PMSG and energy storage coordinated adaptive virtual inertia control method based on frequency change rate

Abstract

The invention discloses a PMSG and energy storage coordinated adaptive virtual inertia control method based on a frequency change rate. At present, a cooperative control strategy of a wind power generation system and energy storage equipment is not mature, and how to reasonably distribute control rights, optimize control parameters and optimize the overall performance of the system is an urgent problem to be solved in current research. In order to solve the problem, the invention provides a self-adaptive control method which can effectively reduce the fluctuation of the system frequency and improve the stability of the system. In addition, the method is also combined with the control of the energy storage battery, so that the quick response to the system frequency and the conversion from the state change of the energy storage system to the active power reference value of the virtual synchronous machine are realized. The invention aims to provide the PMSG and energy storage coordinated adaptive virtual inertia control method based on the frequency change rate, the effect of an energy storage link can be fully played, the virtual inertia is coordinated to adaptively adjust the system frequency, the power fluctuation is inhibited, and the stability of the system is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system control, and in particular relates to a PMSG and energy storage coordinated adaptive virtual inertia control method based on frequency change rate. Background Art

[0002] With the widespread application of renewable energy in power systems, wind power generation, as an important component, has attracted increasing attention for its impact on system stability. Traditional wind power generation systems, especially wind turbines based on permanent magnet synchronous generators (PMSG), have poor adaptability to grid frequency fluctuations due to their rapid response characteristics, which may lead to system stability problems when the grid is disturbed. The present invention proposes an adaptive virtual inertia control method based on PMSG and energy storage coordination based on frequency change rate, and adopts an adaptive algorithm to dynamically adjust the virtual inertia to achieve efficient and stable power system operation. Summary of the invention

[0003] In view of the deficiencies in the prior art, an object of the present invention is to provide a method for adaptive virtual inertia control of PMSG and energy storage coordination based on frequency change rate.

[0004] The purpose of the present invention can be achieved through the following technical solutions:

[0005] In view of the shortcomings of the prior art, the present invention provides a method for adaptive virtual inertia control of PMSG and energy storage based on frequency change rate, aiming to improve the stability of the power system under different working conditions. The core of this technical solution is to dynamically adjust the control parameters of the virtual synchronous generator by adopting real-time monitoring and adaptive adjustment mechanism. It mainly includes the following steps:

[0006] Step S1: Establish a direct-drive permanent magnet synchronous wind turbine generator coordinated energy storage grid-connected model. It is mainly divided into the following steps:

[0007] 1) To establish a complete model of the grid-side inverter, it is necessary to establish the corresponding input and output three-phase voltage equations and the switching function of the inverter based on the clear grid-side inverter topology, and convert them into a mathematical model in the DQ coordinate system. Then, according to the above equations, the frequency and phase angle of the AC phase voltage of the grid-side inverter are expressed, and the expressions of active power and reactive power are obtained. At this point, the grid-side inverter model is established.

[0008] 2) To establish a complete model of the machine-side converter, it is necessary to establish the corresponding input and output three-phase voltage equations and the converter switching function based on the clear machine-side converter topology, convert them into a mathematical model in the DQ coordinate system, and then express the equivalent outer loop voltage value and inner loop current value according to the above equations, and calculate the electromagnetic torque of the permanent magnet synchronous generator. At this point, the machine-side converter model is established.

[0009] 3) To establish a complete mathematical model of pitch angle control, its control principle needs to be clarified. When the output power of the wind turbine is higher than the maximum power setting value, the given value of the pitch angle is increased. Under the action of the pitch angle controller, the pitch angle of the blade increases, and the wind turbine capture power decreases accordingly, and is eventually limited to a given range. According to the principle, the transfer function relationship between the given value and the actual value of the pitch angle is established. At this point, the pitch angle control model is established.

[0010] 4) By detecting the phase voltage V from the three terminal voltages of the converter b To obtain the phase angle, the d-axis and q-axis components of the current are calculated based on the phase angle and the three-phase current, and the active power and reactive power of the converter are respectively calculated by V b The d-axis and q-axis components are independently controlled. Finally, V b The two components are converted into three-phase AC voltage. At this point, the model of the energy storage part is completed.

[0011] 5) The models in steps 1), 2), 3) and 4) are connected according to the structure of the synchronous generator energy supply system to form the coordinated energy storage and grid-connected model of the direct-drive permanent magnet synchronous wind turbine described in claim 1.

[0012] Step S2: By analyzing the dynamic characteristics of the power angle oscillation and the rotor angular frequency response, the mathematical relationship between the inertia and damping coefficient and the system frequency change is derived, and the adaptive control algorithm is designed by integrating the energy storage conversion control strategy and the VSG droop control characteristics to achieve the VSG system to smooth power fluctuations and maintain stability under different working conditions;

[0013] Through frequency dynamic stability analysis, the inertia support characteristics and damping characteristics of the VSG system are studied. It is generally believed that when the load of the wind turbine power generation system changes, the system frequency deviation is determined by the system inertia. Therefore, by analogy with the second-order motion equation of the synchronous generator rotor, a small signal model between the VSG output electromagnetic power and the output command signal can be derived. By analyzing the dynamic response characteristics of the model, the relationship between the system dynamic performance and the virtual inertia can be known.

[0014] Step S3: According to the small signal model obtained in step S2, the energy storage conversion control strategy and the VSG droop control characteristics are integrated to realize the conversion of the energy storage SOC state change to the VSG active reference value;

[0015] Since the step response of an overdamped second-order system has no overshoot, the main performance parameter is the adjustment time. By analyzing the system expression of the underdamped state, it can be seen that the adjustment time is only related to the virtual inertia and the frequency change rate.

[0016] The converter in the energy storage module can work in both rectification mode and inverter mode. By utilizing the bidirectional flow of energy, the bus voltage stability and the power balance within the system can be maintained by controlling the charging and discharging of the battery, thereby maintaining the stability of the system.

[0017] Step S4: Design an adaptive control algorithm to further coordinate the adaptive virtual inertia and participate in the frequency regulation process of the system; the algorithm can dynamically adjust the control parameters of the VSG system according to the real-time operating status of the power system. The algorithm design is mainly divided into the following steps:

[0018] 1) According to the principle of adaptive moment of inertia, in the response and recovery process of the system caused by sudden changes, the frequency deviation is used as the judgment standard, so that the regulation can be divided into two modes within one oscillation cycle. In the frequency degradation mode, the frequency deviation Δω increases continuously to the inflection point, and dω / dt gradually decreases from the maximum value to 0. Then, in the frequency recovery mode, the frequency deviation decreases from the inflection point to 0. Based on the above ideas, the adaptive inertia expression can be designed as follows:

[0019]

[0020] The overshoot suppression factor k j , k ω , virtual inertia constant J 0 are pre-designed constants that meet the system requirements. The curve is smooth at the function switch connection points, so the coefficients must meet the constraints determined by these pre-designed constants.

[0021] 2) According to the working state of energy storage, it can be divided into fully charged state, chargeable and dischargeable state, and fully discharged state, which can be expressed by the expression:

[0022]

[0023] Where SOC represents the state of charge of the energy storage module, c and f are the charging and discharging limits of the energy storage, respectively. According to the working state of the energy storage, a coordinated control strategy for energy storage is designed to cooperate with VSG to achieve adaptive adjustment of the system. The algorithm automatically adjusts the control parameters of the VSG system by acquiring real-time data in the power system to improve control accuracy and response speed. Its expression is as follows:

[0024]

[0025] Mode switching point Δω 0 It is a pre-designed constant that meets the system requirements, and the curve is smooth at the function switch connection point. The essence of this control method is to coordinate the system virtual inertia power regulation process according to the operating status of the energy storage, improve the system regulation efficiency, and better ensure the stable operation of the system.

[0026] Compared with the prior art, the beneficial effects are:

[0027] 1. By monitoring the frequency changes of the power grid in real time and dynamically adjusting the virtual inertia of the virtual synchronous generator, it can effectively cope with various power grid conditions. This adaptive adjustment mechanism can ensure that the necessary damping force is provided quickly and accurately in the case of fluctuations in renewable energy output or sudden load changes, thereby enhancing the stability of the power grid. Compared with fixed parameter control, this method makes the power system more resilient in the face of complex changes and improves its adaptability to dynamic events in the power grid.

[0028] 2. By coordinating the participation of energy storage in the virtual inertia control of VSG, the frequency response speed and stability are optimized, so that the power grid can better absorb and utilize renewable energy. With the continuous increase in the proportion of renewable energy connected to the grid, the problem of grid frequency fluctuation is becoming increasingly serious. By applying the adaptive control method of the present invention, it is possible to quickly adjust the parameters of the virtual synchronous generator to maintain the stability of the grid frequency when the output of renewable energy is unstable. This not only helps to improve the utilization efficiency of renewable energy, but also reduces the pressure of traditional stabilization measures on the power grid, supports the widespread application of green energy, and promotes the optimization and upgrading of the energy structure. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a flow chart of a method for coordinating and adaptive virtual inertia control of PMSG and energy storage based on frequency change rate of the present invention;

[0030] Figure 2 is a topological structure diagram of a grid-side inverter of the present invention;

[0031] Figure 3 is a topological structure diagram of the machine-side converter of the present invention;

[0032] Figure 4 is a pitch angle control flow chart of the present invention;

[0033] Figure 5 It is a topological structure diagram of the energy storage module of the present invention;

[0034] Figure 6 This is a structural diagram of a coordinated energy storage and grid-connected model of a direct-drive permanent magnet synchronous wind turbine generator of the present invention;

[0035] Figure 7 It is a control flow chart of the present invention. DETAILED DESCRIPTION

[0036] The following is a detailed description of a specific implementation of a PMSG and energy storage coordinated adaptive virtual inertia control method based on frequency change rate of the present invention in conjunction with the accompanying drawings.

[0037] like Figure 1 As shown, the PMSG and energy storage coordinated adaptive virtual inertia control method based on frequency change rate of the present invention includes the following steps:

[0038] Step S1: Establish a direct-drive permanent magnet synchronous wind turbine generator coordinated energy storage grid-connected model. It is mainly divided into the following steps:

[0039] 1) Establish a complete model of the grid-side inverter, and the grid-side inverter topology diagram is shown in the figure. Figure 2 shown.

[0040] 2) Establish a complete model of the machine-side converter, and the topological structure diagram of the machine-side converter is shown in the figure. Figure 3 shown.

[0041] 3) Establish a complete mathematical model for pitch angle control and obtain the accompanying figure of pitch angle control flow chart, such as Figure 4 shown.

[0042] 4) Establish a complete mathematical model of energy storage coordinated control, and the topological structure diagram of the energy storage part is illustrated in the figure, such as Figure 5 shown.

[0043] 5) Connecting the models in steps 1), 2), 3) and 4) according to the structure of the synchronous generator energy supply system can form a direct-drive permanent magnet synchronous wind turbine coordinated energy storage grid-connected model, as shown in the accompanying drawings. Figure 6 shown.

[0044] Step S2: By analyzing the dynamic characteristics of the power angle oscillation and the rotor angular frequency response, the mathematical relationship between the inertia and damping coefficient and the system frequency variation is derived.

[0045] By analyzing the dynamic characteristics of power angle oscillation and the rotor angular frequency response, the mathematical relationship between inertia and damping coefficient and system frequency change is derived, and the adaptive control algorithm is designed by integrating the energy storage conversion control strategy and VSG droop control characteristics to achieve VSG system to smooth power fluctuations and maintain stability under different working conditions;

[0046] Through frequency dynamic stability analysis, the inertia support characteristics and damping characteristics of the VSG system are studied. It is generally believed that when the load of the wind turbine power generation system changes, the system frequency deviation is determined by the system inertia. Therefore, by analogy with the second-order motion equation of the synchronous generator rotor, a small signal model between the VSG output electromagnetic power and the output command signal can be derived. By analyzing the dynamic response characteristics of the model, the relationship between the system dynamic performance and the virtual inertia can be known.

[0047] Step S3: integrating the energy storage conversion control strategy and the VSG droop control characteristics to realize the conversion of the energy storage SOC state change to the VSG active reference value;

[0048] According to the small signal model obtained in step S2, the transfer function of VSG output power-reference power is obtained after transformation. This is a second-order system. When a step disturbance occurs in the VSG power command, the dynamic response characteristics of the VSG output are closely related to the characteristic roots of the second-order link.

[0049] Since the step response of an overdamped second-order system has no overshoot, the main performance parameter is the adjustment time. By analyzing the system expression of the underdamped state, it can be seen that the adjustment time is only related to the virtual inertia and the frequency change rate.

[0050] The converter in the energy storage module works in both rectification mode and inverter mode, utilizing the bidirectional flow of energy and controlling the charging and discharging of the battery to maintain the stability of the bus voltage and the power balance within the system, thereby maintaining the stability of the system.

[0051] Step S4: Design an adaptive control algorithm and obtain a control flow chart, as shown in the accompanying drawings. Figure 7 A virtual inertia adaptive algorithm is designed to achieve adaptive adjustment of the VSG system; the virtual inertia adaptive algorithm automatically adjusts the control parameters of the VSG system by acquiring real-time data in the power system to improve the control accuracy and response speed; the algorithm design is mainly divided into the following steps:

[0052] 1) According to the principle of adaptive moment of inertia, in the response and recovery process of the system caused by sudden changes, the frequency deviation is used as the judgment standard, so that the regulation can be divided into two modes within one oscillation cycle. In the frequency degradation mode, the frequency deviation Δω increases continuously to the inflection point, and dω / dt gradually decreases from the maximum value to 0. Then, in the frequency recovery mode, the frequency deviation decreases from the inflection point to 0. Based on the above ideas, the adaptive inertia expression can be designed as follows:

[0053]

[0054] The overshoot suppression factor k j , k ω , virtual inertia constant J 0 is a pre-designed constant that meets the system requirements, and J is the virtual moment of inertia of the system. The curve is smooth at the function switch connection point, so the coefficients must meet the constraints determined by these pre-designed constants. Mode switching point Δω 0 It is a pre-designed constant that meets the system requirements. The curve is smooth at the segmented connection points of the function. The frequency change rate is changed according to the system operation status, and then the virtual inertia is changed to limit the overshoot and ensure that the system works in a stable state.

[0055] 2) According to the working state of energy storage, it can be divided into fully charged state, chargeable and dischargeable state, and fully discharged state, which can be expressed by the expression:

[0056]

[0057] Where SOC represents the state of charge of the energy storage module, c and f are the charging and discharging limits of the energy storage, respectively. According to the working state of the energy storage, a coordinated control strategy for energy storage is designed to cooperate with VSG to achieve adaptive adjustment of the system. The algorithm automatically adjusts the control parameters of the VSG system by acquiring real-time data in the power system to improve control accuracy and response speed. Its expression is as follows:

[0058]

[0059] Mode switching point Δω 0 It is a pre-designed constant that meets the system requirements, and the curve is smooth at the function switch connection point. The essence of this control method is to coordinate the system virtual inertia power regulation process according to the operating status of the energy storage, improve the system regulation efficiency, and better ensure the stable operation of the system.

[0060] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not intended to limit the present invention. A person skilled in the art should understand that the specific implementation of the present invention can be modified or replaced by equivalents, but these modifications or changes are within the scope of protection of the pending claims.

Claims

1. A PMSG and energy storage coordinated adaptive virtual inertia control method based on frequency change rate, characterized in that: The following steps are involved: Step S1: Establishing a direct-drive permanent magnet synchronous wind turbine generator coordinated energy storage and grid connection model; Step S2: by analyzing the dynamic characteristics of the power angle oscillation and the rotor angular frequency response, the mathematical relationship between the inertia and damping coefficient and the system frequency variation is derived; Step S3: integrating the energy storage conversion control strategy and the VSG droop control characteristics to realize the conversion of the energy storage SOC state change to the VSG active reference value; Step S4: Design an adaptive algorithm to further coordinate the adaptive virtual inertia and participate in the frequency modulation process of the system.

2. The method for adaptive virtual inertia control of PMSG and energy storage based on frequency change rate according to claim 1 is characterized in that: By analyzing the dynamic characteristics of power angle oscillation and the rotor angular frequency response, the mathematical relationship between inertia and damping coefficient and system frequency variation is derived. The energy storage conversion control strategy and VSG droop control characteristics are integrated to design an adaptive control algorithm to achieve the VSG system to smooth power fluctuations and maintain stability under different operating conditions.

3. The method for controlling PMSG and energy storage coordinated adaptive virtual inertia based on frequency change rate according to claim 1 is characterized in that: In step S2, the specific method for deriving the mathematical relationship between the inertia and damping coefficient and the system frequency change by analyzing the dynamic characteristics of the power angle oscillation and the rotor angular frequency response is: In the frequency dynamic stability analysis, it is generally believed that when the load of the wind turbine power generation system changes, the system frequency deviation is determined by the system inertia. Therefore, by analogy with the second-order motion equation of the synchronous generator rotor, a small signal model between the VSG output electromagnetic power and the output command signal is derived. The dynamic response characteristics of the model are analyzed to understand the relationship between the system dynamic performance and virtual inertia.

4. The method for controlling PMSG and energy storage coordinated adaptive virtual inertia based on frequency change rate according to claim 1 is characterized in that: The converter in the energy storage module works in both rectification and inversion modes, utilizing the bidirectional flow of energy and controlling the charging and discharging of the battery to maintain the stability of the bus voltage and the power balance within the system, thereby maintaining the stability of the system.

5. The method for controlling PMSG and energy storage coordinated adaptive virtual inertia based on frequency change rate according to claim 1 is characterized in that: In step S4, a virtual inertia adaptive algorithm is designed to achieve adaptive adjustment of the VSG system; the virtual inertia adaptive algorithm automatically adjusts the control parameters of the VSG system by acquiring real-time data in the power system to improve control accuracy and response speed; That The adaptive expression is as follows: Where J is the virtual moment of inertia of the system, J0 is the inertia constant, k j , k w Represents the overshoot suppression factor of the virtual inertia; the mode switching point Δω0 is a pre-designed constant that meets the system requirements, and the curve is smooth at the segmented connection points of the function; the frequency change rate is changed according to the system operating state, and then the virtual inertia is changed to limit the overshoot and ensure that the system works in a stable state.

6. The method for controlling PMSG and energy storage coordinated adaptive virtual inertia based on frequency change rate according to claim 5 is characterized in that: In step S4, according to the working state of the energy storage, it is divided into a fully charged state, a chargeable and dischargeable state, and a fully discharged state, which can be expressed by the expression: Where SOC represents the state of charge of the energy storage module, c and f are the charging and discharging limits of the energy storage, respectively; according to the working state of the energy storage, a coordinated control strategy for energy storage is designed to cooperate with VSG to achieve adaptive adjustment of the system; the algorithm automatically adjusts the control parameters of the VSG system by acquiring real-time data in the power system to improve control accuracy and response speed; Its expression is as follows:

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