Magnetic network electric energy router enhanced control set dynamic optimization prediction method and system

Abstract

The invention discloses a magnetic network electric energy router enhanced control set dynamic optimization prediction method, and the method comprises the steps: firstly building a power transmission model based on a router topology and modulation principle, discretizing a phase shift angle output range, calculating an adaptive phase shift angle through combining a voltage reference, and finally setting M preset discrete phase shift angles. Secondly, establishing a router output voltage model, and deducing a discrete model of the router output voltage model; then, based on the output voltage discrete model and the phase shift angle range, two enhanced phase shift angles are designed to replace head and tail phase shift angles in an original preset set, and M enhanced control set phase shift angles are formed; and finally, predicting the output voltage corresponding to M phase shift angles at the future moment by using the enhanced control set phase shift angle and the output voltage discrete model. And evaluating and selecting an optimal phase-shifting angle through a value function to be applied to a next control period, and realizing output voltage regulation by combining single phase-shifting modulation. According to the method, the dynamic performance of the magnetic network electric energy router under the sudden change operation condition is improved, and the method has application expansibility.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of isolated power electronic converters, and particularly relates to a dynamic optimization prediction method for enhanced control set of a magnetic network power router. BACKGROUND

[0002] Recently, the progress of renewable energy generation and energy storage technology has driven the development of modern power systems. In these systems, DC-DC converters can flexibly integrate and efficiently utilize various renewable energy sources, achieve multi-directional energy transmission and active management, and become a key component of modern power systems. Among them, the magnetic network power router is highly regarded due to its high power density, bidirectional energy transmission, inherent electrical isolation, and easy realization of soft switching. These advantages make it widely used in various energy conversion systems.

[0003] In energy conversion systems, the integration of a large amount of renewable energy will lead to complex and variable operating conditions. These characteristics require the magnetic network power router to have excellent dynamic performance in voltage control. In order to effectively regulate the output voltage of the magnetic network power router, due to its superior dynamic performance and multi-objective control ability, the moving discrete control set model predictive control has been widely applied. The moving discrete control set model predictive control designs a moving discrete control set according to the modulation method used by the magnetic network power router, predicts the future system state using the sampling information and discrete model of the magnetic network power router, and selects the optimal output variable according to the cost function evaluation.

[0004] The voltage performance of the moving discrete control set model predictive control depends on the number of moving discrete control sets. When the number of moving discrete control sets is large, the dynamic performance of the magnetic network power router will be improved, but the computational burden of the moving discrete control set model predictive control will also increase. Conversely, when the number of moving discrete control sets is small, the dynamic performance of the magnetic network power router will be affected. In order to improve the dynamic performance of the moving discrete control set model predictive control, some scholars have proposed a moving discrete control set with an adaptive step mechanism. However, the dynamic performance improvement of the moving discrete control set model predictive control is limited. SUMMARY

[0005] The technical problem to be solved by the present application is to provide a dynamic optimization prediction method for an enhanced control set of a magnetic network power router, which designs an adaptive discrete phase-shifting angle according to the operating state of the magnetic network power router, then establishes a discrete model, designs two enhanced phase-shifting angles, and finally forms an enhanced control set phase-shifting angle, thereby improving the phase-shifting angle adjustment range and the dynamic performance of the magnetic network power router.

[0006] To solve the above technical problems, the technical scheme adopted by the present application is as follows:

[0007] Firstly, the application provides a dynamic optimization prediction method for enhanced control set of magnetic network power router, comprising the following steps:

[0008] Step S1, according to the topology structure of the magnetic network power router and the modulation principle, the power transmission model is obtained, and the phase shift angle output range is obtained based on the power transmission model, and the discrete processing is carried out; at the same time, combined with the voltage reference, M adaptive phase shift angles are calculated, and M is an odd number greater than or equal to 3;

[0009] Step S2, according to the topology structure of the magnetic network power router, the output voltage is modeled; then combined with the power transmission model, the discrete model of the output voltage of the magnetic network power router is calculated;

[0010] Step S3, according to the discrete model of the output voltage of the magnetic network power router and the phase shift angle output range, 2 enhanced phase shift angles are designed to replace the 1st and Mth discrete phase shift angles in the M discrete phase shift angles in step S1, forming M enhanced control set phase shift angles;

[0011] Step S4, according to the enhanced control set phase shift angle range of the magnetic network power router and the output voltage discrete model, the output voltage corresponding to the future time of the M enhanced control set phase shift angles is predicted, the dynamic performance optimization voltage prediction control is realized; the optimal enhanced control set phase shift angle is evaluated by using the value function and applied in the next control period, combined with the single phase shift modulation principle, the output voltage regulation of the magnetic network power router is realized.

[0012] As a further optimization scheme of the dynamic optimization prediction method for enhanced control set of magnetic network power router, the power transmission model established in step S1 is: , wherein P is the power transmission model of the magnetic network power router, Ts is the control period, v ab is the input side alternating voltage of the transformer, i r is the series leakage inductance current of the transformer, n is the number of turns ratio of the transformer, v i is the input voltage of the magnetic network power router, v o is the output voltage of the magnetic network power router, L r is the series leakage inductance of the transformer, and D is the phase shift angle. The phase shift angle D output range is 0~0.5, which can be discretely processed as {0, ΔD, 2ΔD,..., 0.5}, wherein ΔD is the discrete phase shift angle.

[0013] As a further optimization scheme of the dynamic optimization prediction method for enhanced control set of magnetic network power router, the adaptive phase shift angle constructed in step S1 is: , wherein ΔD a(k) is the adaptive phase-shift angle of the magnetic network power router at k moment, ε is the adjustment coefficient, Δv(k) is the discrete voltage error of the magnetic network power router at k moment. The M adaptive discrete phase-shift angles are set as {Dop(k)-(M-1) / 2*ΔD a (k), …, Dop(k), …, Dop(k)+(M-1) / 2*ΔD a (k)}. N (k+1), …, D M (k+1)}.

[0014] As a further optimization scheme of the dynamic optimization prediction method of the enhanced control set of the magnetic network power router, the discrete model of the output voltage of the magnetic network power router established in step S2 is:

[0015]

[0016] wherein v ox (k+1) is the output voltage of the magnetic network power router at k+1 moment, which corresponds to the xth adaptive discrete phase-shift angle, i s (k) is the average output current of the magnetic network power router at k moment, i o (k) is the output current of the magnetic network power router at k moment. D x (k+1) is the xth adaptive discrete phase-shift angle of the magnetic network power router at k+1 moment, C o is the output capacitance of the magnetic network power router.

[0017] As a further optimization scheme of the dynamic optimization prediction method of the enhanced control set of the magnetic network power router, the two enhanced phase-shift angles designed in step S3 are:

[0018] wherein: D m1 (k) is the first enhanced phase-shift angle at k moment, D m2 (k) is the second enhanced phase-shift angle at k moment. G1 is the value function when the phase-shift angle is 0, G2 is the value function when the phase-shift angle is D op (k), and G3 is the value function when the phase-shift angle is 0.5. Instead of the first and Mth discrete phase-shift angles in the M discrete phase-shift angles in step S1, the M enhanced control set phase-shift angles finally formed are {D m1 (k), …, D N (k+1), …, D m2 (k)}.

[0019] ​As a further optimization scheme of the dynamic optimization prediction method for the enhanced control set of a magnetic network power router, the future output voltage corresponding to the phase shift angle of the M enhanced control sets in step S4 is:

[0020]

[0021] Among them, v o1 (k+2) is the phase shift angle D of the first enhanced control set at time k+2. m1 (k) corresponds to the voltage prediction, v oN (k+2) is the phase shift angle D of the Nth enhanced control set at time k+2. J Voltage prediction corresponding to (k+1), v oM (k+2) is the phase shift angle D of the Mth enhanced control set at time k+2. m2 Voltage prediction corresponding to (k).

[0022] As a further optimization scheme of the dynamic optimization prediction method for enhanced control set of magnetic network power routers, in order to predict voltage v o1 (k+2)~v oM In step S4, the optimal predicted value is selected from (k+2), and the value function is established as follows: Where N is the value function at time k+2. Finally, the voltage prediction value with the minimum value function J can be selected as the optimal prediction value, and the corresponding enhanced control set phase shift angle is the optimal phase shift angle applied in the next control cycle. Combined with the single-phase modulation principle, the output voltage regulation of the magnetic network power router is realized.

[0023] Secondly, 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, the instructions being executed by the at least one processor to enable the at least one processor to perform the method steps of the present invention.

[0024] Finally, the present invention also provides a computer-readable storage medium storing computer instructions for causing the computer to perform the method steps of the present invention.

[0025] The present invention, by adopting the above technical solution, has the following beneficial effects:

[0026] (1) The dynamic optimization prediction method for the enhanced control set of the magnetic network power router proposed in this invention improves the dynamic performance of voltage control of the magnetic network power router by modeling the control set of the magnetic network power router and designing the enhanced phase shift angle.

[0027] (2) The enhanced control set proposed in this invention has a simple principle and can be easily extended to magnetic network power routers with different parameters and power levels. Therefore, this invention has a wider range of applications, faster dynamic performance, and higher practical value. Attached Figure Description

[0028] Figure 1 This is a structural diagram of a magnetic network power router.

[0029] Figure 2 This is a control block diagram of the adaptive data-driven power control method for magnetic network power routers proposed in this invention.

[0030] Figure 3 This is a flowchart of the adaptive data-driven power control method for magnetic network power routers proposed in this invention. Detailed Implementation

[0031] Embodiments of the present invention are described in detail below, examples of which are illustrated 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 construed as limiting the present invention.

[0032] This invention proposes a dynamic optimization and prediction method for the enhanced control set of a magnetic network power router.

[0033] Based on the structure and modulation principle of the magnetic network power router, its power transmission model is established, and the phase shift angle output range is obtained. An adaptive discrete phase shift angle is designed, and then a discrete model is established. Two enhanced phase shift angles are designed, and finally an enhanced control set phase shift angle is formed to improve the phase shift angle adjustment range, thereby improving the dynamic performance of the magnetic network power router.

[0034] The topology of a magnetic network power router is as follows: Figure 1 As shown, the magnetic network power router includes an input H-bridge, an output H-bridge, an input capacitor, an output capacitor, and a high-frequency isolation transformer.

[0035] like Figure 2 The diagram shown is a control block diagram of the dynamic performance optimization prediction method proposed in this invention applied to a magnetic network router. The specific steps of the proposed method are as follows: Figure 3 As shown, it includes the following steps:

[0036] S1. Based on the topology and modulation principle of the magnetic network power router, its power transmission model is obtained, and the phase shift angle output range is obtained based on the power transmission model and discretized. At the same time, M adaptive phase shift angles are calculated in combination with the voltage reference, where M is an odd number greater than or equal to 3.

[0037] S2. Based on the topology of the magnetic network power router, model its output voltage; then, combined with the power transmission model, calculate the discrete model of the output voltage of the magnetic network power router.

[0038] S3. Based on the discrete model of the output voltage of the magnetic network power router and the output range of the phase shift angle, design two enhanced phase shift angles to replace the first and Mth discrete phase shift angles in the M discrete phase shift angles in step S1, forming M enhanced control set phase shift angles;

[0039] S4. Based on the phase shift angle range of the enhanced control set and the discrete output voltage model of the magnetic network power router, predict the output voltage at future times corresponding to the phase shift angles of the M enhanced control sets, and realize dynamic performance optimization voltage prediction control; use the value function to evaluate the optimal enhanced control set phase shift angle and apply it in the next control cycle, and combine the single phase shift modulation principle to realize the output voltage regulation of the magnetic network power router.

[0040] Example 1: M is set to 3. The topology and modulation principle of the magnetic network power router in S1 are as follows: Figure 1 As shown. Based on the topology and modulation principle of the magnetic network power router, the power transmission model of the magnetic network power router can be obtained as follows:

[0041] (1)

[0042] In the formula: P is the power transmission model of the magnetic network power router, Ts is the control period, and v ab i is the AC voltage on the input side of the transformer. r The current is the leakage inductance current in the transformer series, where n is the transformer turns ratio, and v is the current. i The input voltage for the magnetic network power router, V o L is the output voltage of the magnetic network power router. r D is the leakage inductance of the transformer in series, and D is the phase shift angle.

[0043] Based on the power transmission model of the magnetic network power router, the output range of the phase shift angle D can be obtained as 0~0.5. Therefore, the discrete form of the phase shift angle D can be expressed as {0, ΔD, 2ΔD, ..., 0.5}, where ΔD is the discrete phase shift angle. According to the discrete form of the phase shift angle D {0, ΔD, 2ΔD, ..., 0.5}, the number of discrete phase shift angles increases as ΔD decreases, and decreases as ΔD increases. When the discrete phase shift angle ΔD decreases, it results in an excessive number of discrete values, increasing the computational load of system control; when the discrete phase shift angle ΔD increases, it results in an insufficient number of discrete values, affecting the system control accuracy.

[0044] Therefore, based on the voltage reference of the magnetic network power router, the discrete voltage error is calculated as follows:

[0045] (2)

[0046] In the formula: Δv(k) is the discrete voltage error of the magnetic network power router at time k, v oref (k) represents the voltage reference of the magnetic network power router at time k, v o (k) represents the output voltage of the magnetic network power router at time k, v m (k) represents the maximum voltage error of the magnetic network power router at time k.

[0047] Based on the discrete voltage error Δv(k) of the magnetic network power router at time k, the adaptive phase shift angle can be obtained as follows:

[0048] (3)

[0049] Where: ΔD a (k) represents the adaptive phase shift angle of the magnetic network power router at time k, and ε is the adjustment coefficient. Ultimately, M is set to 3, based on the adaptive phase shift angle ΔD of the magnetic network power router at time k. a (k) Set the 3 adaptive discrete phase shift angles as: {Dop(k)-ΔD a (k), Dop(k), Dop(k)+ΔD a (k)}. Where Dop(k) is the optimal phase shift angle selected at time k-1.

[0050] For simplicity, the three adaptive discrete phase shift angles are represented as: {D1(k+1), D2(k+1), D3(k+1)}. Here, D1(k+1) is the first adaptive discrete phase shift angle, which is related to Dop(k) - ΔD a (k) corresponds to; D2(k+1) is the second adaptive discrete phase shift angle, which corresponds to Dop(k); D3(k+1) is the third adaptive discrete phase shift angle, which corresponds to Dop(k)+ΔD a (k) corresponds.

[0051] Based on the topology of the magnetic network power router in S2, its output voltage is modeled and expressed as follows:

[0052] (4)

[0053] In the formula: C o For the output capacitor of the magnetic network power router, i s i represents the average output current of the magnetic network power router. o This is the output current for the magnetic network power router.

[0054] Based on the power transmission model shown in formula (1) and formula (4), the discrete model of the output voltage of the magnetic network power router can be obtained as follows:

[0055] (5)

[0056] In the formula: v ox (k+1) represents the output voltage of the magnetic network power router at time k+1, which corresponds to the x-th adaptive discrete phase shift angle, i s (k) represents the average output current of the magnetic network power router at time k, i o (k) represents the output current of the magnetic network power router at time k. x (k+1) represents the x-th adaptive discrete phase shift angle of the magnetic network power router at time k+1.

[0057] The discrete model of the output voltage of the magnetic network power router in S3 is shown in formula (5), and the phase shift angle output range is 0~0.5. Therefore, two enhanced phase shift angles are designed as follows:

[0058] (6)

[0059] In the formula: D m1 (k) is the first enhanced phase shift angle at time k, D m2 (k) represents the second enhanced phase shift angle at time k. G1 is the value function when the phase shift angle is 0, and G2 is the value function when the phase shift angle is D. op The value function at (k) is G3, and the value function at a phase shift angle of 0.5 is G3.

[0060] G1, G2, and G3 can be represented as:

[0061] (7)

[0062] In the formula: v oref (k+1) represents the voltage reference of the magnetic network power router at time k+1, v o_1 (k+1) represents the voltage prediction at time k+1 when the phase shift angle is 0, v o_2 (k+1) represents the phase shift angle at time k+1, which is D. op Voltage prediction at (k), v o_3 (k+1) represents the voltage prediction at time k+1 when the phase shift angle is 0.5.

[0063] Among them, v o_1 (k+1), v o_2 (k+1) and v o_3 (k+1) can be represented as:

[0064] (8)

[0065] According to formula (6), the first enhanced phase shift angle D m1 (k) and the second enhanced phase shift angle D m2 (k) can be in the range of 0~D respectively. op (k), D op The phase shift angle is adaptively adjusted between (k) and 0.5 to meet the needs of dynamic voltage regulation. Therefore, replacing the first adaptive discrete phase shift angle D1(k+1) and the third adaptive discrete phase shift angle D3(k+1) with this angle enhances the dynamic output capability of the magnetic network power router. Meanwhile, the second adaptive discrete phase shift angle D2(k+1) ensures the steady-state performance of the magnetic network power router. Ultimately, three enhanced control set phase shift angles {D} are formed. m1 (k), D2(k+1), D m2 (k)}.

[0066] The predicted future output voltage corresponding to the phase shift angle of the three enhanced control sets in S4 can be expressed as:

[0067] (8)

[0068] In the formula: v o1 (k+2) is the phase shift angle D of the first enhanced control set at time k+2. m1 (k) corresponds to the voltage prediction, v o2 (k+2) represents the voltage prediction corresponding to the phase shift angle D2(k+1) of the second enhanced control set at time k+2, v o3 (k+2) is the phase shift angle D of the third enhanced control set at time k+2. m2 Voltage prediction corresponding to (k).

[0069] In order to predict voltage v o1 (k+2), v o2 (k+2), v o3 Select the optimal predicted value from (k+2) and establish the value function as follows:

[0070] (9)

[0071] In the formula: J is the value function at time k+2. According to formula (9), the voltage prediction value with the minimum value function J can be selected as the optimal prediction value, and the corresponding enhanced control set phase shift angle is the optimal phase shift angle applied in the next control cycle. Combined with the single phase shift modulation principle, the output voltage regulation of the magnetic network power router is realized.

[0072] Example 2: M is set to 5. The topology and modulation principle of the magnetic network power router in S1 are as follows: Figure 1As shown. Based on the topology and modulation principle of the magnetic network power router, the power transmission model of the magnetic network power router can be obtained as follows:

[0073] (1)

[0074] In the formula: P is the power transmission model of the magnetic network power router, Ts is the control period, and v ab i is the AC voltage on the input side of the transformer. r The current is the leakage inductance current in the transformer series, where n is the transformer turns ratio, and v is the current. i The input voltage for the magnetic network power router, V o L is the output voltage of the magnetic network power router. r D is the leakage inductance of the transformer in series, and D is the phase shift angle.

[0075] Based on the power transmission model of the magnetic network power router, the output range of the phase shift angle D can be obtained as 0~0.5. Therefore, the discrete form of the phase shift angle D can be expressed as {0, ΔD, 2ΔD, ..., 0.5}, where ΔD is the discrete phase shift angle. According to the discrete form of the phase shift angle D {0, ΔD, 2ΔD, ..., 0.5}, the number of discrete phase shift angles increases as ΔD decreases, and decreases as ΔD increases. When the discrete phase shift angle ΔD decreases, it results in an excessive number of discrete values, increasing the computational load of system control; when the discrete phase shift angle ΔD increases, it results in an insufficient number of discrete values, affecting the system control accuracy.

[0076] Therefore, based on the voltage reference of the magnetic network power router, the discrete voltage error is calculated as follows:

[0077] (2)

[0078] In the formula: Δv(k) is the discrete voltage error of the magnetic network power router at time k, v oref (k) represents the voltage reference of the magnetic network power router at time k, v o (k) represents the output voltage of the magnetic network power router at time k, v m (k) represents the maximum voltage error of the magnetic network power router at time k.

[0079] Based on the discrete voltage error Δv(k) of the magnetic network power router at time k, the adaptive phase shift angle can be obtained as follows:

[0080] (3)

[0081] Where: ΔD a (k) represents the adaptive phase shift angle of the magnetic network power router at time k, and ε is the adjustment coefficient. Ultimately, M is set to 5, based on the adaptive phase shift angle ΔD of the magnetic network power router at time k.a (k) Set 5 adaptive discrete phase shift angles as: {Dop(k)-2*ΔD} a (k), Dop(k)-ΔD a (k), Dop(k), Dop(k)+ΔD a (k), Dop(k) + 2*ΔD a (k)}. Where Dop(k) is the optimal phase shift angle selected at time k-1.

[0082] For simplicity, the five adaptive discrete phase shift angles are represented as: {D1(k+1), D2(k+1), D3(k+1), D4(k+1), D5(k+1)}. Here, D1(k+1) is the first adaptive discrete phase shift angle, which is related to Dop(k) - 2*ΔD. a (k) corresponds to; D2(k+1) is the second adaptive discrete phase shift angle, which is related to Dop(k)-*ΔD a (k) corresponds to; D3(k+1) is the third adaptive discrete phase shift angle, which corresponds to Dop(k); D4(k+1) is the fourth adaptive discrete phase shift angle, which corresponds to Dop(k)+ΔD a (k) corresponds to; D5(k+1) is the fifth adaptive discrete phase shift angle, which is related to Dop(k)+2*ΔD a (k) corresponds.

[0083] Based on the topology of the magnetic network power router in S2, its output voltage is modeled and expressed as follows:

[0084] (4)

[0085] In the formula: C o For the output capacitor of the magnetic network power router, i s i represents the average output current of the magnetic network power router. o This is the output current for the magnetic network power router.

[0086] Based on the power transmission model shown in formula (1) and formula (4), the discrete model of the output voltage of the magnetic network power router can be obtained as follows:

[0087] (5)

[0088] In the formula: v ox (k+1) represents the output voltage of the magnetic network power router at time k+1, which corresponds to the x-th adaptive discrete phase shift angle, i s (k) represents the average output current of the magnetic network power router at time k, i o (k) represents the output current of the magnetic network power router at time k. x(k+1) represents the x-th adaptive discrete phase shift angle of the magnetic network power router at time k+1.

[0089] The discrete model of the output voltage of the magnetic network power router in S3 is shown in formula (5), and the phase shift angle output range is 0~0.5. Therefore, two enhanced phase shift angles are designed as follows:

[0090] (6)

[0091] In the formula: D m1 (k) is the first enhanced phase shift angle at time k, D m2 (k) represents the second enhanced phase shift angle at time k. G1 is the value function when the phase shift angle is 0, and G2 is the value function when the phase shift angle is D. op The value function at (k) is G3, and the value function at a phase shift angle of 0.5 is G3.

[0092] G1, G2, and G3 can be represented as:

[0093] (7)

[0094] In the formula: v oref (k+1) represents the voltage reference of the magnetic network power router at time k+1, v o_1 (k+1) represents the voltage prediction at time k+1 when the phase shift angle is 0, v o_2 (k+1) represents the phase shift angle at time k+1, which is D. op Voltage prediction at (k), v o_3 (k+1) represents the voltage prediction at time k+1 when the phase shift angle is 0.5.

[0095] Among them, v o_1 (k+1), v o_2 (k+1) and v o_3 (k+1) can be represented as:

[0096] (8)

[0097] According to formula (6), the first enhanced phase shift angle D m1 (k) and the second enhanced phase shift angle D m2 (k) can be in the range of 0~D respectively. op (k), D opThe phase shift angle is adaptively adjusted between (k) and 0.5 to meet the needs of dynamic voltage regulation. Therefore, replacing the first adaptive discrete phase shift angle D1(k+1) and the fifth adaptive discrete phase shift angle D5(k+1) respectively enhances the dynamic output capability of the magnetic network power router. Meanwhile, the second adaptive discrete phase shift angle D2(k+1), the third adaptive discrete phase shift angle D3(k+1), and the fourth adaptive discrete phase shift angle D4(k+1) ensure the steady-state performance of the magnetic network power router. Ultimately, five enhanced control set phase shift angles {D} are formed. m1 (k), D2(k+1), D3(k+1), D4(k+1), D m2 (k)}.

[0098] The predicted future output voltage corresponding to the phase shift angle of the five enhanced control sets in S4 can be expressed as:

[0099] (8)

[0100] In the formula: v o1 (k+2) is the phase shift angle D of the first enhanced control set at time k+2. m1 (k) corresponds to the voltage prediction, v o2 (k+2) represents the voltage prediction corresponding to the phase shift angle D2(k+1) of the second enhanced control set at time k+2, v o3 (k+2) represents the voltage prediction corresponding to the phase shift angle D3(k+1) of the third enhanced control set at time k+2, v o4 (k+2) represents the voltage prediction corresponding to the phase shift angle D4(k+1) of the fourth enhanced control set at time k+2, v o5 (k+2) is the phase shift angle D of the fifth enhanced control set at time k+2. m2 Voltage prediction corresponding to (k).

[0101] In order to predict voltage v o1 (k+2), v o2 (k+2), v o3 (k+2), v o4 (k+2), v o5 Select the optimal predicted value from (k+2) and establish the value function as follows:

[0102] (9)

[0103] In the formula: J is the value function at time k+2. According to formula (9), the voltage prediction value with the minimum value function J can be selected as the optimal prediction value, and the corresponding enhanced control set phase shift angle is the optimal phase shift angle applied in the next control cycle. Combined with the single phase shift modulation principle, the output voltage regulation of the magnetic network power router is realized.

[0104] Example 3: M is set to 7. The topology and modulation principle of the magnetic network power router in S1 are as follows: Figure 1 As shown. Based on the topology and modulation principle of the magnetic network power router, the power transmission model of the magnetic network power router can be obtained as follows:

[0105] (1)

[0106] In the formula: P is the power transmission model of the magnetic network power router, Ts is the control period, and v ab i is the AC voltage on the input side of the transformer. r The current is the leakage inductance current in the transformer series, where n is the transformer turns ratio, and v is the current. i The input voltage for the magnetic network power router, V o L is the output voltage of the magnetic network power router. r D is the leakage inductance of the transformer in series, and D is the phase shift angle.

[0107] Based on the power transmission model of the magnetic network power router, the output range of the phase shift angle D can be obtained as 0~0.5. Therefore, the discrete form of the phase shift angle D can be expressed as {0, ΔD, 2ΔD, ..., 0.5}, where ΔD is the discrete phase shift angle. According to the discrete form of the phase shift angle D {0, ΔD, 2ΔD, ..., 0.5}, the number of discrete phase shift angles increases as ΔD decreases, and decreases as ΔD increases. When the discrete phase shift angle ΔD decreases, it results in an excessive number of discrete values, increasing the computational load of system control; when the discrete phase shift angle ΔD increases, it results in an insufficient number of discrete values, affecting the system control accuracy.

[0108] Therefore, based on the voltage reference of the magnetic network power router, the discrete voltage error is calculated as follows:

[0109] (2)

[0110] In the formula: Δv(k) is the discrete voltage error of the magnetic network power router at time k, v oref (k) represents the voltage reference of the magnetic network power router at time k, v o (k) represents the output voltage of the magnetic network power router at time k, v m (k) represents the maximum voltage error of the magnetic network power router at time k.

[0111] Based on the discrete voltage error Δv(k) of the magnetic network power router at time k, the adaptive phase shift angle can be obtained as follows:

[0112] (3)

[0113] Where: ΔD a(k) represents the adaptive phase shift angle of the magnetic network power router at time k, and ε is the adjustment coefficient. Ultimately, M is set to 7, based on the adaptive phase shift angle ΔD of the magnetic network power router at time k. a (k) Set 7 adaptive discrete phase shift angles as: {Dop(k)-3*ΔD} a (k), Dop(k)-2*ΔD a (k), Dop(k)-ΔD a (k), Dop(k), Dop(k)+ΔD a (k), Dop(k) + 2*ΔD a (k), Dop(k) + 3*ΔD a (k)}. Where Dop(k) is the optimal phase shift angle selected at time k-1.

[0114] For simplicity, the seven adaptive discrete phase shift angles are represented as: {D1(k+1), D2(k+1), D3(k+1), D4(k+1), D5(k+1), D6(k+1), D7(k+1)}. Here, D1(k+1) is the first adaptive discrete phase shift angle, which is related to Dop(k) - 3*ΔD a (k) corresponds to; D2(k+1) is the second adaptive discrete phase shift angle, which is related to Dop(k)-2*ΔD a (k) corresponds to; D3(k+1) is the third adaptive discrete phase shift angle, which is related to Dop(k)-ΔD a (k) corresponds to; D4(k+1) is the fourth adaptive discrete phase shift angle, which corresponds to Dop(k); D5(k+1) is the fifth adaptive discrete phase shift angle, which corresponds to Dop(k)+ΔD a (k) corresponds to; D6(k+1) is the sixth adaptive discrete phase shift angle, which corresponds to Dop(k)+2*ΔD a (k) corresponds to; D7(k+1) is the seventh adaptive discrete phase shift angle, which corresponds to Dop(k)+3*ΔD a (k) corresponds.

[0115] Based on the topology of the magnetic network power router in S2, its output voltage is modeled and expressed as follows:

[0116] (4)

[0117] In the formula: C o For the output capacitor of the magnetic network power router, i s i represents the average output current of the magnetic network power router. o This is the output current for the magnetic network power router.

[0118] Based on the power transmission model shown in formula (1) and formula (4), the discrete model of the output voltage of the magnetic network power router can be obtained as follows:

[0119] (5)

[0120] In the formula: v ox (k+1) represents the output voltage of the magnetic network power router at time k+1, which corresponds to the x-th adaptive discrete phase shift angle, i s (k) represents the average output current of the magnetic network power router at time k, i o (k) represents the output current of the magnetic network power router at time k. x (k+1) represents the x-th adaptive discrete phase shift angle of the magnetic network power router at time k+1.

[0121] The discrete model of the output voltage of the magnetic network power router in S3 is shown in formula (5), and the phase shift angle output range is 0~0.5. Therefore, two enhanced phase shift angles are designed as follows:

[0122] (6)

[0123] In the formula: D m1 (k) is the first enhanced phase shift angle at time k, D m2 (k) represents the second enhanced phase shift angle at time k. G1 is the value function when the phase shift angle is 0, and G2 is the value function when the phase shift angle is D. op The value function at (k) is G3, and the value function at a phase shift angle of 0.5 is G3.

[0124] G1, G2, and G3 can be represented as:

[0125] (7)

[0126] In the formula: v oref (k+1) represents the voltage reference of the magnetic network power router at time k+1, v o_1 (k+1) represents the voltage prediction at time k+1 when the phase shift angle is 0, v o_2 (k+1) represents the phase shift angle at time k+1, which is D. op Voltage prediction at (k), v o_3 (k+1) represents the voltage prediction at time k+1 when the phase shift angle is 0.5.

[0127] Among them, v o_1 (k+1), v o_2 (k+1) and v o_3 (k+1) can be represented as:

[0128] (8)

[0129] According to formula (6), the first enhanced phase shift angle D m1 (k) and the second enhanced phase shift angle D m2 (k) can be in the range of 0~D respectively. op (k), D op The phase shift angle is adaptively adjusted between (k) and 0.5 to meet the needs of dynamic voltage regulation. Therefore, replacing the first adaptive discrete phase shift angle D1(k+1) and the seventh adaptive discrete phase shift angle D7(k+1) respectively enhances the dynamic output capability of the magnetic network power router. Meanwhile, the second, third, fourth, fifth, and sixth adaptive discrete phase shift angles D2(k+1), D3(k+1), D4(k+1), D5(k+1), and D6(k+1) ensure the steady-state performance of the magnetic network power router. Ultimately, seven enhanced control set phase shift angles {D} are formed. m1 (k), D2(k+1), D3(k+1), D4(k+1), D5(k+1), D6(k+1), D m2 (k)}.

[0130] The predicted future output voltage corresponding to the phase shift angle of the 7 enhanced control sets in S4 can be expressed as:

[0131] (8)

[0132] In the formula: v o1 (k+2) is the phase shift angle D of the first enhanced control set at time k+2. m1 (k) corresponds to the voltage prediction, v o2 (k+2) represents the voltage prediction corresponding to the phase shift angle D2(k+1) of the second enhanced control set at time k+2, v o3 (k+2) represents the voltage prediction corresponding to the phase shift angle D3(k+1) of the third enhanced control set at time k+2, v o4 (k+2) represents the voltage prediction corresponding to the phase shift angle D4(k+1) of the fourth enhanced control set at time k+2, v o5 (k+2) represents the voltage prediction corresponding to the phase shift angle D5(k+1) of the fifth enhanced control set at time k+2, v o6 (k+2) represents the voltage prediction corresponding to the phase shift angle D6(k+1) of the sixth enhanced control set at time k+2, v o7 (k+2) is the phase shift angle D of the seventh enhanced control set at time k+2. m2 Voltage prediction corresponding to (k).

[0133] In order to predict voltage v o1 (k+2), v o2 (k+2), vo3 (k+2), v o4 (k+2), v o5 (k+2), v o6 (k+2), v o7 Select the optimal predicted value from (k+2) and establish the value function as follows:

[0134] (9)

[0135] In the formula: J is the value function at time k+2. According to formula (9), the voltage prediction value with the minimum value function J can be selected as the optimal prediction value, and the corresponding enhanced control set phase shift angle is the optimal phase shift angle applied in the next control cycle. Combined with the single phase shift modulation principle, the output voltage regulation of the magnetic network power router is realized.

[0136] Example 4: This example proposes an electronic system, including: 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, the instructions being executed by the at least one processor to enable the at least one processor to perform the method steps of the present invention.

[0137] Example 5: This example proposes a computer-readable storage medium storing computer instructions, which are used to cause the computer to perform the steps of the method described in this invention.

[0138] It should be noted that the processing flows of Embodiments 4 and 5 correspond to the specific steps of the method provided in the embodiments of the present invention, and have the corresponding functional modules and beneficial effects of the method. Technical details not described in detail in this embodiment can be found in the method provided in the embodiments of the present invention.

[0139] The program code used to implement the methods of this application may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0140] In the context of this application, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0141] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0142] The above embodiments are merely illustrative of the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solutions based on the technical concept proposed in this invention shall fall within the scope of protection of this invention.

Claims

1. A dynamic optimization prediction method for an enhanced control set of a magnetic network power router, characterized in that, Includes the following steps: Step S1: Based on the topology and modulation principle of the magnetic network power router, obtain its power transmission model, and obtain the phase shift angle output range based on the power transmission model, and discretize it; at the same time, calculate M adaptive phase shift angles in combination with voltage reference, where M is an odd number greater than or equal to 3. Step S2: Based on the topology of the magnetic network power router, model its output voltage; then, combined with the power transmission model, calculate the discrete model of the output voltage of the magnetic network power router. Step S3: Based on the discrete model of the output voltage of the magnetic network power router and the output range of the phase shift angle, design two enhanced phase shift angles to replace the first and Mth discrete phase shift angles in the M discrete phase shift angles in step S1, forming M enhanced control set phase shift angles; Step S4: Based on the phase shift angle range of the enhanced control set of the magnetic network power router and the discrete model of the output voltage, predict the output voltage at future times corresponding to the phase shift angles of the M enhanced control sets, and realize dynamic performance optimization voltage prediction control; use the value function to evaluate the optimal phase shift angle of the enhanced control set and apply it in the next control cycle, and combine the single phase shift modulation principle to realize the output voltage regulation of the magnetic network power router.

2. The method according to claim 1, characterized in that, The power transfer model established in step S1 is as follows: ， Where P is the power transmission model of the magnetic network power router, and T s To control the cycle, v ab i is the AC voltage on the input side of the transformer. r The current is the leakage inductance current in the transformer series, where n is the transformer turns ratio, and v is the current. i The input voltage for the magnetic network power router, V o L is the output voltage of the magnetic network power router. r The leakage inductance is the series leakage inductance of the transformer, and D is the phase shift angle. The output range of the phase shift angle D is 0~0.

5. Its discretization is {0, ΔD, 2ΔD, ..., 0.5}, where ΔD is the discrete phase shift angle.

3. The method according to claim 2, characterized in that, The adaptive phase shift angle constructed in step S1 is: , Where, ΔD a (k) is the adaptive phase shift angle of the magnetic network power router at time k, ε is the adjustment coefficient, and Δv(k) is the discrete voltage error of the magnetic network power router at time k.

4. The method according to claim 3, characterized in that, Finally, M adaptive discrete phase shift angles are set as {D} op (k)-(M-1) / 2*ΔD a (k), ..., D op (k), ..., D op (k)+ (M-1) / 2*ΔD a (k)}, where D op (k) represents the optimal phase shift angle selected at time k-1. The M adaptive discrete phase shift angles are represented as: {D1(k+1), ..., D N (k+1), ..., D M (k+1)}, where N=(M+1) / 2.

5. The method according to claim 4, characterized in that, The discrete model of the output voltage of the magnetic network power router established in step S2 is as follows: ， Among them, v ox (k+1) represents the output voltage of the magnetic network power router at time k+1, which corresponds to the x-th adaptive discrete phase shift angle, i s (k) represents the average output current of the magnetic network power router at time k, i o (k) represents the output current of the magnetic network power router at time k; D x (k+1) represents the x-th adaptive discrete phase shift angle of the magnetic network power router at time k+1, and C o This is the output capacitor for the magnetic network power router.

6. The method according to claim 5, characterized in that, The two enhanced phase shift angles designed in step S3 are: Where: D m1 (k) is the first enhanced phase shift angle at time k, D m2 (k) represents the second enhanced modulation phase shift angle at time k. G1 is the value function when the phase shift angle is 0, and G2 is the value function when the phase shift angle is D. op The value function at (k) is G3, and the value function at a phase shift angle of 0.5 is G3; replacing the 1st and Mth discrete phase shift angles among the M discrete phase shift angles in step S1, the final M enhanced control sets with phase shift angles of {D} are formed. m1 (k), ..., D N (k+1), ..., D m2 (k)}.

7. The method according to claim 6, characterized in that, In step S4, the future output voltage corresponding to the phase shift angle of the M enhanced control sets is: Among them, v o1 (k+2) is the phase shift angle D of the first enhanced control set at time k+2. m1 (k) corresponds to the voltage prediction, v oN (k+2) is the phase shift angle D of the Nth enhanced control set at time k+2. J Voltage prediction corresponding to (k+1), v oM (k+2) is the phase shift angle D of the Mth enhanced control set at time k+2. m2 Voltage prediction corresponding to (k).

8. The method according to claim 7, characterized in that, In step S4, the value function is established as follows: ， Where J is the value function at time k+2, the voltage prediction value with the minimum value function J is selected as the optimal prediction value, and the corresponding enhanced control set phase shift angle is the optimal phase shift angle applied in the next control cycle. Combined with the single phase shift modulation principle, the output voltage regulation of the magnetic network power router is realized.

9. 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 to enable the at least one processor to perform the method according to any one of claims 1-8.

10. A computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-8.

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