Control method of dual-active-bridge micro inverter and related equipment

Abstract

The embodiment of the invention provides a control method of a dual-active-bridge micro inverter and related equipment, and belongs to the technical field of power electronic converter control. The method comprises the following steps: firstly, establishing a unilateral asymmetric duty ratio modulation model based on three variables of an external phase shift angle D1, an internal phase shift angle D2 and switching frequency, and deducing transmission power and soft switching constraint conditions; secondly, the switching frequency is dynamically adjusted according to the voltage phase of the power grid, and high frequency is adopted near voltage zero crossing to expand the soft switching range; thirdly, in a rated frequency interval of main power transmission, taking minimization of current stress as a target, adopting an improved self-adaptive particle swarm algorithm to carry out on-line optimization on D2 under the soft switching constraint, and calculating a corresponding D1; and finally, combining the optimized frequency with the phase shift angle feed-forward quantity and the feedback compensation quantity to generate a PWM driving signal. According to the invention, the large-range soft switching of a whole power frequency period is realized, the conduction loss is obviously reduced, and the system efficiency and reliability are improved.

Description

Technical Field

[0001] This application relates to the field of power electronic converter control technology, and in particular to a control method and related equipment for a dual active bridge microinverter. Background Technology

[0002] Microinverters are widely used in distributed photovoltaic power generation systems due to their advantages such as module-level maximum power point tracking (MPPT), high security, and easy scalability. Compared with two-stage topologies, single-stage dual-active-bridge (DAB) microinverters have advantages such as compact structure, high efficiency, and bidirectional energy flow, demonstrating great development potential.

[0003] The core control objective of DAB circuits is to achieve high-efficiency power transfer while ensuring zero-voltage switching (ZVS) of the switching transistors to reduce switching losses and electromagnetic interference. Traditional single-phase-shift (SPS) modulation control is simple, but under voltage gain deviations of 1 or light load conditions, the soft-switching range is severely limited, and the inductor current stress is high, leading to increased conduction losses. Extended-phase-shift (EPS) modulation, by introducing an additional inner phase shift angle, provides more control freedom and can optimize current stress and expand the soft-switching range to some extent. However, its optimization capability is limited; under light load or full voltage range conditions, it is still difficult to achieve full-cycle soft switching, and there is still room for further optimization of current stress.

[0004] In existing technologies, there are schemes that use frequency conversion control to improve soft-switching performance, but these typically treat frequency as a fixed or simply varying parameter without deep co-optimization with phase-shifting modulation strategies. Furthermore, some studies have attempted to use intelligent algorithms (such as particle swarm optimization) to optimize the control parameters of the DAB, but these algorithms are often applied to offline calculations or parameter tuning, failing to consider the time-varying and nonlinear optimization problems caused by the periodic sinusoidal variation of the grid voltage under inverter conditions, making it difficult to achieve real-time online optimization within the power frequency cycle.

[0005] Therefore, designing a control method that can collaboratively extend the soft-switching range across the entire power frequency cycle and dynamically optimize current stress within this range has become a key technical challenge for improving the overall performance of DAB micro-inverters. Summary of the Invention

[0006] This application aims to overcome the shortcomings of the prior art and provide a control method, electronic equipment, storage medium, and program product for a dual active bridge microinverter. By organically combining frequency conversion strategy, single-sided asymmetric modulation, and improved online optimization algorithm, it aims to achieve the following objectives: 1) Achieve zero-voltage switching (ZVS) for all switching transistors throughout the entire power frequency cycle, especially near the zero-crossing point of the grid voltage, effectively reducing switching losses.

[0007] 2) While ensuring soft switching, actively optimize control parameters to minimize inductor current stress, thereby reducing conduction losses and device stress, and improving system efficiency and reliability.

[0008] 3) Provide a dynamic and adaptive control framework to cope with the periodic changes in grid voltage and achieve global performance optimization.

[0009] To achieve the above objectives, one aspect of this application proposes a control method for a dual active bridge microinverter, the method comprising: S1: Establish the mathematical model of the dual active bridge microinverter, and set the outer phase shift angle D1, the inner phase shift angle D2, and the switching frequency. Three-variable control strategy; S2: Based on the single-sided asymmetric duty cycle modulation strategy, perform circuit mode analysis to obtain the inductor current expression, transmission power P, and soft switching constraints. S3: Within one power frequency cycle, according to the phase of the grid voltage Dynamically adjust the switching frequency To extend the soft-switching range of the dual active bridge microinverter, a switching frequency higher than the rated frequency f is used in the phase interval near the zero crossing of the grid voltage. S4: Within the main power transmission phase interval where the switching frequency is the rated frequency f, based on the soft switching constraint, the inner phase shift angle D2 is optimized online using an optimization algorithm to minimize the inductor current stress. S5: Calculate the corresponding outer phase shift angle D1 based on the optimal inner phase shift angle D2 obtained in step S4 and the transmission power P; S6: The freq determined in step S3, D2 determined in step S4, and D1 determined in step S5 are used as feedforward control quantities, and combined with the feedback control compensation quantity, to generate a PWM signal to drive the dual active bridge micro inverter.

[0010] In some embodiments, in step S3, the step of determining the grid voltage phase... Dynamically adjust the switching frequency ,include: The power frequency cycle is divided into a high-frequency region, a transition region, and a rated frequency region. In the high-frequency region, the switching frequency is taken as ,in ; In the rated frequency range, the switching frequency is taken as ; In the transition region, the switching frequency is and The values ​​change continuously according to a preset function.

[0011] In some embodiments, the high-frequency region corresponds to the grid voltage phase. satisfy: , ,or The interval; The rated frequency zone corresponds to the grid voltage phase. satisfy: ,or The interval; The transition region corresponds to the grid voltage phase. satisfy: , , ,or The interval; in, and This is the preset phase angle threshold.

[0012] In some embodiments, in the transition region, the switching frequency The expression is: .

[0014] In some embodiments, the soft-switching constraint conditions obtained in step S2 are based on the condition of achieving zero-voltage turn-on for all switching transistors. and The derivation is presented as a two-sided inequality constraint concerning the inner phase shift angle D2, voltage gain k, and per-unit power m.

[0015] In some embodiments, in step S4, when the switching frequency is not within the main power transmission phase range of the rated frequency f, in order to simplify control, the inner phase shift angle D2 is directly taken as a fixed value or boundary value within the range defined by the soft switching constraint.

[0016] In some embodiments, the optimization algorithm used in step S4 is an improved adaptive particle swarm optimization algorithm, wherein the inertia weight of the adaptive particle swarm optimization algorithm is... and / or learning factors , During the iteration process, adjustments are made dynamically based on the nonlinear function to avoid getting trapped in local optima.

[0017] In some embodiments, the inertia weights of the improved adaptive particle swarm optimization algorithm The update formula is:

[0018] in, This represents the current iteration number. The maximum number of iterations, As the initial inertia weight, To terminate the inertia weight, The adjustment coefficient is between 0.5 and 1.

[0019] In some embodiments, the learning factor of the improved adaptive particle swarm optimization algorithm , During the iteration process, each from the maximum value , Decreasing linearly to the minimum value , .

[0020] In some embodiments, step S5, calculating the corresponding outer phase shift angle D1 based on the obtained optimal inner phase shift angle D2 and the transmission power P, includes: According to the per-unit expression of the transmission power P The outer phase shift angle D1 is calculated from the optimal inner phase shift angle D2 obtained through optimization, and its expression is as follows: .

[0022] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0023] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described above.

[0024] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer program product, including a computer program that, when executed by a processor, implements the method described above.

[0025] Compared with the prior art, this application has the following beneficial effects: 1) Full-cycle wide-range soft switching is achieved: By combining frequency conversion strategy with single-sided asymmetric modulation, the problem of soft switching loss near the zero crossing point of grid voltage in traditional methods is innovatively solved. Theoretically, ZVS of all switching transistors can be achieved throughout the entire power frequency cycle, which greatly reduces switching losses and electromagnetic interference and improves system reliability.

[0026] 2) Significantly reduced conduction losses and device stress: Within the "safe zone" guaranteed by soft switching, an improved intelligent optimization algorithm actively and dynamically seeks the operating point with minimum current stress. This online optimization method can adapt to changes in grid voltage and power in real time, minimizing conduction losses and thermal stress of inductors and switching transistors, thereby systematically improving overall system efficiency.

[0027] 3) Enhanced control intelligence and adaptability: This application does not simply superimpose control parameters, but constructs an intelligent control framework of "zonal management and collaborative optimization." The frequency conversion strategy expands the feasible solution space for the optimization algorithm, while the optimization algorithm achieves performance limit exploration in the most critical power transmission range. This architecture enables the control system to have stronger environmental adaptability and global optimization capabilities.

[0028] 4) Enriched the control theory of DAB inverters: A comprehensive solution integrating time-domain analysis, frequency modulation and intelligent optimization was proposed, providing new ideas and methods for the design and control of high-frequency, high-efficiency and high-power-density DAB micro-inverters. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the topology of the dual active bridge microinverter used in the embodiments of this application.

[0030] Figure 2 This is a flowchart illustrating the steps of a control method for a dual active bridge microinverter according to an embodiment of this application.

[0031] Figure 3 This is a schematic diagram of the key waveforms of the single-sided asymmetric duty cycle phase shift modulation strategy used in the embodiments of this application.

[0032] Figure 4 The switching frequency within a single power frequency cycle With grid voltage phase A schematic diagram of an adaptively changing curve.

[0033] Figure 5 To determine the feasible region of soft switching with an inner phase shift angle D2 at fixed frequencies f and 3f, depending on the phase... A diagram illustrating the changes.

[0034] Figure 6This is a system architecture diagram in simulation software for a specific implementation of this application.

[0035] Figure 7 This is a waveform verification diagram for the key switching transistor to achieve zero-voltage turn-on (ZVS) in a simulation experiment.

[0036] Figure 8 This is the actual waveform diagram of smooth switching of the switching frequency within a complete power frequency cycle in the simulation experiment.

[0037] Figure 9 The image shows the grid-connected current waveform obtained using the control method described in this application during a simulation experiment. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0039] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0040] Before providing a detailed description of the embodiments of this application, some of the nouns and terms involved in the embodiments of this application will be explained first. The nouns and terms involved in the embodiments of this application are subject to the following interpretations.

[0041] 1) Single-sided asymmetric duty cycle modulation (SSADM) is a control method for dual active bridge converters (DABs) designed to address the low efficiency of traditional single-phase-shift modulation over a wide voltage range, particularly excelling under light load conditions. The core principle of this strategy lies in achieving asymmetric control by adjusting the duty cycle of one bridge arm. Specifically, different duty cycles are allocated during the positive and negative half-cycles, thereby altering the average and peak-to-peak values ​​of the inductor current and effectively regulating the transmitted power. Based on the degrees of freedom of control, SSADM can be divided into two operating modes, and the steady-state characteristics of the inductor current and transmitted power can be derived through time-domain analysis.

[0042] Microinverters are widely used in residential photovoltaic (PV) systems and commercial rooftop PV systems. Compared to centralized and string inverters, microinverters have a dedicated inverter for each PV panel, allowing each panel to operate independently. This avoids issues such as reduced efficiency due to shading affecting the overall system efficiency or a single panel failure impacting the entire PV system. Currently, most microinverters on the market are two-stage topologies, with a flyback circuit boosting voltage in the front stage and inversion in the back stage. These often require large electrolytic capacitors between the two stages. The complex two-stage topology results in a significant efficiency reduction compared to a single-stage topology. Furthermore, the flyback circuit's unidirectional power transmission limits its application scenarios. Dual active bridge microinverters, with their high-efficiency single-stage structure and bidirectional energy transmission advantages, have great development potential. For example, see [link to example]. Figure 1 The dual active bridge micro inverter includes: primary side circuit. Isolation transformer, inductor, secondary side circuit Filter; the primary-side circuit It consists of two branches connected in parallel, the first of which is a switch transistor. and switching transistor The second branch is connected in series, and the second branch is a switch transistor. and switching transistor The two circuits are connected in series and together form a full-bridge circuit; the secondary side circuit... It consists of two parallel branches, forming a half-bridge circuit. The first branch consists of two pairs of anti-parallel series-connected switching transistors. With switching transistor Reverse series connection, then with the switching transistor. and switching transistor The second branch consists of two capacitors connected in reverse series. and These two branches are connected in series and together form a half-bridge structure.

[0043] However, current control methods for dual active bridge inverters mainly include single-phase-shift modulation and extended-phase-shift modulation. Single-phase-shift modulation is simple to control, but it is prone to soft-switching loss under high voltage gain and light load conditions, and it also has a high effective value for the inductor current. Extended-phase-shift modulation offers a significant efficiency improvement over single-phase-shift modulation, but it also suffers from soft-switching loss under light load conditions, and there is still room for optimization of the inductor current when using only extended-phase-shift modulation.

[0044] In view of this, this application provides a control method, electronic device, storage medium, and program product for a dual active bridge microinverter based on frequency modulation and single-sided asymmetric duty cycle modulation. The scheme includes: establishing a mathematical model of the dual active bridge inverter, obtaining mathematical expressions for inductor current, transmission power, voltage gain, and power transfer ratio at each stage, and normalizing the transmission power; obtaining soft-switching constraints based on circuit time-domain analysis, determining the range of the inner phase shift angle based on the transmission power and soft-switching conditions, and expanding the range of soft-switching conditions by adjusting the frequency according to the voltage phase within the power frequency cycle; and optimizing the current stress during periods of high current stress within the power frequency cycle using an improved adaptive particle swarm optimization algorithm within the soft-switching range. The frequency adjustment strategy, single-sided asymmetric duty cycle modulation strategy, and current stress optimization strategy combined in this application embodiment achieve a wide range of soft switching within the power frequency cycle while reducing current stress, enriching the control methods for dual active bridge microinverters.

[0045] The control method for a dual active bridge microinverter provided in this application relates to the field of power electronic converter control technology. The control method for a dual active bridge microinverter provided in this application can be applied to a terminal, a server, or software running on a terminal or server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or vehicle terminal, but is not limited to these. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing the control method for the dual active bridge microinverter, but is not limited to the above forms.

[0046] like Figure 2 As shown, this embodiment provides a control method for a dual active bridge microinverter, including the following steps: Step S1: Establish the mathematical model of the dual active bridge inverter and set the control strategy.

[0047] A precise mathematical model of the dual active bridge microinverter under single-sided asymmetric duty cycle modulation is established. In the modulation strategy, the switches of the same arm of the primary-side full bridge are complementary in conduction, but the phase shift angle between the drive signals of the upper and lower arms is adjustable, and their duty cycle is not fixed at 50%; the switches of the secondary-side half-bridge operate alternately in the positive and negative half-cycles, and the conduction time is fixed at half a switching cycle. See [link to relevant documentation] Figure 1Three control variables are defined: the outer phase shift angle D1 (the ratio of the phase difference between the primary-side switch S1 and the secondary-side switch S5 to half a switching period), the inner phase shift angle D2 (the ratio of the conduction time of the primary-side switch S1 to half a switching period), and the switching frequency of the switches. .

[0048] Step S2: Modal analysis and characteristic derivation.

[0049] Based on the model in step S1, the circuit's multiple operating modes within one switching cycle are analyzed, and the instantaneous inductor current expression for each mode is derived. Through integration of the inductor current and voltage, the transmission power P with respect to D1, D2, and... The precise mathematical expression. Meanwhile, based on the conditions for achieving ZVS (such as...). and The constraints that D2 must satisfy to ensure soft switching of all switching transistors are derived. These constraints are related to the voltage gain k and the per-unit power m.

[0050] As one embodiment, step S2 specifically includes: The DC input voltage of the dual active bridge inverter is defined as follows: The AC output grid-connected voltage is The turns ratio of the isolation transformer is The inductance is , This is half a switching cycle; Based on the analysis of the single-sided asymmetric duty cycle modulation of the dual active bridge inverter, six operating modes are obtained, and the instantaneous value expression of the inductor current corresponding to each mode is as follows:

[0051] In the formula:

[0052] By utilizing the symmetry of inductor current, we can obtain By substituting the instantaneous value expression of the inductor current into the expression for the instantaneous value of the inductor current at each moment, the expression for the instantaneous value of the inductor current at each moment can be obtained. Based on this expression, the maximum current stress can be obtained. The expression:

[0053] In the formula, for The instantaneous value of the inductor current at a given moment is also the maximum current stress value. This represents the switching frequency of the switching transistor.

[0054] Analyzing each mode and integrating, we obtain the mathematical expression for the transmission power of the dual active bridge inverter:

[0055] The process of converting the transmission power P to a per-unit value p is called per-unit normalization. The normalized transmission power p can be expressed by the following formula:

[0056]

[0057] Define the expression for voltage gain k:

[0058] Define the mathematical expression for the power transfer ratio m:

[0059] In the formula Indicates rated power. Indicates AC output power. This indicates the phase of the power grid voltage during the power frequency cycle.

[0060] Step S3: Adaptive frequency conversion control based on power frequency cycle.

[0061] See Figure 5 To address the issue identified in step S2 that the soft-switching range at a fixed frequency is limited near the zero-crossing point of the grid voltage, a method based on the grid voltage phase is introduced. The dynamic frequency conversion strategy divides the power frequency cycle into: 1) High-frequency region: The small-angle range near the zero-crossing of the mains voltage (e.g., , , ), increasing the switching frequency to ( ,For example This significantly expands the soft-switching range of the region.

[0062] 2) Rated frequency range: The main power transmission range near the peak voltage of the power grid (e.g., , The switching frequency is maintained at the rated frequency f to ensure high power density and efficiency.

[0063] 3) Smooth transition zone: See Figure 4 A transition zone is set between the two zones (e.g., (etc.), switching frequency According to a preset linear or nonlinear function from A smooth transition to f (or vice versa) is achieved to avoid current surges and harmonic problems caused by frequency steps. This process is as follows: Figure 4 and Figure 8 As shown, smooth, shock-free frequency switching is achieved.

[0064] As one embodiment, step S3 specifically includes: Under the unilateral asymmetric duty cycle modulation method, modal analysis can yield the following results: At this time, soft switching of all switching transistors can be achieved. Based on the above soft switching constraints, the following constraints can be obtained: ;

[0065] In the transition region, the switching frequency The expression is:

[0066] Furthermore, when the frequency is taken as f and in the frequency transition range, in order to simplify control, the phase angle is shifted inward. The value can be:

[0067] Step S4: Online optimization of current stress in the main intervals.

[0068] Within the rated frequency range of switching frequency f, the transmission power is high, and current stress becomes a prominent issue. In this range, the soft-switching constraint derived in step S2 is used as the search boundary. With minimizing the inductor current peak or stress as the objective function, an improved adaptive particle swarm optimization algorithm is employed to perform online real-time optimization of D2. This improved algorithm enhances the balance between global search capability and local convergence accuracy by designing a nonlinear inertia weight decay curve and an asynchronously changing learning factor, effectively avoiding getting trapped in local optima under complex constraints.

[0069] As one embodiment, step S4 specifically includes: In the main power transmission range at frequency f, an improved adaptive particle swarm optimization algorithm is used to solve the current stress optimization problem, and the objective equation is constructed as follows:

[0070] The update formula for the improved adaptive particle swarm optimization algorithm is as follows:

[0071]

[0072]

[0073]

[0074]

[0075] In the formula, This represents the position information of the i-th particle in iteration t. This represents the velocity information of the i-th particle in iteration t. The optimized inertia weight is given by the following formula: , This refers to the optimized learning factor.

[0076] Step S5: Calculate the complementary control parameters.

[0077] Based on the optimal inner phase shift angle D2 obtained in step S4, and the per-unit power m calculated from the power command and the current state, the corresponding optimal outer phase shift angle D1 is obtained by inverse solving using the power expression. The relationship is as follows:

[0078] Step S6: Combined feedforward and feedback control.

[0079] The dynamic frequency determined in step S3 The optimized D2 from step S4 and the calculated D1 from step S5 are used as feedforward control quantities. Simultaneously, the system introduces dual closed-loop feedback control for output voltage and current. The outer loop (voltage loop) typically uses the MPPT algorithm to provide a current reference, while the inner loop (current loop) uses a proportional resonant (PR) or other controller to generate compensation quantities. The feedforward control quantity and the feedback compensation quantity are superimposed and input to the PWM generator to generate the final PWM signal driving the switching transistor, achieving precise control of the inverter.

[0080] The above control method enables soft switching throughout the entire cycle. In the 20kHz frequency range, which is the main part of inverter power transmission, reducing the inductor current in a dual active bridge inverter effectively reduces losses in the inductor, transformer, and switching transistors, thereby improving the overall converter efficiency. Therefore, in this range, phase shift angle optimization is performed while ensuring soft switching to reduce the effective value of the inductor current. Current stress not only improves system efficiency and reliability but also enhances dynamic performance and reduces system cost, which is of great significance for improving the overall performance of the DAB converter.

[0081] Here, a particle swarm optimization algorithm is used for optimization, with the objective of minimizing current stress. Due to the symmetry of the inductor current, The current stress is maximum at time t, and the objective function is: It can be obtained through algorithms. The value can be obtained using the formula above. The above calculation results, combined with the results from the current loop, are input to the PWM generator to generate control waveforms and achieve circuit control.

[0082] The solutions of the embodiments of this application will be described in detail below with reference to the accompanying drawings and specific application examples.

[0083] This embodiment provides a control method for a dual active bridge microinverter, applicable to, for example... Figure 6 In the simulation software system shown, the control waveform is as follows: Figure 3 As shown, the primary-side switches in the same bridge arm conduct complementaryly, and S1 and S3 are set with a fixed phase shift angle of 180°. Compared to the traditional control method, the conduction time of the primary-side switches is not fixed at half a switching cycle, while the conduction time of the secondary-side switches is still half a switching cycle. Therefore, it is called single-sided asymmetric duty cycle phase shift modulation. Compared to the dual active bridge circuit, the conversion of the dual active bridge inverter circuit is from DC / DC to DC / AC. When the AC side voltage is greater than 0, switches S5 and S7 conduct complementaryly, while S6 and S8 conduct continuously. When the AC side voltage is less than 0, switches S6 and S8 conduct complementaryly, while S5 and S7 conduct continuously. The phase shift angle D1 is defined as the ratio of the phase difference between the primary-side switch S1 and the secondary-side switch S5 to half a switching cycle, and the phase shift angle D2 is the ratio of the conduction time of the primary-side switches S1 and S3 to half a switching cycle. This invention introduces frequency (freq) control into the single-sided asymmetric duty cycle modulation. This method has three control quantities: D1, D2, and freq, where freq is the switching frequency of the switching transistor. Implementing the inverter function includes the following steps: The modes of the single-sided asymmetric duty cycle modulation method of the dual active bridge inverter are analyzed, and the results are obtained. to , to , to Modal inductor current expression: ; ; ; Based on the symmetry of the inductor current, it can be calculated that , , The expression for node inductance current:

[0084]

[0085]

[0086] right to Integrating over a time interval yields the power expression:

[0087] Where f represents the switching frequency of the switching transistor, and Lk represents the inductance value. Indicates the DC side voltage. This represents the grid voltage. D1 is the outward phase shift angle, and D2 is the inward phase shift angle.

[0088] Based on the rated power and other parameters of the designed micro-inverter, the voltage gain value k and the power transfer ratio m are calculated:

[0089]

[0090] By standardizing the power P, another expression for m can be obtained from the power expression:

[0091] The expression for D1 with respect to D2 and m can be obtained from the above formula:

[0092] In the single-sided asymmetric duty cycle modulation method, since all switches are connected in parallel with capacitors, the switches are clamped after being turned on. Therefore, the turn-off of all switches can be approximated as zero-voltage turn-off. Modal analysis can yield the following results. At this time, due to the effect of the anti-parallel diode, the primary-side switches S1 and S4 can achieve zero-voltage conduction. Furthermore, based on the symmetry of the inductor current, it can be deduced that S2 and S3 can also achieve zero-voltage conduction. Similarly... This allows for zero-voltage conduction of S5, S7, S6, and S8. Based on the aforementioned soft-switching constraints and simplification of the above equation, the following constraints can be obtained: ;

[0093] Based on the soft-switching conditions, the analysis only needs to consider the soft-switching situation within half a power frequency cycle due to the symmetry of the grid voltage. The value range of D2 with the grid voltage phase varies with the switching frequency f, as shown in the figure. When the grid voltage approaches 0, soft switching may be lost. The value range of D2 with the grid voltage phase varies with the switching frequency of 3f, as shown in the figure. It can be seen that increasing the frequency effectively extends the soft-switching phase, but near the peak grid voltage, the soft-switching range is only a single line. Because the charging and discharging of the switch junction capacitance has a minimum current limit, achieving full-frequency cycle soft switching is difficult when the frequency is only 3f.

[0094] Based on the above analysis, this embodiment proposes a single-sided asymmetric duty cycle modulation method combined with frequency adjustment to achieve a soft-switching range within the extended power frequency cycle. This is achieved within a phase angle of 0 to... The switching frequency is set to 3f. To achieve smooth frequency switching, a linear frequency transition is performed between 3f and f, with the transition phase angle set to . The frequency after the transition is taken as f. Frequency switching is performed throughout the entire power frequency cycle according to the above transformation method, that is, when the grid voltage phase is (0, ... ), ( -α, +α), (2 -α,2 When the switching frequency is 3f, the phase of the grid voltage is ( + , - - ), ( + + ,2 - - When the switching frequency is f, the switching frequency is taken as f; to ensure the continuity of frequency switching, the phase of the grid voltage is taken as ( , + ), ( - - , - ), ( + , + + (2) - - ,2 - During these four segments, a frequency switching transition occurs. The frequency transition expressions for these four segments are as follows:

[0095] The above control method enables soft switching over the entire power frequency cycle. Within the switching frequency range of 2f and the frequency transition range, the current stress is relatively small. To simplify control and improve calculation speed, the value of D2 only meets the requirements of soft switching. D2 is set as follows:

[0096] In the frequency range of f, which is the main part of inverter power transmission, the current stress is relatively large. Reducing the current stress of dual active bridge inverters can not only improve system efficiency and reliability, but also enhance dynamic performance and reduce system cost, which is of great significance for improving the overall performance of the converter.

[0097] For the switching frequency range f, an improved particle swarm optimization algorithm is used to optimize the phase shift angle D2 within the constraints of the soft-switching range. The point of maximum current stress throughout the entire cycle is... and The current at these two moments is equal in absolute value due to symmetry. Therefore, the optimization objective is... ;Use the previously calculated soft-switching conditions to determine the range of values ​​for D2 optimization; Within a specified search interval, a swarm of particles is randomly generated. Simultaneously, the initial position and initial velocity of each particle are randomly set, and the particle's attributes are characterized by three parameters: position, velocity, and fitness. Position represents the value of D2 under soft-switching conditions, velocity represents the increase or decrease of each particle's D2 value, and the fitness value is obtained from the fitness function, i.e., the objective function corresponding to each particle. The particle's velocity update comprehensively considers its current position, current velocity, and global optimal position. ) and the individual's optimal position ( This is achieved through [the following], and its update formula is shown below:

[0098]

[0099]

[0100]

[0101] The update formula includes updates to particle velocity and position, since the optimized parameters are only a few. We only need to consider one-dimensional information, in which... This represents the position information of the i-th particle in iteration t. This represents the velocity information of the i-th particle in iteration t. and The learning factor determines the step size at which a particle moves towards the global optimum and the individual optimum. In particle swarm optimization, a fixed value is usually chosen. Here, an asynchronous learning factor is used to dynamically change the learning factor. In the early stages of iteration, this promotes global exploration and avoids getting stuck in local optima. In the later stages of iteration, it performs a fine-grained local search to improve convergence accuracy. , A random number between 0 and 1 This represents the inertial weight of the particle in the i-th iteration.

[0102]

[0103] Where T is the maximum number of iterations, initialized... of The value is 0.8, which is the value when the maximum number of iterations is reached. value Setting the inertia weight to 0.2, the value gradually decreases with the number of iterations. However, as the number of iterations increases, the proportion of the inertia weight decreases further, potentially leading to local optima due to the existence of local optima. To address this issue, an improved nonlinear inertia weight decay algorithm is employed. This method, which suddenly increases the inertia weight when the maximum number of iterations reaches half, can mitigate the problem of jumping into local optima to some extent, thus allowing for the determination of the optimal inductor current. value.

[0104]

[0105] If you want to get The corresponding objective function is less than The corresponding fitness function is then ,on the contrary, Simultaneously, update the position of the optimal particle in the population to obtain... By performing multiple iterations, an approximate optimal solution can be obtained, which is the optimal solution that minimizes the current stress at a frequency of 3f. From the optimal solution Substituting into the formula, we can obtain the value at the corresponding time. The value will be freq, , The compensation value from the current loop is fed into the PWM generator to control the inverter and achieve the inverter function.

[0106] In the simulation experiment, the waveform of the key switching transistor achieving zero-voltage turn-on (ZVS) is as follows: Figure 7 As shown, the actual waveform of smooth switching of the switching frequency within a complete power frequency cycle is as follows: Figure 8 As shown, Figure 9 This demonstrates that the method of this embodiment has good sinusoidal properties and low harmonic content.

[0107] In summary, this application proposes a modulation strategy for a dual active bridge inverter combining frequency modulation with single-sided asymmetric duty cycle modulation, which is an innovative modulation strategy. Compared to single-phase-shift modulation, the single-sided asymmetric duty cycle modulation strategy, by adding a degree of freedom, can better optimize the soft-switching range while transmitting power, and frequency adjustment can further broaden the soft-switching range. Compared to extended phase-shift modulation, by introducing frequency adjustment, a wider range of soft-switching regulation is achieved, while reducing inductor current and thus reducing conduction losses. Furthermore, this new control method combining frequency modulation with single-sided asymmetric duty cycle modulation enriches the control strategies for dual active bridge microinverters.

[0108] Furthermore, the embodiments of this application employ an improved adaptive particle swarm optimization algorithm to optimize current stress in the main power transmission grid voltage phase range. By dynamically changing the learning factor and optimizing the decreasing inertia weight, the algorithm can effectively avoid getting trapped in local optima and find the true global optimum, thereby optimizing current stress to the greatest extent and achieving the effects of reducing losses, improving device reliability, and improving system efficiency.

[0109] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0110] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0111] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0112] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0113] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0114] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0115] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented in the embodiments of this program product are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments. The executable computer program code or "code" used to perform the various embodiments can be written in high-level programming languages ​​such as C, C++, Python, Smalltalk, Java, JavaScript, Visual Basic, Structured Query Language (e.g., Transact-SQL), Perl, or in various other programming languages.

[0116] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0117] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0118] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0119] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0120] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0121] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0122] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0123] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0124] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0125] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0126] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A control method for a dual active bridge microinverter, characterized in that, The method includes the following steps: A mathematical model of the dual active bridge microinverter is established, and the outer phase shift angle D1, the inner phase shift angle D2, and the switching frequency are defined. Three-variable control strategy; Based on a single-sided asymmetric duty cycle modulation strategy, circuit mode analysis is performed to obtain the inductor current expression, transmission power P, and soft-switching constraints. Within one power frequency cycle, according to the grid voltage phase Dynamically adjust the switching frequency To extend the soft-switching range of the dual active bridge microinverter, a switching frequency higher than the rated frequency f is used in the phase interval near the zero crossing of the grid voltage. Within the main power transmission phase range where the switching frequency is the rated frequency f, based on the soft-switching constraint, the inner phase shift angle D2 is optimized online using an optimization algorithm to minimize the inductor current stress. Based on the obtained optimal inner phase shift angle D2 and the transmission power P, calculate the corresponding outer phase shift angle D1; The obtained switching frequency The inner phase shift angle D2 and the outer phase shift angle D1 are used as feedforward control quantities, which, together with the feedback control compensation quantity, generate a PWM signal to drive the dual active bridge micro inverter.

2. The method according to claim 1, characterized in that, According to the grid voltage phase Dynamically adjust the switching frequency ,include: The power frequency cycle is divided into a high-frequency region, a transition region, and a rated frequency region. In the high-frequency region, the switching frequency is taken as ,in It is an integer greater than 1; In the rated frequency range, the switching frequency is taken as ; In the transition region, the switching frequency is and The values ​​change continuously according to a preset function.

3. The method according to claim 2, characterized in that, The high-frequency region corresponds to the power grid voltage phase. satisfy: , ,or The interval; The rated frequency zone corresponds to the power grid voltage phase. satisfy: ,or The interval; The transition region corresponds to the grid voltage phase. satisfy: , , ,or The interval; in, and This is the preset phase angle threshold.

4. The method according to claim 3, characterized in that, In the transition region, the switching frequency The expression is: 。 5. The method according to claim 1, characterized in that, The soft-switching constraint is based on the condition of achieving zero-voltage turn-on for all switching transistors. and The derivation is presented as a two-sided inequality constraint concerning the inner phase shift angle D2, voltage gain k, and per-unit power m.

6. The method according to claim 5, characterized in that, When the switching frequency is not within the main power transmission phase range of the rated frequency f, in order to simplify control, the inner phase shift angle D2 is directly taken as a fixed value or boundary value within the range defined by the soft switching constraint.

7. The method according to claim 1, characterized in that, The optimization algorithm is an improved adaptive particle swarm optimization algorithm, and the inertia weight of the adaptive particle swarm optimization algorithm is... and / or learning factors , During the iteration process, adjustments are made dynamically based on the nonlinear function to avoid getting trapped in local optima.

8. The method according to claim 7, characterized in that, The inertia weight of the improved adaptive particle swarm algorithm The update formula is: in, This represents the current iteration number. The maximum number of iterations, As the initial inertia weight, To terminate the inertia weight, This is the adjustment coefficient.

9. The method according to claim 1, characterized in that, The step of calculating the corresponding outer phase shift angle D1 based on the obtained optimal inner phase shift angle D2 and the transmission power P includes: According to the per-unit expression of the transmission power P The outer phase shift angle D1 is calculated from the optimal inner phase shift angle D2 obtained through optimization, and its expression is as follows: 。 10. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 9.

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