CN109919422A - A kind of Comprehensive assessment of power quality method considering data dynamic fuzzy dependence - Google Patents
A kind of Comprehensive assessment of power quality method considering data dynamic fuzzy dependence Download PDFInfo
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Abstract
The invention discloses a kind of Comprehensive assessment of power quality methods for considering data dynamic fuzzy dependence, are related to technical field of power systems.Now either traditional algorithm or intelligent algorithm are mostly based on static power quality evaluation method, the dynamic changes for the power quality that is beyond expression out.The invention includes the following steps: determining power quality evaluation index；To data prediction；Dynamic analog gelatinization；Weight distribution；Data sum and obtain evaluation result.The technical program is blurred power quality data on the basis of existing evaluation and judges the taxis of data, while considering the weight distribution between evaluation index and the weight distribution in index time series, to reach objective reasonable purpose.The present invention largely remains information entrained by power quality data, while having given expression to the dynamic variation characteristic of data, and the electricity consumption behavior of power supply and distribution behavior and user side for supplier of electricity has positive reference value.
Description
Technical field
The present invention relates to technical field of power systems more particularly to a kind of electric energy for considering data dynamic fuzzy dependence
Quality overall evaluation method.
Background technique
With plant-scale continuous expansion, scientific and technical continuous development, the demand of electric energy is also being continuously increased.With
People's quality of the life is gradually increased, and application of the equipment and instrument of many emerging technologies in life and production is also increasingly wider
It is general.These device efficiencies are high, favorable working performance, but also relatively high to the variation sensitivity of electric energy, these new and high technologies are set
Standby application is so that people constantly propose high request to power quality.At the same time, economic fast development, the degree of automation is not
The rapid advances of disconnected raising and power electronic technique, so that electric load gradually complicated and diversification.A large amount of non-linear, punching
Hitting property equipment is applied to electric system, and the operation and failure problems during power transmission and distribution will lead to electric system and cannot run
Under ideal working condition.
The superiority and inferiority of power quality is it may be said that closely bound up with economic development.Power quality problem can not only reduce user
Productivity, weaken the competitiveness of enterprise, in some instances it may even be possible to influence whether the problem of employment, significant impact is caused to society.Electric power
The ideal operation state of system is the different voltages grade according to regulation, provides the uninterrupted of 50Hz nominal frequency for different user
Sine wave alternating current, and its operating parameter is not influenced by electric load.China promulgates state related with power quality at present
It include: " power quality admissible deviation of supply volt- age ", " power quality utility network harmonic wave ", " power quality voltage in family's standard
Fluctuation and flickering ", " power quality three-phase voltage allow degree of unbalancedness ", " power quality power system frequency tolerance " etc.,
But the standard of related comprehensive power quality evaluation is not put into effect.The different industries of requirement with to(for) power quality is more and more tighter
It is severe, meanwhile, the cry of electricity market " fixing price according to quality " is also higher and higher.Therefore, it is commented if a synthesis can be provided to power quality
It is for reference to determine result, no matter will be all advantageous to supplier of electricity or to electricity consumption side.
The research that domestic and foreign scholars evaluate power quality is broadly divided into conventional method and intelligent method.Conventional method can be with
Data characteristic between power quality is preferably disclosed, but has the defects of complex steps, bad adaptability；Intelligent method is more focused on calculation
Method itself, and the characteristic of power quality itself is had ignored, and intelligent algorithm usually requires to carry out data advanced treating to adapt to
The input data format of algorithm, but this will lead to the loss of partial information entrained by data.Therefore, either traditional algorithm is also
Intelligent algorithm, be mostly based on static power quality evaluation method, the dynamic changes for the power quality that is beyond expression out, and
The dynamic characteristic of data is also to reflect one of the important realization form of power quality superiority and inferiority, researching value with higher.
Summary of the invention
The technical problem to be solved in the present invention and the technical assignment of proposition are to be improved and improved to prior art,
A kind of Comprehensive assessment of power quality method for considering data dynamic fuzzy dependence is provided, to reduce the influence mesh of subjective factor
's.For this purpose, the present invention takes following technical scheme.
A kind of Comprehensive assessment of power quality method considering data dynamic fuzzy dependence, comprising the following steps:
1) power quality evaluation index is determined；
The initial data for obtaining the electrical node to be evaluated of index of correlation, establishes Comprehensive assessment of power quality index system,
Complete the data preparation of index of correlation；
2) to data prediction；
Integrity check is carried out to data, related data is rejected if data are there are repeated sampling situation, if depositing
It is adopted in leakage, carries out interpolation, interpolating method uses Hermite interpolation method, and find out has more preferable adaptation between original data stream
Missing data；After carrying out integrity check to data, error of each sampled point relative to the index ideal value is found out, then right
Each scale error is normalized；
3) dynamic analog is gelatinized；
Dynamic analog gelatinization is carried out to the data after normalization, obtains fuzzy matrix, it is opposite to each point value of fuzzy matrix
The taxis of data is judged in the variation of previous moment, obtains Dynamic Fuzzy Diagnosis Matrix；
4) weight distributes；
Weight distribution twice is carried out to index, the weight of weight and index in time series between index is determined, is passed through
The evaluations matrix of secondary right distribution: Eij=EMij×ωl×ωc；EMijFor Dynamic Fuzzy Diagnosis Matrix, ωlThe weight between index,
ωcFor weight in time series；
5) data sum and obtain evaluation result；
A) the identical value of each index taxis is added, for single metrics evaluation value:
M is the number of sampling points that the index becomes bad in sampling time sequence in formula, and n is the index in sampling time sequence
On the number of sampling points that turns for the better, if Valuew> Valueb, then final evaluation of estimate is Valuei, the index is in sampling time sequence
On become bad；If Valuew< Valueb, then final evaluation of estimate is Valuei, which is to turn for the better in sampling time sequence
's；If Valuew=Valueb, then final evaluation of estimate is Valuei, which runs smoothly in sampling time sequence；
B) for the evaluation of estimate of institute's evaluation node:
In formula, u is that the node becomes the number of bad index, and v is that the node turns for the better the number of index, ifThen final evaluation of estimate is Valueij, which becomes bad in sampling time sequence；IfThen final evaluation of estimate is Valueij, which turns for the better in sampling time sequence；IfThen final evaluation of estimate is Valueij, which runs smoothly in sampling time sequence.
The present invention proposes a kind of Comprehensive assessment of power quality method for considering data dynamic fuzzy dependence.This method exists
The taxis of data is blurred and judged on the basis of existing evaluation to power quality data, while considering the power between evaluation index
Reassignment and the weight distribution in index time series, to reach objective reasonable purpose.The present invention largely retains
Information entrained by power quality data, while the dynamic variation characteristic of data has been given expression to, for the power supply and distribution of supplier of electricity
Behavior and the electricity consumption behavior of user side have positive reference value.
As optimization technique means: when step 1) determines power quality evaluation index, power quality evaluation index includes
Three-phase voltage deviation, three-phase imbalance, frequency departure, voltage fluctuation and flicker；Obtain the electrical node to be evaluated of index of correlation
Initial data, establish Comprehensive assessment of power quality index system, complete the data preparation of index of correlation, then initial evaluation matrix
Are as follows:
I indicates node number in formula, and j indicates sampling time sequence.
As optimization technique means: in step 2), when pretreatment, normalized processing formula are as follows:
Wherein, xijIndicate j-th of sample magnitude of i-th of node, rijIndicate the numerical value after normalization, then mark is changed
Evaluations matrix afterwards are as follows:
As optimization technique means: in step 3), dynamic fuzzy turns to the sampled data compared to previous moment, currently
The trend of the sampled data at moment, including turn for the better, become bad, the variation of data on a timeline embodies dynamic process, taxis
Uncertainty embody the process of blurring, after being blurred to data, evaluations matrix are as follows:
After finding out fuzzy matrix, the taxis of data is judged relative to the variation of previous moment further according to each point value, i.e., should
It is that the data of sampled point turn for the better or become bad, at this point, evaluations matrix are as follows:
As optimization technique means: in step 4),
The weight between index is determined first, i.e., the weight of index is determined according to the significance level of different indexs, for comparing
Important index then distributes greater weight, and so on, weight between index are as follows:
ωl=(ωl1,ωl2,…,ωli)T
Determine that the weight in each index time series, the weight in index time series reflect Appraising subject to difference again
The attention degree of time series, for the time series t evaluated1,t2,…,tnIf evaluation time sequence is considered as comparably
Position, then the weight of each sampled point is identical, then can be its distribution compared with authority to prominent leading portion or the sampled value of rear end time
It is heavy, weight in time series are as follows:
ωc=(ωc1,ωc2,…,ωcj)。
The utility model has the advantages that
1, the technical program is blurred to power quality data on the basis of existing evaluation and is judged the taxis of data,
Consider the weight distribution in the weight distribution and index time series between evaluation index, simultaneously to reach objective reasonable purpose.
The present invention largely remains information entrained by power quality data, while the dynamic change for having given expression to data is special
Property, the electricity consumption behavior of power supply and distribution behavior and user side for supplier of electricity has positive reference value.
2, the technical program can provide beneficial reference for electricity market.As the cry of current electric power " fixing price according to quality " is got over
Come it is higher, market in urgent need one kind can intuitively reflect the index of power quality level as price reference.
3, the technical program can become the useful supplement for first having power quality national standard.The power quality that country promulgates
Some column standards, specify only the limiting value of each index, and qualified analysis can only be made whether to power quality, simple fixed
Property analysis can not complete, true, reflection power grid comprehensively power quality situation.Power quality evaluation is the synthesis of a multi objective
Evaluation process, therefore this algorithm can be used as the useful supplement of existing national standard.
4, the technical program can become the standard of specification user side electricity consumption.Due to the rapidly development of industry, commercial power
Various impacts can be brought to bulk power grid, current national standard has only made relevant regulations to source, and does not advise to load side
It is fixed, thus the algorithm can specification user side to a certain extent electricity consumption behavior.
5, the technical program can provide power quality early warning for supplier of electricity.The result of power quality evaluation can be very intuitive
The superiority and inferiority for finding out the evaluation period node power quality node that power quality constantly deteriorates then is needed to take corresponding
Management measure is given warning in advance with reaching, and reduces the purpose of implicit hidden danger.
Detailed description of the invention
Fig. 1 is structure chart of the invention.
Specific embodiment
Technical solution of the present invention is described in further detail below in conjunction with Figure of description.
As shown in Figure 1, the present invention the following steps are included:
(1) power quality evaluation index is determined first, such as three-phase voltage deviation, three-phase imbalance, frequency departure, voltage wave
Dynamic and flickering etc., obtains the initial data to be evaluated of index of correlation, establishes the index system of Comprehensive assessment of power quality method, complete
At the data preparation of index of correlation, then initial evaluation matrix are as follows:
I indicates node number in formula, and j indicates sampling time sequence.
(2) to data prediction.Whether inspection data is complete first, i.e., inspection data are adopted with the presence or absence of repeated sampling or leakage
Situations such as, such situation then reject to related data if it exists and interpolation, interpolating method use Hermite interpolation method, benefit
Construct interpolation polynomial with functional value of the unknown function f (x) on interpolation knot and derivative value, find out with original data stream it
Between have the missing data of more preferable adaptation.After carrying out integrity check to data, each sampled point is found out relative to the index
The error of ideal value, then each scale error is normalized, concrete mode are as follows:
Wherein, xijIndicate j-th of sample magnitude of i-th of node, rijIndicate the numerical value after normalization, then mark is changed
Evaluations matrix afterwards are as follows:
(3) dynamic analog gelatinization is carried out to the data after normalization.Dynamic analog gelatinization meaning refer to compared to it is previous when
The sampled data at quarter, the sampled data at current time, which is likely to be, to turn for the better, it is also possible to become bad, at this point, data when
Between variation on axis embody dynamic process, the uncertainty of taxis embodies the process of blurring, obscures to data
After change, evaluations matrix are as follows:
After finding out fuzzy matrix, the taxis of data is judged relative to the variation of previous moment further according to each point value, i.e., should
It is that the data of sampled point turn for the better or become bad, at this point, evaluations matrix are as follows:
(4) after obtaining Dynamic Fuzzy Diagnosis Matrix, weight distribution twice is carried out to index.The weight between index is determined first, i.e.,
Meeting target weight is determined according to the significance level of different indexs, greater weight is then distributed for important index, with such
It pushes away, weight between index are as follows:
ωl=(ωl1,ωl2,…,ωli)T (7)
Determine that the weight in each index time series, the weight in index time series reflect Appraising subject to difference again
The attention degree of time series, for the time series t evaluated1,t2,…,tnIf evaluation time sequence is considered as comparably
Position, then the weight of each sampled point is identical, then can be its distribution compared with authority to prominent leading portion or the sampled value of rear end time
It is heavy, weight in time series are as follows:
ωc=(ωc1,ωc2,…,ωcj) (8)
Obtain the evaluations matrix through secondary weight distribution are as follows:
Eij=EMij×ωl×ωc (9)
(5) after obtaining evaluations matrix, the identical value of each index taxis is added, for single metrics evaluation value:
M is the number of sampling points that the index becomes bad in sampling time sequence in formula, and n is the index in sampling time sequence
On the number of sampling points that turns for the better, if Valuew> Valueb, then final evaluation of estimate is Valuei, the index is in sampling time sequence
On become bad；If Valuew< Valueb, then final evaluation of estimate is Valuei, which is to turn for the better in sampling time sequence
's；If Valuew=Valueb, then final evaluation of estimate is Valuei, which runs smoothly in sampling time sequence.
For the evaluation of estimate of institute's evaluation node:
In formula, u is that the node becomes the number of bad index, and v is that the node turns for the better the number of index, ifThen final evaluation of estimate is Valueij, which becomes bad in sampling time sequence；IfThen final evaluation of estimate is Valueij, which turns for the better in sampling time sequence；IfThen final evaluation of estimate is Valueij, which runs smoothly in sampling time sequence.
Shown in figure 1 above it is a kind of consider data dynamic fuzzy dependence Comprehensive assessment of power quality method be this hair
Bright specific embodiment has embodied substantive distinguishing features of the present invention and progress, needs can be used according to actual, in the present invention
Enlightenment under, equivalent modifications, the column in the protection scope of this programme are carried out to it.
Claims (5)
1. a kind of Comprehensive assessment of power quality method for considering data dynamic fuzzy dependence, it is characterised in that including following step
It is rapid:
1) power quality evaluation index is determined；
The initial data for obtaining the electrical node to be evaluated of index of correlation, establishes Comprehensive assessment of power quality index system, completes
The data preparation of index of correlation；
2) to data prediction；
Integrity check is carried out to data, related data is rejected if data are there are repeated sampling situation, is leaked if it exists
It adopts, carries out interpolation, interpolating method uses Hermite interpolation method, finds out the missing for having more preferable adaptation between original data stream
Data；After carrying out integrity check to data, error of each sampled point relative to the index ideal value is found out, then to each finger
Mark error is normalized；
3) dynamic analog is gelatinized；
Dynamic analog gelatinization is carried out to the data after normalization, obtains fuzzy matrix, to each point value of fuzzy matrix relative to preceding
The taxis of data is judged in the variation at one moment, obtains Dynamic Fuzzy Diagnosis Matrix；
4) weight distributes；
Weight distribution twice is carried out to index, determines the weight of weight and index in time series between index, is obtained through secondary
The evaluations matrix of right distribution: Eij=EMij×ωl×ωc；EMijFor Dynamic Fuzzy Diagnosis Matrix, ωlThe weight between index, ωcFor
Weight in time series；
5) data sum and obtain evaluation result；
A) the identical value of each index taxis is added, for single metrics evaluation value:
In formula Chinese style, ValuewBecome the sum of bad point evaluation of estimate for evaluation index, ValuebFor evaluation index turn for the better an evaluation of estimate it
With, the number of sampling points that m becomes bad for the index in sampling time sequence, what n turned for the better in sampling time sequence for the index
Number of sampling points, if Valuew> Valueb, then final evaluation of estimate is Valuei, which is to become bad in sampling time sequence
's；If Valuew< Valueb, then final evaluation of estimate is Valuei, which turns for the better in sampling time sequence；If
Valuew=Valueb, then final evaluation of estimate is Valuei, which runs smoothly in sampling time sequence；
B) for the evaluation of estimate of institute's evaluation node:
In formula, in formula, ValuewpFor the evaluation of estimate for the bad point that becomes in institute's evaluation node, ValuebvIt turns for the better evaluation a little for institute's evaluation point
Value, u are that the node becomes the number of bad index, and v is that the node turns for the better the number of index, ifThen most
Final review value is Valueij, which becomes bad in sampling time sequence；IfIt is then final
Evaluation of estimate is Valueij, which turns for the better in sampling time sequence；IfThen most final review
Value is Valueij, which runs smoothly in sampling time sequence.
2. a kind of Comprehensive assessment of power quality method for considering data dynamic fuzzy dependence according to claim 1,
It is characterized by: power quality evaluation index includes three-phase voltage deviation, three when step 1) determines power quality evaluation index
Mutually imbalance, frequency departure, voltage fluctuation and flicker；The initial data of the electrical node to be evaluated of index of correlation is obtained, is established
Comprehensive assessment of power quality index system completes the data preparation of index of correlation, then initial evaluation matrix are as follows:
I indicates node number in formula, and j indicates sampling time sequence.
3. a kind of Comprehensive assessment of power quality method for considering data dynamic fuzzy dependence according to claim 2,
It is characterized by: in step 2), when pretreatment, normalized processing formula are as follows:
Wherein, xijIndicate j-th of sample magnitude of i-th of node, rijIndicate the numerical value after normalization, then commenting after mark is changed
Valence matrix are as follows:
4. a kind of Comprehensive assessment of power quality method for considering data dynamic fuzzy dependence according to claim 3,
It is characterized by: dynamic fuzzy turns to the sampled data compared to previous moment, the sampled data at current time in step 3)
Trend, including turn for the better, become bad, the variation of data on a timeline embodies dynamic process, and the uncertain of taxis embodies
The process of blurring, after being blurred to data, evaluations matrix are as follows:
The sampled data for indicating the evaluation point may be to become badOr turn for the better
After finding out fuzzy matrix, the taxis of data, the i.e. sampling are judged relative to the variation of previous moment further according to each point value
Point data turn for the better or become bad, at this point, evaluations matrix are as follows:
The sampled data for indicating the evaluation point isOrIn one.
5. a kind of Comprehensive assessment of power quality method for considering data dynamic fuzzy dependence according to claim 2,
It is characterized by: in step 4),
The weight between index is determined first, i.e., the weight between index is determined according to the significance level of different indexs, for than heavier
The index wanted then distributes greater weight, and so on, weight between index are as follows:
ωl=(ωl1,ωl2,…,ωli)T
Determine that the weight in each index time series, the weight in index time series reflect Appraising subject to different time again
The attention degree of sequence, for the time series t evaluated1,t2,…,tnIf evaluation time sequence is considered as par,
The weight of each sampled point is identical, to prominent leading portion or the sampled value of rear end time, then can distribute greater weight, time for it
Weight in sequence are as follows:
ωc=(ωc1,ωc2,…,ωcj)。
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