CN107527131A - A kind of cluster electric system energy consumption level evaluation method and device - Google Patents
A kind of cluster electric system energy consumption level evaluation method and device Download PDFInfo
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Abstract
A kind of cluster electric system energy consumption level evaluation method and device, wherein methods described provided by the invention include：S1, the cluster electric system assessment indicator system based on foundation, the fuzzy overall evaluation matrix of the assessment indicator system destination layer is obtained by fuzzy synthetic appraisement method；S2, the evaluation result of the cluster electric system energy consumption level is obtained according to the fuzzy overall evaluation matrix of the destination layer.This invention ensures that accuracy and correctness that energy consumption level is assessed, can obtain more accurately energy consumption level evaluation result in actual applications, reference Help is provided for cluster electric system energy analysis and combustion adjustment.
Description
Technical field
The present invention relates to electrical engineering technical field, is evaluated more particularly, to a kind of cluster electric system energy consumption level
Method and device.
Background technology
The research of electric system energy consumption includes the many-sides such as energy consumption factor, energy consumption model, conservation measures, cluster department of electrical engineering
The research of system energy consumption level is also current study hotspot, but its energy consumption level evaluation study achievement is less.To cluster electric system
The research of energy consumption level is not only related to the analysis of each motor device energy consumption level in system, also relates to system load characteristic
Researched and analysed with the consideration of operating mode, in addition to some other energy consumption factor.Conjunction to cluster electric system energy consumption level
A+E is managed, integrally can not only have known with energy level to system and control, more can be to Energy Saving Design and optimization
Operation provides reference.
At present, cluster electric system horizontal can consume appraisal procedure, including：According to the variable loss parameter of cluster electric system
With power-balance relation, the energy consumption calculation model of cluster electric system is established；Using the input energy in the energy consumption calculation model
The mode of parameter is consumed, with reference to actual measurement service data, calculates the energy consumption for obtaining cluster electric system, wherein, the energy consumption parameter is
The variable loss of cluster electric system；The energy consumption level of the cluster electric system is assessed according to the energy consumption.
But in existing cluster electric system energy consumption level appraisal procedure, energy consumption calculation model is excessively simple, and input
The energy consumption parameter of energy consumption calculation model is very few, causes the energy consumption level assessment result of cluster electric system obtained using this method
Error is larger.
The content of the invention
For above-mentioned technical problem, the present invention provides a kind of cluster electric system energy consumption level evaluation method and device.
In a first aspect, the present invention provides a kind of cluster electric system energy consumption level evaluation method, including：S1, based on foundation
Cluster electric system assessment indicator system, the mould of the assessment indicator system destination layer is obtained by fuzzy synthetic appraisement method
Paste synthetic evaluation matrix；S2, the cluster electric system energy water consumption is obtained according to the fuzzy overall evaluation matrix of the destination layer
Flat evaluation result；Wherein, the assessment indicator system includes：Destination layer, rule layer and indicator layer；The destination layer is cluster
Electric system energy consumption level；The rule layer includes：Load factor, motor device factor and other factorses；The other factorses
Including：Power grid quality, extra energy ratio and energy-saving effect.
Wherein, the S1 includes：S11, obtain in the assessment indicator system with each factor of layer relative to upper strata factor
Weight vectors；S12, the degree of membership based on each factor of indicator layer in the assessment indicator system relative to each element in evaluate collection,
Obtain the fuzzy evaluating matrix of rule layer；S13, mesh is obtained based on the fuzzy evaluating matrix of the weight vectors and the rule layer
Mark the fuzzy overall evaluation matrix of layer；
Wherein, the evaluate collection be V=it is excellent, it is good, in, it is poor={ υ1, υ2, υ3, υ4}。
Wherein, the S2 includes：According to maximum membership grade principle, choose in the fuzzy overall evaluation matrix of the destination layer
Maximum in the evaluate collection corresponding sentence as evaluation result.
Wherein, the S11 includes：S111, the factor of each layer in the assessment indicator system is compared and assigned two-by-two
Scale value is given, the judgment matrix of each layer is established according to the scale value；S112, the maximum for obtaining the judgment matrix of each layer are special
Characteristic vector corresponding to value indicative, and the characteristic vector is normalized, each factor of same layer is obtained relative to upper strata
The weight vectors of factor；S113, to the judgment matrix carry out consistency check, if the uniformity effect of the judgment matrix compared with
Difference, then the judgment matrix is modified.
Wherein, the S12 includes：S121, the assessment indicator system indicator layer is obtained by the membership function of determination
In quantitative target relative to each element in the evaluate collection degree of membership；S122, obtained by way of expert judging described
Qualitative index in assessment indicator system indicator layer relative to each element in the evaluate collection degree of membership；S123, according to described
Quantitative target is relative to the degree of membership of each element in the evaluate collection, and qualitative index is relative to each element in the evaluate collection
Degree of membership, obtain the fuzzy evaluating matrix of rule layer.
Wherein, the S13 includes：S131, choose product summation type Fuzzy Arithmetic Operators, according to the indicator layer relative to
The fuzzy evaluating matrix of the weight vectors of rule layer and the rule layer obtains the fuzzy overall evaluation matrix of rule layer；S132,
According to the fuzzy overall evaluation matrix of the rule layer and the rule layer relative to the weight vectors of destination layer, destination layer is obtained
Fuzzy overall evaluation matrix.
Wherein, consistency check is carried out to the judgment matrix by below equation,
If C.R. ＜ 0.1, the uniformity effect of judgment matrix is preferable；Or if C.R. >=0.1, judgment matrix it is consistent
Property effect is poor；
Wherein, C.I. is coincident indicator；R.I. it is Aver-age Random Consistency Index, is referred to by looking into mean random uniformity
The table of comparisons is marked to obtain；M is judgment matrix exponent number, λmaxFor the eigenvalue of maximum of judgment matrix.
Second aspect, the present invention provide a kind of cluster electric system energy consumption level evaluating apparatus, including：Acquisition module, use
In the cluster electric system assessment indicator system based on foundation, the assessment indicator system is obtained by fuzzy synthetic appraisement method
The fuzzy overall evaluation matrix of destination layer；Evaluation module, institute is obtained for the fuzzy overall evaluation matrix according to the destination layer
State the evaluation result of cluster electric system energy consumption level；Wherein, the assessment indicator system includes：Destination layer, rule layer and refer to
Mark layer；The destination layer is cluster electric system energy consumption level；The rule layer includes：Load factor, motor device factor and
Other factorses；The other factorses include：Power grid quality, extra energy ratio and energy-saving effect.
The third aspect, the present invention provide a kind of cluster electric system energy consumption level valuator device, including：At least one processing
Device；And
At least one memory being connected with the processor communication, wherein：
The memory storage has and by the programmed instruction of the computing device, the processor described program can be called to refer to
Order is able to carry out above-mentioned method.
Fourth aspect, the present invention provide a kind of non-transient computer readable storage medium storing program for executing, and the non-transient computer is readable
Storage medium stores computer instruction, and the computer instruction makes the computer perform above-mentioned method.
A kind of cluster electric system energy consumption level evaluation method provided by the invention and device, include target by establishing
The cluster electric system assessment indicator system of layer, rule layer and indicator layer, can fully be reflected and cluster electric system energy consumption
Horizontal related influence factor；And the fuzzy of destination layer is obtained by fuzzy synthetic appraisement method based on the assessment indicator system
Synthetic evaluation matrix, and the evaluation knot of cluster electric system energy consumption level is obtained according to the fuzzy overall evaluation matrix of the destination layer
Fruit, it ensure that the accuracy and correctness of the evaluation result so that the evaluation method can be commented accurately in actual applications
Valency energy consumption level, reference Help is provided for cluster electric system energy analysis and combustion adjustment.
Brief description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
There is the required accompanying drawing used in technology description to be briefly described, it should be apparent that, drawings in the following description are the present invention
Some embodiments, for those of ordinary skill in the art, on the premise of not paying creative work, can also basis
These accompanying drawings obtain other accompanying drawings.
Fig. 1 is the flow chart of cluster electric system energy consumption level evaluation method provided in an embodiment of the present invention；
Fig. 2 is the structured flowchart of the assessment indicator system in Fig. 1 cluster electric system energy consumption level evaluation method；
Fig. 3 is the structured flowchart of cluster electric system energy consumption level evaluating apparatus provided in an embodiment of the present invention；
Fig. 4 is the structured flowchart of cluster electric system energy consumption level valuator device provided in an embodiment of the present invention.
Embodiment
To make the purpose, technical scheme and advantage of the embodiment of the present invention clearer, below in conjunction with the embodiment of the present invention
In accompanying drawing, the technical scheme in the embodiment of the present invention is explicitly described, it is clear that described embodiment be the present invention
Part of the embodiment, rather than whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art are not having
The every other embodiment obtained under the premise of creative work is made, belongs to the scope of protection of the invention.
Fig. 1 is the flow chart of cluster electric system energy consumption level evaluation method provided in an embodiment of the present invention, such as Fig. 1 institutes
Show, this method includes：S1, the cluster electric system assessment indicator system based on foundation, is obtained by fuzzy synthetic appraisement method
The fuzzy overall evaluation matrix of the assessment indicator system destination layer；S2, according to the fuzzy overall evaluation matrix of the destination layer
Obtain the evaluation result of the cluster electric system energy consumption level；
Wherein, the assessment indicator system includes：Destination layer, rule layer and indicator layer；The destination layer is cluster motor
System energy consumption is horizontal；The rule layer includes：Load factor, motor device factor and other factorses；The other factorses include：
Power grid quality, extra energy ratio and energy-saving effect.
Wherein, assessment indicator system refers to by sign evaluation object each side characteristic and its multiple indexs connected each other,
The organic whole with immanent structure formed.
Wherein, Field Using Fuzzy Comprehensive Assessment is a kind of integrated evaluating method based on fuzzy mathematics.The comprehensive evaluation according to
Qualitative evaluation is converted into quantitative assessment by the topology degree that is subordinate to of fuzzy mathematics, i.e., with fuzzy mathematics to being restricted by many factors
Things or object make the evaluation of a totality.It has the characteristics of result is clear, and systematicness is strong, can preferably solve to obscure
, be difficult to quantify the problem of, be adapted to various uncertain problems solution.
Specifically, energy consumption level is the major part for weighing electric system energy situation, and its horizontal height directly affects
To enterprise, the system operation ability and economy of garden.The design of assessment indicator system is with scientific, completeness and operability
For principle, accurately objectively reflect cluster electric system energy consumption level.Domain U be include all factors of assessment indicator system because
Element collection, it then follows the similar factor of property is divided into one group of principle, and wherein U is top set of factors, includes l content set, UiFor
Content set, each content set include n bottom set of factors, i.e.,
U={ U1,U2,…,Ul}
Ui={ ui1,ui2,…,uin}
In the embodiment of the present invention, assessment indicator system is divided into three layers by researching and analysing, i.e., destination layer, rule layer and referred to
Layer is marked, then set of factors U={ U1,U2,U3, and content set is respectively：
U1={ u11,u12,u13}
U2={ u21,u22,u23,u24}
U3={ u31,u32,u33}
Assessment indicator system destination layer is cluster electric system energy consumption level；Rule layer includes：Load factor, motor device
Factor and other factorses；Indicator layer includes：Belong to the part throttle characteristics, the total operational efficiency of load and load factor of load factor；Belong to
Total fixed loss of motor device factor, total specified variable loss, cluster electric system operational efficiency and motor duty and volume
It is fixed；And belong to the power grid quality of other factorses, extra energy ratio and energy-saving effect, as shown in Figure 2.Then by fuzzy comprehensive
The fuzzy overall evaluation matrix that evaluation method obtains destination layer in the assessment indicator system is closed, and according to the fuzzy comprehensive of the destination layer
Close Evaluations matrix and obtain the evaluation result of the cluster electric system energy consumption level.
In embodiments of the present invention, the cluster electric system that destination layer, rule layer and indicator layer are included by establishing is evaluated
Index system, it can fully reflect the influence factor related to cluster electric system energy consumption level；And referred to based on the evaluation
Mark system obtains the fuzzy overall evaluation matrix of destination layer by fuzzy synthetic appraisement method, and according to the fuzzy comprehensive of the destination layer
Close Evaluations matrix and obtain the evaluation result of cluster electric system energy consumption level, ensure that the accuracy of the evaluation result with it is correct
Property so that the evaluation method can obtain accurately evaluating energy consumption level in actual applications, be cluster electric system energy analysis
Reference Help is provided with combustion adjustment.
On the basis of above-described embodiment, the S1 includes：S11, obtain in the assessment indicator system with each factor of layer
Relative to the weight vectors of upper strata factor；S12, based on each factor of indicator layer in the assessment indicator system relative in evaluate collection
The degree of membership of each element, obtain the fuzzy evaluating matrix of rule layer；S13, the mould based on the weight vectors and the rule layer
Paste the fuzzy overall evaluation matrix that Evaluations matrix obtains destination layer；Wherein, the evaluate collection be V=it is excellent, it is good, in, it is poor=
{υ1, υ2, υ3, υ4}。
Wherein, weight is a relative concept, for a certain index.The weight of a certain index refers to that the index exists
Relative importance in the overall evaluation.Weight is that weight is separated from some evaluation indexes, one group of assessment indicator system
Corresponding weight constitutes proportional system.
Wherein, if to the either element x in domain (scope of research) U, there is number A (x) ∈ [0,1] right therewith
Should, then A is referred to as the fuzzy set on U, and A (x) is referred to as degrees of membership of the x to A.When x changes in U, A (x) is exactly a function, is claimed
For A membership function.Degree of membership A (x) is closer to 1, and the degree that expression x belongs to A is higher, and A (x) belongs to closer to 0 expression x
A degree is lower.X, which is characterized, with membership function A (x) of the value in section (0,1) belongs to A degree just.Degree of membership belongs to mould
Paste the concept in evaluation function：Fuzzy overall evaluation be things affected by many factors is made thoroughly evaluating it is a kind of very
Effective Multifactor Decision Making method, it is not utterly positive or negative to be characterized in evaluation result, but with a fuzzy set
To represent.
Specifically, when evaluating cluster electric system energy consumption level, obtained and commented by fuzzy synthetic appraisement method
The fuzzy overall evaluation matrix of valency index system destination layer includes：Obtain in the assessment indicator system with each factor of layer relative to upper
The weight vectors of layer factor, then obtain indicator layer each factor in assessment indicator system and be subordinate to relative to each element in evaluate collection
Degree.I-th of factor u in assessment indicator system indicator layeri, relative to j-th of element v in evaluate collectionjDegree of membership can represent
For rij, and 0 ＜ rij＜ 1, so as to obtain the degree of membership of each factor in assessment indicator system indicator layer, and can according to the degree of membership
To establish the fuzzy evaluating matrix of rule layer.
Fuzzy evaluating matrix and indicator layer further according to rule layer obtain rule layer relative to the weight vectors of rule layer
Fuzzy overall evaluation matrix, the fuzzy evaluating matrix of destination layer is then obtained according to the fuzzy overall evaluation matrix of rule layer, then
The fuzzy synthesis that destination layer is obtained relative to the weight vectors of destination layer according to the fuzzy evaluating matrix of destination layer and rule layer is commented
Valency matrix.The evaluation knot of the energy consumption level of cluster electric system can be finally obtained according to the fuzzy overall evaluation matrix of destination layer
Fruit.
In embodiments of the present invention, by obtaining the weight in assessment indicator system with each factor of layer relative to upper strata factor
Vector, the significance level of each factor of cluster electric system energy consumption level can be intuitively influenceed in reflected appraisal index system；Pass through
The fuzzy evaluation of rule layer is obtained relative to the degree of membership of each element in evaluate collection based on each factor of bottom in assessment indicator system
Matrix；And the fuzzy overall evaluation matrix of destination layer is obtained based on the fuzzy evaluating matrix of weight vectors and rule layer, and root
The evaluation result of cluster electric system energy consumption level is obtained according to the fuzzy overall evaluation matrix of the destination layer.It ensure that energy consumption level
The accuracy and correctness of assessment, can obtain more accurately energy consumption level evaluation result in actual applications, be cluster department of electrical engineering
Energy analysis of uniting provides reference Help with combustion adjustment.
On the basis of the various embodiments described above, the S2 includes：According to maximum membership grade principle, the destination layer is chosen
Maximum in fuzzy overall evaluation matrix in the evaluate collection corresponding sentence as evaluation result.
Wherein, maximum membership grade principle (maximum membership principle), the basic principle of fuzzy mathematics
One of, it is a kind of direct method that Model Identification is carried out with fuzzy set theory.
Specifically, after the fuzzy overall evaluation matrix of destination layer is got by fuzzy synthetic appraisement method, according to most
Big degree of membership principle, choose the corresponding sentence conduct in evaluate collection of the maximum in the fuzzy overall evaluation matrix of destination layer and comment
Valency result, for example, the maximum in the fuzzy overall evaluation matrix of destination layer is second element, then second element is being commented
Sentence corresponding to valency concentration is good, that is, the evaluation result for obtaining cluster electric system energy consumption level is good.
In embodiments of the present invention, by maximum membership grade principle, the fuzzy overall evaluation matrix of the destination layer is chosen
In maximum in the evaluate collection corresponding sentence as evaluation result, cluster electric system energy consumption level can be caused
Evaluation result is more accurate, and reference Help is provided for cluster electric system energy analysis and combustion adjustment.
On the basis of the various embodiments described above, the S11 includes：S111, by the assessment indicator system each layer because
Element is compared and assigns scale value two-by-two, and the judgment matrix of each layer is established according to the scale value；S112, obtain each layer
Judgment matrix eigenvalue of maximum corresponding to characteristic vector, and the characteristic vector is normalized, obtained same
Each factor of layer relative to upper strata factor weight vectors；S113, consistency check is carried out to the judgment matrix, if the judgement
The uniformity effect of matrix is poor, then the judgment matrix is modified.
Specifically, using analytic hierarchy process (AHP) (Analytic Hierarchy Process, AHP) to cluster electric system energy
Each layer factor carries out weight calculation in the flat assessment indicator system of water consumption, wherein, AHP is by the element always relevant with decision-making point
Solution carries out the decision-making technique of qualitative and quantitative analysis into levels such as target, criterion, schemes herein on basis.First, it is based on
Satty propose 1-9 scales the factor two-by-two in each layer of assessment indicator system is compared and assigns scale value, then according to
The judgment matrix design criterions of table 1, scale value is assigned, and scale value meets aij=1/aji。
The judgment matrix design criterions of table 1
The judgment matrix of construction rules layer and indicator layer is distinguished according to the scale value, for example, the judgment matrix of rule layer
For A=(aij)n×n, i.e.,
Then the characteristic vector corresponding to the eigenvalue of maximum of rule layer judgment matrix is asked for, and by this feature vector normalizing
Change is handled, and is calculated and is obtained weight vectors of the rule layer relative to destination layer；And ask for the maximum feature of indicator layer judgment matrix
The corresponding characteristic vector of value, and by this feature vector normalized, calculating obtains weight of the indicator layer relative to rule layer
Vector.And the vector that weight is formed turns into Mode of Level Simple Sequence vector.Generally use summation and square-root method etc. obtain weight
Vector.
The judgment matrix for being then aligned with then layer and indicator layer carries out consistency check, can be effectively by consistency check
The reasonability of judgment matrix is evaluated, then can be used for obtaining corresponding weight vectors for the preferable judgment matrix of uniformity；It is right
Need to be modified it and perfect in the poor judgment matrix of uniformity, after upchecking to its consistency, could be used to obtain
Take corresponding weight vectors.The basis of weight vectors is obtained by selecting the preferable judgment matrix of uniformity to be used as so that cluster
The evaluation of electric system energy consumption level is more rationally and accurate.
In embodiments of the present invention, by being compared the factor of each layer in assessment indicator system two-by-two and assigning scale
Value, so as to establish the judgment matrix of each layer according to the scale value so that judgment matrix can more fully reflected appraisal index system
In the horizontal influence degree of each factors on energy consumption, and uniformity is carried out to judgment matrix and tested so that judgment matrix
Uniformity is corrected and perfect, and then the weighted value in the weight vectors for be obtained based on the judgment matrix can intuitively be reflected
The significance level of each energy consumption level influence factor of cluster electric system.
On the basis of the various embodiments described above, the S12 includes：S121, by described in the membership function acquisition of determination
Quantitative target in assessment indicator system indicator layer relative to each element in the evaluate collection degree of membership；S122, pass through expert
The mode of judge obtains person in servitude of the qualitative index in the assessment indicator system indicator layer relative to each element in the evaluate collection
Category degree；S123, the degree of membership according to the quantitative target relative to each element in the evaluate collection, and the qualitative index phase
For the degree of membership of each element in the evaluate collection, the fuzzy evaluating matrix of rule layer is obtained.
Wherein, quantitative target is can be defined with accurate quantity, accurately weigh and can set the performance assessment criteria of performance objective.
In quantitative assessing index system, the metewand value of each index is to weigh the evaluation whether this index meets production basic demand
Benchmark.
Wherein, qualitative index refers to that directly assay content can not be calculated by data, need to carry out visitor to evaluation object
See description and analysis carrys out the index of reflected appraisal result.
Specifically, after obtaining with weight vectors of each factor of layer relative to upper strata factor, first, evaluation index body is selected
It is the quantitative target and qualitative index in indicator layer, i.e. quantitative target is：Load total operational efficiency, load factor situation, department of electrical engineering
Uniting operational efficiency, total specified variable loss, total fixed loss, power grid quality and extra use can situation；Qualitative index is：Load is special
Property, motor duty with quota and energy-saving effect.Then it is true according to data such as assessment indicator system indicator layer middle finger target numbers
Membership function is determined, for example, membership function is：
Wherein, a is that the selection of index is minimum；B is the maximum of index；x1、x2To insert the equivalent point in section [a, b],
And x1＜ x2；xv (1)、xv (2)、xv (3)With xv (4)Represent respectively in evaluate collection " excellent ", " good ", " in " with difference membership function；U is
Quantitative target current data.Then obtain the degree of membership of each index in bottom, for example, in quantitative target i-th of factor degree of membership
For rij(j=1,2 ..., s), and 0 ＜ rij＜ 1, wherein, j is j-th of element in evaluate collection, and s is the quantity of quantitative target.
Then according to quantitative target in the membership function acquisition indicator layer determined relative to the degree of membership of each element in evaluate collection.
Qualitative index needs to be assessed by quantification treatment, by Statistics Method, allows multidigit expert to each qualitative
Index is judged, for example, selection 10 is judged for expert, and expert is in the degree of membership of judge qualitative index, according to table 2
Qualitative index evaluation criterion shown in shown qualitative index evaluation content and table 3 is judged, but is not limited thereto.So as to
Get degree of membership of the qualitative index relative to each factor in evaluate collection.
The qualitative index evaluation content of table 2
The qualitative index evaluation criterion of table 3
The degree of membership of each index in indicator layer relative to each element in evaluate collection is obtained according to calculating and judge above, so
The fuzzy evaluating matrix of rule layer is established according to obtained degree of membership afterwards, for example, the fuzzy evaluating matrix established is：
The fuzzy evaluating matrix contains evaluate collection V and is evaluated indicator layer in assessment indicator system obtained whole
Information, and the i-th row that can be seen that from the fuzzy evaluating matrix R R reflects i-th factor and influences evaluation object and is under the jurisdiction of respectively
The degree of individual evaluate collection；R jth row, which then reflect all factors, influences the journey that evaluation object is under the jurisdiction of j-th of evaluate collection element
Degree.
Then standard can be obtained relative to the weight vectors of rule layer according to the fuzzy evaluating matrix of rule layer and indicator layer
The then fuzzy overall evaluation matrix of layer, the fuzzy overall evaluation matrix of destination layer then can be got, then is subordinate to according to maximum
Spend principle, choose destination layer fuzzy overall evaluation matrix in maximum in evaluate collection corresponding sentence, and using sentence as
The evaluation result of cluster electric system energy consumption level.
In embodiments of the present invention, the degree of membership for referring to each index in system indicator layer by the evaluation based on acquisition establishes standard
The then fuzzy evaluating matrix of layer so that the fuzzy evaluating matrix being capable of each index pair fully in reflected appraisal index system indicator layer
The influence degree of energy consumption level, and then cause the fuzzy overall evaluation of destination layer obtained according to the fuzzy evaluating matrix of rule layer
Matrix can more accurately reflect the energy consumption level of cluster electric system.
Wherein, the calculation of each index includes in quantitative target：Total operational efficiency is loaded, to each in cluster electric system
The calculating of the individual total operational efficiency of load, i.e., all load output powers and motor shaft power ratio, i.e., logical in cluster electric system
Cross below equation and try to achieve the total operational efficiency of load,
In above formula, ηLFor the total operational efficiency of system load；N is group system number of motors；PLiFor i-th load output work
Rate；P2lI-th output power of motor, i.e. shaft power.
System load rate situation, load factor in cluster electric system is assessed, it is main to consider load in cluster electric system
Whether distribution is reasonable, avoids low load with strong power phenomenon and overload phenomenon, ensures that each motor device is run under greater efficiency.Calculate
All motor device load factors in cluster electric system are, it is specified that load factor in 60%-100% is qualified, statistical system load factor
Qualified data volume, computational load rate situation, i.e., system load rate situation is tried to achieve by below equation,
In above formula, α is the qualified ratio of load factor；nIt is qualifiedFor the qualified quantity of motor device load factor in system；N is in system
Total number of motors.
Electric system operational efficiency, service data under stable state is chosen, and carry out system effectiveness calculating, that is, pass through below equation
Electric system operational efficiency is tried to achieve,
In above formula, n is group system number of motors；P2iFor i-th output power of motor；P1It is always defeated for cluster electric system
Enter power.
System always specified variable loss and the total fixed loss of system, is transported in the recent period based on energy consumption model combination cluster electric system
Row data, data content includes system total power input and each motor device power output, with parameter fitting optimization method pair
Always specified variable loss calculates system with total fixed loss.Wherein, the formula of energy consumption model is as follows,
In above formula, n is number of motors in system；PTFor system total power input；βjFor the load factor of jth platform motor device；
Pj2For the power output of jth platform motor device, Δ PjaNFor the specified variable loss of jth platform motor device；ΔPB is totalIt is total for system
Fixed loss；P2' can power for extra use in system；ΔPAN is totalFor the total specified variable loss of system.
Power grid quality, measurement or computing system voltage deviation degree, power grid quality is tried to achieve by below equation,
Extra to use energy ratio, simultaneously computing system additionally with energy ratio, is tried to achieve by below equation and additionally uses energy ratio for measurement,
In above formula, λ is system additionally with energy ratio.
On the basis of the various embodiments described above, the S13 includes：S131, choose product summation type Fuzzy Arithmetic Operators, root
According to the indicator layer relative to the weight vectors of rule layer and the fuzzy evaluating matrix of the rule layer, the fuzzy of rule layer is obtained
Synthetic evaluation matrix；S132, according to the fuzzy overall evaluation matrix of the rule layer and the rule layer relative to destination layer
Weight vectors, obtain the fuzzy overall evaluation matrix of destination layer.
Wherein, fuzzy relation synthesis computing "." it is referred to as fuzzy operator, and fuzzy operator is artificially defined, therefore also may be used
With to "." with different definition, so as to more any fuzzy operator, control effect is optimal, more adduction in fuzzy reasoning process
Reason.Common fuzzy operator has：" Min-Max " fuzzy operator, " product-with " fuzzy operator, " Min/ products-with " fuzzy operator,
" Min- products " fuzzy operator, " Min- and " fuzzy operator.
Specifically, obtain the fuzzy evaluating matrix of rule layer and with each factor of layer relative to the weight vectors on upper strata after,
By choosing suitable Fuzzy Arithmetic Operators, by the fuzzy evaluating matrix of rule layer and indicator layer relative to rule layer weight to
Amount carries out fuzzy composition, obtains the fuzzy overall evaluation matrix of rule layer.In embodiments of the present invention, product summation pattern is selected
Composite operator is pasted, the fuzzy evaluating matrix of rule layer and indicator layer are subjected to fuzzy composition relative to the weight vectors of rule layer,
So as to obtain the fuzzy overall evaluation matrix of rule layer.
Then, the fuzzy overall evaluation matrix based on the rule layer obtains the fuzzy evaluating matrix of destination layer, then uses and multiply
Product summation type Fuzzy Arithmetic Operators, the fuzzy evaluating matrix of destination layer and rule layer are carried out relative to the weight vectors of destination layer
Fuzzy composition, the fuzzy overall evaluation matrix of destination layer is obtained, for example, the fuzzy overall evaluation matrix of obtained destination layer is：
BH=[0.3488 0.4257 0.2226 0.0079],
Finally, according to maximum membership grade principle, by the maximum in destination layer fuzzy overall evaluation matrix in evaluate collection
Corresponding sentence is as evaluation result, for example, above-mentioned BHMaximum in matrix is 0.4257, and it is corresponding in evaluate collection
Sentence is good, is " good " so as to obtain the assessment situation of cluster electric system energy consumption level.
In embodiments of the present invention, by selecting product summation type Fuzzy Arithmetic Operators, by indicator layer relative to rule layer
Weight vectors and rule layer fuzzy evaluating matrix carry out fuzzy composition, obtain the fuzzy overall evaluation matrix of rule layer；With
And similarly, according to the fuzzy overall evaluation matrix of rule layer and rule layer relative to the weight vectors of destination layer, obtain target
The fuzzy overall evaluation matrix of layer, acquisition fuzzy overall evaluation matrix so in layer so that the destination layer finally obtained
Fuzzy overall evaluation matrix, the energy consumption level of cluster electric system can be reflected exactly.
On the basis of the various embodiments described above, consistency check is carried out to the judgment matrix by below equation,
If C.R. ＜ 0.1, the uniformity effect of judgment matrix is preferable；Or if C.R. >=0.1, judgment matrix it is consistent
Property effect is poor；
Wherein, C.I. is coincident indicator；R.I. it is Aver-age Random Consistency Index, is referred to by looking into mean random uniformity
The table of comparisons is marked to obtain；M is judgment matrix exponent number, λmaxFor the eigenvalue of maximum of judgment matrix.
Specifically, it is necessary to judgment matrix to each layer after the judgment matrix of each layer is correspondingly established according to the scale value
Consistency check is carried out, for example, being tested to the judgment matrix of rule layer, the maximum of calculation criterion layer judgment matrix first is special
Value indicative λmax, then according to formulaCalculate coincident indicator, then can to obtain mean random consistent by tabling look-up 4
Property index R.I., further according to formulaCalculate the uniformity of rule layer judgment matrix.
If C.R. ＜ 0.1, show that the uniformity of rule layer judgment matrix is preferable；Or if C.R. >=0.1, show
The uniformity of rule layer judgment matrix is poor, it is necessary to be modified to rule layer judgment matrix and perfect.Pass through consistency check
The reasonability of judgment matrix can be effectively evaluated, then can be used for obtaining corresponding power for the preferable judgment matrix of uniformity
Weight vector；Need to be modified it for the poor judgment matrix of uniformity and perfect, after upchecking to its consistency,
It can be used to obtain corresponding weight vectors.So that the weight vectors obtained by the preferable judgment matrix of uniformity more can be exactly
Each factor influences the significance level of cluster electric system energy consumption level in reflected appraisal index system so that cluster electric system energy
The flat evaluation of water consumption is more rationally and accurate.
| Matrix exponent number | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
| R.I. | 0 | 0 | 0.52 | 0.89 | 1.12 | 1.26 | 1.36 | 1.41 | 1.46 |
The Aver-age Random Consistency Index table of comparisons of table 4
The embodiment of the present invention is illustrated below, but not limited the scope of the invention.Cluster electric system
The assessment indicator system of energy consumption level is as shown in table 5, and then layer is respectively aligned to 1-9 Scale Methods based on analytic hierarchy process (AHP) (AHP)
It is compared with the factor two-by-two in indicator layer and assigns scale value, the foundation for assigning scale value is factor relative to destination layer
Significance level.By taking rule layer as an example, expert is by understanding the information such as cluster electric system structure and motor device model, to criterion
Factor two-by-two in layer and indicator layer is compared and assigns scale value, as shown in table 6.
The assessment indicator system of table 5
| H | A | B | C |
| A | 1 | 1/2 | 3 |
| B | 2 | 1 | 4 |
| C | 1/3 | 1/4 | 1 |
The rule layer factor comparable situation of table 6
Then the judgment matrix of rule layer is established, i.e.,Then rule layer relative to destination layer weight
Vector isHaveEigenvalue of maximum λ can be obtainedmax=3.0191.To the judgment matrix of rule layer
Carry out consistency check,
Pass through consistency check.
Similarly, the judgment matrix that can obtain indicator layer is respectively：
It is w to calculate corresponding weight vectors by judgment matrixA=[0.164 0.297 0.539]T,
wB=[0.142 0.671 0.132 0.055]T, wC=[0.633 0.106 0.261]T。
Consistency check is carried out to above weight vectors to meet the requirements.By obtaining rule layer weight vectors and indicator layer
The product of weight vectors, so as to obtain the weight situation of each layer factor of cluster electric system, as shown in table 7, as can be seen from Table 7,
The factor maximum to cluster electric system energy consumption level influence degree is " system always specified variable loss ", is secondly " load
Rate ", the relatively small factor of influence degree are " extra energy situation " and " motor duty and quota ".
Each layer factor weight of the cluster electric system of table 7
Then, obtain assessment indicator system in each factor of indicator layer relative to each element in evaluate collection degree of membership.For
Quantitative target, is arranged or the computing cluster electric system service data of nearly 1 year, data break are one month, totally 12 groups of data, number
Include according to amount：Each motor device load factor, load total operational efficiency, running efficiency of system, system always specified variable loss, system
Total fixed loss, magnitude of voltage use energy ratio with extra, build evaluate collection scope based on data, regather or computing system is currently transported
Above-mentioned each data under row situation, the degree of membership of each index is calculated with reference to membership function；For qualitative index, it is special to select 10
Family is judged each index, obtains the degree of membership of each factor in indicator layer, as shown in table 8.
The index degree of membership of table 8
Then fuzzy evaluating matrix is built according to degree of membership, show that the fuzzy evaluating matrix of rule layer is as follows：
In the fuzzy evaluating matrix of the rule layer, row vector represents degree of membership of the index for each factor in evaluate collection.
Finally, according to the obtained weight vectors of each layer and the fuzzy evaluating matrix of rule layer, calculated based on product summation type
The fuzzy overall evaluation matrix of method calculation criterion layer is：
In above formula, BA、BB、BC" load factor ", " motor device factor " and the mould of " other factorses " are represented in rule layer
Paste synthetic evaluation matrix.According to maximum membership grade principle, each factor of rule layer can be obtained in assessment indicator system to the cluster motor
The horizontal comprehensive assessment situation of system energy consumption, as shown in table 9.
The rule layer comprehensive assessment situation of table 9
The destination layer fuzzy evaluating matrix of cluster electric system is obtained according to the fuzzy overall evaluation matrix of the rule layer,
That is,
The fuzzy overall evaluation matrix of computing cluster electric system destination layer, i.e.
The comprehensive assessment situation for obtaining destination layer is as shown in table 10.
The aims of systems layer comprehensive assessment situation of table 10
According to maximum membership grade principle, it is " good " that can obtain the system energy consumption level comprehensive and assess situation.
Fig. 3 is the structured flowchart of cluster electric system energy consumption level evaluating apparatus provided in an embodiment of the present invention, such as Fig. 3 institutes
Show, the device includes：Acquisition module 301 and evaluation module 302.Acquisition module 301 is used for the cluster electric system based on foundation
Assessment indicator system, the fuzzy overall evaluation square of the assessment indicator system destination layer is obtained by fuzzy synthetic appraisement method
Battle array；Evaluation module 302 is used to obtain the cluster electric system energy water consumption according to the fuzzy overall evaluation matrix of the destination layer
Flat evaluation result；Wherein, the assessment indicator system includes：Destination layer, rule layer and indicator layer；The destination layer is cluster
Electric system energy consumption level；The rule layer includes：Load factor, motor device factor and other factorses；The other factorses
Including：Power grid quality, extra energy ratio and energy-saving effect.
Specifically, the design of assessment indicator system is accurately objectively anti-using scientific, completeness and operability as principle
Reflect cluster electric system energy consumption level.Domain U is the set of factors for including all factors of assessment indicator system, it then follows property is similar
Factor is divided into one group of principle, and wherein U is top set of factors, includes l content set, UiFor content set, each content set bag
Containing n bottom set of factors, i.e.,
U={ U1,U2,…,Ul}
Ui={ ui1,ui2,…,uin}
In the embodiment of the present invention, assessment indicator system is divided into three layers by researching and analysing, i.e., destination layer, rule layer and referred to
Layer is marked, then set of factors U={ U1,U2,U3, and content set is respectively：
U1={ u11,u12,u13}
U2={ u21,u22,u23,u24}
U3={ u31,u32,u33}
Assessment indicator system destination layer is cluster electric system energy consumption level；Rule layer includes：Load factor, motor device
Factor and other factorses；Indicator layer includes：Belong to the part throttle characteristics, the total operational efficiency of load and load factor of load factor；Belong to
Total fixed loss of motor device factor, total specified variable loss, cluster electric system operational efficiency and motor duty and volume
It is fixed；And belong to the power grid quality of other factorses, extra energy ratio and energy-saving effect, as shown in Figure 2.Then acquisition module
301 obtain the fuzzy overall evaluation matrix of destination layer in the assessment indicator system, evaluation module by fuzzy synthetic appraisement method
302 obtain the evaluation result of the cluster electric system energy consumption level according to the fuzzy overall evaluation matrix of the destination layer.
In embodiments of the present invention, the cluster electric system that destination layer, rule layer and indicator layer are included by establishing is evaluated
Index system, it can fully reflect the influence factor related to cluster electric system energy consumption level；And acquisition module is based on
The assessment indicator system obtains the fuzzy overall evaluation matrix of destination layer by fuzzy synthetic appraisement method, and evaluation module is according to this
The fuzzy overall evaluation matrix of destination layer obtains the evaluation result of cluster electric system energy consumption level, ensure that the evaluation result
Accuracy and correctness so that the evaluation method can obtain accurately evaluating energy consumption level in actual applications, be cluster motor
System energy analysis provides reference Help with combustion adjustment.
Fig. 4 is the structured flowchart of cluster electric system energy consumption level valuator device provided in an embodiment of the present invention, such as Fig. 4 institutes
Show, the equipment includes：Processor (processor) 401, memory (memory) 402 and bus 403；
Wherein, the processor 401 and memory 402 complete mutual communication by the bus 403；
The processor 401 is used to call the programmed instruction in the memory 402, to perform above-mentioned each method embodiment
The method provided, such as including：Cluster electric system assessment indicator system based on foundation, passes through fuzzy synthetic appraisement method
Obtain the fuzzy overall evaluation matrix of the assessment indicator system destination layer；According to the fuzzy overall evaluation matrix of the destination layer
Obtain the evaluation result of the cluster electric system energy consumption level.
The present embodiment provides a kind of non-transient computer readable storage medium storing program for executing, the non-transient computer readable storage medium storing program for executing
Computer instruction is stored, the computer instruction makes the computer perform the method that above-mentioned each method embodiment is provided, example
Such as include：Cluster electric system assessment indicator system based on foundation, the evaluation is obtained by fuzzy synthetic appraisement method and referred to
The fuzzy overall evaluation matrix of mark system destination layer；The cluster electricity is obtained according to the fuzzy overall evaluation matrix of the destination layer
The horizontal evaluation result of machine system energy consumption.
One of ordinary skill in the art will appreciate that：Realizing all or part of step of above method embodiment can pass through
Programmed instruction related hardware is completed, and foregoing program can be stored in a computer read/write memory medium, the program
Upon execution, the step of execution includes above method embodiment；And foregoing storage medium includes：ROM, RAM, magnetic disc or light
Disk etc. is various can be with the medium of store program codes.
Finally it should be noted that：The above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although
The present invention is described in detail with reference to the foregoing embodiments, it will be understood by those within the art that：It still may be used
To be modified to the technical scheme described in foregoing embodiments, or equivalent substitution is carried out to which part technical characteristic；
And these modification or replace, do not make appropriate technical solution essence depart from various embodiments of the present invention technical scheme spirit and
Scope.
Claims (10)
- A kind of 1. cluster electric system energy consumption level evaluation method, it is characterised in that including：S1, the cluster electric system assessment indicator system based on foundation, the evaluation is obtained by fuzzy synthetic appraisement method and referred to The fuzzy overall evaluation matrix of mark system destination layer；S2, the evaluation knot of the cluster electric system energy consumption level is obtained according to the fuzzy overall evaluation matrix of the destination layer Fruit；Wherein, the assessment indicator system includes：Destination layer, rule layer and indicator layer；The destination layer is cluster electric system Energy consumption level；The rule layer includes：Load factor, motor device factor and other factorses；The other factorses include：Power network Quality, extra energy ratio and energy-saving effect.
- 2. according to the method for claim 1, it is characterised in that the S1 includes：S11, obtain the weight vectors with each factor of layer relative to upper strata factor in the assessment indicator system；S12, the degree of membership based on each factor of indicator layer in the assessment indicator system relative to each element in evaluate collection, obtain accurate The then fuzzy evaluating matrix of layer；S13, the fuzzy overall evaluation square of destination layer is obtained based on the fuzzy evaluating matrix of the weight vectors and the rule layer Battle array；Wherein, the evaluate collection be V=it is excellent, it is good, in, it is poor={ v1, v2, v3, v4}。
- 3. according to the method for claim 1, it is characterised in that the S2 includes：According to maximum membership grade principle, the maximum in the fuzzy overall evaluation matrix of the destination layer is chosen in the evaluate collection In corresponding sentence as evaluation result.
- 4. according to the method for claim 2, it is characterised in that the S11 includes：S111, the factor of each layer in the assessment indicator system is compared two-by-two and assigns scale value, according to the scale Value establishes the judgment matrix of each layer；S112, the characteristic vector corresponding to the eigenvalue of maximum for the judgment matrix for obtaining each layer, and to the characteristic vector It is normalized, obtains weight vectors of each factor of same layer relative to upper strata factor；S113, consistency check is carried out to the judgment matrix, if the uniformity effect of the judgment matrix is poor, to described Judgment matrix is modified.
- 5. according to the method for claim 2, it is characterised in that the S12 includes：S121, by the quantitative target in the membership function acquisition assessment indicator system indicator layer of determination relative to described The degree of membership of each element in evaluate collection；S122, the qualitative index in the assessment indicator system indicator layer is obtained by way of expert judging relative to institute's commentary Valency concentrates the degree of membership of each element；S123, the degree of membership according to the quantitative target relative to each element in the evaluate collection, and qualitative index relative to The degree of membership of each element in the evaluate collection, obtain the fuzzy evaluating matrix of rule layer.
- 6. according to any described methods of claim 2-5, it is characterised in that the S13 includes：S131, product summation type Fuzzy Arithmetic Operators are chosen, according to the indicator layer relative to the weight vectors of rule layer and institute The fuzzy evaluating matrix for stating rule layer obtains the fuzzy overall evaluation matrix of rule layer；S132, the weight vectors according to the fuzzy overall evaluation matrix of the rule layer and the rule layer relative to destination layer, Obtain the fuzzy overall evaluation matrix of destination layer.
- 7. according to the method for claim 4, it is characterised in that uniformity is carried out to the judgment matrix by below equation Examine,<mrow> <mi>C</mi> <mo>.</mo> <mi>R</mi> <mo>.</mo> <mo>=</mo> <mfrac> <mrow> <mi>C</mi> <mo>.</mo> <mi>I</mi> <mo>.</mo> </mrow> <mrow> <mi>R</mi> <mo>.</mo> <mi>I</mi> <mo>.</mo> </mrow> </mfrac> <mo>,</mo> <mi>C</mi> <mo>.</mo> <mi>I</mi> <mo>.</mo> <mo>=</mo> <mfrac> <mrow> <msub> <mi>&lambda;</mi> <mi>max</mi> </msub> <mo>-</mo> <mi>m</mi> </mrow> <mrow> <mi>m</mi> <mo>-</mo> <mn>1</mn> </mrow> </mfrac> </mrow>If C.R. ＜ 0.1, the uniformity effect of judgment matrix is preferable；Or if C.R. >=0.1, the uniformity effect of judgment matrix Fruit is poor；Wherein, C.I. is coincident indicator；R.I. it is Aver-age Random Consistency Index, by looking into Aver-age Random Consistency Index pair Obtained according to table；M is judgment matrix exponent number, λmaxFor the eigenvalue of maximum of judgment matrix.
- A kind of 8. cluster electric system energy consumption level evaluating apparatus, it is characterised in that including：Acquisition module, for the cluster electric system assessment indicator system based on foundation, obtained by fuzzy synthetic appraisement method The fuzzy overall evaluation matrix of the assessment indicator system destination layer；Evaluation module, for obtaining the cluster electric system energy consumption level according to the fuzzy overall evaluation matrix of the destination layer Evaluation result；Wherein, the assessment indicator system includes：Destination layer, rule layer and indicator layer；The destination layer is cluster electric system Energy consumption level；The rule layer includes：Load factor, motor device factor and other factorses；The other factorses include：Power network Quality, extra energy ratio and energy-saving effect.
- A kind of 9. cluster electric system energy consumption level valuator device, it is characterised in that including：At least one processor；AndAt least one memory being connected with the processor communication, wherein：The memory storage has can be by the programmed instruction of the computing device, and the processor calls described program instruction energy Enough perform the method as described in claim 1 to 7 is any.
- 10. a kind of non-transient computer readable storage medium storing program for executing, it is characterised in that the non-transient computer readable storage medium storing program for executing is deposited Computer instruction is stored up, the computer instruction makes the computer perform the method as described in claim 1 to 7 is any.
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| CN110196811A (en) * | 2019-06-04 | 2019-09-03 | 上海浦东软件平台有限公司 | A kind of method and apparatus for evaluation software quality |
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| CN110196811A (en) * | 2019-06-04 | 2019-09-03 | 上海浦东软件平台有限公司 | A kind of method and apparatus for evaluation software quality |
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| CN110196811B (en) * | 2019-06-04 | 2024-02-13 | 上海浦东软件平台有限公司 | Method and equipment for evaluating software quality |
| CN112653121A (en) * | 2019-10-11 | 2021-04-13 | 国网甘肃省电力公司电力科学研究院 | Method and device for evaluating power grid frequency modulation participation capability of new energy microgrid |
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| CN112465145A (en) * | 2020-12-02 | 2021-03-09 | 西北工业大学 | Unmanned cluster intelligent qualitative evaluation method based on logical reasoning and fuzzy synthesis |
| CN112465145B (en) * | 2020-12-02 | 2024-04-05 | 西北工业大学 | Unmanned cluster intelligent qualitative evaluation method based on logic reasoning and fuzzy synthesis |
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