Distributed Adaptive Virtual Inertia Calculation Method for Parallel Operation of Multiple Virtual Synchronous Machines

Abstract

The distributed adaptive virtual inertia calculation method for parallel operation of multiple virtual synchronous machines disclosed by the present invention proposes a frequency oscillation inertia control strategy for multiple virtual synchronous machines. First, the output angular frequency of the virtual synchronous machine is obtained according to the active frequency control model of the virtual synchronous machine. Secondly, the output angular frequencies of neighboring virtual synchronous machines are obtained based on the distributed control architecture, and the average value of the output angular frequencies of neighboring virtual synchronous machines is thus obtained. On this basis, the additional inertia term is calculated to obtain the improved rotor motion equation. After the application of the present invention, the dynamic characteristics of the multiple virtual synchronous machine system are significantly improved, the transient response time of the system is shortened, the frequency oscillation is effectively suppressed, and the frequency stability of the system is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system frequency stability analysis, and particularly relates to a distributed adaptive virtual inertia calculation method for parallel operation of multiple virtual synchronous machines. Background Art

[0002] With the energy transformation and technological progress, power systems with a high proportion of renewable energy and a high proportion of power electronic devices have gradually become an important trend in the development of new power systems. However, as the proportion of new energy power generation systems connected to the grid through power electronic devices gradually increases, the system exhibits characteristics of low inertia and low damping, and power disturbances in the system may lead to frequency instability, threatening the safe and stable operation of the system and the power supply reliability.

[0003] In response to the above problems, some scholars have proposed the concept of virtual synchronous generators. The control of virtual synchronous generators is to describe the control characteristics of synchronous generators with mathematical equations and introduce them into the inverter control algorithm, so as to simulate the inertia and damping characteristics of synchronous generators. The introduction of virtual synchronous machine control technology provides damping and inertia support for the system, improves the transient stability of the system, but also inevitably introduces the oscillation characteristics of synchronous generators, especially frequency oscillations will occur when disturbances occur during the parallel operation of multiple virtual synchronous machines. In order to optimize the frequency characteristics of the virtual synchronous machine system, domestic and foreign scholars have designed and modeled controllers for single-machine systems, and rarely studied the frequency stability of multi-machine parallel systems. Summary of the Invention

[0004] The purpose of the present invention is to provide a distributed adaptive virtual inertia calculation method for parallel operation of multiple virtual synchronous machines, which solves the problem of frequency oscillation caused by parallel connection of multiple virtual synchronous machines in the prior art.

[0005] The technical solution adopted by the present invention is as follows: A distributed adaptive virtual inertia calculation method for parallel operation of multiple virtual synchronous machines, comprising the following steps:

[0006] Step 1: Obtain the output angular frequency ω of the i-th virtual synchronous machine according to the active frequency control model of the virtual synchronous machine i ;

[0007] Step 2: Perform distributed information interaction between the i-th virtual synchronous machine and its neighbor virtual synchronous machines to obtain the output angular frequencies ω of each neighbor virtual synchronous machine ij , and further obtain the average value of the neighbor output angular frequencies

[0008] Step 3: Calculate the additional inertia term according to the output angular frequency ω obtained in Step 1 i and the average value of the neighbor output angular frequencies obtained in Step 2 ;

[0009] Step 4: Add an additional inertia term to the rotor motion equation of the virtual synchronous machine to suppress frequency oscillation.

[0010] The features of the present invention also lie in that

[0011] In Step 1, the expression of the active frequency control model of the virtual synchronous machine is:

[0012]

[0013] In the formula, P 0i , ω0 are the mechanical power and angular frequency of the i-th virtual synchronous machine under rated conditions, P Ti , ω i are the mechanical power and the actually output angular frequency of the i-th virtual synchronous machine during actual operation, K ωi is the active droop coefficient of the i-th virtual synchronous machine; J i is the rotor inertia of the i-th virtual synchronous machine, D i is the damping coefficient of the i-th virtual synchronous machine; P ei is the electromagnetic power output by the i-th virtual synchronous machine, which is obtained by calculating the active power of the three-phase voltage and current output by the virtual synchronous machine; δ i is the phase angle of the i-th virtual synchronous machine, Δω i is the angular frequency change of the i-th virtual synchronous machine, and t is the operation time of the system.

[0014] In Step 2, the calculation formula of the average value is:

[0015]

[0016] In the formula, N i is the set definition of the neighbors of the i-th virtual synchronous machine, ω ij is the output angular frequency of the j-th virtual synchronous machine adjacent to the i-th virtual synchronous machine, n is the number of neighbor virtual synchronous machines of the i-th virtual synchronous machine, is the average value of the output angular frequencies of the neighbors of the i-th virtual synchronous machine.

[0017] In Step 3, the expression of the additional inertia term is:

[0018]

[0019] In the formula, ΔJ i is the change in the rotor inertia of the i-th virtual synchronous machine, J xi is defined as the additional inertia coefficient of the i-th virtual synchronous machine, J mi is defined as the interactive inertia coefficient of the i-th virtual synchronous machine, N iDefine the set of neighbors of the \(i\)-th virtual synchronous machine, and \(t\) is the operating time of the system.

[0020] In step 4, an additional inertia term is added to the rotor motion equation of the virtual synchronous machine, and the improved rotor motion equation is expressed as:

[0021]

[0022] In the formula, \(J\) i is the rotor inertia of the \(i\)-th virtual synchronous machine, \(\Delta J\) i is the change in rotor inertia of the \(i\)-th virtual synchronous machine, \(D\) i is the damping coefficient of the \(i\)-th virtual synchronous machine; \(P\) Ti is the mechanical power during the actual operation of the \(i\)-th virtual synchronous machine, \(P\) ei is the electromagnetic power output by the \(i\)-th virtual synchronous machine, which is obtained by calculating the active power of the three-phase voltage and current output by the virtual synchronous machine; \(\Delta\omega\) i is the angular frequency change of the \(i\)-th virtual synchronous machine, and \(t\) is the operating time of the system.

[0023] The beneficial effects of the present invention are as follows: The distributed adaptive virtual inertia calculation method for parallel operation of multiple virtual synchronous machines of the present invention proposes a frequency oscillation inertia control strategy for multiple virtual synchronous machines. By adaptively adjusting the inertia of each virtual synchronous machine, the dynamic characteristics of the multiple virtual synchronous machine system are significantly improved, the transient response time of the system is shortened, frequency oscillation is effectively suppressed, and the frequency stability of the system is improved. Brief Description of the Drawings

[0024] Figure 1 is a schematic flow chart of the distributed adaptive virtual inertia calculation method for parallel operation of multiple virtual synchronous machines of the present invention;

[0025] Figure 2 is a control block diagram of the virtual synchronous machine of the distributed adaptive virtual inertia calculation method for parallel operation of multiple virtual synchronous machines of the present invention;

[0026] Figure 3 is a system structure diagram of multiple virtual synchronous machines of the distributed adaptive virtual inertia calculation method for parallel operation of multiple virtual synchronous machines of the present invention;

[0027] Figure 4 is a communication topology diagram of the multiple virtual synchronous machine system of the distributed adaptive virtual inertia calculation method for parallel operation of multiple virtual synchronous machines of the present invention;

[0028] Figure 5 is a frequency diagram of the multiple virtual synchronous machine system without using the control strategy;

[0029] Figure 6It is the frequency diagram of a multi-virtual synchronous machine system controlled by the distributed adaptive virtual inertia calculation method control strategy for parallel operation of multi-virtual synchronous machines of the present invention. Detailed implementation manners

[0030] The present invention will be described in detail below with reference to the accompanying drawings and specific implementation manners.

[0031] Embodiment 1

[0032] The present invention provides a distributed adaptive virtual inertia calculation method for parallel operation of multi-virtual synchronous machines. As Figure 1 shown, a frequency oscillation inertia control strategy for multi-virtual synchronous machines is proposed. Since the relationship between the angular frequency ω and the frequency f is ω = 2πf, the angular frequency ω is used to reflect the characteristics of the frequency f. First, the output angular frequency of the virtual synchronous machine is obtained according to the active frequency control model of the virtual synchronous machine. Secondly, based on the distributed control architecture, the output angular frequencies of the neighboring virtual synchronous machines are obtained, and thus the average value of the output angular frequencies of the neighboring virtual synchronous machines is obtained. On this basis, the additional inertia term is calculated to obtain the improved rotor motion equation. After the application of the present invention, the dynamic characteristics of the multi-virtual synchronous machine system are significantly improved, the transient response time of the system is shortened, the frequency oscillation is effectively suppressed, and the frequency stability of the system is improved.

[0033] Embodiment 2

[0034] The present invention provides a distributed adaptive virtual inertia calculation method for parallel operation of multi-virtual synchronous machines, which is used to improve the dynamic characteristics of the system in a multi-machine parallel virtual synchronous machine system so as to effectively suppress frequency oscillation, and specifically includes the following steps:

[0035] Step 1: Obtain the output angular frequency ω of the i-th virtual synchronous machine according to the active frequency control model of the virtual synchronous machine i ; wherein, the active frequency control model of a single virtual synchronous machine can be expressed as the following formula, and then the output angular frequency ω of the i-th virtual synchronous machine is calculated through the following formula i :

[0036]

[0037] In the formula, P 0i , ω0 are the mechanical power and angular frequency of the i-th virtual synchronous machine under the rated condition, P Ti , ω i are the mechanical power and the actually output angular frequency of the i-th virtual synchronous machine during actual operation, K ωi is the active droop coefficient of the i-th virtual synchronous machine; J i is the rotor inertia of the i-th virtual synchronous machine, D i is the damping coefficient of the i-th virtual synchronous machine; P eiis the electromagnetic power output by the i-th virtual synchronous machine, which is obtained by calculating the active power of the three-phase voltage and current output by the virtual synchronous machine; δ i is the phase angle of the i-th virtual synchronous machine, Δω i is the angular frequency change of the i-th virtual synchronous machine, and t is the running time of the system.

[0038] Step 2: Perform distributed information interaction between the i-th virtual synchronous machine and its neighbor virtual synchronous machines to obtain the output angular frequencies ω ij of each neighbor virtual synchronous machine, so as to obtain the average value of the neighbor output angular frequencies which is expressed as:

[0039]

[0040] In the formula, N i is the set definition of the neighbors of the i-th virtual synchronous machine, ω ij is the output angular frequency of the j-th virtual synchronous machine adjacent to the i-th virtual synchronous machine, n is the number of neighbor virtual synchronous machines of the i-th virtual synchronous machine, is the average value of the output angular frequencies of the neighbors of the i-th virtual synchronous machine.

[0041] Step 3: Calculate the additional inertia term according to the output angular frequency ω i obtained in Step 1 and the average value of the neighbor angular frequencies obtained in Step 2. Based on the distributed communication architecture, the additional control amount for calculating inertia is controlled by the angular frequency difference between adjacent virtual synchronous machines. The additional inertia term can be expressed as:

[0042]

[0043] In the formula, ΔJ i is the change in the rotor moment of inertia of the i-th virtual synchronous machine, J xi is defined as the additional inertia coefficient of the i-th virtual synchronous machine, J mi is defined as the interactive inertia coefficient of the i-th virtual synchronous machine, N i is the set definition of the neighbors of the i-th virtual synchronous machine, ω i is the angular frequency of the i-th virtual synchronous machine, is the average value of the output angular frequencies of the neighbors of the i-th virtual synchronous machine.

[0044] Step 4: Add the additional inertia term to the rotor motion equation of the virtual synchronous machine to suppress frequency oscillation. The improved rotor motion equation obtained by adding the additional inertia term is expressed as:

[0045]

[0046] In the formula, Ji is the rotor inertia of the i-th virtual synchronous machine, ΔJ i is the change in rotor inertia of the i-th virtual synchronous machine, D i is the damping coefficient of the i-th virtual synchronous machine. P Ti is the mechanical power during the actual operation of the i-th virtual synchronous machine, P ei is the electromagnetic power output by the i-th virtual synchronous machine, obtained by calculating the active power of the three-phase voltage and current output by the virtual synchronous machine. ω i is the actual angular frequency of the i-th virtual synchronous machine, Δω i is the change in angular frequency of the i-th virtual synchronous machine.

[0047] Embodiment 3

[0048] The simulation example of the present invention uses a multi-virtual synchronous machine system under a distributed control mode for case analysis. The multi-virtual synchronous machine system established in the MATLAB / Simulink platform contains five distributed power sources, and each distributed power source uses a virtual synchronous machine control strategy, with different droop coefficients and inertias. Each virtual synchronous machine exchanges information with its neighbors to control the virtual inertia, thereby realizing the suppression of frequency oscillation of multi-machine parallel virtual synchronous machines.

[0049] The control block diagram of the virtual synchronous machine is as Figure 2 shown, and the structure diagram of the multi-virtual synchronous machine system is as Figure 3 shown, and the communication network topology diagram of the multi-virtual synchronous machine system is as Figure 4 shown. The simulation parameters of the multi-virtual synchronous machine system are shown in Table 1. The line voltage level of the microgrid is 380V, and the frequency is 50HZ. The load Load1 is initially connected, and the load Load2 is connected at 2s and removed at 4s.

[0050] The frequency change diagram without using the multi-virtual synchronous machine frequency oscillation coordinated suppression method based on distributed control is as Figure 5 shown. During the startup process of each virtual synchronous machine, during the process of connecting the load at 2s and removing the load at 4s, during the rise and fall of the frequency, there is a frequency oscillation process.

[0051] Table 1 Simulation parameters of the multi-virtual synchronous machine system

[0052]

[0053] The frequency change diagram using the multi-virtual synchronous machine frequency oscillation coordinated suppression method based on distributed control is as Figure 6 shown. During the startup process of each virtual synchronous machine, during the process of connecting the load at 2s and removing the load at 4s, the severity of the frequency oscillation accompanied during the rise and fall of the frequency is compared withFigure 5 Significantly reduced.

[0054] Analyze the operation of VSG5. When the load Load2 is connected at 2s, the maximum frequency before using the control strategy reaches 49.804Hz, and the maximum frequency after using the strategy is 49.7882Hz. The minimum frequency before using the control strategy reaches 49.7687Hz, and the minimum frequency after using the strategy is 49.779Hz. The finally reached steady-state frequency is 49.7824Hz. The deviation from the maximum frequency is reduced from 0.0216Hz to 0.0058Hz, and the deviation from the minimum frequency is reduced from 0.0137Hz to 0.0034Hz. After using the strategy during the frequency oscillation process, the deviation between the frequency extreme values and the steady-state frequency is significantly reduced.

[0055] When the load Load2 is removed at 4s, the minimum frequency before using the control strategy reaches 49.8864Hz, and the minimum frequency after using the strategy is 49.9055Hz. The maximum frequency before using the control strategy reaches 49.9304Hz, and the maximum frequency after using the strategy is 49.9166Hz. The finally reached steady-state frequency is 49.9127Hz. The deviation from the minimum frequency is reduced from 0.0263Hz to 0.0072Hz, and the deviation from the maximum frequency is reduced from 0.0177Hz to 0.0039Hz. After using the strategy during the frequency oscillation process, the deviation between the frequency extreme values and the steady-state frequency is significantly reduced.

Claims

1. A distributed adaptive virtual inertia calculation method for multiple virtual synchronous machines in parallel operation, characterized in that: The following steps are involved: Step 1: According to the virtual synchronous machine active frequency control model, the output angular frequency ω of the i-th virtual synchronous machine is obtained: i ; Step 2: Perform distributed information exchange between the ith virtual synchronous machine and the neighboring virtual synchronous machines to obtain the output angular frequency ω of each neighboring virtual synchronous machine. ij , and then find the average value of the neighbor output angular frequency Step 3: The output angular frequency ω obtained in step 1 i And the average of the neighbor output angular frequencies obtained in step 2 The additional inertia term is calculated; the expression of the additional inertia term is: Where, ΔJ i is the change in the rotor inertia of the i-th virtual synchronous machine, J xi Defined as the additional inertia coefficient of the ith virtual synchronous machine, J mi Defined as the interaction inertia coefficient of the ith virtual synchronous machine, N i is defined as the set of neighbors of the ith virtual synchronizer, and t is the running time of the system; Step 4: Add an additional inertia term to the virtual synchronous machine rotor motion equation to obtain an improved rotor motion equation to achieve frequency oscillation suppression; the improved rotor motion equation is expressed as: In the formula, J i is the rotor inertia of the i-th virtual synchronous machine, ΔJ i is the change in the rotor inertia of the ith virtual synchronous machine, D i is the damping coefficient of the ith virtual synchronous machine; P Ti is the mechanical power of the ith virtual synchronous machine during actual operation, P ei is the electromagnetic power output by the i-th virtual synchronous machine, which is obtained by calculating the active power of the three-phase voltage and current output by the virtual synchronous machine; Δω i is the change in angular frequency of the i-th virtual synchronous machine, and t is the operating time of the system.

2. The distributed adaptive virtual inertia calculation method for multiple virtual synchronous machines in parallel operation according to claim 1 is characterized in that: The expression of the active frequency control model of the virtual synchronous machine in step 1 is: Where P 0i , ω0 is the mechanical power and angular frequency of the ith virtual synchronous machine under rated working conditions, P Ti ,ω i is the mechanical power and actual output angular frequency of the ith virtual synchronous machine during actual operation, K ωi is the active power droop coefficient of the i-th virtual synchronous machine; J i is the rotor inertia of the ith virtual synchronous machine, D i is the damping coefficient of the ith virtual synchronous machine; P ei is the electromagnetic power output by the i-th virtual synchronous machine, which is obtained by calculating the active power of the three-phase voltage and current output by the virtual synchronous machine; δ i is the phase angle of the i-th virtual synchronous machine, Δω i is the change in angular frequency of the i-th virtual synchronous machine, and t is the operating time of the system.

3. The distributed adaptive virtual inertia calculation method for multiple virtual synchronous machines in parallel operation according to claim 1, characterized in that: The average value in step 2 The calculation formula is: Where N i Defined as the set of neighbors of the ith virtual synchronizer, ω ij is the output angular frequency of the jth virtual synchronous machine adjacent to the ith virtual synchronous machine, n is the number of neighboring virtual synchronous machines of the ith virtual synchronous machine, is the average value of the angular frequency output by the neighbors of the i-th virtual synchronizer.

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