Sampling and filtering parameter setting method and device for improving power grid stability judgment accuracy

Abstract

The application discloses a sampling and filtering parameter setting method and device for improving power grid stability judgment accuracy, comprising: determining the key line of sampling and filtering parameter setting; generating multiple noise signals according to the distribution characteristics of the measurement noise signal; determining the phase frequency trajectory curve of the node pair under the condition of not considering noise through short-circuit fault, obtaining the concave-convex coefficient and concave-convex conversion point discrimination time according to the phase frequency trajectory curve; in the short-circuit fault, applying any one noise signal, obtaining the concave-convex coefficient and concave-convex conversion point discrimination time of the phase frequency trajectory curve considering noise; determining the discrimination time difference according to the concave-convex conversion point discrimination time without and considering noise; determining the misjudgment rate according to the concave-convex coefficient without and considering noise; determining the value of the sampling and filtering parameter according to the discrimination time difference and the misjudgment rate, and completing the setting of the value of the filtering parameter. The application solves the problem of low accuracy of power grid power angle stability judgment under different noise conditions in the prior art.

Description

Technical Field

[0001] This application relates to the field of power system security and stability analysis, specifically to a method and apparatus for adjusting sampling and filtering parameters to improve the accuracy of power grid stability assessment. Background Technology

[0002] The safe and stable operation of a power grid depends on monitoring its stability. Accurately identifying the instability state and timing of the grid is crucial for timely implementation of targeted emergency control measures and for restoring the grid to stable operation as quickly as possible. Regarding power angle instability, the power angle stability of the power grid can be determined by the concavity / convexity of the phase frequency trajectories of characteristic node pairs. When the phase frequency trajectories of characteristic node pairs are concave, the power angle is stable; when they are convex, the power angle is unstable. The moment the phase frequency trajectory changes from concave to convex corresponds to the moment of power angle instability. This stability assessment method has the advantages of online implementation, simple criteria, and independence from model parameters.

[0003] Since the signals measured by WAMS from the power grid inevitably contain varying degrees of noise, this noise will affect the phase frequency trajectories of characteristic node pairs, causing them to deviate and thus impacting the accuracy of stability determination. Existing research on improving the noise resistance of stability criteria mainly relies on empirical measurements of noise intensity, lacking quantitative evidence for noise simulation, and the quantitative study of the impact of noise on stability determination results is insufficient. Summary of the Invention

[0004] To address the aforementioned problems, this application provides a method for tuning sampling and filtering parameters to improve the accuracy of power grid stability assessment, comprising:

[0005] Identify the key lines used for sampling and tuning filtering parameters to ensure the accuracy of power grid stability assessment;

[0006] Based on the distribution characteristics of the measured noise signal, multiple noise signals are randomly generated;

[0007] Set the range of values ​​for the fitting time window, the filtering cutoff frequency, and the sampling and filtering parameters;

[0008] By simulating a three-phase AC short-circuit fault on the critical line, the phase frequency trajectory curve of the node pair is determined without considering noise. Based on the phase frequency trajectory curve, the concavity / convexity coefficient and the concavity / convexity transition point judgment time of the phase frequency trajectory curve without considering noise are obtained.

[0009] In each simulated AC three-phase short-circuit fault, a randomly generated noise signal is applied to obtain the concavity coefficient and concavity transition point discrimination time of the phase frequency trajectory curve taking noise into account.

[0010] The discrimination time difference is determined based on the concavity / convexity transition point of the phase frequency trajectory curve without noise and the concavity / convexity transition point of the phase frequency trajectory curve with noise. The misjudgment rate with noise is determined based on the concavity / convexity coefficient of the phase frequency trajectory curve without noise and the concavity / convexity coefficient of the phase frequency trajectory curve with noise.

[0011] Based on the discrimination time difference and the false judgment rate considering noise, the values ​​of the sampling and filtering parameters are determined, and the tuning of the values ​​of the filtering parameters is completed.

[0012] Furthermore, the critical circuit includes: a swing unit and a reference unit; the voltage amplitudes of the swing unit and the reference unit are E... s and E r The reference generator power angle is denoted as 0°, then δ is the swing generator power angle; the voltage phasor amplitude and phase at the critical circuit nodes m and n are denoted as U. m U n and θ m θ n And denote the node pair consisting of nodes m and n as NP. mn Node pair NP mn The phase difference and frequency difference are θ mn =θ m -θ n f mn =dθ mn / dt=f m -f n .

[0013] Furthermore, based on the distribution characteristics of the measurement noise, multiple noise signals are randomly generated, including:

[0014] Based on the characteristic that the measured noise signal follows a zero-mean Gaussian distribution, multiple noise signals following a zero-mean Gaussian distribution are randomly generated while keeping the signal-to-noise ratio constant.

[0015] Furthermore, while maintaining a constant signal-to-noise ratio, multiple noise signals following a zero-mean Gaussian distribution are randomly generated, including:

[0016] Let the random measurement noise signal X N The mean E(X) N ) and variance D(X N It satisfies the relationship shown in the following formula.

[0017]

[0018] Since the mean of the measured noise is 0, according to the above formula, the average power P of the noise is... N It can be represented as follows,

[0019]

[0020] The signal power of the measured signal is denoted as P. S Then the signal-to-noise ratio (SNR) and P S P N The relationship is as follows:

[0021]

[0022] Under the premise of constant signal-to-noise ratio, multiple noise signals that follow a zero-mean Gaussian distribution are randomly generated.

[0023] Furthermore, the range of values ​​for the fitting time window, the filtering cutoff frequency, and the sampling and filtering parameters are set, including:

[0024] The ranges of the fitting time window, the sampling frequency cutoff frequency, and the filtering parameters are respectively [T]. c1， T c2 ] and [f c1 f c2 The two-phase parameters take values ​​in an arithmetic progression within their respective ranges, with (T) ci f cj ) represents one of the parameter combinations i = 1, 2, ..., n, j = 1, 2, ..., l, where n and l are respectively [T c1 T c2 ] and [f c1 f c2 The total number of parameter combinations is n*l, which is the number of values ​​that can be taken within the two intervals.

[0025] Furthermore, by simulating a three-phase AC short-circuit fault on the critical line, the phase frequency trajectory curves of the node pairs are determined without considering noise. Based on the phase frequency trajectory curves, the concavity / convexity coefficients and concavity / convexity transition point judgment times of the phase frequency trajectory curves without considering noise are obtained, including:

[0026] Simulate a three-phase AC short-circuit fault on the critical line, and set the fault duration to t. f ;

[0027] The node pair NP was obtained by calculation without considering noise. mn The time-domain curves of the phase difference and frequency difference are denoted as θ. mn (t) and f mn (t);

[0028] Phase difference θ mn and frequency difference f mn The phase frequency trajectory curve at the current time t is as follows:

[0029] f mn (t)-F t [θmn [(t)]=0t∈(t0-T) c ,t0)

[0030] Define the concavity / convexity coefficient C of the phase frequency trajectory curve mn (t),

[0031] C mn (t)=f mn (t)F t "[θ mn (t)]

[0032] During the transient process, if NP mn Always satisfy C mn If (t) < 0, then the phase frequency trajectory exhibits concave characteristics, corresponding to the transient stability of the system; if there exists a time t such that NP mn Satisfy C mn If (t)>0, then the phase frequency trajectory exhibits convex characteristics, and the system is transiently unstable; if there exists a time t such that NP mn The first time C is satisfied mn If (t) = 0, then the phase frequency trajectory running point at that moment is the phase frequency trajectory concavity / convexity transition point;

[0033] The concavity / convexity transition point of the phase frequency trajectory, neglecting noise, is determined by t. i0 express.

[0034] Furthermore, in each simulated AC three-phase short-circuit fault, a randomly generated noise signal is applied to obtain the concavity / convexity coefficient and the concavity / convexity transition point discrimination time of the phase frequency trajectory curve considering the noise, including:

[0035] In each simulated AC three-phase short-circuit fault, a randomly generated noise signal is applied to obtain the node pair NP considering the noise. mn The time-domain curves of the phase difference and frequency difference are denoted as θ'. mn (t) and f' mn (t);

[0036] Based on the time-domain curves of the phase difference and frequency difference, the phase difference θ' mn and frequency difference f' mn The phase frequency trajectory curve at time t during the sampling period;

[0037] Define the concavity / convexity coefficient C' of the phase frequency trajectory curve mn (t), calculate the time of the concavity / convexity transition point of the phase frequency trajectory considering noise, and use t in express.

[0038] Furthermore, the discrimination time difference is determined based on the transition point of the phase frequency trajectory curve without noise and the transition point of the phase frequency trajectory curve including noise; the misjudgment rate considering noise is determined based on the concavity coefficient of the phase frequency trajectory curve without noise and the concavity coefficient of the phase frequency trajectory curve including noise, including:

[0039] Based on the concavity coefficient C of the phase frequency trajectory curve neglecting noise mn (t) and the concavity coefficient C' of the phase frequency trajectory curve considering noise mn (t), calculate the time difference t between the two. i0 -t in , using Δt in =t i0 -t in express;

[0040] If C mn (t) and C' mn (t) If the signs of positive and negative signs are inconsistent at the same moment during the running time, it is called a misjudgment of the system taking noise into account, and the misjudgment rate (%) is represented by ξ.

[0041] Furthermore, based on the discrimination time difference and the false judgment rate considering noise, the values ​​of the sampling and filtering parameters are determined, and the tuning of the filtering parameters is completed, including:

[0042] Based on the discriminant time difference Δt in Based on the false positive rate ξ considering noise, the sampling and filtering parameters T are determined. c and f c The values ​​of the filtering parameters are determined; the tuning of the values ​​of the filtering parameters is completed.

[0043] This application also provides a sampling and filtering parameter tuning device for improving the accuracy of power grid stability assessment, including:

[0044] The critical path determination unit is used to determine the critical path for power grid stability assessment accuracy sampling and filter parameter tuning.

[0045] The noise signal generation unit is used to randomly generate multiple noise signals based on the distribution characteristics of the measured noise signals;

[0046] The value range setting unit is used to set the value range of the fitting time window and the filtering cutoff frequency sampling and filtering parameters;

[0047] The first concavity / convexity coefficient and discrimination time determination unit is used to determine the phase frequency trajectory curve of the node pair without considering noise by simulating the AC three-phase short circuit fault on the critical line, and obtain the concavity / convexity coefficient and the concavity / convexity transition point discrimination time of the phase frequency trajectory curve without considering noise based on the phase frequency trajectory curve.

[0048] The second concavity coefficient and discrimination time determination unit is used to apply a randomly generated noise signal in each simulated AC three-phase short-circuit fault, thereby obtaining the concavity coefficient and concavity transition point discrimination time of the phase frequency trajectory curve taking noise into account.

[0049] The misjudgment rate determination unit is used to determine the discrimination time difference based on the discrimination time of the concavity-convexity transition point of the phase frequency trajectory curve without noise and the discrimination time of the concavity-convexity transition point of the phase frequency trajectory curve with noise; and to determine the misjudgment rate with noise in mind based on the concavity-convexity coefficient of the phase frequency trajectory curve without noise and the concavity-convexity coefficient of the phase frequency trajectory curve with noise.

[0050] The tuning unit is used to determine the values ​​of the sampling and filtering parameters based on the discrimination time difference and the false judgment rate considering noise, and to complete the tuning of the values ​​of the filtering parameters. Attached Figure Description

[0051] Figure 1 This is a flowchart illustrating a sampling and filtering parameter tuning method for improving the accuracy of power grid stability assessment provided in an embodiment of this application.

[0052] Figure 2 This is a typical two-machine system involved in the power angle stability analysis of the embodiments of this application;

[0053] Figure 3 This application's embodiments involve the discrimination of phase frequency trajectory convexity and concavity features under different combinations of sampling and filtering parameters (N=500 times);

[0054] Figure 4 This is a schematic diagram of a sampling and filtering parameter tuning device for improving the accuracy of power grid stability assessment, provided in an embodiment of this application. Detailed Implementation

[0055] Many specific details are set forth in the following description to provide a full understanding of this application. However, this application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of this application; therefore, this application is not limited to the specific embodiments disclosed below.

[0056] The following explanations are given for the keywords with specific meanings in this invention:

[0057] WAMS: Wide Area Measurement System, is a widely used system in power systems for online measurement and data collection.

[0058] SNR: Signal Noise Ratio.

[0059] Measurement noise: refers to the noise present in the measurement data collected by WAMS from the power grid.

[0060] Characteristic node pair: A node pair consisting of two nodes in a power grid that have the largest phase difference.

[0061] Phase frequency trajectory concavity / convexity: The concavity / convexity of the phase frequency trajectory curve composed of characteristic node pairs.

[0062] Sampling and filtering parameters: The two parameters are the sampling fitting time window Tc and the filtering cutoff frequency fc.

[0063] like Figure 1 As shown, this invention provides a method for tuning sampling and filtering parameters to improve the accuracy of power grid stability assessment, comprising the following steps:

[0064] Step S101: Determine the key lines used for sampling and tuning filtering parameters to ensure the accuracy of power grid stability assessment.

[0065] The critical path includes: the swing unit and the reference unit; the voltage amplitudes of the swing unit and the reference unit are E... s and E r The reference generator power angle is denoted as 0°, then δ is the swing generator power angle; the voltage phasor amplitude and phase at nodes m and n at both ends of the line are denoted as U. m U n and θ m θ n And denote the node pair consisting of nodes m and n as NP. mn Node pair NP mn The phase difference and frequency difference are θ mn =θ m -θ n f mn =dθ mn / dt=f m -f n .

[0066] The following description of the invention is based on... Figure 2 The typical two-machine system shown in Figure 2 is used as an example. It should be noted that the two-machine system shown in Figure 2 is an equivalent and simplified version of the actual power system, but the method described in this invention is not limited to the two-machine system and is also applicable to critical lines in actual power systems that are prone to power angle instability.

[0067] In this paper, a critical path is identified in the power system as the research object. The critical path is the path consisting of nodes m and n, and the node pairs formed by them are denoted as NP. mn Using power system transient simulation software to build such a system Figure 2 The simulation system shown.

[0068] Step S102: Based on the distribution characteristics of the measured noise signal, multiple noise signals are randomly generated.

[0069] Statistical analysis of the data shows that the WAMS measurement noise follows a zero-mean Gaussian distribution. Let the WAMS random measurement noise signal X... N The mean E(X) N ) and variance D(X N It satisfies the relationship shown in equation (1).

[0070]

[0071] Since the mean of the measurement noise is 0, that is, E(X) N ) = 0, according to equation (1), the average power P of the noise N It can be represented as shown in equation (2).

[0072]

[0073] The signal power of the measured signal is denoted as P. S Then the signal-to-noise ratio (SNR) and P S P N The relationship is shown in equation (3):

[0074]

[0075] While maintaining P S and P N Under the premise of keeping the SNR constant, multiple noise waveforms with different zero-mean Gaussian distributions are randomly generated and added to the measurement signal to simulate a variety of different noisy measurement signals.

[0076] Step S103: Set the range of values ​​for the fitting time window, the sampling and filtering parameters for the filter cutoff frequency.

[0077] Set the fitting time window T c and filter cutoff frequency f c The range of values ​​for these two sampling and filtering parameters is defined by [T]. c1 T c2 ] and [f c1 f c2 This indicates that both parameters take values ​​in an arithmetic progression within their respective ranges, with (T) ci f cj ) represents one of the parameter combinations (i = 1, 2, ..., n, j = 1, 2, ..., l, n and l are respectively [T c1 T c2 ] and [f c1 f c2The number of values ​​that can be taken in the two intervals is set by the user, so the total number of parameter combinations is n*l.

[0078] Step S104: By simulating a three-phase AC short-circuit fault on the critical line, determine the phase frequency trajectory curve of the node pair under noise-free conditions. Based on the phase frequency trajectory curve, obtain the concavity / convexity coefficient and the concavity / convexity transition point judgment time of the phase frequency trajectory curve under noise-free conditions.

[0079] In the simulation system, a three-phase AC short-circuit fault is set up on the line, and the fault duration is set to t. f The node pair NP was calculated by simulation software without considering noise. mn The time-domain curves of the phase difference and frequency difference are denoted as θ. mn (t) and f mn (t). According to equation (4), the phase difference θ is obtained. mn and frequency difference f mn The phase frequency trajectory curve at time t during the sampling period.

[0080] f mn (t)-F t [θ mn [(t)]=0 t∈(t0-T) c ,t0) (4)

[0081] According to equation (5), the concavity / convexity coefficient C of the phase frequency trajectory curve is defined. mn (t).

[0082] C mn (t)=f mn (t)F t "[θ mn (t)] (5)

[0083] During the transient process, if NP mn Always satisfy C mn If (t) < 0, then the phase frequency trajectory exhibits concave characteristics, corresponding to the transient stability of the system; if there exists a time t such that NP mn Satisfy C mn If (t)>0, then the phase frequency trajectory exhibits convex characteristics, and the system is transiently unstable; if there exists a time t such that NP mn The first time C is satisfied mn If (t) = 0, then the phase frequency trajectory running point corresponding to that moment is the phase frequency trajectory concavity / convexity transition point. The time for determining the concavity / convexity transition point of the phase frequency trajectory, neglecting noise, is calculated using t. i0 express.

[0084] Step S105: In each simulated AC three-phase short-circuit fault, a randomly generated noise signal is applied to obtain the concavity coefficient and concavity transition point discrimination time of the phase frequency trajectory curve taking noise into account.

[0085] In each simulated three-phase AC short-circuit fault, one of several randomly generated noise signals is applied, and all generated noise waveforms are iterated over to perform N simulation calculations. After each simulation calculation, the simulation software calculates the node pair NP considering the noise. mn The time-domain curves of the phase difference and frequency difference are denoted as θ'. mn (t) and f' mn (t). According to equation (4), the phase difference θ' is obtained. mn and frequency difference f' mn The phase frequency trajectory curve at time t during the sampling period. According to equation (5), the concavity / convexity coefficient C' of the phase frequency trajectory curve is defined. mn (t), and calculate the time of the concavity / convexity transition point of the phase frequency trajectory considering noise, using t in express.

[0086] Step S106: Determine the discrimination time difference based on the concavity / convexity transition point of the phase frequency trajectory curve without noise and the concavity / convexity transition point of the phase frequency trajectory curve with noise; determine the misjudgment rate with noise consideration based on the concavity / convexity coefficient of the phase frequency trajectory curve without noise and the concavity / convexity coefficient of the phase frequency trajectory curve with noise.

[0087] Based on the concavity coefficient C of the phase frequency trajectory curve neglecting noise mn (t) and the concavity coefficient C' of the phase frequency trajectory curve considering noise mn (t), calculate the time difference t between the two. i0 -t in , using Δt in =t i0 -t in Indicates; if C mn (t) and C' mn (t) If the signs of the positive and negative signs are inconsistent at the same moment during the entire simulation time, it is said that the system has made a misjudgment when noise is taken into account. The misjudgment rate (%) is represented by ξ, which is the percentage of the number of misjudgments in the total number of simulations N.

[0088] Step S107: Determine the values ​​of the sampling and filtering parameters based on the discrimination time difference and the false judgment rate considering noise, and complete the tuning of the values ​​of the filtering parameters.

[0089] Based on the above calculation results, plot the x-axis as T. c The vertical coordinates are Δt inThe two graphs for ξ. Based on the aforementioned time difference Δt... in Based on the false positive rate ξ considering noise, the sampling and filtering parameters T are determined. c and f c The values ​​of the filtering parameters are determined; the tuning of the values ​​of the filtering parameters is completed.

[0090] Specific application examples are as follows:

[0091] Build such a system in power system transient simulation software Figure 2 The power transmission system shown consists of two generators connected by two parallel branches. The line reactance is 0.04 pu; the transformer leakage reactance Xs = Xr = 0.01 pu; the generator transient reactance X'd is 0.01 pu; and the steady-state power of the oscillating generator is 1000 MW. A three-phase short-circuit interruption fault occurs on the line node m side, lasting 7.7 ms. At this time, the system will experience power angle instability.

[0092] Set the SNR of the measurement signal to 50dB and N to 500, which means generating 500 random noise waveforms; set the sampling fitting time window T. c The value range is [0.15s, 0.45s], and the number of values ​​n = 7; the filter cutoff frequency f is set. c The value range is [0.5Hz, 10Hz], and the number of values ​​is l = 20; according to steps four and five, the node pair NP is calculated for each combination of filter parameters. mn The concavity coefficient C of the phase frequency trajectory curve mn (t) and C' mn (t), and calculate Δt based on this. in And ξ. We obtain as follows: Figure 3 The key line phase frequency trajectory concavity and convexity feature discrimination curves are shown in (a) and (b) under different combinations of sampling and filtering parameters.

[0093] from Figure 3 (a) It can be seen that as T c Decrease, and f c As the value increases, the misjudgment rate ξ of the stable phase frequency trajectory increases, and the values ​​at each cutoff frequency f also increase. c The minimum T corresponding to the minimum T that guarantees no misjudgment of the transiently stable phase frequency trajectory (i.e., ξ = 0%). c ,from Figure 3 (b) It can be seen that, with f c Increase the filter delay Δt in Reduce the discrimination delay Δt corresponding to the inflection point of the unstable phase frequency trajectory. in Shorten, and Δt in Basically does not follow T c change.

[0094] A lower cutoff frequency helps reduce misjudgments of trajectory concavity / convexity, but it also introduces a discrimination delay. Furthermore, increasing the fitting time window T... c This can reduce the misjudgment rate of trajectory concavity / convexity. Based on the above analysis, the parameter tuning method in the filtering and sampling system is as follows: In Δt in Under the condition of meeting the delay requirement (e.g., Δt) in <0.1s), corresponding to Figure 3 (b) Obtain the minimum cutoff frequency (f) cmin =2Hz), combined Figure 3 (a) The expected misclassification rate is ξ = 0%, which corresponds to the minimum fitting window (T). cmin =0.25s). In practical power system applications involving multiple generators, to ensure the misjudgment rate ξ = 0% for the phase frequency trajectory concavity and convexity after filtering, and the misjudgment delay Δt in If the time is less than 0.1s, set f. c ≥2Hz, and T c >0.30s, i.e., T c It contains at least 30 sampling points.

[0095] Based on the same inventive concept, this invention also provides a sampling and filtering parameter tuning device 400 to improve the accuracy of power grid stability assessment, such as... Figure 4 As shown, it includes:

[0096] Critical path determination unit 410 is used to determine the critical path for power grid stability assessment accuracy sampling and filter parameter tuning.

[0097] The noise signal generation unit 420 is used to randomly generate multiple noise signals based on the distribution characteristics of the measured noise signals;

[0098] The value range setting unit 430 is used to set the value range of the fitting time window and the filtering cutoff frequency sampling and filtering parameters;

[0099] The first concavity / convexity coefficient and discrimination time determination unit 440 is used to determine the phase frequency trajectory curve of the node pair under noise-free conditions by simulating the AC three-phase short circuit fault on the critical line, and obtain the concavity / convexity coefficient and concavity / convexity transition point discrimination time of the phase frequency trajectory curve under noise-free conditions based on the phase frequency trajectory curve.

[0100] The second concavity coefficient and discrimination time determination unit 450 is used to apply a randomly generated noise signal in each simulated AC three-phase short-circuit fault, thereby obtaining the concavity coefficient and concavity transition point discrimination time of the phase frequency trajectory curve taking noise into account.

[0101] The false positive rate determination unit 460 is used to determine the discrimination time difference based on the discrimination time of the concavity-convexity transition point of the phase frequency trajectory curve without noise and the discrimination time of the concavity-convexity transition point of the phase frequency trajectory curve with noise; and to determine the false positive rate with noise in mind based on the concavity-convexity coefficient of the phase frequency trajectory curve without noise and the concavity-convexity coefficient of the phase frequency trajectory curve with noise.

[0102] The tuning unit 470 is used to determine the values ​​of the sampling and filtering parameters based on the discrimination time difference and the false judgment rate considering noise, and to complete the tuning of the values ​​of the filtering parameters.

[0103] This invention defines the concavity / convexity coefficient C of the phase frequency trajectory curve. mn (t) It realizes the quantitative assessment of the power angle stability of the power grid, which facilitates the accurate identification of the power angle instability characteristics of the power grid; by utilizing the statistical characteristic that random noise follows a zero-mean Gaussian distribution, it realizes the quantitative generation of random noise, and can simulate a large number of random noise waveforms that conform to this characteristic based on the SNR value of noise measured by WAMS in actual engineering, avoiding the problem that the noise simulated based on human experience does not match the actual situation; by defining the discrimination time difference Δt in The system incorporates the false positive rate ξ to quantitatively assess the impact of random noise on the accuracy of the judgment stability test. Furthermore, it considers the effects of different combinations of sampling and filtering parameters, as well as varying noise levels, on the judgment time difference Δt. in Through calculation and statistical experiments of the false positive rate ξ, the quantitative tuning of sampling and filtering parameters was realized. The sampling and filtering parameters tuned according to this method can effectively improve the accuracy of power angle stability judgment under different noise conditions.

[0104] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of the claims of the present invention.

Claims

1. A method for tuning sampling and filtering parameters to improve the accuracy of power grid stability assessment, characterized in that, include: Identify the key lines used for sampling and tuning filtering parameters to ensure the accuracy of power grid stability assessment; The critical circuit includes: a swing unit and a reference unit; the voltage amplitudes of the swing unit and the reference unit are respectively... E s and E r The reference generator power angle is denoted as 0°, then δ is the swing generator power angle; the voltage phasor amplitude and phase at the critical circuit nodes m and n are respectively denoted as... U m , U n and θ m , θ n And denote the node pair consisting of nodes m and n as NP mn Node pairs NP mn The phase difference and frequency difference are respectively θ mn = θ m - θ n , f mn = dθ mn / dt = f m - f n ; Based on the distribution characteristics of the measured noise signal, multiple noise signals are randomly generated. Specifically, based on the characteristic that the measured noise signal follows a zero-mean Gaussian distribution, multiple noise signals following a zero-mean Gaussian distribution are randomly generated while keeping the signal-to-noise ratio constant. Set the value ranges for the fitting time window and the filtering cutoff frequency, specifically including: the value ranges for the fitting time window and the filtering cutoff frequency, and the sampling and filtering parameters, are respectively [T c1， T c2 ]and[ f c1， f c2 Both parameters take values ​​in an arithmetic progression within their respective ranges, with (T) ci， f cj ) represents one of the parameter combinations i = 1,2,...,n, j=1,2,...,l, where n and l are respectively [T c1， T c2 ]and[ f c1， f c2 The total number of parameter combinations is n*l, calculated by finding the number of values ​​that can be taken within the two intervals. By simulating a three-phase AC short-circuit fault on the critical line, the phase frequency trajectory curve of the node pair is determined without considering noise. Based on the phase frequency trajectory curve, the concavity / convexity coefficient and the concavity / convexity transition point judgment time of the phase frequency trajectory curve without considering noise are obtained. In each simulated AC three-phase short-circuit fault, a randomly generated noise signal is applied to obtain the concavity coefficient and concavity transition point discrimination time of the phase frequency trajectory curve taking noise into account. The discrimination time difference is determined based on the transition point of the phase frequency trajectory curve without noise and the transition point of the phase frequency trajectory curve including noise. The misjudgment rate under noise is determined based on the concavity / convexity coefficients of the phase frequency trajectory curve without noise and the phase frequency trajectory curve including noise. Specifically, this includes determining the misjudgment rate based on the concavity / convexity coefficient C of the phase frequency trajectory curve without noise. mn ( t ) and the concavity / convexity coefficient C' of the phase frequency trajectory curve taking into account noise mn ( t ), calculate the time difference t between the two. i0 -t in , using Δt in = t i0 -t in Indicates; if C mn ( t ) and C' mn ( t If the signs of the positive and negative signs are inconsistent at the same moment during the runtime, it is called a misjudgment of the system taking noise into account, and the misjudgment rate (%) is represented by ξ. Based on the discrimination time difference and the false judgment rate considering noise, the values ​​of the sampling and filtering parameters are determined, and the tuning of the values ​​of the filtering parameters is completed.

2. The method according to claim 1, characterized in that, While maintaining a constant signal-to-noise ratio, multiple noise signals following a zero-mean Gaussian distribution are randomly generated, including: Suppose a random measurement noise signal X N mean E ( X N ) and variance D ( X N It satisfies the relationship shown in the following formula. Since the mean of the measured noise is 0, according to the above formula, the average power of the noise is... P N It can be represented as follows, The signal power of the measured signal is denoted as P S Then the signal-to-noise ratio (SNR) and P S , P N The relationship is as follows: Under the premise of constant signal-to-noise ratio, multiple noise signals that follow a zero-mean Gaussian distribution are randomly generated.

3. The method according to claim 1, characterized in that, By simulating a three-phase AC short-circuit fault on the critical line, the phase frequency trajectory curves of the node pairs are determined under noise-neglected conditions. Based on the phase frequency trajectory curves, the concavity / convexity coefficients and concavity / convexity transition point judgment times of the noise-neglected phase frequency trajectory curves are obtained, including: Simulate a three-phase AC short-circuit fault on the critical line, and set the fault duration to [value missing]. t f ; The node pairs were obtained by calculation without considering noise. The time-domain curves of the phase difference and frequency difference are denoted as follows: θ mn ( t )and f mn ( t ); Phase difference θ mn and frequency difference f mn The phase frequency trajectory curve at the current time t is as follows: Define the concavity / convexity coefficient C of the phase frequency trajectory curve mn ( t ), During the transient process, if NP mn Always satisfy C mn ( t If ) < 0, then the phase frequency trajectory exhibits concave characteristics, corresponding to the system being transiently stable; if there exists a time t such that satisfy If the phase frequency trajectory exhibits convex characteristics, the system will experience transient instability; if there exists a time... have to First time satisfied Then the phase frequency trajectory running point corresponding to that moment is the phase frequency trajectory concavity-convexity transition point; The concavity / convexity transition point of the phase frequency trajectory, neglecting noise, is determined by t. i0 express.

4. The method according to claim 1, characterized in that, In each simulated AC three-phase short-circuit fault, a randomly generated noise signal is applied to obtain the concavity / convexity coefficient and the concavity / convexity transition point discrimination time of the phase frequency trajectory curve considering the noise, including: In each simulated AC three-phase short-circuit fault, a randomly generated noise signal is applied to obtain the node pairs considering the noise. NP mn The time-domain curves of the phase difference and frequency difference are denoted as follows: θ' mn ( t )and f’ mn ( t ) ; Based on the time-domain curves of the phase difference and frequency difference, the phase difference is obtained. θ' mn and frequency difference f’ mn The phase frequency trajectory curve at time t during the sampling period; Define the concavity / convexity coefficient C' of the phase frequency trajectory curve mn ( t ), calculate the time of the concavity / convexity transition point of the phase frequency trajectory considering noise, and use t in express.

5. The method according to claim 1, characterized in that, Based on the discrimination time difference and the false judgment rate considering noise, the values ​​of the sampling and filtering parameters are determined, and the tuning of the filtering parameters is completed, including: Based on the discriminant time difference Δt in Based on the false positive rate ξ considering noise, the sampling and filtering parameters T are determined. c and f c The values ​​of the filtering parameters are determined; the tuning of the values ​​of the filtering parameters is completed.

6. A sampling and filtering parameter tuning device for improving the accuracy of power grid stability assessment, utilizing the sampling and filtering parameter tuning method for improving power grid stability assessment according to any one of claims 1-5, characterized in that, include: The critical path determination unit is used to determine the critical path for power grid stability assessment accuracy sampling and filter parameter tuning. The noise signal generation unit is used to randomly generate multiple noise signals based on the distribution characteristics of the measured noise signals; The value range setting unit is used to set the value range of the fitting time window and the filtering cutoff frequency sampling and filtering parameters; The first concavity / convexity coefficient and discrimination time determination unit is used to determine the phase frequency trajectory curve of the node pair without considering noise by simulating the AC three-phase short circuit fault on the critical line, and obtain the concavity / convexity coefficient and the concavity / convexity transition point discrimination time of the phase frequency trajectory curve without considering noise based on the phase frequency trajectory curve. The second concavity coefficient and discrimination time determination unit is used to apply a randomly generated noise signal in each simulated AC three-phase short-circuit fault, thereby obtaining the concavity coefficient and concavity transition point discrimination time of the phase frequency trajectory curve taking noise into account. The misjudgment rate determination unit is used to determine the discrimination time difference based on the discrimination time of the concavity-convexity transition point of the phase frequency trajectory curve without noise and the discrimination time of the concavity-convexity transition point of the phase frequency trajectory curve with noise; and to determine the misjudgment rate with noise in mind based on the concavity-convexity coefficient of the phase frequency trajectory curve without noise and the concavity-convexity coefficient of the phase frequency trajectory curve with noise. The tuning unit is used to determine the values ​​of the sampling and filtering parameters based on the discrimination time difference and the false judgment rate considering noise, and to complete the tuning of the values ​​of the filtering parameters.

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