Control method of grid-connected inverter of distributed energy system based on inertia self-adaption

Abstract

The application provides a control method of an inertia adaptive based distributed energy system grid-connected inverter, and relates to the technical field of control of grid-connected inverters in distributed energy systems. The method comprises the following steps: collecting output voltage and output power of a virtual synchronous generator (VSG) in a distributed information energy system, and establishing a VSG model of a cascaded micro-grid in the distributed information energy system according to the collected data; determining a control strategy by using a distributed learning algorithm, and performing phase-locked loop control on the phase of the angular frequency of the VSG according to the control strategy, so as to obtain the load demand of the active power and the reactive power of the VSG; designing an adaptive virtual inertia control law according to the change process of the real-time angular frequency of the VSG, establishing an adaptive virtual inertia model of the VSG and designing a deviation term, so as to obtain an adaptive virtual inertia strategy. According to the method, the power grid state and the actual situation of the distributed information energy system are dynamically responded to the strategy, and the stability and response capability of the grid-connected inverter are improved.

Claims

1. A control method for a grid-connected inverter in a distributed energy system based on inertial adaptive control, characterized in that, The method includes the following steps: Step 1: Collect the output voltage and output power of the virtual synchronous generator (VSG) in the distributed information energy system, and establish a virtual synchronous generator (VSG) model of the cascaded microgrid of the distributed information energy system based on the collected data. The cascaded microgrid contains a total of... One VSG; Step A1: Collect the output voltage and output power of the virtual synchronous generator (VSG) in the distributed information energy system, and establish a power transmission characteristic model of the VSG in the cascaded microgrid based on the collected data; Step A2: Based on the power transmission characteristic model of VSG in cascaded microgrids, establish... The droop control equation; The power transmission characteristic model of the VSG in the cascaded microgrid is expressed as follows: （1）； （2）； in Indicates the first The active power output of each VSG, and , Indicates shared ownership One VSG; Indicates the first The reactive power output of each VSG; and These represent the active power and reactive power input to the VSG, respectively. and These represent the active power loss and reactive power loss within the VSG, respectively. and Indicates the first The voltage amplitude and phase angle of each VSG; and Indicates except the first Of the remaining VSGs besides the first VSG, the first The voltage amplitude and phase angle of each VSG, where ,and ; This represents the impedance modulus of a generalized load, including transmission lines. This represents the phase angle of the generalized load; The reactive power output of the VSG obtained from the power transmission characteristic model of the VSG. ,make Cascaded microgrids The droop control equation is expressed as: （3）； （4）； in Indicates the first The angular frequency of a VSG; Indicates the nominal angular frequency of the VSG; Indicates the first The voltage amplitude of each VSG; Indicates the nominal voltage amplitude of the VSG; Indicates the nominal active power of the VSG; It is a positive coefficient; This represents the time constant of the low-pass filter; Let be a complex variable, representing the complex frequency in the frequency domain; for The sign function; Step A3: Rewrite step A2 using the chain rule. The droop control equation yields a new one. The droop control equation; Step A4: Define the nominal voltage amplitude of the load voltage, including the voltage drop across the transmission lines, as [value missing]. And according to the new Calculation of inertia coefficient using droop control equations and damping coefficient ; Step A5: According to the new A virtual synchronous generator (VSG) model of a cascaded microgrid is established using droop control equations, inertia coefficients, and damping coefficients. Step 2: Based on the virtual synchronous generator (VSG) model of the cascaded microgrid, a distributed learning algorithm is used to determine the control strategy. According to the control strategy, a phase-locked loop (PLL) is designed to control the phase of the VSG's angular frequency, thereby obtaining the load demand of the VSG's active and reactive power. Step 3: Design an adaptive virtual inertial control law based on the real-time angular frequency change process of the VSG, establish an adaptive virtual inertial model of the VSG, and design the deviation term. According to the deviation item Design the nominal inertia term of VSG, and then obtain the adaptive virtual inertia strategy; The adaptive virtual inertial model is represented as follows: （16）； in For the first The adaptive virtual inertial model of the VSG, also known as the first Total inertia of each VSG; Indicates the first The nominal inertia term of each VSG; For the first Real-time angular frequency of each VSG; It is an adaptive compensation inertia term used to adjust the dynamic response speed of the frequency; under the nominal steady state of a distributed information energy system, this is the case for the first cascaded microgrid. For VSG ,in The real-time angular frequency of the transmission line current of the VSG; The second term of the adaptive virtual inertial model is 0, and the total inertia equals ; It is the positive inertia compensation coefficient; Indicates the first The angular frequency difference of each VSG, and ; Indicates time.

2. The control method for a grid-connected inverter in a distributed energy system based on inertial adaptation as described in claim 1, characterized in that, Step 2 includes: Step 2.1: Treat each VSG in the cascaded microgrid as a node, use a distributed learning algorithm to construct the action value function of the node, take the output voltage of the VSG at a certain moment as the current state of the corresponding node, take the phase angle of the VSG at that moment as the action selection of the corresponding node, iteratively update the estimated value of the action value function according to the current state and action selection of the node, and select the optimal result from all the estimated values ​​obtained by the iteration as the control strategy of the node. Step 2.2: Based on the node control strategy, the phase of the VSG's angular frequency is controlled by a phase-locked loop (PLL) to establish the load demand model of the VSG's active and reactive power, thereby obtaining the load demand of the VSG's active and reactive power.

3. The control method for grid-connected inverters in a distributed energy system based on inertial adaptation as described in claim 2, characterized in that, Step 2.2 includes: Step 2.2.1: Use the phase of the angular frequency of the VSG as the input signal of the phase-locked loop, and use the phase of the nominal angular frequency of the VSG as the reference signal of the phase-locked loop. Calculate the phase error between the input signal and the reference signal. Step 2.2.2: Calculate the control voltage in the phase-locked loop based on the phase error between the input signal and the reference signal in the phase-locked loop; Step 2.2.3: Based on the control voltage in the phase-locked loop, establish the load demand model for the active and reactive power of the VSG; Step 2.2.4: Based on the load demand model of active and reactive power of VSG and the VSG model of cascaded microgrid, obtain the load demand of active and reactive power of VSG.

4. The control method for a grid-connected inverter in a distributed energy system based on inertial adaptation as described in claim 3, characterized in that, Step 3 includes: Step 3.1: Design an adaptive virtual inertial control law based on the real-time angular frequency change process of the VSG, use a phase-locked loop to obtain the real-time angular frequency of the VSG transmission line current, and establish an adaptive virtual inertial model of the VSG. Step 3.2: Design the deviation term based on the adaptive virtual inertia model of VSG and the VSG model of cascaded microgrids. And the adaptive virtual inertial control law and adaptive virtual inertial model are used to adjust the deviation term. Solve the problem; Step 3.3: Utilize the resolved deviation term Design the nominal inertia term of VSG, and then obtain the adaptive virtual inertia strategy.

5. The control method for a grid-connected inverter in a distributed energy system based on inertial adaptation as described in claim 4, characterized in that, Step 3.2 includes: Step 3.2.1: Combine the adaptive virtual inertial model with the VSG model of the cascaded microgrid, and design the deviation term based on the combined model. ; Step 3.2.2: Based on the adaptive virtual inertial control law and the adaptive virtual inertial model, solve for the deviation term using Vieta's theorem. The solution for the deviation term is obtained. and ; Step 3.2.3: To avoid the solution of the deviation term and There exists a singularity in the deviation term. The solution is rewritten to obtain the deviation term with no differential action, and the deviation term is resolved according to step 3.2.

2. .

6. The control method for a grid-connected inverter in a distributed energy system based on inertial adaptation as described in claim 5, characterized in that, Step 3.3 includes: Step 3.3.1: When the cascaded microgrid... When the positive coefficient in the droop control equation is less than the preset maximum positive coefficient, the angular frequency of the VSG is controlled within the feasible range, and an effective expression for the deviation term without differential action is established. Step 3.3.2: When the distributed information energy system is fed by the RC load in the system, the adaptive virtual inertial control of the system reaches stability. The nominal inertial term of VSG is designed based on the validity expression of the deviation term without differential action. Step 3.3.3: Combine the expression of the deviation term without differential action with the VSG model of the cascaded microgrid, and obtain the adaptive virtual inertia strategy based on the nominal inertia term of VSG.

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