A single-phase rectifier control method, device and medium

Abstract

This application discloses a single-phase rectifier control method, device, and medium, relating to the field of single-phase rectifier control technology. Addressing the problems of traditional control schemes failing to suppress secondary ripple and requiring the introduction of additional circuits to absorb it, this application provides a single-phase rectifier control method. This method replaces the PI-type controller with a PD-type iterative learning controller corrected by a nonlinear hyperbolic tangent function, thereby achieving control of the single-phase rectifier. Firstly, the PD-type iterative learning controller itself can suppress secondary ripple without introducing a secondary LC resonant circuit, avoiding the problems associated with poor frequency adaptability, increased DC bus parallel capacitance, and weakened system dynamic response speed. Secondly, the differential element in the PD-type iterative learning controller of this method is corrected by a nonlinear hyperbolic tangent function, effectively overcoming the problem of iteration error accumulation caused by low-frequency ripple in the differential element of traditional PD-type iterative learning controllers.

Description

Technical Field

[0001] This application relates to the field of single-phase rectifier control technology, and in particular to a single-phase rectifier control method, device and medium. Background Technology

[0002] In railway electric traction transportation and other fields, pulse-width modulation (PWM) based single-phase rectifiers are widely used due to their advantages such as high power factor, low current ripple, and bidirectional energy flow. Currently, the control schemes for single-phase voltage-source rectifiers mostly employ direct current control with dual closed-loop voltage and current control. However, the inherent secondary ripple of the DC bus voltage in single-phase rectifiers affects the DC bus voltage feedback, and this abnormal feedback leads to the generation of odd harmonics in the grid-side current.

[0003] In related technologies, a common solution is to connect a secondary inductor-capacitor (LC) resonant circuit in parallel with the DC bus to absorb secondary ripple. While this approach effectively suppresses secondary ripple, it suffers from poor frequency adaptability and increases the parallel capacitance of the DC bus, thus weakening the dynamic response speed of the control system.

[0004] Therefore, those skilled in the art urgently need a single-phase rectifier control method to solve a series of problems existing in the currently used secondary ripple suppression scheme. Summary of the Invention

[0005] The purpose of this application is to provide a single-phase rectifier control method, device and medium to solve the problems of reduced system response speed and poor frequency adaptability caused by the need to introduce a secondary LC resonant circuit to absorb secondary ripple.

[0006] To solve the above-mentioned technical problems, this application provides a single-phase rectifier control method, including:

[0007] Obtain the input electromotive force, input current, output voltage, and target reference voltage of the single-phase rectifier;

[0008] Based on the iterative learning controller, the reference current is determined according to the output voltage and the target reference voltage; wherein, the iterative learning controller is a PD-type iterative learning controller whose differential element is corrected by a nonlinear hyperbolic tangent function;

[0009] The input reference voltage is determined based on the reference current, the input electromotive force, and the input current.

[0010] The corresponding PWM control signal is determined based on the input reference voltage to control the single-phase rectifier.

[0011] In one optional embodiment, the step of establishing the iterative learning controller includes:

[0012] Determine the voltage tracking error; wherein, the voltage tracking error is: the difference between the actual DC bus voltage measured in the current iteration cycle and the target reference voltage;

[0013] Based on the voltage tracking error, determine the rate of change of the voltage tracking error;

[0014] The voltage tracking error and the rate of change of the voltage tracking error are processed by a nonlinear hyperbolic tangent function, and the corresponding hyperbolic tangent value is determined.

[0015] The learning amount for the current iteration cycle is determined based on the hyperbolic tangent value; wherein the learning amount is composed of a weighted sum of a proportional term and a differential term, the weight of the proportional term being a proportional coefficient, and the weight of the differential term being a differential coefficient.

[0016] The error learning law function is determined based on the learning amount, and the iterative learning controller is established based on the error learning law function.

[0017] In one optional embodiment, the control equation of the iterative learning controller is:

[0018] ;

[0019] In the formula, and K represents the reference current at time t in the k-th and (k+1)-th iteration cycles, respectively; P and K D These are the proportionality coefficient and the differential coefficient, respectively; tanh() is the nonlinear hyperbolic tangent function; e k+1 (t) and These are the voltage tracking error and the rate of change of the voltage tracking error at time t in the (k+1)th iteration cycle, respectively. The target reference voltage; Let be the actual DC bus voltage at time t in the (k+1)th iteration cycle.

[0020] In one optional embodiment, the proportional coefficient and the differential coefficient satisfy the system convergence condition;

[0021] The system convergence condition is as follows:

[0022] ;

[0023] In the formula, P(jω) is the system's output transfer function, j is the imaginary unit, and ω is the grid angular frequency.

[0024] In one optional embodiment, determining the input reference voltage based on the reference current, the input electromotive force, and the input current includes:

[0025] The virtual electromotive force and virtual current of the virtual phase are determined by the input electromotive force and the input current using a second-order generalized integrator.

[0026] The input electromotive force and the virtual electromotive force are subjected to dq transformation to obtain the d-axis electromotive force and the q-axis electromotive force; the input current and the virtual current are subjected to dq transformation to obtain the d-axis current and the q-axis current.

[0027] Based on the reference current, the d-axis electromotive force and q-axis electromotive force, the d-axis current and q-axis current, the optimal voltage vectors on the d-axis and q-axis are determined based on the deadbeat principle.

[0028] The optimal voltage vectors on the d-axis and q-axis are subjected to an inverse dq transformation to obtain the input reference voltage.

[0029] In an optional embodiment, determining the virtual electromotive force and virtual current of the virtual phase based on the input electromotive force and the input current using a second-order generalized integrator includes:

[0030] The virtual electromotive force and the virtual current are determined by the transfer function of the second-order generalized integrator.

[0031] The transfer function of the second-order generalized integrator is:

[0032] ;

[0033] In the formula, X(s) represents the input signal, X α (s) and X β (s) and ω are the output α-axis and β-axis signals, respectively, where the signal on the virtual phase corresponds to the β-axis signal; s is a complex variable; k and ω SOGI These are the scaling factor and characteristic angular frequency of the second-order generalized integrator, respectively.

[0034] In one optional embodiment, the deadbeat principle is:

[0035] i d (k+1)=i d_ref ;

[0036] i q (k+1)=i q_ref ;

[0037] In the formula, i d (k+1) and i q (k+1) represent the d-axis current and the q-axis current respectively in the (k+1)th iteration period; i d_ref and i q_ref These are the components of the reference current on the d-axis and q-axis, respectively;

[0038] The expression for determining the optimal voltage vector is:

[0039] ;

[0040] In the formula, u d (k) and u q (k) represent the optimal voltage vectors on the d-axis and q-axis respectively in the k-th iteration cycle; L g R g T s ω and i represent the grid-side filter inductance, grid-side filter resistance, control cycle duration, and grid angular frequency, respectively; d (k) and i q (k) represent the d-axis current and the q-axis current respectively in the k-th iteration period; e d (k) and e q (k) represents the d-axis electromotive force and the q-axis electromotive force in the k-th iteration cycle, respectively.

[0041] To address the aforementioned technical problems, this application also provides a single-phase rectifier control device, comprising:

[0042] The parameter acquisition module is used to acquire the input electromotive force, input current, output voltage, and target reference voltage of the single-phase rectifier.

[0043] A current determination module is used to determine a reference current based on the output voltage and the target reference voltage, using an iterative learning controller; wherein the iterative learning controller is a PD-type iterative learning controller whose differential element is corrected by a nonlinear hyperbolic tangent function.

[0044] A voltage determination module is used to determine an input reference voltage based on the reference current, the input electromotive force, and the input current.

[0045] The control output module is used to determine the corresponding PWM control signal based on the input reference voltage to control the single-phase rectifier.

[0046] To address the aforementioned technical problems, this application also provides a single-phase rectifier control device, comprising:

[0047] Memory, used to store computer programs;

[0048] A processor is configured to execute the computer program to implement the steps of the single-phase rectifier control method as described above.

[0049] To address the aforementioned technical problems, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the single-phase rectifier control method described above.

[0050] This application provides a single-phase rectifier control method that replaces the proportional-integral (PI) controller in the original voltage-current dual closed-loop control scheme with a proportional-derivative (PD) iterative learning controller corrected by a nonlinear hyperbolic tangent function to achieve control of the single-phase rectifier. Firstly, by replacing the traditional PI controller with a PD-type iterative learning controller, this method can suppress secondary ripple without introducing a secondary inductor-capacitor (LC) resonant circuit, thus avoiding the problems caused by the secondary LC resonant circuit, such as poor frequency adaptability, increased DC bus parallel capacitance, and weakened system dynamic response speed. Secondly, the iterative learning controller in this method is a PD-type iterative learning controller with its derivative element corrected by a nonlinear hyperbolic tangent function, effectively overcoming the problem of iteration error accumulation caused by low-frequency ripple in the derivative element of the traditional PD-type iterative learning controller. Therefore, the single-phase rectifier control based on this method can suppress the influence of secondary ripple in the DC bus voltage and improve the control accuracy and response speed of the single-phase rectifier.

[0051] The single-phase rectifier control device and computer-readable storage medium provided in this application correspond to the above method and have the same effect. Attached Figure Description

[0052] To more clearly illustrate the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 A flowchart of a single-phase rectifier control method provided in an embodiment of the present invention;

[0054] Figure 2 A control principle diagram of a single-phase rectifier control method provided in an embodiment of the present invention;

[0055] Figure 3 A flowchart of an iterative learning method provided in an embodiment of the present invention;

[0056] Figure 4 A structural diagram of a single-phase rectifier control device provided in an embodiment of the present invention;

[0057] Figure 5 This is a structural diagram of another single-phase rectifier control device provided in an embodiment of the present invention. Detailed Implementation

[0058] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of this application.

[0059] The core of this application is to provide a single-phase rectifier control method, device, and medium.

[0060] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0061] The mathematical model of a single-phase rectifier is:

[0062] (1);

[0063] In the formula, e g i g and u g These are the grid electromotive force, grid input current, and single-phase rectifier input voltage, respectively; u dc m g and i o These are the DC bus voltage, rectifier modulation, and DC load current, respectively; L g R g C and C are the values ​​of the grid-side filter inductance, filter resistor, and DC support capacitor, respectively.

[0064] When a single-phase rectifier is operating normally, its DC bus voltage secondary ripple content u d2 Satisfy the following formula:

[0065] (2);

[0066] In the formula, U g I g and U dc ω and θ represent the effective values ​​of the input voltage, input current, and constant component of the DC bus voltage of the single-phase rectifier, respectively; ω and θ represent the grid angular frequency and power factor angle, respectively; sin() is the sine function.

[0067] In related technologies, the current control of single-phase rectifiers employs direct current control with dual closed-loop voltage and current control, implemented through a proportional-integral (PI) controller. As mentioned above, single-phase rectifiers inherently exhibit a second-order ripple in the DC bus voltage. Traditional PI controllers cannot suppress this second-order ripple in the voltage outer loop feedback. Therefore, this second-order ripple affects the PI controller's bus voltage feedback, resulting in a second-order ripple component in the grid-side current amplitude, and the generated grid-side current reference value will be accompanied by a third harmonic. Furthermore, the interaction of the third-order ripple current with the fundamental frequency will generate even-order voltage ripple and odd-order current ripple, thus requiring improvements to the voltage outer loop controller to suppress the second-order ripple.

[0068] To address this, the current solution is to connect a secondary inductor-capacitor (LC) resonant circuit in parallel with the DC bus to absorb secondary ripple on the DC bus and avoid the aforementioned problems. However, this secondary ripple suppression scheme introduces new issues: the secondary LC resonant circuit has poor frequency adaptability, increases the parallel capacitance of the DC bus, and weakens the system's dynamic response speed.

[0069] To address the aforementioned problems, this application provides a single-phase rectifier control method, such as... Figure 1 As shown, it includes:

[0070] S11: Obtain the input electromotive force, input current, output voltage, and target reference voltage of the single-phase rectifier.

[0071] S12: Based on the iterative learning controller, the reference current is determined according to the output voltage and the target reference voltage.

[0072] Among them, the iterative learning controller is a PD-type iterative learning controller after the differential element has been corrected by a nonlinear hyperbolic tangent function.

[0073] S13: Determine the input reference voltage based on the reference current, input electromotive force, and input current.

[0074] S14: Determine the corresponding PWM control signal based on the input reference voltage to control the single-phase rectifier.

[0075] Step S11 above involves acquiring or setting the electrical parameters required for controlling the single-phase rectifier. In the single-phase rectifier control implemented by this method, the electrical parameters that need to be determined include: input electromotive force e. g Input current i g Output voltage u dc and target reference voltage Among them, the input electromotive force e g Input current i g Output voltage udc The actual parameters obtained through acquisition or measurement are all common electrical parameters in power grid scenarios, and can be acquired using appropriate sensors or acquisition circuits. The target reference voltage... This is a set value, representing the target value of the voltage output by the controller.

[0076] In step S12, this method uses a proportional-derivative (PD) iterative learning controller to replace the PI controller in related technologies to control the single-phase rectifier. Since the secondary ripple is a periodic signal, closed-loop iterative learning control is very effective for controlling periodic signals. The principle of closed-loop iterative learning is as follows.

[0077] For a system that operates repeatedly, the dynamic equation can be expressed as:

[0078] (3);

[0079] In the formula, x k y k u k Let f() and g() represent the state, output, and input variables of the system during its k-th run, respectively; f() and g() are two functional relationships, and t is the time variable.

[0080] Assume the expected output of the system is y d (t), where the control law and error propagation of the closed-loop iterative learning are:

[0081] (4);

[0082] In the formula, L[u k (t), e k+1 [(t)] is the error learning law function, e k+1 (t) refers to the output error (voltage tracking error) of the (k+1)th iteration cycle (i.e., the (k+1)th iteration).

[0083] Furthermore, substituting the mathematical model of the rectifier voltage outer loop represented by equation (1) into equation (4), we can obtain:

[0084] (5);

[0085] In the formula, and These are the reference currents (d-axis) at time t in the k-th and (k+1)-th iteration cycles, respectively, which are also the input variables of the iterative learning controller (DC voltage). The target reference voltage (DC link); Let be the actual DC bus voltage at time t in the (k+1)th iteration cycle.

[0086] Therefore, it can be seen that the traditional PD-type error learning law satisfies K P and K D These are the proportional and derivative coefficients, respectively. They can effectively track the reference value and suppress oscillations. That is, in step S12 of this method, a PD-type iterative learning controller replaces the traditional PI-type controller, which can effectively control the secondary ripple, including its suppression.

[0087] However, the differential element in the traditional PD-type error learning law is essentially the difference of the error. When the output signal contains low frequencies (i.e., the second ripple of the bus voltage), the error will accumulate continuously, thus deteriorating the iterative learning effect. Therefore, the iterative learning controller used in step S12 of this method is a new learning iterative controller obtained by improving the traditional PD-type iterative learning controller. Specifically, this method corrects the error through a nonlinear hyperbolic tangent function, achieving the effect of "large error, small gain; small error, large gain". The improved iterative learning law function is as follows:

[0088] (6);

[0089] In the formula, exp(x) represents an exponential function of the variable x with base e; e k+1 (t) and Let be the voltage tracking error and the rate of change of voltage tracking error (the derivative of voltage tracking error) at time t in the (k+1)th iteration cycle, respectively.

[0090] Based on this improved iterative learning law function, the PD-type learning iterative controller (control equation) used in step S12 of this method after error correction by the nonlinear hyperbolic tangent function can be determined, thereby realizing the control of the single-phase rectifier.

[0091] For steps S13 and S14, that is, based on the parameter reference current i output by the learning iterative controller in step S12. ref (including d-axis component i) d_ref and q-axis component i q_ref The reference current in step S12 above mainly refers to the d-axis component i. d_ref Because the q-axis component i can be obtained by controlling the unity power factor of a single-phase rectifier. q_ref =0; therefore, all subsequent values ​​are based on the d-axis component i. d_refThe steps involve obtaining the final pulse width modulation (PWM) signal used to control the single-phase rectifier (referring to the reference current). Referring to direct current control with a voltage-current dual closed loop in related technologies, this method also provides a voltage-current dual closed-loop control. However, the difference lies in that the inner loop in this method is current control, and the outer loop is voltage control. Specifically, step S12 corresponds to the outer loop voltage control, and step S13 corresponds to the inner loop current control. The reference current i obtained from the outer loop voltage control corresponding to step S12... q_ref This serves as a guide for inner-loop current control. Finally, after dual closed-loop control of the outer-loop voltage and inner-loop current, the final input reference voltage u used to determine the PWM signal is obtained. g_opt (k), while the input reference voltage u g_opt (k) The process of converting into a PWM signal that can directly control a single-phase rectifier corresponds to step S14 above.

[0092] The specific control architecture is as follows: Figure 2 As shown, after passing through the dual closed-loop control logic of outer loop voltage and inner loop current, the input reference voltage u that guides the control of the single-phase rectifier is obtained. g_opt (k) to generate the PWM signal S for controlling the single-phase rectifier. a1 S a2 S b1 S b2 These correspond to the conduction control of the four switching transistors in a single-phase rectifier. Figure 2 The PWM rectifier mentioned here refers to a single-phase rectifier controlled by a PWM signal.

[0093] It should be noted that this embodiment focuses on improving the outer loop voltage control in the aforementioned dual closed-loop control. A closed-loop PD-type iterative learning controller with nonlinear hyperbolic tangent function correction is used instead of a PI controller to address the problem that current control schemes cannot suppress DC bus voltage. And as... Figure 2 As shown, this application also provides an inner-loop current control scheme. However, it should be noted that the improvement on the outer-loop voltage control described above in this embodiment is sufficient to solve the problem that current control schemes cannot suppress DC bus voltage. Therefore... Figure 2 The inner loop current control scheme shown is only one optional implementation scheme and is not limited to the inner loop current control necessarily adopting the following method: Figure 2 The control scheme shown. Except Figure 2 In addition to the inner loop current control scheme shown, other suitable control schemes can be freely selected according to actual needs. This embodiment does not impose any restrictions on this.

[0094] In summary, the single-phase rectifier control method provided in this application uses a closed-loop PD-type iterative learning controller with nonlinear hyperbolic tangent function correction instead of a PI controller. This avoids the problem of traditional PI controllers failing to suppress the second-order ripple of the DC bus voltage, leading to harmonic distortion in the output current reference. Furthermore, it eliminates the accumulation of iteration errors caused by low-frequency ripple in the differential stage of the traditional PD-type iterative learning controller, thus achieving better control of the single-phase rectifier.

[0095] Furthermore, as can be seen from the above embodiments, this method effectively suppresses secondary ripple through closed-loop iterative learning control, specifically through an improved PD-type iterative learning controller. However, the above embodiments do not strictly limit the specific iterative learning process for obtaining the PD-type iterative learning controller. To further illustrate this method, this embodiment also provides an optional implementation scheme for the iterative learning process of the iterative learning controller, such as... Figure 3 As shown, it includes:

[0096] S21: Determine the voltage tracking error.

[0097] Among them, voltage tracking error e k+1 (t) can be obtained from the above equation (5), which is: the actual DC bus voltage measured in the current iteration cycle (the (k+1)th iteration cycle). , and the target reference voltage The difference between them.

[0098] It should be noted that, Figure 3 The diagram shown is only the iterative learning process in one iteration cycle (the (k+1)th iteration cycle).

[0099] S22: Determine the rate of change of voltage tracking error based on the voltage tracking error.

[0100] This step involves checking the voltage tracking error e. k+1 Differentiate (t). Figure 3 The delay element e in k (t) represents the "memory" of the iterative learning controller, which stores the iterative output error (i.e., voltage tracking error) of the previous iteration cycle (the k-th iteration cycle), and is the basis for calculating the error derivative in step S22. The voltage tracking error change rate is... In the formula, T s This is the duration of the iteration cycle.

[0101] S23: The voltage tracking error and the rate of change of voltage tracking error are processed by a nonlinear hyperbolic tangent function, and the corresponding hyperbolic tangent value is determined.

[0102] This step is the core step in improving the traditional PD-type iterative learning controller, calculating the voltage tracking error and the hyperbolic tangent function of the rate of change of the voltage tracking error. The purpose of using the hyperbolic tangent (tanh) function is to achieve the correction effect of "small gain for large errors and large gain for small errors". When the error is large, the output of the tanh function is close to ±1, avoiding excessive gain that could cause system oscillation; when the error is small, the tanh function is approximately linear, providing a large gain, thereby quickly eliminating small errors and improving control accuracy.

[0103] S24: Determine the learning amount for the current iteration cycle based on the hyperbolic tangent value.

[0104] In this step, the "learning amount" for the current iteration cycle is calculated based on the hyperbolic tangent value obtained in step S23. According to equation (6) above, this learning amount L[u] k (t),e k+1 [t] is composed of a weighted average of proportional and differential terms. The weight of the proportional term is the proportional coefficient, and the weight of the differential term is the differential coefficient.

[0105] S25: Determine the error learning law function based on the learning amount, and establish an iterative learning controller based on the error learning law function.

[0106] This step corresponds to the input update and output of the iterative learning controller. For example... Figure 3 As shown, Figure 3 The delay element in u k (t) also represents the "memory" of the iterative learning controller. However, it differs from the aforementioned delay element e. k The difference is that it stores the final control output u of the previous iteration cycle. k (t) (i.e., the reference current output in the kth iteration cycle) This is also the basis for the adjustment of the learning controller in this iteration.

[0107] The iterative learning controller will use the previous control command u k (t) and the currently calculated learning amount L[u k (t), e k+1 Adding (t) together, we obtain the optimized new control instruction u for this iteration (k+1th iteration). k+1 (t). This control command is the final output of this iterative learning controller (voltage outer loop controller), which actually refers to the aforementioned reference current. It is then sent to the inner current loop as its core control objective.

[0108] As described above, the essence of the iterative learning process provided in this embodiment is to correct the current error based on previous experience. This results in a more accurate control output, enabling more precise control of the single-phase rectifier.

[0109] Furthermore, based on the iterative learning process provided in this embodiment, the iterative learning law function corresponding to equation (6) in the above embodiment is further derived to obtain the control equation of the iterative learning controller:

[0110] Based on the iterative learning law function of equation (6), the corresponding error propagation equation is:

[0111] (7);

[0112] In the formula, P(s) is the system's output transfer function, and satisfies y(t) = P(s)[u(t)]. u(t) and y(t) are the system's input and output, respectively.

[0113] Let the function σ = tanh(τ), where σ and τ represent the result variable after processing with the hyperbolic tangent function tanh and the original variable before processing, respectively. According to the Lagrange Mean Value Theorem, we can obtain:

[0114] (8);

[0115] In the formula, c1 and c2 are the Lagrange medians, respectively.

[0116] Substituting the Lagrange mean values ​​c1 and c2 obtained above into equation (7), we get:

[0117] (9);

[0118] In the formula, sech() is the hyperbolic secant function.

[0119] Furthermore, in practical applications, the control system of a single-phase rectifier is required to be convergent, that is, it needs to satisfy |e k+1 |<|e k The statement that "constantly holds true" is equivalent to:

[0120] (10);

[0121] After sorting, we can obtain:

[0122] (11);

[0123] As can be seen from equation (11) above, compared with the traditional closed-loop PD-type iterative learner, the effective gain of the nonlinear system provided in this embodiment never exceeds the set fixed gain. Therefore, the convergence of the system is consistent with that of the traditional closed-loop PD-type iterative learner, that is, when the proportional coefficient K P and differential coefficient K D The controller converges when the following equation is satisfied:

[0124] (12);

[0125] In the formula, P(jω) is the output transfer function P(s) of the system after Laplace transform.

[0126] Based on this, this embodiment provides an optional embodiment:

[0127] The proportional coefficient K in the control equation of the iterative learning controller P and differential coefficient K D The system convergence condition of equation (12) above is satisfied.

[0128] Furthermore, the control equations of the iterative learning controller described above are now:

[0129] (13);

[0130] In the formula, e k+1 (t) and Let be the voltage tracking error and the rate of change of voltage tracking error at time t in the (k+1)th iteration period, respectively. The target reference voltage; Let be the actual DC bus voltage at time t in the (k+1)th iteration cycle.

[0131] The target reference voltage can be determined using the above formula (13). and the output voltage in this iteration (That is, the DC bus voltage measured in the (k+1)th iteration) yields the reference current output. .

[0132] Based on this, in an optional embodiment, step S12 above is to determine the reference current according to the control equation of the iterative learning controller shown in equation (13).

[0133] On the other hand, as illustrated in the above embodiments, Figure 2 An optional inner-loop current control scheme is shown. This embodiment further explains this inner-loop current control scheme. Specifically, step S13 corresponding to the inner-loop current control includes:

[0134] S131: The virtual electromotive force and virtual current of the virtual phase are determined by the second-order generalized integrator based on the input electromotive force and input current.

[0135] S132: Perform dq transformation on the input electromotive force and the virtual electromotive force to obtain the d-axis electromotive force and the q-axis electromotive force; perform dq transformation on the input current and the virtual current to obtain the d-axis current and the q-axis current.

[0136] S133: Based on the reference current, d-axis electromotive force, q-axis electromotive force, d-axis current, and q-axis current, determine the optimal voltage vectors on the d-axis and q-axis according to the deadbeat principle.

[0137] S134: Perform an inverse dq transformation on the optimal voltage vectors on the d-axis and q-axis to obtain the input reference voltage.

[0138] It should be noted that the inner-loop DC control scheme provided in this embodiment is an improvement upon the traditional direct current control, further suppressing secondary ripple, improving the accuracy of grid current phase control, and accelerating the system response speed. Specifically, this embodiment leverages the superiority of a three-phase PWM rectifier controlled by the dq-axis current in the dq-axis coordinate system, and performs a similar transformation on the electrical variables of the single-phase rectifier. That is, a second-order generalized integrator (SOGI) is used to generate a virtual β-axis that intersects with the original rectifier.

[0139] Specifically, in an optional embodiment, step S131 above can be implemented using the transfer function of SOGI to achieve a mathematical transformation, thereby efficiently and conveniently obtaining the virtual current and virtual electromotive force on the virtual phase. The transfer function of SOGI is shown below:

[0140] (14);

[0141] In the formula, X(s) represents the input signal, X α (s) and X β (s) and ω are the output α-axis and β-axis signals, respectively, where the signal on the virtual phase corresponds to the β-axis signal; s is a complex variable; k and ω SOGI These are the scaling factor and characteristic angular frequency of the second-order generalized integrator, respectively.

[0142] Based on equation (14), when the single-phase electrical signals (including electromotive force, current, and voltage) of a single-phase rectifier are input into SOGI, mutually orthogonal α-β coordinate signals can be generated. However, it should be noted that the generated α-β coordinate signals are still time-varying signals. In order to convert them into time-invariant signals, the single-phase rectifier α-β coordinate signals generated by SOGI need to be transformed into dq coordinate system signals according to the grid voltage orientation angle ωt. The dq transformation process is as follows:

[0143] (15);

[0144] In the formula, X d (s) and X q (s) represent the d-axis and q-axis signals after transformation, respectively; cos and sin represent the cosine and sine functions, respectively. The above step S132 can be implemented by equation (15).

[0145] Based on the signal obtained after the above dq transformation, the mathematical model of the single-phase rectifier in the dq coordinate system is transformed as follows:

[0146] (16);

[0147] In the formula, the subscripts d and q represent the corresponding variables on the d-axis and q-axis, respectively (i is the current, e is the electromotive force, u is the voltage, and m is the rectifier modulation degree).

[0148] Furthermore, after obtaining the variables (current, electromotive force) transformed to the dq coordinate system, the corresponding control variable (voltage) is determined based on these variables. As in step S133 above, this embodiment combines the deadbeat principle to achieve deadbeat current control, which can improve the system response speed. Specifically, this embodiment also provides an optional solution for the specific implementation of step S133 above:

[0149] Discretize the above equation (16) using forward Euler discretization, and based on the principle of no-difference beats, i.e. d (k+1)=i d_ref i q (k+1)=i q_ref The optimal voltage vector is determined as follows:

[0150] (17);

[0151] In the formula, i d (k+1) and i q (k+1) represent the d-axis current and q-axis current in the (k+1)th iteration cycle, respectively; i d_ref and i q_ref These are the components of the reference current on the d-axis and q-axis, respectively; u d (k) and u q (k) represent the optimal voltage vectors on the d-axis and q-axis respectively in the k-th iteration cycle; L g R g T s ω and i represent the grid-side filter inductance, grid-side filter resistance, control cycle duration, and grid angular frequency, respectively; d (k) and i q(k) represent the d-axis current and q-axis current in the k-th iteration cycle, respectively; e d (k) and e q (k) represents the d-axis electromotive force and q-axis electromotive force in the k-th iteration cycle, respectively.

[0152] Since the optimal voltage vector obtained in equation (17) is a variable in the dq coordinate system, it needs to be converted back to single-phase in actual control to meet the control requirements of the single-phase rectifier. Corresponding to step S134, the optimal voltage vector obtained in equation (17) is subjected to an inverse dq transformation to be converted back to single-phase to obtain the input reference voltage u. g_opt (k), the transformation process is as follows:

[0153] (18);

[0154] Thus, a reference value for the actual input voltage of the single-phase rectifier is obtained. Based on this, a PWM signal can be generated to achieve effective control of the single-phase rectifier. It should be noted that the inner-loop current control scheme provided in this embodiment combines dq decoupling of the second-order generalized integrator with deadbeat current control. On the one hand, converting the time-varying sinusoidal signal into a DC signal in the dq coordinate system can improve the static current tracking performance of the single-phase rectifier. On the other hand, this embodiment uses deadbeat current control instead of direct current control, which can further improve the system's response speed and phase control accuracy.

[0155] In the above embodiments, a single-phase rectifier control method has been described in detail. This application also provides an embodiment corresponding to a single-phase rectifier control device. It should be noted that this application describes the device embodiment from two perspectives: one based on functional modules and the other based on hardware.

[0156] From the perspective of functional modules, such as Figure 4 As shown, this embodiment provides a single-phase rectifier control device, including:

[0157] The parameter acquisition module 11 is used to acquire the input electromotive force, input current, output voltage and target reference voltage of the single-phase rectifier.

[0158] The current determination module 12 is used to determine the reference current based on the output voltage and the target reference voltage according to the iterative learning controller; wherein, the iterative learning controller is a PD-type iterative learning controller after the differential element is corrected by a nonlinear hyperbolic tangent function.

[0159] The voltage determination module 13 is used to determine the input reference voltage based on the reference current, the input electromotive force, and the input current.

[0160] The control output module 14 is used to determine the corresponding PWM control signal to output based on the input reference voltage, so as to control the single-phase rectifier.

[0161] Since the embodiments of the apparatus and the embodiments of the method correspond to each other, please refer to the description of the embodiments of the method for the embodiments of the apparatus, which will not be repeated here.

[0162] Figure 5 A structural diagram of a single-phase rectifier control device provided in another embodiment of this application is shown below. Figure 5 As shown, a single-phase rectifier control device includes: a memory 20 for storing computer programs;

[0163] The processor 21 is used to execute a computer program to implement the steps of a single-phase rectifier control method as described in the above embodiment.

[0164] The single-phase rectifier control device provided in this embodiment may include, but is not limited to, a computer, a workstation, etc.

[0165] The processor 21 may include one or more processing cores, such as a quad-core processor or an octa-core processor. The processor 21 may be implemented using at least one of the following hardware forms: Digital Signal Processor (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 21 may also include a main processor and a coprocessor. The main processor, also known as the Central Processing Unit (CPU), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, the processor 21 may integrate a Graphics Processing Unit (GPU), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, the processor 21 may also include an Artificial Intelligence (AI) processor, which is used to handle computational operations related to machine learning.

[0166] The memory 20 may include one or more computer-readable storage media, which may be non-transitory. The memory 20 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In this embodiment, the memory 20 is used to store at least the following computer program 201, which, after being loaded and executed by the processor 21, is capable of implementing the relevant steps of a single-phase rectifier control method disclosed in any of the foregoing embodiments. In addition, the resources stored in the memory 20 may also include an operating system 202 and data 203, and the storage method may be temporary or permanent storage. The operating system 202 may include Windows, Unix, Linux, etc. The data 203 may include, but is not limited to, a single-phase rectifier control method.

[0167] In some embodiments, a single-phase rectifier control device may further include a display screen 22, an input / output interface 23, a communication interface 24, a power supply 25, and a communication bus 26.

[0168] Those skilled in the art will understand that Figure 5 The structure shown does not constitute a limitation on a single-phase rectifier control device and may include more or fewer components than shown.

[0169] This application provides a single-phase rectifier control device, including a memory and a processor. When the processor executes a program stored in the memory, it can implement the following method: a single-phase rectifier control method.

[0170] Finally, this application also provides an embodiment corresponding to a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps described in the above method embodiments.

[0171] It is understood that if the methods in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and executes all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0172] The foregoing provides a detailed description of a single-phase rectifier control method, apparatus, and medium provided in this application. The various embodiments in the specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section. It should be noted that those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of this application.

[0173] It should also be noted that, in this specification, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A single-phase rectifier control method, characterized in that, include: Obtain the input electromotive force, input current, output voltage, and target reference voltage of the single-phase rectifier; Based on the iterative learning controller, the reference current is determined according to the output voltage and the target reference voltage; wherein, the iterative learning controller is a PD-type iterative learning controller whose differential element is corrected by a nonlinear hyperbolic tangent function; The input reference voltage is determined based on the reference current, the input electromotive force, and the input current. The corresponding PWM control signal is determined based on the input reference voltage to control the single-phase rectifier; The steps for establishing the iterative learning controller include: Determine the voltage tracking error; wherein, the voltage tracking error is: the difference between the actual DC bus voltage measured in the current iteration cycle and the target reference voltage; Based on the voltage tracking error, determine the rate of change of the voltage tracking error; The voltage tracking error and the rate of change of the voltage tracking error are processed by a nonlinear hyperbolic tangent function, and the corresponding hyperbolic tangent value is determined. The learning amount for the current iteration cycle is determined based on the hyperbolic tangent value; wherein the learning amount is composed of a weighted sum of a proportional term and a differential term, the weight of the proportional term being a proportional coefficient, and the weight of the differential term being a differential coefficient. The error learning law function is determined based on the learning amount, and the iterative learning controller is established based on the error learning law function; The control equation of the iterative learning controller is: ； In the formula, and K represents the reference current at time t in the k-th and (k+1)-th iteration cycles, respectively; P and K D These are the proportionality coefficient and the differential coefficient, respectively; tanh() is the nonlinear hyperbolic tangent function; e k+1 (t) and These are the voltage tracking error and the rate of change of the voltage tracking error at time t in the (k+1)th iteration cycle, respectively. The target reference voltage; Let be the actual DC bus voltage at time t in the (k+1)th iteration cycle.

2. The single-phase rectifier control method according to claim 1, characterized in that, The proportionality coefficient and the differential coefficient satisfy the system convergence condition; The system convergence condition is as follows: ； In the formula, P(jω) is the system's output transfer function, j is the imaginary unit, and ω is the grid angular frequency.

3. The single-phase rectifier control method according to claim 1 or 2, characterized in that, Determining the input reference voltage based on the reference current, the input electromotive force, and the input current includes: The virtual electromotive force and virtual current of the virtual phase are determined by the input electromotive force and the input current using a second-order generalized integrator. The input electromotive force and the virtual electromotive force are subjected to dq transformation to obtain the d-axis electromotive force and the q-axis electromotive force; the input current and the virtual current are subjected to dq transformation to obtain the d-axis current and the q-axis current. Based on the reference current, the d-axis electromotive force and q-axis electromotive force, the d-axis current and q-axis current, the optimal voltage vectors on the d-axis and q-axis are determined based on the deadbeat principle. The optimal voltage vectors on the d-axis and q-axis are subjected to an inverse dq transformation to obtain the input reference voltage.

4. The single-phase rectifier control method according to claim 3, characterized in that, The step of determining the virtual electromotive force and virtual current of the virtual phase based on the input electromotive force and the input current using a second-order generalized integrator includes: The virtual electromotive force and the virtual current are determined by the transfer function of the second-order generalized integrator. The transfer function of the second-order generalized integrator is: ； In the formula, X(s) represents the input signal, X α (s) and X β (s) and ω are the output α-axis and β-axis signals, respectively, where the signal on the virtual phase corresponds to the β-axis signal; s is a complex variable; k and ω SOGI These are the scaling factor and characteristic angular frequency of the second-order generalized integrator, respectively.

5. The single-phase rectifier control method according to claim 3, characterized in that, The principle of zero-delay beats is as follows: i d (k+1)=i d_ref ; i q (k+1)=i q_ref ; In the formula, i d (k+1) and i q (k+1) represent the d-axis current and the q-axis current respectively in the (k+1)th iteration period; i d_ref and i q_ref These are the components of the reference current on the d-axis and q-axis, respectively; The expression for determining the optimal voltage vector is: ； In the formula, u d (k) and u q (k) represent the optimal voltage vectors on the d-axis and q-axis respectively in the k-th iteration cycle; L g R g T s ω and i represent the grid-side filter inductance, grid-side filter resistance, control cycle duration, and grid angular frequency, respectively; d (k) and i q (k) represent the d-axis current and the q-axis current respectively in the k-th iteration period; e d (k) and e q (k) represents the d-axis electromotive force and the q-axis electromotive force in the k-th iteration cycle, respectively.

6. A single-phase rectifier control device, characterized in that, include: The parameter acquisition module is used to acquire the input electromotive force, input current, output voltage, and target reference voltage of the single-phase rectifier. A current determination module is used to determine a reference current based on the output voltage and the target reference voltage, using an iterative learning controller; wherein the iterative learning controller is a PD-type iterative learning controller whose differential element is corrected by a nonlinear hyperbolic tangent function. A voltage determination module is used to determine an input reference voltage based on the reference current, the input electromotive force, and the input current. The control output module is used to determine the corresponding PWM control signal based on the input reference voltage to control the single-phase rectifier; The steps for establishing the iterative learning controller include: Determine the voltage tracking error; wherein, the voltage tracking error is: the difference between the actual DC bus voltage measured in the current iteration cycle and the target reference voltage; Based on the voltage tracking error, determine the rate of change of the voltage tracking error; The voltage tracking error and the rate of change of the voltage tracking error are processed by a nonlinear hyperbolic tangent function, and the corresponding hyperbolic tangent value is determined. The learning amount for the current iteration cycle is determined based on the hyperbolic tangent value; wherein the learning amount is composed of a weighted sum of a proportional term and a differential term, the weight of the proportional term being a proportional coefficient, and the weight of the differential term being a differential coefficient. The error learning law function is determined based on the learning amount, and the iterative learning controller is established based on the error learning law function; The control equation of the iterative learning controller is: ； In the formula, and K represents the reference current at time t in the k-th and (k+1)-th iteration cycles, respectively; P and K D These are the proportionality coefficient and the differential coefficient, respectively; tanh() is the nonlinear hyperbolic tangent function; e k+1 (t) and These are the voltage tracking error and the rate of change of the voltage tracking error at time t in the (k+1)th iteration cycle, respectively. The target reference voltage; Let be the actual DC bus voltage at time t in the (k+1)th iteration cycle.

7. A single-phase rectifier control device, characterized in that, include: Memory, used to store computer programs; A processor, configured to implement the steps of the single-phase rectifier control method as described in any one of claims 1 to 5 when executing the computer program.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the single-phase rectifier control method as described in any one of claims 1 to 5.

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