VSG adaptive control method

Abstract

The invention discloses a VSG adaptive control method, which comprises the following steps: establishing an inverter model based on VSG control, introducing a virtual rotational inertia J and a damping coefficient D into the inverter model, introducing an SG2-order rotor motion equation to simulate the rotational inertia and realize primary frequency modulation characteristics, and deducing a control equation; establishing a VSG active-frequency loop small signal model, and introducing a transfer function of reference power-frequency and output power reference power to determine a variation range corresponding to the virtual inertia J and the damping coefficient D; and designing a fuzzy controller to dynamically adjust the virtual inertia J and the damping coefficient D in a variation range corresponding to the virtual inertia J and the damping coefficient D so as to realize real-time adjustment of the virtual inertia J and the damping coefficient D. According to the VSG adaptive control method, parameter adjustment is more sensitive in the face of small errors, and the selection range of J and D can be fully utilized and adjusted in real time.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual synchronous machine control, and in particular to a VSG adaptive control method. Background Art

[0002] With the development of science and technology and the increase in the proportion of renewable energy in total energy utilization, more and more distributed energy is connected to the power grid through power electronic inverters. However, power electronic converters have fast response speeds, but do not have the mechanical inertia and damping characteristics of traditional synchronous generators. They cannot provide inertia and damping for the system. Too many connections will significantly reduce the inertia level of the system and weaken the anti-disturbance ability. In severe cases, it may even cause relay equipment to malfunction. Virtual synchronous generator (VSG) is created to solve the low inertia problem caused by the grid connection of such new energy. It introduces the rotor motion equation into the control algorithm of the inverter, controls the energy storage device to absorb and release energy, and simulates the mechanical energy in the rotor of the synchronous generator, so that the inverter has the anti-interference characteristics of the synchronous generator when responding to disturbances, which can effectively improve the inertia and damping level of the system.

[0003] Traditional VSG may experience inverter output frequency and power oscillation under conditions such as renewable energy output fluctuations and load switching. Virtual inertia and damping coefficient are the main parameters affecting oscillation. Unreasonable parameter settings will further aggravate the oscillation and damage the inverter in severe cases. Existing VSG control methods are generally achieved by adding fixed damping or transient damping, but this will increase the steady-state deviation, and the transient active overshoot under grid frequency disturbance is very large. Summary of the invention

[0004] In order to solve the technical problems existing in the background technology, the present invention proposes a VSG adaptive control method.

[0005] A VSG adaptive control method proposed by the present invention comprises the following steps:

[0006] S1. Establish an inverter model based on VSG control, introduce the virtual moment of inertia J and damping coefficient D into the inverter model, introduce the SG2-order rotor motion equation to simulate the moment of inertia and realize the primary frequency modulation characteristics, and derive the control equation;

[0007] S2. Establish a small signal model of the VSG active-frequency loop and introduce the transfer function of reference power-frequency and output power reference power to determine the variation range of the virtual inertia J and the damping coefficient D.

[0008] S3. Design a fuzzy controller to dynamically adjust the virtual inertia J and the damping coefficient D within the range of variation corresponding to the virtual inertia J and the damping coefficient D, so as to achieve real-time adjustment of the virtual inertia J and the damping coefficient D.

[0009] Preferably, the inverter model controlled by the VSG includes a distributed power generation unit, an energy storage system, an inverter bridge, an LC filter circuit, and a VSG control module. The VSG control module includes an active-frequency loop unit, a reactive-voltage loop unit, and a stator voltage unit; the control equation includes an active-frequency loop equation, a reactive-voltage loop equation, and a stator voltage equation; the distributed power generation unit is connected in parallel with the energy storage system and then connected to the load or the power grid via an inverter bridge and an LC filter circuit; the active-frequency loop unit and the reactive-voltage loop unit are connected in parallel and then connected in series with the stator voltage unit.

[0010] Preferably, the active power-frequency loop equation is specifically:

[0011]

[0012] Where J is the virtual inertia; D is the damping coefficient; W and W g are the operating angular frequency and the rated angular frequency respectively; W g is the grid angular frequency; P m With P e are mechanical power and output active power respectively; θ and θ0 are VSG output phase angle and rated phase angle; δ is power angle;

[0013] The output voltage stability and primary voltage regulation function are achieved through reactive power-voltage loop control. The reactive power-voltage loop equation is as follows:

[0014]

[0015] Where E is the virtual electromotive force; K is the integral coefficient; K q is the reactive power-voltage droop coefficient; U N and U are the reference value and actual voltage of the machine end voltage respectively; Q ref and Q e They are reactive power reference value and actual output reactive power respectively.

[0016] Preferably, the stator voltage equation is as follows:

[0017]

[0018] Among them, u dref is the d-axis reference value of the terminal voltage; u qref is the q-axis reference value of the terminal voltage; i od is the d-axis component of the output current; i oq is the q-axis component of the output current; R v is a virtual resistor; L v is the virtual inductor.

[0019] Preferably, the VSG active power-frequency loop small signal model specifically includes:

[0020]

[0021] Among them, δ0 is the power angle of the steady-state operating point; Δw s , Δw g (s), Δδ(s), ΔP m (s), ΔP e (s) are the small disturbances of angular frequency, grid frequency, power angle, mechanical power and output active power near the steady-state operating point; X k is the equivalent reactance; E is the virtual electromotive force, and U is the actual voltage.

[0022] Preferably, the transfer function of reference power-frequency and output power-reference power is specifically:

[0023]

[0024] Among them, ΔP m (s), ΔP e (s) are small disturbances of mechanical power and output active power near the steady-state operating point; S E is the elastic coefficient of the system; w0 is the natural frequency of the system; Δw(s) is the small disturbance of the angular velocity near the steady-state operating point.

[0025] Preferably, the natural oscillation angular frequency w of the system corresponding to the transfer function of the reference power-frequency and the output power reference power is n and the damping ratio v are:

[0026]

[0027] Among them, S E is the elastic coefficient of the system; w0 is the natural frequency of the system.

[0028] Preferably, the variation range of the virtual inertia J and the damping coefficient D is specifically:

[0029]

[0030] Among them, ΔP max , Δw max They are the maximum active power deviation and the maximum angular frequency deviation of the inverter output respectively.

[0031] Preferably, step S3 specifically includes:

[0032] The variation range of the virtual inertia J and the damping coefficient D is converted into the corresponding fuzzy rule constraints through the fuzzy controller;

[0033] Define fuzzy variables and determine input and output variables. The input variables are the system error and rate of change, and the output variables are the virtual inertia J and damping coefficient D.

[0034] Monitor input variables in real time, and continuously update fuzzy reasoning and defuzzification processes on the input variables monitored in real time through a fuzzy controller to obtain a moment of inertia adjustment value ΔJ and a damping coefficient adjustment value ΔD;

[0035] The output variable is adjusted based on the rotational inertia adjustment value ΔJ and the damping coefficient adjustment value ΔD as well as fuzzy rule constraints to achieve real-time adjustment of the virtual inertia J and the damping coefficient D.

[0036] Preferably, the distributed power generation unit is used to convert natural energy into electrical energy; the energy storage system is used to store excess energy so as to provide energy when power generation is insufficient, thereby ensuring a stable and reliable power supply;

[0037] Inverter bridge, used to convert DC power from distributed generation units and energy storage systems into AC power suitable for loads or power grids; also used to control the amplitude and frequency of the output voltage to maintain synchronization with the power grid;

[0038] LC filter circuit, used to smooth the AC waveform output by the inverter bridge and eliminate high-frequency harmonics and current fluctuations;

[0039] Active power-frequency loop unit, used to control the relationship between the active power output and frequency of the system. By adjusting the output of active power, it can ensure that the frequency of the power system remains within a stable range;

[0040] Reactive-voltage loop unit, used to adjust the reactive power output of the system to control the voltage level, and adjust the reactive power so as to maintain the grid voltage within a preset range;

[0041] The stator voltage unit is used to calculate the relationship between the stator voltage and current of the motor. The voltage and current dual closed-loop control compares the voltage and current feedback to ensure that the voltage and current output by the inverter are consistent with the preset target values ​​to generate a sinusoidal pulse width modulation (PWM) signal to achieve precise power output.

[0042] In the present invention, the proposed VSG adaptive control method establishes an inverter model based on VSG control, introduces virtual moment of inertia J and damping coefficient D into the inverter model, introduces SG2-order rotor motion equation to simulate the moment of inertia and realize the primary frequency modulation characteristics, and derives the control equation; establishes a VSG active-frequency loop small signal model, and introduces the transfer function of reference power-frequency and output power reference power to determine the variation range corresponding to the virtual inertia J and the damping coefficient D; designs a fuzzy controller to dynamically adjust the virtual inertia J and the damping coefficient D within the variation range corresponding to the virtual inertia J and the damping coefficient D, so as to realize the real-time adjustment of the virtual inertia J and the damping coefficient D. When facing a small error, the parameter adjustment is more sensitive, and the selection range of J and D can be fully utilized and adjusted in real time. At the same time, it can suppress power and frequency oscillations and accelerate dynamic recovery, reduce the impact of new energy output and load fluctuations, provide certain support for system frequency, and improve the power grid's acceptance capacity for high-proportion new energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A schematic diagram of the work flow structure of a VSG adaptive control method proposed by the present invention;

[0044] Figure 2 A VSG main circuit topology diagram of a VSG adaptive control method proposed by the present invention;

[0045] Figure 3 A reactive-voltage loop control block diagram of a VSG adaptive control method proposed by the present invention;

[0046] Figure 4 A stator voltage equation control block diagram of a VSG adaptive control method proposed by the present invention;

[0047] Figure 5 This is a diagram of an active-frequency loop small signal model of a VSG adaptive control method proposed by the present invention;

[0048] Figure 6 This is a fuzzy adaptive control structure diagram of a VSG adaptive control method proposed in the present invention. DETAILED DESCRIPTION

[0049] Reference Figure 1-6 The present invention proposes a VSG adaptive control method, comprising the following steps:

[0050] S1. Establish an inverter model based on VSG control, introduce the virtual moment of inertia J and damping coefficient D into the inverter model, introduce the SG2-order rotor motion equation to simulate the moment of inertia and realize the primary frequency modulation characteristics, and derive the control equation.

[0051] In this embodiment, the VSG main circuit is connected to the load or grid through the inverter bridge and LC filter circuit after the distributed generation unit and energy storage are connected in parallel. The VSG control part consists of three parts: active-frequency loop, reactive-voltage loop and stator voltage equation.

[0052] The SG2-order rotor motion equation is introduced into the inverter model based on VSG control to simulate the moment of inertia and realize the primary frequency modulation characteristics.

[0053] Specifically, the inverter model of VSG control includes a distributed generation unit, an energy storage system, an inverter bridge, an LC filter circuit, and a VSG control module. The VSG control module includes an active-frequency loop unit, a reactive-voltage loop unit, and a stator voltage unit; the control equation includes an active-frequency loop equation, a reactive-voltage loop equation, and a stator voltage equation; the distributed generation unit is connected in parallel with the energy storage system and then connected to the load or the grid via an inverter bridge and an LC filter circuit; the active-frequency loop unit and the reactive-voltage loop unit are connected in parallel and then connected in series with the stator voltage unit.

[0054] In this embodiment, the distributed generation unit is used to convert natural energy into electrical energy; the energy storage system is used to store excess energy so as to provide energy when power generation is insufficient, thereby ensuring a stable and reliable power supply;

[0055] Inverter bridge, used to convert DC power from distributed generation units and energy storage systems into AC power suitable for loads or power grids; also used to control the amplitude and frequency of the output voltage to maintain synchronization with the power grid;

[0056] LC filter circuit, used to smooth the AC waveform output by the inverter bridge and eliminate high-frequency harmonics and current fluctuations;

[0057] Active power-frequency loop unit, used to control the relationship between the active power output and frequency of the system. By adjusting the output of active power, it can ensure that the frequency of the power system remains within a stable range;

[0058] Reactive-voltage loop unit, used to adjust the reactive power output of the system to control the voltage level, and adjust the reactive power so as to maintain the grid voltage within a preset range;

[0059] The stator voltage unit is used to calculate the relationship between the stator voltage and current of the motor. The voltage and current dual closed-loop control compares the voltage and current feedback to ensure that the voltage and current output by the inverter are consistent with the preset target values ​​to generate a sinusoidal pulse width modulation (PWM) signal to achieve precise power output.

[0060] Specifically, the active power-frequency loop equation is:

[0061]

[0062] Where J is the virtual inertia; D is the damping coefficient; W and W g are the operating angular frequency and the rated angular frequency respectively; W g is the grid angular frequency; P m With P e are mechanical power and output active power respectively; θ and θ0 are VSG output phase angle and rated phase angle; δ is power angle;

[0063] The output voltage stability and primary voltage regulation function are achieved through reactive power-voltage loop control. The reactive power-voltage loop equation is as follows:

[0064]

[0065] Where E is the virtual electromotive force; K is the integral coefficient; K q is the reactive power-voltage droop coefficient; U N and U are the reference value and actual voltage of the machine end voltage respectively; Q ref and Q e They are reactive power reference value and actual output reactive power respectively.

[0066] Specifically, the stator voltage equation is as follows:

[0067]

[0068] Among them, u dref is the d-axis reference value of the terminal voltage; u qref is the q-axis reference value of the terminal voltage; i od is the d-axis component of the output current; i oq is the q-axis component of the output current; R v is a virtual resistor; L v is the virtual inductor.

[0069] S2. Establish a small signal model of the VSG active-frequency loop and introduce the transfer functions of reference power-frequency and output power reference power to determine the variation range of the virtual inertia J and the damping coefficient D.

[0070] Specifically, the VSG active power-frequency loop small signal model includes:

[0071]

[0072] Among them, δ0 is the power angle of the steady-state operating point; Δw s , Δw g (s), Δδ(s), ΔP m (s), ΔP e (s) are the small disturbances of angular frequency, grid frequency, power angle, mechanical power and output active power near the steady-state operating point; X kis the equivalent reactance; E is the virtual electromotive force, and U is the actual voltage.

[0073] Specifically, the transfer function of reference power-frequency and output power reference power is:

[0074]

[0075] Among them, ΔP m (s), ΔP e (s) are small disturbances of mechanical power and output active power near the steady-state operating point; S E is the elastic coefficient of the system; w0 is the natural frequency of the system; Δw(s) is the small disturbance of the angular velocity near the steady-state operating point.

[0076] In this embodiment, the natural oscillation angular frequency w of the system corresponding to the transfer function of reference power-frequency and output power reference power is n and the damping ratio v are:

[0077]

[0078] Among them, S E is the elastic coefficient of the system; w0 is the natural frequency of the system.

[0079] In this embodiment, the variation range of the virtual inertia J and the damping coefficient D is specifically:

[0080]

[0081]

[0082] Among them, ΔP max , Δw max They are the maximum active power deviation and the maximum angular frequency deviation of the inverter output respectively.

[0083] In this embodiment, as D increases, the system stability increases, the system working state changes from underdamping to overdamping, and the dynamic performance deteriorates. An excessively large J value is not conducive to system stability and dynamic recovery.

[0084] In this embodiment, the rated capacity is selected as 30 kV·A, and the value range of D is [12.16, 30.4]. Taking the overshoot and adjustment time into consideration, the damping ratio interval is set to [0.7, 1], and the value range of J is [0.03, 0.58].

[0085] S3. Design a fuzzy controller to dynamically adjust the virtual inertia J and the damping coefficient D within the range of variation corresponding to the virtual inertia J and the damping coefficient D, so as to achieve real-time adjustment of the virtual inertia J and the damping coefficient D.

[0086] In this embodiment, step S3 specifically includes:

[0087] The variation range of the virtual inertia J and the damping coefficient D is converted into the corresponding fuzzy rule constraints through the fuzzy controller;

[0088] Define fuzzy variables and determine input and output variables. The input variables are the system error and rate of change, and the output variables are the virtual inertia J and damping coefficient D.

[0089] Monitor input variables in real time, and continuously update fuzzy reasoning and defuzzification processes on the input variables monitored in real time through a fuzzy controller to obtain a moment of inertia adjustment value ΔJ and a damping coefficient adjustment value ΔD;

[0090] The output variable is adjusted based on the rotational inertia adjustment value ΔJ and the damping coefficient adjustment value ΔD as well as fuzzy rule constraints to achieve real-time adjustment of the virtual inertia J and the damping coefficient D.

[0091] In this embodiment, the fuzzy reasoning process uses a fuzzy reasoning mechanism (such as the Mamdani method) to obtain a fuzzy output. The defuzzification process converts the results obtained from the fuzzy reasoning into specific J and D values. According to the changes in the system state, the input variables are monitored in real time, and the fuzzy reasoning and defuzzification processes are continuously updated to dynamically adjust the J and D values.

[0092] In this embodiment, Δw and dw / dt are used as fuzzy inputs e and e c , after quantization factor k e With k ec After scaling, the input domain is [-6,6], and the fuzzy reasoning input is obtained, completing the fuzzification. Quantization factor k e With k ec The basic domain should be set as follows:

[0093]

[0094] Where, dw max is the maximum angular frequency change rate.

[0095] In this embodiment, the fuzzy reasoning process adopts the Mamdani algorithm; the defuzzification process adopts the centroid method to obtain the output, and the output domain is [-6, 6].

[0096] The inference output is scaled by factor k outJ With k outD After expansion and contraction, the moment of inertia adjustment value ΔJ and the damping coefficient adjustment value ΔD are obtained. Proportional factor k outJ With k outD According to the maximum adjustment value ΔJ max , ΔD max The settings are as follows:

[0097]

[0098] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A VSG adaptive control method, characterized in that: The following steps are involved: S1. Establish an inverter model based on VSG control, introduce the virtual moment of inertia J and damping coefficient D into the inverter model, introduce the SG2-order rotor motion equation to simulate the moment of inertia and realize the primary frequency modulation characteristics, and derive the control equation; S2. Establish a small signal model of the VSG active-frequency loop and introduce the transfer function of reference power-frequency and output power reference power to determine the variation range of the virtual inertia J and the damping coefficient D. S3. Design a fuzzy controller to dynamically adjust the virtual inertia J and the damping coefficient D within the range of variation corresponding to the virtual inertia J and the damping coefficient D, so as to achieve real-time adjustment of the virtual inertia J and the damping coefficient D.

2. The VSG adaptive control method according to claim 1, characterized in that: The inverter model controlled by the VSG includes a distributed power generation unit, an energy storage system, an inverter bridge, an LC filter circuit, and a VSG control module. The VSG control module includes an active-frequency loop unit, a reactive-voltage loop unit, and a stator voltage unit; the control equation includes an active-frequency loop equation, a reactive-voltage loop equation, and a stator voltage equation; the distributed power generation unit is connected in parallel with the energy storage system and then connected to the load or the power grid via the inverter bridge and the LC filter circuit; the active-frequency loop unit and the reactive-voltage loop unit are connected in parallel and then connected in series with the stator voltage unit.

3. The VSG adaptive control method according to claim 2, characterized in that: The active power-frequency loop equation is specifically: Where J is the virtual inertia; D is the damping coefficient; W and W g are the operating angular frequency and the rated angular frequency respectively; W g is the grid angular frequency; P m With P e are mechanical power and output active power respectively; θ and θ0 are VSG output phase angle and rated phase angle; δ is power angle; The output voltage stability and primary voltage regulation function are achieved through reactive power-voltage loop control. The reactive power-voltage loop equation is as follows: Where E is the virtual electromotive force; K is the integral coefficient; K q is the reactive power-voltage droop coefficient; U N and U are the reference value and actual voltage of the machine end voltage respectively; Q ref and Q e They are reactive power reference value and actual output reactive power respectively.

4. The VSG adaptive control method according to claim 2, characterized in that: The stator voltage equation is as follows: Among them, u dref is the d-axis reference value of the terminal voltage; u qref is the q-axis reference value of the terminal voltage; i od is the d-axis component of the output current; i oq is the q-axis component of the output current; R v is a virtual resistor; L v is the virtual inductor.

5. The VSG adaptive control method according to claim 1, characterized in that: The VSG active power-frequency loop small signal model specifically includes: Among them, δ0 is the power angle of the steady-state operating point; Δw s , Δw g (s), Δδ(s), ΔP m (s), ΔP e (s) are the small disturbances of angular frequency, grid frequency, power angle, mechanical power and output active power near the steady-state operating point; X k is the equivalent reactance; E is the virtual electromotive force, and U is the actual voltage.

6. The VSG adaptive control method according to claim 5, characterized in that: The transfer function of reference power-frequency and output power reference power is specifically: Among them, ΔP m (s), ΔP e (s) are small disturbances of mechanical power and output active power near the steady-state operating point; S E is the elastic coefficient of the system; w0 is the natural frequency of the system; Δw(s) is the small disturbance of the angular velocity near the steady-state operating point.

7. The VSG adaptive control method according to claim 6, characterized in that: Reference power-frequency and output power The transfer function of reference power corresponds to the natural oscillation angular frequency w of the system n and the damping ratio v are: Among them, S E is the elastic coefficient of the system; w0 is the natural frequency of the system.

8. The VSG adaptive control method according to claim 7, characterized in that: The variation range of the virtual inertia J and the damping coefficient D is specifically: Among them, ΔP max , Δw max They are the maximum active power deviation and the maximum angular frequency deviation of the inverter output respectively.

9. The VSG adaptive control method according to claim 1, characterized in that: Step S3 specifically includes: The variation range of the virtual inertia J and the damping coefficient D is converted into the corresponding fuzzy rule constraints through the fuzzy controller; Define fuzzy variables and determine input and output variables. The input variables are the system error and rate of change, and the output variables are the virtual inertia J and damping coefficient D. Monitor input variables in real time, and continuously update fuzzy reasoning and defuzzification processes on the input variables monitored in real time through a fuzzy controller to obtain a moment of inertia adjustment value ΔJ and a damping coefficient adjustment value ΔD; The output variable is adjusted based on the rotational inertia adjustment value ΔJ and the damping coefficient adjustment value ΔD as well as fuzzy rule constraints to achieve real-time adjustment of the virtual inertia J and the damping coefficient D.

10. The VSG adaptive control method according to claim 2, characterized in that: The distributed generation unit is used to convert natural energy into electrical energy; the energy storage system is used to store excess energy so as to provide energy when power generation is insufficient, thereby ensuring a stable and reliable power supply; Inverter bridge, used to convert DC power from distributed generation units and energy storage systems into AC power suitable for loads or power grids; also used to control the amplitude and frequency of the output voltage to maintain synchronization with the power grid; LC filter circuit, used to smooth the AC waveform output by the inverter bridge and eliminate high-frequency harmonics and current fluctuations; Active power-frequency loop unit, used to control the relationship between the active power output and frequency of the system. By adjusting the output of active power, it can ensure that the frequency of the power system remains within a stable range; Reactive-voltage loop unit, used to adjust the reactive power output of the system to control the voltage level, and adjust the reactive power so as to maintain the grid voltage within a preset range; The stator voltage unit is used to calculate the relationship between the stator voltage and current of the motor. The voltage and current dual closed-loop control compares the voltage and current feedback to ensure that the voltage and current output by the inverter are consistent with the preset target values ​​to generate a sinusoidal pulse width modulation (PWM) signal to achieve precise power output.

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