CN103390117A - Feature extracting method for power load dynamic features - Google Patents

Abstract

The invention discloses a feature extracting method for power load dynamic features. The method comprises the steps of S1, preprocessing the three-phase transient state current data reflecting the power load dynamic features, and selecting the sampling data in one cycle before a fault and in two cycles after the fault as samples; S2, selecting a wavelet basis, and determining the lifting scheme of the wavelet basis; S3, selecting a resolving layer number m and performing m-layer resolving on the three-phase transient state current data of each sample by utilizing the wavelet packet conversion with the lifting scheme; S4, selecting the wavelet packet to reconstruct original signals, wherein the signal number in the wavelet packet is marked as r; and S5, extracting a corresponding reconstruction coefficient and calculating an energy value Mij(k) as the sample number, wherein j is equal to 1, 2 and 3 respectively representing the three phases of currents, k is equal to 1, 2, 3, ellipsis and r represents a frequency band. The multiplication operation number of the wavelet packet conversion based on the lifting scheme is reduced by about a half, so a faster conversion speed is realized.

Description

A kind of feature extracting method of electric load dynamic characteristic

Technical field

The present invention relates to a kind of feature extracting method of electric load dynamic characteristic.

Background technology

Load model is to affect power system digital simulation one of of paramount importance factor of accuracy and confidence as a result, but due to the difficulty of load modeling itself, power system load modeling is there is no the problem of fine solution during Power System Analysis is calculated always.For load modeling, in order to reflect exactly part throttle characteristics, the different load node in the same time different load models should not arranged, yet the region that aggregate power load forms random time variation dispersed and part throttle characteristics has determined to set up a universal model of containing all part throttle characteristics, namely produced thus the engineering practicability of load modeling and the contradiction between model exactness, the main path that solves this contradiction is that the classification of dynamic load model is with comprehensive.

Classification of dynamic load characteristics, with comprehensive, refer to that with the merger that in the dynamic load noisy data of same synthetic load different time collection, load structure is close be a class, and describes the part throttle characteristics of this classification with same load model.The classification of dynamic load model and the main path that is comprehensively change problem while solving in the load modeling process, the basis of classification of dynamic load characteristics and prerequisite are the dynamic load model feature extractions.

In order to extract accurately the proper vector of reflection dynamic load model, realize the accurate classification of dynamic load model, conduct in-depth research both at home and abroad, many methods have been proposed, but all there is defect in various degree in these methods, as extract time of part throttle characteristics sample collection and the season method as proper vector, and can not reflect the essential characteristic of dynamic load model, have larger subjectivity; Model response take each parameter of induction motor unified model as proper vector and under the normal voltage of using excitation is two kinds of feature extracting methods now commonly used as proper vector, the line parameter identification just can obtain the dynamic load model proper vector but these two kinds of methods all must the preference pattern structure be gone forward side by side, model structure error, parameter identification error will be inevitable, and calculated amount is large.In addition, respond as proper vector for using the lower model of normal voltage excitation, because the voltage-drop amplitude of the voltage-drop amplitude of normal voltage excitation and each load disturbance data is generally not identical, be subjected to the impact of the interpolation extrapolability of selected load model, the model response under the normal voltage excitation also there will be certain deviation.

Summary of the invention

The deficiency that exists for solving prior art, the invention discloses a kind of feature extracting method of electric load dynamic characteristic, the invention provides a kind of feature extracting method of realizing the electric load dynamic characteristic simple, that calculated amount is little, degree of accuracy is high, overcome in above-mentioned existing dynamic load model feature extracting method the error problem that the line parameter identification of must the preference pattern structure going forward side by side brings, realized the accurate extraction of the proper vector of reflection dynamic load model.

For achieving the above object, concrete scheme of the present invention is as follows:

A kind of feature extracting method of electric load dynamic characteristic comprises the following steps:

Step 1: the three-phase transient current data to reflection electric load dynamic characteristic are carried out pre-service, choose the previous cycle of fault and fault and start the sampled data of latter two cycle as sample;

Step 2: choose wavelet basis, determine the Via Lifting Scheme of selected wavelet basis;

Step 3: choose the decomposition number of stories m, utilize the wavelet package transforms of Via Lifting Scheme to carry out respectively the decomposition of m layer to the three-phase transient current data of each sample;

Step 4: choose wavelet packet, original signal is reconstructed, in the note wavelet packet, the signal number is r;

Step 5: extract corresponding reconstruction coefficients and calculating energy value M _ij(k), i is sample number, j=1, and 2,3 represent respectively three-phase current, k=1,2,3 ..., r represents frequency band, k is positive integer, take the average energy square of three-phase current signal, constructs the proper vector T of sample i as element _i

$T_i=\left(\underset{j=1}{\overset{3}{\mathrm{\Σ}}}\right[M_{\mathrm{ij}}\left(1\right),M_{\mathrm{ij}}\left(2\right),...,M_{\mathrm{ij}}\left(r\right)\left]\right)/\underset{j=1}{\overset{3}{\mathrm{\Σ}}}{\left({\left|\right|X_{\mathrm{ij}}\left|\right|}_2\right)}^2$

Wherein, X _i1, X _i2, X _i3Be respectively the three-phase current signal of sample i.

In described step 1, sample is designated as: X _i=[X _i1(n); X _i2(n); X _i3(n)],

Wherein i is sample number, and n is the number of sampled point in three cycles, X _i1(n), X _i2(n), X _i3(n) be respectively the sampled signal of three-phase transient current.

Selection of Wavelet Basis db4 small echo in described step 2.

The direct transform formula of described db4 small echo is as follows

$d_l^{\left(1\right)}=x_{2l+1}-\sqrt{3}{x}_{2l}$

$s_l^{\left(1\right)}=x_{2l}+\sqrt{3}/{4d}_l^{\left(1\right)}+(\sqrt{3}-2)/{4d}_{l+1}^{\left(1\right)}$

$d_l^{\left(2\right)}=d_l^{\left(1\right)}+s_{l-1}^{\left(1\right)}$

$s_l=(\sqrt{3}+1)/\sqrt{2}{s}_l^{\left(1\right)}$

$d_l=(\sqrt{3}-1)/\sqrt{2}{d}_l^{\left(2\right)}$

In formula: d ⁽¹⁾, d ⁽²⁾Different intermediate quantity during for calculating high fdrequency component d; s ⁽¹⁾Intermediate quantity during for calculating low frequency component s, data or the signal of Lifting Wavelet packet transform carried out in the x representative, and subscript l represents l numerical value in each variable.

The inverse transformation formula of described db4 small echo is:

$s_l^{\left(1\right)}=(\sqrt{3}-1)/\sqrt{2}{s}_l$

$d_l^{\left(2\right)}=(\sqrt{3}+1)/\sqrt{2}{d}_l$

$d_l^{\left(1\right)}=d_l^{\left(2\right)}-s_{l-1}^{\left(1\right)}$

$x_{2l}=s_l^{\left(1\right)}-\sqrt{3}/{4d}_l^{\left(1\right)}-(\sqrt{3}-2)/{4d}_{l+1}^{\left(1\right)}$

$x_{2l+1}=d_l^{\left(1\right)}+\sqrt{3}{x}_{2l}$

Decompose number of plies m=5 in described step 3, utilize the wavelet package transforms of Via Lifting Scheme to carry out 5 layers of decomposition to the three-phase current data decomposition of each sample;

Described wavelet packet is employing (5,0), (5,1), (4,1), (3,1), (2,1), the wavelet packet that (1,1) signal forms.

In described step 3 and step 4, decomposition and reconstruct are all completed in the MATLAB emulation platform.

Beneficial effect of the present invention:

1. Via Lifting Scheme is allowed the calculating on the spot of fast wavelet transform, that is to say that the wavelet transformation of whole Via Lifting Scheme can be completed on current location, does not need extra storage space;

2. reduced only about half ofly based on the wavelet package transforms multiplying number of Via Lifting Scheme, had conversion rate faster;

3. Via Lifting Scheme does not rely on Fourier transform, and the inversion transducing directly obtains by the arithmetic operation negate to direct transform at once, realizes simple;

4. arithmetic accuracy is high, and between reconstruction signal and original signal, error is very little.

The feature extracting method of the present invention's electric load dynamic characteristic is applicable to the dynamic load model feature extraction for same transformer station load time variation, also be applicable to consider the feature extraction of the different substation part throttle characteristics of region dispersiveness, can meet the requirement of load classification to feature extraction, have engineering practical value.

Description of drawings

The feature extracting method flow diagram of Fig. 1 electric load dynamic characteristic of the present invention;

Fig. 2 (a) is based on the db4 small echo direct transform transition structure block diagram of Via Lifting Scheme;

Fig. 2 (b) is based on the db4 wavelet inverse transformation transition structure block diagram of Via Lifting Scheme;

Fig. 3 is based on the tree construction of 5 layers of WAVELET PACKET DECOMPOSITION of Via Lifting Scheme.

Embodiment:

The present invention is described in detail below in conjunction with accompanying drawing:

As shown in Figure 1, execution step 01, start;

Then, execution step 02, carry out pre-service to the three-phase transient current data of reflection dynamic load model, chooses sampled data that the previous cycle of fault and fault start latter two cycle as sample, is designated as X _i=[X _i1(n); X _i2(n); X _i3(n)], wherein i is sample number, and n is the number of sampled point in three cycles, X _i1(n), X _i2(n), X _i3(n) be respectively the sampled signal of three-phase current.

Then, execution step 03, choose wavelet basis, determines the Via Lifting Scheme of selected wavelet basis.Small echo is more smooth, and the operand of boosting algorithm reduces more obviously.Compare with other db small echos, the db4 small echo has better performance at aspects such as spectrum leakage and resolution, therefore select the db4 small echo.Db4 small echo direct transform formula based on Via Lifting Scheme is as follows:

$d_l^{\left(1\right)}=x_{2l+1}-\sqrt{3}{x}_{2l}$

$s_l^{\left(1\right)}=x_{2l}+\sqrt{3}/{4d}_l^{\left(1\right)}+(\sqrt{3}-2)/{4d}_{l+1}^{\left(1\right)}$

$d_l^{\left(2\right)}=d_l^{\left(1\right)}+s_{l-1}^{\left(1\right)}$

$s_l=(\sqrt{3}+1)/\sqrt{2}{s}_l^{\left(1\right)}$

$d_l=(\sqrt{3}-1)/\sqrt{2}{d}_l^{\left(2\right)}$

In formula: d ⁽¹⁾, d ⁽²⁾Different intermediate quantity when calculating high fdrequency component d; s ⁽¹⁾Intermediate quantity during for calculating low frequency component s, data or the signal of Lifting Wavelet packet transform carried out in the x representative, and subscript l represents l numerical value in each variable, as d _l, s _lRepresent respectively l numerical value of high fdrequency component and low frequency component, x _2l+1, x _2l2l+1 and 2l numerical value of representing respectively its data x.

After execution of step 03, execution step 04, get and decompose number of plies m=5, utilizes the wavelet package transforms of Via Lifting Scheme to carry out 5 layers of decomposition to the three-phase current data decomposition of each sample.

Then, execution step 05, adopt in Fig. 3 (5,0), (5,1), (4,1), (3,1), (2,1), (1,1) signal forms wavelet packet, utilizes the db4 wavelet inverse transformation based on Via Lifting Scheme to be reconstructed original signal, based on the db4 wavelet inverse transformation formula of Via Lifting Scheme, is:

$s_l^{\left(1\right)}=(\sqrt{3}-1)/\sqrt{2}{s}_l$

$d_l^{\left(2\right)}=(\sqrt{3}+1)/\sqrt{2}{d}_l$

$d_l^{\left(1\right)}=d_l^{\left(2\right)}-s_{l-1}^{\left(1\right)}$

$x_{2l}=s_l^{\left(1\right)}-\sqrt{3}/{4d}_l^{\left(1\right)}-(\sqrt{3}-2)/{4d}_{l+1}^{\left(1\right)}$

$x_{2l+1}=d_l^{\left(1\right)}+\sqrt{3}{x}_{2l}$

Then, execution step 06, extract corresponding reconstruction coefficients and calculating energy value M _ij(k), i is sample number, j=1, and 2,3 represent respectively three-phase current, k=1, the wavelet packet signal of choosing in 2,3,4,5,6 expression steps 05.Average energy square take three-phase current signal is constructed the proper vector T of sample i as element _i

$T_i=\left(\underset{j=1}{\overset{3}{\mathrm{\Σ}}}\right[M_{\mathrm{ij}}\left(1\right),M_{\mathrm{ij}}\left(2\right),...,M_{\mathrm{ij}}\left(6\right)\left]\right)/\underset{j=1}{\overset{3}{\mathrm{\Σ}}}{\left({\left|\right|X_{\mathrm{ij}}\left|\right|}_2\right)}^2$

Finally, execution step 07, finish.

Fig. 2 (a) is depicted as the db4 small echo direct transform transition structure block diagram based on Via Lifting Scheme, and its process mainly comprises prediction and renewal link, and predictive operator is respectively

With-z, the renewal operator is

Utilize predictive operator and upgrade operator and carry out computing as shown in Fig. 2 (a) also namely based on the db4 wavelet decomposition process of Via Lifting Scheme; Fig. 2 (b) is depicted as the db4 wavelet inverse transformation transition structure block diagram based on Via Lifting Scheme, and its process mainly comprises anti-prediction and the anti-link of upgrading, and anti-predictive operator and anti-renewal operator should be identical with the factor pair in direct transform, and anti-predictive operator is respectively

With-z, the anti-operator that upgrades is

Utilize anti-predictive operator and the anti-operator that upgrades to carry out computing as shown in Fig. 2 (b) also namely based on the restructuring procedure of the db4 small echo of Via Lifting Scheme.

Figure 3 shows that the tree construction based on 5 layers of WAVELET PACKET DECOMPOSITION of Via Lifting Scheme.In the present embodiment, consider the accuracy of decomposing, calculate simplicity and engineering practicability, choose (5,0), (5,1), (4,1), (3,1), (2,1), (1,1) signal configuration wavelet packet.

Although above-mentioned the specific embodiment of the present invention is described; but be not limiting the scope of the invention; one of ordinary skill in the art should be understood that; on the basis of technical scheme of the present invention, those skilled in the art do not need to pay various modifications that creative work can make or distortion still in protection scope of the present invention.

Claims (8)

1. the feature extracting method of an electric load dynamic characteristic, is characterized in that, comprises the following steps:

Step 1: the three-phase transient current data to reflection electric load dynamic characteristic are carried out pre-service, choose the previous cycle of fault and fault and start the sampled data of latter two cycle as sample;

Step 2: choose wavelet basis, determine the Via Lifting Scheme of selected wavelet basis;

Step 3: choose the decomposition number of stories m, utilize the wavelet package transforms of Via Lifting Scheme to carry out respectively the decomposition of m layer to the three-phase transient current data of each sample;

Step 4: choose wavelet packet, original signal is reconstructed, in the note wavelet packet, the signal number is r;

Step 5: extract corresponding reconstruction coefficients and calculating energy value M _ij(k), i is sample number, j=1, and 2,3 represent respectively three-phase current, k=1,2,3 ..., r represents frequency band, k is positive integer, take the average energy square of three-phase current signal, constructs the proper vector T of sample i as element _i

$T_i=\left(\underset{j=1}{\overset{3}{\mathrm{\Σ}}}\right[M_{\mathrm{ij}}\left(1\right),M_{\mathrm{ij}}\left(2\right),...,M_{\mathrm{ij}}\left(r\right)\left]\right)/\underset{j=1}{\overset{3}{\mathrm{\Σ}}}{\left({\left|\right|X_{\mathrm{ij}}\left|\right|}_2\right)}^2$

Wherein, X _i1, X _i2, X _i3Be respectively the three-phase current signal of sample i.

2. a kind of feature extracting method of electric load dynamic characteristic as claimed in claim 1, is characterized in that, in described step 1, sample is designated as: X _i=[X _i1(n); X _i2(n); X _i3(n)],

Wherein i is sample number, and n is the number of sampled point in three cycles, X _i1(n), X _i2(n), X _i3(n) be respectively the sampled signal of three-phase transient current.

3. a kind of feature extracting method of electric load dynamic characteristic as claimed in claim 1, is characterized in that the Selection of Wavelet Basis db4 small echo in described step 2.

4. a kind of feature extracting method of electric load dynamic characteristic as claimed in claim 3, is characterized in that, the direct transform formula of described db4 small echo is as follows

$d_l^{\left(1\right)}=x_{2l+1}-\sqrt{3}{x}_{2l}$

$s_l^{\left(1\right)}=x_{2l}+\sqrt{3}/{4d}_l^{\left(1\right)}+(\sqrt{3}-2)/{4d}_{l+1}^{\left(1\right)}$

$d_l^{\left(2\right)}=d_l^{\left(1\right)}+s_{l-1}^{\left(1\right)}$

$s_l=(\sqrt{3}+1)/\sqrt{2}{s}_l^{\left(1\right)}$

$d_l=(\sqrt{3}-1)/\sqrt{2}{d}_l^{\left(2\right)}$

In formula: d ⁽¹⁾, d ⁽²⁾Different intermediate quantity when calculating high fdrequency component d; s ⁽¹⁾Intermediate quantity during for calculating low frequency component s, data or the signal of Lifting Wavelet packet transform carried out in the x representative, and subscript l represents l numerical value in each variable.

5. a kind of feature extracting method of electric load dynamic characteristic as claimed in claim 3, is characterized in that, the inverse transformation formula of described db4 small echo is:

$s_l^{\left(1\right)}=(\sqrt{3}-1)/\sqrt{2}{s}_l$

$d_l^{\left(2\right)}=(\sqrt{3}+1)/\sqrt{2}{d}_l$

$d_l^{\left(1\right)}=d_l^{\left(2\right)}-s_{l-1}^{\left(1\right)}$

$x_{2l}=s_l^{\left(1\right)}-\sqrt{3}/{4d}_l^{\left(1\right)}-(\sqrt{3}-2)/{4d}_{l+1}^{\left(1\right)}$

$x_{2l+1}=d_l^{\left(1\right)}+\sqrt{3}{x}_{2l}$

In formula: d ⁽¹⁾, d ⁽²⁾Intermediate quantity during for calculating high fdrequency component d; s ⁽¹⁾Intermediate quantity during for calculating low frequency component s, data or the signal of Lifting Wavelet packet transform carried out in the x representative, and subscript l represents l numerical value in each variable.

6. a kind of feature extracting method of electric load dynamic characteristic as claimed in claim 1, is characterized in that, decomposes number of plies m=5 in described step 3, utilizes the wavelet package transforms of Via Lifting Scheme to carry out 5 layers of decomposition to the three-phase current data decomposition of each sample.

7. a kind of feature extracting method of electric load dynamic characteristic as claimed in claim 1, is characterized in that, described wavelet packet is for adopting (5,0), (5,1), (4,1), (3,1), (2,1), the wavelet packet that (1,1) signal forms.

8. a kind of feature extracting method of electric load dynamic characteristic as claimed in claim 1, is characterized in that, in described step 3 and step 4, decomposes and reconstruct is all completed in the MATLAB emulation platform.

Images