Four-port magnetic network energy router control method and system based on data model

Abstract

The invention discloses a four-port magnetic network energy router prediction control method and system based on a data model, and belongs to the technical field of multi-port isolation type power electronic converters. The method comprises the following steps of: establishing a power model of the four-port magnetic network electric energy router and a power data model of each port, discretizing and matrix the data model, establishing an error between the discrete data model and actual data, and updating the data model in real time; predicting the power of each port in the future according to the data model of each port, establishing a phase shift angle set range of each port in combination with the actual power, and calculating power prediction corresponding to different phase shift angle sets; and finally, selecting a phase shift angle set corresponding to the minimum power error as an optimal set, and applying the optimal set to the next control period. According to the method, challenges caused by external environment changes and internal parameter fluctuations can be effectively handled, and the router is ensured to stably and efficiently work under different operation conditions.

Description

Technical Field

[0001] The present invention belongs to the field of multi-port isolated power electronic converters, and in particular relates to a four-port magnetic network energy router data model and a predictive control method. Background Art

[0002] Multi-port magnetic network energy routers play a key role in AC / DC hybrid microgrids, enabling efficient conversion and distribution between multiple energy forms. With the rapid development of renewable energy, AC / DC hybrid microgrids have become an important technical means to achieve flexible energy scheduling and efficient energy utilization. Multi-port magnetic network energy routers leverage their flexible port design and powerful energy management capabilities to flexibly convert and optimize scheduling between various energy sources, not only improving the stability and reliability of microgrids but also promoting the green transformation of energy structures. Therefore, the development of multi-port magnetic network energy routers is of great significance for improving the overall performance of microgrids and promoting efficient energy utilization.

[0003] Magnetic network energy routers rely on the principle of electromagnetic mutual induction during energy transmission. Their core principle lies in the efficient transfer and flexible conversion of energy between different ports through the use of cleverly designed mutual inductance devices. This mechanism leverages the electromagnetic coupling properties of mutual inductance devices to achieve energy transmission and distribution without the need for physical connections. However, to ensure the efficiency and stability of this transmission, the accuracy of the mutual inductance parameters is crucial. These parameters not only affect the control accuracy of the router but also directly impact its operational efficiency and reliability. Mutual inductance is a key parameter affecting the performance of energy routers, but it is not static. In actual operation, these parameters may vary due to a variety of factors. Material properties are one key factor. The properties of the magnetic materials and capacitor dielectrics used can affect the specific values of mutual inductance and capacitance. Furthermore, temperature fluctuations can affect material properties and device parameters to a certain extent, leading to parameter drift. Frequency fluctuations are another influencing factor, with parameter changes becoming more pronounced at higher frequencies. Finally, device aging and wear can also lead to parameter deviations. These parameter variations can cause circuit characteristics to deviate from the intended design, thus affecting the stable and efficient operation of the router.

[0004] To address the challenges posed by these parameter changes, researchers have proposed several improvement strategies, such as adaptive control algorithms and online parameter identification techniques, which have improved the robustness of the system to a certain extent. However, these methods often rely on complex calculations and real-time monitoring, which can lead to additional system overhead and delays. To fundamentally address these issues, it is particularly important to establish a comprehensive data model for magnetic network energy routers and control them based on this model. This data model can better predict and compensate for the impact of parameter changes, achieve precise control of the routers, and thus improve the overall efficiency and performance stability of the system. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to address the deficiencies of the above-mentioned background technology and provide a data model and predictive control method for a four-port magnetic network energy router. By analyzing the operating status of the four-port magnetic network energy router, its data model is established, and then the future state of the router is predicted based on the model, thereby solving the technical problem of control deviation caused by fluctuations in mutual inductance parameters and achieving the invention purpose of robust control of the energy router.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0007] The present invention proposes a four-port magnetic network energy router control method based on a data model, comprising the following steps:

[0008] Step S1, based on the principle of single phase shift modulation, obtain the power model between any two ports of the four-port magnetic network power router and establish the power data model of each port;

[0009] The power model between any two ports is expressed as:

[0010]

[0011] Where: P i is the power at port i, T s is the control period, v i is the voltage at port i, v j is the voltage at port j, d ij is the phase shift angle between port i and port j, n ij is the turns ratio between port i and port j, L ij is the mutual inductance between port i and port j.

[0012] The power data model of each port of the four-port magnetic network power router is as follows:

[0013] P1=-(P2+P3+P4)

[0014] P2=X 21 D21 +X 23 D 23 +X 24 D 24 ,

[0015] P3=Y 31 D 31 +Y 32 D 32 +Y 34 D 34

[0016] P4=Z 41 D 41 +Z 42 D 42 +Z 43 D 43

[0017] Among them, P1, P2, P3, and P4 are the four port powers, D ij is the phase shift angle set between port i and port j, X ij 、Y ij , Z ij are the data sets between the current port and other ports respectively, i=2,3,4, j=1,2,3,4, i≠j.

[0018] Step S2: discretize and matrix the data model of the four-port magnetic network power router, establish the error between the discrete data model and the actual data, and update the power data model of each port in real time;

[0019] The four-port magnetic network power router data model is discretized and expressed as:

[0020] P2(k)=X 21 (k)D 21 (k)+X 23 (k)D 23 (k)+X 24 (k)D 24 (k)

[0021] P3(k)=Y 31 (k)D 31 (k)+Y 32 (k)D 32 (k)+Y 34 (k)D 34 (k),

[0022] P4(k)=Z 41 (k)D 41 (k)+Z 42 (k)D 42 (k)+Z 43 (k)D43 (k)

[0023] Matrix it and express it as:

[0024]

[0025] Among them, k represents the time, is the phase shift angle set of each port, θ i For each port data set, y i is the actual measurement data of each port, i=2,3,4.

[0026] The error between the discrete data model and the actual data is expressed as:

[0027]

[0028] Among them, E i is the error between the data model and the actual measurement of each port; is the estimated value of each port data set, i=2,3,4.

[0029] Update the power data model of each port in real time, specifically:

[0030]

[0031]

[0032] Among them, K i 、U i ,λ i They are the intermediate matrix, iteration matrix, and forgetting factor when calculating each port data set; i=2, 3, 4.

[0033] Step S3: predicting the power of each port at a future time based on the power data model of each port, establishing a range of phase shift angles for each port using the power reference and actual power of each port, and calculating power predictions corresponding to different phase shift angle sets based on the power of each port at a future time;

[0034] The phase shift angle set range of each port is established, specifically:

[0035] D 21 (k+1)={D 21 (k)-ΔD 2a (k)D 21 (k)D 21 (k)+ΔD 2a (k)}

[0036] D 23 (k+1)={D 23 (k)-ΔD 2a (k)D23 (k)D 23 (k)+ΔD 2a (k)}

[0037] D 24 (k+1)={D 24 (k)-ΔD 2a (k)D 24 (k)D 24 (k)+ΔD 2a (k)}

[0038] D 31 (k+1)={D 31 (k)-ΔD 3a (k)D 31 (k)D 31 (k)+ΔD 3a (k)}

[0039] D 32 (k+1)={D 32 (k)-ΔD 3a (k)D 32 (k)D 32 (k)+ΔD 3a (k)},

[0040] D 34 (k+1)={D 34 (k)-ΔD 3a (k)D 34 (k)D 34 (k)+ΔD 3a (k)}

[0041] D 41 (k+1)={D 41 (k)-ΔD 4a (k)D 41 (k)D 41 (k)+ΔD 4a (k)}

[0042] D 42 (k+1)={D 42 (k)-ΔD 4a (k)D 42 (k)D 42 (k)+ΔD 4a (k)}

[0043] D 43 (k+1)={D 43 (k)-ΔD 4a (k)D 43 (k)D 43 (k)+ΔD4a (k)}

[0044] Where ΔD ia is the adaptive phase shift angle set of each port, i=2,3,4.

[0045] Calculate the power prediction corresponding to different phase shift angle sets, specifically:

[0046]

[0047] in, Estimated values for the data set between the current port and other ports respectively.

[0048] Step S4: Subtract the power prediction corresponding to different phase shift angle sets of each port from the power reference of each port to obtain the power error of each port, add the power errors of each port, select the phase shift angle set corresponding to the power error with the smallest error as the optimal set, and apply it to the next control cycle.

[0049] The power error of each port is:

[0050]

[0051] Among them, g i is the power error of each port, P iref The power reference of each port at time k+1 is used. The power errors of each port are added together, and the phase shift angle set corresponding to the power error with the smallest error is selected as the optimal set and applied in the next control cycle.

[0052] On the other hand, the present invention also proposes an electronic system comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method steps of the present invention.

[0053] The present invention adopts the above technical solution and has the following beneficial effects:

[0054] The present invention significantly improves the overall performance and operating efficiency of a four-port magnetic network power router through a series of innovative steps.

[0055] (1) First, through precise analysis and modeling, we can gain a deeper understanding and management of the router's power distribution problem. This improvement not only promotes the precise control of power routing, but also lays a solid foundation for further system optimization and energy efficiency improvement. The direct result is improved system reliability, enabling it to maintain stable and efficient operation under various complex working conditions.

[0056] (2) Secondly, the discretization, matrixization, and real-time updating methods proposed in this invention significantly enhance the system's adaptability and response speed. This dynamic update mechanism enables the data model to promptly reflect the actual operating conditions and quickly adjust to environmental changes and load fluctuations. This not only significantly improves the robustness and flexibility of the system, but also reduces the power transmission instability caused by external disturbances or internal parameter changes, thereby improving the system's operational continuity and safety.

[0057] (3) Finally, through precise power prediction and phase shift angle optimization methods, the present invention achieves more refined power management and resource utilization efficiency. By deeply analyzing and optimizing the phase shift angle set, the system can select the optimal control strategy to implement the next step of control while minimizing power errors. This precise optimization process ensures that the power router can achieve an ideal balance between energy efficiency and performance, not only improving the overall efficiency of power transmission, but also providing important support for the efficient use of renewable energy and the intelligent management of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 This is a topology diagram of a 4-port magnetic network energy router.

[0059] Figure 2 This is a block diagram of the data model and predictive control method of the four-port magnetic network energy router proposed in the present invention.

[0060] Figure 3 This is a flow chart of the data model and predictive control method of the four-port magnetic network energy router proposed by the present invention. DETAILED DESCRIPTION

[0061] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be interpreted as limiting the present invention.

[0062] The present invention discloses a four-port magnetic network energy router data model and a predictive control method. The topology of the four-port magnetic network energy router is as follows: Figure 1As shown, it mainly includes a power module with four ports. Each power module is composed of an H-bridge and a capacitor. The energy transmission between each port is achieved through a four-winding magnetic network transformer. For details, please refer to patent document CN116247958A (publication date: June 9, 2023, name: A multi-port magnetic network energy router and control method and device). The four-port magnetic network energy router includes a double-magnetic plate press-fit four-winding transformer with a distributed magnetic core column, first to fourth full-bridge converters; first to fourth full-bridge converters, each full-bridge converter includes first to fourth reverse-conducting IGBTs, a DC capacitor and a DC source; wherein, the collector of the first reverse-conducting IGBT and the collector of the third reverse-conducting IGBT are respectively connected to the positive pole of the DC capacitor and the DC source; the emitter of the second reverse-conducting IGBT and the emitter of the fourth reverse-conducting IGBT are respectively connected to the negative pole of the DC capacitor and the DC source; the emitter of the first reverse-conducting IGBT is connected to the collector of the second reverse-conducting IGBT, and the connection point is respectively connected to the lower end of the first to fourth power windings; the emitter of the third reverse-conducting IGBT is connected to the collector of the fourth reverse-conducting IGBT, and the connection point is respectively connected to the upper end of the first to fourth power windings.

[0063] like Figure 2 The figure shows the data model and predictive control method block diagram of the four-port magnetic network energy router proposed by the present invention. The method flow is as follows:

[0064] The specific steps of the proposed method are as follows Figure 3 As shown, the following steps are included:

[0065] Step S1: Analyze the power model of the four-port magnetic network power router based on the principle of single phase shift modulation; and design the power data model of each port of the four-port magnetic network power router based on the power model of the four-port magnetic network power router;

[0066] Step S2: discretize and matrix the data model of the four-port magnetic network power router, establish the error between the discrete data model and the actual data, and design a real-time update method for the data model to ensure the timeliness and predictability of the data model of each port;

[0067] Step S3: predicting the power of each port at a future time based on the data model of each port, establishing a range of phase shift angles for each port using the power reference and actual power of each port, and calculating power predictions corresponding to different phase shift angle sets based on the power of each port at a future time;

[0068] Step S4: Subtract the power prediction corresponding to different phase shift angle sets of each port from the power reference of each port to obtain the power error of each port, add the power errors of each port, select the phase shift angle set corresponding to the power error with the smallest error as the optimal set, and apply it to the next control cycle.

[0069] The topology of the four-port magnetic network power router in step S1 is as follows: Figure 1 As shown. In the topology of the four-port magnetic network power router, the energy transfer between each port is determined by the phase shift angle between each H-bridge. When single-phase shift modulation is used, each H-bridge applies a 50% duty cycle. By adjusting the phase shift angle of the four H-bridges of the magnetic network power router converter, the output voltage of each port can be effectively controlled. At this time, the power model between any two ports of the four-port magnetic network power router can be expressed as:

[0070]

[0071] Where: P i is the power at port i, T s is the control period, v i is the voltage at port i, v j is the voltage at port j, d ij is the phase shift angle between port i and port j, n ij is the turns ratio between port i and port j, L ij is the mutual inductance between port i and port j.

[0072] According to formula (1), the power model of ports 1 to 4 can be obtained and expressed as:

[0073]

[0074] in:

[0075]

[0076] Where: P1 is the power at port 1, P2 is the power at port 2, P3 is the power at port 3, and P4 is the power at port 4. v1 is the voltage at port 1, v2 is the voltage at port 2, v3 is the voltage at port 3, and v4 is the voltage at port 4. 21 is the phase shift angle between port 2 and port 1, d 23 is the phase shift angle between port 2 and port 3, d 24 is the phase shift angle between port 2 and port 4, d 31 is the phase shift angle between port 3 and port 1, d 32 is the phase shift angle between port 3 and port 2, d 34 is the phase shift angle between port 3 and port 4, d 41 is the phase shift angle between port 4 and port 1, d 42 is the phase shift angle between port 4 and port 2, d 43 is the phase shift angle between port 4 and port 3. 21 is the turns ratio between port 2 and port 1, n 23 is the turns ratio between port 2 and port 3, n 24is the turns ratio between port 2 and port 4, n 31 is the turns ratio between port 3 and port 1, n 32 is the turns ratio between port 3 and port 2, n 34 is the turns ratio between port 3 and port 4, n 41 is the turns ratio between port 4 and port 1, n 42 is the turns ratio between the 4-port and the 2-port, n 43 is the turns ratio between 4-port and 3-port. 21 is the mutual inductance between port 2 and port 1, L 23 is the mutual inductance between port 2 and port 3, L 24 is the mutual inductance between port 2 and port 4, L 31 is the mutual inductance between port 3 and port 1, L 32 is the mutual inductance between port 3 and port 2, L 34 is the mutual inductance between port 3 and port 4, L 41 is the mutual inductance between port 4 and port 1, L 42 is the mutual inductance between port 4 and port 2, L 43 is the mutual inductance between port 4 and port 3.

[0077] According to formula (2), the power data model of 2 to 4 ports can be established:

[0078]

[0079] in:

[0080]

[0081] Where: D 21 is the phase shift angle set between port 2 and port 1, D 23 is the phase shift angle set between port 2 and port 3, D 24 is the phase shift angle set between 2-port and 4-port, D 31 is the phase shift angle set between port 3 and port 1, D 32 is the phase shift angle set between 3-port and 2-port, D 34 is the phase shift angle set between port 3 and port 4, D 41 is the phase shift angle set between port 4 and port 1, D 42 is the phase shift angle set between 4-port and 2-port, D 43 is the phase shift angle set between 4-port and 3-port. 21 is the data set between port 2 and port 1, X 23 is the data set between 2 ports and 3 ports, X 24 is the data set between 2 ports and 4 ports, Y 31 is the data set between port 3 and port 1, Y32 is the data set between 3 ports and 2 ports, Y 34 is the data set between 3 ports and 4 ports, Z 41 is the data set between 4 ports and 1 port, Z 42 is the data set between 4 ports and 2 ports, Z 43 It is the data collection between 4 ports and 3 ports.

[0082] The discretization result of the four-port magnetic network power router data model in step S2 can be expressed as:

[0083]

[0084] Where: k represents time k. The power at time k of ports 2 to 4 can be obtained based on the measured voltage and current:

[0085]

[0086] Where: i1 is the current of port 1, i2 is the current of port 2, i3 is the current of port 3, and i4 is the current of port 4.

[0087] Combining formulas (7) and (8), we can obtain:

[0088]

[0089] Formula (9) can be expressed as a matrix:

[0090]

[0091] in:

[0092]

[0093]

[0094] Where: is the 2-port phase shift angle set; θ2 is the 2-port data set; y2 is the 2-port actual measurement data. is the 3-port phase shift angle set; θ3 is the 3-port data set; y3 is the 3-port actual measurement data. is the 4-port phase shift angle set; θ4 is the 4-port data set; y4 is the 4-port actual measurement data.

[0095] According to the above formula, the error Ei(k) between the data model of each port and the actual measurement can be obtained and expressed as:

[0096]

[0097] in:

[0098]

[0099] Where k-1 represents the k-1 moment. E2 is the error between the 2-port data model and the actual measurement; E3 is the error between the 3-port data model and the actual measurement; and E4 is the error between the 4-port data model and the actual measurement. Estimate values for the 2-port dataset; Estimate values for the 3-port dataset; Estimate values for the 4-port dataset. is the estimated value for the data set between port 2 and port 1, is the estimated value for the data set between 2 ports and 3 ports, is the estimated value for the data set between 2 ports and 4 ports, is the estimated value for the data set between 3-port and 1-port, is the estimated value for the data set between 3-port and 2-port, is the estimated value for the data set between 3 ports and 4 ports, is the estimated value for the data set between 4-port and 1-port, is the estimated value for the data set between 4 ports and 2 ports, Estimates are made for data sets between 4-port and 3-port.

[0100] According to the error E between the data model of each port and the actual measurement i (k) The estimated value of the data set at each port k can be calculated:

[0101]

[0102] in:

[0103]

[0104] Where: K2 is the intermediate matrix when calculating the 2-port data set; U2 is the iterative matrix when calculating the 2-port data set; λ2 is the forgetting factor when calculating the 2-port data set; K3 is the intermediate matrix when calculating the 3-port data set; U3 is the iterative matrix when calculating the 3-port data set; λ3 is the forgetting factor when calculating the 3-port data set; K4 is the intermediate matrix when calculating the 4-port data set; U4 is the iterative matrix when calculating the 4-port data set; λ4 is the forgetting factor when calculating the 4-port data set.

[0105] In order to ensure the timeliness and predictability of the data model of each port, it is necessary to obtain the data model of each port at time k+1:

[0106]

[0107] in:

[0108]

[0109] Where: k+1 represents the k+1 moment.

[0110] In step S3, the power of each port at a future moment is predicted based on the data model of each port as follows:

[0111]

[0112] In a four-port magnetic network energy router, due to the phase shift angle d between any two ports ij The range is -0.5 to 0.5, so the phase shift angle set D ij The range is -0.25 to 0.25. The phase shift angle set range can be set to {D ij (k)-ΔD ia (k), D ij (k), D ij (k)+ΔD ia (k)}. Among them, D ij (k) is the optimal phase shift angle set of the previous control cycle, ΔD ia (k) is the set of adaptive phase shift angles at port i, which can be calculated and expressed as:

[0113]

[0114] in:

[0115]

[0116] Where: ΔD 2a is the 2-port adaptive phase shift angle set, ΔD2 is the 2-port discrete phase shift angle set, ε2 is the 2-port phase shift angle set adjustment coefficient, ΔP2 is the 2-port power error, P 2m is the maximum power error of the 2-port, P 2ref ΔD is the 2-port power reference. 3a is the 3-port adaptive phase shift angle set, ΔD3 is the 3-port discrete phase shift angle set, ε3 is the 3-port phase shift angle set adjustment coefficient, ΔP3 is the 3-port power error, P 3m is the 3-port maximum power error, P 3ref ΔD is the 3-port power reference. 4a is the 4-port adaptive phase shift angle set, ΔD4 is the 4-port discrete phase shift angle set, ε4 is the 4-port phase shift angle set adjustment coefficient, ΔP4 is the 4-port power error, P 4m is the 4-port maximum power error, P 4ref 4-port power reference.

[0117]

[0118] in:

[0119]

[0120] In step S4, the power prediction corresponding to the different phase shift angle sets of each port is subtracted from the power reference of each port to obtain the power error of each port:

[0121]

[0122] Where: g2 is the power error at port 2; g3 is the power error at port 3; g4 is the power error at port 4.

[0123]

[0124] Where G is the total power error of the four-port magnetic network energy router. According to formula (28), the phase shift angle set corresponding to the power error with the smallest error is selected as the optimal set and applied in the next control cycle.

[0125] The control method proposed in this paper significantly improves the power control robustness of a four-port magnetic network power router, enabling it to maintain excellent performance under complex and variable operating conditions. Through precise parameter adjustment and intelligent control strategies, this method effectively addresses the challenges posed by changes in the external environment and fluctuations in internal parameters, ensuring stable and efficient operation of the router under diverse operating conditions. This improvement not only enhances the overall reliability of the system but also strengthens its adaptability to various emergencies, ensuring accurate and efficient distribution and transmission of electricity, and providing strong support for the safe and stable operation of the microgrid.

[0126] Example 2:

[0127] This embodiment proposes an electronic system, including a processor; and a memory communicatively connected to the processor; wherein the memory stores instructions that can be executed by the processor, and the instructions are executed by the processor to enable the processor to perform the steps of the method described in the present invention, which will not be repeated here.

[0128] It should be noted that the processing flow of Example 2 corresponds to the specific steps of the method provided in the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not fully described in this embodiment, please refer to the method provided in the embodiment of the present invention.

[0129] The program code for implementing the methods of the present application can be written in any combination of one or more programming languages. Such program code can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the program code is executed by the processor or controller, the functions / operations specified in the flow charts and / or block diagrams are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0130] In the context of the present application, a machine-readable medium can be a tangible medium that can contain or store a program for use by an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0131] The above embodiments are only for illustrating the technical idea of the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the present invention.

Claims

1. A four-port magnetic network energy router control method based on a data model, characterized in that: The steps include: Step S1, based on the principle of single phase shift modulation, obtain the power model between any two ports of the four-port magnetic network power router and establish the power data model of each port; Step S2: discretize and matrix the data model of the four-port magnetic network power router, establish the error between the discrete data model and the actual data, and update the power data model of each port in real time; Step S3: predicting the power of each port at a future time based on the power data model of each port, establishing a range of phase shift angles for each port using the power reference and actual power of each port, and calculating power predictions corresponding to different phase shift angle sets based on the power of each port at a future time; Step S4: Subtract the power prediction corresponding to different phase shift angle sets of each port from the power reference of each port to obtain the power error of each port, add the power errors of each port, select the phase shift angle set corresponding to the power error with the smallest error as the optimal set, and apply it to the next control cycle.

2. The method according to claim 1, characterized in that The power model between any two ports is expressed as: Where: P i is the power at port i, T s is the control period, v i is the voltage at port i, v j is the voltage at port j, d ij is the phase shift angle between port i and port j, n ij is the turns ratio between port i and port j, L ij is the mutual inductance between port i and port j.

3. The method according to claim 2, characterized in that The power data model of each port of the four-port magnetic network power router is as follows: Among them, P1, P2, P3, and P4 are the four port powers, D ij is the phase shift angle set between port i and port j, X ij 、Y ij , Z ij are the data sets between the current port and other ports respectively, i=2,3,4, j=1,2,3,4, i≠j.

4. The method according to claim 3, characterized in that Step S2 discretizes the four-port magnetic network power router data model and expresses it as: Matrix it and express it as: Among them, k represents the time, is the phase shift angle set of each port, θ i For each port data set, y i is the actual measurement data of each port, i=2,3,4.

5. The method according to claim 4, characterized in that Step S2 establishes the error between the discrete data model and the actual data, expressed as: Among them, E i is the error between the data model and the actual measurement of each port; is the estimated value of each port data set, i=2,3,4.

6. The method according to claim 5, characterized in that Step S2 updates the power data model of each port in real time, specifically: Among them, K i 、U i ,λ i They are the intermediate matrix, iteration matrix, and forgetting factor when calculating each port data set; i=2, 3, 4.

7. The method according to claim 6, characterized in that In step S3, the phase shift angle set range of each port is established, specifically: Where ΔD ia is the adaptive phase shift angle set of each port, i=2,3,4.

8. The method according to claim 7, characterized in that In step S3, the power prediction corresponding to different phase shift angle sets is calculated as follows: in, Estimated values for the data set between the current port and other ports respectively.

9. The method according to claim 8, characterized in that The power error of each port obtained in step S4 is: Among them, g i is the power error of each port, P iref The power reference of each port at time k+1 is used. The power errors of each port are added together, and the phase shift angle set corresponding to the power error with the smallest error is selected as the optimal set and applied in the next control cycle.

10. An electronic system comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, characterized in that the instructions are executed by the at least one processor so that the at least one processor can execute the method described in any one of claims 1-9.

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