CN109738716A - A kind of power quality determination method - Google Patents
A kind of power quality determination method Download PDFInfo
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- CN109738716A CN109738716A CN201811254664.5A CN201811254664A CN109738716A CN 109738716 A CN109738716 A CN 109738716A CN 201811254664 A CN201811254664 A CN 201811254664A CN 109738716 A CN109738716 A CN 109738716A
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Abstract
The invention discloses a kind of power quality determination methods, comprise the following steps that step 1: statistics can open up Classical field and save the interval range in domain, and establish matrix model；Step 2: matter-element to be measured is divided into each class set feature according to the set number of Classical field and section domain；Step 3: stipulating the weight coefficient of each feature；Step 4: calculating the sizes values of matter-element characteristic and the degree of association of all categories to be measured；Step 5: calculating correlation function, determine that determinand is explicitly classified and generic；Step 6: degree of association normalization, by the relative value of each class set degree of association of operation, allow the association angle value of each class set all fall within<1, -1>between, the power quality of matter-element to be measured is determined according to association angle value.The present invention can effectively detect power quality disturbance waveform, and characteristic needed for capturing waveform is reduced, and shorten detection time and operation time, improve the accuracy of testing result.
Description
Technical field
The present invention relates to a kind of power quality determination methods, more particularly to a kind of electric power matter based on chaology
Measure determination method.
Background technique
Electrical Engineer needs first to carry out statistical data or system a degree of when analyzing power quality problem
Understand, the detection for electric power signal carries out suitable classification and identification, to achieve the purpose that accurate analyzing and diagnosing.It is existing each
The detection of the power quality of kind mainly utilizes long-term voltage monitoring, by rms voltage in the unit of account time, and sees
Changing value of its numerical value within certain unit time is examined, to judge voltage dip, rise sharply or one of power breakdown, and by
The variation of voltage peak and frequency discriminates whether the problems such as voltage flicker or electric harmonic occurs.It is various in order to accurately analyze
Power quality problem must also usually measure a variety of electrical natures, cause to consume the considerable time on electric power analysis.
Power disturbance is the general designation of voltage disturbance and current disturbing, refer mainly to voltage or electric current deviate ideal sine wave and
Power grid or electrical equipment are caused potentially to negatively affect.In the correlative study of power quality disturbance, can distinguish becomes variation
(variation) and event (event): variation refers to that the small offset to sine wave occurs during normal operation, such as: wave
Shape distortion or the variation of voltage amplitude；And event is the big offset occurred in handover operation or failure, and such as: normal switching
The anomalous event that normal event caused by operating or failure occur.In embodiments of the present invention, be all included in normal event with
And the variation range of two kinds of power waveforms of anomalous event.Power quality is for can be divided into the effect of electric system and client
Two ways: (a) power grid influences client by voltage (quality of voltage)；(b) client influences power grid by electric current (current quality)；
Therefore the present invention lists the power quality disturbance situation of following several types, and its occurrence cause is described as follows:
(1) voltage dip (Sag):
Voltage dip refer to fundamental frequency voltages root-mean-square value circle between specified 0.1~0.9 mark (Per unit, p.u.), and
Continued for 0.5 period to one minute, according to duration length can be divided into instantaneous (Instantaneous), of short duration (Momentary),
Temporarily (Temporary) voltage dip, duration were respectively 0.5~30 period, 30 period~3 second with 3 seconds~1 minute, and
Prolonged abnormal voltage, which reduces situation, can also claim low-voltage (Under voltage)；
(2) voltage swells (Swell):
The phenomenon that voltage swells, refers to rms voltage circle between specified 1.1~1.8 mark, and time of origin continues
0.5 is more than the period, and voltage swells can be divided into transient voltage according to the length of its duration and rise sharply, and the duration is 0.5~30 week
Phase, rms voltage circle is between 1.1~1.8 specified marks；
(3) power breakdown (Interruption):
The referred to as power breakdown when voltage value only has 0.1 specified mark or less, the measurement system of usual power breakdown holds according to power-off
The continuous time can be divided into short time and long-time power breakdown, short period of power interruption can be subdivided into again it is of short duration with it is temporary, when continuing
Between be respectively 0.5 period~3 second, 3 seconds~1 minute, and 1 minute or more long-time power breakdown phenomenon is also known as persistently broken
Electricity；
(4) electric harmonic (Harmonics):
It is almost pure sine wave by the voltage that power plant sends out, ideal power supply is containing only fundamental component, but when sending to user terminal,
Because through long-distance conveying and nonlinear load or non-linear power electronic equipment so that voltage waveform contains harmonic components,
The as harmonic pollution when these non-fundamental components are excessive.
Summary of the invention
The present invention provides a kind of power quality determination methods, to be detected for power quality disturbance waveform,
Characteristic needed for capturing waveform is reduced, detection time and operation time is reduced, improves the accuracy of testing result.
The present invention provides a kind of power quality determination methods, comprise the following steps that
Step 1: statistics can open up Classical field and save the interval range in domain, and establish matrix model；
Step 2: matter-element to be measured is divided into each class set feature according to the set number of Classical field and section domain；
Step 3: stipulating the weight coefficient of each feature；
Step 4: calculating the sizes values of matter-element characteristic and the degree of association of all categories to be measured；
Step 5: calculating correlation function, determine that determinand is explicitly classified and generic；
Step 6: degree of association normalization by the relative value of each class set degree of association of operation, uses following formula
Allow the association angle value of each class set all fall within<1, -1>between, the electric power matter of matter-element to be measured is determined according to association angle value
Amount.
Further, statistics can open up Classical field and save domain interval range and establish the specific method of matrix model in the step 1
It is as follows:
Things R is divided into the category set of k grade first, Classical field can be opened up by, which stipulating, is shown below:
Wherein, R is things, Nk(k=1~m) indicates the divided respective matter-element title of k class set, the matter-element title
All features are with Ci(i=1~n) is indicated, XkiExpression is characterized CiRange of characteristic values, this range of characteristic values ties up to k-th etc.
The distribution of i-th feature of grade, and by akiIndicate the maximum value of the matter-element class set feature, on the contrary bkiThen indicate the object
The minimum value of first class set feature；The above method is repeated, to save domain substitution Classical field.Establish section domain matrix.
Further, matter-element to be measured according to the set number of Classical field and section domain is divided into each class set in the step 2
Formula used in feature is as follows:
Wherein, q indicates this group of character numerical value, and xiFor feature C in qiSpecific features Value Data, i.e. things to be measured detection gained
Specific data.
Further, in the step 3, things R is by each group feature CiIt is formed, each feature will be for this things
Also there is different influence degrees, this step determines each feature to the weight percentage of things using the relationship of weight coefficient；It is as follows
It is shown:
Further, in the step 4 matter-element characteristic and degree of association sizes values of all categories to be measured specific calculating side
Method is as follows:
Calculate a characteristic value x in matter-element to be measurediFor the distance difference of all Classical field central points, and calculate central point and warp
The distance difference of allusion quotation domain bound, by this distance difference be defined as away from, this feature value for each Classical field away from numerical value then such as
Shown in following formula,
Calculate a characteristic value x in matter-element to be measurediFor the distance difference of all section domains central point, and calculate central point and section domain
This distance difference is defined as away from this feature value is for each section domain away from numerical value then such as following formula institute by the distance difference of bound
Show,
Further, the specific method is as follows for calculating correlation function in the step 5:
Each Classical field is completed with section domain away from calculating, according to the operation for being associated function by following formula
Calculate correlation function value；Again via following formula
Accumulating operation is carried out to the degree of membership of determinand generic respectively and is indicated, belonging to determining that determinand explicitly classifies
Classification.
The present invention compared with the existing technology, detects by Chaotic Synchronous and can open up identification method and examine to power disturbance signal
It surveys, there is 97% or more accuracy in the case where noiseless influences, and still be able to have under the influence of there is 5% noise
96% accuracy has preferable detection effect to power quality analysis.Simultaneously as the maximum value of voltage, current cycle,
When minimum value is applied to power quality analysis, electric harmonic and noise can not effectively be analyzed, but the present invention passes through
Chaotic Synchronous detection is added, improves its recognition capability to harmonic wave and noise, can effectively distinguish harmonic wave and noise in electric power
Effect.
Detailed description of the invention
Fig. 1 is that chaos system is tracked to synchronous schematic diagram；
Fig. 2 is the correlation function of Region place value；
Fig. 3 is the flow chart of Chaotic Synchronous extension detecting；
Fig. 4 is all kinds of power disturbance signal figures；
Fig. 5 is additive white Gaussian waveform；
Fig. 6 is all kinds of power disturbance signal figures being added after noise；
Fig. 7 is chaos scatter diagram when electric system is normal；
Chaos scatter diagram when Fig. 8 is voltage dip；
Chaos scatter diagram when Fig. 9 is voltage swells；
Chaos scatter diagram when Figure 10 is voltage harmonic；
Figure 11 is chaos scatter diagram when 5% noise is added in electric system；
Chaos scatter diagram when Figure 12 is voltage dip and 5% noise of addition；
Chaos scatter diagram when Figure 13 is voltage swells and 5% noise of addition；
Chaos scatter diagram when Figure 14 is voltage harmonic and 5% noise of addition.
Specific embodiment
It in order to enable those skilled in the art to better understand the solution of the present invention, below will be to the skill in the embodiment of the present invention
Art scheme is clearly and completely described, it is clear that and the described embodiment is only a part of the embodiment of the present invention, without
It is whole embodiments.
The characteristic using chaology motion profile is proposed in the embodiment of the present invention, looks for the characteristic of track and combination can open up
Normal, voltage swells, voltage dip, voltage interruption, voltage are humorous to the disturbance waveform such as voltage of electric system for theoretical discrimination method
Wave etc. is effectively differentiated.When another aspect, general conventional method is when electric system has the disturbance waveforms such as noise, Wu Fazhun
True identification and feature is found out, and the chaology characteristic that the present invention is mentioned can still find out specific spy under the influence of noise
Sign, the ability with accurate recognition electric system quality.
It should be noted that counting the specific source of language used in the embodiment of the present invention and being explained as follows:
(1) Chaotic Synchronous detection method
Chaos (Chaos) theory is a kind of unpredictable and irregular movement track nonlinear system theory, therefore there are many
Scholars study how control chaotic system, and nineteen ninety propose Chaotic Synchronous idea, mainly be propose two it is identical
Chaos system is referred to as main system (Master) and servant's system (Slave), when master and servant's system initial value difference, then can make two
The motion profile of chaos system is different；And servant's System Back-end is just tracked main system plus controller and utilized by scholars
Controller makes two chaos systems that can make track movement equal under same time, and such tracking state is Chaotic Synchronous, such as formula
(1) shown in；Fig. 1 is that chaos system is tracked to synchronous schematic diagram；
XSlave-XMaster→0 (1)
Wherein XMaster is chaos main system (Master), and XSlave is chaos servant system (Slave).In the embodiment of the present invention
In detect the chaos locus of power signal using this mode, master and servant's chaos system of the present invention is Lorenz
System, specific as follows:
Master:
Slave:
α, β, γ are positive value in formula (2)-(7), and error state is en=xn-yn(n=1,2,3), and dynamic error isAnd above formula (2)-(7) are brought into and become matrix mathematical expression, as shown in formula (8):
Seeking characteristic vector from formula (8) is formula (9)
λ=- γ
It, can be mixed as system according to chaology condition if can determine that systematic (8) is in stable condition when characteristic value is negative
Ignorant attractor；Can by the motion profile of this attractor be used to study various system acting states, such as periodicity, aperiodicity, with
The behavior of machine signal horizon state.Therefore, the embodiment of the present invention is using chaos error equation as power quality state of disturbance
Basis；Its signal is set as shown in formula (10) using formula (8):
Wherein x in formula (10)i(i=1,2,3) is normal signal, and by yi(i=1,2,3) is set as signal to be measured, utilizes this survey
Amount mode classifies this signal output waveform again, therefrom extracts less characteristic.The embodiment of the present invention uses e2Upper half
Wave waveform, and according to e1Value is divided into 3 features, is formed shown in equation such as formula (11)-(13)；It is various according to power quality signal
State waveform includes: that 5 kinds of normal, voltage swells, voltage dip, interruption and harmonic wave etc. is different classes of, and theoretical object can be opened up by establishing
Meta-model, to carry out classification belonging to signal recognition:
C in formula (11)-(13)1、c2、c3Feature name respectively used by the embodiment of the present invention；e21,i、e22,i、e23,i(i=
1,2 ..., n) it respectively represents in e2When > 0, three difference e1The value of chaos object difference in section be (- 1.5~-0.5), (-
0.5~0.5), (0.5~1.5)；Therefore, the embodiment of the present invention takes this equation average value calculated as the spy of identification
Value indicative confirms the operation time for being conducive to shorten classification through sunykatuib analysis, and can effectively improve the accuracy of classification.
(2) discrimination method can be opened up
Cai Wen is proposed in the paper of the nineteen eighty-three Region place value delivered and incompatibility problem to formalization processing contradictory problems
Research, has opened up a new field.The basic theories of extension science is can to open up theory, and method system is extenics method, logic base
Plinth is can to open up logic, and application technology is extension engineering method.Can open up theoretical theoretical pillar be matter-element theory, Region place value it is theoretical and
Logic can be opened up, is resolved contradiction and incompatible problem with the idea of matter-element；Region place value is exactly to go to handle using extension asses
The contradictory problems that traditional mathematics and fuzzy mathematics cannot be handled, by the range of fuzzy set from<0,1>be extended to<-∞, ∞>,
And by away from definition, place value of the description point with section in the zone can quantitatively describe either element through correlation function
Belong to positive domain (Positive field), negative domain (Negative field) or zero boundary (Zero boundary).
(a) matter-element theory
Matter-element theory can be opened up by the various people in daily life, thing, object, all are commonly referred to as " things ", but in order to explicitly distinguish
The difference of various things, therefore it is respectively indicated with different " things titles ".Its various things has description different
Function, pattern, kenel and things occur opposite partaker and are commonly referred to as its " features ", and each features also have relatively
" characterizing magnitudes " answered, therefore the matter-element (Matter-Element) to a things is described in detail then need to have basic
Three elements are respectively to stipulate things title N (Name), features C (Characteristic) and characterizing magnitudes V
(Value) matter-element constituted；Matter-element Mathematical representation is formula (14):
R=(N, c, v) (14)
A usual things only singly will not have a Xiang Tezheng, so its matter-element model also can be multidimensional matter element；Such as formula (15)
It show multidimensional matter element:
(b) definition of Region place value
If U is domain, if either element u and u ∈ U in U, a corresponding real number is had, K (u) ∈<-∞, ∞>, then it can open up
The definition of set is shown in formula (16):
A=(u, y) | u ∈ U, y=K (u) ∈ (- ∞, ∞) } (16)
Wherein y=K (u) be Region place value A correlation function, and K (u) be the degree of association of the u about Region place value A, range from
<-∞, ∞>.Region place value A is represented by domain U
A=A+∪A0∪A- (17)
Wherein
A+=(u, y) | u ∈ U, y=K (u) > 0 } (18)
A0=(u, y) | u ∈ U, y=K (u)=0 } (19)
A-=(u, y) | u ∈ U, y=K (u) < 0 } (20)
Wherein A+、A-、A0Sequentially it is known as positive domain, negative domain and zero boundary in Region place value A, as shown in Figure 2:
(c) extension dependent function
Equipped with Classical field X0=<a, b>with section domain X=<c, d>are real number field<-∞, ∞>on two sections, and X0∈ X, if x
For a bit in real number field, then the definition of correlation function such as formula (21) and formula (22) are shown.
Wherein
That is K (x) is x and X0Between correlation degree x can be claimed to belong to X when K (x)≤00Degree；When K (x)≤0, then claim x not
Belong to X0Degree.
The specific method is as follows for the detection and analysis of power quality of the embodiment of the present invention:
Step 1: statistics stipulates the interval range that can be opened up Classical field and save domain, and classics can be opened up in this step by stipulating first
Shown in domain such as formula (25), things R is divided into the category set of k grade by theory system, and referred to as each set opens up Classical field,
Wherein Nk(k=1~m) indicates that the divided respective matter-element title of k class set, all features of matter-element title are Ci(i
=1~n) it indicates, range of characteristic values is by XkiShown, this range of characteristic values belongs to the distribution model of k-th of grade, i-th feature
It encloses, and characteristic value size is then by akiIndicate the maximum value of the matter-element class set feature, on the contrary bkiThen indicate the matter-element grade collection
Close the minimum value of feature；Its foundation for saving domain matrix mode need to substitute Classical field using section domain, and repeat the above method, be saved
Domain matrix.
Step 2: determine matter-element to be measured, a unknown matter-element be divided into each class set feature, class set have to
Classical field is equal with the section set number in domain, then claims to be matter-element to be measured；As shown in formula (26).
Wherein the q in formula (23) indicates this group of character numerical value, and xiFor q feature CiCharacteristic value data.
Step 3: stipulating the weight coefficient of each feature, things R is by each group feature CiIt is formed, each feature will be for this things
With different degrees of influence, this step determines each feature to the weight percentage of things using the relationship of weight coefficient；Specifically
Shown in formula such as formula (27).
Step 4: calculating a characteristic value x in matter-element to be measurediFor the distance difference of all Classical field central points, and calculate center
The distance difference of point and Classical field bound, this distance difference is defined as away from this feature value is for each Classical field away from number
Value is then shown below,
Calculate a characteristic value x in matter-element to be measurediFor the distance difference of all section domains central point, and calculate central point and section domain
This distance difference is defined as away from this feature value is for each section domain away from numerical value then such as following formula institute by the distance difference of bound
Show,
Step 5: calculate correlation function, if be completed each Classical field and save domain away from calculating, can carry out connection function operation, by
Formula (29) calculates correlation function value (both degrees of membership).It carries out accumulating operation respectively via formula (30) and formula (31) again and represents
The degree of membership of determinand generic, so as to from the classification belonging to the clear determinand of this step.
Step 6: degree of association normalization, by the relative value of each class set degree of association of operation, the embodiment of the present invention uses formula
(32) allow the association angle value of each class set all fall within<1, -1>between, this step predominantly enables a user to explicitly know
The degree of road generic；Its overall flow figure is as shown in Figure 3.
The embodiment of the present invention uses IEEE Std 1159-1995 (IEEE recommended practice in step 1
For monitoring electric power quality) standard, list power breakdown, voltage dip, voltage swells,
The power quality problem of electric harmonic and voltage transient, it is six periods that analog waveform, which takes each form, and each cycle is with 128
Point is described.General measuring instrument usually captures the virtual value of voltage, but caused by this only changes voltage amplitude
Problem has the ability of discrimination, and harmonic wave, voltage transient, frequency variation can not then be discovered, therefore, arranged in embodiments of the present invention
The signal that several power qualities for reference noise disturb out, the electric power that falls into 5 types is normal, electric power rises sharply, electric power rapid drawdown, in electric power
Disconnected, electric harmonic is as shown in figure 4, Fig. 5 is mainly to simulate when electric system input signal is by the white high of outer step noise jamming
This noise waveform, the Power System Disturbances signal after power disturbance signal to be added to noise waveform are as shown in Figure 6.
The embodiment of the present invention captures power breakdown, voltage using existing hand-held power quality detecting instrument in step 2
Rapid drawdown, voltage swells, electric harmonic and power quality data when mixing some noises.The virtual value of voltage is usually captured, but
The problem that this only changes voltage amplitude has the ability of discrimination, for harmonic wave, frequency variation, outer step noise jamming then without
Method is discovered.Therefore, in embodiments of the present invention, with the viewpoint of energy, by normal electricity quality waveform and problematic electric power matter
It measures waveform and carries out chaos conversion, then extract the track of chaotic motion as the input feature vector that can open up change knowledge method, by chaos rail
Waveform diagram after mark conversion is as shown in Fig. 7~Figure 10；Chaotic waves are as shown in Figure 11~Figure 14 if it joined 5% noise.
It can be seen that chaotic waves around central point (0,0), it can thus be appreciated that chaos charmed particle interrogates this from Fig. 7~Figure 10
Number it is attracted to central point (0,0), does not depart from it, and generate chaos signal；It is rapid from the chaotic waves and Fig. 8 of the normal signal of Fig. 7
Drop waveform is compared, it can be seen that Fig. 8 compared with the closer central point of the charmed particle of Fig. 7 (0,0), i.e. central point attraction is stronger；And phase
Compared with Fig. 9 and Figure 10, Fig. 9 and Figure 10 charmed particle will be obvious farther out from central point, i.e. central point attraction is weaker.Separately
On the one hand, the chaotic waves figure such as Figure 11~Figure 14 that joined noise may be used also other than the strong and weak problem of the charmed particle of chaos
Learn that its charmed particle is more intensive, conversely, no noise added chaotic waves are more uniform；Therefore in the embodiment of the present invention
If charmed particle is interfered by external noise, significant change occurs for chaotic motion track, and technical staff can be from chaotic motion
The problem of extracting the characteristic value of each power signal in track, which kind of power disturbance belonged to the waveform of identification system output signal,
Power quality is detected and improved conducive to Electrical Engineer.
Via above-listed chaos locus waveform diagram relatively after, the origin that waveform can be made to surround due to chaotic characteristic is surround, and is made
Gradually there is symmetrical trend at the waveform above and below left and right, therefore the embodiment of the present invention only selects e1Between -1.5~-0.5 section, -
The upper half-wave e in 0.5~0.5 section, 0.5~1.5 section2The characteristic that is extracted as the embodiment of the present invention of equalization point numerical value
Value, table 1 are the matter-element model of established signal classification；It is 1/3 that setting, which can open up the weight of each feature, in the embodiment of the present invention,
According to the state of disturbance of determination method of embodiment of the present invention input signal and the accurate detection power signal.
All kinds of voltage signals of table 1 open up matter-element model
The calculating structure of power signal is compared with by the established matter-element module of table 1, by each news in the embodiment of the present invention
Number classification is using 100 groups of consideration noises and does not consider that the signal of noise detects, and is classified as 5 kinds of signals detections, each voltage
The results are shown in Table 2 for the accuracy rate of signal classification detection；It can be seen that no matter voltage signal has noiseless from table, through the present invention
After the identification of the mentioned method of embodiment, accuracy rate all big about 96% or more demonstrates the embodiment of the present invention and uses detection side
The validity of method.
Accuracy of each voltage signal of table 2 whether there is or not influence of noise
The embodiment of the present invention detects by Chaotic Synchronous and can open up identification method and detects to power disturbance signal, makes an uproar in nothing
Sound shadow has 97% or more accuracy in the case where ringing, and the standard with 96% still is able under the influence of with 5% noise
Exactness has preferable detection effect to power quality analysis.Simultaneously as the maximum value of voltage, current cycle, minimum value quilt
When applied to power quality analysis, electric harmonic and noise can not effectively be analyzed, but the embodiment of the present invention is by adding
Enter Chaotic Synchronous detection, improves its recognition capability to harmonic wave and noise, can effectively distinguish the work of harmonic wave and noise in electric power
With.
Finally it should be noted that the above embodiments are merely illustrative of the technical scheme of the present invention and are not intended to be limiting thereof, to the greatest extent
Invention is explained in detail referring to above-described embodiment for pipe, it should be understood by a person of ordinary skill in the art that technology
Personnel read present specification after still can with modifications or equivalent substitutions are made to specific embodiments of the invention, but this
A little modifications are changed all without departing from the present patent application accompanying claims protection scope.
Claims (6)
1. a kind of power quality determination method, which is characterized in that the determination method comprises the following steps that
Step 1: statistics can open up Classical field and save the interval range in domain, and establish matrix model；
Step 2: matter-element to be measured is divided into each class set feature according to the set number of Classical field and section domain；
Step 3: stipulating the weight coefficient of each feature；
Step 4: calculating the sizes values of matter-element characteristic and the degree of association of all categories to be measured；
Step 5: calculating correlation function, determine that determinand is explicitly classified and generic；
Step 6: degree of association normalization by the relative value of each class set degree of association of operation, uses following formula
Allow the association angle value of each class set all fall within<1, -1>between, the electric power matter of matter-element to be measured is determined according to association angle value
Amount.
2. power quality determination method according to claim 1, which is characterized in that statistics can open up in the step 1
Classical field and section domain interval range simultaneously establish matrix model the specific method is as follows:
Things R is divided into the category set of k grade first, Classical field can be opened up by, which stipulating, is shown below:
Wherein, R is things, Nk(k=1~m) indicates the divided respective matter-element title of k class set, the matter-element title institute
There is feature with Ci(i=1~n) is indicated, XkiExpression is characterized CiRange of characteristic values, this range of characteristic values ties up to k-th of grade
The distribution of i-th feature, and by akiIndicate the maximum value of the matter-element class set feature, on the contrary bkiThen indicate the matter-element
The minimum value of class set feature；The above method is repeated, to save domain substitution Classical field.Establish section domain matrix.
3. power quality determination method according to claim 1, which is characterized in that matter-element to be measured in the step 2
It is as follows to be divided into formula used in each class set feature according to the set number of Classical field and section domain:
Wherein, q indicates this group of character numerical value, and xiFor feature C in qiSpecific features Value Data, i.e. things to be measured detection gained
Specific data.
4. power quality determination method according to claim 1, which is characterized in that in the step 3, things R
System is by each group feature CiIt is formed, each feature will also have this things different influence degrees, this step utilizes weight system
Several relationships determines each feature to the weight percentage of things；As follows
。
5. power quality determination method according to claim 1, which is characterized in that matter-element to be measured in the step 4
The circular of characteristic and degree of association sizes values of all categories is as follows:
Calculate a characteristic value x in matter-element to be measurediFor the distance difference of all Classical field central points, and calculate central point and warp
The distance difference of allusion quotation domain bound, by this distance difference be defined as away from, this feature value for each Classical field away from numerical value then such as
Shown in following formula,
Calculate a characteristic value x in matter-element to be measurediFor the distance difference of all section domains central point, and calculate central point and section domain
This distance difference is defined as away from this feature value is for each section domain away from numerical value then such as following formula institute by the distance difference of bound
Show,
6. power quality determination method according to claim 1, which is characterized in that calculate association in the step 5
The specific method is as follows for function:
Each Classical field is completed with section domain away from calculating, according to the operation for being associated function by following formula
Calculate correlation function value；Again via following formula
Accumulating operation is carried out to the degree of membership of determinand generic respectively and is indicated, belonging to determining that determinand explicitly classifies
Classification.
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