Direct-current micro-grid Buck converter adaptive passive control method based on large signal stability

Abstract

The invention provides a direct-current micro-grid Buck converter adaptive passive control method based on large signal stability. According to the control method, on the basis of a passive control theory, system impedance is optimized and adjusted again through a virtual impedance technology so that the whole system can be in a passive stable state. Aiming at steady-state errors caused by deviation from a rated working state due to system uncertainty, a high-order disturbance observer technology is designed and applied to carry out feed-forward compensation on the steady-state errors caused by the uncertainty, so that stable and error-free operation of the system under the condition that disturbance and uncertainty exist is ensured. In addition, in order to ensure the stability of a large signal of the algorithm, a mixed potential energy function theory is adopted to carry out stability analysis, and a parameter selection criterion is given. And finally, verifying the effectiveness and superiority of the proposed algorithm through a simulation platform.

Description

Technical Field

[0001] The present invention relates to the technical field of direct current microgrids, and in particular to an adaptive passive control method for a direct current microgrid Buck converter based on large signal stabilization. Background Art

[0002] In recent years, microgrids have attracted widespread attention as small distribution systems that effectively integrate renewable energy, energy storage equipment, and different types of loads. Since there is no reactive power, phase, and frequency in DC microgrids, DC microgrids also have the advantages of low system cost, low loss, and easy control. It is precisely because of the above advantages that DC microgrids can absorb distributed energy output more efficiently and reliably. Unlike traditional AC grid architecture systems, due to the use of DC power supply, the only indicator for measuring the stable operation of the grid in DC microgrids is the amplitude of the DC bus voltage. In addition, Buck converters, as one of the most common interfaces of DC microgrids, can reduce the redundant energy conversion stages of DC power supplies and loads. However, the negative incremental impedance characteristics of constant power loads seriously threaten the stable operation of the system. Therefore, it is very necessary to explore controllers that can achieve fast bus voltage regulation and maintain stable operation of DC microgrids.

[0003] In order to eliminate the instability problem caused by constant power load, the solutions initially proposed can be classified into passive damping method and active damping method based on linear control technology. The passive damping method mainly optimizes the impedance distribution of the system by adding components such as inductance, capacitance or LC filter, improves the damping characteristics of the system and suppresses the occurrence of limit cycle oscillation. Although the stability of the system is ensured, this method will lead to an increase in system weight and energy loss. The active damping method relies on virtual impedance technology to suppress system oscillation by changing the system control loop to ensure system stability. However, since the small signal model is used to analyze the system near the steady-state operating point and the linear control algorithm is used to suppress the oscillation of the system, the above active damping method can only ensure the normal and stable operation of the system in the neighborhood of the steady-state operating point. In order to overcome the above defects, a series of nonlinear control strategies based on large signal stability are proposed to solve the instability problem caused by constant power load, such as robust control, backstepping control, sliding mode control, model predictive control, deep reinforcement learning control, etc. However, the above nonlinear controllers cannot avoid problems such as difficult design and complex parameter setting. Passive control has made great progress in the industry, including the field of power electronics, due to its advantages such as simplicity, high efficiency and ease of application. When this method is used to solve the instability problem caused by constant power load, passive control ensures the passivity of the controlled system through damping injection to eliminate the limit cycle oscillation phenomenon, and maintains a dynamic balance of energy absorbed, stored and dissipated in the system through energy reshaping, thereby ultimately ensuring the stable operation of the system under its rated working state. In addition, in order to eliminate the static error caused by system uncertainty, a control strategy combining the integral link with passive control is proposed. However, due to the influence of the feedback link, there are defects such as high overshoot and slow dynamic response. Summary of the invention

[0004] The purpose of the present invention is to solve the problems in the prior art to meet the needs of actual situations, and proposes an adaptive passive control method for a DC microgrid Buck converter based on large signal stability.

[0005] The present invention is realized by the following technical scheme. The present invention proposes an adaptive passive control method for a DC microgrid Buck converter based on large signal stability. The control method is based on passive control theory and re-optimizes and adjusts the impedance of the DC microgrid system through virtual impedance technology to make the entire DC microgrid system in a passive stable state. The method is specifically as follows:

[0006] A high-order disturbance observer based on circuit topology is used to perform online estimation and feedforward compensation for the steady-state error caused by centralized disturbances. An improved passive control algorithm is used to ensure the large-signal stability of the DC microgrid system and compensate for centralized disturbances. The large-signal stability of the improved passive control algorithm is analyzed through the mixed potential function theory, and the stable boundary conditions and parameter selection criteria are given.

[0007] Furthermore, assuming that the Buck converter and its CPL operate in continuous current mode, the average state model of the DC microgrid system is expressed as:

[0008]

[0009] In the above formula, P CPL is the power of the constant power load, μ is the duty cycle; from formula (1), it can be seen that the changes in the inductance and its equivalent series resistance value, the capacitance and its equivalent series resistance value, the input voltage, the resistance and the constant power load power can all affect the output voltage; and the disturbances involved in the control method are mainly the changes in the input voltage, the constant power load and the constant impedance load. When the above factors are taken into account, formula (1) is rewritten as:

[0010]

[0011] In formula (2), d1 and d2 represent the disturbance caused by input voltage and load fluctuation respectively, which can be expressed as

[0012]

[0013] In formula (3), E0, R0, P CPL0 They are the rated values ​​of input voltage, constant impedance load and constant power load power respectively;

[0014] Define the following vector group:

[0015]

[0016] Then formula (1) and formula (2) can be rewritten into the following matrix forms:

[0017]

[0018]

[0019] The control method aims to achieve global asymptotic stability in the presence of disturbances, that is, to achieve

[0020]

[0021] In the above formula, for the initial value z of the elements in the matrix Z10 ,z 20 , there exists 10 ≥0, z 20 >ε;Z d is the reference trajectory matrix of the vector group Z, and ε is an arbitrarily small positive real number.

[0022] Furthermore, the design of the passive controller in the improved passive control algorithm is as follows: according to the passive control theory, when the energy provided by the power supply to the DC microgrid system is equal to the sum of the energy dissipated by the DC microgrid system and the energy stored by the DC microgrid system, the entire DC microgrid system is passive; based on the passive control theory, by means of virtual resistance, that is, by adjusting the inductor series virtual resistance R in the DC microgrid system 1d Connect a virtual resistor R in parallel with the capacitor 2d , while not generating extra energy loss in the DC microgrid system, the impedance distribution of the DC microgrid system is optimized to ensure the passivity of the DC microgrid system, and the energy of each part is balanced through energy reshaping to achieve stable control of the Buck circuit with constant power load and constant impedance load.

[0023] Furthermore, the energy reshaping of the DC microgrid system is specifically as follows: considering the changing trend of the reshaped energy, the energy in the inductor and capacitor both achieves the minimum value at the new balance. Therefore, the following transformation is performed on equation (6) to achieve the energy reshaping;

[0024]

[0025] in Represents the Z deviation trajectory reference value vector group Z d Amplitude.

[0026] Furthermore, damping injection is performed on the DC microgrid system. The damping injection introduces virtual impedance into the DC microgrid system, thereby changing the system impedance distribution to make it passive so as to ultimately achieve the purpose of eliminating energy oscillations in the system. The introduction process of virtual impedance is achieved by modifying equation (8).

[0027] Furthermore, a virtual impedance matrix is ​​constructed as shown in equation (9):

[0028]

[0029] Let R i =R(z r )+R d , and add Available

[0030]

[0031] When an appropriate virtual resistor is introduced into the system, it can ensure that the excess transient energy in the system is fully dissipated and the global asymptotic stability of the system is ensured, so that Therefore, it can be seen that the left side of equation (10) will approach zero, that is,

[0032]

[0033] The above proof can be implemented by Lyapunov's stability rule; let is the equilibrium point of formula (11), and To include domain; let S:D→R be a continuous integral function, S(0)=0 and In D-{0}, In D, the equilibrium point is stable, and if In D-{0}, the equilibrium point is asymptotically stable; let the total energy stored in the system be

[0034]

[0035] Since the matrix H is a positive definite matrix, the system energy storage function Taking the derivative of equation (12) we can get

[0036]

[0037] Transform formula (11) and substitute formula (13) into formula (11) to obtain

[0038]

[0039] In formula (14), since G is an antisymmetric matrix, Since R(z) is a symmetric matrix, we only need to ensure that the diagonal elements are all positive numbers to ensure that the value of equation (14) is a negative value less than zero. According to the definition of Lyapunov direct method, the entire system will eventually approach a globally stable asymptotic equilibrium point without being affected by the action of the converter switch. Therefore, the left side of the equal sign in equation (10) is equal to zero, and rewriting it yields

[0040]

[0041] Rewriting the above formula into a system of equations, we can obtain the following passive control dynamic characteristic equation:

[0042]

[0043] Since the Buck converter is a non-minimum phase system, i Lref With v Cref Replace i respectivelyLd With v Cd , and let i Ld With v Cd The derivative value is zero, which means that the reference voltage can be tracked.

[0044]

[0045] After rearranging equation (17), the voltage loop control equation and current loop control equation for the Buck converter PBC control algorithm are shown in equation (18) and equation (19) respectively.

[0046] μ=(v Cref +R 1d (i Lref -i L )) / E (18)

[0047]

[0048] Under the rated state, a passive control algorithm is constructed according to equations (18) and (19). The passive control algorithm is based on a dual-loop control architecture. The voltage outer loop calculates the difference between the output voltage and the voltage reference value through equation (19) to generate a reference value of the inductor current while tracking the reference voltage. The current inner loop then compares the inductor current with the reference current and calculates through equation (18) to generate a corresponding PWN signal to achieve current tracking.

[0049] Furthermore, the design of the high-order disturbance observer is specifically as follows: the dynamic characteristic equation is converted into a general nonlinear equation model:

[0050]

[0051] where x∈R n ,u∈R m ,d∈R r are the state variable matrix, controlled input matrix and disturbance matrix of the system respectively. f(x,μ; t) is the system equation, which and the matrix F of rank r are both known. Assuming that the disturbance changes slowly, that is, The disturbance can be regarded as a constant disturbance, and since the state variable x is measurable and its initial value is known, the system shown in equation (20) can be reduced to

[0052]

[0053] In the above formula, F+ is the pseudo-inverse matrix of the matrix F, as shown in formula (20). The general form of the constant disturbance observer of the system is as follows:

[0054]

[0055] In the above formula, is the disturbance estimation vector group, and Γ0=diag{γ 01 ,γ 02 ...,γ 0i}, γ 0i >0(i=1,2...,r) is the observer gain coefficient matrix that needs to be debugged; let the observer error be

[0056]

[0057] Combining equations (21) to (23), we can obtain the observer error derivative equation as follows:

[0058]

[0059] From the above formula, we can see that when the observer gain coefficient matrix -Γ0 is a Hurwitz matrix, the disturbance estimate is will approach the disturbance value d at infinity; the high-order disturbance observer that observes the input voltage disturbance is as follows

[0060]

[0061] The high-order disturbance observer for constant impedance load and constant power load disturbances is shown below:

[0062]

[0063] By substituting equations (18), (19), (25), and (26) into equation (2), the control equations of the adaptive passive control algorithm taking disturbance into account can be obtained:

[0064]

[0065]

[0066] Further, the large signal stability is determined by the large signal stability criterion as shown below:

[0067]

[0068] The present invention also proposes an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method for adaptive passive control of a DC microgrid Buck converter based on large signal stability are implemented.

[0069] The present invention also proposes a computer-readable storage medium for storing computer instructions, which, when executed by a processor, implement the steps of the method for adaptive passive control of a DC microgrid Buck converter based on large signal stability.

[0070] Beneficial effects of the present invention:

[0071] The present invention proposes an adaptive passive control method for a DC microgrid Buck converter based on large signal stability, which eliminates the influence of the negative impedance characteristics of constant power load on the stable operation of the Buck converter in the DC microgrid, and improves the transient performance of the system when the load in the DC microgrid is disturbed. The control method is based on passive control theory, and the system impedance is re-optimized and adjusted through virtual impedance technology to put the entire system in a passive stable state. In view of the steady-state error caused by deviation from the rated working state due to system uncertainty, the present invention designs and applies high-order disturbance observer technology to feedforward compensation for the steady-state error caused by uncertainty, thereby ensuring the stable and error-free operation of the system under disturbance and uncertainty. In addition, in order to ensure the large signal stability of the proposed algorithm, the hybrid potential energy function theory is used to perform stability analysis and provide parameter selection criteria. Finally, the effectiveness and superiority of the proposed algorithm are verified through a simulation platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0073] Figure 1 It is a schematic diagram of the Buck converter topology with constant impedance load and constant power load;

[0074] Figure 2 This is the passive control schematic diagram of the Buck converter with constant impedance load and constant power load;

[0075] Figure 3 It is a schematic diagram of the large signal equivalent model of the Buck converter with constant impedance load and constant power load;

[0076] Figure 4 is the system large signal stability boundary diagram of the proposed algorithm; (a) is the system large signal boundary diagram when the constant impedance load changes, and (b) is the system large signal boundary diagram when the constant power load changes;

[0077] Figure 5 It is the output comparison waveform of adaptive PBC and traditional PBC when the input voltage changes;

[0078] Figure 6 It is a waveform diagram comparing the output of adaptive PBC and traditional PBC when the constant power load changes;

[0079] Figure 7 It is a waveform comparison between the output of adaptive PBC and traditional PBC when the constant impedance load changes;

[0080] Figure 8 It is the output comparison waveform of adaptive PBC, IPBC and PBC+NDO when the input voltage changes;

[0081] Fig. 9 It is the output comparison waveform of adaptive PBC, IPBC and PBC+NDO when the constant power load changes;

[0082] Fig.10 It is the output comparison waveform of adaptive PBC, IPBC and PBC+NDO when the constant impedance load changes;

[0083] Fig.11 It is a schematic diagram of the overall system structure of the DC microgrid. DETAILED DESCRIPTION

[0084] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0085] In order to ensure the stable and error-free operation of the DC microgrid Buck converter with constant power load under system uncertainties including resistance, constant power load power, input voltage disturbance, etc., the present invention proposes an adaptive passive control method. In this method, a high-order disturbance observer based on circuit topology is used for online estimation and feedforward compensation of concentrated disturbances, and an improved passive control algorithm is used to ensure the large signal stability of the studied circuit system (DC microgrid system) and compensation for concentrated disturbances. In addition, the large signal stability analysis of the proposed algorithm is carried out by the mixed potential function theory, and the stable boundary conditions and parameter selection criteria are given. Finally, the effectiveness and superiority of the proposed algorithm are verified by MATLAB / Simulink simulation.

[0086] Specifically, combined Figure 1-Figure 11 The present invention proposes an adaptive passive control method for a DC microgrid Buck converter based on large signal stability. The control method is based on passive control theory and re-optimizes and adjusts the impedance of the DC microgrid system through virtual impedance technology to make the entire DC microgrid system in a passive stable state; the method is specifically as follows:

[0087] A high-order disturbance observer based on circuit topology is used to perform online estimation and feedforward compensation for the steady-state error caused by centralized disturbances. An improved passive control algorithm is used to ensure the large-signal stability of the DC microgrid system and compensate for centralized disturbances. The large-signal stability of the improved passive control algorithm is analyzed through the mixed potential function theory, and the stable boundary conditions and parameter selection criteria are given.

[0088] The method of the present invention is based on Figure 1 The buck converter with parallel constant impedance load and constant power load is designed as shown in FIG. 1 , where E, L, C, and R are the input voltage, inductance, capacitance, and constant impedance load resistance of the system, respectively, and i L 、v C 、i C 、i R 、i CPL They represent the inductor current, capacitor voltage, capacitor current, resistor current, and the current of the constant power load, respectively. The power bus is powered by the power generation unit, and the DC / DC buck converter is used to reduce the voltage and provide energy to the load bus. The resistive load and the constant power load absorb electrical energy from the load bus as loads, and the negative impedance characteristics of the constant power load are not conducive to system stability. Therefore, it is necessary to design a suitable controller for the interface converter to achieve stable operation of the DC microgrid.

[0089] Assumptions Figure 2 The Buck converter and its CPL operate in continuous current mode, and the average state model of the DC microgrid system is expressed as:

[0090]

[0091] In the above formula, P CPL is the power of the constant power load, μ is the duty cycle; from formula (1), it can be seen that the changes in the inductance and its equivalent series resistance value, the capacitance and its equivalent series resistance value, the input voltage, the resistance and the constant power load power can all affect the output voltage; however, when the passive control introduces the virtual resistance, the equivalent series resistance of the inductance and the capacitance can be ignored, and under normal working conditions, the inductance and capacitance values ​​are not easy to change significantly, while the change of the input voltage will directly lead to the change of the output voltage, and the change of the constant power load and the constant impedance load will change the passive state of the system and cause output errors, and even cause the appearance of limit cycle oscillation in severe cases. The disturbances involved in the control method are mainly the changes in the input voltage, the constant power load and the constant impedance load. When the above factors are taken into account, formula (1) is rewritten as:

[0092]

[0093] In formula (2), d1 and d2 represent the disturbance caused by input voltage and load fluctuation respectively, which can be expressed as

[0094]

[0095] In formula (3), E0, R0, P CPL0 They are the rated values ​​of input voltage, constant impedance load and constant power load power respectively;

[0096] Define the following vector group:

[0097]

[0098] Then formula (1) and formula (2) can be rewritten into the following matrix forms:

[0099]

[0100] The control method aims to achieve global asymptotic stability in the presence of disturbances, that is, to achieve

[0101]

[0102] In the above formula, for the initial value z of the elements in the matrix Z 10 ,z 20 , there exists 10 ≥0, z 20 >ε;Z d is the reference trajectory matrix of the vector group Z, and ε is an arbitrarily small positive real number.

[0103] The design of the passive controller in the improved passive control algorithm is specifically as follows: According to the passive control theory, when the energy provided by the power supply to the DC microgrid system is equal to the sum of the energy dissipated by the DC microgrid system and the energy stored by the DC microgrid system, the entire DC microgrid system is passive; however, due to the negative incremental impedance introduced by the constant power load, the transient energy stored in the capacitors and inductors in the circuit system cannot be consumed. Therefore, the system is in an extreme oscillation state, causing energy oscillations between the energy storage elements, resulting in unstable system output. Therefore, the present invention is based on the passive control theory by means of virtual resistance, that is, by adjusting Figure 2 The inductor is connected in series with a virtual resistor R in the DC microgrid system shown in the figure. 1d Connect a virtual resistor R in parallel with the capacitor 2d , while not generating extra energy loss in the DC microgrid system, the impedance distribution of the DC microgrid system is optimized to ensure the passivity of the DC microgrid system, and the energy of each part is balanced through energy reshaping to achieve stable control of the Buck circuit with constant power load and constant impedance load.

[0104] The energy reshaping of the DC microgrid system is as follows: considering the changing trend of the reshaped energy, the energy in the inductor and capacitor both achieves the minimum value at the new balance. Therefore, the following transformation is performed on equation (6) to achieve the energy reshaping;

[0105]

[0106] in Represents the Z deviation trajectory reference value vector group Z d Amplitude.

[0107] The DC microgrid system is subjected to damping injection. The damping injection introduces virtual impedance into the DC microgrid system, thereby changing the system impedance distribution to make it passive so as to ultimately eliminate the energy oscillation in the system. The introduction of virtual impedance is achieved by modifying equation (8).

[0108] Construct a virtual impedance matrix as shown in equation (9):

[0109]

[0110] Let R i =R(z r )+R d , and add Available

[0111]

[0112] When an appropriate virtual resistor is introduced into the system, it can ensure that the excess transient energy in the system is fully dissipated and the global asymptotic stability of the system is ensured, so that Therefore, it can be seen that the left side of equation (10) will approach zero, that is,

[0113]

[0114] The above proof can be implemented by Lyapunov's stability rule; let is the equilibrium point of formula (11), and To include domain; let S:D→R be a continuous integral function, S(0)=0 and In D-{0}, In D, the equilibrium point is stable, and if In D-{0}, the equilibrium point is asymptotically stable; let the total energy stored in the system be

[0115]

[0116] Since the matrix H is a positive definite matrix, the system energy storage function Taking the derivative of equation (12) we can get

[0117]

[0118] Transform formula (11) and substitute formula (13) into formula (11) to obtain

[0119]

[0120] In formula (14), since G is an antisymmetric matrix, Since R(z) is a symmetric matrix, we only need to ensure that the diagonal elements are all positive numbers to ensure that the value of equation (14) is a negative value less than zero. According to the definition of Lyapunov direct method, the entire system will eventually approach a globally stable asymptotic equilibrium point without being affected by the action of the converter switch. Therefore, the left side of the equal sign in equation (10) is equal to zero, and rewriting it yields

[0121]

[0122] Rewriting the above formula into a system of equations, we can obtain the following passive control dynamic characteristic equation:

[0123]

[0124] Since the Buck converter is a non-minimum phase system, i Lref With v Cref Replace i respectively Ld With v Cd , and let i Ld With v Cd The derivative value is zero, which means that the reference voltage can be tracked.

[0125]

[0126] After rearranging equation (17), the voltage loop control equation and current loop control equation for the Buck converter PBC control algorithm are shown in equation (18) and equation (19) respectively.

[0127] μ=(v Cref +R 1d (i Lref -i L )) / E (18)

[0128]

[0129] Under the rated state, a passive control algorithm is constructed according to equations (18) and (19). The passive control algorithm is based on a dual-loop control architecture. The voltage outer loop calculates the difference between the output voltage and the voltage reference value through equation (19) to generate a reference value of the inductor current while tracking the reference voltage. The current inner loop then compares the inductor current with the reference current and calculates through equation (18) to generate a corresponding PWN signal to achieve current tracking.

[0130] The design of the high-order disturbance observer is specifically as follows: The PBC control algorithm proposed in the present invention can ensure Figure 2 The circuit system shown in the figure operates stably and without error under rated working conditions. However, considering the randomness and uncertainty of the source and load ends, Figure 2 The power of the constant power load, the constant impedance load and the input voltage in the circuit shown usually change with time. In order to enable the system to still achieve stable and error-free tracking of the reference value when the above variables change, a control algorithm architecture based on disturbance observer technology will be adopted. The control algorithm designed based on this architecture can realize real-time observation of concentrated disturbances in the system through a disturbance observer, and compensate for it through the modified control algorithm, thereby ensuring stable and error-free operation of the system. Therefore, the present invention realizes online estimation of concentrated disturbance information based on high-order disturbance observer technology, and compensates for the influence of disturbances by corresponding adaptive modification of PBC, thereby improving its robustness so that it can still operate normally in the presence of disturbances. Therefore, the dynamic characteristic equation is first converted into a general nonlinear equation model:

[0131]

[0132] where x∈R n ,u∈R m ,d∈R r are the state variable matrix, controlled input matrix and disturbance matrix of the system respectively. f(x,μ; t) is the system equation, which and the matrix F of rank r are both known. Assuming that the disturbance changes slowly, that is, The disturbance can be regarded as a constant disturbance, and since the state variable x is measurable and its initial value is known, the system shown in equation (20) can be reduced to

[0133]

[0134] In the above formula, F+ is the pseudo-inverse matrix of matrix F. Since F is the unit matrix in this paper, F+ can also be taken as the unit matrix, as shown in formula (20). The general form of the constant disturbance observer of the system is as follows:

[0135]

[0136] In the above formula, is the disturbance estimation vector group, and Γ0=diag{γ 01 ,γ 02 ...,γ 0i}, γ 0i >0(i=1,2...,r) is the observer gain coefficient matrix that needs to be debugged; let the observer error be

[0137]

[0138] Combining equations (21) to (23), we can obtain the observer error derivative equation as follows:

[0139]

[0140] From the above formula, we can see that when the observer gain coefficient matrix -Γ0 is a Hurwitz matrix, the disturbance estimate is will approach the disturbance value d at infinity; the high-order disturbance observer that observes the input voltage disturbance is as follows

[0141]

[0142] The high-order disturbance observer for constant impedance load and constant power load disturbances is shown below:

[0143]

[0144] By substituting equations (18), (19), (25), and (26) into equation (2), the control equations of the adaptive passive control algorithm taking disturbance into account can be obtained:

[0145]

[0146] Large signal stability analysis based on hybrid potential function: The large signal stability analysis method based on hybrid potential function theory was first proposed in 1960. Before being applied to the field of power electronics, it was mainly used for stability analysis of nonlinear circuits. The general form of the hybrid potential function is as follows:

[0147] P(i,v)=-A(i)+B(v)+(i,γv-a) (29)

[0148] In the above formula, A(i) is the current potential energy equation, B(v) is the voltage potential energy equation, γ is a constant equation related to the circuit structure, and α is a constant vector. There are three theorems in the hybrid potential energy theory. However, since the current potential energy function in the first theorem and the voltage potential energy function in the second theorem are both linear, the third theorem is used to analyze the large signal stability of the system proposed in the present invention. Assume μ1 and μ2 are L -1 / 2 Aii (i)L -1 / 2 and C -1 / 2 B vv (v)C -1 / 2 The minimum eigenvalue of . If the condition is met

[0149] μ1+μ2≥δ(δ>0) (30)

[0150] And when |i|+|v|→∞, there exists

[0151]

[0152] In the above formula, P i is the partial derivative of P(i,v) with respect to current. When time approaches infinity, all solutions corresponding to the system tend to a stable operating point. For the circuit system in the present invention, the output current of the power supply is proportional to the output terminal voltage, and the current absorbed by the constant power load from the bus is also proportional to the bus voltage. Therefore, the power supply and constant power load in the circuit system can be equivalent to a controlled current source. The equivalent circuit of the system described in the present invention is as follows: Figure 3 shown.

[0153] based on Figure 3 The hybrid potential energy function of the DC microgrid system of the present invention can be written as

[0154]

[0155] In order to facilitate the large signal stability analysis of the system, equation (32) is rewritten as the general form of the mixed potential function as shown below:

[0156]

[0157] After the damping injection of the PBC algorithm, the power supply can quickly track the reference current, that is, i L =i Lref Therefore, we can get the following expressions for μ1 and μ2:

[0158]

[0159] Therefore, the large signal stability is determined by the large signal stability criterion as shown below:

[0160]

[0161] From formula (35), it can be seen that for systems under different working conditions, it is only necessary to adjust the capacitor virtual parallel resistance R 2dThe large signal stability of the system can be achieved. Although for the buck circuit system with a constant impedance load as the single load, the change of the constant impedance load within the power supply capacity will not affect the large signal stability of the system, however, the load considered in the present invention is a mixed load. When the constant impedance load value changes to the point where it loses its dominant position, the large signal stability of the system will be destroyed. Therefore, it is necessary to analyze the large signal stability when the constant power load and the constant impedance load fluctuate. When the constant power load and the constant impedance load change respectively, the large signal stability boundary diagram of the system is as follows Figure 4 In the large signal stability boundary diagram, the blue area is the large signal stability area. When the constant power load or constant impedance load power is used as the horizontal axis, the capacitor virtual parallel resistor R 2d When the point composed of the vertical coordinates is located in the blue area, the system is in a large signal stable state. 2d The value of is not only related to the large signal stability of the system, but also to the 2d The value will reduce the system response characteristics and cause output errors, while the lower R 2d Although it can ensure that the system obtains fast dynamic response characteristics, it will also cause serious ripples in the output waveform. Therefore, after weighing the above factors, the virtual capacitor in the present invention is connected in parallel with the resistor R 2d Finally, 0.16 was selected. Figure 4 It can be seen from the two major signal stability boundary diagram that this value can ensure the large signal stability of the system when the constant impedance load and constant power load fluctuate.

[0162] The method proposed in the present invention is verified by MATLAB / Simulink simulation. 1d , R 2d The values ​​are 10 6 and 0.16, and the disturbance observer coefficients are Γ1=1000 and Γ2=4000 respectively. On this basis, two types of case studies are conducted: a) steady-state performance comparison test under various source-load side disturbances; b) transient performance comparison test under various source-load side disturbances. The detailed parameters of the system are shown in Table 1.

[0163] Table 1 System parameter configuration

[0164]

[0165] Case a): This case studies the steady-state performance of the proposed controller when facing a step disturbance on the source-load side to verify the effectiveness of the method proposed in this invention. Figure 5The output comparison simulation diagram of the proposed adaptive passive control method (red in the figure) and the traditional passive control algorithm (blue in the figure) when the input and voltage change. The input voltage of the system increases from the rated value of 1500V to 2000V at 0.04s, and returns to the rated value after 0.02s. Figure 5 It can be seen that the change of input voltage has no obvious effect on the output voltage of the proposed algorithm, while the traditional passive control output voltage has a steady-state error.

[0166] Figure 6 The output comparison simulation diagram of the proposed adaptive passive control algorithm (red in the figure) and the traditional passive control algorithm (blue in the figure) when the constant power load undergoes a step change. At 0.04s, the power of the constant power load jumps from 14.4kW to 21.7kW, and recovers to 14.4kW after 0.06s. Figure 6 It can be seen that the traditional passive control does not take the change of the constant power load into account. Therefore, although the system is in a stable state, its output voltage has a steady-state error compared to the rated value. The proposed adaptive passive control algorithm produces a certain overshoot when the constant power load changes, and its output recovers to the rated value of 750V after about 2ms.

[0167] Figure 7 The output comparison simulation diagram of the adaptive passive control method (red in the figure) and the traditional passive control method (blue in the figure) when the constant impedance load undergoes a step change. The constant impedance load of the system changes from 50Ω to 33.33Ω at 0.04s and returns to 50Ω at 0.06s. Figure 7 It can be seen that the traditional passive control output voltage has a steady-state error. However, the proposed adaptive passive control algorithm produces an overshoot of 0.4V when the load resistance decreases and recovers to the rated value after 2ms.

[0168] Case b): This case studies the transient performance of the proposed controller when facing a step disturbance on the source-load side to verify the superiority of the method proposed in this invention. In order to further verify the transient performance of the adaptive passive control algorithm, this invention conducts a comparative simulation with passive integral control (Passivity based Control with Integrator, IPBC) and nonlinear disturbance observer based passive control (PBC+NDO). In the construction of passive control with integral link, its virtual resistor R 1d , R 2d The value of is the same as that of the proposed algorithm, and the integral coefficient is selected as 300. Figures 8 to 10The output comparison simulation diagrams of the proposed algorithm (red in the figure) and the IPBC (green in the figure) and PBC+NDO (blue in the figure) algorithms when disturbances occur in the input voltage, constant power load, and constant impedance load.

[0169] Figure 8 The input voltage changes with Figure 5 The working conditions are the same. Figure 8 It can be seen that when the input voltage changes, the proposed algorithm and IPBC can achieve error-free voltage tracking, but a very small static error is found in the PBC+NDO voltage output. Fig. 9 The constant power load changes with Figure 6 The working conditions are the same. Fig. 9 It can be seen that when the power of the constant power load steps, the proposed adaptive passive control has a faster dynamic response speed and smaller overshoot compared with the other two algorithms. Fig.10 The constant impedance load changes with Figure 7 The working conditions are the same. Fig.10 It can be seen that when a step occurs in the constant impedance load, the recovery time and overshoot amplitude of the adaptive passive control algorithm are both optimal, followed by PBC+NDO and IPBC.

[0170] The schematic diagram of the DC microgrid structure is as follows: Fig.11 As shown, it is composed of a DC / DC buck converter topology and a corresponding controller. The present invention proposes an adaptive passive control algorithm based on a high-order disturbance observer technology, which can solve the instability problem caused by a constant power load in a buck circuit. In the proposed algorithm, a high-order disturbance observer is used to realize real-time observation of the disturbance, and the passive control balances the impedance distribution in the system through virtual impedance technology to ensure the large signal stability of the system, and realizes stable tracking of the reference voltage by reshaping the system energy. In addition, the improved passive control algorithm also realizes compensation for concentrated disturbances to improve the robustness of the algorithm. In order to verify the large signal stability of the proposed algorithm, the present invention analyzes the large signal stability of the system based on the hybrid potential energy theory, and proves the large signal stability of the system of the present invention. After MATLAB simulation, the proposed control algorithm can successfully suppress the influence of disturbances such as input voltage and load fluctuations on the output, and has good dynamic response performance.

[0171] The present invention also proposes an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method for adaptive passive control of a DC microgrid Buck converter based on large signal stability are implemented.

[0172] The present invention also proposes a computer-readable storage medium for storing computer instructions, which, when executed by a processor, implement the steps of the method for adaptive passive control of a DC microgrid Buck converter based on large signal stability.

[0173] The memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus RAM (DRRAM). It should be noted that the memory of the method described in the present invention is intended to include, but is not limited to, these and any other suitable types of memory.

[0174] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a high-density digital video disc (DVD)), or a semiconductor medium (eg, a solid state disc (SSD)).

[0175] In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in a processor or an instruction in the form of software. The steps of the method disclosed in conjunction with the embodiment of the present application can be directly embodied as a hardware processor for execution, or a combination of hardware and software modules in a processor for execution. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in conjunction with its hardware. To avoid repetition, it is not described in detail here.

[0176] It should be noted that the processor in the embodiment of the present application can be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method embodiment can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiment of the present application can be directly embodied as a hardware decoding processor to perform, or the hardware and software modules in the decoding processor can be combined and performed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0177] The above is a detailed introduction to the adaptive passive control method of a DC microgrid Buck converter based on large signal stability proposed in the present invention. This article uses specific examples to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. An adaptive passive control method for a DC microgrid Buck converter based on large signal stabilization, characterized in that: The control method is based on passive control theory and uses virtual impedance technology to re-optimize and adjust the impedance of the DC microgrid system so that the entire DC microgrid system is in a passive stable state; the method is specifically as follows: A high-order disturbance observer based on circuit topology is used to perform online estimation and feedforward compensation for the steady-state error caused by centralized disturbances. An improved passive control algorithm is used to ensure the large-signal stability of the DC microgrid system and compensate for centralized disturbances. The large-signal stability of the improved passive control algorithm is analyzed through the mixed potential function theory, and the stable boundary conditions and parameter selection criteria are given.

2. The method according to claim 1, characterized in that Assuming that the Buck converter and its CPL operate in continuous current mode, the average state model of the DC microgrid system is expressed as: In the above formula, P CPL is the power of the constant power load, μ is the duty cycle; from formula (1), it can be seen that the changes in the inductance and its equivalent series resistance value, the capacitance and its equivalent series resistance value, the input voltage, the resistance and the constant power load power can all affect the output voltage; and the disturbances involved in the control method are mainly the changes in the input voltage, the constant power load and the constant impedance load. When the above factors are taken into account, formula (1) is rewritten as: In formula (2), d1 and d2 represent the disturbance caused by input voltage and load fluctuation respectively, which can be expressed as In formula (3), E0, R0, P CPL0 They are the rated values ​​of input voltage, constant impedance load and constant power load power respectively; Define the following vector group: Then formula (1) and formula (2) can be rewritten into the following matrix forms: The control method aims to achieve global asymptotic stability in the presence of disturbances, that is, to achieve In the above formula, for the initial value z of the elements in the matrix Z 10 ,z 20 , there exists 10 ≥0, z 20 >ε;Z d is the reference trajectory matrix of the vector group Z, and ε is an arbitrarily small positive real number.

3. The method according to claim 2, characterized in that The design of the passive controller in the improved passive control algorithm is as follows: According to the passive control theory, when the energy provided by the power supply to the DC microgrid system is equal to the sum of the energy dissipated by the DC microgrid system and the energy stored by the DC microgrid system, the entire DC microgrid system is passive; based on the passive control theory, by means of virtual resistance, that is, by adjusting the inductor series virtual resistance R in the DC microgrid system 1d Connect a virtual resistor R in parallel with the capacitor 2d , while not generating additional energy loss in the DC microgrid system, the impedance distribution of the DC microgrid system is optimized to ensure the passivity of the DC microgrid system, and the energy of each part is balanced through energy reshaping to achieve stable control of the Buck circuit with constant power load and constant impedance load.

4. The method according to claim 3, characterized in that The energy reshaping of the DC microgrid system is as follows: considering the changing trend of the reshaped energy, the energy in the inductor and capacitor both achieves the minimum value at the new balance. Therefore, the following transformation is performed on equation (6) to achieve the energy reshaping; in Represents the Z deviation trajectory reference value vector group Z d Amplitude.

5. The method according to claim 4, characterized in that The DC microgrid system is subjected to damping injection. The damping injection introduces virtual impedance into the DC microgrid system, thereby changing the system impedance distribution to make it passive so as to ultimately eliminate the energy oscillation in the system. The introduction of virtual impedance is achieved by modifying equation (8).

6. The method according to claim 5, characterized in that Construct a virtual impedance matrix as shown in equation (9): Let R i =R(z r )+R d , and add Available When an appropriate virtual resistor is introduced into the system, it can ensure that the excess transient energy in the system is fully dissipated and the global asymptotic stability of the system is ensured, so that Therefore, it can be seen that the left side of equation (10) will approach zero, that is, The above proof can be implemented by Lyapunov's stability rule; let is the equilibrium point of formula (11), and To include domain; let S:D→R be a continuous integral function, S(0)=0 and In D-{0}, In D, the equilibrium point is stable, and if In D-{0}, the equilibrium point is asymptotically stable; let the total energy stored in the system be Since the matrix H is a positive definite matrix, the system energy storage function Taking the derivative of equation (12) we can get Transform formula (11) and substitute formula (13) into formula (11) to obtain In formula (14), since G is an antisymmetric matrix, R(z) is a symmetric matrix. It only needs to ensure that the diagonal elements are all positive numbers to ensure that the value of equation (14) is a negative value less than zero. According to the definition of Lyapunov direct method, the entire system will eventually approach the globally stable asymptotic equilibrium point without being affected by the action of the converter switch; Therefore, the left side of the equal sign in formula (10) is equal to zero, and rewriting it yields Rewriting the above formula into a system of equations, we can obtain the following passive control dynamic characteristic equation: Since the Buck converter is a non-minimum phase system, i Lref With v Cref Replace i respectively Ld With v Cd , and let i Ld With v Cd The derivative value is zero, which means that the reference voltage can be tracked. After rearranging equation (17), the voltage loop control equation and current loop control equation for the Buck converter PBC control algorithm are shown in equation (18) and equation (19) respectively. μ=(v Cref +R 1d (i Lref -i L )) / E (18) Under the rated state, a passive control algorithm is constructed according to equations (18) and (19). The passive control algorithm is based on a dual-loop control architecture. The voltage outer loop calculates the difference between the output voltage and the voltage reference value through equation (19) to generate a reference value of the inductor current while tracking the reference voltage. The current inner loop then compares the inductor current with the reference current and calculates through equation (18) to generate a corresponding PWN signal to achieve current tracking.

7. The method according to claim 6, characterized in that The design of the high-order disturbance observer is specifically as follows: the dynamic characteristic equation is converted into a general nonlinear equation model: where x∈R n ,u∈R m ,d∈R r are the state variable matrix, controlled input matrix and disturbance matrix of the system respectively. f(x,μ; t) is the system equation, which and the matrix F of rank r are both known. Assuming that the disturbance changes slowly, that is, The disturbance can be regarded as a constant disturbance, and since the state variable x is measurable and its initial value is known, the system shown in equation (20) can be reduced to In the above formula, F+ is the pseudo-inverse matrix of the matrix F, as shown in formula (20). The general form of the constant disturbance observer of the system is as follows: In the above formula, is the disturbance estimation vector group, and Γ0=diag{γ 01 ,γ 02 ...,γ 0i }, γ 0i >0(i=1,2...,r) is the observer gain coefficient matrix that needs to be debugged; let the observer error be Combining equations (21) to (23), we can obtain the observer error derivative equation as follows: From the above formula, we can see that when the observer gain coefficient matrix -Γ0 is a Hurwitz matrix, the disturbance estimate is will approach the disturbance value d at infinity; the high-order disturbance observer that observes the input voltage disturbance is as follows The high-order disturbance observer for constant impedance load and constant power load disturbances is shown below: By substituting equations (18), (19), (25), and (26) into equation (2), the control equations of the adaptive passive control algorithm taking disturbance into account can be obtained:

8. The method according to claim 7, characterized in that The large signal stability is determined by the large signal stability criterion shown below:

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium for storing computer instructions, characterized in that: When the computer instructions are executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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