Deadbeat Predictive Control Method for Distributed Power Flow Controller

Abstract

The present invention discloses a zero-beat predictive control method for a distributed power flow controller. Starting from the DPFC topology, the present invention analyzes the equivalent circuit model of the DPFC in detail and derives a predictive control model of the DPFC with an LC filter; considering the difference in the actual parameters of the filter inductor and the filter capacitor in the LC filter, a model reference adaptive method is proposed to accurately identify the DPFC model parameters. The control method of the present invention has the advantages of good dynamic response performance and convenient modeling. It can avoid complex control parameter setting and eliminate the influence of model parameter deviation without affecting the dynamic performance, thereby improving the stability of control. Applying the control method of the present invention to a distributed power flow controller can effectively improve the control accuracy of the DPFC device, and has a positive effect on promoting the application of DPFC in improving the flexibility and controllability of power grid operation.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system operation and stability control, and in particular relates to a deadbeat predictive control method of a distributed power flow controller based on adaptive parameter identification. Background Art

[0002] A distributed power flow controller (DPFC) is a distributed series flexible AC transmission device that utilizes stacked low-voltage converter modules for line compensation. Each converter module has an independent control unit and can be installed in stations, along lines, or on towers to collaboratively meet the diverse control needs of the power grid. As a flexible transmission device characterized by its distributed structure, the DPFC effectively regulates line power flows, handles congestion, and provides functions such as three-phase asymmetry compensation and harmonic suppression, making it a cost-effective option for flexible power grid control.

[0003] The core of DPFC control lies in the control of the H-bridge voltage source converter (VSC). The quality of the converter control strategy directly impacts the effective performance of the DPFC. Currently, several control strategies for H-bridge VSCs include hysteresis current PWM control and dq decoupling control. Hysteresis control offers fast response and strong robustness, but it also suffers from large steady-state current ripple, an unstable switching frequency, and significant current tracking accuracy affected by the hysteresis width. dq decoupling control can achieve decoupled control of active and reactive power, but due to the use of a large number of PI controllers, parameter tuning is complex and dynamic response is poor. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to overcome the defects of the above-mentioned prior art and provide a distributed power flow controller zero-beat predictive control method based on adaptive parameter identification to obtain better dynamic response performance and avoid complex control parameter setting, and to eliminate the influence of model parameter deviation without affecting dynamic performance by online identification of model parameters using a model reference adaptive parameter identification algorithm.

[0005] To this end, the present invention adopts the following technical solutions: a deadbeat predictive control method for a distributed power flow controller, which includes a predictive control method and a parameter identification method;

[0006] The predictive control method comprises the following steps:

[0007] 11) Determine the DC capacitor voltage command and sampling period T s ;

[0008] 12) In the kth sampling cycle, the current I1 flowing into the DPFC unit, the current I2 flowing through the filter inductor, and the voltage of the filter capacitor are sampled. The voltage of the filter capacitor is the output voltage V of the DPFC unit. se And through the coordinate rotation transformation, we can get the d and q axis components in the synchronous rotating coordinate system: I 1d (k), I 1q (k), I 2d (k), I 2q (k), V sed (k) and V seq (k);

[0009] 13) Calculate the given value of the d-axis component of the DPFC unit output voltage Will As the given value at time k+1, that is,

[0010] 14) Determine the q-axis component of the output voltage V given by the system-level controller seq The given value Will As the given value at time k+1, that is,

[0011] 15) Calculate the d and q components of the modulation voltage V at time k+1 using the DPFC predictive control model rd (k+1), V rq (k+1), the modulation voltage V is obtained by coordinate rotation inverse transformation r (k+1);

[0012] 16) The obtained modulation voltage V r (k+1) generates a trigger pulse by SPWM modulation method, and the obtained trigger pulse is used to control the H-bridge converter in the DPFC;

[0013] 17) In the next sampling period, repeat steps 12)-16);

[0014] The parameter identification method comprises the following steps:

[0015] 21) Determine the initial value of the filter capacitor in the adjustable model and the initial value of the filter inductor

[0016] 22) In the kth sampling period, the sampled I 1d (k), I 1q (k), V sed (k) and V seq (k) is calculated and we get and The symbol ^ represents the parameters in the adjustable model and further calculates the generalized error;

[0017] 23) Adjust the capacitance parameters in the adjustable model and inductance parameters

[0018] 24) Determine whether the generalized error converges; if the generalized error converges, use and Replace the AC filter capacitor C in the DPFC unit f and DPFC unit AC filter inductor L f ; If the generalized error does not converge, repeat steps 22)-23) in the next sampling period.

[0019] The research objects are divided into system level and device level. The system is the environment in which the device works. To directly change the electrical quantities at the system level, such as line flow, a system-level controller is required to convert the system-level adjustment instructions into the instructions required for the device to output through calculation.

[0020] Compared with traditional vector control, deadbeat predictive control can achieve higher dynamic response performance and smaller current harmonic components. The present invention applies the deadbeat predictive control method to DPFC, which can achieve better dynamic response performance and avoid complex parameter setting. At the same time, the model reference adaptive method is used to accurately identify the DPFC model parameters, which can eliminate the influence of parameter deviation without sacrificing dynamic performance and improve the stability of the deadbeat predictive control method.

[0021] Furthermore, the loop equation of DPFC in the synchronous rotating coordinate system is obtained from the DPFC unit structure:

[0022]

[0023]

[0024] Where ω is the power frequency angular frequency, subscripts d and q represent the d-axis and q-axis components of the variable respectively; V r is the modulation voltage output by the H-bridge voltage source converter, R f is the epitaxial resistance of the DPFC unit AC filter inductor.

[0025] Furthermore, the DPFC predictive control method is: at each sampling moment, the DPFC output voltage V se The given value and formula (2) and the current current and voltage sampling values are used to calculate the predicted value of the inductor current at the next moment, and then the required modulation voltage V is calculated based on this predicted value and the current current and voltage sampling values. rFinally, the H-bridge voltage source converter is inverted to produce a modulated voltage vector through SPWM modulation, so that the output voltage V se Ability to follow a given value.

[0026] Furthermore, when the sampling period T s Less than 1ms, using forward difference to discretize the differential, we have:

[0027]

[0028] Where V sed (k), V seq (k) is the sampling value of the output voltage d-axis and q-axis at time k, V sed (k+1), V seq (k+1) is the given value of the output voltage d-axis and q-axis at time k+1;

[0029] Substituting formula (3) into formula (1), the predicted value of the inductor current at time k+1 is obtained as follows:

[0030]

[0031] in,

[0032] Where, I 1d (k), I 1q (k) is the sampling value of the line current d-axis and q-axis at time k, I 2d (k+1), I 2q (k+1) is the predicted value of the inductor current d-axis and q-axis at time k+1;

[0033] The same method is used to obtain the predicted value of the modulation voltage vector at time k+1 as follows:

[0034]

[0035] in,

[0036] Where V rd (k+1), V rq (k+1) is the predicted value of the modulation voltage d-axis and q-axis at time k+1;

[0037] Formula (4) and Formula (5) are the predictive control models of DPFC. Formula (4) and Formula (5) can be used to calculate the predicted value of the modulation voltage according to the given value of the output voltage, so that the DPFC output voltage can follow the given value at the next moment.

[0038] Furthermore, in order to control the stability of the DC capacitor voltage, the DC capacitor voltage outer loop is used to calculate Vsed Given a value of , a PI controller is used to ensure zero-error control, so:

[0039]

[0040] Where, is the given value of DC capacitor voltage, K P , K I are the PI controller parameters;

[0041] The given value of the output voltage calculated by formula (6) is sent to formula (4) and formula (5) for the next calculation. seq The given value Given by the system level controller.

[0042] Furthermore, the control system model consists of three parts: a reference model, an adjustable model, and a parameter adaptive law. The reference model is an actual DPFC unit, and the adjustable model is an artificially constructed DPFC mathematical model. The parameters of the adjustable model are adjusted using the parameter adaptive law so that the parameters of the adjustable model converge to the actual values.

[0043] The parameter identification of DPFC includes the parameters of filter capacitor and filter inductor. The state space equations of the two are similar, so the parameter adaptation laws of the two are also similar.

[0044] Furthermore, the state space equation of the filter capacitor is shown in formula (1), and the adjustable model of the filter capacitor is constructed as follows:

[0045]

[0046] Where, is the capacitance parameter in the adjustable model, are the d-axis and q-axis output voltages of the adjustable model respectively.

[0047] Furthermore, the adjustable model of the filter inductor is constructed as follows:

[0048]

[0049] Where, is the inductance parameter in the adjustable model, are the d-axis and q-axis output currents of the adjustable model respectively.

[0050] Furthermore, combining formula (1) and formula (7), we get:

[0051]

[0052] Where, [I d I q] T =[I 1d -I 2d I 1q -I 2q ] T .

[0053] Furthermore, the design principle of the parameter adaptive law is to make the generalized error of the control system tend to zero by adjusting the parameters of the adjustable model online. In order to achieve the error-free identification of the adjustable model parameters, the adjustable model capacitor parameters The adaptive law is:

[0054]

[0055] Where K P1 , K I1 is the first PI controller parameter; e d 、e q They represent the generalized errors of the d-axis and q-axis in the constructed adjustable filter capacitor model respectively;

[0056] For the identification of filter inductance parameters, the same method is used to obtain the filter inductance parameters The adaptive law is:

[0057]

[0058] Where K P2 , K I2 is the second PI controller parameter; e′ d 、e′ q is the generalized error of the d-axis and q-axis in the constructed adjustable filter inductance model.

[0059] The present invention has the following beneficial effects: A deadbeat predictive control method for a distributed power flow controller based on adaptive parameter identification is proposed. This control method offers advantages such as good dynamic response performance and convenient modeling. It avoids complex control parameter tuning and eliminates the effects of model parameter deviation without compromising dynamic performance, thereby improving control stability. Application of this control method to a distributed power flow controller effectively improves the control accuracy of DPFC devices, contributing positively to the promotion of DPFC applications and enhanced flexibility and controllability of power grid operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 A single-line diagram for the existing DPFC access system;

[0061] Figure 2 is an equivalent circuit model diagram of an existing DPFC subunit;

[0062] Figure 3This is a block diagram of the model reference adaptive parameter identification principle of the present invention;

[0063] Figure 4 This is a flow chart of the deadbeat predictive control method of the distributed power flow controller of the present invention. DETAILED DESCRIPTION

[0064] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings. It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with those in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless defined as herein.

[0065] Starting from the DPFC topology, the present invention analyzes the DPFC equivalent circuit model in detail and derives the predictive control model of the DPFC with LC filter. Taking into account the difference in the actual parameters of the filter inductor and filter capacitor in the LC filter, a model reference adaptive method is proposed to accurately identify the DPFC model parameters, eliminate the influence of parameter deviation, and improve the stability of the control algorithm.

[0066] DPFC is a device that is connected to the AC grid in series with two or more independently operable submodules. By controlling the output inductive or capacitive voltage, the line impedance and line flow are equivalently adjusted. The single-line diagram of the DPFC access system is as follows: Figure 1 shown.

[0067] The DPFC subunit consists of a coupling transformer, IGBT, DC capacitor, filter circuit, bypass unit, energy extraction circuit, control unit and other modules. Its equivalent circuit model is as follows: Figure 2 As shown. Among them, I1 is the current flowing into the DPFC unit, I2 is the current flowing through the filter inductor, V se is the voltage of the filter capacitor, which is also the output voltage of the DPFC unit, V r is the modulation voltage output by the H-bridge voltage source converter, V dc is the DC capacitor voltage, C f is the DPFC unit AC filter capacitor, L f is the DPFC unit AC filter inductor, R f is the epitaxial resistance of the DPFC unit AC filter inductor, C dc It is the DC filter capacitor of DPFC unit.

[0068] Depend on Figure 2The equivalent circuit model of the DPFC unit can be obtained as follows:

[0069]

[0070]

[0071] Where ω is the power angular frequency, and the subscripts d and q represent the d-axis and q-axis components of the variable, respectively.

[0072] The control goal of DPFC for the power system is to regulate the line flow while ensuring the stability of its own DC capacitor voltage. The essence of this function is to control the DPFC output voltage V se Therefore, the DPFC predictive control principle is: at each sampling moment, the output voltage V se The given value and formula (2) and the current current and voltage sampling values are used to calculate the predicted value of the inductor current at the next moment, and then the required modulation voltage V is calculated based on this predicted value and the current current and voltage sampling values. r Finally, the H-bridge voltage source converter is inverted to produce a modulated voltage vector through SPWM modulation, so that the output voltage V se Ability to follow a given value.

[0073] When the sampling period T s If the time is less than 1ms, the forward difference can be used to discretize the differential, and we have:

[0074]

[0075] Where V sed (k), V seq (k) is the sampling value of the output voltage at time k, V sed (k+1), V seq (k+1) is the given value of the output voltage at time k+1.

[0076] Substituting formula (3) into formula (1), the predicted value of the inductor current at time k+1 can be obtained as follows:

[0077]

[0078] in

[0079] Where I 1d (k), I 1q (k) is the sampling value of the line current at time k, I 2d (k+1), I 2q (k+1) is the predicted value of the inductor current at time k+1.

[0080] Using the same method, the predicted value of the modulation voltage at time k+1 can be obtained as follows:

[0081]

[0082] in

[0083] Where V rd (k+1), V rq (k+1) is the predicted value of the modulation voltage at time k+1.

[0084] Formula (4) and Formula (5) are the predictive control models of DPFC. Formula (4) and Formula (5) can be used to calculate the predicted value of the modulation voltage according to the given value of the output voltage, so that the DPFC output voltage can follow the given value at the next moment.

[0085] V sed is parallel to the line current vector, so V sed The size of V determines the active power exchanged between DPFC and the outside. In order to control the stability of the DC capacitor voltage, the DC capacitor voltage outer loop is usually used to calculate V sed To ensure zero-error control, a PI controller can be used, so

[0086]

[0087] In the formula is the given value of DC capacitor voltage, K P , K I are the PI controller parameters.

[0088] The given value of the output voltage calculated by formula (6) can be sent to formula (4) and formula (5) for the next calculation. seq The given value Usually given by a system-level controller.

[0089] The principle of model reference adaptive parameter identification is as follows Figure 3 As shown in Figure 3, the control system model consists of three parts: a reference model, an adjustable model, and a parameter adaptation law. The reference model is the actual DPFC unit, and the adjustable model is an artificially constructed DPFC mathematical model. The parameters of the adjustable model can be adjusted using the parameter adaptation law so that the parameters of the adjustable model converge to the actual values.

[0090] The parameter identification of DPFC includes the parameters of filter capacitor and filter inductor. The state space equations of the two are similar, so the parameter adaptation laws of the two are also similar.

[0091] The state space equation of the filter capacitor is shown in formula (1), and the adjustable model of the filter capacitor is constructed as follows:

[0092]

[0093] Where, is the capacitance parameter in the adjustable model, is the voltage output of the adjustable model.

[0094] The adjustable model of filter inductance is constructed as follows:

[0095]

[0096] Where, is the inductance parameter in the adjustable model, is the current output of the adjustable model.

[0097] Combining formula (1) and formula (7), we can get

[0098]

[0099] Where, [I d I q ] T =[I 1d -I 2d I 1q -I 2q ] T .

[0100] The design principle of the parameter adaptive law is to make the generalized error of the control system tend to zero by adjusting the parameters of the adjustable model online. In order to achieve the error-free identification of the adjustable model parameters, the adjustable model parameters The adaptive law is:

[0101]

[0102] For the identification of filter inductance parameters, the same method can be used to obtain the filter inductance parameters. The adaptive law is:

[0103]

[0104] Where, e′ d 、e′ q is the generalized error in the constructed adjustable filter inductor model.

[0105] like Figure 4 The distributed power flow controller zero-beat predictive control method shown includes a predictive control method and a parameter identification method.

[0106] The predictive control method comprises the following steps:

[0107] 11) Determine the DC capacitor voltage command and sampling period T s ;

[0108] 12) In the kth sampling cycle, the current I1 flowing into the DPFC unit, the current I2 flowing through the filter inductor, and the voltage of the filter capacitor are sampled. The voltage of the filter capacitor is the output voltage V of the DPFC unit. se And through the coordinate rotation transformation, we can get the d and q axis components in the synchronous rotating coordinate system: I 1d (k), I 1q (k), I 2d (k), I 2q (k), V sed (k) and V seq (k);

[0109] 13) Calculate the given value of the d-axis component of the DPFC unit output voltage according to formula (6): Will As the given value at time k+1, that is,

[0110] 14) Determine the q-axis component of the output voltage V given by the system-level controller seq The given value Will As the given value at time k+1, that is,

[0111] 15) Calculate the d and q components of the modulation voltage V at time k+1 using formulas (4) and (5): rd (k+1), V rq (k+1), the modulation voltage V is obtained by coordinate rotation inverse transformation r (k+1);

[0112] 16) The obtained modulation voltage V r (k+1) generates a trigger pulse by SPWM modulation method, and the obtained trigger pulse is used to control the H-bridge converter in the DPFC;

[0113] 17) In the next sampling period, repeat steps 12)-16);

[0114] The parameter identification method comprises the following steps:

[0115] 21) Determine the initial value of the filter capacitor in the adjustable model and the initial value of the filter inductor

[0116] 22) In the kth sampling period, the sampled I 1d (k), I 1q (k), V sed (k) and V seq Substituting (k) into formulas (7) and (8), we get and The symbol ^ represents the parameters in the adjustable model and further calculates the generalized error;

[0117] 23) Adjust the capacitance parameters in the adjustable model using formulas (10) and (11). and inductance parameters

[0118] 24) Determine whether the generalized error converges; if the generalized error converges, use and Replace the AC filter capacitor C in formula (4) and formula (5) f and DPFC unit AC filter inductor L f ; If the generalized error does not converge, repeat steps 22)-23) in the next sampling period.

[0119] The above descriptions are only partial embodiments of the present invention. It should be pointed out that ordinary technicians in this technical field can make several improvements and modifications without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A deadbeat predictive control method for a distributed power flow controller, characterized in that: Including predictive control methods and parameter identification methods; The predictive control method comprises the following steps: 11) Determine the DC capacitor voltage command and sampling period T s ; 12) In the kth sampling cycle, the current I1 flowing into the DPFC unit, the current I2 flowing through the filter inductor, and the voltage of the filter capacitor are sampled. The voltage of the filter capacitor is the output voltage V of the DPFC unit. se And through the coordinate rotation transformation, we can get the d and q axis components in the synchronous rotating coordinate system: I 1d (k), I 1q (k), I 2d (k), I 2q (k), V sed (k) and V seq (k); 13) Calculate the given value of the d-axis component of the DPFC unit output voltage Will As the given value at time k+1, that is, 14) Determine the q-axis component of the output voltage V given by the system-level controller seq The given value Will As the given value at time k+1, that is, 15) Calculate the d and q components of the modulation voltage V at time k+1 using the DPFC predictive control model rd (k+1), V rq (k+1), the modulation voltage V is obtained by coordinate rotation inverse transformation r (k+1); 16) The obtained modulation voltage V r (k+1) generates a trigger pulse by SPWM modulation method, and the obtained trigger pulse is used to control the H-bridge converter in the DPFC; 17) In the next sampling period, repeat steps 12)-16); The parameter identification method comprises the following steps: 21) Determine the initial value of the filter capacitor in the adjustable model and the initial value of the filter inductor 22) In the kth sampling period, the sampled I 1d (k), I 1q (k), V sed (k) and V seq (k) is calculated and we get and The symbol ^ represents the parameters in the adjustable model and further calculates the generalized error; 23) Adjust the capacitance parameters in the adjustable model and inductance parameters 24) Determine whether the generalized error converges; if the generalized error converges, use and Replace the AC filter capacitor C in the DPFC unit f and DPFC unit AC filter inductor L f ; If the generalized error does not converge, repeat steps 22)-23) in the next sampling period.

2. The deadbeat predictive control method for a distributed power flow controller according to claim 1, characterized in that: The loop equation of DPFC in the synchronous rotating coordinate system is obtained from the DPFC unit structure: Where ω is the power frequency angular frequency, subscripts d and q represent the d-axis and q-axis components of the variable respectively; V r is the modulation voltage output by the H-bridge voltage source converter, R f is the epitaxial resistance of the DPFC unit AC filter inductor.

3. The deadbeat predictive control method for a distributed power flow controller according to claim 2, characterized in that: The DPFC predictive control method is: at each sampling moment, the DPFC output voltage V se The given value and formula (2) and the current current and voltage sampling values are used to calculate the predicted value of the inductor current at the next moment, and then the required modulation voltage V is calculated based on this predicted value and the current current and voltage sampling values. r Finally, the H-bridge voltage source converter is inverted to produce a modulated voltage vector through SPWM modulation, so that the output voltage V se Ability to follow a given value.

4. The deadbeat predictive control method for a distributed power flow controller according to claim 3, characterized in that: When the sampling period T s Less than 1ms, using forward difference to discretize the differential, we have: Where V sed (k), V seq (k) is the sampling value of the output voltage d-axis and q-axis at time k, V sed (k+1), V seq (k+1) is the given value of the output voltage d-axis and q-axis at time k+1; Substituting formula (3) into formula (1), the predicted value of the inductor current at time k+1 is obtained as follows: in, Where, I 1d (k), I 1q (k) is the sampling value of the line current d-axis and q-axis at time k, I 2d (k+1), I 2q (k+1) is the predicted value of the inductor current d-axis and q-axis at time k+1; The same method is used to obtain the predicted value of the modulation voltage vector at time k+1 as follows: in, Where V rd (k+1), V rq (k+1) is the predicted value of the modulation voltage d-axis and q-axis at time k+1; Formula (4) and Formula (5) are the predictive control models of DPFC. Formula (4) and Formula (5) can be used to calculate the predicted value of the modulation voltage according to the given value of the output voltage, so that the DPFC output voltage can follow the given value at the next moment.

5. The deadbeat predictive control method for a distributed power flow controller according to claim 4, characterized in that: To control the stability of the DC capacitor voltage, the DC capacitor voltage outer loop is used to calculate V sed Given a value of , a PI controller is used to ensure zero-error control, so: Where, is the given value of DC capacitor voltage, K P , K I are the PI controller parameters; The given value of the output voltage calculated by formula (6) is sent to formula (4) and formula (5) for the next step of calculation. seq The given value Given by the system level controller.

6. The deadbeat predictive control method for a distributed power flow controller according to claim 3, characterized in that: The control system model consists of three parts: a reference model, an adjustable model, and a parameter adaptive law. The reference model is the actual DPFC unit, and the adjustable model is an artificially constructed DPFC mathematical model. The parameters of the adjustable model are adjusted using the parameter adaptive law so that the parameters of the adjustable model converge to the actual values. The parameter identification of DPFC includes the parameters of filter capacitor and filter inductor. The state space equations of the two are similar, and the parameter adaptation laws of the two are also similar.

7. The deadbeat predictive control method for a distributed power flow controller according to claim 6, characterized in that: The state space equation of the filter capacitor is shown in formula (1), and the adjustable model of the filter capacitor is constructed as follows: Where, is the capacitance parameter in the adjustable model, are the d-axis and q-axis output voltages of the adjustable model respectively.

8. The deadbeat predictive control method for a distributed power flow controller according to claim 7, characterized in that: The adjustable model of filter inductance is constructed as follows: Where, is the inductance parameter in the adjustable model, are the d-axis and q-axis output currents of the adjustable model respectively.

9. The deadbeat predictive control method for a distributed power flow controller according to claim 8, characterized in that: Combining formula (1) and formula (7), we get: In the ceremony, [I d I q ] T =[I 1d -I 2d I 1q -I 2q ] T .

10. The deadbeat predictive control method for a distributed power flow controller according to claim 9, characterized in that: The design principle of the parameter adaptive law is to make the generalized error of the control system tend to zero by adjusting the parameters of the adjustable model online. In order to achieve the error-free identification of the adjustable model parameters, the adjustable model capacitance parameters The adaptive law is: Where K P1 , K I1 is the first PI controller parameter; e d 、e q They represent the generalized errors of the d-axis and q-axis in the constructed adjustable filter capacitor model respectively; For the identification of filter inductance parameters, the same method is used to obtain the filter inductance parameters The adaptive law is: Where K P2 , K I2 is the second PI controller parameter; e′ d 、e′ q is the generalized error of the d-axis and q-axis in the constructed adjustable filter inductance model.

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