Method and device for online identification of capacitance of DC support capacitor

Abstract

The present invention provides a method and device for online identification of the capacitance of a DC support capacitor, the method comprising: sampling the input voltage and output voltage of the intermediate DC circuit of a train converter online during the precharging process, and establishing a precharging model of the intermediate DC circuit of the train converter based on the input voltage and the output voltage; applying a controlled autoregressive sliding average model to estimate model parameters according to the precharging model to obtain preset parameters at the current moment; and calculating the capacitance of the DC support capacitor at the current moment based on the relationship between the capacitance of the DC support capacitor in the precharging model and the preset parameters according to the preset parameters. The present invention can quickly and concisely realize accurate identification of the capacitance of the DC support capacitor.

Description

Technical Field

[0001] The invention belongs to the technical field of online monitoring, and in particular relates to a method and a device for online identification of the capacitance of a DC support capacitor. Background Art

[0002] In power conversion circuits such as converters, capacitors often gradually deteriorate due to factors such as high working environment temperature and excessive working current, posing safety hazards. Therefore, online monitoring of the health status of capacitors in converters is very important to ensure circuit safety.

[0003] In the prior art, online monitoring technology for capacitor capacitance often requires the addition of some additional circuit components, which increases the circuit cost, and the calculation and evaluation process is relatively complicated. It is difficult to accurately identify the capacitance of the DC support capacitor within a limited time, which is not conducive to promotion and implementation.

[0004] It can be seen that which method is simple and does not require the addition of additional devices to perform online identification of the capacitance of a capacitor is a technical problem that needs to be solved urgently by those skilled in the art. Summary of the invention

[0005] The present invention provides a method and device for online identification of the capacitance of a DC support capacitor, so as to solve the problem that the existing online identification of the capacitance of a DC support capacitor is complicated and has low accuracy.

[0006] Based on the above purpose, an embodiment of the present invention provides an online identification method for the capacitance of a DC support capacitor, comprising: online sampling of the input voltage and the output voltage of the intermediate DC circuit of a train converter during the pre-charging process, and establishing a pre-charging model of the intermediate DC circuit of the train converter based on the input voltage and the output voltage based on the intermediate DC circuit of the train converter; estimating model parameters according to the pre-charging model using a controlled autoregressive sliding average model to obtain preset parameters at the current moment; calculating the calculated value of the capacitance at the current moment based on the preset parameters and the relationship between the capacitance of the DC support capacitor in the pre-charging model and the preset parameters.

[0007] Optionally, the pre-charging model of the intermediate DC circuit of the train converter is established based on the intermediate DC circuit of the train converter according to the input voltage and the output voltage, including: obtaining a charging circuit model according to the intermediate DC circuit of the train converter, and obtaining a transfer function of the charging circuit model; calculating the current flowing through the DC support capacitor at the current moment based on the charging circuit model according to the input voltage and the output voltage; and establishing the pre-charging model of the intermediate DC circuit of the train converter according to the output voltage, the current and the transfer function.

[0008] Optionally, establishing a pre-charging model of the intermediate DC circuit of the train converter based on the output voltage, the current and the transfer function includes: obtaining a transfer function in the complex frequency domain based on the output voltage and the current; performing a bilinear transformation on the transfer function in the complex frequency domain to obtain a discrete form of the transfer function in the complex frequency domain, and obtaining the pre-charging model of the intermediate DC circuit of the train converter.

[0009] Optionally, the pre-charging model is applied with a controlled autoregressive moving average model to perform model parameter estimation to obtain preset parameters at the current moment, including: introducing noise according to the pre-charging model to establish a controlled autoregressive moving average model; using recursive augmented least squares method to perform noise estimation and model parameter estimation on the controlled autoregressive moving average model to obtain the preset parameters at the current moment.

[0010] Optionally, the recursive augmented least squares method is used to perform noise estimation and model parameter estimation on the controlled autoregressive moving average model to obtain the preset parameters at the current moment, including: obtaining a high-order controlled autoregressive model based on the controlled autoregressive moving average model; calculating a smoothed estimate of the noise using the recursive least squares method based on the high-order controlled autoregressive model; and estimating model parameters of the controlled autoregressive model using the recursive augmented least squares method based on the smoothed estimate of the noise to obtain the preset parameters at the current moment.

[0011] Optionally, the high-order controlled autoregressive model is:

[0012] The vector form is:

[0013]

[0014]

[0015]

[0016] Among them, α i and β i are u2(ki) and i C (ki), ρ is the coefficient vector, n0 is the order of the high-order controlled autoregressive model, u2(ki) is the output voltage at time ki, i C (ki) is the current flowing through the DC support capacitor at time ki, ε(k) is the noise at time k, u2(k) is the output voltage at time k, is the transposed vector of the high-order factor vector at time k;

[0017] The method of calculating the smoothed estimated value of the noise by using the recursive least squares method according to the high-order controlled autoregressive model includes: obtaining an estimated value of a coefficient vector by using the recursive least squares method; replacing the coefficient vector with the estimated value of the coefficient vector, and obtaining the smoothed estimated value of the noise by applying the following relationship:

[0018]

[0019] in, is the smoothed estimate of the noise at time i, u2(i) is the output voltage at time i, is the transposed vector of the high-order factor vector at time i, ρ(k+1) is the coefficient vector at time k+1, n d is the order of ε(k).

[0020] Optionally, the method of estimating model parameters of the controlled autoregressive model using the recursive augmented least squares method according to the smoothed estimated value of the noise to obtain the preset parameters at the current moment includes: performing recursive augmented least squares processing on the controlled autoregressive moving average model to switch the controlled autoregressive moving average model into a vector form:

[0021]

[0022]

[0023]

[0024] Wherein, u2(k) is the output voltage at time k, is the transposed vector of the factor vector at time k, ε(k) is the noise at time k, n d is the order of ε(k), u2(k-1) is the output voltage at time k-1, i C (k) is the current flowing through the DC support capacitor at time k, i C (k-1) is the current flowing through the DC support capacitor at time k-1, ε(k-1) is the noise at time k-1, ε(kn d ) is kn d The noise at the moment, θ is the parameter vector, b0 and b1 are the preset parameters, d1, They are ε(k-1) and ε(kn d ) coefficients; replacing the noise in the factor vector with a smoothed estimate of the noise, and using the recursive augmented least squares method to estimate the model parameters of the controlled autoregressive model to obtain the preset parameters at the current moment.

[0025] Based on the same inventive concept, an embodiment of the present invention also proposes an online identification device for the capacitance of a DC support capacitor, comprising: a pre-charging model acquisition unit, used to perform online sampling of the input voltage and the output voltage of the intermediate DC circuit of the train converter during the pre-charging process, and establish a pre-charging model of the intermediate DC circuit of the train converter based on the intermediate DC circuit of the train converter according to the input voltage and the output voltage; a model parameter estimation unit, used to perform model parameter estimation based on the pre-charging model using a controlled autoregressive sliding average model to obtain the preset parameters at the current moment; a capacitance identification unit, used to calculate the calculated value of the capacitance at the current moment based on the preset parameters and the relationship between the capacitance of the DC support capacitor in the pre-charging model and the preset parameters.

[0026] Based on the same inventive concept, an embodiment of the present invention further proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the aforementioned method when executing the program.

[0027] Based on the same inventive concept, an embodiment of the present invention further proposes a computer storage medium, in which at least one executable instruction is stored, and the executable instruction enables a processor to execute the aforementioned method.

[0028] The beneficial effects of the present invention are as follows: as can be seen from the above description, an embodiment of the present invention provides a method and device for online identification of the capacitance of a DC support capacitor, the method comprising: online sampling of the input voltage and the output voltage of the intermediate DC circuit of the train converter during the precharging process, and establishing a precharging model of the intermediate DC circuit of the train converter based on the intermediate DC circuit of the train converter according to the input voltage and the output voltage; applying a controlled autoregressive sliding average model to estimate model parameters according to the precharging model to obtain preset parameters at the current moment; calculating the calculated value of the capacitance at the current moment based on the relationship between the capacitance of the DC support capacitor in the precharging model and the preset parameters according to the preset parameters, so as to quickly and concisely realize accurate identification of the capacitance of the DC support capacitor. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0030] Figure 1 Schematic diagram of the flow of the method for online identification of the capacitance of a DC support capacitor in an embodiment of the present invention;

[0031] Figure 2 is a schematic diagram of a topological structure of an intermediate DC circuit of a train converter in an embodiment of the present invention;

[0032] Figure 3 is a schematic diagram of an equivalent circuit of a thin film capacitor in an embodiment of the present invention;

[0033] Figure 4 Schematic diagram of a charging circuit model of an intermediate DC circuit of a train converter in an embodiment of the present invention;

[0034] Figure 5 Schematic diagram of the calculation process of online identification of the capacitance of a DC support capacitor in an embodiment of the present invention;

[0035] Figure 6 Schematic diagram of the structure of an online identification device for the capacitance of a DC support capacitor in an embodiment of the present invention;

[0036] Figure 7 Schematic diagram of an electronic device in an embodiment of the present invention. DETAILED DESCRIPTION

[0037] In order to make the objectives, technical solutions and advantages of the present disclosure more clearly understood, the present disclosure is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0038] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present invention should be understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in the embodiments of the present invention do not represent any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0039] The embodiment of the present invention provides a method for online identification of the capacitance of a DC support capacitor. The method for online identification of the capacitance of a DC support capacitor according to the embodiment of the present invention is applied to a server. Figure 1 As shown, the online identification method of the DC support capacitor capacitance includes:

[0040] Step S11: During the pre-charging process, the input voltage and the output voltage of the intermediate DC circuit of the train converter are sampled online, and a pre-charging model of the intermediate DC circuit of the train converter is established based on the intermediate DC circuit of the train converter according to the input voltage and the output voltage.

[0041] In the embodiment of the present invention, the intermediate DC circuit topology of the train converter is as follows: Figure 2 As shown, the first voltage sensor TV201 is connected in parallel on the input side to detect the input voltage u1(t). The first contactor KM201 is a double-pole double-throw switch. The first end of the first contactor KM201 is connected to the positive end of the input side through the fuse FU201, the second end is connected to the anode of the diode D201 through the second contactor KM202, the third end is connected to the negative end of the input side, and the fourth end is connected to the negative end of the output side. The charging resistor 201 is connected in parallel to both ends of the second contactor KM202. The DC support capacitor C201 is a film capacitor, one end of which is connected to the cathode of the diode D201 and the other end is connected to the negative end of the output side. The discharge resistor R202 is connected in series with the third contactor KM203 and then connected in parallel to both ends of the output side. The first series resistor R203 and the second series resistor R204 are connected in series with each other and then connected in parallel to the DC support capacitor C201. The second voltage sensor TV202 is connected in parallel to both ends of the discharge resistor R202 and the third contactor KM203 which are connected in series, so as to detect the output voltage u2(t). The third contactor KM203 is a discharge contactor.

[0042] Before the inverter is started, the DC support capacitor C201 needs to be charged to keep it within the rated working voltage range. If the first voltage sensor TV201 detects that the main circuit voltage is greater than DC 500V, the first contactor KM201 is energized, the discharge contactor KM203 is automatically disconnected, and pre-charging begins. When the voltage difference between the first voltage sensor TV201 and the second voltage sensor TV202 is less than DC 50V, the second contactor KM202 is energized to end the pre-charging state. After the pre-charging is completed, the AC output contactor (not shown) is energized, and the inverter starts output according to variable voltage and variable frequency (VVVF). When the first voltage sensor TV201 detects that the DC 600V power supply voltage is lower than DC 500V, the first contactor KM201 is disconnected, the second contactor KM202 is disconnected, the third contactor KM203 is energized, the AC output contactor is disconnected, and the DC support capacitor is mainly discharged via the discharge resistor R202.

[0043] In an embodiment of the present invention, the first voltage sensor TV201 and the second voltage sensor TV202 are used to perform online sampling of the input voltage and the output voltage of the intermediate DC circuit of the train converter respectively during the pre-charging process, and the sampling period is T. Then, a pre-charging model of the intermediate DC circuit of the train converter is established based on the sampled input voltage and output voltage and the intermediate DC circuit of the train converter. Specifically, first, a charging circuit model is obtained based on the intermediate DC circuit of the train converter, and a transfer function of the charging circuit model is obtained; then, the current flowing through the DC support capacitor at the current moment is calculated based on the input voltage and the output voltage based on the charging circuit model; and then, a pre-charging model of the intermediate DC circuit of the train converter is established based on the output voltage, the current and the transfer function.

[0044] In general, such as Figure 3 As shown in the figure, the film capacitor can be equivalent to the series connection of capacitor C and equivalent series resistance ESR in the circuit. C is one of the most important electrical parameters of the film capacitor and directly affects the quality of the capacitor. ESR is related to the effective conductive cross-sectional area of ​​the film capacitor electrode. When the effective conductive cross-sectional area is very small, a smaller ESR (usually less than milliohm level) will be generated. When the film capacitor is in the charging state, the intermediate DC circuit of the train converter can be expressed as Figure 4 The charging circuit model is shown. The first resistor R1 and the second resistor R2 are branch resistors with known resistance values. The transfer function H(s) of the charging circuit model is:

[0045]

[0046] Among them, G1 and G2 are constants,

[0047]

[0048]

[0049] After converting the above transfer function into discrete form, the capacitance C can be estimated by the values ​​of input voltage and output voltage. For the convenience of calculation, the relationship described in equation (1) can be further simplified. According to Kirchhoff's law, the current i flowing through the DC support capacitor can be calculated by the voltage u1(t) and u2(t). C (t) is:

[0050]

[0051] Get the current i of the DC support capacitor C After (t), the transfer function in the complex frequency domain can be obtained based on the output voltage and the current. When the DC support capacitor voltage and current are known, in the complex frequency domain,

[0052]

[0053] Then, the transfer function in the complex frequency domain is bilinearly transformed to obtain the transfer function in discrete form in the complex frequency domain, thereby obtaining the pre-charging model of the intermediate DC circuit of the train converter.

[0054]

[0055] The transfer function of equation (5) can be transformed into the discrete form of the complex frequency domain as follows:

[0056] (1-z -1 )U2(z -1 )=(b0+b1z -1 )I C (z -1 ) (7)

[0057] in,

[0058]

[0059]

[0060] The discrete form of the transfer function in the complex frequency domain is the pre-charging model of the intermediate DC loop of the train converter.

[0061] When the sampling period T is small enough, the amplitude-frequency characteristics and phase-frequency characteristics in discrete time are consistent with those in continuous time. It can be seen that the capacitance C of the DC support capacitor can be effectively identified by obtaining the estimated values ​​of the preset parameters including b0 and b1.

[0062]

[0063] In order to obtain accurate estimates of the preset parameters b0 and b1 and further calculate reliable capacitance estimation, the embodiment of the present invention uses recursive extended least squares (RELS) to iteratively calculate the parameters in equation (7), and achieves the purpose of unbiased parameter estimation by modeling the noise during the process of model parameter identification.

[0064] Step S12: according to the pre-charging model, a controlled autoregressive moving average model is applied to estimate model parameters to obtain preset parameters at the current moment.

[0065] In step S12, a controlled autoregressive moving average model may be established by first introducing measurement noise according to the pre-charging model; and the recursive least squares method may be used to perform noise estimation and model parameter estimation on the controlled autoregressive moving average model to obtain the preset parameters at the current moment.

[0066] In the embodiment of the present invention, considering the influence of the voltage sensor measurement error and the time limit of the pre-charging process, a two-stage recursive least squares (RLS)-RELS method is used to identify the preset parameters b0 and b1 in equation (7), so as to obtain an estimated value of the capacitance C of the DC support capacitor.

[0067] During the pre-charging process of the DC support capacitor, the voltage sensor inevitably has a certain amount of measurement noise, which can be regarded as a comprehensive reflection of factors such as internal and external disturbances of the system and inherent errors of the device. Therefore, the noise of the voltage sensor during actual measurement is not necessarily statistically independent white noise. Considering the influence of noise, equation (7) can be rewritten as a controlled autoregressive moving average (CARMA) model:

[0068] u2(k)=u2(k-1)+b0i C (k)+b1i C (k-1)+ε(k) (11)

[0069] Wherein, u2(k) is the output voltage at time k, u2(k-1) is the output voltage at time k-1, b0 and b1 are the preset parameters, i C (k) is the current flowing through the DC support capacitor at time k, i C (k-1) is the current flowing through the DC support capacitor at time k-1, ε(k) is the noise at time k, and the variance is unknown.

[0070] The controlled autoregressive moving average model is processed by recursive augmented least squares method and switched to the controlled autoregressive moving average model in vector form:

[0071]

[0072]

[0073]

[0074] Wherein, u2(k) is the output voltage at time k, is the transposed vector of the factor vector at time k, ε(k) is the noise at time k, n d is the order of ε(k), u2(k-1) is the output voltage at time k-1, i C (k) is the current flowing through the DC support capacitor at time k, i C (k-1) is the current flowing through the DC support capacitor at time k-1, ε(k-1) is the noise at time k-1, ε(kn d ) is knd The noise at the moment, θ(k) is the parameter vector, b0 and b1 are the preset parameters, d1, They are ε(k-1) and ε(kn d ) coefficient.

[0075] Since the transpose vector is contains an unmeasurable amount of noise ε(k-1), i=1, 2, ..., n d .Will The ε(k-1) in is replaced by its estimated value, and we have

[0076]

[0077] in, is the noise estimate.

[0078] By recursive least squares method, we can get:

[0079]

[0080]

[0081] Among them, P(k) is the first intermediate parameter, is the estimated value of the parameter vector θ(k+1), P(k) is the first intermediate parameter, The estimate of the noise in is given by:

[0082]

[0083] in, is the smoothed estimate of the noise at time i, is the estimated value of the parameter vector at time i-1. And the initial value is:

[0084]

[0085] The above recursive parameter estimation method can accurately identify the preset parameters b0 and b1 by estimating the noise. Usually, the estimation of θ(k) of the algorithm is consistent, that is, when k tends to infinity, the estimated value of θ converges to the true value. Therefore, when k is large enough, the capacitance C can be effectively estimated by formula (10).

[0086] Taking into account that in some cases the charging time of the DC support capacitor is short, and the noise estimation and parameter estimation are mutually coupled through equations (16) to (18), which leads to a slow convergence speed of the algorithm and a low noise estimation accuracy within a limited time, the embodiment of the present invention adopts an RLS algorithm to fit a high-order controlled auto-regressive (CAR) model to obtain a noise estimation separately, and uses it for RELS to estimate the model parameters of the controlled auto-regressive model, thereby optimizing the estimation method shown in equation (18) to achieve the purpose of improving the parameter identification accuracy and convergence speed.

[0087] In an embodiment of the present invention, when the recursive augmented least squares method is used to perform noise estimation and model parameter estimation on the controlled autoregressive moving average model, a high-order controlled autoregressive model is first obtained based on the controlled autoregressive moving average model; then, the recursive least squares method is used to calculate the smoothed estimate of the noise based on the high-order controlled autoregressive model.

[0088] From the stationary and reversible properties, we know that the above controlled autoregressive moving average (CARMA) model can be replaced by the following high-order CAR model:

[0089]

[0090] Among them, n0 is the order of the high-order controlled autoregressive model, α i and β i are u2(ki) and i C (ki) and α0 = β0 = 1. The order n0 is set to be sufficiently large, for example, 10 to 20.

[0091] The high-order CAR model is processed by recursive least squares method to obtain the high-order CAR model in vector form:

[0092]

[0093]

[0094]

[0095] Wherein, ρ is the coefficient vector, u2(ki) is the output voltage at time ki, i C (ki) is the current flowing through the DC support capacitor at time ki, ε(k) is the noise at time k, u2(k) is the output voltage at time k, is the transposed vector of the high-order factor vector.

[0096] Based on the high-order CAR model in vector form, the recursive least squares method can be used to obtain the estimated value of the coefficient vector:

[0097]

[0098]

[0099] in, The estimated value of the coefficient vector is used to replace the coefficient vector, and then the smoothed estimate of the noise can be obtained according to the following relationship:

[0100]

[0101] in, is the noise estimation value at time i, u2(i) is the output voltage at time i, is the transposed vector of the high-order factor vector at time i, ρ(k+1) is the coefficient vector at time k+1, n d is the order of ε(k).

[0102] Compared with the original error estimation algorithm, that is, the model parameter estimation affects the error estimation through equation (18), the estimation method given by equation (26) is independent of the model parameter estimation and is used independently for the calculation of RELS. When the order of the fitted high-order CAR model is large enough, a high-precision smooth estimation of the error can be obtained through equation (26), while improving the convergence speed of parameter identification.

[0103] After obtaining the smoothed estimate of the noise, the model parameters of the controlled autoregressive model are estimated by using the recursive augmented least squares method according to the smoothed estimate of the noise, and the preset parameters at the current moment are obtained. Specifically, the controlled autoregressive moving average model is processed by the recursive augmented least squares method and switched to the controlled autoregressive moving average model in vector form; then the smoothed estimate of the noise replaces the noise in the factor vector, and the model parameters of the controlled autoregressive model are estimated by using the recursive augmented least squares method to obtain the preset parameters at the current moment. That is, the transposed vector of the factor vector is calculated according to formula (15): Then, according to the equations (16) and (17) obtained by processing the controlled autoregressive model using the recursive augmented least squares method, the estimated value of the parameter vector θ(k) is obtained: And as the parameter vector θ(k). According to the parameter vector θ(k), the preset parameters b0 and b1 contained therein can be obtained.

[0104] Step S13: calculating the capacitance at the current moment according to the preset parameters based on the relationship between the capacitance of the DC support capacitor in the pre-charging model and the preset parameters.

[0105] After obtaining the preset parameters b0 and b1, the capacitance C of the DC support capacitor at the current moment can be calculated according to the formula (10) obtained based on the pre-charging model, thereby completing the online identification of the capacitance C of the DC support capacitor.

[0106] In the embodiment of the present invention, during the pre-charging process, the capacitance of the DC support capacitor can be accurately identified by collecting the input voltage u1(t) and the output voltage u2(t), combining the pre-charging model, RELSE parameter estimation and error estimation. The entire calculation process is as follows: Figure 5 shown.

[0107] First, the current i of the DC support capacitor at the current moment is calculated from the input voltage u1(t) and the output voltage u2(k) at the moment k. C (k), and the output voltage u2(k) and the current i of the DC support capacitor C (k) is used as the algorithm input. Then, at each moment, the smoothed estimate of the noise is calculated by the fitted high-order CAR model Specifically, the transposed vector of the high-order factor vector is calculated according to formula (22): Then, according to equations (24) and (25), we can obtain the coefficient vector ρ and the second intermediate parameter Then, the smoothed estimate of the noise is calculated according to formula (26): Then, a smoothed estimate of the noise is calculated Replace the transposed vector of the factor vector The noise estimate originally calculated by RELS is According to formula (15), the transposed vector of the factor vector is calculated as Then, according to equations (16) and (17), we can obtain the estimated values ​​of the parameter vector θ(k) and the first intermediate parameter P(k). The parameter vector θ(k) is the model parameter of the controlled autoregressive moving average model. As the parameter vector θ(k), the preset parameters b0 and b1 contained therein can be obtained according to the parameter vector θ(k). Finally, the accurate calculated value of the capacitance C is obtained through the relationship between the preset parameters b0 and b1 and the capacitance in the pre-charge model, that is, formula (10).

[0108] Compared with the existing recursive algorithm calculation process, the online identification method of the DC support capacitor capacitance proposed in the embodiment of the present invention can effectively ensure the convergence speed of the recursive algorithm while considering the error influence and improving the identification accuracy, and realize the accurate identification of the DC support capacitor capacitance within a limited time.

[0109] The following Table 1 shows the identification results of the DC support capacitor capacitance at different sampling frequencies using the online identification method for the DC support capacitor capacitance of the embodiment of the present invention, where the step size, i.e., the number of steps, represents the order n0 of the high-order controlled autoregressive model. It can be seen that at different sampling frequencies, the identification results of the embodiment of the present invention are all within 1%.

[0110] Table 1 Identification results of DC link capacitor capacitance at different sampling frequencies

[0111]

[0112] In summary, the online identification method of the capacitance of the DC support capacitor in the embodiment of the present invention performs online sampling of the input voltage and the output voltage of the intermediate DC circuit of the train converter during the pre-charging process, and establishes a pre-charging model of the intermediate DC circuit of the train converter based on the input voltage and the output voltage based on the intermediate DC circuit of the train converter; estimates model parameters based on the pre-charging model using a controlled autoregressive sliding average model to obtain preset parameters at the current moment; calculates the capacitance at the current moment based on the preset parameters based on the relationship between the capacitance of the DC support capacitor in the pre-charging model and the preset parameters, thereby enabling fast and concise accurate identification of the capacitance of the DC support capacitor.

[0113] The above specific embodiments of the present invention are described. In some cases, the actions or steps recorded in the embodiments of the present invention can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0114] Based on the same concept, the embodiment of the present invention also provides a device for online identification of the capacitance of a DC support capacitor. Applicable to a server. Figure 6 As shown, the online identification device for the capacitance of a DC support capacitor includes: a pre-charge model acquisition unit, a model parameter estimation unit and a capacitance identification unit.

[0115] A pre-charging model acquisition unit, used to perform online sampling of the input voltage and output voltage of the intermediate DC circuit of the train converter during the pre-charging process, and establish a pre-charging model of the intermediate DC circuit of the train converter based on the intermediate DC circuit of the train converter according to the input voltage and the output voltage;

[0116] A model parameter estimation unit, used to estimate model parameters by applying a controlled autoregressive sliding average model according to the pre-charging model to obtain preset parameters at a current moment;

[0117] A capacitance identification unit is used to calculate the capacitance at a current moment according to the preset parameters based on the relationship between the capacitance of the DC support capacitor in the pre-charging model and the preset parameters.

[0118] For the convenience of description, the above device is described as various modules according to their functions. Of course, when implementing the embodiment of the present invention, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0119] The device of the above embodiment is applied to the corresponding method in the above embodiment, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0120] Based on the same inventive concept, an embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in any one of the above embodiments is implemented.

[0121] An embodiment of the present invention provides a non-volatile computer storage medium, wherein the computer storage medium stores at least one executable instruction, and the computer executable instruction can execute the method described in any of the above embodiments.

[0122] Figure 7 A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 701, a memory 702, an input / output interface 703, a communication interface 704, and a bus 705. The processor 701, the memory 702, the input / output interface 703, and the communication interface 704 are connected to each other in communication within the device through the bus 705.

[0123] The processor 701 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solution provided by the method embodiment of the present invention.

[0124] The memory 702 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 702 can store an operating system and other application programs. When the technical solution provided by the method embodiment of the present invention is implemented by software or firmware, the relevant program code is stored in the memory 702 and called and executed by the processor 701.

[0125] The input / output interface 703 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0126] The communication interface 704 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB, network cable, etc.) or a wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

[0127] The bus 705 includes a path for transmitting information between various components of the device (eg, the processor 701 , the memory 702 , the input / output interface 703 , and the communication interface 704 ).

[0128] It should be noted that, although the above device only shows the processor 701, the memory 702, the input / output interface 703, the communication interface 704 and the bus 705, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiment of the present invention, and does not necessarily include all the components shown in the figure.

[0129] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application is limited to these examples. Based on the concept of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the present application as described above, which are not provided in detail for the sake of simplicity.

[0130] This application is intended to cover all such substitutions, modifications and variations that fall within the broad scope of the embodiments of the present invention. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present invention should be included in the scope of protection of this application.

Claims

1. A method for online identification of the capacitance of a DC support capacitor, characterized in that: The method comprises: During the pre-charging process, the input voltage and the output voltage of the intermediate DC circuit of the train converter are sampled online, and a pre-charging model of the intermediate DC circuit of the train converter is established based on the intermediate DC circuit of the train converter according to the input voltage and the output voltage; According to the pre-charging model, a controlled autoregressive moving average model is applied to estimate model parameters to obtain preset parameters at the current moment; Calculating the capacitance at the current moment according to the preset parameters based on the relationship between the capacitance of the DC support capacitor in the pre-charging model and the preset parameters; The method of establishing a pre-charging model of the intermediate DC circuit of the train converter based on the intermediate DC circuit of the train converter according to the input voltage and the output voltage includes: Acquire a charging circuit model according to the intermediate DC circuit of the train converter, and acquire a transfer function of the charging circuit model; Calculate the current flowing through the DC support capacitor at the current moment based on the charging circuit model according to the input voltage and the output voltage; Establishing a pre-charging model of the intermediate DC circuit of the train converter according to the output voltage, the current and the transfer function; The method of establishing a pre-charging model of the intermediate DC circuit of the train converter according to the output voltage, the current and the transfer function includes: Acquire a transfer function in the complex frequency domain based on the output voltage and the current; perform a bilinear transformation on the transfer function in the complex frequency domain to acquire a transfer function in a discrete form in the complex frequency domain, and obtain the pre-charging model of the intermediate DC circuit of the train converter; The method of applying a controlled autoregressive moving average model to estimate model parameters according to the pre-charging model to obtain preset parameters at the current moment includes: Introducing noise according to the pre-charging model to establish a controlled autoregressive moving average model; Using the recursive augmented least squares method to perform noise estimation and model parameter estimation on the controlled autoregressive moving average model, and obtain the preset parameters at the current moment; The adopting the recursive augmented least square method to perform noise estimation and model parameter estimation on the controlled autoregressive moving average model to obtain the preset parameters at the current moment includes: Obtaining a high-order controlled autoregressive model according to the controlled autoregressive moving average model; Calculating a smoothed estimate of the noise using a recursive least squares method according to the high-order controlled autoregressive model; The model parameters of the controlled autoregressive model are estimated using the recursive augmented least squares method according to the smoothed estimated value of the noise to obtain the preset parameters at the current moment.

2. The method according to claim 1, characterized in that: The high-order controlled autoregressive model is: , the vector form is: in, and They are and The coefficient of is the coefficient vector, is the order of the high-order controlled autoregressive model, is the output voltage at time ki, is the current flowing through the DC support capacitor at time ki, is the noise at time k, is the output voltage at time k, is the transposed vector of the high-order factor vector at time k; The method of calculating the smoothed estimate of noise using the recursive least squares method according to the high-order controlled autoregressive model includes: The recursive least squares method is used to obtain the estimated value of the coefficient vector; The estimated value of the coefficient vector is substituted for the coefficient vector, and the smoothed estimate of the noise is obtained by applying the following relationship: in, is the smoothed estimate of the noise at time i, is the output voltage at time i, is the transposed vector of the high-order factor vector at time i, is the coefficient vector at time k+1, for The order of.

3. The method according to claim 1, characterized in that: The method of estimating model parameters of the controlled autoregressive model using the recursive augmented least squares method according to the smoothed estimated value of the noise to obtain the preset parameters at the current moment includes: The controlled autoregressive moving average model is processed by recursive augmented least squares method and switched to the controlled autoregressive moving average model in vector form: in, is the output voltage at time k, is the transposed vector of the factor vector at time k, is the noise at time k, for The order of is the output voltage at time k-1, is the current flowing through the DC support capacitor at time k, is the current flowing through the DC support capacitor at time k-1, is the noise at time k-1, for The noise at the moment, θ is the parameter vector, , is the preset parameter, , They are and The coefficient of The noise in the factor vector is replaced by a smoothed estimated value of the noise, and the model parameters of the controlled autoregressive model are estimated by using the recursive augmented least squares method to obtain the preset parameters at the current moment.

4. A device for online identification of the capacitance of a DC support capacitor, characterized in that: The device comprises: A pre-charging model acquisition unit, used to perform online sampling of the input voltage and output voltage of the intermediate DC circuit of the train converter during the pre-charging process, and establish a pre-charging model of the intermediate DC circuit of the train converter based on the intermediate DC circuit of the train converter according to the input voltage and the output voltage; A model parameter estimation unit, used to estimate model parameters by applying a controlled autoregressive sliding average model according to the pre-charging model to obtain preset parameters at a current moment; A capacitance identification unit, configured to calculate a calculated value of the capacitance at a current moment based on the relationship between the capacitance of the DC support capacitor in the pre-charging model and the preset parameter according to the preset parameter; The method of establishing a pre-charging model of the intermediate DC circuit of the train converter based on the intermediate DC circuit of the train converter according to the input voltage and the output voltage includes: Acquire a charging circuit model according to the intermediate DC circuit of the train converter, and acquire a transfer function of the charging circuit model; Calculate the current flowing through the DC support capacitor at the current moment based on the charging circuit model according to the input voltage and the output voltage; Establishing a pre-charging model of the intermediate DC circuit of the train converter according to the output voltage, the current and the transfer function; The method of establishing a pre-charging model of the intermediate DC circuit of the train converter according to the output voltage, the current and the transfer function includes: Acquire a transfer function in the complex frequency domain based on the output voltage and the current; perform a bilinear transformation on the transfer function in the complex frequency domain to acquire a transfer function in a discrete form in the complex frequency domain, and obtain the pre-charging model of the intermediate DC circuit of the train converter; The method of applying a controlled autoregressive moving average model to estimate model parameters according to the pre-charging model to obtain preset parameters at the current moment includes: Introducing noise according to the pre-charging model to establish a controlled autoregressive moving average model; Using the recursive augmented least squares method to perform noise estimation and model parameter estimation on the controlled autoregressive moving average model, and obtain the preset parameters at the current moment; The adopting the recursive augmented least square method to perform noise estimation and model parameter estimation on the controlled autoregressive moving average model to obtain the preset parameters at the current moment includes: Obtaining a high-order controlled autoregressive model according to the controlled autoregressive moving average model; Calculating a smoothed estimate of the noise using a recursive least squares method according to the high-order controlled autoregressive model; The model parameters of the controlled autoregressive model are estimated using the recursive augmented least squares method according to the smoothed estimated value of the noise to obtain the preset parameters at the current moment.

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method according to any one of claims 1 to 3 is implemented.

6. A computer storage medium, characterized in that: The storage medium stores at least one executable instruction, and the executable instruction enables the processor to execute the method as described in any one of claims 1-3.

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