CN112016819B - Comprehensive assessment method for electric energy quality of low-voltage transformer area - Google Patents
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Abstract
The invention provides a comprehensive assessment method for the electric energy quality of a low-voltage transformer area. The method comprises the steps of establishing a primary electric energy quality index set and a secondary electric energy 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 the monitoring values of the selected indexes are normalized, positive and negative ideal solution sequences of the selected indexes are calculated, the resolution coefficient and positive and negative correlation coefficients of a gray correlation method are further calculated, then the indexes are aggregated by utilizing a log 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 further calculated, the positive and negative comprehensive correlation degrees are further calculated, and a comprehensive evaluation index is constructed for evaluating the overall power quality of the monitoring points. The invention improves the distinguishing capability of the severe indexes and realizes the evaluation result which meets the electric energy quality evaluation target better.
Description
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 construction of the tail end of the power distribution network represented by rural remote areas is weaker, the load is lighter, the power transmission distance is longer, the power transmission radius of a power distribution network power supply line is exceeded, the power transmission loss on the line is larger, the tail end power supply capacity is insufficient, and various power quality problems represented by low voltage are caused. Reactive compensation is conventionally performed by adopting measures such as parallel capacitors, tap joints of a regulating transformer and the like, so that voltage is regulated; meanwhile, in order to fully develop reactive compensation potential of distributed power generation represented by photovoltaic, measures for low-voltage management by adopting photovoltaic power generation have been attracting attention. In order to better measure the performance of the photovoltaic and other distributed power sources connected to the power distribution network, an effective method is required to comprehensively evaluate the electric energy management of the low-voltage transformer area.
Along with the development of economic and social production in China, the demand of users for electric energy quality and the requirement of electric energy quality are also higher and higher, and how to utilize the existing single electric energy quality index national standard in China to carry out reasonable comprehensive evaluation on electric energy quality is the basis of electric energy quality pricing in electric power marketization. Two key links in the comprehensive evaluation of the electric energy quality are index weighting 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 objective weighting method cannot effectively reflect the nonlinearity and the emergent characteristic of the index set, and misjudgment is easy to occur when the index value with smaller weight changes severely. To avoid this problem, researchers have conducted studies on the theory of varying weights in various fields. A fuzzy model of the power quality index is defined in literature, and a weight-changing scheme is designed according to the inverse of the index fuzzy quality; there are also documents that propose to construct the comprehensive weights first and then to use the equalization function to perform the weight change.
The electric energy quality evaluation is a multi-index joint decision process, is influenced by the randomness of the monitored data, shows a gray characteristic, and a gray correlation method is a common evaluation method for the problems. The traditional gray correlation method realizes comprehensive sorting by constructing positive and negative ideal solutions and describing the similarity degree between index sets by a gray correlation function. The approximation degree between the two is described by using the Euclidean distance function by the ideal solution, and the effective evaluation can be realized on the index set which cannot be distinguished by the gray correlation method by introducing the ideal solution. However, in the research of the existing improved gray correlation method, the gray correlation degree is often calculated by adopting a linear index aggregation mode, so that the effects of high-quality indexes and high-weight indexes are highlighted, and the accuracy 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, so as to avoid the limitation of determining weights by a Chang Quan method; an ideal solution is introduced to make up for the defects of the traditional gray correlation method, and a gray correlation degree model between index sets to be evaluated is established; based on the wooden barrel theory, a logarithmic aggregation coefficient adjustment gray correlation calculation formula is introduced to embody punishment on 'bad' indexes, and comprehensive sequencing is carried out on the optimal calculation comprehensive correlation.
In order to achieve the above purpose, the technical scheme adopted by the invention is a comprehensive assessment method for the electric energy quality of a low-voltage station, which comprises the following specific implementation steps:
step 1: establishing a primary power quality index set and a secondary power quality index set;
step 2: constructing a judgment matrix, combining the judgment matrix, determining subjective weights of the primary power quality index and the secondary power quality index through an application scale expansion method, determining objective weights of the primary power quality index and the secondary power quality index through a weight changing theory, and further determining comprehensive weights of the primary power quality index and the secondary power quality index;
step 3: calculating positive ideal solutions and negative ideal solutions of the secondary power quality indexes of the primary power quality indexes through normalized monitoring values of the secondary power quality indexes at the monitoring points, constructing positive ideal solution sequences and negative ideal solution sequences, calculating total index solving expression maximum values in the secondary power quality index range under the primary power quality indexes, calculating resolution coefficients of a gray correlation method by combining the positive ideal solution sequences, calculating positive correlation coefficients and negative correlation coefficients, carrying out index aggregation on the basis of a log method of a wooden barrel theory to obtain correlation degree aggregation degree, calculating weighted positive gray correlation degree, weighted negative gray correlation degree, calculating positive Euclidean distance and negative Euclidean distance, further calculating positive comprehensive correlation degree and negative comprehensive correlation degree, and constructing comprehensive evaluation indexes through the positive comprehensive correlation degree and the negative comprehensive correlation degree for evaluating the whole power quality of the monitoring points.
Preferably, in the step 1, the establishing a primary power quality index set and a secondary power quality index set is as follows:
i 1 ∈[1,m]，j 1 ∈[1,n]，k 1 ∈[1,s i ]
wherein m is the number of monitoring points, n is the number of primary power quality indexes, s i For the number of the secondary power quality indexes under the primary power quality index i,
indicating the monitoring point i 1 At the first level of power quality index j 1 The lower two-level electric energy quality index k 1 Is a monitored value of (2);
the primary power quality indicators in step 1 include, but are not limited to, voltage deviation, voltage flicker, voltage fluctuation, harmonic distortion, three-phase imbalance, frequency deviation, and power supply reliability;
the secondary power quality indicators in step 1 include, but are not limited to, voltage amplitude average deviation, voltage deviation duration, flicker level, flicker duration, voltage average fluctuation amplitude, fluctuation duration, total harmonic content, harmonic duration, unbalance degree, unbalance duration, frequency average deviation, frequency deviation duration, voltage sag, and interruption duration.
Preferably, the construction judgment matrix in the step 2 is:
wherein ,W* Representing the judgment matrix and having complete consistency, is a positive-to-negative matrix of n, n is the number of primary power quality indexes, and is equal to one The relative importance ranking is carried out on the level electric energy quality indexes, t k Represents the importance of the kth primary power quality index relative to the kth+1st primary power quality index, k is [1, n-1 ]]The method comprises the following steps:
if X k Relative to X k-1 Equally important, then t k Taking a 1 ；
If X k Relative to X k-1 Slightly important, t k Taking a 2 ；
If X k Relative to X k-1 Obviously important, t k Taking a 3 ；
If X k Relative to X k-1 Very important, t k Taking a 4 ；
If X k Relative to X k-1 Extremely important, t k Taking a 5 。
wherein ,a1 ＜a 2 ＜a 3 ＜a 4 ＜a 5 ，
To judge the ith in the matrix 0 Line j 0 The elements of the column, i.e. representing the ith 0 The first-level electric energy quality index is relative to the j 0 Subjective weight of each primary electric energy quality index reflects the ith 0 The first-level electric energy quality index is relative to the j 0 Importance degree of each first-level electric energy quality index, i 0 ∈[1,n]，j 0 ∈[1,n]，i 0 ≠j 0 ；
And step 2, determining subjective weights for selecting the primary power quality index and the secondary power quality index by combining the judgment matrix through an application scale expansion method, wherein the subjective weights are as follows:
for a power quality index set
Defining m as the number of monitoring points, n as the first-level power quality index number, s i For the number of secondary electric energy quality index under the primary electric energy quality index i, <>
The types of indexes can be classified into a deviation type index, a duration type index and a reliability index.
First-level power quality index i 0 Is recorded as the subjective weight value of (2)
The specific calculation is as follows:
first-level power quality index i 0 The relative weight of the lower-level power quality index is recorded as
Is a row vector and the number of vector elements is +.>
For->
Two secondary electric energy quality index->
Relative importance ranking is performed. Wherein (1)>
Represents the kth 0 The second electric energy quality index is relative to the kth 0 Importance of +1 secondary power quality indicators. k (k) 0 The value method is the same as the k value method.
First-level power quality index i 0 Subjective weight of the next-level power quality index is recorded as
The specific calculation is as follows:
wherein, the first-level electric energy quality index i 0 The following k 0 The subjective weight concrete value of each secondary electric energy quality index is
And step 2, determining and selecting objective weights of a primary electric energy quality index and a secondary electric energy quality index according to a variable weight theory, wherein the objective weights are as follows:
the comprehensive electric energy quality on-line monitoring device is used for monitoring n electric energy quality secondary electric energy quality indexes of m monitoring points in the power distribution network, and an original electric energy quality evaluation index monitoring data set is obtained as
In the actual process, generally, the measurement units of different electric energy quality indexes are different, and in order to make each index have the same expressive force in the comprehensive evaluation process, the electric energy quality comprehensive evaluation index data needs to be normalized to obtain ∈ - >
The normalization process is to limit the index fluctuation range to the interval [0,1 ]]And 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:
if the index data is smaller and better, the normalization method is as follows:
i 1 ∈[1,m]，j 1 ∈[1,n]，
wherein m is the number of monitoring points, n is the number of primary power quality indexes,
is the first-level electric energy quality index j 1 The number of lower secondary power quality indicators, +.>
Is the first-level electric energy quality index j 1 The following k 1 Maximum value of m monitoring data of two secondary electric energy quality indexes, < >>
Is the first-level electric energy quality index j 1 The following k 1 The minimum value in m monitoring data of the two-level power quality indexes. For the power quality index set->
Defining m as the number of monitoring points, n as the number of primary power quality indexes, < >>
Is the first-level electric energy quality index j 1 The number of the next-level electric energy quality indexes is defined as r, the number of electric energy quality grades is defined as r, and the membership degree set of the electric energy quality index set is defined as +.>
wherein ,i1 ∈[1,m]，j 1 ∈[1,n]，k 1 ∈[1,s j ]，l 1 ∈[1,r]；/>
Indicating the monitoring point i 1 At the first level of power quality index j 1 The lower two-level electric energy quality index k 1 For power quality class l 1 Membership value of (2).
The membership value calculation method of the deviation index comprises the following steps:
i 1 ∈[1,m]，j 1 ∈[1,n]，
wherein m is the number of monitoring points, n is the number of primary power quality indexes,
is the first-level electric energy quality index j 1 The number of lower secondary power quality indicators, +.>
Is the first-level electric energy quality index j 1 The lower two-level electric energy quality index k 1 The upper limit of the allowable operation value specified by the national standard,/->
Is the first-level electric energy quality index j 1 The lower two-level electric energy quality index k 1 The lower limit of the allowable operation value specified by the national standard.
The membership value calculation method of the duration class index comprises the following steps:
i 1 ∈[1,m]，j 1 ∈[1,n]，
wherein m is the number of monitoring points, n is the number of primary power quality indexes,
is the first-level electric energy quality index j 1 The number of lower secondary power quality indicators, +.>
Is the first-level electric energy quality index j 1 The lower two-level electric energy quality index k 1 The allowable duration, k, of the deviation value specified by the national standard 0 The fixed coefficient was taken as 0.13.
The membership degree calculation method of the reliability index comprises the following steps:
i 1 ∈[1,m]，j 1 ∈[1,n]，
wherein ,
and normalizing the power quality comprehensive evaluation index data to obtain a value. />
Deviation class indicators include, but are not limited to, voltage amplitude average deviation, flicker level, voltage average ripple 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 break duration.
The reliability index comprises power supply reliability;
defining a monitoring point i 1 The fuzzy relation between the membership degree of the electric energy index and the electric energy quality evaluation set is that
The fuzzy relation calculating method comprises the following steps:
i 1 ∈[1,m]，j 1 ∈[1,n]，
wherein m is the number of monitoring points, n is the number of primary power quality indexes,
is the first-level electric energy quality index j 1 The number of lower secondary power quality indicators; />
Indicating the monitoring point i 1 At the first level of power quality index j 1 The lower two-level electric energy quality index k 1 For power quality class l 1 Is a fuzzy mapping relation of the (a);
the information entropy value calculating method comprises the following steps:
in the formula ,
is the index of primary electric energy quality index j 1 Lower secondary power quality index k 1 Information entropy value of>
For monitoring point i 1 At the first level of power quality index j 1 The lower two-level electric energy quality index k 1 Chang Quan information entropy of (2);
introducing a weight changing theory to process the information entropy, and realizing the weight changing of the information entropy to highlight the influence of the bad index on the comprehensive judgment result;
with the qualified electric energy quality as a standard, the monitoring point i 1 The membership of the overall power quality at the grade of being qualified J and above is recorded as
Information entropy change objective weight>
The calculation method comprises the following steps:
i 1 ∈[1,m]，j 1 ∈[1,n]，
in the formula ,
for monitoring point i 1 At the first level of power quality index j 1 Lower secondary power quality index k 1 For electric energy quality grade l 1 Is a membership of (1). />
For monitoring point i 1 N primary power quality indexes and each primary power quality index j 1 Lower->
A second-level power quality index, which is qualified for the power quality grade l 1 E [1, 2., J). />
Smaller indicates the monitoring point i 1 The worse the overall power quality level of (2), the first power quality index j at that point 1 Lower secondary power quality index k 1 Variable weight coefficient->
The larger, where a is the performance balance correction coefficient;
and step 2, further determining the comprehensive weight of the primary power quality index and the secondary power quality index as follows:
wherein ,
is the first-level electric energy quality index j 1 Lower secondary power quality index k 1 Subjective weight value of +.>
For monitoring point i 1 At the first level of power quality index j 1 Lower secondary power quality index k 1 Objective weight value of information entropy change of +.>
For monitoring point i 1 At the first level of power quality index j 1 Lower secondary power quality fingerMark k 1 Is used for determining the final comprehensive weight value of the (a);
Preferably, the monitoring value of the normalization processing of the secondary power quality index under the primary power quality index at the monitoring point in the step 3 is:
for a power quality index set
Indicating the monitoring point i 1 At the first level of power quality index j 1 The lower two-level electric energy quality index k 1 The index after normalization treatment is +.>
And step 3, calculating a positive ideal solution and a negative ideal solution of the secondary electric energy quality index of the primary electric energy quality index as follows:
first-level power quality index j 1 The lower two-level electric energy quality index k 1 Is to say that
The method comprises the following steps:
first-level power quality index j 1 The lower two-level electric energy quality index k 1 Negative ideal solution of (i)
The method comprises the following steps:
in the formula ,
is the first-level electric energy quality index j 1 The lower two-level electric energy quality index k 1 At m monitorsMaximum value of the measured point value,/">
Is the first-level electric energy quality index j 1 The lower two-level electric energy quality index k 1 The minimum value of the m monitoring point values;
and 3, constructing a positive ideal solution sequence and a negative ideal solution sequence as follows:
from the following components
The positive ideal sequence of the composition is marked by +.>
The negative ideal solution sequence of the composition is marked as R - The following are respectively indicated:
and 3, calculating the total index in the range of the secondary electric energy quality index under the primary electric energy quality index, wherein the maximum value of the expression is as follows:
in the formula ,
represents n primary power quality indexes of m monitoring points and each primary power quality index j 1 Corresponding->
Solving the maximum value of the expression of all indexes in the range of the two-level electric energy quality indexes; />
And 3, calculating the resolution coefficient of the gray correlation method as follows:
wherein ρ is the resolution coefficient of the gray correlation method, and the value should be satisfied: x is X Δ X is less than 1/3 Δ ≤ρ≤1.5X Δ The method comprises the steps of carrying out a first treatment on the surface of the When X is Δ 1.5X when not less than 1/3 Δ ≤ρ≤2X Δ ；
And 3, calculating a forward correlation coefficient as follows:
record monitoring point i 1 At the first level of power quality index j 1 The lower two-level electric energy quality index k 1 Normalized index of (2)
And the first-level power quality index j 1 The lower two-level electric energy quality index k 1 Is->
The degree of correlation of (2) is a positive correlation coefficient, which is marked as +.>
The specific calculation method comprises the following steps:
and 3, calculating a negative correlation coefficient as follows:
record monitoring point i 1 Primary electric energy qualityIndex j 1 The lower two-level electric energy quality index k 1 Normalized index of (2)
And the first-level power quality index j 1 The lower two-level electric energy quality index k 1 Negative ideal solution->
The correlation degree negative correlation coefficient of (2) is marked as +.>
The specific calculation method comprises the following steps:
and 3, performing index polymerization on the log method based on the wooden barrel theory to obtain a degree of association polymerization as follows:
index aggregation is carried out by introducing a log method based on a barrel theory, and the degree of association aggregation is as follows:
in the formula ,
is the first-level electric energy quality index j 1 The lower two-level electric energy quality index k 1 And I jc ∈[1,5]，/>
Is the first-level electric energy quality index j 1 The lower two-level electric energy quality index k 1 Is a comprehensive weight value of (1);
and 3, respectively calculating the weighted positive gray correlation degree and the weighted negative gray correlation degree as follows:
in the formula ,
indicating the monitoring point i 1 Overall power quality index and positive ideal solution R + The degree of grey-color correlation between the two,
indicating the monitoring point i 1 Overall power quality index and negative ideal solution R - Gray correlation between;
and 3, respectively calculating the positive Euclidean distance and the negative Euclidean distance as follows:
in the formula ,
indicating the monitoring point i 1 Overall power quality index and positive ideal solution R + European distance between->
Indicating the monitoring point i 1 Overall power quality index and negative ideal solution R - Euclidean distance between them.
And 3, calculating positive comprehensive association degree and negative comprehensive association degree as follows:
construction comprehensive positive comprehensive association degree
And negative comprehensive relevance->
The method comprises the following steps:
in the formula ,α1 For a first linear coefficient, alpha 2 Is a second linear coefficient, alpha 1 ，α 2 ∈[0,1]And alpha is 1 +α 2 ＝1；
And 3, constructing comprehensive evaluation indexes by the positive comprehensive association degree and the negative comprehensive association degree as follows:
in the formula ,
the larger the value is, the more the monitoring point i is represented 1 The better the overall power quality at the location;
comprehensive evaluation index (namely
The value is used to evaluate the overall power quality of the monitoring point.
The beneficial effects of the invention are as follows:
the traditional information entropy weighting method is adjusted by combining a variable weighting theory, so that a higher objective weight is given to the bad indexes, and the importance of the decision scheme to the bad indexes is improved;
an ideal solution is introduced to modify the traditional gray correlation method, so that the distinguishing capability of the evaluation method on different index sets to be evaluated is improved;
the log aggregation method based on the wooden barrel theory is used for replacing the traditional linear aggregation method, and the association degree calculation formula of the gray association method is adjusted, so that the distinguishing capability of severe indexes is improved, and the evaluation result which meets the electric energy quality evaluation target better is realized.
Drawings
Fig. 1: is a flow chart of the invention;
Detailed Description
The technical solutions 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 will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Fig. 1 is a flowchart of the present invention, and the present embodiment is implemented by the following technical solutions, and a method for comprehensively evaluating the power quality of a low-voltage station area is characterized by comprising the following steps:
step 1: establishing a primary power quality index set and a secondary power quality index set;
preferably, in the step 1, the establishing a primary power quality index set and a secondary power quality index set is as follows:
i 1 ∈[1,m]，j 1 ∈[1,n]，k 1 ∈[1,s i ]
wherein m is the number of monitoring points, n is the number of primary power quality indexes, s i For the number of the secondary power quality indexes under the primary power quality index i,
indicating the monitoring point i 1 At the first level of power quality index j 1 The lower two-level electric energy quality index k 1 Is a monitored value of (2);
the primary power quality indicators in step 1 include, but are not limited to, voltage deviation, voltage flicker, voltage fluctuation, harmonic distortion, three-phase imbalance, frequency deviation, and power supply reliability;
the secondary power quality indicators in step 1 include, but are not limited to, voltage amplitude average deviation, voltage deviation duration, flicker level, flicker duration, voltage average fluctuation amplitude, fluctuation duration, total harmonic content, harmonic duration, unbalance degree, unbalance duration, frequency average deviation, frequency deviation duration, voltage sag, and interruption duration.
Step 2: constructing a judgment matrix, combining the judgment matrix, determining subjective weights of the primary power quality index and the secondary power quality index through an application scale expansion method, determining objective weights of the primary power quality index and the secondary power quality index through a weight changing theory, and further determining comprehensive weights of the primary power quality index and the secondary power quality index;
and step 2, constructing a judgment matrix:
wherein ,W* Representing the judgment matrix and having complete consistency, and being a positive-to-negative matrix of n, n being the number of primary power quality indexes, and ordering the relative importance of the primary power quality indexes, t k Represents the importance of the kth primary power quality index relative to the kth+1st primary power quality index, k is [1, n-1 ]]The method comprises the following steps:
if X k Relative to X k-1 Equally important, then t k Taking a 1 ；
If X k Relative to X k-1 Slightly important, t k Taking a 2 ；
If X k Relative to X k-1 Obviously important, t k Taking a 3 ；
If X k Relative to X k-1 Very important, t k Taking a 4 ；
If X k Relative to X k-1 Extremely important, t k Taking a 5 。
wherein ,a1 ＜a 2 ＜a 3 ＜a 4 ＜a 5 ，
To judge the ith in the matrix 0 Line j 0 The elements of the column, i.e. representing the ith 0 The first-level electric energy quality index is relative to the j 0 Subjective weight of each primary electric energy quality index reflects the ith 0 The first-level electric energy quality index is relative to the j 0 Importance degree of each first-level electric energy quality index, i 0 ∈[1,n]，j 0 ∈[1,n]，i 0 ≠j 0 ；
wherein ,tk The specific value of (a) is a 1 ＝1，a 2 ＝1.2，a 3 ＝1.4，a 4 ＝1.6，a 5 The step 2 of combining the judgment matrix determines that subjective weights of the primary power quality index and the secondary power quality index are selected by applying a scale expansion method as follows:
for a power quality index set
Defining m as the number of monitoring points, n as the first-level power quality index number, s i For the number of secondary electric energy quality index under the primary electric energy quality index i, <>
The types of indexes can be classified into a deviation type index, a duration type index and a reliability index.
First-level power quality index i 0 Is recorded as the subjective weight value of (2)
The specific calculation is as follows:
first-level power quality index i 0 The relative weight of the lower-level power quality index is recorded as
Is a row vector and the number of vector elements is +.>
For->
Two secondary electric energy quality index->
Relative importance ranking is performed. Wherein (1)>
Represents the kth 0 The second electric energy quality index is relative to the kth 0 Importance of +1 secondary power quality indicators. k (k) 0 The value method is the same as the k value method.
First-level power quality index i 0 Subjective weight of the next-level power quality index is recorded as
The specific calculation is as follows:
wherein, the first-level electric energy quality index i 0 The following k 0 The subjective weight concrete value of each secondary electric energy quality index is
And step 2, determining and selecting objective weights of a primary electric energy quality index and a secondary electric energy quality index according to a variable weight theory, wherein the objective weights are as follows:
the comprehensive electric energy quality on-line monitoring device is used for monitoring n electric energy quality secondary electric energy quality indexes of m monitoring points in the power distribution network, and an original electric energy quality evaluation index monitoring data set is obtained as
In the actual process, generally, the measurement units of different electric energy quality indexes are different, and in order to make each index have the same expressive force in the comprehensive evaluation process, the electric energy quality comprehensive evaluation index data needs to be normalized to obtain ∈ ->
The normalization process is to limit the index fluctuation range to the interval [0,1 ]]And 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:
if the index data is smaller and better, the normalization method is as follows:
i 1 ∈[1,m]，j 1 ∈[1,n]，
wherein m is the number of monitoring points, n is the number of primary power quality indexes,
is the first-level electric energy quality index j 1 The number of lower secondary power quality indicators, +. >
Is the first-level electric energy quality index j 1 The following k 1 Maximum value of m monitoring data of two secondary electric energy quality indexes, < >>
Is the first-level electric energy quality index j 1 The following k 1 The minimum value in m monitoring data of the two-level power quality indexes. For the power quality index set->
Defining m as the number of monitoring points, n as the number of primary power quality indexes, < >>
Is the first-level electric energy quality index j 1 The number of the next-level electric energy quality indexes is defined as r, the number of electric energy quality grades is defined as r, and the membership degree set of the electric energy quality index set is defined as +.>
wherein ,i1 ∈[1,m]，j 1 ∈[1,n]，k 1 ∈[1,s j ]，l 1 ∈[1,r]；/>
Indicating the monitoring point i 1 At the first level of power quality index j 1 The lower two-level electric energy quality index k 1 For power quality class l 1 Membership value of (2).
the membership value calculation method of the deviation index comprises the following steps:
i 1 ∈[1,m]，j 1 ∈[1,n]，
wherein m is the number of monitoring points, n is the number of primary power quality indexes,
is the first-level electric energy quality index j 1 The number of lower secondary power quality indicators, +.>
Is the first-level electric energy quality index j 1 The lower two-level electric energy quality index k 1 The upper limit of the allowable operation value specified by the national standard,/->
Is the first-level electric energy quality index j 1 The lower two-level electric energy quality index k 1 The lower limit of the allowable operation value specified by the national standard.
The membership value calculation method of the duration class index comprises the following steps:
i 1 ∈[1,m]，j 1 ∈[1,n]，
wherein m is the number of monitoring points, n is the number of primary power quality indexes,
is the first-level electric energy quality index j 1 The number of lower secondary power quality indicators, +.>
Is the first-level electric energy quality index j 1 The lower two-level electric energy quality index k 1 The allowable duration, k, of the deviation value specified by the national standard 0 The fixed coefficient was taken as 0.13.
The membership degree calculation method of the reliability index comprises the following steps:
i 1 ∈[1,m]，j 1 ∈[1,n]，
wherein ,
and normalizing the power quality comprehensive evaluation index data to obtain a value. />
Deviation class indicators include, but are not limited to, voltage amplitude average deviation, flicker level, voltage average ripple 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 break duration.
The reliability index comprises power supply reliability;
defining a monitoring point i 1 The fuzzy relation between the membership degree of the electric energy index and the electric energy quality evaluation set is that 
The fuzzy relation calculating method comprises the following steps:
i 1 ∈[1,m]，j 1 ∈[1,n]，
wherein m is the number of monitoring points, n is the number of primary power quality indexes,
is the first-level electric energy quality index j 1 The number of lower secondary power quality indicators; />
Indicating the monitoring point i 1 At the first level of power quality index j 1 The lower two-level electric energy quality index k 1 For power quality class l 1 Is a fuzzy mapping relation of the (a);
the information entropy value calculating method comprises the following steps:
in the formula ,
is the index of primary electric energy quality index j 1 Lower secondary power quality index k 1 Information entropy value of>
For monitoring point i 1 At the first level of power quality index j 1 The lower two-level electric energy quality index k 1 Chang Quan information entropy of (2);
introducing a weight changing theory to process the information entropy, and realizing the weight changing of the information entropy to highlight the influence of the bad index on the comprehensive judgment result;
with the qualified electric energy quality as a standard, the monitoring point i 1 The membership of the overall power quality at the grade of being qualified J and above is recorded as
Information entropy change objective weight>
The calculation method comprises the following steps:
i 1 ∈[1,m]，j 1 ∈[1,n]，
in the formula ,
for monitoring point i 1 At the first level of power quality index j 1 Lower secondary power quality index k 1 For electric energy quality grade l 1 Is a membership of (1). />
For monitoring point i 1 N primary power quality indicators, under each primary power quality indicator j1 +. >
A second-level power quality index, which is qualified for the power quality grade l 1 E [1, 2., J). />
Smaller indicates the monitoring point i 1 The worse the overall power quality level of (2), the first power quality index j at that point 1 Lower secondary power quality index k 1 Variable weight coefficient->
The larger, where a is the performance balance correction coefficient; />
And step 2, further determining the comprehensive weight of the primary power quality index and the secondary power quality index as follows:
wherein ,
is the first-level electric energy quality index j 1 Lower secondary power quality index k 1 Subjective weight value of +.>
For monitoring point i 1 At the first level of power quality index j 1 Lower secondary power quality index k 1 Objective weight value of information entropy change of +.>
For monitoring point i 1 At the first level of power quality index j 1 Lower secondary power quality index k 1 Is used for determining the final comprehensive weight value of the (a);
step 3: calculating positive ideal solutions and negative ideal solutions of the secondary power quality indexes of the primary power quality indexes through normalized monitoring values of the secondary power quality indexes at the monitoring points, constructing positive ideal solution sequences and negative ideal solution sequences, calculating total index solving expression maximum values in the secondary power quality index range under the primary power quality indexes, calculating resolution coefficients of a gray correlation method by combining the positive ideal solution sequences, calculating positive correlation coefficients and negative correlation coefficients, carrying out index aggregation on the basis of a log method of a wooden barrel theory to obtain correlation degree aggregation degree, calculating weighted positive gray correlation degree, weighted negative gray correlation degree, calculating positive Euclidean distance and negative Euclidean distance, further calculating positive comprehensive correlation degree and negative comprehensive correlation degree, and constructing comprehensive evaluation indexes through the positive comprehensive correlation degree and the negative comprehensive correlation degree for evaluating the whole power quality of the monitoring points.
And 3, the normalized monitoring value of the secondary electric energy quality index under the primary electric energy quality index at the monitoring point is:
for a power quality index set
Indicating the monitoring point i 1 At the first level of power quality index j 1 The lower two-level electric energy quality index k 1 The index after normalization treatment is +.>
And step 3, calculating a positive ideal solution and a negative ideal solution of the secondary electric energy quality index of the primary electric energy quality index as follows:
first-level power quality index j 1 The lower two-level electric energy quality index k 1 Is to say that
The method comprises the following steps:
first-level power quality index j 1 The lower two-level electric energy quality index k 1 Negative ideal solution of (i)
The method comprises the following steps:
in the formula ,
is the first-level electric energy quality index j 1 The lower two-level electric energy quality index k 1 At the maximum of the m monitoring point values, +.>
Is the first-level electric energy quality index j 1 The lower two-level electric energy quality index k 1 The minimum value of the m monitoring point values;
and 3, constructing a positive ideal solution sequence and a negative ideal solution sequence as follows:
from the following components
The positive ideal sequence of the composition is marked by +.>
The negative ideal solution sequence of the composition is marked as R - The following are respectively indicated:
and 3, calculating the total index in the range of the secondary electric energy quality index under the primary electric energy quality index, wherein the maximum value of the expression is as follows:
in the formula ,
represents n primary power quality indexes of m monitoring points and each primary power quality index j 1 Corresponding->
Solving the maximum value of the expression of all indexes in the range of the two-level electric energy quality indexes;
and 3, calculating the resolution coefficient of the gray correlation method as follows:
wherein ρ is the resolution coefficient of the gray correlation method, and the value should be satisfied: x is X Δ X is less than 1/3 Δ ≤ρ≤1.5X Δ The method comprises the steps of carrying out a first treatment on the surface of the When X is Δ 1.5X when not less than 1/3 Δ ≤ρ≤2X Δ ；
And 3, calculating a forward correlation coefficient as follows:
record monitoring point i 1 At the first level of power quality index j 1 The lower two-level electric energy quality index k 1 Normalized index of (2)
And the first-level power quality index j 1 The lower two-level electric energy quality index k 1 Is->
The degree of correlation of (2) is a positive correlation coefficient, which is marked as +.>
The specific calculation method comprises the following steps:
and 3, calculating a negative correlation coefficient as follows:
record monitoring point i 1 At the first level of power quality index j 1 The lower two-level electric energy quality index k 1 Normalized index of (2)
And the first-level power quality index j 1 The lower two-level electric energy quality index k 1 Negative ideal solution->
The correlation degree negative correlation coefficient of (2) is marked as +.>
The specific calculation method comprises the following steps:
and 3, performing index polymerization on the log method based on the wooden barrel theory to obtain a degree of association polymerization as follows:
index aggregation is carried out by introducing a log method based on a barrel theory, and the degree of association aggregation is as follows:
in the formula ,
is the first-level electric energy quality index j 1 The lower two-level electric energy quality index k 1 And I jc ∈[1,5]，/>
Is the first-level electric energy quality index j 1 The lower two-level electric energy quality index k 1 Is a comprehensive weight value of (1);
and 3, respectively calculating the weighted positive gray correlation degree and the weighted negative gray correlation degree as follows:
in the formula ,
indicating the monitoring point i 1 Overall power quality index and positive ideal solution R + The degree of grey-color correlation between the two,
indicating the monitoring point i 1 Overall power quality index and negative ideal solution R - Gray correlation between;
and 3, respectively calculating the positive Euclidean distance and the negative Euclidean distance as follows:
in the formula ,
indicating the monitoring point i 1 Overall power quality index and positive ideal solution R + European distance between->
Indicating the monitoring point i 1 Overall power quality index and negative ideal solution R - Euclidean distance between them.
And 3, calculating positive comprehensive association degree and negative comprehensive association degree as follows:
construction comprehensive positive comprehensive association degree
And negative comprehensive relevance->
The method comprises the following steps:
in the formula ,α1 For a first linear coefficient, alpha 2 Is a second linear coefficient, alpha 1 ，α 2 ∈[0,1]And alpha is 1 +α 2 ＝1；
And 3, constructing comprehensive evaluation indexes by the positive comprehensive association degree and the negative comprehensive association degree as follows:
in the formula ,
the larger the value is, the more the monitoring point i is represented 1 The better the overall power quality at the location;
comprehensive evaluation index (namely
The value is used to evaluate the overall power quality of the monitoring point.
The number of the applicable index is Dat3= { x 1 ，x 3 ，x 5 ，x 7 ，x 9 ，x 11 -a }; membership function of duration class index, number of applicable index is Dat4= { x 2 ，x 4 ，x 6 ，x 8 ，x 10 ，x 12 -a }; membership function of reliability index, number of applicable index is Dat5= { x 13 }.
And carrying out power quality evaluation based on data of 3 monitoring points in a certain 10kV power distribution system. A is that 1 ～A 5 For five power quality grades from good to bad, the index group is determined according to national relevant standards and industry expert experience; b (B) 1 ～B 3 For 3 monitoring points, statistics is carried out on the monitoring data collected in a certain week, and the 95% probability large value is used as the original index data to be filled in the table 1.
Table 1 raw monitoring data of index
The comprehensive weight is calculated based on a scale expansion method and a variable weight theory as follows:
in the above, ω i Is subjective weight value, v i1 Objective weight value for constant entropy，v i2B1 ，v i2B2 and vi2B3 For introducing index set information entropy objective weight value of variable weight theory, alpha i And the final comprehensive weight value.
Solving to obtain the comprehensive weight alpha i Obtaining a weighted decision matrix R through normalization and weighting * The positive and negative ideal solutions are obtained 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]
calculate delta v ＝0.0254，X Δ ＝0.2003<1/3, and the resolution coefficient is determined to be ρ=0.3.
Comprehensive relevance G when ρ=0.5 under linear weighting is calculated respectively 1 And the comprehensive association degree G when rho=0.3 selected according to the method 2 ：
ΔG 1 ＝G 1max -G 1min ＝0.7199-0.2634＝0.4565
ΔG 2 ＝G 2max -G 2min ＝0.7712-0.209＝0.5622
ΔG can be seen 1 <ΔG 2 The method has the advantages that the association degree distribution interval obtained by selecting the rho value according to the method is larger, and the interference of the severe value of the observation sequence on the evaluation result can be well restrained. Calculating the comprehensive gray correlation value
and />
Listed in Table 2, final decision criteria S i Also listed in table 2.
Table 2 results of power quality assessment
From the original monitoring data shown in Table 3, monitoring point B 2 In the data of (a), voltage deviation is sustainedThe time and the harmonic duration are in the "bad" level, and the comprehensive evaluation result of the electric energy quality should trend to the "bad" level. Based on the judgment of Table 3, analyzing the data of Table 4, monitoring point B 2 In the comprehensive evaluation results of (a), the conventional gray correlation method is rated as "medium", the method is adopted to rate as "bad", the reason for the difference of the results of the two methods is mainly the linear information aggregation mode adopted by the conventional gray correlation method and is due to the index x 2 And index x 8 The weight of (2) is lower, and the bad influence is covered by other high-weight excellent indexes, so that the evaluation value tends to be better. Likewise applying the methods herein, reference B 2 Index x of (2) 2 The monitoring value is modified to 6.0, the influence of the bad indexes is weakened, the final grade rating value is changed to be 'medium', and the rating result is consistent with the traditional grey correlation method.
It should be understood that parts of the specification not specifically set forth herein are all prior art.
It should be understood that the foregoing description of the preferred embodiments is not intended to limit the scope of the invention, but rather to limit the scope of the claims, and that those skilled in the art can make substitutions or modifications without departing from the scope of the invention as set forth in the appended claims.
Claims (4)
1. The comprehensive evaluation method for the electric energy quality of the low-voltage transformer area is characterized by comprising the following steps of:
step 1: establishing a primary power quality index set and a secondary power quality index set;
step 2: constructing a judgment matrix, combining the judgment matrix, determining subjective weights of the primary power quality index and the secondary power quality index through an application scale expansion method, determining objective weights of the primary power quality index and the secondary power quality index through a weight changing theory, and further determining comprehensive weights of the primary power quality index and the secondary power quality index;
step 3: calculating positive ideal solutions and negative ideal solutions of the secondary power quality indexes of the primary power quality indexes through normalized monitoring values of the secondary power quality indexes at the monitoring points, constructing positive ideal solution sequences and negative ideal solution sequences, calculating total index solving expression maximum values in the secondary power quality index range under the primary power quality indexes, calculating resolution coefficients of a gray correlation method by combining the positive ideal solution sequences, calculating positive correlation coefficients and negative correlation coefficients, carrying out index aggregation on the basis of a log method of a wooden barrel theory to obtain correlation degree aggregation degree, calculating weighted positive gray correlation degree, weighted negative gray correlation degree, calculating positive Euclidean distance and negative Euclidean distance, further calculating positive comprehensive correlation degree and negative comprehensive correlation degree, and constructing comprehensive evaluation indexes through the positive comprehensive correlation degree and the negative comprehensive correlation degree for evaluating the whole power quality of the monitoring points.
2. The method for comprehensively evaluating the power quality of a low-voltage area according to claim 1, wherein the method comprises the following steps of:
step 1, a primary power quality index set and a secondary power quality index set are established as follows:
wherein m is the number of monitoring points, n is the number of primary power quality indexes, s i For the number of the secondary power quality indexes under the primary power quality index i,
indicating the monitoring point i 1 At the first level of power quality index j 1 The lower two-level electric energy quality index k 1 Is a monitored value of (2);
the primary power quality indicators in step 1 include, but are not limited to, voltage deviation, voltage flicker, voltage fluctuation, harmonic distortion, three-phase imbalance, frequency deviation, and power supply reliability;
the secondary power quality indicators in step 1 include, but are not limited to, voltage amplitude average deviation, voltage deviation duration, flicker level, flicker duration, voltage average fluctuation amplitude, fluctuation duration, total harmonic content, harmonic duration, unbalance degree, unbalance duration, frequency average deviation, frequency deviation duration, voltage sag, and interruption duration;
and step 2, constructing a judgment matrix:
wherein ,W* Representing the judgment matrix and having complete consistency, and being a positive-to-negative matrix of n, n being the number of primary power quality indexes, and ordering the relative importance of the primary power quality indexes, t k Represents the importance of the kth primary power quality index relative to the kth+1st primary power quality index, k is [1, n-1 ]]The method comprises the following steps:
if X k Relative to X k-1 Equally important, then t k Taking a 1 ；
If X k Relative to X k-1 Slightly important, t k Taking a 2 ；
If X k Relative to X k-1 Obviously important, t k Taking a 3 ；
If X k Relative to X k-1 Very important, t k Taking a 4 ；
If X k Relative to X k-1 Extremely important, t k Taking a 5 ；
wherein ,a1 ＜a 2 ＜a 3 ＜a 4 ＜a 5 ，
To judge the ith in the matrix 0 Line j 0 The elements of the column, i.e. representing the ith 0 The first-level electric energy quality index is relative to the j 0 Subjective weight of primary electric energy quality index, reflecti 0 The first-level electric energy quality index is relative to the j 0 Importance degree of each first-level electric energy quality index, i 0 ∈[1,n]，j 0 ∈[1,n]，i 0 ≠j 0 ；
And step 2, determining subjective weights for selecting the primary power quality index and the secondary power quality index by combining the judgment matrix through an application scale expansion method, wherein the subjective weights are as follows:
for a power quality index set
Defining m as the number of monitoring points, n as the first-level power quality index number, s i The power quality index d is the number of the second-level power quality index under the first-level power quality index i i1,j1,k1 The method can be divided into deviation type indexes, duration type indexes and reliability indexes according to index types;
first-level power quality index i 0 Is recorded as the subjective weight value of (2)
The specific calculation is as follows:
First-level power quality index i 0 The relative weight of the lower-level power quality index is recorded as
Is a row vector and the number of vector elements is +.>
For->
Two-stage power quality indexMark->
Performing relative importance ranking; wherein,
represents the kth 0 The second electric energy quality index is relative to the kth 0 Importance of +1 secondary power quality indicators; k (k) 0 The value method is the same as the value method of k;
first-level power quality index i 0 Subjective weight of the next-level power quality index is recorded as
The specific calculation is as follows:
wherein, the first-level electric energy quality index i 0 The following k 0 The subjective weight concrete value of each secondary electric energy quality index is
3. The method for comprehensively evaluating the power quality of a low-voltage area according to claim 1, wherein the method comprises the following steps of:
and step 2, determining and selecting objective weights of the primary electric energy quality index and the secondary electric energy quality index according to a variable weight theory, wherein the objective weights are as follows:
the comprehensive electric energy quality on-line monitoring device is used for monitoring n electric energy quality secondary electric energy quality indexes of m monitoring points in the power distribution network, and an original electric energy quality evaluation index monitoring data set is obtained as
In practice, in general, different power quality meansThe target measurement units are different, and in order to make each index have the same expressive force in the comprehensive evaluation process, the electric energy quality comprehensive evaluation index data needs to be normalized to obtain +. >
The normalization process is to limit the index fluctuation range to the interval [0,1 ]]And 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:
if the index data is smaller and better, the normalization method is as follows:
wherein m is the number of monitoring points, n is the number of primary power quality indexes,
is the first-level electric energy quality index j 1 The number of lower secondary power quality indicators, +.>
Is the first-level electric energy quality index j 1 The following k 1 Maximum value of m monitoring data of two secondary electric energy quality indexes, < >>
Is the first-level electric energy quality index j 1 The following k 1 The minimum value in m monitoring data of the two-level electric energy quality indexes; for the power quality index set->
Defining m as the number of monitoring points, n as the number of primary power quality indexes, < >>
Is the first-level electric energy quality index j 1 The number of the next-level electric energy quality indexes is defined as r, the number of electric energy quality grades is defined as r, and the membership degree set of the electric energy quality index set is defined as +.>
wherein ,i1 ∈[1,m]，j 1 ∈[1,n]，k 1 ∈[1,s j ]，l 1 ∈[1,r]；/>
Indicating the monitoring point i 1 At the first level of power quality index j 1 The lower two-level electric energy quality index k 1 For power quality class l 1 Membership value of (2);
The membership value calculation method of the deviation index comprises the following steps:
wherein m is the number of monitoring points, n is the number of primary power quality indexes,
is the first-level electric energy quality index j 1 The number of lower secondary power quality indicators, +.>
Is the first-level electric energy quality index j 1 The lower two-level electric energy quality index k 1 The upper limit of the allowable operation value specified by the national standard,/->
Is the first-level electric energy quality index j 1 The lower two-level electric energy quality index k 1 The lower limit of the allowable running value specified by the national standard;
the membership value calculation method of the duration class index comprises the following steps:
wherein m is the number of monitoring points, n is the number of primary power quality indexes,
is the first-level electric energy quality index j 1 The number of lower secondary power quality indicators, +.>
Is the first-level electric energy quality index j 1 The lower two-level electric energy quality index k 1 The allowable duration, k, of the deviation value specified by the national standard 0 Taking the fixed coefficient as 0.13; />
The membership degree calculation method of the reliability index comprises the following steps:
wherein ,
a value obtained by normalizing the comprehensive evaluation index data of the electric energy quality;
deviation class indicators include, but are not limited to, voltage amplitude 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 break duration;
the reliability index comprises power supply reliability;
defining a monitoring point i 1 The fuzzy relation between the membership degree of the electric energy index and the electric energy quality evaluation set is that
The fuzzy relation calculating method comprises the following steps:
wherein m is the number of monitoring points, n is the number of primary power quality indexes,
is the first-level electric energy quality index j 1 The number of lower secondary power quality indicators; />
Indicating the monitoring point i 1 At the first level of power quality index j 1 The lower two-level electric energy quality index k 1 For power quality class l 1 Is a fuzzy mapping relation of the (a);
the information entropy value calculating method comprises the following steps:
in the formula ,
is the index of primary electric energy quality index j 1 Lower secondary power quality index k 1 Information entropy value of>
For monitoring point i 1 At the first level of power quality index j 1 The lower two-level electric energy quality index k 1 Chang Quan information entropy of (2);
introducing a weight changing theory to process the information entropy, and realizing the weight changing of the information entropy to highlight the influence of the bad index on the comprehensive judgment result;
with the qualified electric energy quality as a standard, the monitoring point i 1 The membership of the overall power quality at the grade of being qualified J and above is recorded as
Information entropy change objective weight>
The calculation method comprises the following steps:
in the formula ,
for monitoring point i 1 At the first level of power quality index j 1 Lower secondary power quality index k 1 For electric energy quality grade l 1 Membership degree of (3); />
For monitoring point i 1 N primary power quality indexes and each primary power quality index j 1 Lower->
A second-level power quality index, which is qualified for the power quality grade l 1 E [1, 2., J) maximum value of membership value; />
Smaller indicates the monitoring point i 1 The worse the overall power quality level of (2), the first power quality index j at that point 1 Lower secondary power quality index k 1 Variable weight coefficient->
The larger, where a is the performance balance correction coefficient;
and step 2, further determining the comprehensive weight of the primary power quality index and the secondary power quality index as follows:
wherein ,
is the first-level electric energy quality index j 1 Lower secondary power quality index k 1 Subjective weight value of +.>
For monitoring point i 1 At the first level of power quality index j 1 Lower secondary power quality index k 1 Objective weight value of information entropy change of +.>
For monitoring point i 1 At the first level of power quality index j 1 Lower secondary power quality index k 1 Is added to the final composite weight value.
4. The method for comprehensively evaluating the power quality of a low-voltage area according to claim 1, wherein the method comprises the following steps of:
and 3, the normalized monitoring value of the secondary electric energy quality index under the primary electric energy quality index at the monitoring point is:
for a power quality index set
Indicating the monitoring point i 1 At the first level of power quality index j 1 The lower two-level electric energy quality index k 1 The index after normalization treatment is +.>
And step 3, calculating a positive ideal solution and a negative ideal solution of the secondary electric energy quality index of the primary electric energy quality index as follows:
first-level power quality index j 1 The lower two-level electric energy quality index k 1 Is to say that
The method comprises the following steps:
first-level power quality index j 1 The lower two-level electric energy quality index k 1 Negative ideal solution of (i)
The method comprises the following steps:
in the formula ,
is the first-level electric energy quality index j 1 The lower two-level electric energy quality index k 1 At the maximum of the m monitoring point values, +.>
Is the first-level electric energy quality index j 1 The lower two-level electric energy quality index k 1 The minimum value of the m monitoring point values;
and 3, constructing a positive ideal solution sequence and a negative ideal solution sequence as follows:
from the following components
The positive ideal sequence of the composition is marked by +.>
The negative ideal solution sequence of the composition is marked as R - The following are respectively indicated:
and 3, calculating the total index in the range of the secondary electric energy quality index under the primary electric energy quality index, wherein the maximum value of the expression is as follows:
in the formula ,
represents n primary power quality indexes of m monitoring points and each primary power quality index j 1 Corresponding->
Solving the maximum value of the expression of all indexes in the range of the two-level electric energy quality indexes;
and 3, calculating the resolution coefficient of the gray correlation method as follows:
wherein ρ is the resolution coefficient of the gray correlation method, and the value should be satisfied: x is X Δ X is less than 1/3 Δ ≤ρ≤1.5X Δ The method comprises the steps of carrying out a first treatment on the surface of the When X is Δ 1.5X when not less than 1/3 Δ ≤ρ≤2X Δ ；
And 3, calculating a forward correlation coefficient as follows:
record monitoring point i 1 At the first level of power quality index j 1 The lower two-level electric energy quality index k 1 Normalized index of (2)
And the first-level power quality index j 1 The lower two-level electric energy quality index k 1 Is->
The degree of correlation of (2) is a positive correlation coefficient, which is marked as +.>
The specific calculation method comprises the following steps:
and 3, calculating a negative correlation coefficient as follows:
record monitoring point i 1 At the first level of power quality index j 1 The lower two-level electric energy quality index k 1 Normalized index of (2)
And the first-level power quality index j 1 The lower two-level electric energy quality index k 1 Negative ideal solution->
Is marked as the negative correlation coefficient of the correlation degree
The specific calculation method comprises the following steps:
and 3, performing index polymerization on the log method based on the wooden barrel theory to obtain a degree of association polymerization as follows:
index aggregation is carried out by introducing a log method based on a barrel theory, and the degree of association aggregation is as follows:
in the formula ,
is the first-level electric energy quality index j 1 The lower two-level electric energy quality index k 1 And I jc ∈[1,5]，/>
Is the first-level electric energy quality index j 1 The lower two-level electric energy quality index k 1 Is a comprehensive weight value of (1);
and 3, respectively calculating the weighted positive gray correlation degree and the weighted negative gray correlation degree as follows:
in the formula ,
indicating the monitoring point i 1 Integral electrical energy at the siteQuantitative index and positive ideal understanding of R + Gray degree of association between->
Indicating the monitoring point i 1 Overall power quality index and negative ideal solution R - Gray correlation between;
and 3, respectively calculating the positive Euclidean distance and the negative Euclidean distance as follows:
in the formula ,
indicating the monitoring point i 1 Overall power quality index and positive ideal solution R + European distance between->
Indicating the monitoring point i 1 Overall power quality index and negative ideal solution R - A Euclidean distance between them;
and 3, calculating positive comprehensive association degree and negative comprehensive association degree as follows:
construction comprehensive positive comprehensive association degree
And negative comprehensive relevance->
The method comprises the following steps:
in the formula ,α1 For a first linear coefficient, alpha 2 Is a second linear coefficient, alpha 1 ，α 2 ∈[0,1]And alpha is 1 +α 2 ＝1；
And 3, constructing comprehensive evaluation indexes by the positive comprehensive association degree and the negative comprehensive association degree as follows:
in the formula ,
the larger the value is, the more the monitoring point i is represented 1 The better the overall power quality at the location;
comprehensive evaluation index (namely
The value is used to evaluate the overall power quality of the monitoring point. />
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| 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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| 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 |
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