SMSST-CDSSI subsynchronous oscillation mode identification method and device

Abstract

The invention discloses a subsynchronous oscillation mode identification method and device based on SMSST-CDSSI, and the method comprises the steps: employing a ridge extraction method, screening out a time-frequency coefficient representing each subsynchronous oscillation component according to the energy of the time-frequency coefficient in each second-order multiple synchronous compression transformation coefficient matrix, and obtaining an optimal instantaneous frequency track; performing time domain reconstruction of each oscillation mode according to the optimal instantaneous frequency trajectory; the method comprises the following steps: constructing a state space model of an n-order system according to input and output signals of a power system and considering deterministic and random noise, and constructing a corresponding Hankle matrix according to input and output variables; performing LQ decomposition on the input / output Hankel matrix to calculate an oblique projection matrix Oi; and performing singular value decomposition on the oblique projection matrix Oi, calculating a state matrix A of a discrete system, performing eigenvalue decomposition on the state matrix A, solving a discrete eigenvalue of the state matrix A, and solving the frequency fi and the modal damping ratio xii of the subsynchronous oscillation according to the relationship between the discrete system and the continuous time system. The device comprises a processor and a memory.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of synchronous oscillation mode identification, and in particular to a SMSST-CDSSI subsynchronous oscillation mode identification method and device. BACKGROUND

[0002] With the increasing proportion of new energy power generation in the power grid year by year, the power electronic characteristics of the power system are significant, and the new type of subsynchronous oscillation (SSO) problem caused by the interaction between new energy generating units and the power grid is increasingly prominent, leading to frequent subsynchronous oscillation accidents in the actual power grid [1] . Wind power grid-connected system subsynchronous oscillation is one of the most concerned stability problems in academia and industry. It is the mechanical, electromagnetic or coupled energy exchange between the power grid and power electronic equipment, causing periodic oscillation in voltage, current and power in the frequency range of several hertz to several tens of hertz over time, which is fundamentally different from the traditional subsynchronous oscillation caused by turbine shaft torsional vibration. Its generation mechanism is complex, showing large-scale time-varying characteristics of oscillation frequency, oscillation damping, etc.; the oscillation frequency is complex, with multiple oscillation modes coexisting; the oscillation propagation range is wide, and the electrical distance is far [2] .

[0003] Wind power grid-connected system subsynchronous oscillation is one of the most concerned stability problems in academia and industry. It is the mechanical, electromagnetic or coupled energy exchange between the power grid and power electronic equipment, causing periodic oscillation in voltage, current and power in the frequency range of several hertz to several tens of hertz over time, which is fundamentally different from the traditional subsynchronous oscillation caused by turbine shaft torsional vibration. Its generation mechanism is complex, showing large-scale time-varying characteristics of oscillation frequency, oscillation damping, etc.; the oscillation frequency is complex, with multiple oscillation modes coexisting; the oscillation propagation range is wide, and the electrical distance is far. Such oscillation accidents may cause damage to new energy equipment and large-scale disconnection of new energy, seriously affecting the grid-connection and consumption of new energy, and even causing turbine shaft fatigue, leading to generator shaft rupture, and further inducing regional power grid cascading accidents, seriously endangering the safe and stable operation of the power system, equipment safety and power quality. Therefore, when the oscillation occurs, accurately identifying the key parameters such as the frequency and damping ratio of SSO is of great significance for in-depth analysis of the propagation mechanism of subsynchronous oscillation and the development of effective suppression measures.

[0004] The mechanism of new type subsynchronous oscillation is complex and variable, and its frequency and damping ratio show nonlinear time-varying characteristics with the change of the operation state of the power grid and power electronic equipment [3-4]This requires the SSO mode time-frequency extraction technology to have stronger processing capability for non-stationary time-varying signals. Time-frequency extraction techniques mainly include Short Time Fourier Transform (STFT) [5] , Continuous Wavelet Transform (CWT) [6] and S-transform [7] , etc. STFT uses a sliding time window to analyze the signal and performs Fourier transform on the windowed signal to generate a time-frequency spectrum. However, since the time window of STFT is pre-set and the window length is fixed, it cannot provide excellent time-frequency resolution characteristics. Based on this, CWT improves the time window by introducing a scale factor concept, making it exhibit good time and frequency resolution. ST can be seen as a combination of STFT and CWT, with a similar overall structure to STFT, and uses a frequency-dependent Gaussian window to have similar time-frequency resolution to CWT. However, due to the Heisenberg uncertainty principle, the energy of each frequency band in the time-frequency spectrum obtained by the above methods will be dispersed and leaked in a wide range around the center frequency, and the time-frequency concentration ability is insufficient, resulting in low time-frequency resolution.

[0005] Time-frequency extraction techniques can decompose SSO signals into components at different times and frequencies, revealing the dynamic characteristics of SSO. However, in a complex time-varying noise environment, SSO components are easily disturbed by noise, leading to a decrease in recognition accuracy. To improve the recognition accuracy of SSO feature parameters in a complex time-varying noise environment, the above time-frequency extraction techniques usually need to combine Prony [8] or Stochastic Subspace Identification (SSI) [9] methods to accurately identify the oscillation mode feature parameters. Prony method extracts oscillation feature parameters, but Prony algorithm has limited ability to identify the actual order of the system when dealing with noisy nonlinear and non-stationary signals, and the fitting effect on non-stationary signals is not good, reducing the processing accuracy of the algorithm. Using Stochastic Subspace Identification (SSI) to obtain subsynchronous oscillation parameters, since the input signal of the identification system is unknown, it leads to mode aliasing and false mode problems, reducing the accuracy and reliability of the identification results.

[0006] References

[0007] [1] Yi Lidong, Ma Ningning, Ding Maosheng, et al. New power system wideband oscillation prevention and control architecture [J]. Power System Technology, 2023, 47(10): 4092-4102.

[0008] [2] Maning, Xie Xiaorong, He Jingbo, et al. Review of Wideband Oscillation in Power Systems With High Proportion of New Energy and Power Electronic Equipment[J]. Proceedings of the CSEE, 2020, 40(15): 4720-4732.

[0009] [3] Wu Xi, Chen Xi, Lv Wan, et al. Review of Subsynchronous Oscillation Detection and Online Location Technology in Power System[J]. Electric Power Automation Equipment, 2020, 40(9): 129-141.

[0010] [4] Liu Hao, Li Jie, Bi Tianyu, et al. Sub- / ultra-synchronous inter-harmonic identification method based on PMU phasor[J]. Power System Technology, 2017, 41(10): 3237-3243.

[0011] [5] Jiang Tao, Gao Kun, Li Xue, et al. Dissipation energy spectrum method for forced oscillation source location in power system[J]. Proceedings of the CSEE, 2023, 43(08): 2940-2953.

[0012] [6] Liu Chunxiao, Jiang Tao, Li Xue, et al. Comprehensive assessment of power system dynamic stability based on continuous wavelet transform[J]. Electric Power Automation Equipment, 2021, 41(02): 144-152.

[0013] [7] Jiang Li, Shang Wenqing, Jiao Yudong, et al. An adaptive generalized s-transform algorithm for seismic signal analysis[J]. IEEE Access, 2022, 10: 127863-127870.

[0014] [8] Peng Liping, Wei Hui, Liu Guiping, et al. Identification of Subsynchronous Resonance Mode Parameters Caused by AC Series Compensation by Improved Prony Algorithm[J]. Proceedings of the CSEE, 2015, 27(S1): 63-67.

[0015] [9] Li Yugang, Ye Qingwei, Zhou Yu, et al. Improvement of stochastic subspace identification algorithm based on modal parameter extraction[J]. China Mechanical Engineering, 2017, 28(01): 69-74. SUMMARY

[0016] The application provides an SMSST-CDSSI subsynchronous oscillation mode identification method and device, and realizes mode identification of a nonlinear time-varying subsynchronous oscillation signal of a wind power grid-connected system based on actual measurement data of a phasor measurement unit (PMU), effectively compensates for insufficient identification accuracy of a stochastic subspace identification (SSI) method, and focuses on improving the noise resistance of the SSI method, has strong robustness, and details are described below.

[0017] An SMSST-CDSSI subsynchronous oscillation mode identification method, the method comprises:

[0018] A ridge line extraction method is adopted, time-frequency coefficients representing each subsynchronous oscillation component are selected according to the size of the energy of the time-frequency coefficients in each second-order multiple synchronous compression transformation coefficient matrix, and an optimal instantaneous frequency trajectory is obtained; and time-domain reconstruction of each oscillation mode is performed according to the optimal instantaneous frequency trajectory;

[0019] According to input and output signals of the power system, a state space model of an n-order system is constructed by considering the deterministic and random noise, and a corresponding Hankel matrix is constructed according to the input and output variables; the input / output Hankel matrix is subjected to LQ decomposition to calculate a oblique projection matrix O i ;

[0020] The oblique projection matrix O i is subjected to singular value decomposition to calculate a state matrix A of the discrete system, the state matrix A is subjected to eigenvalue decomposition to obtain discrete eigenvalues, and the frequency f i and the modal damping ratio ξ i of the subsynchronous oscillation are obtained according to the relationship between the discrete and continuous time systems.

[0021] The acquisition of each second-order multiple synchronous compression transformation coefficient matrix is as follows:

[0022] The voltage measurement information matrix U is subjected to SMSST decomposition by means of SMSST to obtain a second-order multiple synchronous compression transformation coefficient matrix

[0023] The voltage measurement signal u(t) is extracted to construct a discrete form of the voltage measurement information matrix U:

[0024] U=[u(t1)u(t2)…u(t n )]

[0025] The voltage measurement information matrix U is subjected to transformation by means of SMSST:

[0026]

[0027] In the formula, U is an SMSST coefficient of u(t) at the n-th time coefficient and the m-th frequency coefficient.

[0028] wherein the frequency f of the subsynchronous oscillation is obtained according to the relationship between the discrete and continuous time systems i and the modal damping ratio ξ i are:

[0029] By performing eigenvalue decomposition on the identified system state matrix A, the discrete eigenvalues λ i , i = 1, 2, …, n, the time interval is dt, the frequency f of the subsynchronous oscillation is obtained according to the relationship between the discrete and continuous time systems i and the modal damping ratio ξ i :

[0030]

[0031] The method further comprises:

[0032] The frequency f of the subsynchronous oscillation i and the modal damping ratio ξ i are monitored in real time by the PMU device, and an online modal analysis system is constructed;

[0033] The power system sets the bandwidth of the damping controller according to the frequency f i , and judges whether to trigger a protection action according to the modal damping ratio ξ i , so as to realize adaptive control of the subsynchronous oscillation.

[0034] The second aspect is an SMSST-CDSSI subsynchronous oscillation mode identification device, which comprises a processor and a memory, the memory stores program instructions, and the processor invokes the program instructions stored in the memory to make the device execute the method of any one of the first aspect.

[0035] The third aspect is a computer readable storage medium, which stores a computer program, the computer program comprises program instructions, and the program instructions are executed by a processor to make the processor execute the method of any one of the first aspect.

[0036] The technical scheme provided by the application has the beneficial effects that:

[0037] 1. The second-order multiple synchronous compression transform (SMSST) time-frequency extraction technology can concentrate the time-frequency energy dispersed in each frequency band at the center frequency, efficiently improve the time-frequency energy concentration ability, obtain more concentrated time-frequency expression, and further facilitate accurate extraction and reconstruction of the SSO mode.

[0038] 2. The combined deterministic subspace identification (CDSSI) feature parameter identification method provided by the application is based on SSI, utilizes all input and output information to identify modal parameters, prevents expansion of the system eigenvalue solution space, can solve problems such as modal aliasing and false modal, meanwhile retains advantages such as small SSI calculation amount and stable pole, improves identification precision of the state space expression and reliability of the equivalent model, can dynamically track multiple time-varying SSO modes;

[0039] 3. The application can provide more perfect and sufficient theoretical identification system for power system operation dispatch personnel, and provide technical support and data support for dynamic stability monitoring and control of the power system. BRIEF DESCRIPTION OF DRAWINGS

[0040] Figure 1 It is a flow chart of a wind power grid-connected system subsynchronous oscillation mode identification method;

[0041] Figure 2 It is a local topology schematic diagram of a wind power plant in GY area;

[0042] Figure 3 It is a schematic diagram of LHT wind power measured recording waveforms;

[0043] Figure 4 It is a measured signal time-frequency diagram;

[0044] Figure 5 It is a smooth SSO signal waveform diagram;

[0045] Figure 6 It is a multi-modal smooth signal SMSST time-frequency diagram;

[0046] Figure 7 It is a time-frequency analysis comparison diagram of different methods. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical scheme and advantages of the application more clear, the embodiments of the application are further described in detail below.

[0048] In order to solve the defects and deficiencies of the method based on wide-area measurement information analysis in the background art, the embodiments of the application take improving identification precision and enhancing algorithm robustness as the target, introduce the SMSST method to transform the measurement information to obtain the time-frequency expression of the subsynchronous oscillation signal, that is, first, the measurement information is preprocessed through SMSST; then, accurate instantaneous frequency estimation results are obtained through a ridge line extraction method, and extraction and reconstruction of the subsynchronous oscillation mode are realized; finally, the CDSSI method is used to accurately and quickly identify the feature parameters of the SSO mode, a more complete oscillation identification framework is constructed for the power grid, and more abundant and perfect theoretical analysis basis is provided to improve the dynamic stability of the system.

[0049] Embodiment 1

[0050] An SMSST-CDSSI-based subsynchronous oscillation mode identification method, see Figure 1 , the method comprising the following steps:

[0051] 101: Extracting the voltage measurement signal u(t) to construct a discrete form of the voltage measurement information matrix U, and performing SMSST decomposition on the voltage measurement information matrix U by means of SMSST to obtain a second-order multiple synchronous compression transformation coefficient matrix

[0052] Wherein, the voltage measurement signal u(t) is the voltage measurement data collected by the PMU.

[0053] 102: Using the ridge line extraction method, according to the size of the time-frequency coefficient energy in each second-order multiple synchronous compression transformation coefficient matrix, the time-frequency coefficients that can accurately represent each subsynchronous oscillation component are selected, and the best instantaneous frequency trajectory is obtained, which can accurately obtain the energy characteristics of SSO and significantly improve the accuracy and efficiency of time-frequency analysis; according to the best instantaneous frequency trajectory, the time-domain reconstruction of each oscillation mode is carried out, which further lays the foundation for the subsequent time-domain feature parameter identification;

[0054] 103: According to the input and output signals of the power system, considering the deterministic and random noise, the state space model of the n-order system is constructed, and the corresponding Hankle matrix is constructed according to the input and output variables;

[0055] 104: LQ decomposition is performed on the input / output Hankel matrix to calculate the oblique projection matrix O i ;

[0056] 105: Singular value decomposition is performed on the oblique projection matrix O i to calculate the state matrix A of the discrete system, and further eigenvalue decomposition is performed on the state matrix A to obtain its discrete eigenvalue λ i (i=1,2,…,n), and according to the relationship between the discrete and continuous time systems, the frequency f i and the modal damping ratio ξ i of the subsynchronous oscillation can be obtained.

[0057] 106: The frequency f i and the modal damping ratio ξ i of the subsynchronous oscillation are monitored in real time by the PMU device to construct an online modal analysis system, and the power system sets the bandwidth of the damping controller according to the frequency f i , and judges whether to trigger the protection action according to the modal damping ratio ξ i , effectively realizing the adaptive control of the subsynchronous oscillation.

[0058] In summary, the embodiment of the application improves the SSO oscillation characteristic parameter identification accuracy and the algorithm noise resistance by combining SMSST and CDSSI through the above steps 101-106, realizes the fast and accurate identification and adaptive control of the subsynchronous oscillation mode of the wind power grid-connected system, and overcomes the defects of the traditional method that can only detect the existence of the subsynchronous oscillation, but cannot quantify the frequency and damping ratio parameter characteristics of the subsynchronous oscillation, and thus leads to the blindness of the subsynchronous oscillation control measures.

[0059] Embodiment 2

[0060] The scheme in embodiment 1 is further introduced in combination with specific calculation formulas and examples, and details are described below:

[0061] 201: Real-time extraction of voltage measurement signal u(t) is used to construct a discrete form of voltage measurement information matrix U, and SMSST decomposition is performed on the voltage measurement information matrix U by means of SMSST to obtain a second-order multiple synchronous compression transformation coefficient matrix

[0062] The voltage measurement signal u(t) is extracted to construct a discrete form of voltage measurement information matrix U:

[0063] U = [u(t1) u(t2) … u(t n )] (1)

[0064] The voltage measurement information matrix U is transformed by means of SMSST:

[0065]

[0066] In the formula, is the SMSST coefficient of u(t) at the nth time coefficient and the mth frequency coefficient.

[0067] 202: The ridge line extraction method is used to obtain the best instantaneous frequency estimation position of each mode, and the SMSST method is further used to realize efficient extraction and time domain reconstruction of the SSO mode;

[0068] In order to accurately obtain the instantaneous frequency estimation result, the ridge line extraction method is cited:

[0069]

[0070] In the formula, Ω(t) represents the extracted ridge line of each mode, J is a set of frequencies, and L is the cutoff time of signal search.

[0071] Since the SMSST method only performs synchronous compression in the frequency scale, the method has signal reconstruction capability, and its expression is as follows:

[0072]

[0073] where ds is the reconstruction frequency bandwidth, and Ω(t) is the optimal instantaneous frequency estimate trajectory position.

[0074] 203: Construct the state space model of the n-order system, and construct the corresponding Hankle matrix according to the input and output variables; wherein the state space model of the n-order system can be described as:

[0075]

[0076] where the system matrix A ∈ R n×n ; the output matrix C ∈ R m×n ; the state vector x k ∈ R n ; the output vector y k ∈ R m ; w k , v k are process noise and measurement noise with zero expectation and mutual independence.

[0077] Construct the corresponding Hankle matrix according to the input and output variables:

[0078]

[0079] where i is the block row number; the input and output Hankle matrix is divided into past and future input and output matrixes U p , U f , Y p and Y f .

[0080] 204: Perform LQ decomposition on the input / output Hankle matrix to calculate the oblique projection matrix O i ;

[0081] The LQ decomposition of the input / output Hankle matrix can be expressed as:

[0082]

[0083] where the combined subspace W P = [U p Y p ] T , L ij is a lower triangular matrix, and Q ij is an orthogonal matrix; formula (9) is to calculate the future output Y f on W p along the oblique projection of the past output U f ; Λ i is an extended observable matrix; X i is the Kalman filter state sequence. L32 the column space of Λ i . Therefore, it is enough to obtain the system characteristics from the LQ decomposition of the whole subspace. At this time, the oblique projection matrix O 32 is just L i . 32 .

[0084] 205: singular value decomposition of the oblique projection matrix is performed to calculate the state matrix A of the discrete system;

[0085] The singular value decomposition of the oblique projection matrix is as follows:

[0086]

[0087] The extended observability matrix is expressed as formula (11):

[0088]

[0089] The state matrix A of the discrete system is obtained by solving formula (10) and (11) simultaneously:

[0090]

[0091] In the formula, Λ1 is the first l(i-1) rows of Λ i ; Λ2 is the last l(i-1) rows of Λ i ; and represents the pseudo-inverse.

[0092] 206: according to the discrete eigenvalue λ i of the state matrix A, the frequency f i and the modal damping ratio ξ i of the subsynchronous oscillation are solved;

[0093] The discrete eigenvalue λ i (i = 1, 2, …, n) is solved by performing eigenvalue decomposition on the system state matrix A identified in the above formula, and the time interval is dt. According to the relationship between the discrete and continuous time systems, the frequency f i and the modal damping ratio ξ i of the subsynchronous oscillation are solved:

[0094]

[0095] 207: the frequency f i and the modal damping ratio ξ i of the subsynchronous oscillation are monitored in real time by the PMU device, an online modal analysis system is constructed, the center frequency of the filter is adjusted according to the deviation of f i , and the system operation mode change is adapted; and when ξ iWhen approaching the critical value (here set as 2%), a pre-warning is triggered and the control parameters are automatically adjusted to realize the adaptive control of the subsynchronous oscillation.

[0096] In summary, the embodiment of the application realizes the fast and accurate identification and adaptive control of the subsynchronous oscillation mode based on SMSST-CDSSI through the steps 201-207; on the one hand, the identification accuracy of the traditional signal processing technology can be improved, and on the other hand, the noise resistance of the CDSSI identification algorithm is enhanced, which has wide engineering application value.

[0097] Embodiment 3

[0098] In the following, in combination with a specific example and a synthesized SSO signal, the simulation analysis and verification are carried out on the method for identifying the subsynchronous oscillation mode of the wind power grid-connected system based on the SMSST-CDSSI, and the details are described as follows:

[0099] In this example, the simulation analysis and verification are carried out on the fault recording data of a SSO fault occurred in a certain wind farm in Jibei, and the local topological graph of the wind farm is as shown in Figure 2 Due to the finiteness of the field data, the PMU fault recording data of the LHT wind farm is selected in the embodiment 3 for in-depth analysis, and the A-phase voltage waveform at the PCC point is as shown in Figure 3 The PMU data sampling frequency is 50Hz, and the duration is 400s.

[0100] Firstly, as shown in Figure 3 , the SSO phenomenon occurs in the measured data near 8s, so the voltage measurement data of 8s after the oscillation are selected to construct the measurement information matrix in the form of formula (1), and then the SMSST transformation is carried out on the measurement information matrix U according to the method to obtain the SMSST coefficient matrix in the form of formula (2). Secondly, the ridge line extraction method is used to select the time-frequency coefficients representing the SSO mode to obtain the optimal instantaneous frequency trajectory matrix in the form of formula (3), and then the reconstruction of the subsynchronous oscillation mode is realized by formula (4); then, the input and output Hankel matrices in the form of formula (6) and (7) are constructed according to the input and output variables, and on this basis, the extended observability matrix is solved by formula (10) and (11), and the state matrix A is further solved; finally, the modal frequency f i and the damping ratio ξ i of each subsynchronous oscillation mode are calculated according to formula (13).

[0101] The SMSST coefficient matrix is calculated according to formula (2) The time-frequency distribution diagram is as shown in Figure 4 Figure 4 ​It can be seen that the proposed method accurately concentrates the instantaneous frequency energy near the frequencies of 6.7 Hz and 13.4 Hz, and effectively identifies the two SSO modes existing in the system. This result is consistent with the actual accident recording results, verifying the effectiveness of the proposed method.

[0102] The SSO mode is extracted according to the SMSST-CDSSI algorithm, and the characteristic parameters of each oscillation mode are identified. The identification results are shown in Table 1.

[0103] Table 1 Measured signal parameter identification results

[0104]

[0105] Comparing the parameter identification results of the measured signals in Table 1, it can be seen that the proposed SMSST-CDSSI method can accurately identify the characteristic parameters of two groups of subsynchronous oscillation modes. This result is consistent with the actual accident recording results, which effectively verifies the feasibility of the proposed method in analyzing the actual SSO of the power grid.

[0106] In order to verify the accuracy of the proposed method, this paper constructs a set of multimodal stationary signals containing random noise, whose signal model can be expressed as:

[0107]

[0108] Where: σ(t) is Gaussian white noise with a standard deviation of 0.02 and a signal-to-noise ratio of 45dB. The simulation signal contains three groups of oscillation modes, with a sampling frequency of 100Hz, a sampling time of 4s, and 400 sampling points. The detailed parameters are shown in Table 2, and the waveform is shown in Figure 5 shown.

[0109] Table 2 Specific parameters of multimodal stationary signals

[0110]

[0111] First, the measurement information matrix of the form of formula (2) is constructed for the signal shown in formula (14). Then, according to the proposed method, the measurement information matrix U is transformed by SMSST to obtain the SMSST coefficient matrix of the form of formula (3): Its time-frequency distribution diagram is as follows Figure 6 As shown by Figure 6 It can be seen that the proposed method accurately concentrates the instantaneous frequency energy near the frequencies of 16 Hz, 34 Hz, and 40 Hz, and effectively identifies the three SSO modes existing in the system. This result is consistent with the simulation signal setting, verifying the effectiveness of the proposed method.

[0112] Further, the ridge line extraction method is adopted to screen the time-frequency coefficients representing the SSO mode, to obtain an optimal instantaneous frequency trajectory matrix in the form of formula (3), and then to realize the reconstruction of the SSO mode by formula (4). Finally, the input and output Hankel matrices in the form of formula (6) and (7) are respectively constructed according to the input and output variables. On this basis, the extended observability matrix is solved by formula (10) and (11), and the state matrix A is further solved. According to formula (13), the modal frequency f i and the damping ratio ξ i of each SSO mode are calculated.

[0113] Table 3 Parameter identification results based on SMSST-CDSSI

[0114]

[0115] It can be seen from Table 3 that the method proposed in the embodiment of the application has high precision in the identification of the oscillation frequency, and the identification error is controlled within 0.001%. Further analysis of the identification results of the damping ratio shows that the identification errors of the three modes can be kept below 0.7%, indicating that the proposed method can accurately identify the SSO parameters and meet the engineering error requirements.

[0116] To further verify the superiority of the proposed method compared with the existing SSO identification methods, the proposed method is compared with STFT, SSST and MSST respectively, and the time-frequency analysis results are shown in Figure 7 .

[0117] It can be seen from Figure 7 (a) that the time-frequency energy of STFT shows a dispersion phenomenon, there is serious modal aliasing, and it drifts in a large range, so it is difficult to accurately identify the frequency characteristics of the signal. In comparison, the SSST shown in Figure 7 (b) has improved time-frequency concentration due to the use of second-order instantaneous frequency estimation, but the energy still shows a certain degree of divergence in a small range, which brings challenges to the extraction and identification of the SSO mode. Figure 7 The MSST shown in Figure 7 (c) compresses the frequency accurately to the vicinity of the true frequency from the perspective of multiple channels, but there is still energy distribution ambiguity at the end points of the time-frequency plane, which affects the identification of the time-frequency characteristics of the signal. The method proposed in the embodiment of the application shown in (d) significantly improves the smoothness of the time-frequency curve, overcomes the common time-frequency ambiguity phenomenon in STFT, effectively eliminates the time-frequency coefficients of divergent energy, enhances the ability to distinguish similar SSO frequencies, significantly improves the problem of spectral energy leakage, and accurately concentrates the frequency at 16Hz, 34Hz and 40Hz.

[0118] Further, Figure 5Gaussian white noise with signal-to-noise ratio of 60dB, 45dB and 30dB respectively is injected into the self-synthesized signal to verify the robustness of the proposed method, and the results are shown in Table 4. As shown in Table 4, as the signal-to-noise ratio gradually decreases, the identification error of frequency and damping ratio shows an upward trend, but the identification error always remains within the acceptable range of engineering application, verifying that the proposed method has good anti-noise performance.

[0119] Table 4 Parameter identification results under different noise environments

[0120]

[0121] Further, the parameter identification results of the SSO feature parameters of the method proposed in the embodiment of the application are compared with those of the traditional SSI algorithm and Prony method respectively, and the results are shown in Table 5.

[0122] Table 5 Parameter identification results of different methods

[0123]

[0124]

[0125] As shown in Table 5, by comparing the parameter identification results of the self-synthesized signal by the three methods, it can be seen that the proposed SMSST-CDSSI method has significant advantages in the identification accuracy of the key parameters of frequency and damping ratio. The SMSST-CDSSI method can not only accurately capture and extract the frequency of each oscillation mode in a complex and variable signal environment, but also has smaller error in the identification results compared with the other two methods. At the same time, in the identification of damping ratio parameters, the SMSST-CDSSI method is more accurate, further verifying its superiority in complex SSO signal analysis.

[0126] Embodiment 4

[0127] An SMSST-CDSSI-based subsynchronous oscillation mode identification device, the device comprising: a processor and a memory, the memory storing program instructions, and the processor invoking the program instructions stored in the memory to enable the device to perform the following method steps in Embodiment 1:

[0128] The ridge line extraction method is adopted to screen out time-frequency coefficients representing each subsynchronous oscillation component according to the size of the time-frequency coefficient energy in each second-order multiple synchronous compression transformation coefficient matrix, and to obtain the best instantaneous frequency trajectory; and the time-domain reconstruction of each oscillation mode is performed according to the best instantaneous frequency trajectory;

[0129] According to the input and output signals of the power system, the state space model of the n-order system is constructed by considering both deterministic and random noises, and the corresponding Hankel matrix is ​​constructed according to the input and output variables; the input / output Hankel matrix is ​​decomposed by LQ to calculate the oblique projection matrix O i ;

[0130] Oblique projection matrix O i Perform singular value decomposition, calculate the state matrix A of the discrete system, perform eigenvalue decomposition on the state matrix A, find its discrete eigenvalue, and find the frequency f of the subsynchronous oscillation based on the relationship between discrete and continuous time systems i and modal damping ratio ξ i .

[0131] Among them, the acquisition of each second-order multiple synchronous compression transformation coefficient matrix is:

[0132] The voltage measurement information matrix U is decomposed by SMSST to obtain the second-order multiple synchronous compression transformation coefficient matrix

[0133] Extract the voltage measurement signal u(t) to construct the discrete voltage measurement information matrix U:

[0134] U=[u(t1)u(t2)…u(t n )]

[0135] The voltage measurement information matrix U is transformed with the help of SMSST:

[0136]

[0137] Where, is the SMSST coefficient of u(t) at the nth time coefficient and the mth frequency coefficient.

[0138] Among them, the frequency f of the subsynchronous oscillation is obtained based on the relationship between discrete and continuous time systems i and modal damping ratio ξ i for:

[0139] By performing eigenvalue decomposition on the identified system state matrix A, its discrete eigenvalue λ is obtained. i , i = 1, 2, ..., n, the time interval is dt, according to the relationship between discrete and continuous time systems, the frequency f of the subsynchronous oscillation is obtained i and modal damping ratio ξ i :

[0140]

[0141] The device further comprises:

[0142] The frequency f of the subsynchronous oscillation is monitored in real time by a PMU device i and the modal damping ratio ξ i , and an online modal analysis system is constructed.

[0143] The power system sets the bandwidth of the damping controller according to the frequency f i and judges whether to trigger a protection action according to the modal damping ratio ξ i , thereby realizing adaptive control of the subsynchronous oscillation.

[0144] It should be noted that the device description in the above embodiments corresponds to the method description in the embodiments, and the embodiments of the present application will not be described here.

[0145] The execution subject of the processor and the memory described above can be a computer, a single-chip microcomputer, a microcontroller, or other devices with computing functions, and the embodiments of the present application do not limit the execution subject in specific implementation, and the execution subject is selected according to the actual application.

[0146] The memory and the processor transmit data signals through a bus, and the embodiments of the present application do not repeat the description.

[0147] Based on the same inventive concept, the embodiments of the present application also provide a computer-readable storage medium, which includes a stored program, and the program controls the device where the storage medium is located to execute the method steps in the above embodiments when the program is running.

[0148] The computer-readable storage medium includes but is not limited to a flash memory, a hard disk, a solid state disk, etc.

[0149] It should be noted that the readable storage medium description in the above embodiments corresponds to the method description in the embodiments, and the embodiments of the present application will not be described here.

[0150] In the above embodiments, all or part of the embodiments can be realized by software, hardware, firmware, or any combination thereof. When realized by software, all or part of the embodiments can be realized in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions according to the embodiments of the present application are generated.

[0151] The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted through a computer-readable storage medium. The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server, data center, etc. integrated with one or more available media. The available medium can be a magnetic medium or a semiconductor medium, etc.

[0152] Those skilled in the art can understand that the drawings are only schematic illustrations of a preferred embodiment, and the above-mentioned embodiment numbers of the present application are only for description, not representing the advantages and disadvantages of the embodiments.

[0153] The above description is only the preferred embodiment of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for identifying SMSST-CDSSI subsynchronous oscillation patterns, characterized in that: The method comprises: Using the ridge extraction method, the time-frequency coefficients representing each synchronous oscillation component are screened out according to the energy of the time-frequency coefficients in each second-order multiple synchronous compression transform coefficient matrix to obtain the optimal instantaneous frequency trajectory. The time domain reconstruction of each oscillation mode is performed based on the optimal instantaneous frequency trajectory. According to the input and output signals of the power system, the state space model of the n-order system is constructed by considering both deterministic and random noises, and the corresponding Hankel matrix is ​​constructed according to the input and output variables; the input / output Hankel matrix is ​​decomposed by LQ to calculate the oblique projection matrix O i ; Oblique projection matrix O i Perform singular value decomposition, calculate the state matrix A of the discrete system, perform eigenvalue decomposition on the state matrix A, find its discrete eigenvalue, and find the frequency f of the subsynchronous oscillation based on the relationship between discrete and continuous time systems i and modal damping ratio ξ i .

2. The SMSST-CDSSI subsynchronous oscillation pattern identification method according to claim 1, characterized in that: The acquisition of each second-order multiple synchronous compression transformation coefficient matrix is: The voltage measurement information matrix U is decomposed by SMSST to obtain the second-order multiple synchronous compression transformation coefficient matrix Extract the voltage measurement signal u(t) to construct the discrete voltage measurement information matrix U: U=[u(t1)u(t2)…u(t n )] The voltage measurement information matrix U is transformed with the help of SMSST: Where, is the SMSST coefficient of u(t) at the nth time coefficient and the mth frequency coefficient.

3. The SMSST-CDSSI subsynchronous oscillation pattern identification method according to claim 1, characterized in that: The frequency f of the subsynchronous oscillation is obtained based on the relationship between discrete and continuous time systems. i and modal damping ratio ξ i for: By performing eigenvalue decomposition on the identified system state matrix A, its discrete eigenvalue λ is obtained. i , i = 1, 2, ..., n, the time interval is dt, according to the relationship between discrete and continuous time systems, the frequency f of the subsynchronous oscillation is obtained i and modal damping ratio ξ i :

4. The SMSST-CDSSI subsynchronous oscillation pattern identification method according to claim 1, characterized in that: The method further comprises: The frequency f of the subsynchronous oscillation is monitored in real time by the PMU device i and modal damping ratio ξ i , build an online modal analysis system; The power system is based on the frequency f i Set the bandwidth of the damping controller and adjust the modal damping ratio ξ i Determine whether to trigger protection action and implement adaptive control of subsynchronous oscillation.

5. A device for identifying SMSST-CDSSI subsynchronous oscillation patterns, characterized in that: The device includes: a processor and a memory, wherein program instructions are stored in the memory, and the processor calls the program instructions stored in the memory to enable the device to execute the method according to any one of claims 1 to 4.

6. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions. When the program instructions are executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 4.

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