CN107807278A - Oscillating signal parameter identification method based on H ∞ EKFs - Google Patents

Oscillating signal parameter identification method based on H ∞ EKFs Download PDF

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CN107807278A
CN107807278A CN201711282838.4A CN201711282838A CN107807278A CN 107807278 A CN107807278 A CN 107807278A CN 201711282838 A CN201711282838 A CN 201711282838A CN 107807278 A CN107807278 A CN 107807278A
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王�义
钟永洁
孙永辉
武小鹏
吕欣欣
翟苏巍
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Hohai University HHU
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Abstract

The invention provides a kind of oscillating signal parameter identification method based on H ∞ EKFs, utilize H ∞ filtering theories, in oscillating signal parameter identification, the influence of effective meter and model uncertainty, avoid the parameter identification error caused by model parameter uncertainty, and due to using noise covariance matrix adaptive technique, dynamic adjustment covariance matrix, so that institute's extracting method has stronger robustness, beneficial to obtaining more accurately oscillating signal parameter identification result.

Description

Oscillating signal parameter identification method based on H ∞ EKFs
Technical field
The present invention relates to a kind of power system, and in particular to a kind of low-frequency oscillation of electric power system method for extracting signal.
Background technology
In recent years, during nationwide integrated power grid interconnection is with west-to-east power transmission, exchange of electric power is more frequent, the peace of power system Full stable problem shows as low-frequency oscillation mostly.Therefore, how effectively to extract what low-frequency oscillation of electric power system signal was characterized Information, it is significant for the security and stability analysis of power system.
In current research, using field measurement Data Analysis Services signal, it is research electricity to obtain oscillation characteristicses parameter A kind of effective way of Force system low-frequency oscillation.Its conventional method mainly includes real-time FFT (fast Fourier transform, FFT), wavelet algorithm, Prony algorithms and expanded Kalman filtration algorithm etc..The precision of real-time FFT Limited by data window, it is impossible to reflect the damping characteristic of vibration；Wavelet algorithm can reflect the time-varying characteristics of signal, but small echo be present The problem of base is difficult to choose；Prony algorithms can directly extract amplitude, phase, frequency and decay factor, and algorithm is easy, therefore quilt It is widely used in the identification of low-frequency oscillation of electric power system pattern.But Prony algorithms are more sensitive to noise, identify that noisy low frequency shakes Error when swinging signal is larger；When oscillation mode increases for multistage and sample rate, the amount of calculation of oscillation amplitude and first phase is identified In exponential increase, matrix inversion operation is difficult.Based on EKF (extended Kalman filter, EKF) Oscillating signal parameter identification method, not only with on-line identification function, and it is low to calculate committed memory, thus application compared with Extensively.It is to be noted, however, that EKF methods can not consider uncertainty introduced in oscillator signal modeling process, and its Identification result is easily influenceed by noise initial variance matrix.
The content of the invention
Goal of the invention：The problem of it is an object of the invention to exist for prior art, it is proposed that one kind is extended based on H ∞ The oscillating signal parameter identification method of Kalman filtering, it is effectively counted and the influence of model uncertainty, and dynamic is adjusted Whole covariance matrix, realize the accurate recognition of low-frequency oscillation of electric power system signal parameter.
Technical scheme：The invention provides a kind of oscillating signal parameter identification based on H ∞ EKFs Method, comprise the following steps：
(1) the related initial value of setting filtering, the state estimation initial value at k=0 moment is includedState estimation error is assisted VarianceSystem noise and the initial value Q for measuring noise covariance matrix0And R0, moving window value L and maximum estimated moment N；
(2) low-frequency oscillation of electric power system measuring signal sequence inputting value y is obtainedk, it measures function and is defined as follows：
yk=h (xk)+vk
H () represents known and measures function, x in formulakFor the parameter true value at k moment, vkThe measurement noise at k moment is represented, Its covariance matrix met is Rk；
(3) the parameter prediction value at k moment is calculatedCalculation formula is as follows：
System function known to f () expressions in formula,For the estimates of parameters at k-1 moment；
(4) the parameter prediction error covariance at k moment is calculatedCalculation formula is as follows：
In formulaRepresent that nonlinear function f () existsThe Jacobian matrix at place, Qk-1When representing k-1 The system noise covariance matrix at quarter；
(5) k moment H ∞ EKFs gain Gs are calculatedk, calculation formula is as follows：
In formula ()-1To ask inverse of a matrix computing,The nonlinear function h () of expression existsPlace Jacobian matrix；
(6) the evaluated error covariance at k moment is calculatedCalculation formula is as follows：
I is the unit matrix of corresponding dimension in formula, RE, kCalculation formula is as follows：
Wherein parameter γ set calculation formula be：
In formula λ be it is to be placed be more than 1 positive parameter, interval is [1,30] when parameters of electric power system recognizes, eig () represents to take the characteristic value of corresponding matrix, and max () represents to take maximum；
(7) estimates of parameters at k moment is calculatedCalculation formula is as follows：
Y in formulakFor the measuring value at k moment；
(8) innovation sequence is calculated, calculation formula is as follows：
(9) when to take moving window size be L, innovation sequence s in calculation windowkAverage value, i.e., newly breath Matrix Cvk, it is counted It is as follows to calculate formula：
(10) on the basis of previous step, dynamic calculation k+1 moment system noise covariance matrixes QkAssisted with noise is measured Anti- poor matrix Rk, calculation formula is as follows：
(11) low-frequency oscillation of electric power system signal parameter identification is carried out according to time series according to (3)-(10) step, until Iteration stopping during k+1 ＞ N, output parameter identification result.
Beneficial effect：The present invention utilizes H ∞ filtering theories, in oscillating signal parameter identification, effective meter and mould The probabilistic influence of type, the parameter identification error caused by model parameter uncertainty is avoided, and made an uproar due to using Sound covariance matrix adaptive technique, dynamic adjusts covariance matrix, so that institute's extracting method has stronger robustness, profit In obtaining more accurately oscillating signal parameter identification result.
Brief description of the drawings
Fig. 1 is the flow chart of the inventive method；
Fig. 2 is the low-frequency oscillation of electric power system semaphore measured value of embodiment；
Fig. 3 is embodiment to oscillating signal frequency parameter w identification results；
Fig. 4 is embodiment to oscillating signal damping factor parameter δ identification results；
Fig. 5 is root-mean-square-deviation of the embodiment to oscillating signal parameter identification.
Embodiment
Technical solution of the present invention is described in detail below, but protection scope of the present invention is not limited to the implementation Example.
Generally low-frequency oscillation of electric power system signal can be expressed as the sum of the sinusoidal signal of multiple exponential dampings, Following form can be described as：
In formula, Ai, δi, wi, φiIt is the unknown parameter of real number, N is that the sinusoidal signal of one oscillator signal decay of composition is total Number, subscript i represent that relevant parameter belongs to the cosine signal for forming i-th of oscillating signal decay, and n (t) is one zero equal It is worth white noise.Wherein, δiThe referred to as damping factor of oscillating signal, wiRepresent the frequency of oscillating signal, wi, δiTo treat Estimate parameter, the separate manufacturing firms for including parameter to be estimated in the components of state variables of oscillating signal can be obtained by reasoning Model.Consider the low-frequency oscillation of electric power system signal being made up of the sinusoidal signal summation of N number of exponential damping, its 4N state variable Form can be expressed as follows：
x4i-1, k=wi
x4i, k=δi
It is to belong to i-th of attenuated sinusoidal signal of low-frequency oscillation of electric power system signal that i, which represents these variables and parameter, in formula (i=1 ... N), k represents moment, fsRepresent sample frequency.The state component at k+1 moment is can obtain by inference：
x4i-1, k+1=x4i-1, k+w4i-1, k
x4i, k+1=x4i, k+w4i, k
Then its output equation is：
In formula, k2i-1=cos (φi), k2i=-sin (φi), nkThe white noise for being zero for average, so, power system is low The state-space model of frequency oscillator signal can be typically expressed as：
In formula, f () and h () represent the nonlinear function that can be linearized according to Taylor series expansion, xk+1Table Show the quantity of state at k+1 moment and parametric component to be identified, ykFor the oscillating signal measurement sequence at k moment, wkAnd vkIt is average The Gaussian sequence for being zero, meet covariance matrix Q respectivelykAnd Rk.Specifically, in low-frequency oscillation of electric power system signal：
And function h (xk) form can be expressed as：
H=(k1k200 ..., k2i-1k2i00 ..., k2N1k2N00)
h(xk)=β xk
Constant coefficient matrix known to β expressions in formula.
So far, the state-space model of low-frequency oscillation of electric power system signal model parameter to be estimated is included in components of state variables It has been established that herein on basis, then the method for the invention introduced can be used, to low-frequency oscillation of electric power system signal parameter Identification.
Embodiment：In order to verify the validity of the inventive method and practicality, it is low that the present embodiment chooses following power system Frequency oscillator signal carries out parameter identification analysis
Y (t)=e-δtCos(wt+φ)+n(t)
The oscillating signal is made up of the sinusoidal signal of an exponential damping.Oscillating signal ginseng to be identified Number：Damping factor δ=0.01, frequency w=0.5rad/s, φ=0, n (t) are white Gaussian noises, its covariance square met Battle array is r=10-5, it is T=1s to take the sampling time, and 300 sampling instant measuring values enter before the present embodiment takes when carrying out emulation experiment Row proof of algorithm, i.e. N are 300.
When carrying out parameter identification to embodiment oscillating signal with method proposed by the invention, filtering is taken just Beginning evaluated error covariance and system noise covariance matrix setup values are：
The initial value of parameter identification is chosen forMeasure noise covariance square Battle array initial value is arranged to the 10 of actual value3Times, i.e. R0=10-2；Process noise dynamic estimation window value L is taken as 10, λ values and is 20。
As shown in figure 1, with the inventive method to low-frequency oscillation of electric power system signal parameter discrimination method, it includes as follows Step：
(1) the related initial value of setting filtering, the state estimation initial value at k=0 moment is includedState estimation error is assisted VarianceSystem noise and the initial value Q for measuring noise covariance matrix0And R0, moving window value L and maximum estimated moment N；
(2) low-frequency oscillation of electric power system measuring signal sequence inputting value y is obtainedk, it measures function and is defined as follows
yk=h (xk)+vk
H () represents known and measures function, x in formulakFor the parameter true value at k moment, vkThe measurement noise at k moment is represented, Its covariance matrix met is Rk；
(3) the parameter prediction value at k moment is calculatedCalculation formula is as follows
System function known to f () expressions in formula,For the estimates of parameters at k-1 moment；
(4) the parameter prediction error covariance at k moment is calculatedCalculation formula is as follows
In formulaRepresent that nonlinear function f () existsThe Jacobian matrix at place, Qk-1When representing k-1 The system noise covariance matrix at quarter；
(5) k moment H ∞ EKFs gain Gs are calculatedk, calculation formula is as follows
In formula ()-1To ask inverse of a matrix computing,The nonlinear function h () of expression existsThat locates is refined Gram than matrix, RkRepresent the measurement noise covariance matrix at k moment；
(6) the evaluated error covariance at k moment is calculatedCalculation formula is as follows
I is the unit matrix of corresponding dimension in formula, RE, kCalculation formula is as follows
Wherein parameter γ set calculation formula be
In formula λ be it is to be placed be more than 1 positive parameter, interval is [1,30] when parameters of electric power system recognizes, eig () represents to take the characteristic value of corresponding matrix, and max () represents to take maximum；
(7) estimates of parameters at k moment is calculatedCalculation formula is as follows
Y in formulakFor the measuring value at k moment, h () represents output function；
(8) innovation sequence is calculated, calculation formula is as follows
(9) when to take moving window size be L, innovation sequence s in calculation windowkAverage value, i.e., newly breath Matrix Cvk, it is counted It is as follows to calculate formula
(10) on the basis of previous step, dynamic calculation k+1 moment system noise covariance matrixes QkAssisted with noise is measured Anti- poor matrix Rk, calculation formula is as follows
(11) low-frequency oscillation of electric power system signal parameter identification is carried out according to time series according to (3)-(10) step, until Iteration stopping during k+1 ＞ N, output parameter identification result is for further analysis for the parametric results to the inventive method, this Embodiment carries out measurement analysis using root-mean-square-deviation to parameter identification precision, and it is defined as follows：
In formulaIt is parameter identification estimated result, xkIt is parameter actual value, n represents the iteration moment.
Fig. 2 is embodiment oscillating signal measurement sequence value, and parameter identification analysis is carried out to the oscillating signal, its Middle oscillating signal frequency w identification result is as shown in figure 3, Fig. 4 gives oscillating signal damping factor δ identification knots Fruit, Fig. 5 are that embodiment uses the inventive method to oscillating signal frequency and the root-mean-square-deviation of damping factor identification result. It can be seen that from Fig. 3 and Fig. 4 identification result not true even in measurement noise covariance matrix and the presence of parameter identification initial value In the case of qualitative and deviation, the inventive method still can effectively realize the accurate recognition of oscillating signal parameter, show Show that the inventive method has stronger robustness.From Fig. 5 then can with it is further seen that, the root-mean-square-deviation of parameter identification tends to 0, show that institute's extracting method of the present invention has higher identification precision and constringency performance.
To sum up, it can be deduced that to draw a conclusion, the power system low frequency proposed by the present invention based on H ∞ EKFs Oscillator signal parameter identification method has stronger robustness, effectively reduces the Identification Errors that model parameter uncertainty is brought, Improve the precision of oscillating signal parameter identification.

Claims (1)

  1. A kind of 1. oscillating signal parameter identification method based on H ∞ EKFs, it is characterised in that：Including following Step：
    (1) the related initial value of setting filtering, the state estimation initial value at k=0 moment is includedState estimation error covarianceSystem noise and the initial value Q for measuring noise covariance matrix0And R0, moving window value L and maximum estimated moment N；
    (2) low-frequency oscillation of electric power system measuring signal sequence inputting value y is obtainedk, it measures function and is defined as follows：
    yk=h (xk)+vk
    H () represents known and measures function, x in formulakFor the parameter true value at k moment, vkThe measurement noise at k moment is represented, it is full The covariance matrix of foot is Rk；
    (3) the parameter prediction value at k moment is calculatedCalculation formula is as follows：
    <mrow> <msub> <mover> <mi>x</mi> <mo>~</mo> </mover> <mi>k</mi> </msub> <mo>=</mo> <mi>f</mi> <mrow> <mo>(</mo> <msub> <mover> <mi>x</mi> <mo>^</mo> </mover> <mrow> <mi>k</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>)</mo> </mrow> </mrow>
    System function known to f () expressions in formula,For the estimates of parameters at k-1 moment；
    (4) the parameter prediction error covariance at k moment is calculatedCalculation formula is as follows：
    <mrow> <msub> <mover> <mi>P</mi> <mo>~</mo> </mover> <mi>k</mi> </msub> <mo>=</mo> <msub> <mi>F</mi> <mrow> <mi>k</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <msub> <mover> <mi>P</mi> <mo>^</mo> </mover> <mrow> <mi>k</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <msubsup> <mi>F</mi> <mrow> <mi>k</mi> <mo>-</mo> <mn>1</mn> </mrow> <mi>T</mi> </msubsup> <mo>+</mo> <msub> <mi>Q</mi> <mrow> <mi>k</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> </mrow>
    In formulaRepresent that nonlinear function f () existsThe Jacobian matrix at place, Qk-1Represent that k-1 moment is System noise covariance matrix；
    (5) k moment H ∞ EKFs gain Gs are calculatedk, calculation formula is as follows：
    <mrow> <msub> <mi>G</mi> <mi>k</mi> </msub> <mo>=</mo> <msub> <mover> <mi>P</mi> <mo>~</mo> </mover> <mi>k</mi> </msub> <msubsup> <mi>H</mi> <mi>k</mi> <mi>T</mi> </msubsup> <msup> <mrow> <mo>(</mo> <msub> <mi>H</mi> <mi>k</mi> </msub> <msub> <mover> <mi>P</mi> <mo>~</mo> </mover> <mi>k</mi> </msub> <msubsup> <mi>H</mi> <mi>k</mi> <mi>T</mi> </msubsup> <mo>+</mo> <msub> <mi>R</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msup> </mrow>
    In formula ()-1To ask inverse of a matrix computing,The nonlinear function h () of expression existsThe Ya Ke at place Compare matrix；
    (6) the evaluated error covariance at k moment is calculatedCalculation formula is as follows：
    <mrow> <msub> <mover> <mi>P</mi> <mo>^</mo> </mover> <mi>k</mi> </msub> <mo>=</mo> <mrow> <mo>(</mo> <mi>I</mi> <mo>-</mo> <msub> <mover> <mi>P</mi> <mo>~</mo> </mover> <mi>k</mi> </msub> <mo>&amp;lsqb;</mo> <msubsup> <mi>H</mi> <mi>k</mi> <mi>T</mi> </msubsup> <mi>I</mi> <mo>&amp;rsqb;</mo> <msubsup> <mi>R</mi> <mrow> <mi>e</mi> <mo>,</mo> <mi>k</mi> </mrow> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msubsup> <msup> <mrow> <mo>&amp;lsqb;</mo> <msubsup> <mi>H</mi> <mi>k</mi> <mi>T</mi> </msubsup> <mi>I</mi> <mo>&amp;rsqb;</mo> </mrow> <mi>T</mi> </msup> <mo>)</mo> </mrow> <msub> <mover> <mi>P</mi> <mo>~</mo> </mover> <mi>k</mi> </msub> </mrow>
    I is the unit matrix of corresponding dimension in formula, RE, kCalculation formula is as follows：
    <mrow> <msub> <mi>R</mi> <mrow> <mi>e</mi> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mo>=</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <mrow> <msub> <mi>R</mi> <mi>k</mi> </msub> <mo>+</mo> <msub> <mi>H</mi> <mi>k</mi> </msub> <msub> <mover> <mi>P</mi> <mo>~</mo> </mover> <mi>k</mi> </msub> <msubsup> <mi>H</mi> <mi>k</mi> <mi>T</mi> </msubsup> </mrow> </mtd> <mtd> <msup> <mrow> <mo>(</mo> <msub> <mover> <mi>P</mi> <mo>~</mo> </mover> <mi>k</mi> </msub> <msubsup> <mi>H</mi> <mi>k</mi> <mi>T</mi> </msubsup> <mo>)</mo> </mrow> <mi>T</mi> </msup> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mover> <mi>P</mi> <mo>~</mo> </mover> <mi>k</mi> </msub> <msubsup> <mi>H</mi> <mi>k</mi> <mi>T</mi> </msubsup> </mrow> </mtd> <mtd> <mrow> <mo>-</mo> <msup> <mi>&amp;gamma;</mi> <mn>2</mn> </msup> <mi>I</mi> <mo>+</mo> <msub> <mover> <mi>P</mi> <mo>~</mo> </mover> <mi>k</mi> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow>
    Wherein parameter γ set calculation formula be：
    <mrow> <msup> <mi>&amp;gamma;</mi> <mn>2</mn> </msup> <mo>=</mo> <mi>&amp;lambda;</mi> <mi>m</mi> <mi>a</mi> <mi>x</mi> <mo>{</mo> <mi>e</mi> <mi>i</mi> <mi>g</mi> <msup> <mrow> <mo>(</mo> <msubsup> <mover> <mi>P</mi> <mo>~</mo> </mover> <mi>k</mi> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msubsup> <mo>+</mo> <msubsup> <mi>H</mi> <mi>k</mi> <mi>T</mi> </msubsup> <msubsup> <mi>R</mi> <mi>k</mi> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msubsup> <msub> <mi>H</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msup> <mo>}</mo> </mrow>
    In formula λ be it is to be placed be more than 1 positive parameter, interval is [1,30] when parameters of electric power system recognizes, eig () table Show the characteristic value for taking corresponding matrix, max () represents to take maximum；
    (7) estimates of parameters at k moment is calculatedCalculation formula is as follows：
    <mrow> <msub> <mover> <mi>x</mi> <mo>^</mo> </mover> <mi>k</mi> </msub> <mo>=</mo> <msub> <mover> <mi>x</mi> <mo>~</mo> </mover> <mi>k</mi> </msub> <mo>+</mo> <msub> <mi>G</mi> <mi>k</mi> </msub> <mo>&amp;lsqb;</mo> <msub> <mi>y</mi> <mi>k</mi> </msub> <mo>-</mo> <mi>h</mi> <mrow> <mo>(</mo> <msub> <mover> <mi>x</mi> <mo>~</mo> </mover> <mi>k</mi> </msub> <mo>)</mo> </mrow> <mo>&amp;rsqb;</mo> </mrow>
    Y in formulakFor the measuring value at k moment；
    (8) innovation sequence is calculated, calculation formula is as follows：
    <mrow> <msub> <mi>s</mi> <mi>k</mi> </msub> <mo>=</mo> <msub> <mi>y</mi> <mi>k</mi> </msub> <mo>-</mo> <mi>h</mi> <mrow> <mo>(</mo> <msub> <mover> <mi>x</mi> <mo>~</mo> </mover> <mi>k</mi> </msub> <mo>)</mo> </mrow> </mrow>
    (9) when to take moving window size be L, innovation sequence s in calculation windowkAverage value, i.e., newly breath Matrix Cvk, it calculates public Formula is as follows：
    <mrow> <msub> <mi>C</mi> <mrow> <mi>v</mi> <mi>k</mi> </mrow> </msub> <mo>=</mo> <mfrac> <mn>1</mn> <mi>L</mi> </mfrac> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mi>k</mi> <mo>-</mo> <mi>N</mi> <mo>+</mo> <mn>1</mn> </mrow> <mi>k</mi> </munderover> <msub> <mi>s</mi> <mi>i</mi> </msub> <msubsup> <mi>s</mi> <mi>i</mi> <mi>T</mi> </msubsup> </mrow>
    (10) on the basis of previous step, dynamic calculation k+1 moment system noise covariance matrixes QkPoor square is defenced jointly with noise is measured Battle array Rk, calculation formula is as follows：
    <mrow> <msub> <mi>Q</mi> <mi>k</mi> </msub> <mo>=</mo> <msub> <mi>G</mi> <mi>k</mi> </msub> <msub> <mi>C</mi> <mrow> <mi>v</mi> <mi>k</mi> </mrow> </msub> <msubsup> <mi>G</mi> <mi>k</mi> <mi>T</mi> </msubsup> </mrow>
    <mrow> <msub> <mi>R</mi> <mi>k</mi> </msub> <mo>=</mo> <msub> <mi>C</mi> <mrow> <mi>v</mi> <mi>k</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>H</mi> <mi>k</mi> </msub> <mo>&amp;CenterDot;</mo> <msub> <mover> <mi>P</mi> <mo>^</mo> </mover> <mi>k</mi> </msub> <mo>&amp;CenterDot;</mo> <msubsup> <mi>H</mi> <mi>k</mi> <mi>T</mi> </msubsup> </mrow>
    (11) low-frequency oscillation of electric power system signal parameter identification is carried out according to time series according to (3)-(10) step, until k+1 Iteration stopping during ＞ N, output parameter identification result.
CN201711282838.4A 2017-12-06 2017-12-06 Oscillating signal parameter identification method based on H ∞ EKFs Pending CN107807278A (en)

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CN109274107A (en) * 2018-11-05 2019-01-25 河海大学 The oscillating signal identification model and its parameter identification method of meter and singular value
CN109375111A (en) * 2018-10-12 2019-02-22 杭州电子科技大学 A kind of estimation method of battery dump energy based on UHF
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CN109375111A (en) * 2018-10-12 2019-02-22 杭州电子科技大学 A kind of estimation method of battery dump energy based on UHF
CN109274107A (en) * 2018-11-05 2019-01-25 河海大学 The oscillating signal identification model and its parameter identification method of meter and singular value
CN109274107B (en) * 2018-11-05 2022-01-28 河海大学 Low-frequency oscillation signal parameter identification method considering singular values
CN110021931A (en) * 2019-04-28 2019-07-16 河海大学 It is a kind of meter and model uncertainty electric system assist predicted state estimation method
CN110118936A (en) * 2019-05-06 2019-08-13 杭州电子科技大学 A kind of estimation method of battery dump energy based on EHF
CN110287537A (en) * 2019-05-27 2019-09-27 西北大学 Anti- outlier method for adaptive kalman filtering for frequency marking output transition detection
CN111046327A (en) * 2019-12-18 2020-04-21 河海大学 Prony analysis method suitable for low-frequency oscillation and subsynchronous oscillation identification
CN112234601A (en) * 2020-09-03 2021-01-15 国网浙江省电力有限公司电力科学研究院 Method and system for identifying low-frequency oscillation characteristic parameters of power system on line
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CN112380655B (en) * 2020-11-20 2024-04-26 华南理工大学 Robot inverse kinematics solving method based on RS-CMSA algorithm
CN117277520A (en) * 2023-11-22 2023-12-22 深圳清瑞博源智能科技有限公司 SOC-SOH combined calculation method and device for new energy storage power station
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