A method for online identification of dq impedance of MMC type DC ice melting device

Abstract

The invention discloses an online identification method of dq impedance of an MMC type direct current ice melting device. The method comprises the following steps: connecting the MMC type direct current ice melting device to a power grid via a multi-frequency voltage disturbance source, setting the disturbance source frequency, phase and amplitude parameters, and collecting voltage and current data of a grid-connected point of the direct current ice melting device after the disturbance source is connected; establishing a dual-input and dual-output dq impedance identification model, giving the initial order and parameters of the model, and adopting a recursive least squares algorithm to perform impedance identification; calculating the error between the output voltage of the model and the actual collected voltage under the current order and parameters, and if the error is greater than a given threshold, updating the model order, repeating step 2 to form an iteration until the error is less than the given threshold, and outputting the impedance identification result; the method can meet the demand for fast and accurate impedance identification at an actual engineering site; effectively carrying out stability analysis of the connection of the MMC type direct current ice melting device to a power grid, and also providing basic data support for actual engineering scenarios such as setting value calculation of a relay protection device and device operating condition evaluation.

Description

Technical Field

[0001] The invention belongs to the field of impedance identification of a direct current ice-melting device, and in particular relates to an online identification method of dq impedance of an MMC type direct current ice-melting device. Background Art

[0002] In order to prevent the impact of extreme cold weather on electrical equipment such as transmission lines, MMC-type DC ice melting devices are increasingly used in modern power systems. This type of device is based on power electronic topology, and its multi-scale control dynamics will interact with the power grid, causing harmonic instability problems such as harmonic resonance. The modeling and stability analysis methods based on dq impedance are relatively mature, but the current invention methods all require that the internal information of the ice melting device, such as the structure, parameters, and control method, is known. There are a large number of MMC-type DC ice melting devices that are already in operation and whose internal information is confidential at actual engineering sites. There is no very effective method for how to obtain the dq impedance of such devices online. Summary of the invention

[0003] The technical problem to be solved by the present invention is: to provide an online identification method for the dq impedance of an MMC type DC ice melting device. For an MMC type DC ice melting device with unknown internal information such as structure, parameters, and control mode, a multi-frequency voltage disturbance source is used to stimulate the current response of the device, and the dq impedance is identified with the help of a recursive least squares algorithm. The identification calculation takes a short time and the identification result has a high accuracy.

[0004] The technical solution of the present invention is:

[0005] An online identification method for dq impedance of an MMC type DC ice melting device, comprising:

[0006] Step 1: Connect the MMC type DC ice melting device to the power grid via a multi-frequency voltage disturbance source, set the disturbance source frequency, phase and amplitude parameters, and collect the voltage and current data of the DC ice melting device grid connection point after the disturbance source is connected;

[0007] Step 2: Establish a dual-input dual-output dq impedance identification model, give the initial order and parameters of the model, and use the recursive least squares algorithm to perform impedance identification;

[0008] Step 3: Calculate the error between the model output voltage and the actual collected voltage under the current order and parameters. If it is greater than the given threshold, update the model order and repeat step 2 to form an iteration until it is less than the given threshold, and output the impedance identification result.

[0009] The multi-frequency voltage disturbance source is defined by the following formula:

[0010]

[0011] In the formula, f k , and Ak is the frequency, phase and amplitude of the kth frequency component of the multi-frequency voltage disturbance source, and K is the number of sinusoidal waves contained in the multi-frequency voltage disturbance source.

[0012] The frequency range of the multi-frequency voltage disturbance source is determined by the frequency range of concern of the dq impedance of the MMC type DC ice melting device, and the frequency interval is 50Hz.

[0013] Step 2 establishes a dual-input dual-output dq impedance identification model, and given the initial order and parameters of the model, the method of using the recursive least squares algorithm to perform impedance identification includes: based on the collected voltage and current data, the voltage and current data in the three-phase coordinate system are transformed into the dq0 coordinate system through the abc-dq0 coordinate transformation, and the d-axis and q-axis current I d ,I q is the model input, d-axis and q-axis voltage U d , U q As the model output, the recursive least squares algorithm is used for dq impedance identification.

[0014] The identification model adopts an algorithm structure of inner and outer double loops, in which the outer loop is used to select the optimal order of dq impedance. The initial order of impedance is 1. The order change is based on the error obtained in step 3. When the error threshold is not met, the order increases gradually with a step size of 1. The inner loop is used to solve the parameters of the dq impedance expression. The initial value of the impedance parameter is selected as 10. -4 or 10 4 The parameter change is based on the parameters obtained from the previous test data and the new test data for recursive update; when the inner loop completes the recursive calculation of the last test data point, the loop ends.

[0015] The error index between the model output voltage and the actual collected voltage is calculated by the following formula:

[0016]

[0017] Where: N is the total number of measurements; u m represents the instantaneous value of voltage obtained by the identification model at the mth sampling point; u m0 Indicates the instantaneous value of voltage actually collected at the mth sampling point.

[0018] Calculate ε after one iteration RE , given a threshold ε RE0 , relative error percentage ε RE When it is greater than a given threshold, increase the dq impedance order and return to step 2 for iterative identification until the relative error percentage ε RE If the impedance is less than the given threshold, the impedance identification result is output.

[0019] The method for determining the initial phase of each frequency voltage waveform of the multi-frequency voltage disturbance source includes: defining a peak factor CF to select the initial phase of each frequency voltage waveform,

[0020] Where, T max It is the least common multiple of the periods of all the frequency components of the sine wave voltages that constitute the multi-frequency voltage disturbance source u(t), and T max =1s;

[0021] The larger the peak factor CF is, the larger the peak value of the multi-frequency voltage disturbance source signal is. Therefore, in order to reduce CF, the following formula is used to optimize the phase of the voltage sine wave of each frequency:

[0022]

[0023] Disturbance voltage source amplitude u peak The calculation formula is:

[0024] u peak =max[u(t)]<10%U

[0025] Where U is the rated voltage amplitude of the MMC DC ice melting device under test.

[0026] Beneficial effects of the present invention:

[0027] The present invention provides an online identification method for the dq impedance of an MMC type DC ice melting device. The method takes an MMC type DC ice melting device whose internal information such as structure, parameters, and control mode is unknown as the object, and aims to accurately obtain the dq impedance of the device, and proposes a fast and accurate online identification method. The present invention integrates the algorithm advantages of recursive least squares identification, realizes the online identification of the dq impedance of the MMC type DC ice melting device, improves the impedance identification accuracy, shortens the calculation time, and can meet the actual engineering site's demand for fast and accurate impedance identification. Based on the identification results of dq impedance, the stability analysis of the MMC type DC ice melting device connected to the power grid can be effectively carried out, and basic data support can also be provided for actual engineering scenarios such as relay protection device setting value calculation and device operating condition evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a topological diagram of an MMC type DC ice melting device according to an embodiment of the present invention;

[0029] Figure 2 This is a waveform comparison diagram before and after phase optimization of a multi-frequency disturbance voltage source according to an embodiment of the present invention;

[0030] Figure 3 A flow chart of a recursive least squares algorithm used in an embodiment of the present invention;

[0031] Figure 4This is a principle diagram of an identification method according to an embodiment of the present invention;

[0032] Figure 5 (a), (b), (c), and (d) are diagrams showing the identification results of Zdd, Zdq, Zqd, and Zqq in an embodiment of the present invention; DETAILED DESCRIPTION

[0033] The specific steps of the present invention include:

[0034] A method for online identification of dq impedance of an MMC type DC ice melting device, the method comprising the following steps:

[0035] S1, the MMC type DC ice melting device is connected to the power grid through a multi-frequency voltage disturbance source, the frequency, phase and amplitude parameters of the disturbance source are reasonably set, and the voltage and current data of the DC ice melting device grid connection point after the disturbance is connected are collected;

[0036] S2, establish a dual-input dual-output dq impedance identification model, give the initial order and parameters of the model, and use the recursive least squares algorithm to perform impedance identification;

[0037] S3, calculate the error between the model output voltage and the actual collected voltage under the current order and parameters. If it is greater than a given threshold, update the model order and repeat S2 to form an iteration until it is less than a given threshold, and output the impedance identification result.

[0038] Furthermore, in step S1, the MMC type DC ice melting device is connected to the power grid via a multi-frequency voltage disturbance source, the frequency, phase and amplitude parameters of the disturbance source are reasonably set, and the voltage and current data of the DC ice melting device grid connection point after the disturbance is connected are collected, which specifically includes:

[0039] The MMC type DC ice melting device is connected to the power grid through a multi-frequency voltage disturbance source, which is defined by the following formula:

[0040]

[0041] In the formula, f k , and A k is the frequency, phase and amplitude of the kth frequency component of the multi-frequency voltage disturbance source. K is the number of sine waves contained in the multi-frequency voltage disturbance source. A reasonable multi-frequency voltage disturbance source not only ensures the accurate acquisition of voltage and current data, but also avoids the influence of acquisition noise on the measurement data.

[0042] The frequency range of the multi-frequency voltage disturbance source is determined by the frequency range of concern of the dq impedance of the MMC type DC ice melting device, and the frequency interval is generally 50Hz to ensure the accuracy of the identification of the fundamental integer harmonic impedance.

[0043] The superposition of voltage waveforms of multiple frequencies will produce a large waveform peak, which is easy to cause the operating state of the MMC DC ice melting device to change, thereby affecting the accuracy of dq impedance identification. The key to affecting the multi-frequency voltage disturbance source is the phase of the voltage signal of each frequency. Therefore, the peak coefficient is defined to reasonably select the initial phase of the voltage waveform of each frequency.

[0044] The multi-frequency disturbance voltage source needs to have a large enough amplitude to ensure that the MMC type DC ice melting device produces a strong enough current response. However, too large an amplitude will also cause the device to experience modulation saturation, working condition changes, etc. Therefore, the disturbance voltage source amplitude u is defined as peak To limit the total energy of the disturbance signal.

[0045] The multi-frequency voltage disturbance source is set according to the above criteria. After the system runs stably, the voltage and current data of the grid-connected point of the MMC type DC ice melting device are collected.

[0046] Further, based on step S1, a dual-input dual-output dq impedance identification model is established, the initial order and parameters of the model are given, and the recursive least squares algorithm is used to perform impedance identification, which specifically includes:

[0047] Based on the voltage and current data collected by S1, the voltage and current data in the three-phase coordinate system are transformed to the dq0 coordinate system through abc-dq0 coordinate transformation, with the d-axis and q-axis current I d ,I q is the model input, d-axis and q-axis voltage U d , U q As the model output, the recursive least squares algorithm is used for dq impedance identification.

[0048] The identification model proposed in the present invention adopts an algorithm structure of inner and outer double loops. The outer loop is used to select the optimal order of dq impedance. The initial order of impedance is 1. The order change is based on the error obtained in S3. When the error threshold is not met, the order is increased step by step, with a step size of 1. The inner loop is used to solve the parameters of the dq impedance expression. The initial value of the impedance parameter is selected to be sufficiently small (generally 10 -4 ) or sufficiently large (usually 10 4 ), the parameter change is based on the parameters obtained from the previous test data and the new test data for recursive update. When the inner loop completes the recursive calculation of the last test data point, the loop ends and enters step S3.

[0049] Each time a new set of voltage and current data is obtained, the estimation result is corrected and updated based on the previous set of dq impedance identification results. Therefore, the method proposed by the present invention realizes the online identification of the dq impedance of the MMC type DC ice melting device, and the entire impedance model is dynamic.

[0050] Furthermore, the step S3 calculates the error between the model output voltage and the actual collected voltage under the current order and parameters based on the step S2. If it is greater than a given threshold, the model order is updated, and S2 is repeated to form an iteration until it is less than the given threshold. The output impedance identification result specifically includes:

[0051] Define the error index between the output voltage of the identification model and the actual collected voltage, and calculate it by the following formula:

[0052]

[0053] Where: N is the total number of measurements; u m represents the instantaneous value of voltage obtained by the identification model at the mth sampling point; u m0 Indicates the instantaneous value of voltage actually collected at the mth sampling point.

[0054] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0055] The purpose of the present invention is to provide an online identification method for the dq impedance of an MMC type DC ice melting device. For an MMC type DC ice melting device whose internal information such as structure, parameters, and control mode is unknown, the dq impedance is identified based on a recursive least squares algorithm. The identification calculation takes a short time and the identification result has a high accuracy.

[0056] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0057] Figure 1 This is a topology of an MMC type DC ice melting device. In the figure, u sa 、u sb with u sc Indicates the three-phase voltage of the AC power grid; R and L indicate the equivalent resistance and reactance of the power grid; u ha 、u hb with u hc represents the three-phase voltage of the multi-frequency voltage disturbance source; R0 and L0 represent the grid-connected equivalent resistance and reactance of the full-bridge submodule; i ap with i an Represents the current of the upper bridge arm and the lower bridge arm; I dc , U dc Represents DC current and voltage; T1, T2, T3, and T4 are transistors.

[0058] like Figure 1 As shown, the present invention provides an MMC type DC ice melting device dq impedance online identification method based on Figure 1 The topology structure shown in FIG. 1 comprises the following steps:

[0059] S1, the MMC type DC ice melting device is connected to the power grid through a multi-frequency voltage disturbance source, the frequency, phase and amplitude parameters of the disturbance source are reasonably set, and the voltage and current data of the DC ice melting device grid connection point after the disturbance is connected are collected; specifically including:

[0060] The MMC type DC ice melting device is connected to the power grid through a multi-frequency voltage disturbance source, which is defined by formula (1):

[0061]

[0062] In the formula, f k , and A k is the frequency, phase and amplitude of the kth frequency component of the multi-frequency voltage disturbance source. K is the number of sine waves contained in the multi-frequency voltage disturbance source. A reasonable multi-frequency voltage disturbance source not only ensures the accurate acquisition of voltage and current data, but also avoids the influence of acquisition noise on the measurement data.

[0063] The frequency range of the multi-frequency voltage disturbance source is determined by the frequency range of concern of the dq impedance of the MMC type DC ice melting device, and the frequency interval is generally 50Hz to ensure the accuracy of the identification of the fundamental integer harmonic impedance.

[0064] The superposition of voltage waveforms of multiple frequencies will produce a large waveform peak, which is easy to cause the operating state of the MMC DC ice melting device to change, thereby affecting the accuracy of dq impedance identification. The key to affecting the multi-frequency voltage disturbance source is the phase of the voltage signal of each frequency. Therefore, the peak coefficient CF is defined to reasonably select the initial phase of the voltage waveform of each frequency, as shown in formula (2).

[0065]

[0066] Where, T max It is the least common multiple of the periods of all the frequency components of the sine wave voltages constituting the multi-frequency voltage disturbance source u(t). In the present invention, T max =1s.

[0067] It can be seen from formula (2) that the larger the peak coefficient CF is, the larger the peak value of the multi-frequency voltage disturbance source signal is. Therefore, in order to reduce CF, the present invention uses formula (3) to optimize the phase of the sine wave of each frequency voltage. The waveform comparison before and after optimization is as follows: Figure 2 shown. Figure 2 The waveforms in the figure are composed of 30 voltage sine waves with an amplitude of 1 V. The phase of each frequency waveform of the black solid line is set to 0, and the phase of each frequency waveform of the red dotted line is optimized according to formula (3).

[0068]

[0069] In the formula, generally The meanings of other variables are the same as those in formula (1).

[0070] from Figure 2 It can be seen that the peak value of the waveform amplitude of the multi-frequency voltage disturbance source without optimization is 26V, and the peak value of the waveform amplitude after phase optimization by formula (3) is 14V, which is 46% lower than that before optimization. The phase optimization method proposed in the present invention has a significant effect on reducing the disturbance source CF.

[0071] The multi-frequency disturbance voltage source needs to have a large enough amplitude to ensure that the MMC type DC ice melting device produces a strong enough current response. However, too large an amplitude will also cause the device to experience modulation saturation, working condition changes, etc. Therefore, the disturbance voltage source amplitude u is defined as peak To limit the total energy of the disturbance signal, as shown in formula (4).

[0072] u peak =max[u(t)]<10%U (4)

[0073] Where: U is the rated voltage amplitude of the MMC DC ice melting device under test

[0074] The multi-frequency voltage disturbance source is set according to the above criteria. After the system runs stably, the voltage and current data of the grid-connected point of the MMC type DC ice melting device are collected.

[0075] S2, establish a dual-input dual-output dq impedance identification model, give the initial order and parameters of the model, and use the recursive least squares algorithm to perform impedance identification; specifically including:

[0076] Based on the voltage and current data collected by S1, the voltage and current data in the three-phase coordinate system are transformed into the dq0 coordinate system through abc-dq0 coordinate transformation. d ,I q is the model input, d-axis and q-axis voltage U d , U q The dq impedance model to be identified is represented by the differential equation shown in formula (5):

[0077]

[0078] In the formula, u d (k), i d (k) is the kth observed value of the device d-axis voltage and current, u d (k-1), i d (k-1) is the k-1th observed value of the voltage and current of the d-axis of the device, and so on, the same applies to the q-axis; d ud,m 、e ud,j 、f ud,jare the d-axis voltage coefficients respectively. The d-axis voltage corresponds to the d and q-axis current coefficients. The same is true for the q-axis. m is the mth data point and j is the jth data point. ε ud (k), ε uq To identify the error between the d-axis and q-axis voltages output by the model and the actual voltage; n1 and n2 are the optimal orders of the d-axis and q-axis voltages and currents.

[0079] By performing z-transformation on the differential equation in equation (5), the dq impedance transfer function matrix of the MMC type DC ice melting device can be obtained as follows:

[0080]

[0081] Where:

[0082]

[0083]

[0084]

[0085] It can be seen from formula (6) that the main goal of the dq impedance identification of the MMC type DC ice melting device is to determine the coefficients of voltage and current (impedance parameters to be identified) d, e, f in formula (6) based on the measured voltage and current disturbance data, and finally obtain the dq impedance value as shown in formula (6).

[0086] The present invention adopts the recursive least squares algorithm to solve and identify the dq impedance coefficient, and its basic calculation formula is:

[0087]

[0088]

[0089] Where: θ ud ,θ uq is the d-axis and q-axis voltage parameter vector, that is:

[0090]

[0091] h ud 、h uq To identify the test matrix, satisfy h ud (k)=[U d I d I q ] T ;h uq (k)=[U q I d I q ] T ;in:

[0092]

[0093] The identification model proposed in the present invention adopts an algorithm structure of inner and outer double loops. The outer loop is used to select the optimal order of dq impedance. The initial order of impedance is 1. The order change is based on the error obtained in S3. When the error threshold is not met, the order is increased step by step, with a step size of 1. The inner loop is used to solve the parameters of the dq impedance expression. The initial value of the impedance parameter is selected to be sufficiently small (generally 10 -4 ) or sufficiently large (usually 10 4 ), the parameter change is based on the parameters obtained from the previous test data and the new test data for recursive update. When the inner loop completes the recursive calculation of the last test data point, the loop ends and enters step S3. The algorithm flow chart is as follows Figure 3 shown.

[0094] S3, calculate the error between the model output voltage and the actual collected voltage under the current order and parameters. If it is greater than a given threshold, update the model order and repeat S2 to form an iteration until it is less than a given threshold, and output the impedance identification result; specifically including:

[0095] Define the error index between the output voltage of the identification model and the actual collected voltage, and calculate it through formula (2):

[0096]

[0097] Where: N is the total number of measurements; u m represents the instantaneous value of voltage obtained by the identification model at the mth sampling point; u m0 Indicates the instantaneous value of voltage actually collected at the mth sampling point.

[0098] Calculate ε after one iteration RE , given a threshold ε RE0 , relative error percentage ε RE When it is greater than a given threshold, increase the dq impedance order and return to step 2 for iterative identification until the relative error percentage ε RE If the impedance is less than a given threshold, the impedance identification result is output. The identification principle is as follows: Figure 4 The final identification result is shown in Figure 5 shown.

[0099] The present invention provides an online identification method for the dq impedance of an MMC type DC ice melting device. The method takes an MMC type DC ice melting device whose internal information such as structure, parameters, and control mode is unknown as the object, and aims to accurately obtain the dq impedance of the device, and proposes a fast and accurate online identification method. The present invention integrates the algorithm advantages of recursive least squares identification, realizes the online identification of the dq impedance of the MMC type DC ice melting device, improves the impedance identification accuracy, shortens the calculation time, and can meet the actual engineering site's demand for fast and accurate impedance identification. Based on the identification results of dq impedance, the stability analysis of the MMC type DC ice melting device connected to the power grid can be effectively carried out, and basic data support can also be provided for actual engineering scenarios such as relay protection device setting value calculation and device operating condition evaluation.

Claims

1. A method for online identification of dq impedance of a MMC type DC ice melting device, comprising: Step 1: Connect the MMC type DC ice melting device to the power grid via a multi-frequency voltage disturbance source, set the disturbance source frequency, phase and amplitude parameters, and collect the voltage and current data of the DC ice melting device grid connection point after the disturbance source is connected; Step 2: Establish a dual-input dual-output dq impedance identification model, give the initial order and parameters of the model, and use the recursive least squares algorithm to perform impedance identification; Based on the collected voltage and current data, the voltage and current data in the three-phase coordinate system are transformed into the dq0 coordinate system through abc-dq0 coordinate transformation, with the d-axis and q-axis current I d ,I q is the model input, d-axis and q-axis voltage U d , U q For the model output, the recursive least squares algorithm is used for dq impedance identification; In the formula, u d (k), i d (k) is the kth observed value of the device d-axis voltage and current, u d (k-1), i d (k-1) is the k-1th observed value of the voltage and current of the d-axis of the device, and so on, the same applies to the q-axis; d ud,m 、e ud,j 、f ud,j They are the d-axis voltage coefficients, the d-axis voltage corresponds to the d and q-axis current coefficients, and the same is true for the q-axis. m is the mth data point, and j is the jth data point. ε ud (k), ε uq To identify the error between the d-axis and q-axis voltages output by the model and the actual voltage; n1 and n2 are the optimal orders of the d-axis and q-axis voltages and currents; The recursive least squares algorithm is used to solve and identify the dq impedance coefficient. The calculation formula is: Where: θ ud ,θ uq is the d-axis and q-axis voltage parameter vector, that is: h ud 、h uq To identify the test matrix, satisfy h ud (k)=[U d I d I q ] T ;h uq (k)=[U q I d I q ] T ; Step 3: Calculate the error between the model output voltage and the actual collected voltage under the current order and parameters. If it is greater than the given threshold, update the model order and repeat step 2 to form an iteration until it is less than the given threshold, and output the impedance identification result.

2. According to claim 1, a method for online identification of dq impedance of a MMC type DC ice melting device is characterized in that: The multi-frequency voltage disturbance source is defined by the following formula: In the formula, f k , and A k is the frequency, phase and amplitude of the kth frequency component of the multi-frequency voltage disturbance source, and K is the number of sinusoidal waves contained in the multi-frequency voltage disturbance source.

3. The method for online identification of dq impedance of a MMC type DC ice melting device according to claim 2 is characterized in that: The frequency range of the multi-frequency voltage disturbance source is determined by the frequency range of concern of the dq impedance of the MMC type DC ice melting device, and the frequency interval is 50Hz.

4. The method for online identification of dq impedance of a MMC type DC ice melting device according to claim 1 is characterized in that: The identification model adopts an algorithm structure of inner and outer double loops, in which the outer loop is used to select the optimal order of dq impedance. The initial order of impedance is 1. The order change is based on the error obtained in step 3. When the error threshold is not met, the order increases gradually with a step size of 1. The inner loop is used to solve the parameters of the dq impedance expression. The initial value of the impedance parameter is selected as 10. -4 or 10 4 The parameter change is based on the parameters obtained from the previous test data and the new test data for recursive update; when the inner loop completes the recursive calculation of the last test data point, the loop ends.

5. The method for online identification of dq impedance of a MMC type DC ice melting device according to claim 1 is characterized in that: The error index between the model output voltage and the actual acquisition voltage is calculated by the following formula: Where: N is the total number of measurements; u m represents the instantaneous value of voltage obtained by the identification model at the mth sampling point; u m0 Indicates the instantaneous value of voltage actually collected at the mth sampling point.

6. The method for online identification of dq impedance of a MMC type DC ice melting device according to claim 1 is characterized in that: Calculate ε after one iteration RE , given a threshold ε RE0 , relative error percentage ε RE When it is greater than a given threshold, increase the dq impedance order and return to step 2 for iterative identification until the relative error percentage ε RE If the impedance is less than the given threshold, the impedance identification result is output.

7. The method for online identification of dq impedance of a MMC type DC ice melting device according to claim 2 is characterized in that: The method for determining the initial phase of each frequency voltage waveform of the multi-frequency voltage disturbance source includes: defining a peak factor CF to select the initial phase of each frequency voltage waveform, In the formula, u peak is the amplitude of the multi-frequency voltage disturbance source, T max It is the least common multiple of the periods of all the frequency components of the sine wave voltages that constitute the multi-frequency voltage disturbance source u(t), and T max =1s; The larger the peak factor CF is, the larger the peak value of the multi-frequency voltage disturbance source signal is. Therefore, in order to reduce CF, the following formula is used to optimize the phase of the voltage sine wave of each frequency:

8. The method for online identification of dq impedance of a MMC type DC ice melting device according to claim 7 is characterized in that: Multi-frequency voltage disturbance source amplitude u peak The calculation formula is: (u peak =max[u(t)])<10%U Where U is the rated voltage amplitude of the MMC DC ice melting device under test.

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