CN110221147A - Power Quality Detection analysis method based on more composite optimization algorithms - Google Patents
Power Quality Detection analysis method based on more composite optimization algorithms Download PDFInfo
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Abstract
The present invention relates to a kind of Power Quality Detection analysis methods of more composite optimization algorithms.The present invention first uses EEMD method to denoise, decompose to the electric signal of sampling.Then amplitude, frequency, decay factor and the starting phase angle for calculating and decomposing each obtained IMF are handled using Prony algorithm.In order to improve the precision of calculating, accuracy, the present invention joined improved dragonfly algorithm to combine to obtain new intelligent algorithm (present invention is referred to as DP algorithm for the time being) with Prony algorithm, to solve to IMF during calculating.Better effect in order to obtain, the present invention are introduced signal-to-noise ratio again in DP algorithm, the judge of algorithm accuracy are carried out with this, keep final result more accurate.
Description
Technical field
The present invention relates to a kind of Power Quality Detection analysis methods based on more composite optimization algorithms, belong to the electric energy of power grid
Quality testing analysis technical field.
Background technique
In recent years, the continuous improvement of the rapid development of social economy and social productive forces, living standards of the people obtain quickly
Raising, the electric load of power supply network also obtained rapid growth.Various impact loads and nonlinear-load in power grid
It is quickling increase and the extensive use in the power system of the precision equipment at various high-precision ends, in particular with new energy
Grid-connected, such as wind-power electricity generation, photovoltaic power generation equistability is not high, grid-connected, the electric energy in electric system of the biggish new energy of randomness
The case where quality, also becomes increasingly complex, and power quality problem has caused more and more concerns of various circles of society.For this purpose, related
Department increasingly payes attention to the test problems of power quality.But the electric system discrete data in reality is often simultaneously by multiple
The influence of source of error, such as include attenuating dc component, m-Acetyl chlorophosphonazo, noise in data.It is temporary accurately to extract power failure
The parameters such as amplitude and frequency of periodic signal are to POWER SYSTEM STATE analysis, fault diagnosis, control and protection to pass in state signal
It is important.Domestic and foreign scholars have been carried out extensive and in-depth research.The characteristic information for how extracting Power Quality Disturbance is
The basis that Power Quality Disturbance identifies and correctly classifies.
Summary of the invention
The object of the present invention is to provide a kind of methods of Power Quality Detection analysis.
In order to achieve the above object, the technical solution of the present invention is to provide a kind of power qualities of more composite optimization algorithms
Determination method, which comprises the following steps:
Step 1 carries out EEMD decomposition using power failure transient signal denoising of the EEMD method to sampling later, and EEMD divides
Each IMF component is obtained after solution, and i-th of IMF component is denoted as cki(t), the IMF component obtained every time is done into integrated average place
Reason, then i-th of the IMF component obtained after handling are denoted as ci(t),N is overall that white Gaussian noise is added
Number, the IMF of the more big corresponding white Gaussian noise of N and will tend to 0, and decomposition result also more levels off to true value；
Step 2, setting maximum number of iterations Tmax, snr threshold SNR' is set, the number of iterations t=0 is set；
Each dragonfly is calculated as the substitution of the initial value of dragonfly algorithm in each IMF component that step 1 obtains by step 3
Separating degree, degree of registration, cohesion degree, food attraction and the natural enemy repulsive force of dragonfly individual, in which:
The separating degree of i-th of dragonfly individual is Si, then have:
In formula, X indicates the position of i-th of dragonfly individual, XjIndicate the position of the dragonfly individual adjacent with X, it is adjacent with X
Dragonfly individual shares N number of；
The degree of registration of i-th of dragonfly individual is Ai, then have:
In formula, VjIndicate the speed of the dragonfly individual adjacent with X；
The cohesion degree of i-th of dragonfly individual is Ci, then have:
The food attraction of i-th of dragonfly individual is Fi, then have:
Fi=X+-X
In formula, X+Indicate food source position；
The natural enemy repulsive force of i-th of dragonfly individual is Ei, then have:
Ei=X-+X
In formula, X-Indicate natural enemy position；
Step 4, setting degree of registration weight a, separating degree weight s, cohesion degree weight c, inertial factor w, are calculated improvement
Natural enemy factor e' and food factor f' afterwards:
F'=λ f
E'=β e
In formula, f indicates the food factor before improving, and e indicates the natural enemy factor before improving,
Step 5, improved according to improved natural enemy factor e' and food factor f' after dragonfly update position Xt+1,
Then have:
ΔXt+1=(sSi+aAi+cCi+f'Fi+e'Ei)+wΔXt
Xt+1=Xt+ΔXt+1
In formula, Δ XtIt indicates to update step-length, XtThe position of current i-th of dragonfly individual；
Step 6 and by new location parameter Xt+1It brings intoTo solveIt will
As a result it brings intoTo seek objective function, in which:Indicate the fitting estimated value of x (n), akTable
Show characteristic equation coefficient, bkIndicate the corresponding plural number of x (n) fitting estimated value,Indicate the corresponding plural number of x (n) fitting estimated value,
Z indicates objective function；
A is calculated in step 7k、Ak、θkAnd fk, AkFor kth rank amplitude, fkFor kth order frequency, θkFor kth rank phase:
Ak=| bk|
In formula, zkThe root that representative polynomial solves, Δ t indicate the sampling interval；
SNR is calculated in step 8,T=t+1；
If step 9, t >=TmaxOr SNR < SNR', then terminate this method, otherwise, return step 3.
The present invention first uses EEMD method to denoise, decompose to the electric signal of sampling.Then located using Prony algorithm
Reason calculates amplitude, frequency, decay factor and the starting phase angle for decomposing each obtained IMF.In order to improve the precision of calculating,
Accuracy, it is new to combine to obtain with Prony algorithm that the present invention joined improved dragonfly algorithm during calculating
Intelligent algorithm (present invention is referred to as DP algorithm for the time being), to be solved to IMF.Better effect in order to obtain, the present invention is again
Signal-to-noise ratio is introduced in DP algorithm, the judge of algorithm accuracy is carried out with this, keeps final result more accurate.
Detailed description of the invention
Fig. 1 and Fig. 2 is flow chart of the invention.
Specific embodiment
With reference to the accompanying drawing, the present invention is further explained.It should be understood that these embodiments are merely to illustrate the present invention and do not have to
In limiting the scope of the invention.In addition, it should also be understood that, after reading the content taught by the present invention, those skilled in the art can be with
The present invention is made various changes or modifications, such equivalent forms equally fall within model defined by the application the appended claims
It encloses.
A kind of Power Quality Detection analysis method of more composite optimization algorithms provided by the invention is based on following algorithm:
The first) Prony algorithm
Prony algorithm was widely used in the analysis of signal in recent years, and feasibility has been found.Prony algorithm can
With amplitude, frequency, decay factor and the starting phase angle in direct estimation signal.
Assuming that x (0), x (1) ..., x (N-1) are sampled data.Then:
N=0,1,2 in formula (1) ..., N-1；K=1,2 ..., P
bkIndicate the corresponding plural number of fitting estimated value of x (n),Indicate the corresponding plural number of fitting estimated value of x (n), AkFor
Kth rank amplitude, αkFor kth rank damping factor, fkFor kth order frequency, θkFor kth rank phase, Δ t is the sampling interval.
Construct objective function:
It is given below and is solved using difference equation:
Formula (1) is the homogeneous solution of following LINEAR DIFFERENCE EQUATION WITH CONSTANT COEFFICIENTS:
If evaluated error be e (n) then
It is therein
Therefore, x (n) can be regarded as the output that noise u (n) motivates a P rank AR model to generate.Solve AR model just
The then available parameter a of equationk, substitute into following formula and z can be obtained by polynomial rootingk, wherein k=1,2 ..., P, order P can
To be determined according to the AIC criterion of AR model.
According to formula (1), then have
[b can be acquired using least square solution1,b2…bP]T.By bkIt is available:
Ak=| bk| (4)
In above formula: k=1,2 ..., P
There is a certain error for signal and original signal meeting after fitting, is indicated: being the bigger the better using signal-to-noise ratio here,
Snr threshold SNR' is set, as a standard of judge.
The second) EEMD method
When electric system operates normally, waveform is relatively stable.Using Prony algorithm analyze the frequency of signal, amplitude,
Just make phase and decay factor, analytic process is simple, and analysis result accuracy is high.But when Harmonious Waves in Power Systems is seriously polluted
When, Prony algorithm causes interpretation of result inaccurate vulnerable to noise jamming.At this moment it can will be made an uproar by improved EEMD method
Sound assistant analysis is applied in EMD also be able to achieve good noise reduction simultaneously in suppression mode aliasing to promote anti-mixed decomposition.Noise
Natural mode of vibration component of the signal after EEMD is decomposed preferably discloses the physical connotation of original signal, keeps its physical essence more clear
It is clear.Therefore the present invention first carries out EEMD decomposition to signal, then carries out carrying out Prony analysis to each IMF component after decomposition, from
And quickly and accurately identify Oscillatory mode shape parameter.
The problem of can solve traditional mode aliasing by the multiple EMD decomposition after superposition white Gaussian noise.When original letter
Number be added frequency-flat white noise background when, the signal area of different time scales can be automatically mapped to and background white noise
On relevant appropriate scale.The principle that average statistical using uncorrelated random sequence is 0 is to the totality after multiple addition noise
After taking mean value, noise will be eliminated, and population mean is considered as signal itself to eliminate modal overlap phenomenon.Its algorithm steps
It is rapid as follows.
(1) by normal distribution white noise sequence nk(t) it is added in time series x (t)；
x'k(t)=x (t)+nk(t) (9)
(2) by the time series x after addition normal distribution white noise sequencek(t) EMD points are carried out respectively as a whole
Solution, obtains each IMF component and is denoted as cki(t), r is denoted as with a remainderkn(t)。
(3) step (1) and (2) 100 times are repeated, new normal distribution white noise sequence is added every time；
(4) the IMF component obtained every time is done into integrated average treatment.
C in formulai(t) it indicates to obtain i-th final of IMF component after EEMD is decomposed.N is the totality that white Gaussian noise is added
Number.The IMF of the more big corresponding white noise of N and 0 will be tended to, decomposition result also more levels off to true value.
Third) dragonfly algorithm
In dragonfly algorithm, dragonfly individual behavior specifically include that collision avoidance behavior, peering behavior, Assembling Behavior, foraging behavior with
And enemy's behavior is kept away, the main function of these behaviors is respectively to avoid mutually colliding between individual around dragonfly group, guarantee group
Around rate uniformity between individual, guarantee that individual is mobile to mean place around group, guarantees the close food of individual in population
Source and guarantee that individual in population avoids natural enemy.The specific meaning of these individual behaviors and mathematics expression are as follows:
(1) separating degree refers to and avoids collision between dragonfly and adjacent body
(2) degree of registration, which refers to, tends to keep identical speed between adjacent body.
(3) cohesion degree refers to that dragonfly tends to gather to adjacent body center.
(4) food attraction refers to food to the attraction of dragonfly.
Fi=X+-X (15)
(5) natural enemy repulsive force refers to dragonfly to the repulsive force of natural enemy
Ei=X-+X (16)
In formula, X indicates the position of current dragonfly individual；XjIndicate the position of j-th of adjacent dragonfly individual；VjIt indicates j-th
The speed of adjacent dragonfly individual；N indicates the individual amount adjacent with i-th of dragonfly individual；X+Indicate food source position；X-It indicates
Natural enemy position.According to above-mentioned 5 kinds of dragonfly behaviors, the step-length of next-generation dragonfly and position calculating are as follows:
In above formula, t indicates current iteration number；I indicates i-th of dragonfly individual；XtIndicate current t for population at individual position
It sets；ΔXt+1Indicate next-generation population location updating step-length；Xt+1Indicate next-generation population at individual position；S indicates separating degree weight；
A indicates degree of registration weight；C indicates cohesion degree weight；F indicates the food factor；E indicates the natural enemy factor；W indicates inertia weight.
Four) improved dragonfly algorithm:
For the rapidity and accuracy for improving dragonfly optimization algorithm.It is allowed to better solve objective optimisation problems, propose
Following improvement strategy:
The food factor is indicated to f；E indicates that the natural enemy factor improves
Then according to the update position of dragonfly after the new available improvement of weight are as follows:
Based on above-mentioned algorithm, a kind of Power Quality Detection analysis method based on more composite optimization algorithms provided by the invention
Detailed process is as shown in flow chart 1, and steps are as follows:
(1) EEMD resolution process is carried out to collected electric signal first, specifically according to formula x'k(t)=x (t)+nk
(t) normal distribution white noise is added.By the time series x after addition normal distribution white noise sequencek(t) divide as a whole
Not carry out EMD decomposition, obtain each IMF component and be denoted as cki(t), r is denoted as with a remainderkn(t) namelyThe IMF component obtained every time is done into integrated average treatment.C in formulai(t) table
Show and obtains j-th final of IMF component after EEMD is decomposed.N is the overall number that white Gaussian noise is added, and N is bigger, corresponding white
The IMF of noise and 0 will be tended to, decomposition result also more levels off to true value.
(2) by each ci(t) initial value as dragonfly algorithm is brought into Fi=X+-X；Ei=X-+X.X indicates the position of current dragonfly individual in formula；XjIndicate j-th of adjacent dragonfly
The position of dragonfly individual；VjIndicate the speed of j-th of adjacent dragonfly individual；N indicates the number of individuals adjacent with i-th of dragonfly individual
Amount；X+Indicate food source position；X- indicates natural enemy position.
(3) degree of registration weight a, separating degree weight s, cohesion degree weight c, inertial factor w are set, and according to formula
F'=λ f
E'=β e
Design factor e', f' update coefficient.
(4) basisCalculate follow-on position, and will be new
Location parameter bring formula intoTo solveBring result into formulaTo seek objective function.
(5) make t=t+1 at the same time according to formulaSignal-to-noise ratio is calculated, and carry out judgement to be
It is no to reach the number of iterations and meet situation there are also signal-to-noise ratio.Until meet the requirements, according to formula:
Ak=| bk|
To calculate Ak,ak,θk,fkIt saves and exports result.
Verified, a kind of Power Quality Detection analysis method based on more composite optimization algorithms proposed by the present invention can be more
The power quality of power grid is tested and analyzed well, and realizes that process is relatively easy, there is good practical value.
Claims (1)
1. a kind of Power Quality Detection analysis method of more composite optimization algorithms, which comprises the following steps:
Step 1 carries out EEMD decomposition using power failure transient signal denoising of the EEMD method to sampling later, after EEMD is decomposed
Each IMF component is obtained, i-th of IMF component is denoted as cki(t), the IMF component obtained every time is done into integrated average treatment, then
I-th of the IMF component obtained after processing is denoted as ci(t),N is the overall number that white Gaussian noise is added, N
The IMF of more big corresponding white Gaussian noise and 0 will be tended to, decomposition result also more levels off to true value；
Step 2, setting maximum number of iterations Tmax, snr threshold SNR' is set, the number of iterations t=0 is set；
Each dragonfly is calculated as the substitution of the initial value of dragonfly algorithm in each IMF component that step 1 obtains by step 3
Separating degree, degree of registration, cohesion degree, food attraction and the natural enemy repulsive force of body, in which:
The separating degree of i-th of dragonfly individual is Si, then have:
In formula, X indicates the position of i-th of dragonfly individual, XjIndicate the position of the dragonfly individual adjacent with X, the dragonfly adjacent with X
Individual shares N number of；
The degree of registration of i-th of dragonfly individual is Ai, then have:
In formula, VjIndicate the speed of the dragonfly individual adjacent with X；
The cohesion degree of i-th of dragonfly individual is Ci, then have:
The food attraction of i-th of dragonfly individual is Fi, then have:
Fi=X+-X
In formula, X+Indicate food source position；
The natural enemy repulsive force of i-th of dragonfly individual is Ei, then have:
Ei=X-+X
In formula, X-Indicate natural enemy position；
Step 4, setting degree of registration weight a, separating degree weight s, cohesion degree weight c, inertial factor w, are calculated improved
Natural enemy factor e' and food factor f':
F'=λ f
E'=β e
In formula, f indicates the food factor before improving, and e indicates the natural enemy factor before improving,
Step 5, improved according to improved natural enemy factor e' and food factor f' after dragonfly update position Xt+1, then have:
ΔXt+1=(sSi+aAi+cCi+f'Fi+e'Ei)+wΔXt
Xt+1=Xt+ΔXt+1
In formula, Δ XtIt indicates to update step-length, XtThe position of current i-th of dragonfly individual；
Step 6 and by new location parameter Xt+1It brings intoTo solveBy result band
EnterTo seek objective function, in which:Indicate the fitting estimated value of x (n), akIndicate feature
Equation coefficient, bkIndicate the corresponding plural number of x (n) fitting estimated value,Indicate that the corresponding plural number of x (n) fitting estimated value, z indicate
Objective function；
A is calculated in step 7k、Ak、θkAnd fk, AkFor kth rank amplitude, fkFor kth order frequency, θkFor kth rank phase:
Ak=| bk|
In formula, zkThe root that representative polynomial solves, Δ t indicate the sampling interval；
SNR is calculated in step 8,T=t+1；
If step 9, t >=TmaxOr SNR < SNR', then terminate this method, otherwise, return step 3.
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