative comprehensive correlation degrees are further calculated, and a comprehensive evaluation index is constructed for evaluating the overall electric energy quality of the monitoring point. The method improves the capability of distinguishing the severe indexes and realizes the evaluation result which is more in line with the power quality evaluation target.

Description

Low-voltage transformer area electric energy quality comprehensive evaluation method
Technical Field
The invention belongs to the field of power quality monitoring and management, and particularly relates to a comprehensive evaluation method for power quality of a low-voltage transformer area.
Background
The tail end of a power distribution network represented by rural remote areas is weak in construction, light in load and long in power transmission distance, and exceeds the power supply radius of a power distribution network power supply circuit, so that the power transmission loss on the circuit is large, the tail end power supply capacity is insufficient, and various power quality problems represented by low voltage are caused. Traditionally, the voltage is regulated by adopting measures such as a shunt capacitor, a tap of a regulating transformer and the like to perform reactive compensation; meanwhile, in order to fully exert the reactive compensation potential of distributed power generation represented by photovoltaic, measures for low-voltage treatment by photovoltaic power generation are receiving wide attention. In order to better measure the performance of the photovoltaic and other distributed power supplies connected to the power distribution network, an effective method needs to be adopted to comprehensively evaluate the electric energy management of the low-voltage transformer area.
With the development of economic and social production in China, the requirements of users on the demand and the quality of electric energy are higher and higher, and how to utilize the national standard of the existing single electric energy quality index in China to carry out reasonable comprehensive evaluation on the quality of the electric energy is the basis of pricing the electric energy according to the quality in electric power marketization. Two key links in the comprehensive evaluation of the power quality are index empowerment and comprehensive evaluation. Whether the index weight value is reasonable and accurate directly influences the reliability of the evaluation result, but the traditional subjective weighting method and the traditional objective weighting method cannot effectively reflect the nonlinear and emergent characteristics of the index set, and misjudgment is easy to occur when the index quantity with smaller weight is changed violently. In order to avoid this problem, researchers have conducted studies on weight-varying theory in various fields. A fuzzy model of the power quality index is defined in literature, and a variable weight scheme is designed according to the reciprocal of the fuzzy quality of the index; there are also documents that propose to construct the comprehensive weight first and then to change the weight by using the equalization function.
The power quality evaluation is a multi-index joint decision process, is influenced by the randomness of monitoring data and shows the characteristic of gray, and a gray correlation method is a common evaluation method for the problems. The traditional grey correlation method realizes comprehensive sequencing by constructing positive and negative ideal solutions and describing the similarity degree between index sets by a grey correlation function. The ideal solution method utilizes the Euclidean distance function to describe the proximity degree between the two, and the introduction of the ideal solution method can realize effective evaluation on index sets which cannot be distinguished by the gray correlation method. However, in the existing research of improving the gray correlation method, a linear index polymerization mode is often adopted to calculate the gray correlation degree, so that the effects of high-quality indexes and high-weight indexes are highlighted, and the correctness of an evaluation result is influenced.
Disclosure of Invention
The invention aims to construct variable objective weights by combining a variable weight theory and an information entropy weighting method, and avoid the limitation of determining the weights by a constant weight method; an ideal solution method is introduced to make up the defects of the traditional grey correlation method, and a grey correlation degree model between index sets to be evaluated is established; based on the wooden barrel theory, a logarithmic aggregation coefficient is introduced to adjust a grey correlation calculation formula so as to reflect punishment on 'bad' indexes, and the comprehensive correlation is optimally calculated to carry out comprehensive sequencing.
In order to achieve the purpose, the technical scheme adopted by the invention is a comprehensive evaluation method for the electric energy quality of a low-voltage transformer area, which comprises the following concrete implementation steps:
step 1: establishing a primary power quality index set and a secondary power quality index set;
step 2: establishing a judgment matrix, determining and selecting subjective weights of a first-stage power quality index and a second-stage power quality index by applying a scale expansion method in combination with the judgment matrix, determining and selecting objective weights of the first-stage power quality index and the second-stage power quality index by a variable weight theory, and further determining comprehensive weights of the first-stage power quality index and the second-stage power quality index;
and step 3: calculating a positive ideal solution and a negative ideal solution of a secondary power quality index of a primary power quality index through a normalized monitoring value of the secondary power quality index under the primary power quality index at a monitoring point, constructing a positive ideal solution sequence and a negative ideal solution sequence, calculating the maximum value of an expression of all indexes in a secondary power quality index range under the primary power quality index, calculating a resolution coefficient of a gray correlation method by combining the positive ideal solution sequence, calculating a positive correlation coefficient and a negative correlation coefficient, performing index polymerization by a logarithm method based on a barrel theory to obtain the degree of polymerization of the correlation degree, to calculate the weighted positive gray correlation degree and the weighted negative gray correlation degree, calculate the positive Euclidean distance and the negative Euclidean distance, further calculate the positive comprehensive correlation degree and the negative comprehensive correlation degree, and establishing a comprehensive evaluation index through the positive comprehensive relevance degree and the negative comprehensive relevance degree for evaluating the overall power quality of the monitoring point.
Preferably, the step 1 of establishing the first-stage power quality index set and the second-stage power quality index set includes:
Figure BDA0002636041170000021
i1∈[1,m]，j1∈[1,n]，k1∈[1,si]
wherein m is the number of monitoring points, n is the number of first-level power quality indexes, and siThe number of the second-level power quality indexes under the first-level power quality index i,
Figure BDA0002636041170000022
represents a monitoring point i1First-level power quality index j1Second grade electric energy quality index k1A monitored value of (d);
the first-stage power quality indexes in the step 1 include but are not limited to voltage deviation, voltage flicker, voltage fluctuation, harmonic distortion, three-phase unbalance, frequency deviation and power supply reliability;
the secondary power quality indexes in the step 1 include, but are not limited to, average voltage amplitude deviation, voltage deviation duration, flicker level, flicker duration, average voltage fluctuation amplitude, fluctuation duration, total harmonic content, harmonic duration, unbalance degree, unbalance duration, average frequency deviation, frequency deviation duration, voltage sag, and interruption duration.
Preferably, the step 2 of constructing the judgment matrix is:
Figure BDA0002636041170000023
wherein ,W*Representing the judgment matrix and having complete consistency, is a positive and reciprocal matrix of n x n, n is the number of first-level power quality indexes, relative importance ordering is carried out on the first-level power quality indexes, tkExpressing the importance of the kth first-level power quality index relative to the kth +1 first-level power quality index, wherein k belongs to [1, n-1 ]]The method specifically comprises the following steps:
if XkRelative to Xk-1Equally important, then tkGet a1；
If XkRelative to Xk-1Of slight importance, then tkGet a2；
If XkRelative to Xk-1Of obvious importance, then tkGet a3；
If XkRelative to Xk-1Of great importance, then tkGet a4；
If XkRelative to Xk-1Of extreme importance, then tkGet a5。
wherein ,a1＜a2＜a3＜a4＜a5，
Figure BDA0002636041170000024
To judge the ith in the matrix0Line j (th)0Elements of a column, i.e. representing the ith0The first-level power quality index is relative to the jth0Subjective weight of the first-level power quality index, reflecting the ith0The first-level power quality index is relative to the jth0Importance of individual first-order power quality index, i0∈[1,n]，j0∈[1,n]，i0≠j0；
And 2, determining and selecting the subjective weights of the first-stage power quality index and the second-stage power quality index by combining the judgment matrix through a scale expansion method as follows:
for the set of power quality indicators
Figure BDA0002636041170000025
Defining m as the number of monitoring points, n as the number of first-level power quality indicators, siIs the number of the secondary power quality indexes under the primary power quality index i,
Figure BDA0002636041170000026
the method can be divided into a deviation index, a duration index and a reliability index according to the index types.
First-level power quality index i0Is recorded as a subjective weight
Figure BDA0002636041170000031
The specific calculation is as follows:
Figure BDA0002636041170000032
first-level power quality index i0The relative weight of the next two-stage power quality index is recorded as
Figure BDA0002636041170000033
Is a row vector and the number of vector elements is
Figure BDA0002636041170000034
To pair
Figure BDA0002636041170000035
Individual second grade electric energy quality index
Figure BDA0002636041170000036
Relative importance ranking is performed. Wherein the content of the first and second substances,
Figure BDA0002636041170000037
denotes the kth0Relative kth index of secondary electric energy quality index0And +1 secondary power quality indicators. k is a radical of0The value taking method is the same as the k value taking method.
First-level power quality index i0The subjective weight of the next two-stage power quality index is recorded as
Figure BDA0002636041170000038
The specific calculation is as follows:
Figure BDA0002636041170000039
wherein, the first-level power quality index i0Lower k0The subjective weight specific value of each secondary power quality index is
Figure BDA00026360411700000310
Step 2, determining and selecting objective weights of the first-stage power quality index and the second-stage power quality index according to a variable weight theory, wherein the objective weights are as follows:
monitoring n secondary electric energy quality indexes of the electric energy quality of m monitoring points in the power distribution network by using the comprehensive electric energy quality on-line monitoring device to obtain an original electric energy quality evaluation index monitoring data set
Figure BDA00026360411700000311
In an actual process, generally, measurement units of different power quality indexes are different, and in order to enable each index to have equal expressive force in a comprehensive evaluation process, power quality comprehensive evaluation index data needs to be normalized to obtain power quality comprehensive evaluation index data
Figure BDA00026360411700000312
The normalization process is performed by limiting the index fluctuation range to the interval [0,1 ]]And index numberThe better the data is, the larger the normalized index data is;
if the index data is larger and better, the normalization method is as follows:
Figure BDA00026360411700000313
if the smaller the index data is, the better the low-priority index is, the normalization method is as follows:
Figure BDA00026360411700000314
i1∈[1,m]，j1∈[1,n]，
Figure BDA00026360411700000315
wherein m is the number of monitoring points, n is the number of first-level power quality indexes,
Figure BDA00026360411700000316
is a first-level power quality index j1The number of the next two-stage power quality indexes,
Figure BDA00026360411700000317
is a first-level power quality index j1Lower k1The maximum value of m monitoring data of each secondary power quality index,
Figure BDA00026360411700000318
is a first-level power quality index j1Lower k1And the minimum value of the m monitoring data of each secondary power quality index. For the set of power quality indicators
Figure BDA00026360411700000319
Defining m as the number of monitoring points, n as the number of first-level power quality indexes,
Figure BDA00026360411700000320
is aClass power quality index j1The number of the next two-stage power quality indexes is defined as r, the power quality grade number is defined as r, and the membership degree set of the power quality index set is defined as
Figure BDA00026360411700000321
wherein ,i1∈[1,m]，j1∈[1,n]，k1∈[1,sj]，l1∈[1,r]；
Figure BDA00026360411700000322
Represents a monitoring point i1First-level power quality index j1Second grade electric energy quality index k1For power quality class l1A membership value of.
Figure BDA00026360411700000323
According to the index types, the method can be divided into a deviation index, a duration index and a reliability index;
the method for calculating the membership value of the deviation index comprises the following steps:
Figure BDA0002636041170000041
i1∈[1,m]，j1∈[1,n]，
Figure BDA0002636041170000042
wherein m is the number of monitoring points, n is the number of first-level power quality indexes,
Figure BDA0002636041170000043
is a first-level power quality index j1The number of the next two-stage power quality indexes,
Figure BDA0002636041170000044
is a first-level power quality index j1Second grade electric energy quality index k1The upper limit of the allowable operation value specified by the national standard,
Figure BDA0002636041170000045
is a first-level power quality index j1Second grade electric energy quality index k1The lower limit of the allowable operation value specified by the national standard.
The method for calculating the membership value of the duration class index comprises the following steps:
Figure BDA0002636041170000046
i1∈[1,m]，j1∈[1,n]，
Figure BDA0002636041170000047
wherein m is the number of monitoring points, n is the number of first-level power quality indexes,
Figure BDA0002636041170000048
is a first-level power quality index j1The number of the next two-stage power quality indexes,
Figure BDA0002636041170000049
is a first-level power quality index j1Second grade electric energy quality index k1Allowable duration k of the deviation value specified by the national standard0For a fixed coefficient, it was taken to be 0.13.
The membership calculation method of the reliability index comprises the following steps:
Figure BDA00026360411700000410
i1∈[1,m]，j1∈[1,n]，
Figure BDA00026360411700000411
wherein ,
Figure BDA00026360411700000412
as electric energyAnd normalizing the quality comprehensive evaluation index data to obtain a value.
Deviation class indicators include, but are not limited to, voltage magnitude average deviation, flicker level, voltage average fluctuation amplitude, total harmonic content, three-phase imbalance, frequency average deviation, and voltage sag.
Duration class indicators include, but are not limited to, voltage deviation duration, flicker duration, ripple duration, harmonic duration, imbalance duration, frequency deviation duration, and discontinuity duration.
The reliability index comprises power supply reliability;
defining a monitoring point i1The fuzzy relation between the electric energy index membership degree and the electric energy quality evaluation set is
Figure BDA00026360411700000413
The fuzzy relation calculation method comprises the following steps:
Figure BDA00026360411700000414
i1∈[1,m]，j1∈[1,n]，
Figure BDA00026360411700000415
wherein m is the number of monitoring points, n is the number of first-level power quality indexes,
Figure BDA00026360411700000416
is a first-level power quality index j1The number of the next-level power quality indexes;
Figure BDA00026360411700000417
represents a monitoring point i1First-level power quality index j1Second grade electric energy quality index k1For power quality class l1The fuzzy mapping relationship of (1);
the information entropy value calculation method comprises the following steps:
Figure BDA0002636041170000051
Figure BDA0002636041170000052
in the formula ,
Figure BDA0002636041170000053
first-level power quality index j1Lower secondary electric energy quality index k1The value of the entropy of the information of (c),
Figure BDA0002636041170000054
is a monitoring point i1First-level power quality index j1Second grade electric energy quality index k1Entropy of constant weight information;
introducing a variable weight theory to process the information entropy, and realizing information entropy variable weight to highlight the influence of severe indexes on a comprehensive judgment result;
using the qualified quality of electric energy as standard, monitoring point i1The membership degree of the integral power quality at the qualified level J and above is recorded as
Figure BDA0002636041170000055
Objective weight of information entropy change
Figure BDA0002636041170000056
The calculation method comprises the following steps:
Figure BDA0002636041170000057
Figure BDA0002636041170000058
Figure BDA0002636041170000059
i1∈[1,m]，j1∈[1,n]，
Figure BDA00026360411700000510
in the formula ,
Figure BDA00026360411700000511
is a monitoring point i1First-level power quality index j1Lower secondary electric energy quality index k1For power quality class l1Degree of membership.
Figure BDA00026360411700000512
Is a monitoring point i1N primary power quality indexes are processed, and each primary power quality index j1Is as follows
Figure BDA00026360411700000513
A second grade power quality index of qualified power quality grade l1E [1, 2., J) is the maximum value of the membership value.
Figure BDA00026360411700000514
Smaller represents a monitoring point i1The worse the overall power quality level is, the first-level power quality index j at the point1Lower secondary electric energy quality index k1Coefficient of variable weight
Figure BDA00026360411700000515
The larger, where a is the performance balance correction factor;
step 2, further determining the comprehensive weight of the first-stage power quality index and the second-stage power quality index as follows:
Figure BDA00026360411700000516
wherein ,
Figure BDA00026360411700000517
is aClass power quality index j1Lower secondary electric energy quality index k1The subjective weight value of (a) is,
Figure BDA00026360411700000518
is a monitoring point i1First-level power quality index j1Lower secondary electric energy quality index k1The information entropy of (a) is changed into an objective weight value,
Figure BDA00026360411700000519
is a monitoring point i1First-level power quality index j1Lower secondary electric energy quality index k1The final comprehensive weight value of (1);
preferably, the normalized monitoring value of the secondary power quality index at the monitoring point under the primary power quality index in step 3 is:
for the set of power quality indicators
Figure BDA00026360411700000520
Represents a monitoring point i1First-level power quality index j1Second grade electric energy quality index k1The monitoring value after normalization processing is
Figure BDA00026360411700000521
And 3, calculating a positive ideal solution and a negative ideal solution of the secondary power quality index of the primary power quality index as follows:
first order power quality index j1Second grade electric energy quality index k1Is just like to understand that
Figure BDA00026360411700000522
Comprises the following steps:
Figure BDA00026360411700000523
first order power quality index j1Second grade electric energy quality index k1Is a negative ideal solution of
Figure BDA00026360411700000524
Comprises the following steps:
Figure BDA00026360411700000525
in the formula ,
Figure BDA0002636041170000061
is a first-level power quality index j1Second grade electric energy quality index k1At the maximum of the m monitor point values,
Figure BDA0002636041170000062
is a first-level power quality index j1Second grade electric energy quality index k1Minimum value among m monitoring point values;
and 3, constructing a positive ideal solution sequence and a negative ideal solution sequence as follows:
by
Figure BDA0002636041170000063
The positive ideal solution sequence of the composition is expressed as
Figure BDA0002636041170000064
The negative ideal solution sequence of the composition is marked as R-Respectively, as follows:
Figure BDA0002636041170000065
Figure BDA0002636041170000066
step 3, calculating the maximum value of the expression of all indexes in the range of the secondary power quality indexes under the primary power quality indexes:
Figure BDA0002636041170000067
in the formula ,
Figure BDA0002636041170000068
n primary power quality indexes of m monitoring points and each primary power quality index j1Corresponding to
Figure BDA0002636041170000069
Calculating the maximum value of the expression of all indexes in the range of the secondary power quality indexes;
and 3, calculating the resolution coefficient of the gray correlation method as follows:
Figure BDA00026360411700000610
Figure BDA00026360411700000611
Figure BDA00026360411700000612
in the formula, rho is a resolution coefficient of a gray correlation method, and the value should satisfy: xΔ< 1/3, XΔ≤ρ≤1.5XΔ(ii) a When X is presentΔAt not less than 1/3, 1.5XΔ≤ρ≤2XΔ；
And 3, calculating the forward correlation coefficient as follows:
recording monitoring point i1First-level power quality index j1Second grade electric energy quality index k1Normalized index of
Figure BDA00026360411700000613
And a first-level power quality index j1Second grade electric energy quality index k1Positive idea of (1)
Figure BDA00026360411700000614
Has a degree of association ofForward correlation coefficient, is recorded as
Figure BDA00026360411700000615
The specific calculation method comprises the following steps:
Figure BDA00026360411700000616
and 3, calculating the negative correlation coefficient as follows:
recording monitoring point i1First-level power quality index j1Second grade electric energy quality index k1Normalized index of
Figure BDA00026360411700000617
And a first-level power quality index j1Second grade electric energy quality index k1Negative ideal solution of
Figure BDA00026360411700000618
Is recorded as the negative correlation coefficient
Figure BDA0002636041170000071
The specific calculation method comprises the following steps:
Figure BDA0002636041170000072
and 3, performing index polymerization by using a logarithmic method based on the barrel theory to obtain a degree of association polymerization of:
a logarithmic method based on the barrel theory is introduced for index polymerization, and the degree of polymerization of the degree of correlation is as follows:
Figure BDA0002636041170000073
Figure BDA0002636041170000074
in the formula ,
Figure BDA0002636041170000075
is a first-level power quality index j1Second grade electric energy quality index k1Is scored for health, and Ijc∈[1,5]，
Figure BDA0002636041170000076
Is a first-level power quality index j1Second grade electric energy quality index k1The comprehensive weight value of (2);
step 3, respectively calculating the weighted positive gray correlation degree and the weighted negative gray correlation degree as follows:
Figure BDA0002636041170000077
Figure BDA0002636041170000078
in the formula ,
Figure BDA0002636041170000079
represents a monitoring point i1Overall power quality index and positive ideal solution R+The degree of grey correlation between the two,
Figure BDA00026360411700000710
represents a monitoring point i1Overall power quality index and negative ideal solution R-The grey correlation degree between;
and 3, respectively calculating the positive Euclidean distance and the negative Euclidean distance as follows:
Figure BDA00026360411700000711
Figure BDA00026360411700000712
in the formula ,
Figure BDA00026360411700000713
represents a monitoring point i1Overall power quality index and positive ideal solution R+The euclidean distance between them,
Figure BDA00026360411700000714
represents a monitoring point i1Overall power quality index and negative ideal solution R-The euclidean distance between them.
Step 3, the calculation of the positive comprehensive relevance degree and the negative comprehensive relevance degree is as follows:
construct comprehensive positive comprehensive relevance
Figure BDA00026360411700000715
And negative degree of comprehensive association
Figure BDA00026360411700000716
Comprises the following steps:
Figure BDA00026360411700000717
Figure BDA00026360411700000718
in the formula ,α1Is a first linear coefficient, α2Is the second linear coefficient, alpha1，α2∈[0,1]And alpha is1+α2＝1；
And 3, constructing comprehensive evaluation indexes of the positive comprehensive relevance and the negative comprehensive relevance as follows:
Figure BDA00026360411700000719
in the formula ,
Figure BDA0002636041170000081
the larger the value is, the monitoring point i is represented1The better the overall power quality;
overall evaluation index is
Figure BDA0002636041170000082
The values are used to assess the overall power quality of the monitoring point.
The invention has the beneficial effects that:
the traditional information entropy weighting method is adjusted by combining the variable weight theory, higher objective weight is given to the severe indexes, and the degree of attention of the decision scheme to the severe indexes is improved;
an ideal solution method is introduced to modify the traditional grey correlation method, so that the distinguishing capability of the evaluation method on different index sets to be evaluated is improved;
the logarithm polymerization method based on the barrel theory replaces the traditional linear polymerization method, the correlation degree calculation formula of the grey correlation method is adjusted, the distinguishing capability of severe indexes is improved, and the evaluation result which is more in line with the power quality evaluation target is realized.
Drawings
FIG. 1: is a flow chart of the present invention;
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a flowchart of the present invention, and this embodiment is implemented by the following technical solutions, and a method for comprehensively evaluating power quality of a low-voltage transformer area is characterized by including the following steps:
step 1: establishing a primary power quality index set and a secondary power quality index set;
preferably, the step 1 of establishing the first-stage power quality index set and the second-stage power quality index set includes:
Figure BDA0002636041170000083
i1∈[1,m]，j1∈[1,n]，k1∈[1,si]
wherein m is the number of monitoring points, n is the number of first-level power quality indexes, and siThe number of the second-level power quality indexes under the first-level power quality index i,
Figure BDA0002636041170000084
represents a monitoring point i1First-level power quality index j1Second grade electric energy quality index k1A monitored value of (d);
the first-stage power quality indexes in the step 1 include but are not limited to voltage deviation, voltage flicker, voltage fluctuation, harmonic distortion, three-phase unbalance, frequency deviation and power supply reliability;
the secondary power quality indexes in the step 1 include, but are not limited to, average voltage amplitude deviation, voltage deviation duration, flicker level, flicker duration, average voltage fluctuation amplitude, fluctuation duration, total harmonic content, harmonic duration, unbalance degree, unbalance duration, average frequency deviation, frequency deviation duration, voltage sag, and interruption duration.
Step 2: establishing a judgment matrix, determining and selecting subjective weights of a first-stage power quality index and a second-stage power quality index by applying a scale expansion method in combination with the judgment matrix, determining and selecting objective weights of the first-stage power quality index and the second-stage power quality index by a variable weight theory, and further determining comprehensive weights of the first-stage power quality index and the second-stage power quality index;
step 2, the construction judgment matrix is as follows:
Figure BDA0002636041170000085
wherein ,W*Representing the decision matrix and having complete correspondence, is n x nN is the number of the first-level power quality indexes, relative importance ranking is carried out on the first-level power quality indexes, and t iskExpressing the importance of the kth first-level power quality index relative to the kth +1 first-level power quality index, wherein k belongs to [1, n-1 ]]The method specifically comprises the following steps:
if XkRelative to Xk-1Equally important, then tkGet a1；
If XkRelative to Xk-1Of slight importance, then tkGet a2；
If XkRelative to Xk-1Of obvious importance, then tkGet a3；
If XkRelative to Xk-1Of great importance, then tkGet a4；
If XkRelative to Xk-1Of extreme importance, then tkGet a5。
wherein ,a1＜a2＜a3＜a4＜a5，
Figure BDA0002636041170000091
To judge the ith in the matrix0Line j (th)0Elements of a column, i.e. representing the ith0The first-level power quality index is relative to the jth0Subjective weight of the first-level power quality index, reflecting the ith0The first-level power quality index is relative to the jth0Importance of individual first-order power quality index, i0∈[1,n]，j0∈[1,n]，i0≠j0；
wherein ,tkIs specifically a1＝1，a2＝1.2，a3＝1.4，a4＝1.6，a51.8, determining and selecting the subjective weight of the first-stage power quality index and the second-stage power quality index by combining the judgment matrix in the step 2 through applying a scale expansion method as follows:
for the set of power quality indicators
Figure BDA0002636041170000092
Definition m is monitoringThe number of points, n is the first-level power quality index number, siIs the number of the secondary power quality indexes under the primary power quality index i,
Figure BDA0002636041170000093
the method can be divided into a deviation index, a duration index and a reliability index according to the index types.
First-level power quality index i0Is recorded as a subjective weight
Figure BDA0002636041170000094
The specific calculation is as follows:
Figure BDA0002636041170000095
first-level power quality index i0The relative weight of the next two-stage power quality index is recorded as
Figure BDA0002636041170000096
Is a row vector and the number of vector elements is
Figure BDA0002636041170000097
To pair
Figure BDA0002636041170000098
Individual second grade electric energy quality index
Figure BDA0002636041170000099
Relative importance ranking is performed. Wherein the content of the first and second substances,
Figure BDA00026360411700000910
denotes the kth0Relative kth index of secondary electric energy quality index0And +1 secondary power quality indicators. k is a radical of0The value taking method is the same as the k value taking method.
First-level power quality index i0The subjective weight of the next two-stage power quality index is recorded as
Figure BDA00026360411700000911
The specific calculation is as follows:
Figure BDA00026360411700000912
wherein, the first-level power quality index i0Lower k0The subjective weight specific value of each secondary power quality index is
Figure BDA00026360411700000913
Step 2, determining and selecting objective weights of the first-stage power quality index and the second-stage power quality index according to a variable weight theory, wherein the objective weights are as follows:
monitoring n secondary electric energy quality indexes of the electric energy quality of m monitoring points in the power distribution network by using the comprehensive electric energy quality on-line monitoring device to obtain an original electric energy quality evaluation index monitoring data set
Figure BDA00026360411700000914
In an actual process, generally, measurement units of different power quality indexes are different, and in order to enable each index to have equal expressive force in a comprehensive evaluation process, power quality comprehensive evaluation index data needs to be normalized to obtain power quality comprehensive evaluation index data
Figure BDA00026360411700000915
The normalization process is performed by limiting the index fluctuation range to the interval [0,1 ]]The better the index data is, the larger the normalized index data is;
if the index data is larger and better, the normalization method is as follows:
Figure BDA0002636041170000101
if the smaller the index data is, the better the low-priority index is, the normalization method is as follows:
Figure BDA0002636041170000102
i1∈[1,m]，j1∈[1,n]，
Figure BDA0002636041170000103
wherein m is the number of monitoring points, n is the number of first-level power quality indexes,
Figure BDA0002636041170000104
is a first-level power quality index j1The number of the next two-stage power quality indexes,
Figure BDA0002636041170000105
is a first-level power quality index j1Lower k1The maximum value of m monitoring data of each secondary power quality index,
Figure BDA0002636041170000106
is a first-level power quality index j1Lower k1And the minimum value of the m monitoring data of each secondary power quality index. For the set of power quality indicators
Figure BDA0002636041170000107
Defining m as the number of monitoring points, n as the number of first-level power quality indexes,
Figure BDA0002636041170000108
is a first-level power quality index j1The number of the next two-stage power quality indexes is defined as r, the power quality grade number is defined as r, and the membership degree set of the power quality index set is defined as
Figure BDA0002636041170000109
wherein ,i1∈[1,m]，j1∈[1,n]，k1∈[1,sj]，l1∈[1,r]；
Figure BDA00026360411700001010
Represents a monitoring point i1To get atClass power quality index j1Second grade electric energy quality index k1For power quality class l1A membership value of.
Figure BDA00026360411700001011
According to the index types, the method can be divided into a deviation index, a duration index and a reliability index;
the method for calculating the membership value of the deviation index comprises the following steps:
Figure BDA00026360411700001012
i1∈[1,m]，j1∈[1,n]，
Figure BDA00026360411700001013
wherein m is the number of monitoring points, n is the number of first-level power quality indexes,
Figure BDA00026360411700001014
is a first-level power quality index j1The number of the next two-stage power quality indexes,
Figure BDA00026360411700001015
is a first-level power quality index j1Second grade electric energy quality index k1The upper limit of the allowable operation value specified by the national standard,
Figure BDA00026360411700001016
is a first-level power quality index j1Second grade electric energy quality index k1The lower limit of the allowable operation value specified by the national standard.
The method for calculating the membership value of the duration class index comprises the following steps:
Figure BDA00026360411700001017
i1∈[1,m]，j1∈[1,n]，
Figure BDA00026360411700001018
wherein m is the number of monitoring points, n is the number of first-level power quality indexes,
Figure BDA00026360411700001019
is a first-level power quality index j1The number of the next two-stage power quality indexes,
Figure BDA00026360411700001020
is a first-level power quality index j1Second grade electric energy quality index k1Allowable duration k of the deviation value specified by the national standard0For a fixed coefficient, it was taken to be 0.13.
The membership calculation method of the reliability index comprises the following steps:
Figure BDA0002636041170000111
i1∈[1,m]，j1∈[1,n]，
Figure BDA0002636041170000112
wherein ,
Figure BDA0002636041170000113
the method is a value obtained by normalizing the electric energy quality comprehensive evaluation index data.
Deviation class indicators include, but are not limited to, voltage magnitude average deviation, flicker level, voltage average fluctuation amplitude, total harmonic content, three-phase imbalance, frequency average deviation, and voltage sag.
Duration class indicators include, but are not limited to, voltage deviation duration, flicker duration, ripple duration, harmonic duration, imbalance duration, frequency deviation duration, and discontinuity duration.
The reliability index comprises power supply reliability;
defining a monitoring point i1The fuzzy relation between the electric energy index membership degree and the electric energy quality evaluation set is
Figure BDA0002636041170000114
The fuzzy relation calculation method comprises the following steps:
Figure BDA0002636041170000115
i1∈[1,m]，j1∈[1,n]，
Figure BDA0002636041170000116
wherein m is the number of monitoring points, n is the number of first-level power quality indexes,
Figure BDA0002636041170000117
is a first-level power quality index j1The number of the next-level power quality indexes;
Figure BDA0002636041170000118
represents a monitoring point i1First-level power quality index j1Second grade electric energy quality index k1For power quality class l1The fuzzy mapping relationship of (1);
the information entropy value calculation method comprises the following steps:
Figure BDA0002636041170000119
Figure BDA00026360411700001110
in the formula ,
Figure BDA00026360411700001111
first-level power quality index j1Lower secondary power qualityIndex k1The value of the entropy of the information of (c),
Figure BDA00026360411700001112
is a monitoring point i1First-level power quality index j1Second grade electric energy quality index k1Entropy of constant weight information;
introducing a variable weight theory to process the information entropy, and realizing information entropy variable weight to highlight the influence of severe indexes on a comprehensive judgment result;
using the qualified quality of electric energy as standard, monitoring point i1The membership degree of the integral power quality at the qualified level J and above is recorded as
Figure BDA00026360411700001113
Objective weight of information entropy change
Figure BDA00026360411700001114
The calculation method comprises the following steps:
Figure BDA00026360411700001115
Figure BDA00026360411700001116
Figure BDA00026360411700001117
i1∈[1,m]，j1∈[1,n]，
Figure BDA00026360411700001118
in the formula ,
Figure BDA00026360411700001119
is a monitoring point i1First-level power quality index j1Lower secondary electric energy quality index k1For power quality class l1Is subject toAnd (4) degree.
Figure BDA00026360411700001120
Is a monitoring point i1At n primary power quality indexes and under each primary power quality index j1
Figure BDA00026360411700001121
A second grade power quality index of qualified power quality grade l1E [1, 2., J) is the maximum value of the membership value.
Figure BDA00026360411700001122
Smaller represents a monitoring point i1The worse the overall power quality level is, the first-level power quality index j at the point1Lower secondary electric energy quality index k1Coefficient of variable weight
Figure BDA0002636041170000121
The larger, where a is the performance balance correction factor;
step 2, further determining the comprehensive weight of the first-stage power quality index and the second-stage power quality index as follows:
Figure BDA0002636041170000122
wherein ,
Figure BDA0002636041170000123
is a first-level power quality index j1Lower secondary electric energy quality index k1The subjective weight value of (a) is,
Figure BDA0002636041170000124
is a monitoring point i1First-level power quality index j1Lower secondary electric energy quality index k1The information entropy of (a) is changed into an objective weight value,
Figure BDA0002636041170000125
is a monitoring point i1First-level power quality index j1Lower secondary electricityEnergy quality index k1The final comprehensive weight value of (1);
and step 3: calculating a positive ideal solution and a negative ideal solution of a secondary power quality index of a primary power quality index through a normalized monitoring value of the secondary power quality index under the primary power quality index at a monitoring point, constructing a positive ideal solution sequence and a negative ideal solution sequence, calculating the maximum value of an expression of all indexes in a secondary power quality index range under the primary power quality index, calculating a resolution coefficient of a gray correlation method by combining the positive ideal solution sequence, calculating a positive correlation coefficient and a negative correlation coefficient, performing index polymerization by a logarithm method based on a barrel theory to obtain the degree of polymerization of the correlation degree, to calculate the weighted positive gray correlation degree and the weighted negative gray correlation degree, calculate the positive Euclidean distance and the negative Euclidean distance, further calculate the positive comprehensive correlation degree and the negative comprehensive correlation degree, and establishing a comprehensive evaluation index through the positive comprehensive relevance degree and the negative comprehensive relevance degree for evaluating the overall power quality of the monitoring point.
Step 3, the normalized monitoring value of the secondary power quality index under the primary power quality index at the monitoring point is as follows:
for the set of power quality indicators
Figure BDA0002636041170000126
Represents a monitoring point i1First-level power quality index j1Second grade electric energy quality index k1The monitoring value after normalization processing is
Figure BDA0002636041170000127
And 3, calculating a positive ideal solution and a negative ideal solution of the secondary power quality index of the primary power quality index as follows:
first order power quality index j1Second grade electric energy quality index k1Is just like to understand that
Figure BDA0002636041170000128
Comprises the following steps:
Figure BDA0002636041170000129
first order power quality index j1Second grade electric energy quality index k1Is a negative ideal solution of
Figure BDA00026360411700001210
Comprises the following steps:
Figure BDA00026360411700001211
in the formula ,
Figure BDA00026360411700001212
is a first-level power quality index j1Second grade electric energy quality index k1At the maximum of the m monitor point values,
Figure BDA00026360411700001213
is a first-level power quality index j1Second grade electric energy quality index k1Minimum value among m monitoring point values;
and 3, constructing a positive ideal solution sequence and a negative ideal solution sequence as follows:
by
Figure BDA00026360411700001214
The positive ideal solution sequence of the composition is expressed as
Figure BDA00026360411700001215
The negative ideal solution sequence of the composition is marked as R-Respectively, as follows:
Figure BDA00026360411700001216
Figure BDA00026360411700001217
step 3, calculating the maximum value of the expression of all indexes in the range of the secondary power quality indexes under the primary power quality indexes:
Figure BDA00026360411700001218
in the formula ,
Figure BDA00026360411700001219
n primary power quality indexes of m monitoring points and each primary power quality index j1Corresponding to
Figure BDA00026360411700001220
Calculating the maximum value of the expression of all indexes in the range of the secondary power quality indexes;
and 3, calculating the resolution coefficient of the gray correlation method as follows:
Figure BDA0002636041170000131
Figure BDA0002636041170000132
Figure BDA0002636041170000133
in the formula, rho is a resolution coefficient of a gray correlation method, and the value should satisfy: xΔ< 1/3, XΔ≤ρ≤1.5XΔ(ii) a When X is presentΔAt not less than 1/3, 1.5XΔ≤ρ≤2XΔ；
And 3, calculating the forward correlation coefficient as follows:
recording monitoring point i1First-level power quality index j1Second grade electric energy quality index k1Normalized index of
Figure BDA0002636041170000134
And a first-level power quality index j1Second grade electric energy quality index k1Positive idea of (1)
Figure BDA0002636041170000135
The correlation degree of (2) is a positive correlation coefficient, and is recorded as
Figure BDA0002636041170000136
The specific calculation method comprises the following steps:
Figure BDA0002636041170000137
and 3, calculating the negative correlation coefficient as follows:
recording monitoring point i1First-level power quality index j1Second grade electric energy quality index k1Normalized index of
Figure BDA0002636041170000138
And a first-level power quality index j1Second grade electric energy quality index k1Negative ideal solution of
Figure BDA0002636041170000139
Is recorded as the negative correlation coefficient
Figure BDA00026360411700001310
The specific calculation method comprises the following steps:
Figure BDA00026360411700001311
and 3, performing index polymerization by using a logarithmic method based on the barrel theory to obtain a degree of association polymerization of:
a logarithmic method based on the barrel theory is introduced for index polymerization, and the degree of polymerization of the degree of correlation is as follows:
Figure BDA00026360411700001312
Figure BDA00026360411700001313
in the formula ,
Figure BDA00026360411700001314
is a first-level power quality index j1Second grade electric energy quality index k1Is scored for health, and Ijc∈[1,5]，
Figure BDA00026360411700001315
Is a first-level power quality index j1Second grade electric energy quality index k1The comprehensive weight value of (2);
step 3, respectively calculating the weighted positive gray correlation degree and the weighted negative gray correlation degree as follows:
Figure BDA0002636041170000141
Figure BDA0002636041170000142
in the formula ,
Figure BDA0002636041170000143
represents a monitoring point i1Overall power quality index and positive ideal solution R+The degree of grey correlation between the two,
Figure BDA0002636041170000144
represents a monitoring point i1Overall power quality index and negative ideal solution R-The grey correlation degree between;
and 3, respectively calculating the positive Euclidean distance and the negative Euclidean distance as follows:
Figure BDA0002636041170000145
Figure BDA0002636041170000146
in the formula ,
Figure BDA0002636041170000147
represents a monitoring point i1Overall power quality index and positive ideal solution R+The euclidean distance between them,
Figure BDA0002636041170000148
represents a monitoring point i1Overall power quality index and negative ideal solution R-The euclidean distance between them.
Step 3, the calculation of the positive comprehensive relevance degree and the negative comprehensive relevance degree is as follows:
construct comprehensive positive comprehensive relevance
Figure BDA0002636041170000149
And negative degree of comprehensive association
Figure BDA00026360411700001410
Comprises the following steps:
Figure BDA00026360411700001411
Figure BDA00026360411700001412
in the formula ,α1Is a first linear coefficient, α2Is the second linear coefficient, alpha1，α2∈[0,1]And alpha is1+α2＝1；
And 3, constructing comprehensive evaluation indexes of the positive comprehensive relevance and the negative comprehensive relevance as follows:
Figure BDA00026360411700001413
in the formula ,
Figure BDA00026360411700001414
the larger the value is, the monitoring point i is represented1The better the overall power quality;
overall evaluation index is
Figure BDA00026360411700001415
The values are used to assess the overall power quality of the monitoring point.
The index number of the applicable index is Data3 ═ x1，x3，x5，x7，x9，x11}; membership function of duration class index, and suitable index number is Data4 ═ x2，x4，x6，x8，x10，x12}; membership function of reliability index, and applicable index number is Data5 ═ x13}.
And (3) carrying out power quality evaluation on the basis of data of 3 monitoring points in a certain 10kV power distribution system. A. the1～A5The index group is determined according to the related national standard and the experience of industry experts for five power quality grades from good to bad; b is1～B3For 3 monitoring points, statistics is performed on the monitoring data collected in a certain week, and the 95% probability maximum value is filled in table 1 as the original index data.
Table 1 original monitoring data of indexes
Figure BDA00026360411700001416
Figure BDA0002636041170000151
The comprehensive weight is calculated based on a scale expansion method and a variable weight theory as follows:
Figure BDA0002636041170000152
in the above formula, ωiIs a subjective weight value, vi1Is a constant entropy objective weight value, vi2B1，vi2B2 and vi2B3For introducing an index set information entropy objective weight value alpha of a variable weight theoryiIs the final integrated weight value.
Solve to obtain the comprehensive weight alphaiObtaining a weighted decision matrix R through normalization and weighting*And solving the positive and negative ideal solutions as follows:
R+＝[0.0743 0.0629 0.0680 0.1269 0.0659 0.0717 0.0761 0.0593 0.0718 0.0602 0.0630 0.0741 0.1259]
R-＝[0 0 0 0 0 0 0 0 0 0 0 0 0]
calculated Deltav＝0.0254，XΔ＝0.2003<1/3, determining the resolution coefficient rho to be 0.3.
Respectively calculating the comprehensive association degree G when rho is 0.5 under linear weighting1And the comprehensive association degree G of rho 0.3 selected by the method of the invention2：
ΔG1＝G1max-G1min＝0.7199-0.2634＝0.4565
ΔG2＝G2max-G2min＝0.7712-0.209＝0.5622
It can be seen that Δ G1<ΔG2It is shown that the correlation degree distribution interval obtained by selecting rho values according to the method of the invention is larger, and the interference of the bad values of the observation sequence to the evaluation result can be well inhibited. Calculating the comprehensive grey correlation value
Figure BDA0002636041170000155
And
Figure BDA0002636041170000154
shown in Table 2, Final decision criteria SiAlso listed in table 2.
Table 2 electric energy quality evaluation results
Figure BDA0002636041170000153
As can be seen from the raw monitoring data shown in Table 3, monitoring point B2In the data, the voltage deviation duration and the harmonic duration are in the 'bad' grade, and the comprehensive evaluation result of the power quality tends to the 'bad' grade. Analyzing the Table 4 data based on the determination of Table 3, monitoring Point B2In the comprehensive evaluation results, the traditional grey correlation method is rated as 'middle', the text method is rated as 'poor', the reason for the difference of the results of the two methods is mainly the linear information polymerization mode adopted by the traditional grey correlation method, and the index x is used2And index x8The weight of (a) is low, and the adverse effect thereof is masked by other high-weight good indicators, resulting in a tendency toward a better evaluation value. Also applying the process herein, B2Index x of2The monitoring value is modified to be 6.0, the influence of the bad index is weakened, the final grade evaluation value is changed to be 'middle', and the evaluation result is consistent with the traditional grey correlation method.
It should be understood that parts of the specification not set forth in detail are well within the prior art.
It should be understood that the above description of the preferred embodiments is given for clarity and not for any purpose of limitation, and that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (4)

1. A comprehensive evaluation method for the electric energy quality of a low-voltage transformer area is characterized by comprising the following steps:
step 1: establishing a primary power quality index set and a secondary power quality index set;
step 2: establishing a judgment matrix, determining and selecting subjective weights of a first-stage power quality index and a second-stage power quality index by applying a scale expansion method in combination with the judgment matrix, determining and selecting objective weights of the first-stage power quality index and the second-stage power quality index by a variable weight theory, and further determining comprehensive weights of the first-stage power quality index and the second-stage power quality index;
and step 3: calculating a positive ideal solution and a negative ideal solution of a secondary power quality index of a primary power quality index through a normalized monitoring value of the secondary power quality index under the primary power quality index at a monitoring point, constructing a positive ideal solution sequence and a negative ideal solution sequence, calculating the maximum value of an expression of all indexes in a secondary power quality index range under the primary power quality index, calculating a resolution coefficient of a gray correlation method by combining the positive ideal solution sequence, calculating a positive correlation coefficient and a negative correlation coefficient, performing index polymerization by a logarithm method based on a barrel theory to obtain the degree of polymerization of the correlation degree, to calculate the weighted positive gray correlation degree and the weighted negative gray correlation degree, calculate the positive Euclidean distance and the negative Euclidean distance, further calculate the positive comprehensive correlation degree and the negative comprehensive correlation degree, and establishing a comprehensive evaluation index through the positive comprehensive relevance degree and the negative comprehensive relevance degree for evaluating the overall power quality of the monitoring point.
2. The low-voltage transformer area electric energy quality comprehensive evaluation method according to claim 1, characterized in that:
step 1, establishing a primary power quality index set and a secondary power quality index set as follows:
Figure FDA0002636041160000011
wherein m is the number of monitoring points, n is the number of first-level power quality indexes, and siThe number of the second-level power quality indexes under the first-level power quality index i,
Figure FDA0002636041160000012
represents a monitoring point i1First-level power quality index j1Second grade electric energy quality index k1A monitored value of (d);
the first-stage power quality indexes in the step 1 include but are not limited to voltage deviation, voltage flicker, voltage fluctuation, harmonic distortion, three-phase unbalance, frequency deviation and power supply reliability;
in the step 1, the secondary electric energy quality indexes include, but are not limited to, average voltage amplitude deviation, voltage deviation duration, flicker level, flicker duration, average voltage fluctuation amplitude, fluctuation duration, total harmonic content, harmonic duration, unbalance degree, unbalance duration, average frequency deviation, frequency deviation duration, voltage sag and interruption duration;
step 2, the construction judgment matrix is as follows:
Figure FDA0002636041160000013
wherein ,W*Representing the judgment matrix and having complete consistency, is a positive and reciprocal matrix of n x n, n is the number of first-level power quality indexes, relative importance ordering is carried out on the first-level power quality indexes, tkExpressing the importance of the kth first-level power quality index relative to the kth +1 first-level power quality index, wherein k belongs to [1, n-1 ]]The method specifically comprises the following steps:
if XkRelative to Xk-1Equally important, then tkGet a1；
If XkRelative to Xk-1Of slight importance, then tkGet a2；
If XkRelative to Xk-1Of obvious importance, then tkGet a3；
If XkRelative to Xk-1Of great importance, then tkGet a4；
If XkRelative to Xk-1Of extreme importance, then tkGet a5；
wherein ,a1＜a2＜a3＜a4＜a5，
Figure FDA0002636041160000021
To judge the ith in the matrix0Line j (th)0Elements of a column, i.e. representing the ith0The first-level power quality index is relative to the jth0Subjective weight of the first-level power quality index, reflecting the ith0The first-level power quality index is relative to the jth0Importance of individual first-order power quality index, i0∈[1,n]，j0∈[1,n]，i0≠j0；
And 2, determining and selecting the subjective weights of the first-stage power quality index and the second-stage power quality index by combining the judgment matrix through a scale expansion method as follows:
for the set of power quality indicators
Figure FDA0002636041160000022
Defining m as the number of monitoring points, n as the number of first-level power quality indicators, siIs a secondary power quality index number d under a primary power quality index ii1,j1,k1According to the index types, the method can be divided into a deviation index, a duration index and a reliability index;
first-level power quality index i0Is recorded as a subjective weight
Figure FDA0002636041160000023
The specific calculation is as follows:
Figure FDA0002636041160000024
first-level power quality index i0The relative weight of the next two-stage power quality index is recorded as
Figure FDA0002636041160000025
Figure FDA0002636041160000026
Is a row vector and the number of vector elements is
Figure FDA0002636041160000027
To pair
Figure FDA0002636041160000028
Individual second grade electric energy quality index
Figure FDA0002636041160000029
Carrying out relative importance ranking; wherein,
Figure FDA00026360411600000210
denotes the kth0Relative kth index of secondary electric energy quality index0The importance of +1 secondary power quality indicators; k is a radical of0The value taking method is the same as the k value taking method;
first-level power quality index i0The subjective weight of the next two-stage power quality index is recorded as
Figure FDA00026360411600000211
The specific calculation is as follows:
Figure FDA00026360411600000212
wherein, the first-level power quality index i0Lower k0The subjective weight specific value of each secondary power quality index is
Figure FDA00026360411600000213
3. The low-voltage transformer area electric energy quality comprehensive evaluation method according to claim 1, characterized in that:
step 2, determining and selecting objective weights of the first-stage power quality index and the second-stage power quality index according to a variable weight theory, wherein the objective weights are as follows:
monitoring n secondary electric energy quality indexes of the electric energy quality of m monitoring points in the power distribution network by using the comprehensive electric energy quality on-line monitoring device to obtain the original electric energy quality evaluation index monitoring numberIs according to the data set
Figure FDA00026360411600000214
In an actual process, generally, measurement units of different power quality indexes are different, and in order to enable each index to have equal expressive force in a comprehensive evaluation process, power quality comprehensive evaluation index data needs to be normalized to obtain power quality comprehensive evaluation index data
Figure FDA00026360411600000215
The normalization process is performed by limiting the index fluctuation range to the interval [0,1 ]]The better the index data is, the larger the normalized index data is;
if the index data is larger and better, the normalization method is as follows:
Figure FDA00026360411600000216
if the smaller the index data is, the better the low-priority index is, the normalization method is as follows:
Figure FDA00026360411600000217
Figure FDA0002636041160000031
wherein m is the number of monitoring points, n is the number of first-level power quality indexes,
Figure FDA0002636041160000032
is a first-level power quality index j1The number of the next two-stage power quality indexes,
Figure FDA0002636041160000033
is a first-level power quality index j1Lower k1Two isThe maximum value of m monitoring data of the grade power quality index,
Figure FDA0002636041160000034
is a first-level power quality index j1Lower k1The minimum value of m monitoring data of each secondary power quality index; for the set of power quality indicators
Figure FDA0002636041160000035
Defining m as the number of monitoring points, n as the number of first-level power quality indexes,
Figure FDA0002636041160000036
is a first-level power quality index j1The number of the next two-stage power quality indexes is defined as r, the power quality grade number is defined as r, and the membership degree set of the power quality index set is defined as
Figure FDA0002636041160000037
wherein ,i1∈[1,m]，j1∈[1,n]，k1∈[1,sj]，l1∈[1,r]；
Figure FDA0002636041160000038
Represents a monitoring point i1First-level power quality index j1Second grade electric energy quality index k1For power quality class l1A membership value of;
Figure FDA0002636041160000039
according to the index types, the method can be divided into a deviation index, a duration index and a reliability index;
the method for calculating the membership value of the deviation index comprises the following steps:
Figure FDA00026360411600000310
wherein m is the monitoring pointThe number n is the number of first-level power quality indexes,
Figure FDA00026360411600000311
is a first-level power quality index j1The number of the next two-stage power quality indexes,
Figure FDA00026360411600000312
is a first-level power quality index j1Second grade electric energy quality index k1The upper limit of the allowable operation value specified by the national standard,
Figure FDA00026360411600000313
is a first-level power quality index j1Second grade electric energy quality index k1The lower limit of the allowable operation value specified by the national standard;
the method for calculating the membership value of the duration class index comprises the following steps:
Figure FDA00026360411600000314
wherein m is the number of monitoring points, n is the number of first-level power quality indexes,
Figure FDA00026360411600000315
is a first-level power quality index j1The number of the next two-stage power quality indexes,
Figure FDA00026360411600000316
is a first-level power quality index j1Second grade electric energy quality index k1Allowable duration k of the deviation value specified by the national standard0The coefficient is fixed and is taken as 0.13;
the membership calculation method of the reliability index comprises the following steps:
Figure FDA00026360411600000317
wherein ,
Figure FDA00026360411600000318
the value is obtained by normalizing the comprehensive evaluation index data of the power quality;
deviation class indicators include, but are not limited to, voltage amplitude mean deviation, flicker level, voltage mean fluctuation amplitude, total harmonic content, three-phase imbalance, frequency mean deviation, and voltage sag;
duration class indicators include, but are not limited to, voltage deviation duration, flicker duration, ripple duration, harmonic duration, imbalance duration, frequency deviation duration, and discontinuity duration;
the reliability index comprises power supply reliability;
defining a monitoring point i1The fuzzy relation between the electric energy index membership degree and the electric energy quality evaluation set is
Figure FDA0002636041160000041
The fuzzy relation calculation method comprises the following steps:
Figure FDA0002636041160000042
wherein m is the number of monitoring points, n is the number of first-level power quality indexes,
Figure FDA0002636041160000043
is a first-level power quality index j1The number of the next-level power quality indexes;
Figure FDA0002636041160000044
represents a monitoring point i1First-level power quality index j1Second grade electric energy quality index k1For power quality class l1The fuzzy mapping relationship of (1);
the information entropy value calculation method comprises the following steps:
Figure FDA0002636041160000045
Figure FDA0002636041160000046
in the formula ,
Figure FDA0002636041160000047
first-level power quality index j1Lower secondary electric energy quality index k1The value of the entropy of the information of (c),
Figure FDA0002636041160000048
is a monitoring point i1First-level power quality index j1Second grade electric energy quality index k1Entropy of constant weight information;
introducing a variable weight theory to process the information entropy, and realizing information entropy variable weight to highlight the influence of severe indexes on a comprehensive judgment result;
using the qualified quality of electric energy as standard, monitoring point i1The membership degree of the integral power quality at the qualified level J and above is recorded as
Figure FDA0002636041160000049
Objective weight of information entropy change
Figure FDA00026360411600000410
The calculation method comprises the following steps:
Figure FDA00026360411600000411
Figure FDA00026360411600000412
Figure FDA00026360411600000413
in the formula ,
Figure FDA00026360411600000414
is a monitoring point i1First-level power quality index j1Lower secondary electric energy quality index k1For power quality class l1Degree of membership of;
Figure FDA00026360411600000415
is a monitoring point i1N primary power quality indexes are processed, and each primary power quality index j1Is as follows
Figure FDA00026360411600000416
A second grade power quality index of qualified power quality grade l1Maximum value of membership value of e [1, 2., J));
Figure FDA00026360411600000417
smaller represents a monitoring point i1The worse the overall power quality level is, the first-level power quality index j at the point1Lower secondary electric energy quality index k1Coefficient of variable weight
Figure FDA00026360411600000418
The larger, where a is the performance balance correction factor;
step 2, further determining the comprehensive weight of the first-stage power quality index and the second-stage power quality index as follows:
Figure FDA00026360411600000419
wherein ,
Figure FDA00026360411600000420
is a first-level power quality index j1Lower secondary electric energy quality index k1The subjective weight value of (a) is,
Figure FDA00026360411600000421
is a monitoring point i1First-level power quality index j1Lower secondary electric energy quality index k1The information entropy of (a) is changed into an objective weight value,
Figure FDA0002636041160000051
is a monitoring point i1First-level power quality index j1Lower secondary electric energy quality index k1The final integrated weight value of (2).
4. The low-voltage transformer area electric energy quality comprehensive evaluation method according to claim 1, characterized in that:
step 3, the normalized monitoring value of the secondary power quality index under the primary power quality index at the monitoring point is as follows:
for the set of power quality indicators
Figure FDA0002636041160000052
Figure FDA0002636041160000053
Represents a monitoring point i1First-level power quality index j1Second grade electric energy quality index k1The monitoring value after normalization processing is
Figure FDA0002636041160000054
And 3, calculating a positive ideal solution and a negative ideal solution of the secondary power quality index of the primary power quality index as follows:
first order power quality index j1Second grade electric energy quality index k1Is just like to understand that
Figure FDA0002636041160000055
Comprises the following steps:
Figure FDA0002636041160000056
first order power quality index j1Second grade electric energy quality index k1Is a negative ideal solution of
Figure FDA0002636041160000057
Comprises the following steps:
Figure FDA0002636041160000058
in the formula ,
Figure FDA0002636041160000059
is a first-level power quality index j1Second grade electric energy quality index k1At the maximum of the m monitor point values,
Figure FDA00026360411600000510
is a first-level power quality index j1Second grade electric energy quality index k1Minimum value among m monitoring point values;
and 3, constructing a positive ideal solution sequence and a negative ideal solution sequence as follows:
by
Figure FDA00026360411600000511
The positive ideal solution sequence of the composition is expressed as
Figure FDA00026360411600000512
The negative ideal solution sequence of the composition is marked as R-Respectively, as follows:
Figure FDA00026360411600000513
Figure FDA00026360411600000514
step 3, calculating the maximum value of the expression of all indexes in the range of the secondary power quality indexes under the primary power quality indexes:
Figure FDA00026360411600000515
in the formula ,
Figure FDA00026360411600000516
n primary power quality indexes of m monitoring points and each primary power quality index j1Corresponding to
Figure FDA00026360411600000517
Calculating the maximum value of the expression of all indexes in the range of the secondary power quality indexes;
and 3, calculating the resolution coefficient of the gray correlation method as follows:
Figure FDA00026360411600000518
Figure FDA00026360411600000519
Figure FDA00026360411600000520
in the formula, rho is a resolution coefficient of a gray correlation method, and the value should satisfy: xΔ< 1/3, XΔ≤ρ≤1.5XΔ(ii) a When X is presentΔAt not less than 1/3, 1.5XΔ≤ρ≤2XΔ；
And 3, calculating the forward correlation coefficient as follows:
recording monitoring point i1First-level power quality index j1Second grade electric energy quality index k1Normalized index of
Figure FDA0002636041160000061
And a first-level power quality index j1Second grade electric energy quality index k1Positive idea of (1)
Figure FDA0002636041160000062
The correlation degree of (2) is a positive correlation coefficient, and is recorded as
Figure FDA0002636041160000063
The specific calculation method comprises the following steps:
Figure FDA0002636041160000064
and 3, calculating the negative correlation coefficient as follows:
recording monitoring point i1First-level power quality index j1Second grade electric energy quality index k1Normalized index of
Figure FDA0002636041160000065
And a first-level power quality index j1Second grade electric energy quality index k1Negative ideal solution of
Figure FDA0002636041160000066
Is recorded as the negative correlation coefficient
Figure FDA0002636041160000067
The specific calculation method comprises the following steps:
Figure FDA0002636041160000068
and 3, performing index polymerization by using a logarithmic method based on the barrel theory to obtain a degree of association polymerization of:
a logarithmic method based on the barrel theory is introduced for index polymerization, and the degree of polymerization of the degree of correlation is as follows:
Figure FDA0002636041160000069
Figure FDA00026360411600000610
in the formula ,
Figure FDA00026360411600000611
is a first-level power quality index j1Second grade electric energy quality index k1Is scored for health, and Ijc∈[1,5]，
Figure FDA00026360411600000612
Is a first-level power quality index j1Second grade electric energy quality index k1The comprehensive weight value of (2);
step 3, respectively calculating the weighted positive gray correlation degree and the weighted negative gray correlation degree as follows:
Figure FDA00026360411600000613
Figure FDA00026360411600000614
in the formula ,
Figure FDA00026360411600000615
represents a monitoring point i1To the whole body ofEnergy quality index and positive ideal solution R+The degree of grey correlation between the two,
Figure FDA00026360411600000616
represents a monitoring point i1Overall power quality index and negative ideal solution R-The grey correlation degree between;
and 3, respectively calculating the positive Euclidean distance and the negative Euclidean distance as follows:
Figure FDA00026360411600000617
Figure FDA0002636041160000071
in the formula ,
Figure FDA0002636041160000072
represents a monitoring point i1Overall power quality index and positive ideal solution R+The euclidean distance between them,
Figure FDA0002636041160000073
represents a monitoring point i1Overall power quality index and negative ideal solution R-The Euclidean distance between;
step 3, the calculation of the positive comprehensive relevance degree and the negative comprehensive relevance degree is as follows:
construct comprehensive positive comprehensive relevance
Figure FDA0002636041160000074
And negative degree of comprehensive association
Figure FDA0002636041160000075
Comprises the following steps:
Figure FDA0002636041160000076
Figure FDA0002636041160000077
in the formula ,α1Is a first linear coefficient, α2Is the second linear coefficient, alpha1，α2∈[0,1]And alpha is1+α2＝1；
And 3, constructing comprehensive evaluation indexes of the positive comprehensive relevance and the negative comprehensive relevance as follows:
Figure FDA0002636041160000078
in the formula ,
Figure FDA0002636041160000079
the larger the value is, the monitoring point i is represented1The better the overall power quality;
overall evaluation index is
Figure FDA00026360411600000710
The values are used to assess the overall power quality of the monitoring point.
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Publication number Priority date Publication date Assignee Title
CN103617371A (en) * 2013-12-10 2014-03-05 国家电网公司 Method for comprehensively evaluating electric energy quality based on grey theory
CN107515839A (en) * 2017-07-12 2017-12-26 国网上海市电力公司 The improved quality of power supply THE FUZZY EVALUATING METHOD for assigning power algorithm process
CN108197848A (en) * 2018-03-22 2018-06-22 广东工业大学 A kind of energy quality comprehensive assessment method and device based on intuitionistic fuzzy theory

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103617371A (en) * 2013-12-10 2014-03-05 国家电网公司 Method for comprehensively evaluating electric energy quality based on grey theory
CN107515839A (en) * 2017-07-12 2017-12-26 国网上海市电力公司 The improved quality of power supply THE FUZZY EVALUATING METHOD for assigning power algorithm process
CN108197848A (en) * 2018-03-22 2018-06-22 广东工业大学 A kind of energy quality comprehensive assessment method and device based on intuitionistic fuzzy theory

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