An adaptive data-driven power control method for magnetic network power router

Abstract

The present invention discloses an adaptive data-driven power control method for a magnetic network power router. The method includes establishing a discrete power model for the secondary side of the magnetic network power router, establishing its data model, and identifying the data model in real time based on a gradient optimization approach. The adaptive gradient factor is designed by considering the influence of the gradient factor on the data model identification. An adaptive phase shift angle range is designed based on the secondary side output power and an output power reference to provide a phase shift angle reference for output power prediction calculations. Future output power is predicted based on the data model and the adaptive phase shift angle range to implement data-driven predictive control. The method also utilizes a value function to evaluate the optimal phase shift angle and apply it in the next control cycle to implement adaptive output power control. The method improves the robustness of the power control of the magnetic network power router, enhances the reliability of the magnetic network power router under different operating conditions, and enhances the scalability of the application of magnetic network energy routers with different power control parameters.

Description

Technical Field

[0001] The present invention belongs to the field of isolated power electronic converters, and in particular relates to an adaptive data-driven power control method for a magnetic network power router. Background Art

[0002] A magnetic network power router is a device or system used to manage and optimize the flow of electricity. It monitors and manages the supply and demand of electricity, rationally allocating energy resources based on real-time power conditions and user needs. Through intelligent power scheduling algorithms, it achieves efficient utilization and balance of energy. Furthermore, it can be combined with energy storage devices such as batteries and supercapacitors to improve the reliability and stability of power systems. The magnetic network power router is widely used in smart grids, microgrids, and power management systems. By optimizing power flow and improving energy utilization efficiency, the magnetic network power router makes a significant contribution to the sustainable development of power systems and the transformation of the power sector.

[0003] A magnetic network power router is a device that transmits energy using magnetic coupling technology. It includes key components such as leakage inductance and capacitance. Efficient control of its output power is a key metric for its operation. Traditional methods for controlling the output power of magnetic network power routers use linear feedback control or model predictive control techniques, both of which require accurate leakage inductance and capacitance parameters for precise power control. When the model parameters of leakage inductance and capacitance do not match the actual parameters, traditional power control results in inaccurate steady-state performance and slow dynamic response. However, during actual operation of a magnetic network power router system, the actual values ​​of leakage inductance and capacitance vary with temperature, hardware aging, and changing operating conditions, potentially resulting in parameter errors of up to 50%.

[0004] To improve the robustness of the output power control parameters of magnetic network power routers, researchers have proposed various power control methods based on parameter identification. However, before implementing these parameter identification methods, a relatively accurate system model must be established to determine the relationship between the system identification variables and the input and output variables. Furthermore, these parameter identification methods ignore the influence of parasitic parameters, and identification errors can still affect the output power performance of magnetic network power routers. Summary of the Invention

[0005] The technical problem to be solved by the present invention is: in response to the problems always existing in the prior art, a method for adaptive data-driven power control of a magnetic network power router is provided. By establishing a data-driven model of the magnetic network power router, data-driven power control is realized, and the technical problem of the influence of changes in traditional leakage inductance and capacitance parameters on traditional control is solved, so as to achieve the purpose of robust, fast and accurate power control.

[0006] To solve the above technical problems, the present invention provides the following technical solution: a method for adaptive data-driven power control of a magnetic network power router, comprising the following steps:

[0007] S1. Based on the magnetic network power router model, establish the secondary side output voltage and current model of the magnetic network power router, calculate the discrete model of the secondary side output voltage and output current of the magnetic network power router, and then obtain the discrete model of the secondary side output power of the magnetic network power router;

[0008] S2. Based on the discrete model of the secondary-side output power of the magnetic network power router, a data model of the secondary-side output power of the magnetic network power router is established. Combined with the gradient optimization idea, the discriminant equation is used to identify the data model in real time. Considering the influence of the gradient factor on the data model identification, an adaptive gradient factor is designed.

[0009] S3. Design an adaptive phase shift angle range based on the secondary side output power and output power reference of the magnetic network power router to provide a phase shift angle reference for output power prediction calculation;

[0010] S4. Based on the data model of the secondary side output power of the magnetic network power router and the adaptive phase shift angle range, the output power at future moments is predicted to realize data-driven predictive control; the optimal phase shift angle is evaluated using the value function and applied in the next control cycle to realize adaptive output power control of the magnetic network power router.

[0011] Furthermore, in the aforementioned step S1, the discrete models of the secondary side output voltage and output current of the magnetic network power router are as follows:

[0012]

[0013] Among them, T s is the control period of the magnetic network power router; v1(k) is the sampling value of the primary side input voltage of the magnetic network power router at time k; v2(k) is the sampling value of the secondary side output voltage of the magnetic network power router at time k; i o (k) is the sampling value of the secondary side output current of the magnetic network power router at time k; i s2 (k) is the output current of the H-bridge on the secondary side of the magnetic network power router at time k; v2(k+1) is the output voltage on the secondary side of the magnetic network power router at time k+1; D(k) is the phase shift angle between the primary and secondary sides of the magnetic network power router at time k, n is the turns ratio of the transformer of the magnetic network power router; L r Transformer leakage inductance for the magnetic network power router.

[0014] Furthermore, in the aforementioned step S1, the discrete model of the secondary side output power of the magnetic network power router is as follows:

[0015]

[0016] Among them, i o (k) is the output current of the secondary side of the magnetic network power router at time k, and P2(k) and P2(k+1) are the output powers of the secondary side of the magnetic network power router at time k and k+1 respectively.

[0017] Furthermore, in the aforementioned step S2, the data model of the secondary side output power of the magnetic network power router is:

[0018] ΔP2(k)=A(k)D(k)+B(k)i o (k), ΔP2(k)=P2(k)-P2(k-1),

[0019] Where ΔP2(k) is the output power difference on the secondary side of the magnetic network power router, and A(k) and B(k) are the sets of known items, unknown items, and disturbances of the system.

[0020] Furthermore, in the aforementioned step S2, the identification equation is as follows:

[0021]

[0022] in, λ is the gradient factor.

[0023] Furthermore, in the aforementioned step S2, the adaptive gradient factor established by the gradient optimization idea is:

[0024]

[0025] Among them, q is the natural exponential function, λ0 is the boundary value of the gradient factor, τ is a positive coefficient, and e0 is the boundary value of the data model identification error.

[0026] Furthermore, in the aforementioned step S3, the adaptive phase shift angle range is specifically: {D(k)-ΔD a (k), D(k), D(k)+ΔD a (k)}, where D(k) is the optimal phase shift angle of the previous control cycle, ΔD a (k) is the adaptive phase shift angle, P m is the maximum power error, ε is the adjustment coefficient, ΔP is the power error, P 2ref is the secondary side output power reference.

[0027] Furthermore, in the aforementioned step S4, based on the data model of the secondary side output power of the magnetic network power router and the adaptive phase shift angle range, the output power at the future moment is predicted to implement data-driven predictive control, specifically:

[0028] P 2x (k+1)=P2(k)+ΔP 2x (k)x∈{1,2,3},

[0029] in, ΔP 21 (k+1),ΔP 22 (k+1),ΔP 23 (k+1) are phase shift angles D(k)-ΔD a (k), D(k), D(k)+ΔD a (k) The corresponding power difference.

[0030] Furthermore, in the aforementioned step S4, the value function is:

[0031] g=α1g1+α2g2,

[0032] in, g1 is the value function for reference power tracking, g2 is the value function for ensuring the steady-state performance of the magnetic network power router, α1 and α2 are the weight factors of g1 and g2 respectively. The optimal phase shift angle selected according to the value function is applied to the magnetic network power router to control its secondary side output power.

[0033] Compared with the prior art, the beneficial technical effects of the present invention using the above technical solution are as follows:

[0034] (1) The present invention proposes an adaptive data-driven power control method for a magnetic network power router. By modeling a data-driven model for the power control of the magnetic network power router, the traditional mathematical model is replaced to realize data-driven power prediction control, thereby improving the robustness of the power control of the magnetic network power router.

[0035] (2) The adaptive data-driven power control method proposed in the present invention is used to design an adaptive gradient optimization idea to achieve the recognition accuracy and speed of the data model in multiple working conditions, thereby improving the reliability of power tracking of the magnetic network power router.

[0036] (3) The data-driven power control method proposed in the present invention has a simple principle and can be easily extended to magnetic network power routers with different parameters and power levels. Therefore, the present invention has a wider range of applications and higher practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is the structural diagram of the magnetic network power router.

[0038] Figure 2 This is a control block diagram of a method for adaptive data-driven power control of a magnetic network power router proposed by the present invention.

[0039] Figure 3 The present invention provides a flow chart of a method for adaptive data-driven power control of a magnetic network power router. DETAILED DESCRIPTION

[0040] In order to better understand the technical content of the present invention, specific embodiments are given below in conjunction with the accompanying drawings.

[0041] Various aspects of the present invention are described herein with reference to the accompanying drawings, which show a number of illustrative embodiments. The embodiments of the present invention are not limited to those described in the accompanying drawings. It should be understood that the present invention can be implemented by any of the various concepts and embodiments described above, as well as the concepts and implementations described in detail below, because the concepts and embodiments disclosed herein are not limited to any particular implementation. In addition, some aspects disclosed herein may be used alone or in any appropriate combination with other aspects disclosed herein.

[0042] The present invention proposes an adaptive data-driven power control method for a magnetic network power router. Based on the discrete power model of the secondary side of the magnetic network power router, its data-driven model is established. Based on the gradient optimization idea, the data model of the magnetic network power router is identified in real time. Combined with the adaptive phase shift angle, data-driven power predictive control is realized, thereby achieving the purpose of robust and fast power control of the magnetic network power router under different parameter levels, and improving the accuracy and reliability of power control.

[0043] The structure of the magnetic network power router in the present invention is as follows Figure 1 As shown in the figure, the magnetic network power router includes two power modules on the primary side and the secondary side. Each power module consists of an H-bridge and a capacitor. The energy transmission between the primary side and the secondary side is achieved through a bidirectional transformer.

[0044] like Figure 2 The figure shows a control block diagram of a magnetic network power router adaptive data driven power control method proposed by the present invention. Figure 3 The present invention provides a method for adaptive data-driven power control of a magnetic network power router, comprising the following steps:

[0045] S1. Based on the magnetic network power router model, establish the secondary side output voltage and current model of the magnetic network power router, calculate the discrete model of the secondary side output voltage and output current of the magnetic network power router, and then obtain the discrete model of the secondary side output power of the magnetic network power router;

[0046] S2. Based on the discrete model of the secondary-side output power of the magnetic network power router, a data model of the secondary-side output power of the magnetic network power router is established. Combined with the gradient optimization idea, the discriminant equation is used to identify the data model in real time. Considering the influence of the gradient factor on the data model identification, an adaptive gradient factor is designed.

[0047] S3. Design an adaptive phase shift angle range based on the secondary side output power and output power reference of the magnetic network power router to provide a phase shift angle reference for output power prediction calculation;

[0048] S4. Based on the data model of the secondary side output power of the magnetic network power router and the adaptive phase shift angle range, the output power at future moments is predicted to realize data-driven predictive control; the optimal phase shift angle is evaluated using the value function and applied in the next control cycle to realize adaptive output power control of the magnetic network power router.

[0049] In step S1, the structure of the magnetic network power router is as follows: Figure 1 As shown, according to the structure of the magnetic network power router, we can get Figure 2 The magnetic network power router model is shown, where the secondary side output voltage and output current of the magnetic network power router can be expressed as:

[0050]

[0051] Where: C2 is the output capacitance of the secondary side of the magnetic network power router, v2 is the output voltage of the secondary side of the magnetic network power router, i s2 is the H-bridge output current on the secondary side of the magnetic network power router, i o is the secondary side output current of the magnetic network power router.

[0052] By using the forward Euler method, formula (1) can be discretized and expressed as:

[0053]

[0054] In formula (2), i s2 (k) can be expressed as:

[0055]

[0056] Where: T s is the control period of the magnetic network power router; v1(k) is the sampling value of the primary side input voltage of the magnetic network power router at time k; v2(k) is the sampling value of the secondary side output voltage of the magnetic network power router at time k; i o (k) is the sampling value of the secondary side output current of the magnetic network power router at time k; i s2(k) is the output current of the H-bridge on the secondary side of the magnetic network power router at time k; v2(k+1) is the output voltage on the secondary side of the magnetic network power router at time k+1; D(k) is the phase shift angle between the primary and secondary sides of the magnetic network power router at time k, which ranges from [-0.5 to 0.5]; n is the turns ratio of the transformer of the magnetic network power router; L r Transformer leakage inductance for the magnetic network power router.

[0057] Substituting formula (3) into formula (2), we can obtain the following discrete models of the output voltage and output current on the secondary side of the magnetic network power router:

[0058]

[0059] Considering that the load current changes slowly, it can be considered that i o (k+1)≈i o (k), so the secondary side output power of the magnetic network power router can be expressed as:

[0060]

[0061] Where: i o (k) is the output current of the secondary side of the magnetic network power router at time k, and P2(k) and P2(k+1) are the output powers of the secondary side of the magnetic network power router at time k and k+1 respectively.

[0062] As a preferred embodiment of the present invention, in step S2, the data model of the secondary side output power of the magnetic network power router is as follows:

[0063] According to formula (5), it is established and expressed as:

[0064] ΔP2(k)=A(k)D(k)+B(k)i o (k) (6)

[0065] in:

[0066] ΔP2(k)=P2(k)-P2(k-1) (7)

[0067] Where: ΔP2(k) is the output power difference on the secondary side of the magnetic network power router, A(k) and B(k) are the sets of known items, unknown items, and disturbances of the system, which will be obtained through the identification method without the need to calculate according to the system mathematical model, thus eliminating the influence of parameters.

[0068] The matrix form of formula (6) can be expressed as:

[0069]

[0070] in:

[0071]

[0072] According to the above formula, the data model identification error e(k) can be obtained and expressed as:

[0073]

[0074] In order to obtain the corresponding data matrix θ(k) when the identification error e(k) is minimized, the objective function J can be defined as:

[0075]

[0076] By calculating the gradient of the objective function J along the direction of the data matrix θ(k), the minimum e(k) can be found. When e(k) is minimized, the change in the data matrix θ(k) is close to 0, and the updated parameters are consistent with the actual values.

[0077] The gradient matrix of the objective function J with respect to the data matrix θ(k) can be expressed as:

[0078]

[0079] Therefore, according to the gradient descent law, the estimated data matrix θ(k), that is, the identification equation is expressed as:

[0080]

[0081] Where: λ is the gradient factor.

[0082] In formula (13), the gradient factor λ is generally set to 0<λ<1. When the value of λ is much less than 1, the data matrix identification speed will be improved. Similarly, when the value of λ is close to 1, the accuracy of data matrix identification is enhanced. Considering that the magnetic network power router system operates in various working conditions, in order to improve the performance of data matrix identification, it is necessary to dynamically adjust and adaptively change the value of λ, as shown below:

[0083]

[0084] Where q is the natural exponential function, λ0 is the boundary value of the gradient factor, τ is a positive coefficient, and e0 is the boundary value of the data model identification error.

[0085] The adaptive phase shift angle range of the magnetic network power router in step S3 is as follows:

[0086] In a magnetic network power router with unidirectional power transmission, the phase shift angle range is 0 to 0.5, and its discrete form can be expressed as {0, ΔD, 2ΔD, …, 0.5}. Among them, ΔD can be calculated and expressed as:

[0087]

[0088] Where: T c is the clock cycle of the DSP processor.

[0089] In order to reduce the phase shift angle range to {0, ΔD, 2ΔD, …, 0.5}, the phase shift angle range can be set to {D(k)-ΔD a (k), D(k), D(k)+ΔD a (k)}. Where D(k) is the optimal phase shift angle of the previous control cycle, ΔD a (k) is the adaptive phase shift angle, which can be calculated and expressed as:

[0090] ΔD a (k)=ΔD(k)(1+εΔP 2 (k)) (16)

[0091] in:

[0092]

[0093] Where: P m is the maximum power error, ε is the adjustment coefficient, ΔP is the power error, P 2ref is the secondary side output power reference.

[0094] The data-driven predictive control of the magnetic network power router in step S4 is as follows:

[0095] Based on the data models A(k) and B(k) obtained in step S2 and the phase shift angle {D(k)-ΔD a (k), D(k), D(k)+ΔD a (k)}, the voltage difference corresponding to different phase shift angles can be obtained:

[0096]

[0097] Where: ΔP 21 (k+1),ΔP 22 (k+1),ΔP 23 (k+1) are phase shift angles D(k)-ΔD a (k), D(k), D(k)+ΔD a (k) The corresponding power difference. Combined with the output power sample P2(k) at time k and the power difference ΔP 2x (k+1), we can get the output voltage P at time (k+1) 2x (k+1):

[0098] P 2x (k+1)=P2(k)+ΔP x2(k)x∈{1,2}, (19)

[0099] As shown in formulas (18) to (19), the proposed magnetic network power router predictive power calculation does not require the use of any model parameters, thus realizing data-driven power control and improving the parameter robustness of power control.

[0100] In order to a , D, D+ΔD a} selects the optimal phase shift angle and applies it in the next control cycle, defining the cost function shown in formula (20):

[0101] g=α1g1+α2g2 (20)

[0102] in:

[0103]

[0104] Where g1 is the value function for reference power tracking, g2 is the value function for ensuring the steady-state performance of the magnetic network power router, and α1 and α2 are the weighting factors for g1 and g2, respectively. The optimal phase shift angle selected based on the value function is then applied to the magnetic network power router to control its secondary-side output power.

[0105] While the present invention has been described above with reference to preferred embodiments, this is not intended to limit the present invention. Persons skilled in the art will readily appreciate that various modifications and variations can be made without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A method for adaptive data-driven power control of a magnetic network power router, characterized in that: The steps include: S1. Based on the magnetic network power router model, establish the secondary side output voltage and current model of the magnetic network power router, calculate the discrete model of the secondary side output voltage and output current of the magnetic network power router, and then obtain the discrete model of the secondary side output power of the magnetic network power router; S2. Based on the discrete model of the secondary-side output power of the magnetic network power router, a data model of the secondary-side output power of the magnetic network power router is established. Combined with the gradient optimization idea, the discriminant equation is used to identify the data model in real time. Considering the influence of the gradient factor on the data model identification, an adaptive gradient factor is designed. S3. Design an adaptive phase shift angle range based on the secondary side output power and output power reference of the magnetic network power router to provide a phase shift angle reference for output power prediction calculation; S4. Based on the data model of the secondary side output power of the magnetic network power router and the adaptive phase shift angle range, the output power at future moments is predicted to realize data-driven predictive control; the optimal phase shift angle is evaluated using the value function and applied in the next control cycle to realize adaptive output power control of the magnetic network power router.

2. The method for adaptive data-driven power control of a magnetic network power router according to claim 1, characterized in that: In step S1, the discrete models of the output voltage and output current on the secondary side of the magnetic network power router are as follows: Among them, T s is the control period of the magnetic network power router; v1(k) is the sampling value of the primary side input voltage of the magnetic network power router at time k; v2(k) is the sampling value of the secondary side output voltage of the magnetic network power router at time k; i o (k) is the sampling value of the secondary side output current of the magnetic network power router at time k; i s2 (k) is the output current of the H-bridge on the secondary side of the magnetic network power router at time k; v2(k+1) is the output voltage on the secondary side of the magnetic network power router at time k+1; D(k) is the phase shift angle between the primary and secondary sides of the magnetic network power router at time k, n is the turns ratio of the transformer of the magnetic network power router; L r Transformer leakage inductance for the magnetic network power router.

3. The method for adaptive data-driven power control of a magnetic network power router according to claim 2, characterized in that: In step S1, the discrete model of the secondary side output power of the magnetic network power router is as follows: Among them, i o (k) is the output current of the secondary side of the magnetic network power router at time k, and P2(k) and P2(k+1) are the output powers of the secondary side of the magnetic network power router at time k and k+1 respectively.

4. The method for adaptive data-driven power control of a magnetic network power router according to claim 3, characterized in that: In step S2, the data model of the secondary side output power of the magnetic network power router is: ΔP2(k)=A(k)D(k)+B(k)i o (k),ΔP2(k)=P2(k)-P2(k-1), Where ΔP2(k) is the output power difference on the secondary side of the magnetic network power router, and A(k) and B(k) are the sets of known items, unknown items, and disturbances of the system.

5. The method for adaptive data-driven power control of a magnetic network power router according to claim 4, characterized in that: In step S2, the identification equation is as follows: in, λ is the gradient factor.

6. The method for adaptive data-driven power control of a magnetic network power router according to claim 5, characterized in that: In step S2, the adaptive gradient factor established by the gradient optimization idea is: Where q is the natural exponential function, λ0 is the boundary value of the gradient factor, τ is the positive system, and e0 is the boundary value of the data model identification error.

7. The method for adaptive data-driven power control of a magnetic network power router according to claim 6, characterized in that: In step S3, the adaptive phase shift angle range is specifically: {D(k)-ΔD a (k), D(k), D(k)+ΔD a (k)}, Where ΔD a (k)=ΔD(k)(1+εΔP 2 (k)), D(k) is the optimal phase shift angle of the previous control cycle, ΔD a (k) is the adaptive phase shift angle, P m is the maximum power error, ε is the adjustment coefficient, ΔP is the power error, P 2ref is the secondary side output power reference.

8. The method for adaptive data-driven power control of a magnetic network power router according to claim 7, characterized in that: In step S4, based on the data model of the secondary side output power of the magnetic network power router and the adaptive phase shift angle range, the output power at the future moment is predicted to implement data-driven predictive control, specifically: P 2x (k+1)=P2(k)+ΔP 2x (k) x∈{1,2,3}, in, ΔP 21 (k+1),ΔP 22 (k+1),ΔP 23 (k+1) are phase shift angles D(k)-ΔD a (k), D(k), D(k)+ΔD a (k) The corresponding power difference.

9. The method for adaptive data-driven power control of a magnetic network power router according to claim 8, characterized in that: In step S4, the value function is: g=α1g1+α2g2, in, g1 is the value function for reference power tracking, g2 is the value function for ensuring the steady-state performance of the magnetic network power router, α1 and α2 are the weight factors of g1 and g2 respectively. The optimal phase shift angle selected according to the value function is applied to the magnetic network power router to control its secondary side output power.

Images