Optimal control method for dc microgrid considering time-dependent noise and communication delay
By using optimal filters and orthogonal decomposition techniques to handle time-related noise and communication delays, the problems of control accuracy and stability in DC microgrids were solved, and the system achieved efficient and stable operation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- TIANJIN UNIV
- Filing Date
- 2025-09-22
- Publication Date
- 2026-07-21
AI Technical Summary
Existing technologies are insufficient to effectively address the time-dependent noise and communication delay issues in DC microgrids, leading to a decrease in control accuracy and system stability.
The time-dependent noise is eliminated by using an optimal filter. The system variables are decomposed into independent parts by state augmentation and orthogonal decomposition techniques. An optimal output feedback controller is designed to handle time-dependent noise and communication delay.
It significantly improves the control accuracy and stability of the system in complex noise environments, provides explicit control laws, and facilitates the deployment and application of DC microgrid systems.
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Figure CN121192645B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of DC microgrid control technology, and in particular to an optimal output feedback control method for DC microgrids that takes into account time-dependent noise and communication delay. Background Technology
[0002] DC microgrids are small-scale power systems integrating distributed generation units such as photovoltaics, wind power, and energy storage. They fall under the category of interconnected systems and can operate in both grid-connected and islanded modes. Due to the widespread use of DC equipment, they have attracted considerable attention. During operation, control issues such as voltage stability, handling the randomness of renewable energy sources, and communication constraints need to be addressed. Linear Quadratic Gaussian (LQG) control is a classic method for achieving optimal system control. However, traditional LQG control theory is based on the ideal assumption that each subsystem has complete information and that noise is time-independent white noise, making it difficult to directly apply to practical interconnected systems. In real systems, due to communication channel delays, each subsystem can only obtain partial delayed information, forming a non-classical information pattern. Simultaneously, process noise and measurement noise often exhibit time correlation, which significantly degrades the performance of standard Kalman filters and LQG controllers based on the white noise assumption, even causing them to fail.
[0003] Currently, some studies have attempted to address delay or noise-related issues. See: Wei Liu, Peng Shi, Xiangpeng Xie, Dong Yue, and Shumin Fei. Optimal linear-quadratic-Gaussian control for discrete-time linear systems with white and time-correlated measurement noises. Optimal Control Applications and Methods, 42(5):1467-1486, 2021. and Yan Wang, Rong Su, and Bohui Wang. Optimal control of interconnected systems with time-correlated noises: Application to vehicleplatoon. Automatica, 137:110018, 2022. and Yan Wang and Rong Su. Infinite horizonoptimal LQG control of interconnected systems with application to multiareapower systems. IEEE Transactions on Automatic Control, 69(6):3602-3614, 2024. However, these existing technologies still have significant shortcomings: First, most studies still assume the noise is white noise, failing to fully consider more complex and realistic scenarios where both process and measurement noise are time-dependent. Second, regarding output feedback, although some studies have addressed this through information decomposition, designing an explicit optimal output feedback controller for systems with both time-dependent noise and non-classical information patterns under strongly connected graph delay modes remains an unsolved problem. This results in existing controllers failing to guarantee control accuracy and system stability under complex noise and communication delay environments. Therefore, a new control method is urgently needed to solve the problem of optimal output feedback control for interconnected systems under the combined effects of communication delay and time-dependent noise. Summary of the Invention:
[0004] The purpose of this invention is to propose an optimal control method for DC microgrids that considers time-dependent noise and communication delay to solve the problems of time-dependent noise and communication delay in DC microgrids, and to achieve stable operation and precise voltage regulation of DC microgrids.
[0005] To achieve the above objectives, the present invention adopts the following technical solution:
[0006] An optimal output feedback control method for an interconnected system with time-dependent noise and communication delay specifically includes the following steps:
[0007] Step 1: Based on the projection theorem, apply an optimal filter to eliminate the time correlation between process noise and measurement noise.
[0008] Step 2: Using dynamic programming and mathematical induction, decompose the system's performance indicators into two parts.
[0009] Step 3: Transform the interconnected system based on a set of non-public information using a state augmentation method.
[0010] Step 4: Using orthogonal decomposition technology, the control input, state variables, process noise, and measurement noise of the transformed system are decomposed into two independent parts.
[0011] Step 5: Based on time-dependent noise filter and system variable decomposition, design an optimal output feedback control method for an interconnected system with time-dependent noise and communication delay.
[0012] Preferably, the interconnected system with time-dependent noise and communication delay is modeled as a state-space model, specifically: consider an interconnected system defined on a strongly connected graph G = (v, ε), where v = {1, 2, ..., N} and Let N represent the set of nodes and N represent the set of edges, respectively. This interconnected system consists of N subsystems, each capable of transmitting information to other subsystems via existing communication channels. In this graph, nodes correspond to subsystems, and edges represent communication channels. The neighbors of node i are defined as N... i = {j∈v:(j,i)∈ε}, where (j,i) is a directed edge from node j to node i. The dynamics of the i-th subsystem in this interconnected system are given by the following equation:
[0013]
[0014] in, These are state variables, control inputs, and measurement outputs; and It is time-dependent process and measurement noise; μ i ,η i It is additive Gaussian independent noise; A ij B i C i ,Φ ij and H ij It is a matrix of appropriate dimension given for all i,j∈v.
[0015] The augmented vector and its corresponding matrix are defined as follows:
[0016]
[0017] Therefore, the dynamic equations of the entire interconnected system can be modeled as follows:
[0018] x k+1 =Ax k +Bu k +w k ,
[0019] y k =Cx k +v k ,
[0020] w k+1 =Φw k +μ k ,
[0021] v k+1 =Hv k +η k ,
[0022] Where the initial state x0 is the mean value. covariance is Independent Gaussian random vectors, with initial process noise w0 having a mean of covariance is An independent Gaussian random vector, with initial measurement noise v0 having a mean of covariance is μ is an independent Gaussian random vector, where x0, w0, and v0 are uncorrelated. k and η k The covariances are P μ and P η Independent zero-mean Gaussian noise; system matrices A, B, C, Φ, H and w k ,v k The mean and covariance of x0 are known for all subsystems.
[0023] For an interconnected system defined on a strongly connected graph, its subsystem i can output its measurement y via the shortest path. i Transmitted to other subsystems j. Let l ij Let l represent the shortest path length between subsystem i and subsystem j, where l ii =0. If it takes one time step for information to traverse an edge, then the information that subsystem i can obtain is:
[0024]
[0025] The performance metrics of an interconnected system are defined in the following form:
[0026]
[0027] Where Q≥0 and R>0 are known weight matrices, and the time T is a finite number of steps.
[0028] Preferably, the interconnected system includes a DC microgrid system formed by interconnecting multiple distributed generation units through a communication network. This DC microgrid system is modeled as follows: Consider a DC microgrid consisting of N distributed generation units interconnected by power lines according to a strongly connected graph. The i-th distributed generation unit contains a resistive load R. Li The system includes an RLC filter, a DC / DC buck converter, and a DC voltage source powered by renewable energy. It is assumed that the renewable energy source is equipped with an energy storage system, thus providing a stable power supply. Furthermore, it is assumed that the DC microgrid can be approximated using a quasi-static line approximation method because the line inductance L... ij Minimal. Based on this, the dynamic model of the i-th distributed generation unit can be expressed as:
[0029]
[0030] Among them, V i Indicates capacitor voltage; I fi V represents the inductor current; j d represents the port voltage interconnected with the i-th distributed generation unit; j To generate the duty cycle of the PWM signal used to drive the IGBT; I fi C fi and R fi These are the parameters of the RLC filter. Based on this, the state-space model of the i-th distributed generation unit can be expressed as:
[0031]
[0032] Where, x i =[V i ,I fi ] T u i =d i y i =V i and C i = [0 1]. By direct discretization, the above equation can be discretized as:
[0033]
[0034] in, And T sThis refers to the sampling time. Therefore, the discrete-time state-space model of a DC microgrid is:
[0035] x k+1 =Ax k +Bu k +w k ,
[0036] y k =Cx k +ν k ,
[0037] in, A = [A ij ] i,j∈{1,L,N} B = diag{B 1 ,L,B N And C = diag{C 1 ,L,C N In practice, interference received by a DC microgrid can affect the DC microgrid. These disturbances can be modeled as time-dependent noise, as follows:
[0038] w k+1 =Φw k +μ k ,
[0039] v k+1 =Hv k +η k .
[0040] For a DC microgrid, the control objective is to regulate the voltage of the distributed generation units to track the reference input by minimizing the control input. Based on this, the system performance index is defined as follows:
[0041]
[0042] Among them, V ref Indicates the reference voltage; and r i These are the weighting coefficients.
[0043] Preferably, for interconnected systems where both process noise and measurement noise are time-dependent, the optimal state filter and optimal one-step predictor capable of eliminating noise time dependence are calculated as follows:
[0044]
[0045] Define the gain matrix for:
[0046]
[0047] Meanwhile, the information renewal process in the above formula is defined as follows:
[0048]
[0049] Its covariance matrix The calculation is as follows:
[0050]
[0051] Error covariance matrix of optimal state filter and optimal one-step predictor and They are respectively:
[0052]
[0053] The optimal filter for process noise and measurement noise is calculated as follows:
[0054]
[0055] Define the gain matrix and for:
[0056]
[0057] Error covariance matrix of the noise-optimal filter and They are respectively:
[0058]
[0059] The cross-covariance matrix is calculated as follows:
[0060]
[0061] definition Define the initial values of each quantity as follows: Preferably, the performance indicators of the interconnected system can be decomposed into the following two parts:
[0062] J = J u +J x ,
[0063]
[0064] In the above formula:
[0065]
[0066] And the initial value is M T =Q,H T =0,G T =0.
[0067] Preferably, the interconnected system can be transformed based on a non-public information set using a state augmentation method, as detailed below: Definition Represents all paths l in a strongly connected graph ij The longest path length. Decompose the information observed by the system into a common information set y. 0:k-τ Non-public information set y k-τ+1:k The non-public information set y k-τ+1:k Stacked as augmented vectors The dynamic equations of the interconnected system are transformed into:
[0068]
[0069] In the above formula, the definition is... and
[0070]
[0071] The transformed noise covariance matrix is defined as follows:
[0072]
[0073] The performance metrics of interconnected systems are also converted into:
[0074]
[0075] in,
[0076] Preferably, the control input, state variables, process noise, and measurement noise of the transformed interconnected system can be decomposed as follows: First, for the transformed system, a common information set Δ is defined. k =Y 0:k-τ Then, the control input of the transformed interconnected system can be decomposed into... The calculation is as follows:
[0077]
[0078] in,
[0079]
[0080] The state variables of the interconnected system after transformation can be decomposed into The calculation is as follows:
[0081]
[0082] and
[0083]
[0084] Where, when k>τ, 1≤p≤τ, and The calculation is as follows:
[0085]
[0086] The process noise of the interconnected system after conversion can be decomposed into The calculation is as follows:
[0087]
[0088] and
[0089]
[0090] The measurement noise of the interconnected system after conversion can be decomposed into The calculation is as follows:
[0091]
[0092] and
[0093]
[0094] Preferably, for interconnected systems where both process noise and measurement noise are time-dependent, and given the existence of strong connectivity graph delay, the optimal output feedback controller is designed as follows: First, define the intermediate variable as...
[0095]
[0096] Θ k+1 =Θ k +Ψ k+1 ,
[0097] Its initial values Ξ0, Λ0, Ψ0, and Θ0 are all identity matrices. Using these intermediate variables, we can calculate:
[0098]
[0099]
[0100] Using the above variables, for interconnected systems where both process noise and measurement noise are time-dependent, and given the existence of strong connectivity graph delay, the optimal output feedback controller can be calculated as follows:
[0101]
[0102] Among them, the control gain matrix A set is defined as Must satisfy for any when hour when hour Matrix F can be obtained by solving the following equation:
[0103]
[0104] in, and The definition is as follows:
[0105]
[0106] Beneficial effects
[0107] The advantages of this invention compared to the prior art are:
[0108] (1) By employing an improved optimal state filter, this invention effectively handles time-related noise, significantly improving the control accuracy and stability of the system in complex noise environments;
[0109] (2) This invention innovatively uses orthogonal decomposition technology to decompose system variables into two independent sub-variables based on public information and non-public information, which effectively solves the control problem caused by communication delay;
[0110] (3) Unlike most existing methods which only propose a control structure, this invention provides an explicit control law, which greatly facilitates its deployment and application in DC microgrid systems.
[0111] Additional aspects and advantages 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
[0112] Figure 1 This is a flowchart of the optimal output feedback control method for interconnected systems with time-dependent noise and communication delay proposed in this invention;
[0113] Figure 2 This is a structural diagram of the distributed generation unit in Example 2;
[0114] Figure 3 This is a communication diagram of each distributed generation unit in the DC microgrid in Example 2;
[0115] Figure 4 This refers to the voltage response of the open-loop system in Example 2;
[0116] Figure 5 This is the voltage response under the action of the optimal output feedback controller designed in this invention in Example 2. Detailed Implementation
[0117] The technical solution adopted in this invention is an optimal output feedback control method for interconnected systems with time-dependent noise and communication delay. The invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
[0118] Example 1:
[0119] See Figure 1 This invention proposes an optimal output feedback control method for interconnected systems with time-dependent noise and communication delay. The overall implementation process includes the following steps:
[0120] Step 1: Based on the projection theorem, apply an optimal filter to eliminate the time correlation between process noise and measurement noise. The optimal state filter and the optimal one-step predictor are calculated as follows:
[0121]
[0122] Define the gain matrix for:
[0123]
[0124] Meanwhile, the information renewal process in the above formula is defined as follows:
[0125]
[0126] Its covariance matrix The calculation is as follows:
[0127]
[0128] Error covariance matrix of optimal state filter and optimal one-step predictor and They are respectively:
[0129]
[0130] The optimal filter for process noise and measurement noise is calculated as follows:
[0131]
[0132] Define the gain matrix and for:
[0133]
[0134] Error covariance matrix of the noise-optimal filter and They are respectively:
[0135]
[0136] The cross-covariance matrix is calculated as follows:
[0137]
[0138] definition Define the initial values of each quantity as follows:
[0139] Step 2: Using dynamic programming and mathematical induction, decompose the system's performance indicators into the following two parts:
[0140] J = J u +J x ,
[0141]
[0142] In the above formula:
[0143]
[0144] And the initial value is M T =Q,H T =0,G T =0.
[0145] Step 3: Transform the interconnected system using state augmentation methods based on a set of non-public information. First, define... Represents all paths l in a strongly connected graph ij The longest path length. Decompose the information observed by the system into a common information set y. 0:k-τ Non-public information set y k-τ+1:k Then, the non-public information set y k-τ+1:k Stacked as augmented vectors The dynamic equations of the interconnected system are transformed into:
[0146]
[0147] In the above formula, the definition is... and
[0148]
[0149]
[0150] The transformed noise covariance matrix is defined as follows:
[0151]
[0152] The performance metrics of interconnected systems are also converted into:
[0153]
[0154] in,
[0155] Step 4: Using orthogonal decomposition technology, the control input, state variables, process noise, and measurement noise of the transformed system are all decomposed into two independent parts. First, for the transformed system, a common information set Δ is defined. k =Y 0:k-τ Then, the control input of the transformed interconnected system can be decomposed into... The calculation is as follows:
[0156]
[0157] in,
[0158]
[0159] The state variables of the interconnected system after transformation can be decomposed into The calculation is as follows:
[0160]
[0161] and
[0162]
[0163] Where, when k>τ, 1≤p≤τ, and The calculation is as follows:
[0164]
[0165] The process noise of the interconnected system after conversion can be decomposed into The calculation is as follows:
[0166]
[0167] and
[0168]
[0169] The measurement noise of the interconnected system after conversion can be decomposed into The calculation is as follows:
[0170]
[0171] and
[0172]
[0173] Step 5: Based on time-dependent noise filters and system variable decomposition, design an optimal output feedback control method for an interconnected system with time-dependent noise and communication delay. First, define the intermediate variable as...
[0174]
[0175] Θ k+1 =Θ k +Ψ k+1 ,
[0176] Its initial values Ξ0, Λ0, Ψ0, and Θ0 are all identity matrices. Using these intermediate variables, we can calculate:
[0177]
[0178]
[0179] Using the above variables, for interconnected systems where both process noise and measurement noise are time-dependent, and given the existence of strong connectivity graph delay, the optimal output feedback controller can be calculated as follows:
[0180]
[0181] Among them, the control gain matrix A set is defined as Must satisfy for any when hour when hour Matrix F can be obtained by solving the following equation:
[0182]
[0183] in, and The definition is as follows:
[0184]
[0185] Example 2:
[0186] Based on Example 1, but with a difference, a DC microgrid consisting of three distributed generation units interconnected by power lines according to a strongly connected graph is considered. The structure of the distributed generation unit is as follows: Figure 2 As shown. The system parameter values are designed as follows:
[0187] The DC voltage source is U1 = U2 = U3 = 100V, and the load resistance is R. L1 =R L2 =R L3=10Ω; RLC filter parameters are L f1 =L f2 =L f3 =2mH, C f1 =C f2 =C f3 =15mF and R f1 =R f2 =R f3 =0Ω; for all i,j∈{1,2,3}, the line resistance and line inductance are respectively R ij =0.4Ω and L ij =2μH. Sampling time is set to T. s =1ms. The weighting coefficient of the performance metric is set to... For the time-dependent noise equation, Φ = 0.1 and H = 0.5 are designed. Noise μ k and η k It is zero-mean Gaussian noise with a covariance matrix equal to the identity matrix. Assume that the initial output voltage of all power generation units is V. i =50V, three distributed generation units can be connected via Figure 3 The communication diagram shown transmits its own measurement output. During the period from k=0 to k=500, the reference voltage is set to V. ref =50V; at k=501, the reference voltage becomes V ref =30V; after 500 sampling times, the reference voltage is reset to V. ref =60V.
[0188] Figure 4 The voltage response of the open-loop system from k=0 to k=150 is shown. Figure 5 The voltage response under the optimal output feedback controller designed in this invention is demonstrated. Figure 4 Compared to the case of voltage divergence shown, Figure 5 This demonstrates that the control scheme proposed in this invention can effectively stabilize the system. From... Figure 5 It can also be seen that, except for the times k=200 and k=1200, even if the reference voltage V ref Despite the change, the system voltage can still successfully track the reference voltage V. ref At times k=200 and k=1200, suppose a short-term disturbance lasting one sampling period is suddenly applied to the system. Such disturbances may be caused by factors such as sudden changes in environmental conditions or drastic load changes. When the sudden disturbance occurs, the voltage will deviate significantly from the reference voltage. However, through the optimal output feedback controller designed in this invention, the system voltage can be readjusted to the reference voltage within 10 sampling periods.
[0189] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.
Claims
1. An optimal control method for DC microgrids considering time-dependent noise and communication delay, characterized in that, Specifically, this relates to an optimal output feedback control method for an interconnected system with time-dependent noise and communication delay. The interconnected system comprises a DC microgrid system formed by interconnecting multiple distributed generation units via a communication network. The DC microgrid system is modeled as follows: Consider a N A DC microgrid of distributed generation units interconnected by power lines according to a strongly connected graph; i Each distributed generation unit includes resistive loads. R Li RLC filters, DC / DC buck converters, and DC voltage sources powered by renewable energy sources; Assuming the DC microgrid adopts the quasi-static line approximation method, based on this, the first... The dynamic model of a distributed generation unit is expressed as follows: in, V i Indicates capacitor voltage; I fi Indicates inductor current; V j Indicates the relationship with the first i The port voltages interconnected by the distributed generation units; d i To generate the duty cycle for the PWM signal used to drive the IGBT; L fi , C fi and R fi yes RLC The parameters of the filter; based on this, the first... i The state-space model of a distributed generation unit is represented as follows: in, , , and , , , ; By directly discretizing, the above equation can be discretized as follows: in, , , and It is the sampling time; therefore, the discrete-time state-space model of the DC microgrid is: The interference received by the DC microgrid is modeled as time-dependent noise, as follows: The control objective of a DC microgrid is to regulate the voltage of distributed generation units to track the reference input by minimizing the control input; based on this, the system performance index is defined as: in, V ref Indicates the reference voltage; , and r i These are the weighting coefficients; The state variables of the distributed generation unit include physical quantities that reflect its operating state, and the control input is used to adjust these physical quantities to the target range. The control method includes the following steps: Step 101: Based on the projection theorem, design an optimal state filter and an optimal one-step predictor to eliminate the time correlation of process noise and measurement noise; Step 102: Using dynamic programming and mathematical induction, decompose the performance indicators of the interconnected system into two parts; Step 103: Transform the interconnected system based on a set of non-public information using a state augmentation method; Step 104: Using orthogonal decomposition technology, the control input, state variables, process noise, and measurement noise of the transformed system are all decomposed into two independent parts; Step 105: Based on time-dependent noise filter and system variable decomposition, design an optimal output feedback controller for an interconnected system with time-dependent noise and communication delay.
2. The optimal control method for DC microgrids considering time-dependent noise and communication delay according to claim 1, characterized in that, The interconnected system is modeled as a state-space model, as follows: Consider a definition in a strongly connected graph G =( v , ε The interconnected system on ) v ={1,2,…, N }and Representing the point set and edge set respectively; the interconnection system consists of N It consists of several subsystems, each of which transmits information to other subsystems through existing information channels; in this diagram, nodes correspond to subsystems, and edges represent communication channels. node i The neighbor is defined as ,in( j , i ) is a path from node j Pointing to node i The directed edge in the interconnected system; the first... i The dynamics of the subsystem are given by the following equations: , , , , in, , , These represent state variables, control inputs, and measurement outputs, respectively. and This represents time-dependent process and measurement noise; , It is additive Gaussian independent noise; , , , and For all Given a matrix of appropriate dimension; The augmented vector and its corresponding matrix are defined as follows: B = diag { B 1 ,…, B N}， C = diag { C 1 ,…, C N}； Therefore, the dynamic equations of the entire interconnected system are modeled as follows: x k+1 = Ax k + Bu k + w k ， y k = Cx k + v k ， ， ， Among them, the initial state x 0 is the mean. Covariance is Independent Gaussian random vectors; initial process noise w 0 is the mean. Covariance is Independent Gaussian random vectors; initial measurement noise v 0 is the mean. Covariance is Independent Gaussian random vectors; x 0、 w 0 and v 0. Uncorrelated; μ k and η k The covariances are respectively P μ and P η Independent zero-mean Gaussian noise; system matrix A , B , C , , H as well as w k , v k and x The mean and covariance of 0 are known for all subsystems; For an interconnected system defined on a strongly connected graph, its subsystems i Measure and output using the shortest path. y i Transmitted to other subsystems j ;make l ij Representation Subsystem i With subsystem j The shortest path length between, where l ii =0; if it takes one time step for information to traverse an edge, then the subsystem i The information obtained is as follows: The performance metrics of an interconnected system are defined in the following form: in, Q ≥0 and R >0 indicates a known weight matrix; time T It is a finite number of steps and .
3. The optimal control method for DC microgrids considering time-dependent noise and communication delay according to claim 2, characterized in that, The calculation of the optimal state filter and the optimal one-step predictor in step 101 specifically includes the following: Define the gain matrix for: Meanwhile, the information renewal process in the above formula is defined as follows: Its covariance matrix The calculation is as follows: , Error covariance matrix of optimal state filter and optimal one-step predictor and They are respectively: , The optimal filter for process noise and measurement noise is calculated as follows: Define the gain matrix and for: , Error covariance matrix of the noise-optimal filter and They are respectively: The cross-covariance matrix is calculated as follows: definition , Define the initial values of each quantity as follows: , , , , , , , , , , , , , .
4. The optimal control method for DC microgrids considering time-dependent noise and communication delay according to claim 3, characterized in that, The performance indicators of the interconnected system described in step 102 are specifically broken down into the following two parts: In the above formula: And the initial value is , , .
5. The optimal control method for DC microgrids considering time-dependent noise and communication delay according to claim 4, characterized in that, Step 103 specifically includes the following: definition It represents all paths in a strongly connected graph. l ij The longest path length; decomposing the information observed by the system into a common information set. Non-public information sets non-public information collection Stacked as augmented vectors The dynamic equations of the interconnected system are transformed into: In the above formula, the definition is... , , and The transformed noise covariance matrix is defined as follows: The performance metrics of interconnected systems are transformed into: in, .
6. The optimal control method for DC microgrids considering time-dependent noise and communication delay according to claim 5, characterized in that, Step 104 decomposes the control input, state variables, process noise, and measurement noise of the converted interconnected system as follows: First, for the transformed system, define a common information set. ; Then, the control input of the transformed interconnected system can be decomposed into The calculation is as follows: in, The state variables of the interconnected system after transformation are decomposed into The calculation is as follows: and Among them, when hour, and The calculation is as follows: The process noise of the interconnected system after conversion is decomposed into The calculation is as follows: and The measurement noise of the interconnected system after conversion is decomposed into The calculation is as follows: and 7. The optimal control method for DC microgrids considering time-dependent noise and communication delay according to claim 6, characterized in that, The optimal output feedback controller mentioned in step 105 specifically includes the following: Define intermediate variables as Its initial value , , , All are identity matrices; Calculate using the above intermediate variables: Using the above variables, for interconnected systems where both process noise and measurement noise are time-dependent, the optimal output feedback controller is calculated as follows, given the existence of strong connectivity graph delay: ， Among them, the control gain matrix , A set is defined as It satisfies for any ,when hour ,when hour ;matrix The following matrix equation is obtained by solving: in, and The definition is as follows: 。 8. A computer device, characterized in that, The computer device includes a processor and a memory, the memory storing at least one instruction, at least one program, code set, or instruction set, the instruction, program, code set, or instruction set being loaded and executed by the processor to implement the optimal control method for DC microgrids considering time-dependent noise and communication delay as described in any one of claims 1-7.
9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction, at least one program, code set, or instruction set, which is loaded and executed by a processor to implement the optimal control method for DC microgrids considering time-dependent noise and communication delay as described in any one of claims 1-7.