Optical storage all-in-one machine bidirectional converter control method based on virtual synchronous generator

Abstract

The invention belongs to the technical field of optical storage all-in-one machine control, and particularly relates to an optical storage all-in-one machine bidirectional converter control method based on a virtual synchronous generator. Comprising the following steps: S1, establishing a traditional VSG controller mathematical model; s2, carrying out adaptive rule design; s3, virtual inertia control considering the charge state of the energy storage unit is carried out; s4, the adaptive filtering is fused into the SOGI phase-locked loop; aiming at the problem of low precision of a phase-locked loop in a traditional all-in-one machine, an improved phase-locked loop technology is introduced, the phase detection precision is improved, accurate acquisition of frequency and phase is ensured, and the stability of a system is enhanced; the virtual inertia value can be adjusted in real time according to the SOC of the energy storage unit, the energy storage unit is prevented from being overcharged or overdischarged, and the service life of the energy storage unit is prolonged.

Description

Technical Field

[0001] The present invention belongs to the technical field of photovoltaic and storage integrated machine control, and in particular relates to a photovoltaic and storage integrated machine bidirectional converter control method based on a virtual synchronous generator. Background Art

[0002] As the penetration rate of distributed generation (DG) such as photovoltaic and wind power in the power system continues to increase, the application of power electronics technology is becoming more and more extensive. Photovoltaic power generation systems and energy storage systems achieve efficient power conversion and utilization through photovoltaic and energy storage integrated machines. The integrated machine connects these photovoltaic and energy storage devices to the power grid through a bidirectional converter interface. However, since these distributed power sources themselves lack the rotational inertia of traditional synchronous generators, this leads to a significant reduction in the overall rotational inertia of the power system, which seriously affects the dynamic response and stability of the power system.

[0003] In traditional power systems, rotational inertia provides an important buffering effect, which can maintain system stability when load changes or emergencies occur. In high-penetration distributed power systems, the lack of this buffering effect makes the system more susceptible to disturbances, increasing the risk of frequency fluctuations and voltage instability. In addition, with the large-scale access of converters to the power grid, the harmonic content of the grid voltage increases and the frequency changes, further reducing the operational stability and power quality of the grid.

[0004] In this context, the traditional dual second-order generalized integral phase-locked loop (DSOGI-PLL) is difficult to meet the increasingly stringent filtering requirements in the face of declining grid quality, resulting in reduced phase-locking accuracy and affecting the performance of the inverter in scenarios with severe grid distortion. Therefore, the study of control methods for photovoltaic and storage integrated bidirectional converters has become a hot topic in power electronics technology. Virtual synchronous generator (VSG) control technology improves the frequency stability of microgrids by simulating the characteristics of traditional synchronous generators. This concept was first proposed by Professor Beck of Clausthal University of Technology in Germany. At present, the research on photovoltaic and storage integrated VSG mainly focuses on adaptive inertial control, parameter setting analysis and frequency characteristic optimization. Adaptive inertial control aims to adjust the system inertial response in real time to cope with load changes and improve the dynamic stability of the system; parameter setting analysis enhances the system response speed and stability by optimizing parameters such as inertia coefficient and damping coefficient; frequency characteristic optimization focuses on improving the frequency response characteristics of VSG and improving its ability to regulate the frequency of microgrids.

[0005] The currently constructed VSG-based integrated photovoltaic and energy storage machine does not consider the constraints of the energy storage unit's own charging and discharging characteristics on the system's virtual inertia, and lacks engineering feasibility. With the access of large-scale integrated machines and new energy inverters, the harmonic content of the power grid has increased and the frequency has changed. The traditional phase-locked loop technology based on double second-order generalized integral can no longer meet the filtering requirements of the integrated photovoltaic and energy storage machine using VSG, and it is difficult to accurately lock the voltage frequency and phase. When the integrated photovoltaic and energy storage machine is in the grid-connected working mode, it will seriously damage the power electronic devices and even cause harm to the power grid. In summary, new control methods are needed to improve the performance of the integrated photovoltaic and energy storage machine and the stability of the power grid. Summary of the invention

[0006] The purpose of the present invention is to overcome the shortcomings of the prior art and to provide a photovoltaic and energy storage integrated bidirectional converter control method based on a virtual synchronous generator. By real-time monitoring of the SOC of the energy storage unit, the virtual inertia value is dynamically adjusted to avoid overcharging or over-discharging, thereby improving the performance and life of the energy storage unit.

[0007] The object of the present invention is achieved by: a control method for a photovoltaic storage integrated bidirectional converter based on a virtual synchronous generator, which comprises the following steps:

[0008] S1. Establish the mathematical model of the traditional VSG controller;

[0009] S2, designing adaptive rules;

[0010] S3, performing virtual inertia control taking into account the state of charge of the energy storage unit;

[0011] S4. Integrate adaptive filtering into the SOGI phase-locked loop.

[0012] The step S1 of establishing a mathematical model of a conventional VSG controller includes:

[0013] Active power-frequency droop characteristic equation of traditional synchronous generator:

[0014] P N -P=K f (ff n )

[0015] Among them, P N is the rated active power; P is the actual active power; f n is the rated frequency of the system; f is the actual system frequency; K f is the frequency adjustment coefficient;

[0016] Through the active power-frequency droop characteristics of the traditional synchronous generator, the mathematical model of the virtual speed regulator of VSG can be derived as follows:

[0017]

[0018] Where J is the virtual moment of inertia; D is the virtual damping coefficient; Δω is the angular frequency change of VSG; P ref is the rated active power; P m is the mechanical power; n is the rated angular frequency;

[0019] The reactive voltage droop characteristic equation of the synchronous generator is:

[0020] U n -U=K v (Q n -Q)

[0021] Where Q n is the rated reactive power value; Q is the actual reactive power value; U n is the given value of output voltage; U is the actual value of output voltage; K v is the voltage regulation coefficient;

[0022] The droop characteristics of the reactive voltage of the synchronous generator can be used to obtain the mathematical model of the VSG virtual excitation regulator as follows:

[0023]

[0024] in is the integration factor; D Q is the droop coefficient; E is the excitation electromotive force; U0 is the no-load electromotive force; Q ref is the rated reactive power; Q is the actual reactive power; U ref is the given value of output voltage; U is the actual value of output voltage;

[0025] Referring to the mathematical model of synchronous generator, the mathematical model of VSG is expressed as,

[0026]

[0027] Where, θ is the phase angle of the excitation electromotive force; u g is the grid voltage; u * is the output reference voltage; i ref is the output reference current; L is the inductance in the circuit; T m is the mechanical torque; T e is the electromagnetic torque;

[0028] The basic equation of the active frequency loop of the traditional VSG is expressed as:

[0029] P ref -P m -D p (ω-ω g )=2Jdω / dt

[0030] Where, ω is the angular frequency of the VSG output; ω g is the common bus angular frequency.

[0031] The step S2 of performing adaptive rule design includes:

[0032] In order to maximize the flexible adjustment capability of virtual inertia in VSG control, the adaptive inertia rule adopted by the present invention is as follows:

[0033]

[0034] Where J0 is the steady-state value of virtual inertia; G is the virtual inertia judgment threshold; k is the system compensation coefficient; ω ref The tanh function is introduced to make the moment of inertia change between J0-k and J0+k as the reference value of the system angular frequency. The tanh function characteristic prevents the virtual inertia J from changing too fast or too large.

[0035] The step S3 of performing virtual inertia control considering the state of charge of the energy storage unit includes:

[0036] The working state of the energy storage system is represented by the battery's SOC (state of charge), and is divided into different stages, as follows:

[0037]

[0038] Where a and b represent the SOC critical values ​​of battery over-discharge and over-charge, respectively;

[0039] Simplify the active frequency equation of the traditional VSG.

[0040]

[0041] Among them, P in is the active power of the input converter; ΔP is the change in active power; Δω is the change in angular frequency;

[0042] When the battery is in the over-discharge area and needs to be discharged, it is hoped that the output of the battery will be reduced as much as possible, that is, the active change ΔP will be as small as possible. At this time, it can be approximately assumed that the angular frequency change Δω remains unchanged in a short period of time, so J should be as small as possible to reduce the active output of the battery. When it is in the over-charge area and needs to be charged, it is similar to the situation of discharging in the over-discharge area, and J also needs to be reduced. When the battery is in the over-discharge area but needs to be charged, it is hoped to increase the charging energy of the battery so that it can return to the normal working area as soon as possible. At this time, it is necessary to increase J, thereby increasing ΔP, in order to increase the charging energy of the battery. When it is in the over-charge area but needs to be discharged, it is similar to the situation of charging in the over-discharge area, and J needs to be increased so that the battery can enter a safe working state as soon as possible. Based on these operating conditions, the specific expression of the virtual inertia under the limit SOC can be obtained as follows,

[0043]

[0044] Among them, J f , J c are the virtual inertia of the battery in discharge and charge states respectively; k1 and k2 are the virtual inertia compensation coefficients.

[0045] The step S4 of integrating the adaptive filtering into the SOGI phase-locked loop comprises:

[0046] Construct the corresponding phase-locked time-domain continuous mathematical model in the VSG-based optical storage integrated machine.

[0047]

[0048] Among them, cosωt and sinωt are trigonometric functions output by the phase-locked loop;

[0049] The weight vector matrix is,

[0050]

[0051] in, and They represent the positive and negative sequence components of the grid voltage in the two-phase rotating coordinate system dq coordinate system respectively;

[0052] The expected response input matrix is ​​as follows,

[0053]

[0054] Among them, Y(t) is the system output matrix; and is the positive sequence component of the grid voltage in the αβ coordinate system;

[0055] The error vector matrix is ​​as follows,

[0056] ε(t)=d(t)-Y(t)

[0057] The weight iteration formula is as follows:

[0058] W′(t)=W(t)+με(t)X(t)

[0059] W′(t) is the updated weight matrix; μ is the step size update parameter;

[0060] Variable step size μ design, when the error ε(t) is large, the step size μ will increase accordingly, thereby improving the convergence speed of the algorithm and reducing the dynamic response. When the error ε(t) gradually decreases, the algorithm will automatically adjust the value of the step size μ to make it gradually decrease, thereby improving the steady-state accuracy.

[0061] The variable step length calculation formula is:

[0062] μ(n+1)=ξμ(n)+ηε 2 (n)

[0063] Among them, ξ and η are the step size factor vector and error factor vector of step size update; ε(n) is the autocorrelation estimated mean of the error signal; μ(n) is the update value of each step size cycle.

[0064] Beneficial effects of the present invention: The control method of the bidirectional converter of the integrated photovoltaic and storage machine based on the virtual synchronous generator of the present invention comprises the steps of S1, establishing a mathematical model of the traditional VSG controller; S2, designing an adaptive rule; S3, performing virtual inertia control considering the state of charge of the energy storage unit; S4, integrating adaptive filtering into the SOGI phase-locked loop; through the above steps, the control method of the bidirectional converter of the integrated photovoltaic and storage machine based on the virtual synchronous generator of the present invention solves the problems of dynamic response and stability of the power system caused by the lack of rotational inertia in the distributed power supply system. By real-time monitoring of the state of charge of the energy storage unit, the virtual inertia value is dynamically adjusted to avoid overcharging or over-discharging of the energy storage unit and extend its service life. At the same time, the improved phase-locked loop technology is introduced to improve the phase detection accuracy of pre-synchronization seamless switching, ensure the accurate acquisition of frequency and phase, and enhance the stability of the system and off-grid switching operation. In addition, an adaptive filtering technology is designed to optimize the filtering characteristics of the second-order generalized integral phase-locked loop, effectively suppress high-frequency interference and harmonic components, and improve the steady-state accuracy of the system. These innovations significantly improve the operating reliability of the integrated photovoltaic and storage machine and the service life of the energy storage unit, and provide an effective solution for engineering applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0066] Figure 1 It is a diagram of the improved VSG control system of the integrated optical storage machine.

[0067] Figure 2 This is a diagram of an improved phase-locked loop structure.

[0068] Figure 3 It is the SOC virtual inertia control diagram.

[0069] Figure 4 It is a diagram of an improved active power-frequency loop control system.

[0070] Figure 5 This is a diagram of the traditional phase-locked loop structure.

[0071] Figure 6 This is the topology diagram of the optical storage integrated machine. DETAILED DESCRIPTION

[0072] The present invention will be further described below in conjunction with the accompanying drawings.

[0073] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0074] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0075] like Figure 1-6 As shown, the control method of the photovoltaic storage integrated bidirectional converter based on the virtual synchronous generator of the present invention comprises the following steps:

[0076] S1. Establish the mathematical model of the traditional VSG controller;

[0077] S2, designing adaptive rules;

[0078] S3, performing virtual inertia control taking into account the state of charge of the energy storage unit;

[0079] S4. Integrate adaptive filtering into the SOGI phase-locked loop.

[0080] The present invention aims to solve the problem that the VSG-based integrated photovoltaic and storage device does not consider the constraints of the charging and discharging characteristics of the energy storage unit on the system virtual inertia, resulting in overcharging and over-discharging of the energy storage unit, affecting its performance and life. Figure 1 As shown in Figure 1, an adaptive inertia VSG system is proposed. The system monitors the SOC of the energy storage unit in real time and dynamically adjusts the virtual inertia value to avoid overcharging or over-discharging, thereby improving the performance and life of the energy storage unit. Figure 2 As shown in the figure, the improved phase-locked loop technology is introduced to improve the phase detection accuracy during pre-synchronization control, ensure the accurate acquisition of frequency and phase, and ensure stable operation of the system. When it is detected that the SOC reaches the set upper or lower limit, the system automatically adjusts the charge and discharge power to prevent overcharging or over-discharging, and prolong the service life of the energy storage unit. These improvements significantly improve the operating reliability of the integrated photovoltaic storage unit and the life of the energy storage unit, providing an effective solution for engineering applications.

[0081] First, by analyzing the working principle and control objectives of the virtual synchronous generator, a detailed mathematical model was established, which accurately reflects the dynamic behavior of the traditional VSG controller under different operating conditions. By combining the inertial characteristics of grid synchronization with the characteristics of the virtual motor, the mathematical model not only describes the basic structure of the VSG controller, but also systematically explains its control mechanism in voltage regulation, frequency response, and grid stability. This mathematical model provides a solid theoretical foundation for subsequent system simulation and optimization design, and also provides an important reference for in-depth understanding of the dynamic performance of the VSG controller.

[0082] Furthermore, the step S1 of establishing a mathematical model of a conventional VSG controller includes:

[0083] The primary frequency regulation mechanism of the traditional synchronous generator starts after the system frequency changes. By adjusting the input mechanical power, the system can regain a stable state. The active power-frequency droop characteristic equation of the traditional synchronous generator is:

[0084] P N -P=K f (ff n )

[0085] Among them, P N is the rated active power; P is the actual active power; f n is the rated frequency of the system; f is the actual system frequency; K f is the frequency adjustment coefficient;

[0086] Through the active power-frequency droop characteristics of the traditional synchronous generator, the mathematical model of the virtual speed regulator of VSG can be derived as follows:

[0087]

[0088] Where J is the virtual moment of inertia; D is the virtual damping coefficient; Δω is the angular frequency change of VSG; P ref is the rated active power; P m is the mechanical power; n is the rated angular frequency;

[0089] When the system frequency is stable, the excitation electromotive force and output voltage of the traditional synchronous generator can be regarded as approximately equal. When the system reactive power fluctuates, the excitation electromotive force can be changed by adjusting the excitation current of the synchronous generator to keep the output voltage stable. The reactive voltage droop characteristic equation of the synchronous generator is

[0090] U n -U=K v (Q n -Q)

[0091] Where Q n is the rated reactive power value; Q is the actual reactive power value; U n is the given value of output voltage; U is the actual value of output voltage; K v is the voltage regulation coefficient;

[0092] The droop characteristics of the reactive voltage of the synchronous generator can be used to obtain the mathematical model of the VSG virtual excitation regulator as follows:

[0093]

[0094] in is the integration factor; D Q is the droop coefficient; E is the excitation electromotive force; U0 is the no-load electromotive force; Q ref is the rated reactive power; Q is the actual reactive power; U ref is the given value of output voltage; U is the actual value of output voltage;

[0095] Referring to the mathematical model of synchronous generator, the mathematical model of VSG is expressed as,

[0096]

[0097] Where, θ is the phase angle of the excitation electromotive force; u g is the grid voltage; u * is the output reference voltage; i ref is the output reference current; L is the inductance in the circuit; T m is the mechanical torque; T e is the electromagnetic torque;

[0098] The basic equation of the active frequency loop of the traditional VSG is expressed as:

[0099] Pref -P m -D p (ω-ω g )=2Jdω / dt

[0100] Where, ω is the angular frequency of the VSG output; ω g is the common bus angular frequency.

[0101] In order to improve the robustness and response speed of the system under different operating environments, a set of adaptive rules is designed to enable the control system to adjust its parameters in real time to cope with external disturbances and internal parameter changes. This set of adaptive rules automatically adjusts the gain and dynamic response characteristics of the controller by combining real-time monitoring data with the model prediction algorithm, thereby ensuring that the system can maintain stable output under various operating conditions. At the same time, based on the designed adaptive rules, the system can quickly adjust itself under the influence of uncertain factors such as load fluctuations and environmental changes, thereby enhancing the system's adaptability and anti-interference performance. This design not only improves the control accuracy of the system, but also significantly improves the overall efficiency and reliability of the system.

[0102] Furthermore, the step S2 of performing adaptive rule design includes:

[0103] In order to maximize the flexible adjustment capability of virtual inertia in VSG control, the adaptive inertia rule adopted by the present invention is as follows:

[0104]

[0105] Where J0 is the steady-state value of virtual inertia; G is the virtual inertia judgment threshold; k is the system compensation coefficient; ω ref The tanh function is introduced to make the moment of inertia change between J0-k and J0+k as the reference value of the system angular frequency. The tanh function characteristic prevents the virtual inertia J from changing too fast or too large.

[0106] In the virtual synchronous generator control strategy, an adaptive virtual inertia control method is designed for the SOC of the energy storage unit. This method dynamically adjusts the virtual inertia parameters according to the real-time SOC of the energy storage unit to optimize the inertial response of the system. When the SOC of the energy storage unit is high, the virtual inertia increases to enhance the stability and frequency regulation capability of the system; when the SOC is low, the virtual inertia decreases accordingly to avoid excessive discharge of the energy storage unit. This SOC-based virtual inertia control strategy can not only improve the frequency stability of the system, but also effectively extend the service life of the energy storage unit, ensuring the reliability and economy of the system in long-term operation. At the same time, the control method also has a certain degree of adaptability, and can automatically adjust the control strategy according to the grid conditions and load changes, further enhancing the flexibility and robustness of the VSG system.

[0107] Furthermore, the virtual inertia control considering the state of charge of the energy storage unit in step S3 includes:

[0108] When the battery reaches its charge and discharge limit, the active output of the battery can be changed by adjusting the virtual inertia. This adjustment can make the battery enter a safe state as soon as possible, avoiding deep overcharge or over discharge, thereby optimizing the battery's operating state. The battery's SOC (state of charge) is used to represent the working state of the energy storage system, and it is divided into different stages, as follows:

[0109]

[0110] Where a and b represent the SOC critical values ​​of battery over-discharge and over-charge, respectively;

[0111] Simplify the active frequency equation of the traditional VSG.

[0112]

[0113] Among them, P in is the active power of the input converter; ΔP is the change in active power; Δω is the change in angular frequency;

[0114] When the battery is in the over-discharge area and needs to be discharged, it is hoped that the output of the battery will be reduced as much as possible, that is, the active change ΔP will be as small as possible. At this time, it can be approximately assumed that the angular frequency change Δω remains unchanged in a short period of time, so J should be as small as possible to reduce the active output of the battery. When it is in the over-charge area and needs to be charged, it is similar to the situation of discharging in the over-discharge area, and J also needs to be reduced. When the battery is in the over-discharge area but needs to be charged, it is hoped to increase the charging energy of the battery so that it can return to the normal working area as soon as possible. At this time, it is necessary to increase J, thereby increasing ΔP, in order to increase the charging energy of the battery. When it is in the over-charge area but needs to be discharged, it is similar to the situation of charging in the over-discharge area, and J needs to be increased so that the battery can enter a safe working state as soon as possible. Based on these operating conditions, the specific expression of the virtual inertia under the limit SOC can be obtained as follows,

[0115]

[0116]

[0117] Among them, J f , J c are the virtual inertia of the battery in discharge and charge states respectively; k1 and k2 are the virtual inertia compensation coefficients.

[0118] In order to improve the filtering effect without sacrificing the dynamic response speed, the adaptive filtering technology is cleverly integrated into the second-order generalized integral phase-locked loop. This design enables the system to automatically optimize the filtering characteristics under different power grid conditions by adjusting the filtering parameters in real time, effectively suppressing high-frequency interference and harmonic components, thereby significantly improving the steady-state accuracy of the system. The adaptive filtering SOGI phase-locked loop can still maintain a high phase-locking speed and accuracy when the grid voltage suddenly changes or the frequency fluctuates, thereby ensuring the stable operation of the system. In addition, the adaptive characteristics of this method enable it to have a wider range of application scenarios, especially in complex power grid environments, and can provide excellent performance.

[0119] Furthermore, the step S4 of integrating the adaptive filtering into the SOGI phase-locked loop includes:

[0120] Integrating adaptive filtering into the phase-locked loop can improve the filtering effect without reducing the dynamic response speed, thereby improving the steady-state accuracy.

[0121] Construct the corresponding phase-locked time-domain continuous mathematical model in the VSG-based optical storage integrated machine.

[0122]

[0123] Among them, cosωt and sinωt are trigonometric functions output by the phase-locked loop;

[0124] The weight vector matrix is,

[0125]

[0126] in, and They represent the positive and negative sequence components of the grid voltage in the two-phase rotating coordinate system dq coordinate system respectively;

[0127] The expected response input matrix is ​​as follows,

[0128]

[0129] Among them, Y(t) is the system output matrix; and is the positive sequence component of the grid voltage in the αβ coordinate system;

[0130] The error vector matrix is ​​as follows,

[0131] ε(t)=d(t)-Y(t)

[0132] The weight iteration formula is as follows:

[0133] W′(t)=W(t)+με(t)X(t)

[0134] W′(t) is the updated weight matrix; μ is the step size update parameter;

[0135] Variable step size μ design, when the error ε(t) is large, the step size μ will increase accordingly, thereby improving the convergence speed of the algorithm and reducing the dynamic response. When the error ε(t) gradually decreases, the algorithm will automatically adjust the value of the step size μ to make it gradually decrease, thereby improving the steady-state accuracy.

[0136] The variable step length calculation formula is:

[0137] μ(n+1)=ξμ(n)+ηε 2 (n)

[0138] Among them, ξ and η are the step size factor vector and error factor vector of step size update; ε(n) is the autocorrelation estimated mean of the error signal; μ(n) is the update value of each step size cycle.

[0139] In summary, the control method of the bidirectional converter of the photovoltaic storage integrated machine based on the virtual synchronous generator of the present invention includes the steps of S1, establishing a mathematical model of the traditional VSG controller; S2, designing an adaptive rule; S3, performing virtual inertia control considering the state of charge of the energy storage unit; S4, integrating adaptive filtering into the SOGI phase-locked loop; through the above steps, the control method of the bidirectional converter of the photovoltaic storage integrated machine based on the virtual synchronous generator of the present invention solves the problems of dynamic response and stability of the power system caused by the lack of rotational inertia in the distributed power supply system. By real-time monitoring of the state of charge of the energy storage unit, the virtual inertia value is dynamically adjusted to avoid overcharging or over-discharging of the energy storage unit and extend its service life. At the same time, the improved phase-locked loop technology is introduced to improve the phase detection accuracy of pre-synchronization seamless switching, ensure the accurate acquisition of frequency and phase, and enhance the stability of the system's on-grid and off-grid switching operation. In addition, an adaptive filtering technology is designed to optimize the filtering characteristics of the second-order generalized integral phase-locked loop, effectively suppress high-frequency interference and harmonic components, and improve the steady-state accuracy of the system. These innovations significantly improve the operating reliability of the photovoltaic storage integrated machine and the service life of the energy storage unit, and provide an effective solution for engineering applications.

[0140] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0141] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A control method for a photovoltaic-storage integrated bidirectional converter based on a virtual synchronous generator, characterized in that: The following steps are involved: S1. Establish the mathematical model of the traditional VSG controller; S2, designing adaptive rules; S3, performing virtual inertia control taking into account the state of charge of the energy storage unit; S4. Integrate adaptive filtering into the SOGI phase-locked loop.

2. The control method of a photovoltaic-storage integrated bidirectional converter based on a virtual synchronous generator according to claim 1, characterized in that: The step S1 of establishing a mathematical model of a conventional VSG controller includes: Active power-frequency droop characteristic equation of traditional synchronous generator: P N -P=K f (f-f n ) Among them, P N is the rated active power; P is the actual active power; f n is the rated frequency of the system; f is the actual system frequency; K f is the frequency adjustment coefficient; The mathematical model of the virtual speed regulator of VSG is as follows: Where J is the virtual moment of inertia; D is the virtual damping coefficient; Δω is the angular frequency change of VSG; P ref is the rated active power; P m is the mechanical power; n is the rated angular frequency; The reactive voltage droop characteristic equation of the synchronous generator is: U n -U=K v (Q n -Q) Where Q n is the rated reactive power value; Q is the actual reactive power value; U n is the given value of output voltage; U is the actual value of output voltage; K v is the voltage regulation coefficient; The mathematical model of VSG virtual excitation regulator is as follows: in is the integration factor; D Q is the droop coefficient; E is the excitation electromotive force; U0 is the no-load electromotive force; Q ref is the rated reactive power; Q is the actual reactive power; U ref is the given value of output voltage; U is the actual value of output voltage; The VSG mathematical model is expressed as: Where, θ is the phase angle of the excitation electromotive force; u g is the grid voltage; u * is the output reference voltage; i ref is the output reference current; L is the inductance in the circuit; T m is the mechanical torque; T e is the electromagnetic torque; The basic equation of the active frequency loop of the traditional VSG is expressed as: P ref -P m -D p (oh-oh g )=2Jdω / dt Where, ω is the angular frequency of the VSG output; ω g is the common bus angular frequency.

3. The control method of a photovoltaic-storage integrated bidirectional converter based on a virtual synchronous generator according to claim 1, characterized in that: The step S2 of performing adaptive rule design includes: The adaptive inertia rule is as follows: Where J0 is the steady-state value of virtual inertia; G is the virtual inertia judgment threshold; k is the system compensation coefficient; ω ref is the system angular frequency reference value.

4. The control method of a photovoltaic-storage integrated bidirectional converter based on a virtual synchronous generator according to claim 1, characterized in that: The step S3 of performing virtual inertia control considering the state of charge of the energy storage unit includes: The working state of the energy storage system is represented by the battery's SOC (state of charge), and is divided into different stages, as follows: Where a and b represent the SOC critical values ​​of battery over-discharge and over-charge, respectively; Simplify the active frequency equation of the traditional VSG. Among them, P in is the active power of the input converter; ΔP is the change in active power; Δω is the change in angular frequency; The specific expression of virtual inertia under the limit SOC is as follows: Among them, J f , J c are the virtual inertia of the battery in discharge and charge states respectively; k1 and k2 are the virtual inertia compensation coefficients.

5. The control method of a photovoltaic-storage integrated bidirectional converter based on a virtual synchronous generator according to claim 1, characterized in that: The step S4 of integrating the adaptive filtering into the SOGI phase-locked loop comprises: Construct the corresponding phase-locked time-domain continuous mathematical model in the VSG-based optical storage integrated machine. Among them, cosωt and sinωt are trigonometric functions output by the phase-locked loop; The weight vector matrix is, in, and They represent the positive and negative sequence components of the grid voltage in the two-phase rotating coordinate system dq coordinate system respectively; The expected response input matrix is ​​as follows, Among them, Y(t) is the system output matrix; and is the positive sequence component of the grid voltage in the αβ coordinate system; The error vector matrix is ​​as follows, ε(t)=d(t)-Y(t) The weight iteration formula is as follows: W′(t)=W(t)+με(t)X(t) W′(t) is the updated weight matrix; μ is the step size update parameter; The variable step length calculation formula is: μ(n+1)=ξμ(n)+ηε 2 (n) Among them, ξ and η are the step size factor vector and error factor vector of step size update; ε(n) is the autocorrelation estimated mean of the error signal; μ(n) is the update value of each step size cycle.

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