Quantum-modulated elastic fixed-time control method for voltage regulation of distributed direct current microgrid

Abstract

The present application belongs to the technical field of micro-grid voltage control, and particularly relates to a quantum modulation elastic fixed-time control distributed DC micro-grid voltage regulation method, which comprises the following steps: establishing dynamic equations of each power supply unit in the distributed DC micro-grid, constructing dynamic adjustment parameters based on real-time voltage consensus error and control input, designing an elastic fixed-time stability criterion to dynamically adjust the upper limit of convergence time according to the system state, then using a quantum annealing optimization algorithm to globally optimize the dynamic adjustment parameters to obtain optimal parameters, and finally designing an adaptive update law of the control input based on the optimal parameters to realize voltage consistency regulation within a fixed time. The present application can effectively reduce control energy consumption while ensuring fast voltage regulation, overcome the defects of traditional fixed-time control that cannot balance rapidity and economy, and is suitable for distributed DC micro-grids in complex dynamic environments.

Description

Technical Field

[0001] This invention belongs to the field of microgrid voltage control technology, and particularly relates to a distributed DC microgrid voltage regulation method with quantum modulation elastic fixed-time control. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] With the continued growth of global electricity demand and the rapid development of new energy technologies, the deployment scale of distributed DC microgrids has increased significantly. The core control objective of this type of microgrid is to achieve rapid and low-cost voltage regulation. Failure to achieve rapid and accurate voltage consistency under dynamic operating conditions will directly affect the overall stability and operational performance of the microgrid.

[0004] Currently, most voltage consistency control relies on fixed-time controllers with static convergence boundaries, which have the advantage of ensuring system convergence within a preset time. However, traditional fixed-time control methods have a significant limitation: they lack the flexibility to adaptively adjust the upper limit of the convergence time based on dynamic changes in the system and real-time energy consumption.

[0005] Meanwhile, as a complex network composed of numerous distributed power sources, microgrids face stringent real-time requirements. To ensure optimal dynamic regulation, optimization algorithms must be employed to achieve the best parameter configuration. However, with the increasing complexity of the dynamic characteristics of microgrid networks and the intensification of energy constraints, continuously and accurately locating the optimal convergence boundary becomes extremely challenging.

[0006] Therefore, how to effectively reduce control energy consumption while meeting the requirements of rapid voltage regulation, and how to achieve adaptive optimization of convergence boundary in complex dynamic environments, are the technical problems that urgently need to be solved in the current distributed DC microgrid control. Summary of the Invention

[0007] To overcome the shortcomings of the prior art, this invention provides a distributed DC microgrid voltage regulation method with quantum modulation elastic fixed-time control. It aims to break through the limitation of the upper bound of static convergence time. By introducing dynamic adjustment parameters driven by the real-time state of the distributed DC microgrid and combining them with the quantum annealing optimization algorithm for parameter optimization, the distributed DC microgrid can adaptively adjust the convergence rate according to the actual operating conditions, thereby achieving synergistic optimization of response speed and control energy consumption.

[0008] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions: In the first aspect, a method for voltage regulation of distributed DC microgrids with quantum modulation elastic fixed-time control is disclosed, including: Establish the system dynamic equations for each power source unit in a distributed DC microgrid; Based on the system dynamic equations, the real-time voltage consensus error of each power supply unit is defined. Based on the real-time voltage consensus error and control input, dynamic adjustment parameters are constructed, and the upper bound of the convergence time that varies with the system state is established according to the dynamic adjustment parameters, forming an elastic fixed-time stability criterion. The quantum annealing optimization algorithm is used to perform global optimization on the dynamic adjustment parameters to obtain the globally optimal dynamic adjustment parameters. Based on the aforementioned globally optimal dynamic adjustment parameters, an adaptive update law for the control input is designed for each power supply unit to achieve voltage consistency adjustment within a fixed time period.

[0009] Furthermore, dynamic equations for each power source unit in the distributed DC microgrid are established. Specifically, a second-order dynamic state-space model based on the DC-DC Buck converter is established, considering the time-varying characteristics and higher-order dynamic characteristics of the internal parameters of each power source unit, and an uncertain dynamic equation containing unknown nonlinear dynamic terms is constructed. The unknown nonlinear dynamic term is approximated using a fuzzy logic system to obtain the dynamic equation after fuzzy approximation.

[0010] Furthermore, the real-time voltage consensus error is the weighted difference between the actual output voltage of each power supply unit and the reference voltage signal, after information fusion by combining the communication topology weights.

[0011] Furthermore, the elastic fixed-time stability criterion is specifically: based on dynamically adjusted parameters, the derivative of the Lyapunov function satisfies a fixed-time stability inequality including the dynamically adjusted parameters:

[0012] in, Scalar Lyapunov functions of the system state; Let be the time derivative of the Lyapunov function; , and All are design constants that are strictly greater than zero, and satisfy the condition. ,parameter The range of values ​​is ; This is a dynamically adjustable parameter, and its value range is within... between.

[0013] Furthermore, the quantum annealing optimization algorithm is used to globally optimize the dynamic adjustment parameters to obtain the globally optimal dynamic adjustment parameters. Specifically, the dynamic adjustment parameters in the continuous range are discretized into multiple candidate parameter values, a domain wall encoding strategy is used to construct a quantum bit chain, and each candidate parameter value is mapped to the corresponding multi-qubit tensor product state.

[0014] Furthermore, the quantum annealing optimization algorithm is used to globally optimize the dynamic adjustment parameters to obtain the globally optimal dynamic adjustment parameters. This also includes: constructing a target cost function containing a weighted sum of voltage consistency error and control input energy consumption terms; encoding the target cost function into a target Hamiltonian using a Pauli-Z operator; constructing a constraint Hamiltonian to enforce the validity of domain wall encoding; and combining the target Hamiltonian and the constraint Hamiltonian to form the total Ising Hamiltonian. Furthermore, globally optimal dynamic adjustment parameters are used to design an adaptive update law for the control input of each power supply unit, achieving voltage consistency regulation within a fixed time period, specifically as follows: The speed tracking error is constructed, the virtual control law and the adaptive update law of fuzzy weights are determined, a fixed-time convergence exponent is constructed based on the globally optimal dynamic adjustment parameters, and the adaptive update law of the actual control input signal and current loop is derived based on backstepping design.

[0015] Secondly, a distributed DC microgrid voltage regulation system with quantum modulation elastic fixed-time control is disclosed, including: The dynamic model building module is used to establish the dynamic equations of each power source unit in a distributed DC microgrid. The voltage consensus error definition module is used to define the real-time voltage consensus error of each power supply unit based on the dynamic equation. The stability criterion construction module is used to construct dynamic adjustment parameters and design elastic fixed-time stability criteria based on the real-time voltage consensus error and control input. The parameter optimization module is used to perform global optimization of the dynamic adjustment parameters using the quantum annealing optimization algorithm to obtain the globally optimal dynamic adjustment parameters. The adaptive control module is used to design an adaptive update law for the control input of each power supply unit based on the global optimal dynamic adjustment parameters, so as to achieve voltage consistency adjustment within a fixed time.

[0016] Thirdly, a computer device is disclosed, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to perform the steps of the method described above.

[0017] Fourthly, a computer-readable storage medium is disclosed having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0018] The above one or more technical solutions have the following beneficial effects: This invention abandons the static, preset upper bound of convergence time in traditional fixed-time control, and introduces a dynamically adjustable parameter driven by both real-time voltage consensus error and control input, thus constructing a fixed-time stability criterion with elastic characteristics. This criterion can adaptively adjust the upper bound of convergence time according to the actual operating state of the system: when the voltage consistency error is large, it automatically tightens the convergence boundary to accelerate error correction; when the control input energy consumption is high, it automatically widens the convergence boundary to limit energy consumption.

[0019] This invention addresses the challenge of parameter optimization in complex dynamic environments of microgrids by introducing a quantum annealing optimization algorithm to globally optimize dynamically adjustable parameters. Through a domain wall encoding strategy, parameters are discretized and mapped to a chain of qubits, transforming the parameter optimization problem into a ground-state search problem for the Ising Hamiltonian. The quantum tunneling effect is then utilized to enable the system to efficiently transition to the global optimum.

[0020] This invention fully considers the uncertainties in distributed DC microgrids, such as time-varying parameters, high-order dynamics, and external power disturbances, and utilizes a fuzzy logic system to approximate the unknown nonlinear dynamic terms in the voltage and current loops. This approach does not require precise knowledge of the system model, effectively enhancing the robustness of the control strategy to model uncertainties and external disturbances, and is suitable for complex and variable operating environments in practical engineering.

[0021] This invention, based on Lyapunov stability theory, designs an adaptive update law and a virtual control law that include dynamically adjustable parameters, and derives the actual control input signal using the backstepping method. The derivative of the Lyapunov function of the closed-loop system strictly satisfies the fixed-time stability condition, mathematically guaranteeing that the output voltage of each power supply unit achieves uniform convergence within a fixed time, with a clearly defined and adjustable upper bound on the convergence time.

[0022] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0023] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0024] Figure 1 This is a schematic diagram of the overall structural framework of the distributed DC microgrid voltage regulation method with quantum modulation elastic fixed-time control in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the structure of the distributed DC microgrid in Embodiment 1 of the present invention; Figure 3 This is a schematic diagram illustrating the principle and execution flow of the quantum annealing optimization algorithm in Embodiment 1 of the present invention; Figure 4 The static reference signal in Embodiment 1 of the present invention Voltage tracking diagram below; Figure 5 The time-varying reference signal in Embodiment 1 of the present invention Voltage tracking diagram below; Figure 6 In Embodiment 1 of the present invention, the static reference signal The diagram below illustrates the dynamic feedback adjustment process. Figure 7 In Embodiment 1 of the present invention, the time-varying reference signal The diagram below illustrates the dynamic feedback adjustment process. Figure 8 In Embodiment 1 of the present invention, the microgrid 1 is in the static reference signal Heatmap of the evolutionary trajectory below; Figure 9 For the microgrid 1 in Embodiment 1 of the present invention, the time-varying reference signal The evolutionary trajectory heatmap below. Detailed Implementation

[0025] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0026] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0027] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0028] Example 1 like Figure 1 As shown, this embodiment discloses a distributed DC microgrid voltage regulation method with quantum modulation elastic fixed-time control, including: A dynamic model of each distributed power source unit in a distributed DC microgrid is established. The time-varying nonlinear terms and external power disturbances are approximated by a fuzzy logic system to obtain the dynamic equations. Maintaining voltage stability is a key aspect of control research in distributed DC microgrids. For example... Figure 2As shown, the central control unit is connected to the DC distribution bus to provide a unified DC bus voltage reference signal to each microgrid unit. The DC distribution bus is connected to the input terminals of the converter and controller of each microgrid unit, providing them with DC input voltage. Inside each microgrid unit, the output terminal of the microgrid converter and controller is connected to the unit's filter circuit, which consists of a filter inductor and a filter capacitor, used to filter out high-frequency harmonics from the inverter output.

[0029] Correspondingly, the filtered voltage is output to the filter node via a converter and finally connected to the common DC bus. Simultaneously, an equivalent load resistance is connected in parallel to the common DC bus, representing the load of that unit. The output voltages of each microgrid unit are aggregated at the common DC bus. The central control unit collects the output voltage information of each unit and, combined with the communication topology weights, generates a unified voltage regulation command.

[0030] To analyze the dynamic characteristics of the microgrid control system, the theoretical model of the i-th microgrid in state-space form is expressed as follows:

[0031] in, Indicates the filter capacitor; This represents the inverter-side inductance and its parasitic resistance. Indicates the equivalent load resistance; This is the DC bus voltage; Represents the output voltage; It is the inductor current; The actuator control input corresponding to the i-th microgrid.

[0032] Considering the time-varying characteristics of the internal parameters of each power supply unit and neglecting higher-order dynamics, the uncertainty features are integrated into unknown nonlinear dynamic terms, resulting in an uncertain dynamic equation containing unknown nonlinear dynamic terms:

[0033] in, This represents the regulated output voltage; Represents inductor current; As a measurable output. and Representing unknown nonlinear dynamics, these represent the changes in internal parameters of each power supply unit in the voltage and current loops, as well as external disturbances.

[0034] Using fuzzy logic systems (FLS) to handle unknown nonlinear dynamic terms By approximation, we obtain the dynamic equations in fuzzy approximation form:

[0035] in, and The introduced ideal fuzzy weights are used to approximate the first... Unknown nonlinear dynamics of voltage and current loops in a microgrid system; and For fuzzy basis function vectors; This represents the number of fuzzy rules in a fuzzy logic system. and It is the minimum approximation error generated by the fuzzy logic system when it approximates the unknown nonlinear terms of two subsystems respectively.

[0036] Based on the dynamic equation, the real-time voltage consensus error is defined as the weighted difference between the actual output voltage of each power supply unit and the reference voltage signal after information fusion combined with the communication topology weights. The real-time voltage consensus error of the i-th microgrid is defined as:

[0037] in, For the first The current actual output voltage of the microgrid; For the first The current actual output voltage of the microgrid; This is the reference voltage signal; For communication topology weights; Reference voltage signal Gain; This represents the total number of distributed microgrid units.

[0038] To achieve effective constraints on stability, this embodiment designs a fixed-time stability criterion with elastic characteristics. This criterion introduces a dynamically adjustable parameter driven by both the real-time voltage consensus error and the control input. This ensures that the derivative of the Lyapunov function satisfies the fixed-time stability inequality that includes the dynamically adjusted parameter.

[0039]

[0040] in, Scalar Lyapunov functions of the system state; Let be the time derivative of the Lyapunov function; , and All are design constants that are strictly greater than zero, and satisfy the condition. ,parameter The range of values ​​is ; This is a dynamically adjustable parameter, and its value range is within... between.

[0041] Under this criterion, the upper bound of the system's convergence time Instead of a preset fixed constant, it is a flexible upper bound that can dynamically change according to the power grid's operating state. This transforms the static upper bound of convergence time into a flexible, dynamic upper bound driven by real-time feedback from the system state. Specifically, it includes a convergence time upper bound with a dynamic adjustment mechanism. The formula is: (7) in, and Representing Lyapunov functions respectively in and Convergence time boundaries corresponding to the two different stages; and Design parameters that are positive constants; Due to voltage consistency error and control input Dynamically adjustable parameters driven in real time.

[0042] In the fixed-time control of distributed DC microgrids, if the preset upper limit of convergence time is... If the input is too short, the controller needs to generate a very large control input to quickly eliminate the error, resulting in extremely high energy consumption and power loss; conversely, if the input is set conservatively to reduce energy consumption, the system's dynamic response will be slow and unable to meet the real-time requirements of the power grid.

[0043] To ensure the optimal adjustment of parameters, such as Figure 3 As shown, this embodiment uses the quantum annealing optimization algorithm to globally optimize the dynamic adjustment parameters and obtain the globally optimal dynamic adjustment parameters.

[0044] Since the quantum optimization algorithm operates on a discrete quantum spin model, this embodiment first discretizes the continuously variable dynamic adjustment parameters into multiple candidate parameter values, and then... The feasible range is set as Then, the interval is uniformly divided into M discrete levels. The quantization step size is set to... Then the mathematical expression for the level of the k-th candidate parameter is: .

[0045] To incorporate optimization problems into the quantum computing framework, a domain wall encoding strategy is employed to construct a chain containing M+1 qubits. Under this strategy, each discrete parameter... Corresponding to a specific multi-qubit tensor product state The physical characteristic of this state is that the first k qubits are in a spin-up configuration. The remaining M+1-k qubits are in a spin-down configuration. The boundaries between different spins form "domain walls," and the spatial location of these domain walls uniquely identifies the selected parameter values. The corresponding mathematical expression for the quantum state is as follows:

[0046] To mathematically extract this boundary position, the Pauli-Z matrix is ​​used to evaluate the state differences between adjacent qubits on the chain:

[0047] in, This is the Kronecker function. This mathematical property ensures that it only applies at domain wall locations. The measurement result at one location is 1, while the results at other locations are 0. Represents a multi-qubit tensor product state corresponding to a specific discretization parameter. and These represent the actions performed on the first quantum chain when evaluating state differences. The and the first Pauli-Z operator for adjacent qubits.

[0048] Therefore, by measuring the Pauli-Z matrix, the abstract domain wall positions can be converted into quantifiable numerical labels, thereby identifying the specific candidate parameter values ​​corresponding to the current quantum state.

[0049] Based on this, in order to comprehensively evaluate and coordinate voltage consistency errors and control energy consumption Adjusting classic parameters Transform into quantum operator Construct an objective cost function to minimize a given quantum state. Expected value:

[0050] in, and The weighting coefficients are the first term in the objective function. It can actively drive parameters to increase when the voltage consistency error is large, forcing the system to tighten the convergence boundary to accelerate convergence, thereby ensuring the speed of voltage regulation; the second item When the control input energy consumption is high, the dynamic adjustment parameter is actively driven to decrease, and the upper limit of convergence is relaxed to prevent excessive energy consumption, thereby ensuring the economy of the system.

[0051] Therefore, the dynamic adjustment parameters can be adaptively adjusted according to the real-time state of the system, achieving a dynamic optimization balance between voltage regulation speed and control energy consumption, effectively overcoming the inherent defects of traditional fixed-time control in which the static convergence boundary cannot take into account both speed and economy.

[0052] Based on this, in order to minimize the above objective function using the quantum annealing mechanism, the optimization problem is mathematically transformed into finding the total Ising Hamiltonian operator. ground state This total Hamiltonian consists of a target Hamiltonian. and a constrained Hamiltonian Composition. The total Hamiltonian is constructed as follows:

[0053] in, This is a strictly positive penalty parameter used to control the relative strength of the constraint terms.

[0054] Target Hamiltonian The cost function is directly encoded into the local bias term acting on the qubit using the Pauli-Z operator:

[0055] in, The discretization series for dynamically adjustable parameters; Indicates the first discrete quantum operators The cost function operator for evaluation is used to map voltage consensus error and control energy consumption to quantum states; and Represents the action on the first The and the first Pauli-Z operator with 100 qubits.

[0056] To strictly confine physical evolution within an effective domain wall encoding subspace, a constrained Hamiltonian is introduced. To force strong ferromagnetic coupling between adjacent qubits:

[0057] in, This represents the ferromagnetic coupling strength, used to force adjacent qubits to maintain spin consistency; For boundary pinning field strength; and These represent the Pauli-Z operators acting at the head and tail of the qubit chain, respectively.

[0058] The microgrid independently executes the aforementioned quantum annealing optimization process. Driven by the quantum tunneling effect, the system naturally evolves to its ground state, thereby instantly capturing and outputting optimized control parameters with extremely high computational efficiency. Ultimately, this achieves an optimal balance between voltage regulation time and control energy consumption.

[0059] Based on the aforementioned globally optimal dynamic adjustment parameters, an adaptive update law for the actual control input signal current loop is designed for each distributed power unit to ensure that the distributed DC microgrid achieves voltage consistency within a fixed time period. Specifically, this includes: Define speed tracking error To handle system uncertainty, an adaptive update law for fuzzy weights and a virtual control law for the first step are designed. as follows:

[0060] in, and These are the communication adjacency weight and reference restraint gain in the microgrid network, respectively. This is the positive control gain of the voltage regulation loop; Regularization parameters to mitigate the impact of sudden load changes; and This represents the fixed-time convergence exponent based on the quantum dynamic adjustment mechanism.

[0061] Following the backstepping design steps, the final actual control input signal of the distributed DC microgrid is... and the corresponding adaptive update law of the current loop Designed as follows:

[0062] By constructing the Lyapunov function of the entire closed-loop system, its time derivative is simplified to the standard fixed-time stable form. ,in, Let Lyapunov be the total Lyapunov function of the entire closed-loop system of the constructed distributed DC microgrid. Let be the time derivative of the Lyapunov function. and The value of the centralized control parameter is determined by the minimum value of the controller gain and the adaptive parameter. and These are dynamic power exponents, which together determine the overall convergence speed of the closed-loop system under a fixed-time steady state. The system residual term is a bounded set containing the ideal weights of the fuzzy logic approximation, the minimum approximation error, and the constants introduced when scaling using Young's inequality. In summary, this control scheme mathematically guarantees the voltage consistency regulation of the microgrid.

[0063] Taking a DC microgrid system containing four distributed units as an example, the effectiveness of this embodiment is illustrated: in, Indicates the filter capacitor; This represents the inverter-side inductance and its parasitic resistance. Indicates the equivalent load resistance; This is the DC bus voltage; Represents the output voltage; It is the inductor current; The actuator control input corresponding to the i-th microgrid.

[0064] In a distributed DC microgrid system, the DC bus voltage of each microgrid is set as The parameters of the internal filtering component are selected as follows: The inductor is set as Equivalent load resistance of each microgrid During quantum annealing optimization, the continuous feasible range of the dynamically adjustable parameters is set to... and set There are 10 candidate levels; the weighting coefficients used to coordinate voltage consistency error and control energy consumption are symmetrically selected as follows: and .

[0065] The reference voltage signal is set to include a static trajectory. and continuously time-varying targets .

[0066] like Figure 4 As shown, at the static reference voltage The system then undergoes approximately After the instantaneous overshoot, all output voltages are at Enter quickly within the time frame Error range; such as Figure 5 As shown, in a sinusoidal reference signal that varies with time... Under these conditions, the output voltage also accurately tracks the desired signal, with a settling time of only [time value missing]. The overshoot is extremely low.

[0067] Further analysis of the dynamic feedback regulation process, such as Figures 6 to 7As shown, the curves depicting the dynamic adjustment parameters, voltage consistency error, and control input over time correspond to the two reference signals mentioned above. When the system consistency error decreases and the control input continuously increases, the dynamic parameters decrease accordingly, relaxing the upper limit of the convergence time and effectively avoiding excessive energy consumption. Conversely, when the voltage tracking error increases and the control input remains small, the dynamic parameters immediately increase to tighten the convergence boundary and significantly accelerate the error correction speed. This dynamic process demonstrates the effectiveness of the quantum modulation feedback mechanism, proving that the system can limit energy consumption while maintaining excellent dynamic tracking accuracy, thus proving that the controller designed in this embodiment is effective.

[0068] also, Figure 8 and Figure 9 Heatmaps of the evolution trajectory of microgrid 1 under two reference signals are presented, visually demonstrating the entire process of the dynamic adjustment parameters evolving with the system state. The heatmaps show that the parameter values ​​remain at a high level in the region of large voltage error, and automatically decrease when the error approaches zero and energy consumption increases, further confirming the adaptive adjustment capability of the quantum modulation feedback mechanism.

[0069] Example 2 Based on the method described in Implementation 1, the purpose of this embodiment is to provide a distributed DC microgrid voltage regulation system with quantum modulation elastic fixed-time control, including: a dynamic model establishment module for establishing the dynamic equations of each power supply unit in the distributed DC microgrid; The voltage consensus error definition module is used to define the real-time voltage consensus error of each power supply unit based on the dynamic equation. The stability criterion construction module is used to construct dynamic adjustment parameters and design elastic fixed-time stability criteria based on the real-time voltage consensus error and control input. The parameter optimization module is used to perform global optimization of the dynamic adjustment parameters using the quantum annealing optimization algorithm to obtain the globally optimal dynamic adjustment parameters. The adaptive control module is used to design an adaptive update law for the control input of each power supply unit based on the global optimal dynamic adjustment parameters, so as to achieve voltage consistency adjustment within a fixed time.

[0070] Example 3 The purpose of this embodiment is to provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the above-described method.

[0071] Example 4 The purpose of this embodiment is to provide a computer-readable storage medium.

[0072] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps of the above method.

[0073] The steps and methods involved in the apparatus of the above embodiments correspond to those in Embodiment 1. For specific implementation details, please refer to the relevant description section of Embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood as including any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and enabling the processor to perform any of the methods in this invention.

[0074] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0075] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for voltage regulation of distributed DC microgrids with quantum modulation elastic fixed-time control, characterized in that, include: Establish the dynamic equations for each power source unit in a distributed DC microgrid; Based on the dynamic equation, the real-time voltage consensus error of each power supply unit is defined; Based on the real-time voltage consensus error and control input, dynamic adjustment parameters are constructed, and a flexible fixed-time stability criterion is designed. The quantum annealing optimization algorithm is used to perform global optimization on the dynamic adjustment parameters to obtain the globally optimal dynamic adjustment parameters. Based on the aforementioned globally optimal dynamic adjustment parameters, an adaptive update law for the control input is designed for each power supply unit to achieve voltage consistency adjustment within a fixed time period.

2. The distributed DC microgrid voltage regulation method with quantum modulation elastic fixed-time control as described in claim 1, characterized in that, The establishment of dynamic equations for each power supply unit in the distributed DC microgrid specifically involves: establishing a second-order dynamic state-space model based on the DC-DCBuck converter, considering the time-varying characteristics and higher-order dynamic characteristics of the internal parameters of each power supply unit, and constructing an uncertain dynamic equation containing unknown nonlinear dynamic terms. The unknown nonlinear dynamic term is approximated using a fuzzy logic system to obtain the dynamic equation after fuzzy approximation.

3. The distributed DC microgrid voltage regulation method with quantum modulation elastic fixed-time control as described in claim 1, characterized in that, The real-time voltage consensus error is the weighted difference between the actual output voltage of each power supply unit and the reference voltage signal, after information fusion by combining the communication topology weights.

4. The distributed DC microgrid voltage regulation method with quantum modulation elastic fixed-time control as described in claim 1, characterized in that, The elastic fixed-time stability criterion is specifically: based on a dynamically adjusted parameter, the derivative of the Lyapunov function satisfies a fixed-time stability inequality including the dynamically adjusted parameter. in, Scalar Lyapunov functions of the system state; Let be the time derivative of the Lyapunov function; , and All are design constants that are strictly greater than zero, and satisfy the condition. ,parameter The range of values ​​is ; This is a dynamically adjustable parameter, and its value range is within... between.

5. The distributed DC microgrid voltage regulation method with quantum modulation elastic fixed-time control as described in claim 1, characterized in that, The process of using the quantum annealing optimization algorithm to globally optimize the dynamic adjustment parameters and obtain the globally optimal dynamic adjustment parameters involves: discretizing the dynamic adjustment parameters within a continuous range into multiple candidate parameter values, constructing a quantum bit chain using a domain wall encoding strategy, and mapping each candidate parameter value to a corresponding multi-qubit tensor product state.

6. The distributed DC microgrid voltage regulation method with quantum modulation elastic fixed-time control as described in claim 5, characterized in that, The step of using the quantum annealing optimization algorithm to globally optimize the dynamic adjustment parameters and obtain the globally optimal dynamic adjustment parameters further includes: constructing a target cost function containing a weighted sum of voltage consistency error terms and control input energy consumption terms; encoding the target cost function into a target Hamiltonian using the Pauli-Z operator; constructing a constraint Hamiltonian to enforce the validity of domain wall encoding; and combining the target Hamiltonian and the constraint Hamiltonian to form the total Ising Hamiltonian.

7. The distributed DC microgrid voltage regulation method with quantum modulation elastic fixed-time control as described in claim 1, characterized in that, Based on the aforementioned globally optimal dynamic adjustment parameters, an adaptive update law for the control input is designed for each power supply unit to achieve voltage consistency regulation within a fixed time period, specifically as follows: The speed tracking error is constructed, the virtual control law and the adaptive update law of fuzzy weights are determined, a fixed-time convergence exponent is constructed based on the globally optimal dynamic adjustment parameters, and the adaptive update law of the actual control input signal and current loop is derived based on backstepping design.

8. A distributed DC microgrid voltage regulation system with quantum modulation elastic fixed-time control, characterized in that, include: The dynamic model building module is used to establish the dynamic equations of each power source unit in a distributed DC microgrid. The voltage consensus error definition module is used to define the real-time voltage consensus error of each power supply unit based on the dynamic equation. The stability criterion construction module is used to construct dynamic adjustment parameters and design elastic fixed-time stability criteria based on the real-time voltage consensus error and control input. The parameter optimization module is used to perform global optimization of the dynamic adjustment parameters using the quantum annealing optimization algorithm to obtain the globally optimal dynamic adjustment parameters. The adaptive control module is used to design an adaptive update law for the control input of each power supply unit based on the global optimal dynamic adjustment parameters, so as to achieve voltage consistency adjustment within a fixed time.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the distributed DC microgrid voltage regulation method with quantum modulation elastic fixed-time control as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it performs the steps of the distributed DC microgrid voltage regulation method with quantum modulation elastic fixed-time control as described in any one of claims 1-7.

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