CN104217112A - Multi-type signal-based power system low-frequency oscillation analysis method - Google Patents

Abstract

The invention discloses a multi-type signal-based power system low-frequency oscillation analysis method, and belongs to the field of analyzing the stability of the power system. The method mainly comprises the steps of analyzing internal physical contact among multi-type curves of sets; formulating amplitude deviation and phase deviation evaluation indexes based on the contact, and evaluating the accuracy degree of extracting oscillation mode by the Prony algorithm; in order to avoid the signal in some type from being covered caused by over large amplitude difference of the curves in different types, performing translation processing on the amplitude of the signals in different types; giving a dominant oscillation mode identifying method, establishing a comprehensive evaluation index of a multicomputer signal, and reflecting the reliability of the Prony algorithm. The index system provided by the invention has engineering application value; according to the system, the reliability of extracting the oscillation mode information can be reflected; the appropriate Prony algorithm order can be selected according to the comprehensive index.

Description

A kind of low-frequency oscillation analysis method for power system based on polymorphic type signal

Technical field

The invention belongs to stability of power system analysis field, particularly a kind of low-frequency oscillation analysis method for power system based on polymorphic type signal.

Background technology

Low-frequency oscillation of electric power system directly affects the operation of interacted system, based on the characteristic root method clear physics conception of inearized model, and the informative provided, but to Iarge-scale system dyscalculia, and be difficult to reflect nonlinear impact.The disturbed track of system can comprise non-linear effects; track acquisition affects less by system scale, along with WAMS (WAMS) is introduced, can not rely on system model; the operation of real-time monitoring system, for Low Frequency Oscillation Analysis provides important disturbed track.

Based on trajectory analysis low-frequency oscillation, mainly comprise Stationary Oscillation specificity analysis and nonstationary oscillation specificity analysis, current nonstationary oscillation specificity analysis is mainly based on single track, and common method comprises window Fourier ridge, Wavelet Ridge, HHT etc.; Stationary Oscillation specificity analysis is applicable to single track and is also applicable to the disturbed track of multimachine, and common method is Prony algorithm, and this algorithm calculates simple, but interference free performance is poor, and needs selected suitable model order.At present, determine that Prony algorithm exponent number is determined to common are new determinant method and singular value decomposition method etc., these class methods are mainly used to distinguish valid data space and spatial noise, are difficult to the quality evaluating different rank Prony algorithm identification result.For there is certain nonlinear system, with the exponent number of this class methods determination algorithm, overfitting may be caused.There is polytype curve in the disturbed track of electric system, tradition is general adopts single generator's power and angle curve, speed curves or dominant eigenvalues curve to carry out analysis of the oscillation, have ignored the relation of dissimilar curve.

Summary of the invention

The object of the invention is to propose a kind of low-frequency oscillation analysis method for power system based on polymorphic type signal, based on the internal relation of polymorphic type curve, set up the amplitude excursion and phase deviation index of evaluating the mode of oscillation confidence level that Prony extracts, and formulate the comprehensive evaluation index of Prony algorithm confidence level.

A kind of low-frequency oscillation analysis method for power system based on polymorphic type signal that the present invention proposes, comprises the steps:

(1) read dissimilar curve data, and analyze the relation between dissimilar curve;

(2) conversion process is carried out to the amplitude of dissimilar curve;

(3) the initial exponent number N of multi-machine Prony algorithm is set, the exponent number Δ N of each increase and top step number N _max, comprehensive evaluation index η is set _{amplitude Σ}and η _{phase Σ}desired value

(4) calculating of Prony algorithm is carried out to dissimilar curve, obtain control oscillation modes;

(5) calculate amplitude excursion percentage and the phase deviation percentage of each control oscillation modes, evaluate the accuracy of each control oscillation modes;

(6) the comprehensive evaluation index η of calculated amplitude deviation _{amplitude Σ}with the comprehensive evaluation index η of phase deviation _{phase Σ}, the confidence level of assessment Prony algorithm; If comprehensive assessment index is less than the desired value of setting, then Output rusults; If comprehensive assessment index is greater than the desired value of setting, then increases Prony algorithm exponent number Δ N, and judge whether Prony algorithm exponent number is greater than top step number N _maxif be less than top step number N _max, then return step (4) and recalculate, if be greater than top step number N _max, then η is exported _{amplitude Σ}, η _{phase Σ}result time minimum.

In aforesaid step (1), for genset, its power-angle curve and speed curves are dissimilar curve,

Expression formula is respectively:

${\mathrm{\δ}}_i\left(t\right)={\mathrm{\δ}}_{i0}+\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{A}_j{e}^{-{\mathrm{\σ}}_{j}t}\sin ({\mathrm{\ω}}_{j}t+{\mathrm{\φ}}_{j0})---\left(1\right)$

Wherein: δ _it () represents merit angle, i platform unit relative inertia center, ν _it () represents the rotating speed at i-th relative inertia center of unit ,-σ _j± i ω _jrepresent a jth mode of oscillation, n represents mode of oscillation number, δ _i0represent power-angle curve DC component, A _jrepresent the amplitude of a power-angle curve jth mode of oscillation, φ _j0represent the first phase of a power-angle curve jth mode of oscillation, B _jrepresent the amplitude of a speed curves jth mode of oscillation, represent the first phase of a speed curves jth mode of oscillation.

Relation is there is: v between aforesaid power-angle curve and speed curves _i(t)=δ ' _i(t),

Wherein, δ ' _it () represents δ _ithe derivative of (t);

Obtained by the relation between above-mentioned power-angle curve and speed curves:

Amplitude and relation corresponding to mode of oscillation:

$\frac{B_j}{A_j}=\sqrt{{\mathrm{\σ}}_j^2+{\mathrm{\ω}}_j^2}---\left(3\right)$

Relation between phase differential and mode of oscillation:

In aforesaid step (2), conversion process is carried out to the amplitude of dissimilar curve and comprises the following steps:

2-1) establish same type signal curve x to have m bar, sampled point is q, carries out, after straight process, setting up the mean oscillatory energy of signal of the same type to signal

${\stackrel{\‾}{E}}_x=\frac{\sqrt{\underset{k=1}{\overset{m}{\mathrm{\Σ}}}\underset{i=1}{\overset{q}{\mathrm{\Σ}}}{\left(x_k\left(\mathrm{i\Δt}\right)\right)}^2}}{m}$

Wherein, x _krepresent that kth bars curve is power-angle curve δ _i(t) or speed curves ν _i(t), i represents i-th sampled point, and Δ t is sampling step length;

2-2) another type signal curve y is carried out equally every straight process, obtain mean oscillatory energy

2-3) with signal curve x for reference, all y signal curves are multiplied by carry out amplitude conversion.

In aforesaid step (4), obtain control oscillation modes and comprise the following steps:

4-1) jth mode of oscillation accounts for the percentage η of gross energy _jcalculation expression be:

${\mathrm{\η}}_j=\frac{P_j}{P_{\mathrm{\Σ}}}\×100\%=\frac{\underset{i=1}{\overset{l}{\mathrm{\Σ}}}\left(\underset{k=1}{\overset{q}{\mathrm{\Σ}}}{\left(A_{\mathrm{ij}}{e}^{-{\mathrm{\σ}}_j\mathrm{k\Δt}}\right)}^2\right)}{\underset{j=1}{\overset{n}{\mathrm{\Σ}}}\left(\underset{i=1}{\overset{l}{\mathrm{\Σ}}}\left(\underset{k=1}{\overset{q}{\mathrm{\Σ}}}{\left(A_{\mathrm{ij}}{e}^{-{\mathrm{\σ}}_j\mathrm{k\Δt}}\right)}^2\right)\right)}\×100\%---\left(5\right)$

Wherein: P _jfor the oscillation energy of jth mode of oscillation, P _Σfor the gross energy of all mode of oscillation, l is all dissimilar curve sums, and q is sampling number, and n is mode of oscillation number, A _ijit is the amplitude of i-th curve jth mode of oscillation;

4-2) sort to mode of oscillation according to mode of oscillation energy accounting, and set up threshold epsilon, mode of oscillation energy accounting takes mode of oscillation as the leading factor more than the pattern of ε.

In aforesaid step (5),

The amplitude excursion percentage η of described control oscillation modes j _jAmplitudecomputing formula as follows:

${\mathrm{\η}}_{\mathrm{jAmplitude}}=|(\frac{B_j}{A_j}-\sqrt{{{\mathrm{\σ}}_j}^2+{\mathrm{\ω}}_j^2})/\sqrt{{{\mathrm{\σ}}_j}^2+{\mathrm{\ω}}_j^2}|\×100\%---\left(6\right)$

The phase deviation percentage η of described control oscillation modes j _jPhasecomputing formula as follows:

Described η _jAmplitudeand η _jPhasedata are larger, represent that this control oscillation modes is more insincere.

In aforesaid step (6),

The comprehensive evaluation index η of described amplitude excursion _{amplitude Σ}for:

${\mathrm{\η}}_{\mathrm{Amplitude\Σ}}=\underset{j=1}{\overset{m}{\mathrm{\Σ}}}({\mathrm{\η}}_j\×{\mathrm{\η}}_{\mathrm{jAmplitude}})---\left(8\right)$

The comprehensive evaluation index η of described phase deviation _{phase Σ}for:

${\mathrm{\η}}_{\mathrm{phase\Σ}}=\underset{j=1}{\overset{m}{\mathrm{\Σ}}}({\mathrm{\η}}_j\×{\mathrm{\η}}_{\mathrm{jphase}})---\left(9\right)$

Wherein, m takes mode of oscillation number as the leading factor.

Compared with existing Low Frequency Oscillation Analysis, of the present inventionly to have the following advantages:

(1) low-frequency oscillation of electric power system information is present in polytype curve, tradition selects a certain type curve to carry out information extraction usually, confidence level for analysis result is difficult to pass judgment on, the present invention is to polymorphic type information analysis, based on the physical link between dissimilar curve, build evaluation index, the mode of oscillation accuracy of information that Prony algorithm extracts is evaluated;

(2) the present invention establishes comprehensive evaluation index, can reflect the confidence level of Prony algorithm;

(3) System of Comprehensive Evaluation that the present invention proposes has engineer applied and is worth, and can select suitable Prony algorithm exponent number according to comprehensive evaluation index.

Accompanying drawing explanation

Fig. 1 is method flow diagram of the present invention.

Embodiment

For making the object, technical solutions and advantages of the present invention clearly, below in conjunction with drawings and embodiments, the present invention is described in further detail.

As shown in Figure 1, the low-frequency oscillation analysis method for power system based on polymorphic type signal of the present invention comprises the following steps:

Step 1, reads dissimilar curve data, and for genset, power-angle curve and speed curves belong to two kinds of dissimilar curves.

In multi-computer system, unit power-angle curve and speed curves generally adopt relative inertia center or angular centre display.Because merit angle exists initial phase difference, the power-angle curve of multi-computer system generally comprises DC component, and the generating unit speed curve being in synchronous operation does not generally comprise DC component.Because every platform unit power-angle curve and speed curves comprise identical mode of oscillation information, expression formula is:

${\mathrm{\δ}}_i\left(t\right)={\mathrm{\δ}}_{i0}+\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{A}_j{e}^{-{\mathrm{\σ}}_{j}t}\sin ({\mathrm{\ω}}_{j}t+{\mathrm{\φ}}_{j0})---\left(1\right)$

In formula: δ _it () represents merit angle, i-th unit relative inertia center; ν _it () represents the rotating speed at i-th relative inertia center of unit ,-σ _j± i ω _jrepresent a jth mode of oscillation, n represents mode of oscillation number, δ _i0represent power-angle curve DC component, A _jrepresent the amplitude of a power-angle curve jth mode of oscillation, φ _j0represent the first phase of a power-angle curve jth mode of oscillation, B _jrepresent the amplitude of a speed curves jth mode of oscillation, represent the first phase of a speed curves jth mode of oscillation.

Different with simple signal transacting, electric system is physical system, there is inner link between signal with different type curve, in real system, there is derivative relation between power-angle curve and speed curves, i.e. v _i(t)=δ ' _i(t),

The coefficient of contrast correspondence is known,

There is relation in amplitude and mode of oscillation:

$\frac{B_j}{A_j}=\sqrt{{\mathrm{\σ}}_j^2+{\mathrm{\ω}}_j^2}---\left(3\right)$

Relation is there is between phase differential and mode of oscillation:

Step 2, for avoiding dissimilar curve amplitude to have big difference, forming signal and flooding, and needs to carry out conversion process to the amplitude of dissimilar curve.

If the curve of same type is x type, the curve of another type is y type, and e.g., if power-angle curve is x type, then speed curves is y type.

X type curve, through after straight process, obtains mean oscillatory energy

In formula: q is sampling number; Δ t is step-length; M is x type curve number.

In like manner, y type curve, through after straight process, obtains mean oscillatory energy

With signal curve x for reference, all y signal curves are multiplied by carry out amplitude conversion, eliminate the difference that dissimilar curve causes amplitude huge due to dimension difference.

Such as: the curve of power-angle curve y and y' is as follows, and y' is speed curves, sampling step length is 0.05s, and the sampling time is 5s,

y＝6e ^-0.5tsin(10t)+2e ^-0.1tsin(20t)，

y'＝60.08e ^-0.5tsin(10t+1.52)+40.00e ^-0.1tsin(20t+1.57)，

Because signal amplitude difference is comparatively large, according to step 2 amplitude conversion disposal route, with y reference, y' amplitude is processed, calculates therefore y' signal times is with 13.77.

Step 3, arranges the initial exponent number N of multi-machine Prony algorithm, the exponent number Δ N of each increase and top step number N _max, comprehensive evaluation index η is set _{amplitude Σ}and η _{phase Σ}desired value

Step 4, carries out the calculating of Prony algorithm to dissimilar curve, obtains control oscillation modes.

Consider noise and nonlinear impact, the mode of oscillation information that signal processing method obtains, the signal that energy accounting is larger has higher confidence level.For the mode of oscillation that high-order Prony algorithm obtains, needing the pattern information to extracting to sort, obtaining control oscillation modes.Control oscillation modes is not only relevant with the initial amplitude of vibration also relevant with its damping.For the disturbed track of multimachine, the energy of mode of oscillation is included in all oscillating curves, and jth mode of oscillation accounts for the percentage η of gross energy _jcalculating formula be:

${\mathrm{\η}}_j=\frac{P_j}{P_{\mathrm{\Σ}}}\×100\%=\frac{\underset{i=1}{\overset{l}{\mathrm{\Σ}}}\left(\underset{k=1}{\overset{q}{\mathrm{\Σ}}}{\left(A_{\mathrm{ij}}{e}^{-{\mathrm{\σ}}_j\mathrm{k\Δt}}\right)}^2\right)}{\underset{j=1}{\overset{n}{\mathrm{\Σ}}}\left(\underset{i=1}{\overset{l}{\mathrm{\Σ}}}\left(\underset{k=1}{\overset{q}{\mathrm{\Σ}}}{\left(A_{\mathrm{ij}}{e}^{-{\mathrm{\σ}}_j\mathrm{k\Δt}}\right)}^2\right)\right)}\×100\%---\left(5\right)$

Wherein: for the oscillation energy of jth mode of oscillation,

$P_{\mathrm{\Σ}}=\underset{j=1}{\overset{n}{\mathrm{\Σ}}}\left(\underset{i=1}{\overset{l}{\mathrm{\Σ}}}\left(\underset{k=1}{\overset{q}{\mathrm{\Σ}}}{\left(A_{\mathrm{ij}}{e}^{-{\mathrm{\σ}}_j\mathrm{k\Δt}}\right)}^2\right)\right)$ For global oscillation energy,

L is all dissimilar curve sums, and q is sampling number, and n is mode of oscillation number, A _ijit is the amplitude of i-th curve jth mode of oscillation.

Sort to mode of oscillation according to mode of oscillation energy accounting, and set up threshold epsilon, mode of oscillation energy accounting takes mode of oscillation as the leading factor more than the pattern of ε.

Step 5, calculates amplitude excursion percentage and the phase deviation percentage of each control oscillation modes, evaluates the accuracy of each control oscillation modes;

The amplitude excursion percentage η of control oscillation modes j _jAmplitudewith phase deviation percentage η _jPhase, as shown in formula (6) and (7),

${\mathrm{\η}}_{\mathrm{jAmplitude}}=|(\frac{B_j}{A_j}-\sqrt{{{\mathrm{\σ}}_j}^2+{\mathrm{\ω}}_j^2})/\sqrt{{{\mathrm{\σ}}_j}^2+{\mathrm{\ω}}_j^2}|\×100\%---\left(6\right)$

The accuracy of each control oscillation modes can by corresponding η _jAmplitudeand η _jPhasereflection, data are larger, represent that this control oscillation modes accuracy is lower, otherwise, then show that this control oscillation modes accuracy is high.

Step 6, calculates the comprehensive evaluation index η of the amplitude excursion of Prony algorithm Output rusults _{amplitude Σ}with the comprehensive evaluation index η of phase deviation _{phase Σ}, the confidence level of assessment Prony algorithm.

For the signal with multiple control oscillation modes, need to set up comprehensive evaluation index to reflect the confidence level of Prony algorithm.In multi-computer system, in each unit, there is multiple η _jAmplitudeand η _jPhase, need the confidence level setting up comprehensive evaluation index reflection result.Comprehensive evaluation index needs to reflect the energy accounting of each mode of oscillation and amplitude and phase deviation.Build comprehensive evaluation index such as formula shown in (8) and (9).

${\mathrm{\η}}_{\mathrm{Amplitude\Σ}}=\underset{j=1}{\overset{m}{\mathrm{\Σ}}}({\mathrm{\η}}_j\×{\mathrm{\η}}_{\mathrm{jAmplitude}})---\left(8\right)$

${\mathrm{\η}}_{\mathrm{phase\Σ}}=\underset{j=1}{\overset{m}{\mathrm{\Σ}}}({\mathrm{\η}}_j\×{\mathrm{\η}}_{\mathrm{jphase}})---\left(9\right)$

Wherein, η _{amplitude Σ}for the comprehensive evaluation index of amplitude excursion, η _{phase Σ}for the comprehensive evaluation index of phase deviation, m takes mode of oscillation number as the leading factor, η _jtake the percentage of energy of mode of oscillation j as the leading factor.

If comprehensive assessment index is less than the desired value of setting, namely then show that Prony algorithm calculates credible, then export Prony algorithm result of calculation; If comprehensive assessment index is greater than the desired value of setting, then increases Prony algorithm exponent number Δ N, and judge whether Prony algorithm exponent number is greater than top step number N _maxif be less than top step number N _max, then return step 4 and recalculate, if be greater than top step number N _max, then show that Prony algorithm does not reach accuracy requirement, then search for η in computation process _{amplitude Σ}, η _{phase Σ}prony algorithm time minimum, and export its result.

For aforementioned y and y' signal, noise gets 5dB, 10dB and 20dB respectively, it is 10,20,40 that Prony algorithm gets exponent number respectively, calculates each mode of oscillation energy accounting according to formula (5), to take mode of oscillation Output rusults as the leading factor as shown in table 1 with mode of oscillation energy accounting more than 2%.

Table 1 Prony algorithm Output rusults

As shown in Table 1, the control oscillation modes information of extraction is more accurate, corresponding η _jAmplitude, η _jPhasedeviation is less, visible, and the index of foundation can be used in the accuracy evaluating identification result.Noise can produce certain influence to the Output rusults of Prony algorithm, and the signal amplitude that signal to noise ratio (S/N ratio) is little and phase deviation are comparatively large, and high-order Prony algorithm is conducive to white-noise filtering, makes control oscillation modes result more accurate.Analyzing signal to noise ratio (S/N ratio) is the signal discovery of 5dB and 10dB, and along with the raising of Prony algorithm exponent number, 20 rank models and 40 rank model counting accuracies do not significantly improve, as seen when meeting the demands, without the need to adopting too high model order.

Above embodiment is only and technological thought of the present invention is described, can not limit protection scope of the present invention with this, and every technological thought proposed according to the present invention, any change that technical scheme basis is done, all falls within scope.

Claims (7)

1. based on a low-frequency oscillation analysis method for power system for polymorphic type signal, it is characterized in that, comprise the following steps:

(1) read dissimilar curve data, and analyze the relation between dissimilar curve;

(2) conversion process is carried out to the amplitude of dissimilar curve;

(3) the initial exponent number N of multi-machine Prony algorithm is set, the exponent number Δ N of each increase and top step number N _max, comprehensive evaluation index η is set _{amplitude Σ}and η _{phase Σ}desired value

(4) calculating of Prony algorithm is carried out to dissimilar curve, obtain control oscillation modes;

(5) calculate amplitude excursion percentage and the phase deviation percentage of each control oscillation modes, evaluate the accuracy of each control oscillation modes;

(6) the comprehensive evaluation index η of calculated amplitude deviation _{amplitude Σ}with the comprehensive evaluation index η of phase deviation _{phase Σ}, the confidence level of assessment Prony algorithm; If comprehensive assessment index is less than the desired value of setting, then Output rusults; If comprehensive assessment index is greater than the desired value of setting, then increases Prony algorithm exponent number Δ N, and judge whether Prony algorithm exponent number is greater than top step number N _maxif be less than top step number N _max, then return step (4) and recalculate, if be greater than top step number N _max, then η is exported _{amplitude Σ}, η _{phase Σ}result time minimum.

2. a kind of low-frequency oscillation analysis method for power system based on polymorphic type signal according to claim 1, is characterized in that, in described step (1), for genset, its power-angle curve and speed curves are dissimilar curve,

Expression formula is respectively:

${\mathrm{\δ}}_i\left(t\right)={\mathrm{\δ}}_{i0}+\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{A}_j{e}^{-{\mathrm{\σ}}_{j}t}\sin ({\mathrm{\ω}}_{j}t+{\mathrm{\φ}}_{j0})---\left(1\right)$

Wherein: δ _it () represents merit angle, i platform unit relative inertia center, ν _it () represents the rotating speed at i-th relative inertia center of unit ,-σ _j± i ω _jrepresent a jth mode of oscillation, n represents mode of oscillation number, δ _i0represent power-angle curve DC component, A _jrepresent the amplitude of a power-angle curve jth mode of oscillation, φ _j0represent the first phase of a power-angle curve jth mode of oscillation, B _jrepresent the amplitude of a speed curves jth mode of oscillation, represent the first phase of a speed curves jth mode of oscillation.

3. a kind of low-frequency oscillation analysis method for power system based on polymorphic type signal according to claim 1 and 2, is characterized in that there is relation between described power-angle curve and speed curves: v _i(t)=δ ' _i(t),

Wherein, δ ' _it () represents δ _ithe derivative of (t);

Obtained by the relation between above-mentioned power-angle curve and speed curves:

Amplitude and relation corresponding to mode of oscillation:

$\frac{B_j}{A_j}=\sqrt{{\mathrm{\σ}}_j^2+{\mathrm{\ω}}_j^2}---\left(3\right)$

Relation between phase differential and mode of oscillation:

4. a kind of low-frequency oscillation analysis method for power system based on polymorphic type signal according to claim 1, is characterized in that, in described step (2), carries out conversion process comprise the following steps the amplitude of dissimilar curve:

2-1) establish same type signal curve x to have m bar, sampled point is q, carries out, after straight process, setting up the mean oscillatory energy of signal of the same type to signal

${\stackrel{\‾}{E}}_x=\frac{\sqrt{\underset{k=1}{\overset{m}{\mathrm{\Σ}}}\underset{i=1}{\overset{q}{\mathrm{\Σ}}}{\left(x_k\left(\mathrm{i\Δt}\right)\right)}^2}}{m}$

Wherein, x _krepresent that kth bars curve is power-angle curve δ _i(t) or speed curves ν _i(t), i represents i-th sampled point, and Δ t is sampling step length;

2-2) another type signal curve y is carried out equally every straight process, obtain mean oscillatory energy

2-3) with signal curve x for reference, all y signal curves are multiplied by carry out amplitude conversion.

5. a kind of low-frequency oscillation analysis method for power system based on polymorphic type signal according to claim 1, is characterized in that, in described step (4), obtains control oscillation modes and comprises the following steps:

4-1) jth mode of oscillation accounts for the percentage η of gross energy _jcalculation expression be:

${\mathrm{\η}}_j=\frac{P_j}{P_{\mathrm{\Σ}}}\×100\%=\frac{\underset{i=1}{\overset{l}{\mathrm{\Σ}}}\left(\underset{k=1}{\overset{q}{\mathrm{\Σ}}}{\left(A_{\mathrm{ij}}{e}^{-{\mathrm{\σ}}_j\mathrm{k\Δt}}\right)}^2\right)}{\underset{j=1}{\overset{n}{\mathrm{\Σ}}}\left(\underset{i=1}{\overset{l}{\mathrm{\Σ}}}\left(\underset{k=1}{\overset{q}{\mathrm{\Σ}}}{\left(A_{\mathrm{ij}}{e}^{-{\mathrm{\σ}}_j\mathrm{k\Δt}}\right)}^2\right)\right)}\×100\%---\left(5\right)$

Wherein: P _jfor the oscillation energy of jth mode of oscillation, P _Σfor the gross energy of all mode of oscillation, l is all dissimilar curve sums, and q is sampling number, and n is mode of oscillation number, A _ijit is the amplitude of i-th curve jth mode of oscillation;

4-2) sort to mode of oscillation according to mode of oscillation energy accounting, and set up threshold epsilon, mode of oscillation energy accounting takes mode of oscillation as the leading factor more than the pattern of ε.

6. a kind of low-frequency oscillation analysis method for power system based on polymorphic type signal according to claim 1, is characterized in that, in described step (5),

The amplitude excursion percentage η of described control oscillation modes j _jAmplitudecomputing formula as follows:

${\mathrm{\η}}_{\mathrm{jAmplitude}}=|(\frac{B_j}{A_j}-\sqrt{{{\mathrm{\σ}}_j}^2+{\mathrm{\ω}}_j^2})/\sqrt{{{\mathrm{\σ}}_j}^2+{\mathrm{\ω}}_j^2}|\×100\%---\left(6\right)$

The phase deviation percentage η of described control oscillation modes j _jPhasecomputing formula as follows:

Described η _jAmplitudeand η _jPhasedata are larger, represent that this control oscillation modes is more insincere.

7. a kind of low-frequency oscillation analysis method for power system based on polymorphic type signal according to claim 1, is characterized in that, in described step (6),

The comprehensive evaluation index η of described amplitude excursion _{amplitude Σ}for:

${\mathrm{\η}}_{\mathrm{Amplitude\Σ}}=\underset{j=1}{\overset{m}{\mathrm{\Σ}}}({\mathrm{\η}}_j\×{\mathrm{\η}}_{\mathrm{jAmplitude}})---\left(8\right)$

The comprehensive evaluation index η of described phase deviation _{phase Σ}for:

${\mathrm{\η}}_{\mathrm{phase\Σ}}=\underset{j=1}{\overset{m}{\mathrm{\Σ}}}({\mathrm{\η}}_j\×{\mathrm{\η}}_{\mathrm{jphase}})---\left(9\right)$

Wherein, m takes mode of oscillation number as the leading factor.