A method and device for detecting traction network pressure

Abstract

The present invention discloses a method for detecting traction network voltage, the method comprising: collecting primary voltage data and secondary current data of a vehicle-mounted transformer; processing the primary voltage data and the secondary current data to obtain voltage effective value data and power factor data of the vehicle-mounted transformer; based on the voltage effective value data and the power factor data, obtaining a first oscillation frequency of the voltage effective value data and a second oscillation frequency of the power factor data; when the first oscillation frequency and the second oscillation frequency are both within a preset frequency range, determining that the traction network is in a low-frequency oscillation state. The present invention also discloses a device for detecting traction network voltage.

Description

Technical Field

[0001] The invention relates to a traction network pressure detection technology, and in particular to a traction network pressure detection method and device. Background Art

[0002] With the new high-power high-speed trains put into operation, some complex system compatibility issues have begun to emerge when the high-speed trains and traction power supply systems interact dynamically. Among them, low-frequency oscillation is one of the important issues that has attracted much attention in the stable operation of the traction power supply system. When multiple trains are running at the same time with light loads, the voltage and current of the substation fluctuate greatly, and the phases are sometimes in phase, sometimes staggered, and sometimes completely opposite. In severe cases, it may cause train traction blockage, equipment burning and other faults.

[0003] However, at present, it is impossible to effectively identify the low-frequency oscillation of the traction grid voltage when the traction power supply system is running. The following protection measures can only be adopted after the low-frequency fluctuation of the traction grid voltage occurs:

[0004] 1) Improve the power supply environment and reduce the impedance of the traction network, for example, increase the capacity of the traction substation transformer, etc. However, this method often requires large investment, long construction period and poor flexibility;

[0005] 2) Modify the control parameters of the grid-side rectifier of the electric locomotive. However, this method can only temporarily solve the problem and will still cause low-frequency oscillation again in the future.

[0006] Therefore, it is necessary to study an effective detection method for the low-frequency oscillation phenomenon of traction grid voltage when the traction power supply system is operating normally. Summary of the invention

[0007] In view of this, an embodiment of the present invention hopes to provide a method and device for detecting traction grid voltage, which can effectively detect the low-frequency oscillation phenomenon of the traction grid voltage when the traction power supply system is operating normally, so as to suppress the low-frequency oscillation in time when the traction grid voltage has low-frequency oscillation phenomenon, so as to ensure the normal and stable operation of the traction power supply system.

[0008] The technical solution of the embodiment of the present invention is achieved as follows:

[0009] According to one aspect of an embodiment of the present invention, a method for detecting traction grid voltage is provided, which is applied to an electric locomotive, and the method comprises:

[0010] Collect the primary voltage data and secondary current data of the on-board transformer;

[0011] Processing the primary voltage data and the secondary current data to obtain voltage effective value data and power factor data of the on-board transformer;

[0012] Based on the voltage effective value data and the power factor data, obtaining a first oscillation frequency of the voltage effective value data and a second oscillation frequency of the power factor data;

[0013] When the first oscillation frequency and the second oscillation frequency are both within a preset frequency range, it is determined that the traction network is in a low-frequency oscillation state.

[0014] In the above solution, after determining that the traction network is in a low-frequency oscillation state, the method further includes:

[0015] Obtaining the duration of the traction network being in a low-frequency oscillation state;

[0016] Based on the duration of the traction network being in low-frequency oscillation, the current loop proportional coefficient of the grid-side converter is increased.

[0017] In the above solution, when the first oscillation frequency and / or the second oscillation frequency is outside a preset frequency range, it is determined that the traction network is not in a low-frequency oscillation state.

[0018] In the above solution, after determining that the traction network is not in a low-frequency oscillation state, the method further includes:

[0019] Adjust the current loop proportional coefficient of the grid-side converter to the initial value.

[0020] In the above scheme, the processing of the primary voltage data and the secondary current data to obtain the voltage effective value data and power factor data of the on-board transformer includes:

[0021] Performing root mean square calculation on the primary voltage data to obtain voltage effective value data of the primary voltage data;

[0022] A sine reference signal and a cosine reference signal are obtained by using a local clock; the sine reference signal, the cosine reference signal, the primary voltage data and the secondary current data are calculated to obtain power factor data of the on-board transformer.

[0023] In the above scheme, the obtaining of the first oscillation frequency of the voltage effective value data and the second oscillation frequency of the power factor data based on the voltage effective value data and the power factor data includes:

[0024] Constructing a first Hankel matrix based on the voltage effective value data; performing singular value decomposition on the first Hankel matrix to obtain an orthogonal matrix of a first order in the first Hankel matrix, wherein the orthogonal matrix corresponds to a subspace of the voltage effective value data; performing singular value decomposition on the orthogonal matrix to obtain a reversible matrix; calculating the reversible matrix by total least squares method to obtain eigenvalues ​​of the reversible matrix; calculating the eigenvalues ​​of the reversible matrix to obtain a first oscillation frequency of the voltage effective value;

[0025] A second Hankel matrix is ​​constructed based on the power factor data; a singular value decomposition is performed on the second Hankel matrix to obtain an orthogonal matrix of the first order in the second Hankel matrix, wherein the orthogonal matrix corresponds to a subspace of the power factor; a singular value decomposition is performed on the orthogonal matrix to obtain a reversible matrix; the reversible matrix is ​​calculated by the total least squares method to obtain the eigenvalues ​​of the reversible matrix; the eigenvalues ​​of the reversible matrix are calculated to obtain a second oscillation frequency of the power factor.

[0026] In the above solution, before processing the primary voltage data and the secondary current data, the method further includes:

[0027] The primary voltage data and the secondary current data are respectively subjected to low-pass filtering with a cutoff frequency of 100 Hz to obtain low-frequency data of the primary voltage data and low-frequency data of the secondary current data.

[0028] The processing of the primary voltage data and the secondary current data includes:

[0029] The low-frequency data of the primary voltage data and the low-frequency data of the secondary current data are processed.

[0030] According to a second aspect of an embodiment of the present invention, a device for detecting traction network pressure is provided, the detection device comprising:

[0031] Voltage transformer, used to collect primary voltage data of the on-board transformer;

[0032] A current transformer, used to collect secondary current data of the on-board transformer;

[0033] A processor is used to process the primary voltage data and the secondary current data to obtain the voltage effective value data and the power factor data of the on-board transformer; based on the voltage effective value data and the power factor data, a first oscillation frequency of the voltage effective value data and a second oscillation frequency of the power factor data are obtained; when the first oscillation frequency and the second oscillation frequency are both within a preset frequency range, it is determined that the traction network is in a low-frequency oscillation state.

[0034] In the above scheme, the processor is also used to: obtain the duration of the traction network being in a low-frequency oscillation state; and increase the current loop proportional coefficient of the grid-side converter based on the duration of the traction network being in a low-frequency oscillation state.

[0035] In the above solution, the processor is further used to: when the first oscillation frequency and / or the second oscillation frequency are both not within a preset frequency range, determine that the traction network is not in a low-frequency oscillation state.

[0036] In the above solution, the processor is also used to: when the traction network is not in a low-frequency oscillation state, adjust the current loop proportional coefficient of the grid-side converter to an initial value.

[0037] According to a third aspect of an embodiment of the present invention, a device for detecting traction network pressure is provided, the detection device comprising:

[0038] A collection unit, used to collect primary voltage data and secondary current data of the on-board transformer;

[0039] a processing unit, configured to process the primary voltage data and the secondary current data to obtain voltage RMS data and power factor data of the on-board transformer, and to obtain a first oscillation frequency of the voltage RMS data and a second oscillation frequency of the power factor data based on the voltage RMS data and the power factor data;

[0040] A determining unit is used to determine that the traction network is in a low-frequency oscillation state when the first oscillation frequency and the second oscillation frequency are both within a preset frequency range.

[0041] The embodiment of the present invention provides a method and device for detecting traction network voltage, which collects primary voltage data and secondary current data of an on-board transformer; processes the primary voltage data and the secondary current data to obtain voltage effective value data and power factor data of the on-board transformer, and obtains a first oscillation frequency of the voltage effective value data and a second oscillation frequency of the power factor data based on the voltage effective value data and the power factor data; when the first oscillation frequency and the second oscillation frequency are both within a preset frequency range, it is determined that the traction network is in a low-frequency oscillation state. In this way, by fully considering the characteristics of large voltage fluctuations and constant changes in voltage and current phases when low-frequency oscillation occurs in the traction power supply system, the voltage effective value and power factor are given. When the traction power supply system is operating normally, the low-frequency oscillation phenomenon of the traction network voltage can be effectively detected to improve the accuracy of the low-frequency oscillation phenomenon of the traction network voltage, so that when the low-frequency oscillation phenomenon occurs in the traction network voltage, the low-frequency oscillation can be suppressed in time to ensure the safe and stable operation of the traction power supply system. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a schematic diagram of the implementation process of the method for detecting the traction network pressure in this application;

[0043] Figure 2 This is a schematic diagram of the implementation process of the method for detecting and suppressing traction network pressure in this application;

[0044] Figure 3 It is a waveform diagram of the traction grid voltage when the conventional current dq decoupling control is adopted in the prior art;

[0045] Figure 4 A waveform diagram of the traction network pressure when the traction network pressure detection and suppression method in the present application is adopted;

[0046] Figure 5 Schematic diagram of the adaptive adjustment process of the current loop proportional coefficient of the grid-side converter in this application;

[0047] Figure 6 The structure of the traction network pressure detection device in this application is shown in FIG. Figure 1 ;

[0048] Figure 7 The structure of the traction network pressure detection device in this application is shown in FIG. Figure 2 . DETAILED DESCRIPTION

[0049] The specific implementation of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the present invention, and is not used to limit the present invention.

[0050] Figure 1 This is a schematic diagram of the implementation process of the traction network pressure detection method in this application, such as Figure 1 As shown, the method includes:

[0051] Step 101, collecting primary voltage data and secondary current data of the vehicle-mounted transformer;

[0052] In the present application, the detection method of the traction network voltage is mainly applied to electric locomotives. The electric locomotive includes an on-board transformer and an on-board converter. Since the low-frequency fluctuation of the traction network voltage generally occurs when multiple electric locomotives are simultaneously raising their pantographs in the same power supply range, when the electric locomotive is raising its pantographs for maintenance, the electric locomotive can collect the primary voltage data of the on-board transformer through a voltage transformer and collect the secondary current data of the on-board transformer through a current transformer.

[0053] Here, when the voltage transformer and the current transformer collect data, they can specifically collect data according to a sampling period, for example, the collection period can be 50us or 10us, etc.

[0054] In the present application, after the electric locomotive collects the primary voltage data and the secondary current data of the on-board transformer, it can also use a digital signal processor (DSP) or a field programmable gate array (FPGA) processor to perform low-pass filtering with a cutoff frequency of 100 Hz on the primary voltage data and the secondary current data to filter out the high-frequency components contained in the collected signal and retain the effective low-frequency components, thereby obtaining the low-frequency data of the primary voltage data and the low-frequency data of the secondary current data.

[0055] Step 102, processing the primary voltage data and the secondary current data to obtain voltage effective value data and power factor data of the on-board transformer;

[0056] In the present application, after obtaining the low-frequency data of the primary voltage data and the low-frequency data of the secondary current data, the electric locomotive can process the low-frequency data of the primary voltage data and the low-frequency data of the secondary current data to obtain the voltage effective value data and power factor data of the on-board transformer.

[0057] In the present application, when the electric locomotive processes the low-frequency data of the primary voltage data, it can specifically use a sliding window method with a period of 0.02s to perform root mean square calculation on the low-frequency data of the primary voltage data window by window to obtain the effective voltage value of the primary voltage data.

[0058] Here, the voltage effective value of the primary voltage data is also called the root mean square value of the primary voltage data.

[0059] Specifically, the mathematical expression for obtaining the root mean square value of the primary voltage data can be:

[0060]

[0061] Among them, U i is the i-th primary voltage data collected, and N is the number of primary voltage data collected within the sliding window time (0.02s).

[0062] In the present application, when the electric locomotive processes the primary voltage data and the secondary current data, it can also use the local clock on the processor to obtain a sine reference signal and a cosine reference signal; then calculate the sine reference signal, the cosine reference signal, the primary voltage data and the secondary current data to obtain the phase of the primary voltage and the phase of the secondary current.

[0063] Specifically, the phase of the primary voltage is calculated as:

[0064]

[0065] Wherein, sin(2πft) represents the sine reference signal, cos(2πft) represents the cosine reference signal; N is the number of primary voltage data collected within the sliding window time (0.02s), U i is the i-th primary voltage data collected.

[0066] Calculate the phase of the secondary current as:

[0067]

[0068] Wherein, sin(2πft) represents the sine reference signal, cos(2πft) represents the cosine reference signal; N is the number of primary voltage data collected within the sliding window time (0.02s). i is the collected i-th secondary current data.

[0069] Calculate the power factor of the on-board transformer as:

[0070] PF = cos(φU-φI);

[0071] Among them, φU is the phase of the voltage, φI is the phase of the voltage. In an AC circuit, the cosine of the phase difference (Φ) between voltage and current is called the power factor, which is represented by the symbol cosΦ.

[0072] Step 103, based on the voltage effective value data and the power factor data, obtaining a first oscillation frequency of the voltage effective value data and a second oscillation frequency of the power factor data;

[0073] In the present application, after obtaining the voltage effective value data and power factor data of the on-board transformer, the electric locomotive can also identify the voltage effective value data and the power factor data respectively based on the signal parameter estimation algorithm of the total least squares method-rotational invariance technology to obtain the first oscillation frequency corresponding to the voltage effective value data and the second oscillation frequency corresponding to the power factor data.

[0074] Specifically, when the electric locomotive identifies the voltage effective value data and obtains the first oscillation frequency corresponding to the voltage effective value data, it can first construct a first Hankel matrix based on the voltage effective value data; then perform singular value decomposition on the constructed first Hankel matrix to obtain an orthogonal matrix of the first order in the first Hankel matrix, wherein the orthogonal matrix corresponds to a subspace of the voltage effective value data; then perform singular value decomposition on the orthogonal matrix to obtain a reversible matrix; then calculate the reversible matrix by the total least squares method to obtain the eigenvalues ​​of the reversible matrix; finally, calculate the eigenvalues ​​of the reversible matrix to obtain the first oscillation frequency of the voltage effective value data.

[0075] The specific process of calculating the first oscillation frequency corresponding to the voltage effective value data is as follows:

[0076] (1) Let the data sequence be X = [x(0), x(1), …, x(n-1)];

[0077] Wherein, X is the voltage effective value data, and n is the number of the voltage effective value data obtained;

[0078] (2) Construct an r×m-order Hankel matrix H based on the voltage effective value data:

[0079]

[0080] Wherein, r and m are both orders, and satisfy r+m-1=n. Here, the orders of r and m can be determined according to the set fitting model order P, and r>p, m>p.

[0081] (3) Perform singular value decomposition on the Hankel matrix H:

[0082]

[0083] Among them, U is an r-order orthogonal matrix; Σ is an r×m-order diagonal matrix, and the diagonal elements of Σ are obtained by arranging the singular values ​​of the matrix H in descending order; V is an m-order orthogonal matrix, V T is the conjugate transpose of V. U s U n represents an r×r-order matrix, ∑ s and∑ n denotes the diagonal elements of Σ.

[0084] Here, the singular values ​​of the matrix H are recorded as the singular values ​​ξ1, ξ2, …, ξp, ξp+1, …, ξmax(r,m) of the matrix X. When the signal is composed of only p complex sinusoidal signal components, the rank of the Hankel matrix is ​​p, and ξ1>ξ2>…>ξp>ξp+1=…=ξmax(r,m)=0. However, when the signal is contaminated by noise, the constructed Hankel matrix is ​​full rank, and all singular values ​​are no longer equal to zero.

[0085] In order to estimate the signal parameters from the noise-contaminated sampling data, V can be divided into two parts, namely V = [Vs, Vn], where Vs and Vn correspond to the signal subspace and noise subspace of the voltage effective value data respectively, and the column vector of Vs is the eigenvector corresponding to the p singular values ​​with the largest amplitude of the matrix H, s represents the signal, and n represents the noise.

[0086] (4) For the orthogonal matrix V in the matrix H s Perform singular value decomposition:

[0087]

[0088] Where V1 represents the matrix V s The new matrix obtained after deleting row 1. V2 represents the matrix V s The new matrix obtained by deleting the last row.

[0089] By orthogonal matrix V s Performing singular value decomposition, we know that there is a reversible matrix P such that:

[0090] V2=V1P

[0091] In this way, a reversible matrix P satisfying the relationship V2=V1P can be obtained, and its eigenvalue can be used to calculate the frequency of the data signal.

[0092] Specifically, the oscillation signal x(n) can be expressed as a combination of a series of sinusoidal signals with exponentially changing amplitudes and white noise. At the sampling time n, its expression is as follows:

[0093]

[0094] Where: T s is the sampling period; since the sampled signal is usually a real signal, the model order P is usually twice the number of real sinusoidal components actually contained in the signal; a p ,φ p ,ω p ,σ p are the amplitude, initial phase, angular frequency and attenuation coefficient of the pth attenuation component; f p is the frequency of the pth attenuation component, f p =ωp / (2π); w is white noise with mean 0.

[0095] make The signal can be simplified as:

[0096]

[0097] Where: z p called the signal pole.

[0098] Define the vector x(n) = [x(n), x(n+1), …, x(n+M-1)] T , where M>P, then

[0099]

[0100] Where:

[0101] c=[c1 c2 … c P ] T

[0102] Φ=diag(z1 z2 … z P )

[0103]

[0104] Γ M =[τ M (z1) τ M (z2) … τ M (z P )]

[0105] w(n)=[w(n) w(n+1) … w(n+M-1)] T

[0106] It can be found that: p (p=1,2,…,P) determines the frequency and damping coefficient of each component in the signal and completely determines Φ. Therefore, we can try to indirectly find the signal poles through Φ, and then obtain parameters such as the frequency and damping coefficient of each signal component.

[0107] For the convenience of expression, the symbols ↑ and ↓ are used to represent the new matrix obtained by deleting the first and last row of the matrix. ↓ It represents the new matrix obtained by deleting the last row of matrix S. ↑ Represents the new matrix obtained by deleting the first row from the matrix S.

[0108] S ↓ =Γ M-1 Φ n c=J1c

[0109] S ↑ =Γ M-1 Φ n+1 c=J2c

[0110] J2=J1Φ

[0111] Here, Φ is equivalent to the reversible matrix P in the above formula.

[0112] (5) The reversible matrix P is calculated by the total least squares method (TLS) to obtain the eigenvalue λ of the reversible matrix P i , here, the eigenvalue λ i Oscillation frequency used to estimate the effective value of voltage;

[0113] Specifically, let K = [V1, V2], and perform singular value decomposition (SVD) on K:

[0114] K=U3∑V3 T ;

[0115] Among them, U is an r-order orthogonal matrix; Σ is an r×m-order diagonal matrix, and the diagonal elements of Σ are obtained by arranging the singular values ​​of the matrix K in descending order; V is an m-order orthogonal matrix, V T is the conjugate transpose of V. The diagonal elements of Σ i These are the singular values ​​of K.

[0116] Then, according to the set fitting model order p, the matrix V3 in the matrix K is divided into 4 p×p sub-matrices:

[0117]

[0118] Among them, V 11 、V 12 , V2, V 22 Respectively represent the positions of the corresponding four sub-matrices, for example, V 11 Indicates 1 row and 1 column, V 12 Indicates 1 row and 2 columns, V 21 Indicates 2 rows and 1 column, V 21 Represents 2 rows and 2 columns.

[0119] The total least squares solution of the reversible matrix P is obtained by calculating the submatrices of the matrix V3 using the total least squares method:

[0120]

[0121] Then, find the total least squares solution P of the reversible matrix P tls The eigenvalue λ i (i=1,2,…,p), we can get the oscillation frequency of the sinusoidal component contained in the signal:

[0122]

[0123] Among them, T s is the sampling time of the voltage RMS value, λ i P tls The characteristic value of .

[0124] Here, f i That is the first oscillation frequency of the voltage effective value, denoted as f U .

[0125] In the present application, when the power factor data is identified and the second oscillation frequency of the power factor data is obtained, the power factor data can be obtained first, and then a second Hankel matrix is ​​constructed based on the obtained power factor data; then the constructed second Hankel matrix is ​​subjected to singular value decomposition to obtain an orthogonal matrix of the first order in the second Hankel matrix, wherein the orthogonal matrix corresponds to a subspace of the power factor data; then the orthogonal matrix is ​​subjected to singular value decomposition to obtain a reversible matrix; then the reversible matrix is ​​calculated by the total least squares method to obtain the eigenvalues ​​of the reversible matrix; finally, the eigenvalues ​​of the reversible matrix are calculated to obtain the second oscillation frequency of the power factor data.

[0126] The specific process of calculating the second oscillation frequency of the power factor data is as follows:

[0127] (1) Let the data sequence be X = [x(0), x(1), …, x(n-1)];

[0128] Wherein, X is the power factor data, and n is the number of the power factor data obtained;

[0129] (2) Based on the power factor data, an r×m-order Hankel matrix H is constructed:

[0130]

[0131] Wherein, r and m are both orders, and satisfy r+m-1=n. Here, the orders r and m are determined according to the set fitting model order P, and r>p, m>p.

[0132] (3) Perform singular value decomposition on the Hankel matrix H, which is:

[0133]

[0134] Among them, U is an r-order orthogonal matrix; Σ is an r×m-order diagonal matrix, and the diagonal elements of Σ are obtained by arranging the singular values ​​of the matrix H in descending order; V is an m-order orthogonal matrix, V T is the conjugate transpose of V. U s U n represents an r×r-order matrix, ∑ s and∑ n denotes the diagonal elements of Σ.

[0135] The singular values ​​of the matrix H are recorded as the singular values ​​ξ1, ξ2, …, ξp, ξp+1, …, ξmax(r,m) of the matrix X. When the signal is composed of only p complex sinusoidal signal components, the rank of the Hankel matrix is ​​p, and ξ1>ξ2>…>ξp>ξp+1=…=ξmax(r,m)=0. However, when the signal is contaminated by noise, the constructed Hankel matrix is ​​full rank, and all singular values ​​are no longer equal to zero.

[0136] In order to estimate the signal parameters from the noise-contaminated sampled data, V can be divided into two parts, namely V = [Vs, Vn], where Vs and Vn correspond to the signal subspace and noise subspace of the power factor data respectively, and the column vector of Vs is the eigenvector corresponding to the p singular values ​​with the largest amplitude of the matrix H, s represents the signal, and n represents the noise.

[0137] (4) For the orthogonal matrix V s Perform singular value decomposition:

[0138]

[0139] Where V1 represents the matrix V s The new matrix obtained after deleting row 1. V2 represents the matrix V s The new matrix obtained by deleting the last row.

[0140] By V s Perform singular value decomposition and determine the reversible matrix P such that:

[0141] V2=V1P

[0142] Thus, the eigenvalue of the matrix P that satisfies the relationship V2=V1P can be used to calculate the frequency of the data signal.

[0143] (5) The reversible matrix P is calculated by the total least squares method (TLS) to obtain the eigenvalue λ of the reversible matrix P i , the eigenvalue λ i Oscillation frequency used for subsequent estimation of power factor data;

[0144] Specifically, let K = [V1, V2], and perform singular value decomposition (SVD) on the matrix K:

[0145] K=U3∑V3 T ;

[0146] Among them, U is an r-order orthogonal matrix; Σ is an r×m-order diagonal matrix, and the diagonal elements of Σ are obtained by arranging the singular values ​​of the matrix K in descending order; V is an m-order orthogonal matrix, V T is the conjugate transpose of V. The diagonal elements of Σ i These are the singular values ​​of K.

[0147] Then, according to the set fitting model order p, the matrix V3 in the matrix K is divided into 4 p×p sub-matrices:

[0148]

[0149] Among them, V 11 、V 12 , V2, V 22 Respectively represent the positions of the corresponding four sub-matrices, for example, V 11 Indicates 1 row and 1 column, V 12 Indicates 1 row and 2 columns, V 21 Indicates 2 rows and 1 column, V 21 Represents 2 rows and 2 columns.

[0150] The total least squares solution of the reversible matrix P is obtained based on the submatrices of the matrix V3:

[0151]

[0152] Find the total least squares solution P of the reversible matrix P tls The eigenvalue λ i (i=1,2,…,p), we can get the oscillation frequency of the sinusoidal component contained in the signal:

[0153]

[0154] Among them, T s is the sampling time of the power factor, λ i P tls The characteristic value of .

[0155] Here, f i This is the second oscillation frequency of the power factor, denoted as f PF .

[0156] Step 104: When the first oscillation frequency and the second oscillation frequency are both within a preset frequency range, it is determined that the traction network is in a low-frequency oscillation state.

[0157] In this application, the electric locomotive obtains the first oscillation frequency f of the effective value of the voltage U and the second oscillation frequency f of the power factor PF After that, the first oscillation frequency f of the voltage RMS can be determined. Uand the second oscillation frequency f of the power factor PF Are they both within the range of [0.1, 20] Hz? A judgment result is obtained. If the judgment result represents the first oscillation frequency f U and the second oscillation frequency f PF If both are within the range of [0.1, 20] Hz, it means that the current traction network has a low-frequency network voltage fluctuation phenomenon, and the traction network voltage is currently in a low-frequency oscillation state. On the contrary, if the judgment result indicates that the first oscillation frequency f U and / or the second oscillation frequency f PF If it is outside the range of [0.1, 20] Hz, it means that there is no low-frequency network voltage fluctuation in the traction network, which means that the current traction network is not in a low-frequency oscillation state.

[0158] In this way, through the traction grid voltage detection method provided by the present application, the low-frequency oscillation phenomenon in the traction power supply system is detected in real time by utilizing the characteristics of the continuous change in the phase of the primary voltage and secondary current of the on-board transformer, and the low-frequency oscillation phenomenon in the traction grid voltage can be identified in time, thereby more effectively suppressing the low-frequency oscillation phenomenon of the traction grid voltage.

[0159] In the present application, when low-frequency oscillation occurs in the traction network voltage, the electric locomotive can also obtain the duration of the low-frequency oscillation in the traction network; based on the duration of the low-frequency oscillation, the current loop proportional coefficient of the grid-side converter is increased, that is: K pi =α Δt K pi0 .

[0160] The coefficient α is greater than 1, preferably 1.1, Δt is the duration of the detected low-frequency oscillation, and K pi0 is the initial value of the current loop proportional coefficient of the grid-side converter.

[0161] In the present application, after correcting the current loop proportional coefficient of the grid-side converter, steps 101 to 104 may be continued until the low-frequency oscillation phenomenon is eliminated.

[0162] In the present application, the electric locomotive can also adjust the current loop proportional coefficient of the grid-side converter to the initial value, that is, K, when it is determined that the traction network is not in a low-frequency oscillation state. pi =K pi0 .

[0163] In this way, by online adaptively correcting the grid-side rectifier controller parameters of the electric locomotive, only the existing algorithm needs to be appropriately corrected without additional investment, thereby achieving the effect of suppressing low-frequency oscillations without affecting the normal operation of the traction power supply system.

[0164] Figure 2Schematic diagram of the implementation process of the method for detecting and suppressing traction network pressure in this application, such as Figure 2 As shown:

[0165] Step 201 , collect the primary voltage and secondary current data of the vehicle transformer, and perform low-pass filtering with a cutoff frequency of 100 Hz on the collected data to filter out the high-frequency part and retain the low-frequency part.

[0166] Step 202, using a sliding window method to calculate the voltage effective value and the power factor of the vehicle-mounted transformer.

[0167] Here, a sliding window method with a period of 0.02s is used to obtain the root mean square value (RMS) of the voltage data window by window to obtain the voltage effective value data U rms ; Then, the power factor PF of the on-board transformer is calculated using the sine reference signal and cosine reference signal generated by the local clock, the primary voltage data, and the secondary current data.

[0168] Step 203, using a signal parameter estimation algorithm based on the total least squares method-rotational invariance technology to estimate the effective value of the traction network voltage U rms Identify the power factor PF and obtain the oscillation frequency f U and f PF .

[0169] Specifically, when low-frequency oscillation occurs, the grid voltage amplitude and grid voltage and current phase fluctuations are calculated using the signal parameter estimation algorithm based on the total least squares method-rotational invariance technology to estimate the effective value of the traction grid voltage U rms Identify the power factor PF and obtain the voltage effective value U rms The corresponding oscillation frequency f U The oscillation frequency f corresponding to the power factor PF PF .

[0170] Step 204, determine the oscillation frequency f of the voltage effective value U , the oscillation frequency of the power factor f PF Whether they are within the range of [0.1, 20] Hz, if all are within the range, it is determined to be a low-frequency oscillation phenomenon and step 205 is executed; if at least one is not within the range, it is determined to be normal and step 206 is executed.

[0171] Specifically, the low-frequency oscillation frequency of the domestic traction power supply system is within 20Hz. Therefore, the voltage effective value oscillation frequency f is determined. U , power factor oscillation frequency f PF Whether it is within the interval [0.1, 20] Hz, if it is satisfied at the same time, it is determined that the traction power supply system has a low-frequency oscillation phenomenon, and step 205 is executed. If it is not satisfied, it is judged to be normal, and the current loop proportional coefficient of the EMU network side converter is set to the initial value Kpi0 , return to step 201 to continue testing.

[0172] Step 205: low-frequency oscillation occurs in the traction power supply system, and the current loop proportional coefficient of the grid-side converter is adjusted to: K pi =α Δt K pi0 .

[0173] The coefficient α is greater than 1, preferably 1.1, Δt is the duration of the detected low-frequency oscillation, and K pi0 is the initial value of the current loop proportional coefficient of the grid-side converter.

[0174] Specifically, according to the conclusion of the low-frequency oscillation stability analysis of the existing vehicle-grid coupling system, when the rectifier on the EMU network side adopts the current loop dq decoupling control, appropriately increasing the current loop proportional coefficient can play a good role in suppressing low-frequency oscillation. Therefore, when it is judged to be a low-frequency oscillation state, the current loop proportional coefficient of the EMU network side converter is adjusted online at fixed intervals: K pi =α Δt K pi0 After the coefficient is corrected, the process returns to step 201 and continues testing until the low-frequency oscillation is eliminated.

[0175] Step 206: If the traction power supply system does not experience low-frequency oscillation, adjust the current loop proportional coefficient of the grid-side converter to: K pi =K pi0 .

[0176] After correction, return to step 201 to continue testing.

[0177] Figure 3 FIG. 1 is a waveform diagram of the traction grid voltage when the conventional current dq decoupling control is adopted in the prior art; Figure 3 As shown:

[0178] If there are five trains in the traction power supply system running lightly at the same time, when the rectifier on the EMU grid side adopts the traditional current dq decoupling control, the collected primary voltage and secondary current of the on-board transformer will fluctuate greatly, resulting in continuous low-frequency oscillation.

[0179] Figure 4 Schematic diagram of the waveform of the traction network voltage when the traction network voltage detection and suppression method in this application is adopted; Figure 4 As shown:

[0180] If there are five trains in the traction power supply system running lightly at the same time, when the rectifier on the EMU grid side adopts the traction grid voltage detection and suppression method proposed in this application, the oscillation phenomenon of the primary voltage and secondary current of the on-board transformer collected is significantly suppressed.

[0181] Figure 5 Schematic diagram of the adaptive adjustment process of the current loop proportional coefficient of the grid-side converter in this application, such as Figure 5 As shown:

[0182] The dashed line represents K pi Parameter value; the rectangular line indicates whether low-frequency oscillation is detected, 0 means no detection, 1 means detection. When low-frequency oscillation is not detected, K pi The parameter is a fixed initial value K pi0 , when low frequency oscillation is detected, K pi The parameter is adaptively adjusted to α Δt K pi0 , in order to increase the current loop proportional coefficient of the converter on the EMU network side.

[0183] Figure 6 The structure of the traction network pressure detection device in this application is shown in FIG. Figure 1 ;like Figure 6 As shown, the traction grid voltage detection device 600 can be an electric locomotive or an EMU. Figure 6 The traction grid voltage detection device 600 shown includes: a voltage transformer 601, a current transformer 602, an on-board transformer 603, at least one processor 604 and a grid-side converter 605. The voltage transformer 601, the current transformer 602, the on-board transformer 603, at least one processor 604 and the grid-side converter 605 can be coupled together through a bus system 606. It can be understood that the bus system 606 is used to realize the connection and communication between these components. In addition to the data bus, the bus system 606 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, in Figure 6 Various buses are labeled as bus system 606 .

[0184] The method disclosed in the above embodiment of the present invention can be applied to the processor 604, or implemented by the processor 604. The processor 604 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit in the processor 604 or the instruction in the form of software. The above processor 604 can be a general processor, a digital signal processor (DSP, Digital Signal Processor), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The processor 604 can implement or execute the methods, steps and logic block diagrams disclosed in the embodiment of the present invention. The general processor can be a microprocessor or any conventional processor, etc. In combination with the steps of the method disclosed in the embodiment of the present invention, it can be directly embodied as a hardware decoding processor to execute, or it can be executed by a combination of hardware and software modules in the decoding processor.

[0185] In an exemplary embodiment, the traction grid voltage detection device 600 can be implemented by one or more application-specific integrated circuits (ASIC), DSP, programmable logic device (PLD), complex programmable logic device (CPLD), field programmable gate array (FPGA), general-purpose processor, controller, microcontroller (MCU), microprocessor, or other electronic components to execute the aforementioned method.

[0186] Specifically, the voltage transformer 601 is used to collect the primary voltage data of the vehicle transformer 603;

[0187] The current transformer 602 is used to collect the secondary current data of the on-board transformer 603;

[0188] The processor 604 is used to process the primary voltage data and the secondary current data to obtain the voltage effective value data and the power factor data of the on-board transformer; and based on the voltage effective value data and the power factor data, obtain the first oscillation frequency of the voltage effective value data and the second oscillation frequency of the power factor data; when the first oscillation frequency and the second oscillation frequency are both within a preset frequency range, it is determined that the traction network is in a low-frequency oscillation state.

[0189] As a preferred solution of an embodiment of the present application, the processor 604 is also used to: obtain the duration of the traction network being in a low-frequency oscillation state; and increase the current loop proportional coefficient of the grid-side converter 605 based on the duration of the traction network being in a low-frequency oscillation state.

[0190] As a preferred solution of an embodiment of the present application, the processor 604 is also used to determine that the traction network is not in a low-frequency oscillation state when the first oscillation frequency and / or the second oscillation frequency is outside a preset frequency range, and when in a low-frequency oscillation state, adjust the current loop proportional coefficient of the grid-side converter 605 to an initial value.

[0191] As a preferred solution of an embodiment of the present application, the processor 604 is specifically used to perform root mean square calculation on the primary voltage data to obtain voltage effective value data of the primary voltage data; obtain a sine reference signal and a cosine reference signal using a local clock; calculate the sine reference signal, the cosine reference signal, the primary voltage data and the secondary current data to obtain power factor data of the on-board transformer 603.

[0192] As a preferred solution of an embodiment of the present application, the processor 604 is specifically used to: construct a first Hankel matrix based on the voltage effective value data; perform singular value decomposition on the first Hankel matrix to obtain an orthogonal matrix of the first order in the first Hankel matrix, wherein the orthogonal matrix corresponds to a subspace of the voltage effective value data; perform singular value decomposition on the orthogonal matrix to obtain a reversible matrix; calculate the reversible matrix by total least squares method to obtain the eigenvalues ​​of the reversible matrix; calculate the eigenvalues ​​of the reversible matrix to obtain the first oscillation frequency of the voltage effective value.

[0193] As a preferred solution of an embodiment of the present application, the processor 604 is specifically used to: construct a second Hankel matrix based on the power factor data; perform singular value decomposition on the second Hankel matrix to obtain an orthogonal matrix of the first order in the second Hankel matrix, wherein the orthogonal matrix corresponds to a subspace of the power factor data; perform singular value decomposition on the orthogonal matrix to obtain a reversible matrix; calculate the reversible matrix by total least squares method to obtain the eigenvalues ​​of the reversible matrix; calculate the eigenvalues ​​of the reversible matrix to obtain a second oscillation frequency of the power factor.

[0194] As a preferred solution of an embodiment of the present application, the processor 604 is also specifically used to: perform low-pass filtering with a cutoff frequency of 100 Hz on the primary voltage data and the secondary current data, respectively, to obtain low-frequency data of the primary voltage data and low-frequency data of the secondary current data.

[0195] Figure 7 The structure of the traction network pressure detection device in this application is shown in FIG. Figure 2 ;like Figure 7 As shown, in an exemplary embodiment, the detection device includes:

[0196] The acquisition unit 701 is used to acquire the primary voltage data and the secondary current data of the vehicle-mounted transformer;

[0197] The processing unit 702 is used to process the primary voltage data and the secondary current data to obtain voltage RMS data and power factor data of the on-board transformer, and obtain a first oscillation frequency of the voltage RMS data and a second oscillation frequency of the power factor data based on the voltage RMS data and the power factor data;

[0198] The determining unit 703 is configured to determine that the traction network is in a low-frequency oscillation state when both the first oscillation frequency and the second oscillation frequency are within a preset frequency range.

[0199] In a preferred embodiment, the detection device further includes: an acquisition unit 704 and an adjustment unit 705;

[0200] The acquisition unit 704 is used to acquire the duration of the traction network being in a low-frequency oscillation state;

[0201] The adjustment unit 705 is used to increase the current loop proportional coefficient of the grid-side converter based on the duration of the traction network being in low-frequency oscillation.

[0202] In a preferred embodiment, the determining unit 703 is further configured to determine that the traction network is not in a low-frequency oscillation state when the first oscillation frequency and / or the second oscillation frequency is outside a preset frequency range.

[0203] In a preferred embodiment, the adjustment unit 705 is further configured to adjust the current loop proportional coefficient of the grid-side converter to an initial value when it is determined that the traction network is not in a low-frequency oscillation state.

[0204] In a preferred embodiment, the processing unit 702 is specifically used to perform root mean square calculation on the primary voltage data to obtain voltage effective value data of the primary voltage data;

[0205] A sine reference signal and a cosine reference signal are obtained by using a local clock; the sine reference signal, the cosine reference signal, the primary voltage data and the secondary current data are calculated to obtain power factor data of the on-board transformer.

[0206] In a preferred embodiment, the processing unit 702 is further used to construct a first Hankel matrix based on the voltage effective value data; perform singular value decomposition on the first Hankel matrix to obtain an orthogonal matrix of a first order in the first Hankel matrix, wherein the orthogonal matrix corresponds to a subspace of the voltage effective value data; perform singular value decomposition on the orthogonal matrix to obtain a reversible matrix; calculate the reversible matrix by total least squares method to obtain the eigenvalue of the reversible matrix; calculate the eigenvalue of the reversible matrix to obtain a first oscillation frequency of the voltage effective value;

[0207] A second Hankel matrix is ​​constructed based on the power factor data; a singular value decomposition is performed on the second Hankel matrix to obtain an orthogonal matrix of the first order in the second Hankel matrix, wherein the orthogonal matrix corresponds to a subspace of the power factor data; a singular value decomposition is performed on the orthogonal matrix to obtain a reversible matrix; the reversible matrix is ​​calculated by the total least squares method to obtain the eigenvalues ​​of the reversible matrix; the eigenvalues ​​of the reversible matrix are calculated to obtain a second oscillation frequency of the power factor.

[0208] In a preferred embodiment, the processing unit 702 is further used to perform low-pass filtering with a cutoff frequency of 100 Hz on the primary voltage data and the secondary current data, respectively, to obtain low-frequency data of the primary voltage data and low-frequency data of the secondary current data. The low-frequency data of the primary voltage data and the low-frequency data of the secondary current data are processed.

[0209] It should be noted that: the above-mentioned traction network voltage detection device only uses the division of the above-mentioned program modules as an example to illustrate when implementing traction network voltage detection. In actual applications, the above-mentioned data processing module can be completed by different program modules as needed, that is, the internal structure of the traction network voltage detection device is divided into different program modules to complete all or part of the processing described above. In addition, the above-mentioned traction network voltage detection device and the above-mentioned traction network voltage detection method are of the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0210] The methods disclosed in several method embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments.

[0211] The features disclosed in several product embodiments provided in this application can be arbitrarily combined without conflict to obtain new product embodiments.

[0212] The features disclosed in several method or device embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments or device embodiments.

[0213] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for detecting traction network pressure, characterized in that: The method comprises: Collect the primary voltage data and secondary current data of the on-board transformer; Processing the primary voltage data and the secondary current data to obtain voltage effective value data and power factor data of the on-board transformer; Based on the voltage effective value data and the power factor data, obtaining a first oscillation frequency of the voltage effective value data and a second oscillation frequency of the power factor data; When the first oscillation frequency and the second oscillation frequency are both within a preset frequency range, determining that the traction network is in a low-frequency oscillation state; Wherein, obtaining a first oscillation frequency of the voltage effective value data and a second oscillation frequency of the power factor data based on the voltage effective value data and the power factor data includes: Constructing a first Hankel matrix based on the voltage effective value data; performing singular value decomposition on the first Hankel matrix to obtain an orthogonal matrix of a first order in the first Hankel matrix, wherein the orthogonal matrix corresponds to a subspace of the voltage effective value data; performing singular value decomposition on the orthogonal matrix to obtain a reversible matrix; calculating the reversible matrix by total least squares method to obtain eigenvalues ​​of the reversible matrix; calculating the eigenvalues ​​of the reversible matrix to obtain a first oscillation frequency of the voltage effective value; A second Hankel matrix is ​​constructed based on the power factor data; a singular value decomposition is performed on the second Hankel matrix to obtain an orthogonal matrix of the first order in the second Hankel matrix, wherein the orthogonal matrix corresponds to a subspace of the power factor data; a singular value decomposition is performed on the orthogonal matrix to obtain a reversible matrix; the reversible matrix is ​​calculated by the total least squares method to obtain the eigenvalues ​​of the reversible matrix; the eigenvalues ​​of the reversible matrix are calculated to obtain a second oscillation frequency of the power factor.

2. The method according to claim 1, characterized in that After determining that the traction network is in a low-frequency oscillation state, the method further includes: Obtaining the duration of the traction network being in a low-frequency oscillation state; Based on the duration of the traction network being in low-frequency oscillation, the current loop proportional coefficient of the grid-side converter is increased.

3. The method according to claim 1, characterized in that When the first oscillation frequency and / or the second oscillation frequency is outside a preset frequency range, it is determined that the traction network is not in a low-frequency oscillation state.

4. The method according to claim 3, characterized in that After determining that the traction network is not in a low-frequency oscillation state, the method further includes: Adjust the current loop proportional coefficient of the grid-side converter to the initial value.

5. The method according to claim 1, characterized in that The processing of the primary voltage data and the secondary current data to obtain voltage effective value data and power factor data of the on-board transformer includes: Performing root mean square calculation on the primary voltage data to obtain voltage effective value data of the primary voltage data; A sine reference signal and a cosine reference signal are obtained by using a local clock; the sine reference signal, the cosine reference signal, the primary voltage data and the secondary current data are calculated to obtain power factor data of the on-board transformer.

6. The method according to claim 1, characterized in that Before processing the primary voltage data and the secondary current data, the method further includes: Performing low-pass filtering with a cutoff frequency of 100 Hz on the primary voltage data and the secondary current data respectively to obtain low-frequency data of the primary voltage data and low-frequency data of the secondary current data; The processing of the primary voltage data and the secondary current data includes: The low-frequency data of the primary voltage data and the low-frequency data of the secondary current data are processed.

7. A device for detecting traction network pressure, characterized in that: The detection device comprises: Voltage transformer, used to collect primary voltage data of the on-board transformer; A current transformer, used to collect secondary current data of the on-board transformer; A processor is used to process the primary voltage data and the secondary current data to obtain voltage RMS data and power factor data of the on-board transformer; based on the voltage RMS data and the power factor data, obtain a first oscillation frequency of the voltage RMS data and a second oscillation frequency of the power factor data; when the first oscillation frequency and the second oscillation frequency are both within a preset frequency range, determine that the traction network is in a low-frequency oscillation state; The processor is specifically used to construct a first Hankel matrix based on the voltage effective value data; perform singular value decomposition on the first Hankel matrix to obtain an orthogonal matrix of a first order in the first Hankel matrix, wherein the orthogonal matrix corresponds to a subspace of the voltage effective value data; perform singular value decomposition on the orthogonal matrix to obtain a reversible matrix; calculate the reversible matrix by the total least squares method to obtain the eigenvalues ​​of the reversible matrix; calculate the eigenvalues ​​of the reversible matrix to obtain a first oscillation frequency of the voltage effective value; construct a second Hankel matrix based on the power factor data; perform singular value decomposition on the second Hankel matrix to obtain an orthogonal matrix of a first order in the second Hankel matrix, wherein the orthogonal matrix corresponds to a subspace of the power factor data; perform singular value decomposition on the orthogonal matrix to obtain a reversible matrix; calculate the reversible matrix by the total least squares method to obtain the eigenvalues ​​of the reversible matrix; calculate the eigenvalues ​​of the reversible matrix to obtain a second oscillation frequency of the power factor.

8. The detection device according to claim 7, characterized in that: The processor is also used for: obtaining the duration of the traction network being in a low-frequency oscillation state; and increasing the current loop proportional coefficient of the grid-side converter based on the duration of the traction network being in a low-frequency oscillation state.

9. A device for detecting traction network pressure, characterized in that: The detection device comprises: A collection unit, used to collect primary voltage data and secondary current data of the on-board transformer; a processing unit, configured to process the primary voltage data and the secondary current data to obtain voltage RMS data and power factor data of the on-board transformer, and to obtain a first oscillation frequency of the voltage RMS data and a second oscillation frequency of the power factor data based on the voltage RMS data and the power factor data; a determining unit, configured to determine that the traction network is in a low-frequency oscillation state when both the first oscillation frequency and the second oscillation frequency are within a preset frequency range; The processing unit is specifically used to construct a first Hankel matrix based on the voltage effective value data; perform singular value decomposition on the first Hankel matrix to obtain an orthogonal matrix of a first order in the first Hankel matrix, wherein the orthogonal matrix corresponds to a subspace of the voltage effective value data; perform singular value decomposition on the orthogonal matrix to obtain a reversible matrix; calculate the reversible matrix by the total least squares method to obtain the eigenvalues ​​of the reversible matrix; calculate the eigenvalues ​​of the reversible matrix to obtain a first oscillation frequency of the voltage effective value; construct a second Hankel matrix based on the power factor data; perform singular value decomposition on the second Hankel matrix to obtain an orthogonal matrix of a first order in the second Hankel matrix, wherein the orthogonal matrix corresponds to a subspace of the power factor data; perform singular value decomposition on the orthogonal matrix to obtain a reversible matrix; calculate the reversible matrix by the total least squares method to obtain the eigenvalues ​​of the reversible matrix; calculate the eigenvalues ​​of the reversible matrix to obtain a second oscillation frequency of the power factor.

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