A distributed dynamic performance improvement control method and device

Abstract

The present disclosure relates to a distributed dynamic performance improvement control method and device. The method comprises: establishing and generating an inductor current prediction model for a parallel Buck converter system model based on droop control; converting the inductor current prediction model into a linear function expression based on external disturbance and model uncertainty analysis; establishing a residual-based performance improvement controller, and solving it based on a gradient descent algorithm to improve distributed dynamic performance control. The present disclosure can address external disturbances and model uncertainty issues in the system, while also being plug-and-play and applicable to a wide range of applications.

Description

Technical Field

[0001] The present disclosure relates to the field of power electronics technology, and in particular to a distributed dynamic performance improvement control method and device. Background Art

[0002] In recent years, with the continuous improvement of modernization, electricity consumption has been increasing annually across China. Microgrids, as intelligent power systems that efficiently integrate distributed renewable energy, energy storage systems, and modern flexible loads, have attracted widespread attention. When operating in isolated microgrids, bus voltage must be stabilized by converter control. Under the influence of external disturbances and model uncertainty, converter control performance can easily deteriorate, resulting in bus voltage instability. Step disturbance currents generated by load and converter switching cause temporary rises and dips in bus voltage. The addition of unbalanced devices and nonlinear loads on the AC side can inject non-fundamental disturbance currents, causing bus voltage fluctuations. The randomness and intermittency of distributed power generation output power can cause converter output current to fluctuate, affecting bus voltage stability. Furthermore, due to environmental factors and age, some converter components age, leading to model parameter uncertainty and inadequate control performance of existing controllers. Therefore, robust control strategies are needed within closed-loop control to suppress external disturbances, address system parameter changes, stabilize bus voltage, and improve system performance.

[0003] To address the impact of external disturbances and model uncertainties on converter system performance, many advanced control techniques have been developed and implemented, including backstepping control, adaptive control, approximate time-optimal control, sliding mode control, hierarchical control, neural network control, and observer-based control. Sliding mode control and observer-based control have attracted widespread attention due to their simple structure and strong robustness to various external disturbances and model uncertainties. Sliding mode control is a nonlinear control method that offers excellent disturbance rejection, small steady-state error, fast dynamic response, and minimal overshoot for nonlinear systems such as converters. There are many types of observer-based control strategies, such as disturbance observers, sliding mode observers, and observer-based residual generators. Compared to observer-based control methods, sliding mode control suffers from jitter, which can easily cause system instability. It is also applicable only to specific cycles, making it less versatile. Furthermore, sliding mode control requires modifications to the existing control strategy, making it difficult to plug and play with existing control systems, unlike observer-based control structures. Among observer-based control strategies, disturbance observers offer good compensation for external disturbances, improving system tracking performance. However, their effectiveness in compensating for model uncertainty is limited. Sliding mode observers, while similar to sliding mode control, still suffer from jitter and have limited effectiveness in compensating for high-frequency signals, hindering widespread adoption.

[0004] Therefore, one or more methods are needed to solve the above problems.

[0005] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute prior art known to ordinary technicians in the field. Summary of the Invention

[0006] The purpose of the present disclosure is to provide a distributed dynamic performance improvement control method, device, electronic device and computer-readable storage medium, thereby overcoming one or more problems caused by the limitations and defects of related technologies to at least a certain extent.

[0007] According to one aspect of the present disclosure, a decentralized dynamic performance improvement control method is provided, comprising:

[0008] Establishing a Buck converter parallel system model based on droop control, and performing predictive control analysis on the Buck converter parallel system model to generate an inductor current prediction model of the Buck converter parallel system model;

[0009] Performing external disturbance and model uncertainty analysis based on the Buck converter parallel system model, and converting the inductor current prediction model into a linear function expression;

[0010] A residual-based performance improvement controller is established according to the linear function expression of the inductor current prediction model, and is solved based on a gradient descent algorithm to improve the decentralized dynamic performance.

[0011] In an exemplary embodiment of the present disclosure, the method further includes:

[0012] According to the parallel relationship of n Buck converters in the Buck converter parallel system model, the state space equation of the i-th Buck converter is generated.

[0013]

[0014] y i =C i x i +D i u i ; i=1.....n

[0015] Among them, the local load of Buck converter i is r i , Buck converter j local load is r j , the common load is r p , the common load current is i p , bus voltage is u p , L i 、Ci 、r L,i are the filter inductor, filter capacitor and inductor parasitic resistance of Buck converter i respectively; i L,i is the inductor current; u o,i is the output voltage; u i,i is the input voltage; i o,i is the input current, where the input current can be decomposed into i o,i =i i,i +i d,i ,i i,i is the fundamental component of the input current, i d,i is the non-fundamental component of the input current;

[0016]

[0017] D i =0;

[0018] n i represents all converters adjacent to Buck converter i, x i =(u o,i ,i L,i ) T is the state quantity of the system, u i =(u i,i ,i ri,i +i pi,i ) T is the system input, d i =i d,i is the disturbance input of the system, y i =(u o,i ,i L,i ) T is the output of the system.

[0019] In an exemplary embodiment of the present disclosure, the method further includes:

[0020] The state space equation of the Buck converter is generated based on the state space equation of the Buck converter parallel system model based on droop control.

[0021]

[0022] y i =C i x i +D i u i

[0023] in, D i =0

[0024] x i=(i L,i ,u o,i ) T is the state quantity of converter i; u i =(i o,i ,u i,i ) T is the system input, i o,i is a stable output current, which includes a stable inter-converter current and a stable load current; d i =i d,i The variable input current is generated by load and converter switching, AC side imbalance, and the connection of nonlinear devices; i =(i L,i ,u o,i ) T is the output of the system.

[0025] In an exemplary embodiment of the present disclosure, the method further includes:

[0026] Perform predictive control analysis on the Buck converter parallel system model to generate an inductor current prediction model of the Buck converter parallel system model

[0027]

[0028] in, is the predicted value of the inductor current, is the switching voltage of the input voltage.

[0029] In an exemplary embodiment of the present disclosure, the method further includes:

[0030] Based on the Buck converter parallel system model, external disturbance and model uncertainty analysis are performed to convert the inductor current prediction model into a linear function expression.

[0031] y o,i =k o,i x o,i +b o,i

[0032] Among them, i Ld,i (k) is the inductor current affected by the disturbance, u od,i (k) is the output voltage affected by the disturbance;

[0033]

[0034] In an exemplary embodiment of the present disclosure, the method further includes:

[0035] Establish a residual generator based on a discrete observer, and generate a linear function expression of the performance improvement controller based on the residual generator

[0036] y q,i =k q,i x q,i +b q,i

[0037] in, θ1, θ2, and θ3 are free parameters.

[0038] In an exemplary embodiment of the present disclosure, the method further includes:

[0039] According to the performance improvement controller based on the residual generator and the inductor current prediction model, a discrete state space equation of the performance improvement controller based on the residual is established.

[0040]

[0041] Based on the gradient descent algorithm, the decentralized dynamic performance is improved and controlled.

[0042] In one aspect of the present disclosure, a distributed dynamic performance improvement control device is provided, comprising:

[0043] A prediction model establishment module is used to establish a Buck converter parallel system model based on droop control, and perform predictive control analysis on the Buck converter parallel system model to generate an inductor current prediction model of the Buck converter parallel system model;

[0044] A prediction model conversion module, configured to perform external disturbance and model uncertainty analysis based on the Buck converter parallel system model, and convert the inductor current prediction model into a linear function expression;

[0045] The distributed dynamic performance improvement control module is used to establish a residual-based performance improvement controller according to the linear function expression of the inductor current prediction model, and solve it based on the gradient descent algorithm to improve the distributed dynamic performance.

[0046] In one aspect of the present disclosure, there is provided an electronic device, comprising:

[0047] processor; and

[0048] A memory having computer-readable instructions stored thereon, wherein the computer-readable instructions, when executed by the processor, implement the method according to any one of the above items.

[0049] In one aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method according to any one of the above items is implemented.

[0050] A distributed dynamic performance improvement control method in an exemplary embodiment of the present disclosure includes: establishing and generating an inductor current prediction model for a Buck converter parallel system model based on droop control; converting the inductor current prediction model into a linear function expression based on external disturbance and model uncertainty analysis; establishing a performance improvement controller based on residuals, and solving the problem based on a gradient descent algorithm to improve the distributed dynamic performance. The observer-based residual generator disclosed in the present disclosure can react to external disturbances and model uncertainty problems in the system through residuals, and has plug-and-play characteristics, a wide range of applications, and high research and application value.

[0051] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The above and other features and advantages of the present disclosure will become more apparent by describing in detail example embodiments thereof with reference to the accompanying drawings.

[0053] Figure 1 A flow chart of a distributed dynamic performance improvement control method according to an exemplary embodiment of the present disclosure is shown;

[0054] Figure 2 A Buck converter parallel topology diagram of a distributed dynamic performance improvement control method according to an exemplary embodiment of the present disclosure is shown;

[0055] Figure 3 A schematic diagram showing a topological relationship between a virtual resistor and a Buck converter in parallel in a decentralized dynamic performance improvement control method according to an exemplary embodiment of the present disclosure is shown;

[0056] Figure 4 A schematic diagram of a Buck converter decentralized control based on model prediction according to a decentralized dynamic performance improvement control method according to an exemplary embodiment of the present disclosure is shown;

[0057] Figure 5 A schematic diagram showing an equivalent relationship of vector selection of a decentralized dynamic performance improvement control method according to an exemplary embodiment of the present disclosure is shown;

[0058] Figure 6 A schematic diagram of a performance improvement control structure based on residual error of a decentralized dynamic performance improvement control method according to an exemplary embodiment of the present disclosure is shown;

[0059] Figure 7 Another vector selection equivalent relationship diagram of a decentralized dynamic performance improvement control method according to an exemplary embodiment of the present disclosure is shown;

[0060] Figure 8 A schematic diagram of a performance improvement control structure of a Buck converter based on a residual generator according to a decentralized dynamic performance improvement control method of an exemplary embodiment of the present disclosure is shown;

[0061] Figure 9 A schematic block diagram of a distributed dynamic performance improvement control device according to an exemplary embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0062] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be embodied in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art. Like reference numerals in the drawings represent like or similar parts, and thus repetitive description thereof will be omitted.

[0063] In addition, the described features, structures or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure can be practiced without one or more of the specific details, or other methods, components, materials, devices, steps, etc. can be adopted. In other cases, well-known structures, methods, devices, implementations, materials or operations are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0064] The blocks shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically separate entities. Specifically, these functional entities may be implemented in software, or in one or more software-hardened modules, or in different networks and / or processor devices and / or microcontroller devices.

[0065] In this exemplary embodiment, a distributed dynamic performance improvement control method is first provided; Figure 1 As shown in , the distributed dynamic performance improvement control method may include the following steps:

[0066] Step S110, establishing a Buck converter parallel system model based on droop control, and performing predictive control analysis on the Buck converter parallel system model to generate an inductor current prediction model of the Buck converter parallel system model;

[0067] Step S120, performing external disturbance and model uncertainty analysis based on the Buck converter parallel system model, and converting the inductor current prediction model into a linear function expression;

[0068] In step S130 , a residual-based performance improvement controller is established according to the linear function expression of the inductor current prediction model, and is solved based on a gradient descent algorithm to improve the distributed dynamic performance.

[0069] A distributed dynamic performance improvement control method in an exemplary embodiment of the present disclosure includes: establishing and generating an inductor current prediction model for a Buck converter parallel system model based on droop control; converting the inductor current prediction model into a linear function expression based on external disturbance and model uncertainty analysis; establishing a performance improvement controller based on residuals, and solving the problem based on a gradient descent algorithm to improve the distributed dynamic performance. The observer-based residual generator disclosed in the present disclosure can react to external disturbances and model uncertainty problems in the system through residuals, and has plug-and-play characteristics, a wide range of applications, and high research and application value.

[0070] Next, a distributed dynamic performance improvement control method in this example embodiment will be further described.

[0071] Step S110 : establishing a Buck converter parallel system model based on droop control, and performing predictive control analysis on the Buck converter parallel system model to generate an inductor current prediction model of the Buck converter parallel system model.

[0072] In this exemplary embodiment, the method further includes:

[0073] According to the parallel relationship of n Buck converters in the Buck converter parallel system model, the state space equation of the i-th Buck converter is generated.

[0074]

[0075] y i =C i x i +D i u i ; i=1.....n

[0076] Among them, the local load of Buck converter i is r i , Buck converter j local load is r j , the common load is r p , the common load current is i p , bus voltage is u p , L i 、C i 、r L,i are the filter inductor, filter capacitor and inductor parasitic resistance of Buck converter i respectively; i L,iis the inductor current; u o,i is the output voltage; u i,i is the input voltage; i o,i is the input current, where the input current can be decomposed into i o,i =i i,i +i d,i ,i i,i is the fundamental component of the input current, i d,i is the non-fundamental component of the input current;

[0077]

[0078] D i =0;

[0079] n i represents all converters adjacent to Buck converter i, x i =(u o,i ,i L,i ) T is the state quantity of the system, u i =(u i,i ,i ri,i +i pi,i ) T is the system input, d i =i d,i is the disturbance input of the system, y i =(u o,i ,i L,i ) T is the output of the system.

[0080] In the embodiment of this example, a Buck converter parallel system model is established, such as Figure 2 As shown, there are n converters connected in parallel, each converter has a local load and a common load. The local load of converter i is r i , the local load of converter j is r j , the common load is r p , the common load current is i p , bus voltage is u p .

[0081] according to Figure 2 The differential equation of converter i is obtained:

[0082]

[0083]

[0084] Where, L i 、C i 、r L,iare the filter inductor, filter capacitor and inductor parasitic resistance of converter i respectively; i L,i is the inductor current; u o,i is the output voltage; u i,i is the input voltage; i o,i is the input current, where the input current can be decomposed into i o,i =i i,i +i d,i ,i i,i is the fundamental component of the input current, i d,i is the non-fundamental component of the input current.

[0085] The differential equation of the current between converters i and j is obtained from the line relationship between converters:

[0086]

[0087] Where, L line,ij =L line,i +L line,j 、r line,ij =r line,i +r line,j are the equivalent inductance and resistance of the line between converters i and j, L line,i and L line,j are the equivalent inductance of the circuit of converter i and j, r line,i and r line,j are the line equivalent resistances of converters i and j respectively.

[0088] According to the above formula, the state space equation of converter i when n converters are connected in parallel is obtained:

[0089]

[0090] y i =C i x i +D i u i ; i=1.....n

[0091] in:

[0092] D i =0

[0093] Among them, n i represents all the transformers adjacent to transformer i, x i =(u o,i ,i L,i ) T is the state quantity of the system, u i =(u i,i ,i ri,i +ipi,i ) T is the system input, d i =i d,i is the disturbance input of the system, y i =(u o,i ,i L,i ) T is the output of the system. In the state space equation (A ii ,B i ,C i ,D i ,E i ) defines the state matrix of converter i, A ij x j Represents the coupling relationship between converters i and j.

[0094] According to the above formula, the state space equation of the DC microgrid composed of n converters in parallel is written as:

[0095]

[0096] y s =C s x s +D s u s

[0097] in:

[0098]

[0099]

[0100] x s =(x1,x2,…x n ) T is all the state variables of the system, u s =(u1,u2,…u n ) T is the total input quantity of the system, d s =(d1,d2,…d n ) T is the total disturbance input of the system, y s =(y1,y2,…y n ) T All outputs of the system.

[0101] In this exemplary embodiment, the method further includes:

[0102] The state space equation of the Buck converter is generated based on the state space equation of the Buck converter parallel system model based on droop control.

[0103]

[0104] y i =C i x i +D i u i

[0105] in, D i =0

[0106] x i =(i L,i ,u o,i ) T is the state quantity of converter i; u i =(i o,i ,u i,i ) T is the system input, i o,i is a stable output current, which includes a stable inter-converter current and a stable load current; d i =i d,i The variable input current is generated by load and converter switching, AC side imbalance, and the connection of nonlinear devices; i =(i L,i ,u o,i ) T is the output of the system.

[0107] In the embodiment of this example, an IV characteristic curve droop control strategy is adopted in the parallel converter, and the control strategy expression is:

[0108]

[0109] Where u * o,i The reference value of the voltage loop of converter i is given; v * dc,i The reference value of the droop loop of converter i is the bus voltage value; n i is the droop coefficient of converter i.

[0110] When using the IV characteristic curve, the relationship between the output currents of converters i and j is:

[0111]

[0112] Where n i and n j is the droop coefficient of converter i, j; it can also be called virtual resistance; r line,i and r line,j is the line resistance between converters i and j. The parallel converter topology with virtual resistors is as follows: Figure 3 shown.

[0113] After adding the droop loop, the state space expression of converter i can be rewritten as:

[0114]

[0115] y i =C i x i +D i u i

[0116] in:

[0117] D i =0

[0118] Where x i =(i L,i ,u o,i ) T is the state quantity of converter i; u i =(i o,i ,u i,i ) T is the system input, i o,i is a stable output current, which includes a stable inter-converter current and a stable load current; d i =i d,i The variable input current is generated by load and converter switching, AC side imbalance, and the connection of nonlinear devices; i =(i L,i ,u o,i ) T is the output of the system.

[0119] In this exemplary embodiment, the method further includes:

[0120] Perform predictive control analysis on the Buck converter parallel system model to generate an inductor current prediction model of the Buck converter parallel system model

[0121]

[0122] in, is the predicted value of the inductor current, is the switching voltage of the input voltage.

[0123] In the embodiment of this example, the general expression of the discrete state space equation is obtained based on the state space expression of the converter i.

[0124] x i (k+1)=A i x i(k)+B i u i (k)+E i d i (k)

[0125] y i (k) = C i x i (k)+D i u i (k)

[0126] in:

[0127] d i (k)=[i d,i (k)]

[0128] Where x i (k+1) is the state quantity at time k+1; x i (k) is the state quantity at time k; u i (k) is the input quantity at time k; y i (k) is the output at time k; d i (k) is the disturbance input at time k.

[0129] From the discrete state space equation, we know that in L i 、C i 、r L,i and T s When all the values ​​are known and there is no disturbance input, the inductor current value at time k+1, i.e. the predicted inductor current value, can be calculated based on the input voltage, output voltage and inductor current at time k. This gives the prediction model of the inductor current of the Buck converter:

[0130]

[0131] Where, is the predicted value of the inductor current, is the switching voltage of the input voltage.

[0132] According to the above derivation, the Buck converter decentralized control structure based on model prediction is obtained, as shown in Figure 4 shown.

[0133] Step S120 , performing external disturbance and model uncertainty analysis based on the Buck converter parallel system model, and converting the inductor current prediction model into a linear function expression.

[0134] In this exemplary embodiment, the method further includes:

[0135] Based on the Buck converter parallel system model, external disturbance and model uncertainty analysis are performed to convert the inductor current prediction model into a linear function expression.

[0136] y o,i =k o,i x o,i +b o,i

[0137] Among them, i Ld,i (k) is the inductor current affected by the disturbance, u od,i (k) is the output voltage affected by the disturbance;

[0138]

[0139] In the embodiment of this example, in order to achieve the ideal control effect in the model predictive control of the converter, it is necessary to know L i 、C i and r L,i However, in actual system operation, the model parameters will change due to the influence of environmental factors such as temperature, resulting in differences between the parameters in the model predictive control and the parameters of the actual object, resulting in model uncertainty. The prediction model of the inductor current of the Buck converter with model uncertainty is:

[0140]

[0141] Where Δr is the uncertainty of the inductor resistance, and ΔL is the uncertainty of the inductor.

[0142] Disturbance signal d i (k) = i o,i (k) will affect the output signal y i (k)=(i L,i (k),u o,i (k)) T The prediction model of the Buck converter inductor current considering the disturbance signal and model uncertainty is obtained as follows:

[0143]

[0144] Where i Ld,i (k) is the inductor current affected by the disturbance, u od,i (k) is the output voltage affected by the disturbance.

[0145] With a known input voltage u i,i (k) Get the k-time The predicted value of the inductor current is compared with the given value of the inductor current Compare and select the switch state. In the switch state selection, ui,i (k) as the independent variable, As the dependent variable, we get the expression of a linear function:

[0146] y p.i =k p,i x p,i +b p,i

[0147] in:

[0148]

[0149] Similarly, the prediction model of the Buck converter inductor current considering the disturbance signal and model uncertainty is converted into a linear function expression:

[0150] y o,i =k o,i x o,i +b o,i

[0151] in:

[0152] x o,i =u i,i (k),

[0153]

[0154] Based on the above derivation, the switch state selection problem can be expressed in two-dimensional space, as Figure 5 shown.

[0155] In step S130 , a residual-based performance improvement controller is established according to the linear function expression of the inductor current prediction model, and is solved based on a gradient descent algorithm to improve the distributed dynamic performance.

[0156] In this example, G(s) is defined as the generalized control object, K(s) is the stabilizing controller, u is the control input signal, y is the measured output signal, and w is the sum of all external input signals, such as the given signal, disturbance signal, and noise signal. z is the controlled output signal. When G(s) = C(sI-A) -1 When B+D, (A, B) is stable, and (C, A) is observable, the controlled object G(s) can be expressed in the form of left-right coprime decomposition:

[0157]

[0158] The kernel function expression of the control object is obtained as follows:

[0159]

[0160] Where r is the residual value. When the disturbance and model uncertainty are not considered, the residual is defined as the zero vector.

[0161] When there is model uncertainty in the controlled object, it can be expressed by its left and right coprime decomposition as follows:

[0162]

[0163] Where, Δ N (s) and Δ M (s) represents the uncertainty of the model and is a multiplicative disturbance. When considering the existence of a disturbance signal d in the controlled object, its output signal is expressed as:

[0164] y=G Δ (s)u+G Δf (s)d

[0165] Where G Δf (s) is the transfer function from the disturbance signal to the output.

[0166] From this, we can get the kernel function expression when the controlled object has disturbance and model uncertainty:

[0167]

[0168] Based on the above derivation, the Euler parameterized control structure of the stable controller is obtained, as shown in Figure 6 The control structure shown in FIG1 adds a performance improvement controller Q(s) to achieve disturbance suppression and improve the robustness of the system without changing the original closed-loop control system.

[0169] In this exemplary embodiment, the method further includes:

[0170] Establish a residual generator based on a discrete observer, and generate a linear function expression of the performance improvement controller based on the residual generator

[0171] y q,i =k q,i x q,i +b q,i

[0172] in, θ1, θ2, and θ3 are free parameters.

[0173] In the embodiment of this example, in order to obtain the residual value of the system, a residual generator based on a discrete observer is designed, and its expression is:

[0174]

[0175] Where, is the reconstructed state of converter i; r i (k)=(r i,i (k),r u,i (k)) T is the residual value of the output voltage and the inductor current; L i is the gain matrix, which is designed using the pole placement method.

[0176] Assume that the residual r i The linear function expression of the performance improvement controller Q(s) driven by (k) is:

[0177] y q,i =k q,i x q,i +b q,i

[0178] in:

[0179] x q,i =u i,i (k)k q,i =θ1

[0180] b q,i =θ2r i,i (k)+θ3r u,i (k)

[0181] Where θ1, θ2, and θ3 are free parameters.

[0182] When θ1, θ2, and θ3 are designed to appropriate values, there exists:

[0183] x i,i =x o,i =x q,i =x p,i

[0184] y o,i +y q,i =y p,i

[0185] k o,i +k q,i =k p,i

[0186] b o,i +b q,i =b p,i

[0187] The performance improvement controller Q(s) eliminates the influence of disturbance and model uncertainty on the system. Figure 7 The vector selection relationship shown.

[0188] In this exemplary embodiment, the method further includes:

[0189] According to the performance improvement controller based on the residual generator and the inductor current prediction model, a discrete state space equation of the performance improvement controller based on the residual is established.

[0190]

[0191] Based on the gradient descent algorithm, the decentralized dynamic performance is improved and controlled.

[0192] In this exemplary embodiment, Figure 8 As shown, according to the linear function expression of the performance improvement controller Q(s), we can get:

[0193]

[0194] Convert it into a discrete state space equation:

[0195] x q (k+1)=A q x q (k)+B q,1 u q,1 (k)+B q,2 u q,2 (k)

[0196] y q (k) = C q x q (k)+D q,1 u q,1 (k)+D q,2 u q,2 (k)

[0197] The prediction model of the Buck converter inductor current is converted into a discrete state space equation:

[0198] x p (k+1)=A p x p (k)+B p,1 u p,1 (k)+B p,2 u p,2 (k)

[0199] y p (k) = C p x p (k)+D p,1 u p,1 (k)+D p,2 u p,2 (k)

[0200] The discrete state space equation of the voltage PI controller is written as:

[0201] x c (k+1)=A c x c (k)+B c u c (k)

[0202] y c (k) = C c x c (k)+D c e v (k)

[0203] The four sets of discrete state-space equations for the performance improvement controller Q(s), the inductor current prediction model, the voltage PI controller, and the residual generator based on the state observer are combined to obtain the discrete state-space equation of the overall controller:

[0204] x c (k+1)=A c x c (k)+B c,1 u c,1 (k)+B c,2 u c,2 (k)

[0205] y c (k) = C c x c (k)+D c,1 u c,1 (k)+D c,2 u c,2 (k)

[0206] Define the cost function that minimizes the error and the input signal:

[0207]

[0208] According to the discrete state space equation of the overall controller, the gradient formula of the parameters θ1, θ2 and θ3 of the performance improvement controller Q(s) is obtained:

[0209]

[0210]

[0211] Therefore, the performance improvement controller Q(s) is optimized in real time online through the gradient descent algorithm, thereby achieving performance improvement control of the entire Buck converter.

[0212] In the embodiment of this example, the bus voltage is temporarily increased or decreased due to the switching of loads and converters in the DC microgrid system, the bus voltage fluctuations caused by the connection of unbalanced equipment and nonlinear loads on the AC side, the bus voltage instability caused by the randomness and intermittency of the output power of distributed power sources, and the aging of some components of the converter. This patent proposes a performance improvement control strategy based on a residual generator. According to the parallel model of the Buck converter, on the basis of the original droop control and model predictive control, an observer-based residual generator is designed to map external disturbances and model uncertainty problems into residuals. Through the vector selection relationship in the model predictive control, the analytical disturbance and model uncertainty problem are creatively transformed, thereby defining the parameterized form of the performance improvement controller, which provides a new idea for the research of model predictive control. In solving the performance improvement controller, the traditional PID control, model predictive control and observer-based residual generator control are combined to propose a new online optimization control structure.

[0213] In this exemplary embodiment, an observer-based residual performance enhancement control strategy is combined with the model predictive control of the Buck converter. By adding a performance enhancement controller, external disturbances caused by load and converter switching, AC-side imbalance, and the connection of nonlinear devices are suppressed. Compared with other methods, this control strategy has the following advantages: based on the original Buck converter droop control structure, it only uses local information to achieve decentralized dynamic disturbance compensation, without changing the original controller, which is conducive to plug-and-play of parallel converters; the designed performance enhancement controller has a low order and can effectively suppress the impact of power quality disturbances and model uncertainty on system performance. The proposed control structure can be combined with model predictive control, introduces a residual-driven compensation vector, and improves the optimization performance of model predictive control.

[0214] It should be noted that although the steps of the method disclosed herein are depicted in a particular order in the accompanying drawings, this does not require or imply that the steps must be performed in that particular order, or that all steps must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one, and / or one step may be decomposed into multiple steps.

[0215] In addition, in this exemplary embodiment, a distributed dynamic performance improvement control device is also provided. Figure 9 As shown, the distributed dynamic performance improvement control device 200 may include: a prediction model building module 210, a prediction model conversion module 220 and a distributed dynamic performance improvement control module 230.

[0216] A prediction model building module 210 is used to establish a Buck converter parallel system model based on droop control, and perform predictive control analysis on the Buck converter parallel system model to generate an inductor current prediction model of the Buck converter parallel system model;

[0217] A prediction model conversion module 220 is configured to perform external disturbance and model uncertainty analysis based on the Buck converter parallel system model and convert the inductor current prediction model into a linear function expression;

[0218] The distributed dynamic performance improvement control module 230 is used to establish a residual-based performance improvement controller according to the linear function expression of the inductor current prediction model, and solve it based on the gradient descent algorithm to improve the distributed dynamic performance.

[0219] The specific details of each of the above-mentioned distributed dynamic performance improvement control device modules have been described in detail in the corresponding distributed dynamic performance improvement control method, so they will not be repeated here.

[0220] It should be noted that although the above detailed description mentions several modules or units of a distributed dynamic performance enhancement control device 200, this division is not mandatory. In fact, according to embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in a single module or unit. Conversely, the features and functions of a single module or unit described above can be further divided and embodied by multiple modules or units.

[0221] Those skilled in the art will appreciate that various aspects of the present invention may be implemented as systems, methods, or program products. Accordingly, various aspects of the present invention may be implemented as a complete hardware embodiment, a complete software embodiment (including firmware, microcode, etc.), or a combination of hardware and software embodiments, which may be collectively referred to herein as "circuits," "modules," or "systems."

[0222] Furthermore, the above-described figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention and are not intended to be limiting. It is readily understood that the processes illustrated in the above-described figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0223] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow from the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the claims.

[0224] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A distributed dynamic performance improvement control method, characterized in that: The method comprises: A Buck converter parallel system model based on droop control is established. The parallel system model consists of n The converters are connected in parallel, each converter has a local load. n The converters have a common load, in which the first part of the Buck converter parallel system model based on droop control is i The state space equation of a Buck converter is: in, x i =( i L,i , u o,i ) T For converter i State quantity; u i = ( i o,i , u i,i ) T is the system input, i o,i A stable output current, which includes a stable inter-converter current and a stable load current; d i = i d,i The variable input current is generated by load and converter switching, AC side imbalance, and the connection of nonlinear devices; y i =( i L,i , u o,i ) T is the output of the system, Buck converter i The local load is r i Buck converter j The local load is r j , the common load is r p , the common load current is i p , the bus voltage is u p ，L i 、 C i 、 r L,i Buck converter i Filter inductor, filter capacitor and inductor parasitic resistance; i L,i is the inductor current; u o,i is the output voltage; u i,i is the input voltage; The Buck converter parallel system model is subjected to predictive control analysis to generate the inductor current prediction model of the Buck converter parallel system model: ,in, is the predicted value of the inductor current, is the switching voltage of the input voltage; Based on the Buck converter parallel system model, external disturbance and model uncertainty analysis is performed, and the inductor current prediction model is converted into a linear function expression. The linear function expression is: in, i Ld,i ( k ) is the inductor current affected by disturbances and model uncertainties, u od,i ( k ) is the output voltage affected by the disturbance; A residual-based performance improvement controller is established according to the linear function expression of the inductor current prediction model, and is solved based on a gradient descent algorithm to improve the decentralized dynamic performance.

2. The method according to claim 1, wherein The method further comprises: Establish a residual generator based on a discrete observer, and generate a linear function expression of the performance improvement controller based on the residual generator in, , θ 1. θ 2 and θ 3 is a free parameter.

3. The method according to claim 2, wherein The method further comprises: According to the performance improvement controller based on the residual generator and the inductor current prediction model, a discrete state space equation of the performance improvement controller based on the residual is established. ; Based on the gradient descent algorithm, the decentralized dynamic performance is improved and controlled.

4. A distributed dynamic performance improvement control device, using the method according to any one of claims 1 to 3, characterized in that: The device comprises: A prediction model establishment module is used to establish a Buck converter parallel system model based on droop control, and perform predictive control analysis on the Buck converter parallel system model to generate an inductor current prediction model of the Buck converter parallel system model; A prediction model conversion module, configured to perform external disturbance and model uncertainty analysis based on the Buck converter parallel system model, and convert the inductor current prediction model into a linear function expression; The distributed dynamic performance improvement control module is used to establish a residual-based performance improvement controller according to the linear function expression of the inductor current prediction model, and solve it based on the gradient descent algorithm to improve the distributed dynamic performance.

5. An electronic device, characterized in that: include processor; and A memory having computer-readable instructions stored thereon, wherein the computer-readable instructions are executed by the processor to implement the method according to any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the method according to any one of claims 1 to 3 when executed by a processor.

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