Maximum power point tracking and stability control method for dual-active bridge converter

Abstract

The invention relates to the technical field of new energy power generation control, and particularly discloses a dual active bridge converter maximum power point tracking and stable control method, which deduces an MPPT control principle of a photovoltaic panel in a DAB converter according to a current-voltage characteristic curve of the photovoltaic panel, and designs a variable step size MPPT control algorithm. Therefore, the maximum power point of the photovoltaic system can be quickly tracked under a dynamic condition. And then, deriving a state space model of the photovoltaic and DAB system by using a Kirchhoff voltage and current law, and designing a high-gain observer for the established dynamic model of the photovoltaic system so as to perform real-time estimation and compensation on internal uncertainty and external disturbance of the system. Finally, a composite superspiral sliding mode control method is designed, the chattering phenomenon in traditional sliding mode control is effectively restrained, the dynamic response speed and robustness of the system are improved, and stable operation of the system under large signal disturbance is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of new energy power generation control technology, specifically to a maximum power point tracking and stability control method for a dual active bridge converter. Background Technology

[0002] With the continuous growth of energy demand and increasing emphasis on environmental protection, DC microgrids, as an efficient and flexible energy distribution method, have gradually become a research hotspot in the energy field. (Appendix) Figure 1 This paper showcases a typical structure of a known DC microgrid, which effectively integrates various distributed energy sources such as photovoltaic (PV) and wind power, reducing losses during energy conversion and improving the overall efficiency and reliability of the system. Among numerous distributed energy sources, PV has become an important component of DC microgrids due to its stable and reliable operation and simple maintenance, providing strong support for achieving sustainable energy development.

[0003] In DC microgrids, photovoltaic (PV) panels are typically connected to the DC bus via power electronic converters. Dual Active Bridge (DAB) converters are an ideal choice for connecting PV cells to the DC bus due to their high efficiency, high power density, and excellent bidirectional power transmission capability. Their topology is shown in the attached figure. Figure 2 As shown, the DAB converter achieves power transmission through phase-shift control, enabling flexible adjustment of the photovoltaic system's output power to meet the dynamic demands of the DC bus. However, the photovoltaic output characteristics are significantly affected by environmental factors such as light intensity and temperature. Under different light intensities, the output power will vary, which may lead to problems such as DC bus voltage fluctuations and power imbalances, thereby affecting the normal operation of critical equipment and loads in the DC microgrid.

[0004] Significant progress has been made in recent years in power point tracking (MPPT) control of photovoltaic (PV) systems and stable control of DAB converters. Some studies employ MPPT control algorithms such as the disturbance observation method, incremental conductance method, and fuzzy control method to ensure that PV modules always operate at their maximum power point under current conditions. However, these methods suffer from response lag and decreased tracking accuracy when facing sudden disturbances and rapid fluctuations. Control methods for DAB converters largely revolve around modulation strategies and phase shift angle control, mostly based on average and small-signal models, without establishing a unified model for both PV and DAB systems. PID control, active disturbance rejection control, and direct power control, which use linearized system models to design controllers, are relatively simple to implement and easy to understand and debug. However, under large-signal disturbances, linear control strategies struggle to guarantee global system stability, exhibit slow dynamic response, and poor adaptability to changes in system parameters and load disturbances, failing to meet the high-precision, fast, and stable control requirements of constant power loads in DC microgrids.

[0005] Compared to traditional linear control strategies such as PID control, sliding mode control, as a nonlinear control method, exhibits strong robustness to parameters and external disturbances, and demonstrates good dynamic and steady-state responses. Currently, research on sliding mode control is deepening, encompassing various methods such as time-delay systems and adaptive terminal sliding mode control. However, due to factors such as the discontinuity of its control law, the lag in the switching process, and measurement system errors, the system state cannot strictly follow a predetermined trajectory, leading to chattering. Chattering causes the system state to traverse back and forth on both sides of the sliding surface, resulting in decreased control accuracy. High-frequency chattering also increases the wear of mechanical components, reducing the system's lifespan.

[0006] In summary, to address the issues of photovoltaic system output being greatly affected by environmental factors, slow dynamic response speed of traditional linear control strategies, and poor adaptability to system parameter changes and load disturbances, a maximum power point tracking and stable control method using dual active bridge converters is proposed to quickly track the maximum power of the photovoltaic system and achieve efficient and stable system operation. Summary of the Invention

[0007] The technical problem this invention aims to solve is to provide a maximum power point tracking (MPPT) and stable control method for a dual active bridge (DBC) converter. Based on the IU characteristic curve of the photovoltaic (PV) panel, its MPPT control principle in the DBC converter is derived, and a variable step-size MPPT control algorithm is designed. This algorithm can quickly track the system's maximum power point under dynamic conditions. Using Kirchhoff's voltage and current laws, the state-space model of the PV panel and the DBC converter is derived, laying the model foundation for the subsequent control strategy. A high-gain observer is designed for the established dynamic model of the PV system to estimate and compensate for internal uncertainties and external disturbances in real time. A super-spiral sliding mode control method is introduced, which, together with the designed high-gain observer and MPPT control algorithm, forms a composite controller to suppress chattering in traditional sliding mode control, while significantly improving the system's dynamic response speed and control accuracy.

[0008] To solve the above-mentioned technical problems, the technical solution provided by the present invention is: a method for maximum power point tracking and stable control of a dual active bridge converter, comprising the following steps:

[0009] S1. Based on the current-voltage characteristic curve of the photovoltaic panel, derive the MPPT control principle of the photovoltaic panel in the DAB converter, and determine the control logic of changing the equivalent resistance of the photovoltaic panel by adjusting the phase shift angle of the DAB converter to match the maximum power point.

[0010] S2. Design a variable step size MPPT control algorithm to collect the real-time output voltage and current of the photovoltaic system and calculate the output power. Adjust the voltage disturbance step size according to the comparison result of the power change and the preset threshold, and adjust the reference voltage value in combination with the direction of the derivative of power with respect to voltage to achieve fast and accurate tracking of the maximum power point under dynamic conditions.

[0011] S3. Based on Kirchhoff's voltage and current laws, establish a unified state-space model of the photovoltaic panel and the DAB converter. Reconstruct the dynamic model of the system through feedback linearization technology and coordinate transformation, and clarify the relationship between state variables, disturbance terms and control laws.

[0012] S4. To address the internal uncertainties and external disturbances in the reconstructed dynamic model, a high-gain observer is designed to estimate and compensate for the disturbance terms in real time.

[0013] S5. Design a super-spiral sliding mode controller, define the sliding surface and derive the control law, combine the variable step size MPPT control algorithm with a high-gain observer to form a composite control strategy, and suppress chattering phenomenon in sliding mode control by adjusting the phase shift angle of the DAB converter.

[0014] Furthermore, step S2 is specifically as follows:

[0015] First, the output voltage v of the photovoltaic system at time k is collected. pv (k) and current ipv , calculate its output power P(k) at time k, that is:

[0016] P(k) = v pv (k)·i pv (k);

[0017] The change in power ΔP is obtained by subtracting the power value from the previous moment:

[0018] ΔP = P(k) - P(k-1);

[0019] A power change threshold is set at the maximum power point. The distance between the operating point and the maximum power point is determined by comparing the magnitude of ΔP and the power change threshold. The specific voltage change step size is Δv.

[0020]

[0021] Where ε1 and ε2 are power change thresholds, and v1, v2, and v3 are the step size selections for different power change amounts;

[0022] To determine the derivative of system power with respect to voltage, if the derivative is positive, the operating point is to the left of the maximum power point, and the reference voltage value should be increased; if the derivative is negative, the operating point is to the right of the maximum power point, and the reference voltage value should be decreased. This can be expressed by the formula:

[0023]

[0024] Furthermore, the unified state-space model of the photovoltaic panel and the DAB converter is constructed in the following way:

[0025] Combining Kirchhoff's voltage and current laws, the average value model of the DAB circuit is obtained as follows:

[0026]

[0027] Among them, i pv and v pv These are the output current and output voltage of the photovoltaic module, L is the inductance value of the DAB converter, C is the input capacitance value of the DAB converter, and i... L It is the inductor current of the DAB converter, v dc It is the output voltage of the DAB converter, and D is the phase shift angle generated by the controller;

[0028] By using feedback linearization, we obtain the state variable x1:

[0029]

[0030] By differentiating x1 and combining it with the average value model of the converter, we obtain:

[0031]

[0032] In this formula, a new state variable x2 = -v is selected. pv i L And define an interference term d1 = v pv i pv Taking the derivative of x2 and combining it with the average value model of the converter, we get:

[0033]

[0034] In the obtained formula, an intermediate control law u and a disturbance term d2 are defined, and the dynamic model of the photovoltaic system is reconstructed as follows:

[0035]

[0036] Therefore, the actual control law D of the DAB converter is calculated as follows:

[0037]

[0038] By applying the above coordinate transformation, the control objective of the photovoltaic system changes from reducing the output voltage v of the photovoltaic module to... pv Tracking voltage reference value v ref This allows the state variable x1 to track its reference value x. 1d Specifically, it is expressed as:

[0039]

[0040] Furthermore, the high-gain observer is designed as follows:

[0041]

[0042]

[0043] in, It is an estimate of x1. This is an estimate of d1. yes The estimated value, It is an estimate of x2. It is an estimate of d2, l 11 , l 12 , l 13 , l 21 , l 22 It is the observer gain.

[0044] Furthermore, in the superspiral sliding mode controller, the sliding surface s is defined as:

[0045]

[0046] Where k1 and k2 are adjustable gains, and ξ is an intermediate variable; since the expected value of state x1 is x 1d The tracking error is then: e = x1 - x 1d To further design control strategies, the sliding surface is defined as follows:

[0047]

[0048] Differentiating it, we get:

[0049]

[0050] Based on the above equations, the control law is derived as follows:

[0051]

[0052] The advantages of this invention compared to the prior art are:

[0053] 1. Based on the fundamental principles of photovoltaic systems and DAB converters, this invention designs a variable step size MPPT control algorithm to accelerate the response speed while ensuring that the system tracks the maximum power.

[0054] 2. This invention employs a nonlinear high-gain observer, which significantly improves the robustness and dynamic performance of the control system by real-time estimation and compensation of external disturbances and system uncertainties.

[0055] 3. This invention establishes a unified dynamic model of the photovoltaic system and the DAB converter, and designs a super-spiral sliding mode controller, which improves the system's adaptability to parameter changes and load disturbances, and avoids the chattering phenomenon caused by traditional sliding mode control. Attached Figure Description

[0056] Figure 1 This is a diagram of a DC microgrid structure.

[0057] Figure 2 This is a topology diagram of a photovoltaic panel and a DAB converter.

[0058] Figure 3 This is a graph showing the photovoltaic IU characteristic curve.

[0059] Figure 4 This is the flowchart for photovoltaic variable step size MPPT control.

[0060] Figure 5 This is a circuit waveform diagram of a DAB converter.

[0061] Figure 6 This is a diagram of the composite superspiral sliding mode control structure.

[0062] Figure 7 This is a comparison chart of fixed step size and variable step size MPPT.

[0063] Figure 8 This is a graph showing the estimation results of the high-gain observer.

[0064] Figure 9 This is a comparison chart of composite superspiral sliding mode control and traditional PID control. Detailed Implementation

[0065] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the present invention.

[0066] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the invention or its application or use.

[0067] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0068] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0069] The following detailed description, in conjunction with the accompanying drawings, provides a method for maximum power point tracking and stable control of a dual active bridge converter according to the present invention.

[0070] Combined with appendix Figure 1-9 The specific implementation process of the maximum power point tracking and stability control method for a dual active bridge converter of the present invention is as follows:

[0071] In DC microgrids, photovoltaic (PV) systems are typically connected to the DC bus via power electronic converters (DABs). DAB converters, due to their high efficiency and high power density, are an ideal choice for connecting PV systems to the DC bus. This invention uses PV panels and DAB converters for modeling, control, and analysis. The technical solutions in the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings.

[0072] Step 1: Derivation of MPPT Control for Photovoltaic Panels in DAB Converter

[0073] Under varying conditions of light intensity and weather, the output voltage and current of photovoltaic panels will fluctuate, resulting in different power outputs. The matching curves of the IU characteristics of photovoltaic panels and load characteristics can be obtained from the attached... Figure 3Presentation: When the illuminance is 1, the IU characteristic curve intersects the load characteristic curve at point a, at which point the photovoltaic panel can output maximum power. When the illuminance changes from intensity 1 to intensity 2, the system will operate at point b, but the maximum output power of the photovoltaic is at point c.

[0074] To achieve maximum output power, the circuit's operating point should be matched to the photovoltaic panel's load characteristics, placing it at its maximum power point. Based on the power transfer characteristics of the DAB converter, its transfer power is:

[0075]

[0076] Where n is the transformer turns ratio, v pv This refers to the output voltage of the photovoltaic panel; v dc is the DC bus voltage; D is the phase shift angle of the DAB converter; f is the switching frequency; L is the energy storage inductance value.

[0077] Load power can be expressed as:

[0078]

[0079] Where is the load resistance value.

[0080] From the power conservation law, we can obtain:

[0081]

[0082] pass Substituting the above formulas for load power and photovoltaic panel output voltage, we can obtain the equivalent resistance of the photovoltaic panel as follows:

[0083]

[0084] Therefore, it can be seen that by changing the phase shift angle D between the primary and secondary sides of the DAB converter, the equivalent resistance of the photovoltaic panel can be changed, thereby changing the effect of the photovoltaic panel on the resistance. Figure 3 The slope of the curve is adjusted so that it operates at point c, ensuring that it still outputs the maximum photovoltaic power under a light intensity of 2.

[0085] Step 2: Design of Variable Step Size MPPT Control Algorithm

[0086] MPPT (Maximum Power Point Tracking) has become one of the most commonly used control algorithms for photovoltaic (PV) panels due to its advantages of simple calculation, fast response, high accuracy, and applicability to various PV panels. However, because it uses a fixed step size for perturbation, the output power of the PV panel fluctuates around the maximum power point, affecting the accurate tracking of the maximum power point. When the step size is set too small, the algorithm's speed in finding the maximum power point decreases significantly, especially under conditions of drastic changes in the external environment, making it difficult to quickly locate the maximum power point. Therefore, an unreasonable step size setting will affect the overall performance of the algorithm. It is necessary to adjust the MPPT step size according to specific circumstances to achieve an optimal balance between performance and response time.

[0087] To address this, the present invention employs variable step size MPPT, adjusting the step size based on the distance between the operating point and the maximum power point. The flowchart of this process is attached. Figure 4 As shown, when the light intensity changes, if the power change is small, it indicates that the operating point is close to the maximum power point, and a small step size is used for tracking; otherwise, a large step size is used. This method improves the accuracy of the control algorithm, speeds up the response, and ensures the performance and reliability of MPPT control.

[0088] First, the output voltage v of the photovoltaic system at time k is collected. pv (k) and current i pv , calculate its output power P(k) at time k, that is:

[0089] P(k) = v pv (k)·i pv (k);

[0090] The change in power ΔP is obtained by subtracting the power value from the previous moment:

[0091] ΔP = P(k) - P(k-1);

[0092] The perturbation step size will change with the power. As the curve gradually approaches the peak, a power change threshold is set at the maximum power point. The distance between the operating point and the maximum power point is determined by comparing ΔP with the power change threshold. The specific voltage change step size Δv is as follows:

[0093]

[0094] Where ε1 and ε2 are power change thresholds, and v1, v2, and v3 are the step size selections for different power change amounts;

[0095] Next, determine the derivative of system power with respect to voltage. When the derivative is positive, the operating point is to the left of the maximum power point, and the reference voltage value needs to be increased; when the derivative is negative, the operating point is to the right of the maximum power point, and the reference voltage value needs to be decreased. This can be expressed by the formula:

[0096]

[0097] Step 3: Establishing a unified photovoltaic and DAB model

[0098] In single-phase shift control of a DAB converter, the two IGBTs on the same bridge arm conduct complementaryly, while the diagonal bridge arms conduct simultaneously. Power transfer between the two full bridges can be adjusted by controlling the phase shift ratio D. Figure 5 The waveform diagram of the DAB converter shows that, due to the symmetry of its waveform, the average value of the inductor voltage over time T can be expressed as:

[0099] v L =D(v) pv +v dc )+(1-D)(v pv +v dc );

[0100] Combining Kirchhoff's voltage and current laws, the average value model of the DAB circuit can be obtained as follows:

[0101]

[0102] Among them, i pv and v pv These are the output current and output voltage of the photovoltaic module, L is the inductance value of the DAB converter, C is the input capacitance value of the DAB converter, and i... L It is the inductor current of the DAB converter, v dc It is the output voltage of the DAB converter, and D is the phase shift angle generated by the controller;

[0103] By using feedback linearization, the state variable x1 can be obtained:

[0104]

[0105] By taking the derivative of x1 and combining it with the average value model of the converter, we can obtain:

[0106]

[0107] In this formula, a new state variable x2 = -v can be chosen. pv i L And define an interference term d1 = v pv i pv Similarly, by differentiating x2 and combining it with the average value model of the converter, we can obtain:

[0108]

[0109] Subsequently, by defining an intermediate control law u and a disturbance term d2 in the obtained formula, the dynamic model of the photovoltaic system can be reconstructed as follows:

[0110]

[0111] Therefore, the actual control law D of the DAB converter can be derived as follows:

[0112]

[0113] By applying the above coordinate transformation, the control objective of the photovoltaic system changes from reducing the output voltage v of the photovoltaic module to... pv Tracking voltage reference value v ref This allows the state variable x1 to track its reference value x. 1d It can be represented as:

[0114]

[0115] Step 4: High-gain observer design

[0116] For the disturbance terms d1 and d2 in the obtained dynamic model of the photovoltaic system, a high-gain observer is used to estimate them to facilitate the design of the subsequent super-helical sliding mode controller.

[0117] The high-gain observer is designed as follows:

[0118]

[0119] in, It is an estimate of x1. This is an estimate of d1. yes The estimated value, It is an estimate of x2. It is an estimate of d2, l 11 , l 12 , l 13 , l 21 , l 22 It is the observer gain.

[0120] Step 5: Design of Superspiral Sliding Mode Controller

[0121] In the control of photovoltaic systems, to reduce system chattering, this invention employs a super-helical sliding mode control method. Compared with traditional sliding mode control, super-helical sliding mode control, as a second-order sliding mode control method, obtains the actual control quantity through integration, thus improving the smoothness of control. Here, the sliding surface is defined as s, and it satisfies the following equation:

[0122]

[0123] Where k1 and k2 are adjustable gains, and ξ is an intermediate variable; since the expected value of state x1 is x 1d The tracking error is then: e = x1 - x 1d To further design control strategies, the sliding surface is defined as follows:

[0124]

[0125] Differentiating it, we get:

[0126]

[0127] From the above equations, the control law can be derived as follows:

[0128]

[0129] To provide a clear explanation, a schematic diagram of the control structure of the photovoltaic and DAB converter is attached. Figure 9 As shown in the diagram. In this control method, the maximum power voltage reference value of the photovoltaic panel is determined through MPPT. The control system estimates the disturbance through a high-gain observer and uses a super-spiral sliding mode controller to adjust the PWM signal to optimize the phase shift angle of the DAB converter, thereby achieving MPPT of the photovoltaic panel and ensuring stable and efficient operation of the system.

[0130] Step 6: Simulation Verification

[0131] To verify the control method proposed in this invention, a simulation model of a photovoltaic system and a DAB converter was built in the MATLAB / Simulink environment to verify the effectiveness of variable step size MPPT control and composite superhelical sliding mode control.

[0132] In the variable step size MPPT control, the selected photovoltaic panel has a maximum power of 1066W at 25℃, corresponding to a voltage of 29V; while at 45℃, the maximum power is 976.9W, corresponding to an output voltage of 26.34V. During the simulation, the ambient temperature was adjusted from 25℃ to 45℃ in 0.5s. The comparison results of fixed step size and variable step size MPPT control are shown in the attached figure. Figure 7 As shown. From the appendix Figure 7 (a) It can be observed that the output voltage fluctuates significantly when using a fixed-step MPPT, while the variable-step MPPT outputs 29V in the first 0.5s and 26V in the last 0.5s, almost perfectly matching the theoretical value. (Appendix) Figure 7 (b) shows the duty cycle change of the PWM control signal. At 0.1s, the system has reached a stable state, but the maximum power point cannot be accurately located when using a fixed step size MPPT, causing the system to fluctuate continuously on both sides of the maximum power point, thereby causing voltage fluctuations.

[0133] From the formula As can be seen, the interference term is the output power of the photovoltaic panel; therefore, the interference term will change significantly with changes in light intensity. (Appendix) Figure 8 The figure shows the observation results of the interference term when using a high-gain observer. At 0.3s, the given voltage reference value changes from 40V to 60V, and at 0.6s, the irradiance changes from 1000W / m2 to 700W / m2. As can be seen from the figure, the high-gain observer can accurately estimate the output power of the photovoltaic system when the photovoltaic power changes.

[0134] Appendix Figure 9 The figures show the voltage waveforms of the composite superspiral sliding mode control and the traditional PID control. As can be seen from the results, the traditional PID control exhibits a large overshoot in the output voltage, while the composite superspiral sliding mode control method proposed in this invention can quickly and accurately stabilize the output voltage at the target value.

[0135] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A method for maximum power point tracking and stable control of a dual active bridge converter, characterized in that, Includes the following steps: S1. Based on the current-voltage characteristic curve of the photovoltaic panel, derive the MPPT control principle of the photovoltaic panel in the DAB converter, and determine the control logic of changing the equivalent resistance of the photovoltaic panel by adjusting the phase shift angle of the DAB converter to match the maximum power point. S2. Design a variable step size MPPT control algorithm to collect the real-time output voltage and current of the photovoltaic system and calculate the output power. Adjust the voltage disturbance step size according to the comparison result of the power change and the preset threshold, and adjust the reference voltage value in combination with the direction of the derivative of power with respect to voltage to achieve fast and accurate tracking of the maximum power point under dynamic conditions. S3. Based on Kirchhoff's voltage and current laws, establish a unified state-space model of the photovoltaic panel and the DAB converter. Reconstruct the dynamic model of the system through feedback linearization technology and coordinate transformation, and clarify the relationship between state variables, disturbance terms and control laws. S4. To address the internal uncertainties and external disturbances in the reconstructed dynamic model, a high-gain observer is designed to estimate and compensate for the disturbance terms in real time. S5. Design a super-spiral sliding mode controller, define the sliding surface and derive the control law, combine the variable step size MPPT control algorithm with a high-gain observer to form a composite control strategy, and suppress chattering phenomenon in sliding mode control by adjusting the phase shift angle of the DAB converter.

2. The maximum power point tracking and stability control method for a dual active bridge converter according to claim 1, characterized in that: Step S2 is as follows: First, the output voltage v of the photovoltaic system at time k is collected. pv (k) and current i pv , calculate its output power P(k) at time k, that is: P(k)=v pv (k)·i pv (k); The change in power ΔP is obtained by subtracting the power value from the previous moment: ΔP = P(k) - P(k-1); A power change threshold is set at the maximum power point. The distance between the operating point and the maximum power point is determined by comparing the magnitude of ΔP and the power change threshold. The specific voltage change step size is Δv. Where ε1 and ε2 are power change thresholds, and v1, v2, and v3 are the step size selections for different power change amounts; To determine the derivative of system power with respect to voltage, if the derivative is positive, the operating point is to the left of the maximum power point, and the reference voltage value should be increased; if the derivative is negative, the operating point is to the right of the maximum power point, and the reference voltage value should be decreased. This can be expressed by the formula:

3. The maximum power point tracking and stability control method for a dual active bridge converter according to claim 2, characterized in that: The unified state-space model of the photovoltaic panel and the DAB converter is constructed in the following way: Combining Kirchhoff's voltage and current laws, the average value model of the DAB circuit is obtained as follows: Among them, i pv and v pv These are the output current and output voltage of the photovoltaic module, L is the inductance value of the DAB converter, C is the input capacitance value of the DAB converter, and i... L It is the inductor current of the DAB converter, v dc It is the output voltage of the DAB converter, and D is the phase shift angle generated by the controller; By using feedback linearization, we obtain the state variable x1: By differentiating x1 and combining it with the average value model of the converter, we obtain: In this formula, a new state variable x2 = -v is selected. pv i L And define an interference term d1 = v pv i pv Taking the derivative of x2 and combining it with the average value model of the converter, we get: In the obtained formula, an intermediate control law u and a disturbance term d2 are defined, and the dynamic model of the photovoltaic system is reconstructed as follows: Therefore, the actual control law D of the DAB converter is calculated as follows: By applying the above coordinate transformation, the control objective of the photovoltaic system changes from reducing the output voltage v of the photovoltaic module to... pv Tracking voltage reference value v ref This allows the state variable x1 to track its reference value x. 1d Specifically, it is expressed as:

4. The maximum power point tracking and stability control method for a dual active bridge converter according to claim 3, characterized in that: The high-gain observer is designed as follows: in, It is an estimate of x1. This is an estimate of d1. yes The estimated value, It is an estimate of x2. It is an estimate of d2, l 11 , l 12 , l 13 , l 21 , l 22 It is the observer gain.

5. The maximum power point tracking and stability control method for a dual active bridge converter according to claim 4, characterized in that: In the aforementioned superspiral sliding mode controller, the sliding surface s is defined as: Where k1 and k2 are adjustable gains, and ξ is an intermediate variable; since the expected value of state x1 is x 1d The tracking error is then: e = x1 - x 1d ; To further design control strategies, the sliding surface is defined as follows: Differentiating it, we get: Based on the above equations, the control law is derived as follows:

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