CN105139091B - A kind of capacitor capacitance value and its trend method based on time series method - Google Patents
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Abstract
The present invention relates to power quality analysis technical field, in particular to a kind of capacitor capacitance value and its trend method based on time series method.Capacitor capacitance value and its variation tendency are effectively predicted for prior art shortage, the present invention provides a kind of capacitor capacitance value and its trend method based on time series method.The present invention passes through collecting sample data, establishes capacitance variation trend prediction model, establishes capacitor value prediction model and predict that capacitance realizes effective, the Accurate Prediction to capacitor capacitance value and its variation tendency.It is more thorough that algorithm design of the invention considers, implementation cost is lower, and prediction effect is reliable, all has good suitability with the existing all kinds of capacitors of power industry, has a extensive future.
Description
Technical field
The present invention relates to power quality analysis technical field, in particular to a kind of capacitor capacitance based on time series method
Value and its trend method.
Background technique
With the development of modern industry, the non-linear electrical equipments such as converter, electric arc furnaces and out-of-balance load are straight
The fields such as stream transmission of electricity, metallurgy, chemical industry and electric railway are used widely, these non-linear electrical equipments can produce at runtime
Raw a large amount of reactive power demand, and a large amount of harmonic injection power grids are generated, cause voltage waveform distortion, power quality decline.?
Installation shunt capacitor is compensating power and filters out that harmonic wave is mostly important and application in electric system and vast power consumer
Extensive technological means.
In electric system, if the shunt capacitor installed at substation and load often operate in it is more severe humorous
In wave environment, built-in electrical insulation shortens dramatically accelerated ageing, service life, and may cause accident.The capacitor of aging can
Amplification can be generated to harmonic current, aggravate Harmfulness Caused by Harmonics, there may be harmonic resonances when serious, make several times of amplified harmonic current
Even tens times, the danger of overcurrent and overvoltage is caused, capacitor and reactor is made to damage or burn.With large capacity aggregation type
For capacitor, if its internal existing small capacitances element fault, although having substantially no effect on its power frequency stable state reactive compensation performance,
But harmonic wave be easy to cause its internal fault protection misoperation, influences the normal work of capacitor.
If the capacitance and its variation tendency of capacitor can be predicted, it can tell whether capacitor will damage.Therefore,
It is necessary to predict capacitor capacitance value and its variation tendency, to prevent capacitor catastrophic failure, expand accident range.
Summary of the invention
Capacitor capacitance value and its variation tendency are effectively predicted for prior art shortage, the present invention provides one kind
Capacitor capacitance value and its trend method based on time series method.
The technical solution of the present invention is as follows:
A kind of capacitor capacitance value and its trend method based on time series method, comprising the following steps:
(a) collecting sample data
Putting into operation constantly since capacitor until at the time of prediction, successively the accumulation of capacitor is transported
The row time is divided according to constant duration H, using the average capacitance value of each time interval H inner capacitor as a sample
Ci, it is sample size that wherein i=1,2 ..., N, which are N,；By each sample CiAssign a sample moment T (i), T (i)=i, i=
1,2,…,N。
(b) capacitance variation trend prediction model is established
(b-1) capacitance variation trend Δ C is calculatedN
The capacitance variation trend Δ C at T (i) moment is calculated according to formula 1i:
ΔCi=Ci+1-Ci (1)
In formula 1, Δ CiFor the capacitance variation trend at T (i) moment, CiFor the capacitance at T (i) moment, Ci+1For T (i+
1) capacitance at moment, i=1,2 ..., N；
It is successively calculated T (1) using formula 1 and the collected sample data of step (a), T (2), the electricity at Λ, T (N-1) moment
Capacitance variation tendency Δ C1,ΔC2,…,ΔCN-1；
The capacitance variation trend Δ C at T (N) momentNIt is related with the capacitance variation trend at preceding k moment, see formula 2:
In formula 2, Δ CNFor the capacitance variation trend at T (N) moment, Δ CN-pFor the capacitance variation at T (N-p) moment
Trend, ωpFor the capacitance variation trend Δ C at T (N-p) momentN-pTo the capacitance variation trend Δ C at T (N) momentNInfluence
Weight；P expression T (N) the 1st moment to any one moment between k-th of moment before the moment；
(b-2) weight ω is calculatedp
Weight ω is calculated according to formula 3p:
ωp=rp·sN-p(ΔCN-p) (3)
In formula 3, rpIt is chronological order to weight ωpInfluence factor and
sN-p(ΔCN-p) it is T (N-p) moment capacitance variation speed to weight ωpInfluence factor, sN-p(ΔCN-p)
It calculates according to formula 4.
In formula 4, sj(ΔCj) it is influence factor of T (j) the moment capacitance variation speed to weight,
(b-3) capacitance variation trend prediction model is established
The substitution formula 2 of formula 3~4 is obtained into capacitance variation trend prediction model, sees formula 5:
(c) it establishes capacitor value prediction model and predicts capacitance CN+1
Capacitor value prediction model is established according to formula 1, sees formula 6:
CN+1=CN+ΔCN (6)
In formula 6, CN+1For the capacitance at T (N+1) moment, CNFor the capacitance at T (N) moment, Δ CNFor T (N) moment
Capacitance variation trend；Capacitance CNIt is collected by step (a)；Capacitance variation trend Δ CNIt is calculated by step (b)；
By capacitance CNWith capacitance variation trend Δ CNIt substitutes into formula 6 and prediction capacitance C is calculatedN+1。
Specifically, r in step (b-2)pCalculating according to formula (7):
In formula 7, rpIt is chronological order to weight ωpInfluence factor, p=1,2 ..., k；
Number at the time of k indicates choose when capacitance variation trend prediction.
Specifically, r in step (b-2)pIt is calculated using analytic hierarchy process (AHP).
Specifically, k=4 in step (b).
Beneficial effects of the present invention: the present invention is different from conventionally used prediction technique to the prediction of capacitance, common
Prediction is the direct capacitance using capacitance prediction subsequent time for the previous period.The change of present invention combination capacitance at any time
Law has initially set up capacitance variation trend prediction model, is realized by means of the prediction to capacitance variation trend to electricity
The prediction of capacitance, do so capacitance predict than directly it is more acurrate.Prediction technique in the present invention is not only simply
Time series method, two factors involved in invention, first is that capacitance variation speed, second is that the sequencing of time.Factor one
Influence of the capacitance variation speed to prediction result is calculated using s type distribution function, is not belonging to time series method.Factor two belongs to
Time series method, the calculation method of factor two includes two methods, first is that mean value method, two are combined with adding for analytic hierarchy process (AHP)
Weigh the method for moving average.Present invention combination capacitance variation speed and the sequencing of time the two factors, establish capacitance
Trend model and capacitor value prediction model simultaneously predict capacitance.It is more thorough that algorithm design of the invention considers, real
Apply that cost is relatively low, prediction effect is reliable, all has good suitability, application prospect with the existing all kinds of capacitors of power industry
It is wide.
Specific embodiment
Embodiment uses capacitor capacitance value and its trend method of the invention, and specific implementation step is such as
Under:
(a) collecting sample data
Putting into operation constantly since capacitor until at the time of prediction, successively the accumulation of capacitor is transported
The row time is divided according to constant duration H, using the average capacitance value of each time interval H inner capacitor as a sample
Ci, it is sample size that wherein i=1,2 ..., N, which are N,；By each sample CiAssign a sample moment T (i), T (i)=i, i=
1,2,…,N。
When the Cumulative Elapsed Time to capacitor carries out constant duration division, for the last remaining period, when
Its runing time ignores this section when being less than H/2；When being no less than H/2 between when it is operated, by being averaged for the period inner capacitor
Capacitance is also used as a sample.
In the present embodiment, time interval H value is 15 days.It, can be in sample when time interval H takes 15 days through repetition test
This acquisition not only guarantees sampling precision when calculating, but also can rationally control the calculation amount of sampling, and it is time-consuming to shorten sampling.
(b) capacitance variation trend prediction model is established
(b-1) capacitance variation trend Δ C is calculatedN
The capacitance variation trend Δ C at T (i) moment is calculated according to formula 1i:
ΔCi=Ci+1-Ci (1)
In formula 1, Δ CiFor the capacitance variation trend at T (i) moment, CiFor the capacitance at T (i) moment, Ci+1For T (i+
1) capacitance at moment, i=1,2 ..., N.
It is successively calculated T (1) using formula 1 and the collected sample data of step (a), T (2), the electricity at Λ, T (N-1) moment
Capacitance variation tendency Δ C1,ΔC2,…,ΔCN-1。
The capacitance variation trend Δ C at T (N) momentNIt is related with the capacitance variation trend at preceding k moment, see formula 2:
In formula 2, Δ CNFor the capacitance variation trend at T (N) moment, Δ CN-pFor the capacitance variation at T (N-p) moment
Trend, ωpFor the capacitance variation trend Δ C at T (N-p) momentN-pTo the capacitance variation trend Δ C at T (N) momentNInfluence
Weight, p expression T (N) the 1st moment to any one moment between k-th of moment before the moment.In the present embodiment, the value of k is
4., can be under the premise of guaranteeing capacitance and its trend accuracy when k=4 through repetition test, reasonable control meter
Calculation amount avoids calculating from taking long time.
(b-2) weight ω is calculatedp
The present embodiment is calculating weight ωpWhen do not use only simple time series method, also fully considered influence power
Value ωpTwo factors, first is that capacitance variation speed, second is that the sequencing of time, is shown in formula 3.
ωp=rp·sN-p(ΔCN-p) (3)
In formula 3, rpIt is chronological order to weight ωpInfluence factor, andsN-p(ΔCN-p) it is T
(N-p) moment capacitance variation speed is to weight ωpInfluence factor.
(i.e. capacitance variation speed is to weight ω for factor onepInfluence) using S type distribution function calculate capacitance variation
Influence of the speed to prediction result, is not belonging to time series method.Consider capacitance variation speed to weight ωpInfluence when, it is first
First analyze the operation characteristic of capacitor.In many cases, with the growth of capacitor runing time, the capacitor of most capacitors
Value can show the rule successively decreased, and also have partial capacitor its capacitance within the shorter a period of time for starting to put into operation certainly
It can first increase, but finally can also show downward trend.The variation tendency of capacitance is to reflect the size of capacitance variation.
By the definition of capacitance variation trend it is found that capacitor capacitance value is the increase with its variation tendency and is increased, reduces and subtract
It is small, meet the variation characteristic of S type distribution function.Capacitance variation trend increases or reduces the variation speed that reaction is capacitance
Degree.In conclusion influence s of the capacitance variation speed to capacitanceN-p(ΔCN-p) calculating of S type distribution function can be used, see public affairs
Formula 4.
In formula 4, sj(ΔCj) it is influence factor of T (j) the moment capacitance variation speed to weight,
(sequencing of time is to weight ω for factor twopInfluence) belong to time series method, the calculation method of factor two
Including two methods, first is that mean value method, two are combined with the method for weighted moving average of analytic hierarchy process (AHP).
R is calculated using mean value methodpWhen, rpCalculating according to formula (7):
In formula 7, rpIt is chronological order to weight ωpInfluence factor, p=1,2 ..., k；K indicates to carry out capacitor
Number at the time of selection when value trend.
K=4 in the present embodiment, then r1=r2=r3=r4=1/4.
R is calculated using analytic hierarchy process (AHP)pWhen, Judgement Matricies in the case where k=4 are as follows:
It is computed, r=[0.4658 0.2771 0.1611 0.0960], i.e. r1=0.4658, r2=0.2771, r3=
0.1611, r4=0.0960, that is, as prediction n-hour capacitance variation trend Δ CNWhen, by preceding k (this reality of n-hour
Apply k=4 in example) to be respectively as follows: the N-1 moment be 0.4658 to influence factor caused by the chronological order at a moment, N-2
Moment is 0.2771, and the N-3 moment is 0.1611, and the N-4 moment is 0.0960.
(b-3) capacitance variation trend prediction model is established
The substitution formula 2 of formula 3~4 is obtained into capacitance variation trend prediction model, sees formula 5:
(c) it establishes capacitor value prediction model and predicts capacitance CN+1
Capacitor value prediction model is established according to formula 1, sees formula 6:
CN+1=CN+ΔCN (6)
In formula 6, CN+1For the capacitance at T (N+1) moment, CNFor the capacitance at T (N) moment, Δ CNFor T (N) moment
Capacitance variation trend；Capacitance CNIt is collected by step (a)；Capacitance variation trend Δ CNIt is calculated by step (b)；
By capacitance CNWith capacitance variation trend Δ CNIt substitutes into formula 6 and prediction capacitance C is calculatedN+1。
When using mean value method calculating rpWhen, the average value prediction model of capacitance is obtained, sees formula 8:
In formula 8, CN+1For the capacitance at T (N+1) moment, CNFor the capacitance at T (N) moment, Δ CN-pWhen for T (N-p)
The capacitance variation trend at quarter, sN-p(ΔCN-p) it is influence factor of T (N-p) the moment capacitance variation speed to capacitance.Electricity
Capacitance CNIt is collected by step (a), capacitance variation trend Δ CN-pSample data is collected by step (a) and formula 1 is counted
It obtains, sN-p(ΔCN-p) sample data is collected by step (a) and formula 4 is calculated, k=4.
When using analytic hierarchy process (AHP) calculating rpWhen, the weighted prediction model of capacitance is obtained, sees formula 9:
In formula 9, CN+1For the capacitance at T (N+1) moment, CNFor the capacitance at T (N) moment, Δ CN-pWhen for T (N-p)
The capacitance variation trend at quarter, sN-p(ΔCN-p) it is influence factor of T (N-p) the moment capacitance variation speed to capacitance, rp
It is chronological order to weight ωpInfluence factor.Capacitance CNIt is collected by step (a), capacitance variation trend Δ
CN-pSample data is collected by step (a) and formula 1 is calculated, sN-p(ΔCN-p) sample number collected by step (a)
It is calculated according to formula 4, rpIt is calculated using analytic hierarchy process (AHP), k=4.
The present invention establishes two kinds of capacitor value prediction models of average value and weight estimation.By comparison, average value predicts mould
Type is more simple compared with weighted prediction model, conveniently, is suitable for estimating roughly to capacitance, and the prediction knot of weighted prediction model
Fruit is relatively more accurate, is suitable for the higher occasion of prediction result accuracy requirement.Time series method institute is based in the present invention
The two kinds of capacitor value prediction models established not are directly to predict the capacitance of subsequent time, but when first prediction obtains this
Then the capacitance variation trend at quarter is predicted to obtain the capacitance of subsequent time by capacitance variation trend model.Using this
The reason of kind of modeling pattern, is that the capacitance of most of capacitors can all be showed with the increase of its runing time and successively decreases
Rule also has partial capacitor its capacitance within the shorter a period of time for starting to put into operation that can first increase certainly, but most
Downward trend can be also showed eventually.It can be seen that the variation tendency of certain moment capacitance and the variation at the front several moment become
Gesture has very strong correlation.In view of this changing rule of capacitance is first predicted using prediction model built in the present invention
Then the capacitance variation trend at certain moment predicts subsequent time capacitance, so first it is ensured that capacitance variation direction
The correctness of (increase or reduce), then by considering chronological order and the two factors of capacitance variation speed to electricity
Dynamic weighting function is combined with analytic hierarchy process (AHP), obtains reasonable weight, can be further improved pre- by the influence characteristic of capacitance
Survey the accuracy of capacitance.But if directly capacitance is predicted, then not necessarily can guarantee the correct of its variation tendency
Property.
For example, downward trend is showed as k=4 and in T (N-3), T (N-2), T (N-1) and T (N) moment capacitance,
That is CN≤CN-1≤CN-2≤CN-3When, directly the capacitance at T (N+1) moment is measured in advance using conventional methodThere is CN+1≥CN, the capacitance at gained T (N+1) moment is greater than the capacitance at T (N) moment,
That is capacitance variation trend is changed, and becomes ascendant trend from decline, it is clear that the capacitance prediction accuracy of conventional method
It is extremely low.C is obtained using capacitor value prediction model of the inventionN+1=CN+ΔCN,Due to Δ CN-p≤ 0,
Obtain Δ CN≤ 0, there is CN+1≤CN, capacitance variation trend do not change.It can be seen that capacitance and its variation of the invention
Law forecasting is highly improved compared with the accuracy that conventional method is predicted.
It should be noted that calculating r using analytic hierarchy process (AHP)pTechnology be this field common knowledge, even if of the invention
It is not described in detail, those skilled in the art are it is also evident that above step.
Embodiment described above is merely a preferred embodiment of the present invention, and the simultaneously exhaustion of the feasible implementation of non-present invention.It is right
For persons skilled in the art, any aobvious to made by it under the premise of without departing substantially from the principle of the invention and spirit and
The change being clear to should be all contemplated as falling within claims of the invention.
Claims (4)
1. a kind of capacitor capacitance value and its trend method based on time series method, it is characterised in that it include with
Lower step:
(a) collecting sample data
Putting into operation constantly since capacitor until at the time of prediction, when successively the accumulation of capacitor being run
Between divided according to constant duration H, using the average capacitance value of each time interval H inner capacitor as a sample Ci,
Wherein i=1,2 ..., N, N are sample size；By each sample CiA sample moment T (i), T (i)=i, i=1 are assigned,
2,…,N；
(b) capacitance variation trend prediction model is established
(b-1) capacitance variation trend Δ C is calculatedN
The capacitance variation trend Δ C at T (i) moment is calculated according to formula 1i:
ΔCi=Ci+1-Ci (1)
In formula 1, Δ CiFor the capacitance variation trend at T (i) moment, CiFor the capacitance at T (i) moment, Ci+1When for T (i+1)
The capacitance at quarter, i=1,2 ..., N；
It is successively calculated T (1) using formula 1 and the collected sample data of step (a), T (2) ..., the capacitance at T (N-1) moment
Variation tendency Δ C1,ΔC2,…,ΔCN-1；
The capacitance variation trend Δ C at T (N) momentNIt is related with the capacitance variation trend at preceding k moment, see formula 2:
In formula 2, Δ CNFor the capacitance variation trend at T (N) moment, Δ CN-pFor the capacitance variation trend at T (N-p) moment,
ωpFor the capacitance variation trend Δ C at T (N-p) momentN-pTo the capacitance variation trend Δ C at T (N) momentNInfluence weight；p
Expression T (N) the 1st moment to any one moment between k-th of moment before the moment；
(b-2) weight ω is calculatedp
Weight ω is calculated according to formula 3p:
ωp=rp·sN-p(ΔCN-p) (3)
In formula 3, rpIt is chronological order to weight ωpInfluence factor andsN-p(ΔCN-p) it is T (N-p)
Moment, capacitance variation speed was to weight ωpInfluence factor, sN-p(ΔCN-p) calculating according to formula 4:
In formula 4, sj(ΔCj) it is influence factor of T (j) the moment capacitance variation speed to weight,
(b-3) capacitance variation trend prediction model is established
The substitution formula 2 of formula 3~4 is obtained into capacitance variation trend prediction model, sees formula 5:
(c) it establishes capacitor value prediction model and predicts capacitance CN+1
Capacitor value prediction model is established according to formula 1, sees formula 6:
CN+1=CN+ΔCN (6)
In formula 6, CN+1For the capacitance at T (N+1) moment, CNFor the capacitance at T (N) moment, Δ CNFor the capacitor at T (N) moment
It is worth variation tendency；Capacitance CNIt is collected by step (a)；Capacitance variation trend Δ CNIt is calculated by step (b)；It will be electric
Capacitance CNWith capacitance variation trend Δ CNIt substitutes into formula 6 and prediction capacitance C is calculatedN+1。
2. a kind of capacitor capacitance value and its trend side based on time series method according to claim 1
Method, it is characterised in that r in step (b-2)pCalculating according to formula (7):
In formula 7, rpIt is chronological order to weight ωpInfluence factor, p=1,2 ..., k；
Number at the time of k indicates choose when capacitance variation trend prediction.
3. a kind of capacitor capacitance value and its trend side based on time series method according to claim 1
Method, it is characterised in that r in step (b-2)pIt is calculated using analytic hierarchy process (AHP).
4. a kind of capacitor capacitance value and its variation tendency based on time series method according to claim 1 or 2 or 3 are pre-
Survey method, it is characterised in that k=4 in step (b).
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