A coordinated control method of multi-self-synchronous voltage sources based on ridge estimation damping inertia

Abstract

The application discloses a kind of based on the damping inertia of ridge estimation multi-self-synchronous voltage source coordinated control method, comprising: establishing system transfer function and self-synchronous voltage source model;Self-synchronous voltage source model is linearized and differentiated, and virtual damping and virtual inertia are decoupled by ridge estimation LASSO regression, optimal virtual damping coefficient and virtual moment of inertia under system dynamic and static are solved;According to dynamic performance and optimal second-order system criterion, introduce transient compensation to carry out optimal control to system damping inertia;According to the reasonable matching principle of virtual damping inertia, the virtual inertia and virtual damping coefficient of each self-synchronous voltage source are set, the transition time of multi-machine system is kept consistent, and dynamic and static coordinated control of multi-self-synchronous voltage source is carried out.The application considers the reasonable matching of virtual inertia and virtual damping coefficient under multi-machine parallel system, effectively improves system dynamic characteristics and static stability ability, so that new energy station source side self-synchronous voltage source power quality is higher, and system adaptive performance is better.

Description

Technical Field

[0001] This invention belongs to the field of multi-self-synchronous voltage source control technology for new energy power plants, and relates to a coordinated control strategy for multi-self-synchronous voltage sources, specifically a coordinated control method for multi-self-synchronous voltage sources based on ridge estimation damping inertia. Background Technology

[0002] With the increasing proportion of new energy sources, new energy power plants are characterized by continuously increasing installed capacity and concentrated deployment patterns. The "dual high" of high proportion of new energy and high proportion of power electronic equipment will become the main features of future power systems. Due to the volatility and randomness of new energy resources, as well as the low immunity and weak support of new energy power generation, the transformation of new energy from auxiliary energy to primary power source will bring enormous challenges to my country's energy system. The safe operation of the power system and the efficient consumption of new energy will also face significant challenges, inevitably requiring revolutionary changes in new energy grid connection technology and core equipment. Since self-synchronizing voltage sources possess inertia and reactive power support capabilities, the large-scale application of self-synchronizing voltage source technology is an inevitable trend.

[0003] Patent document CN113346522A discloses a technical solution for "an adaptive control method and system for a self-synchronizing voltage source based on rotational inertia." This method determines an adaptive control strategy through an adaptive control function based on rotational inertia to perform adaptive control of the self-synchronizing voltage source. However, this method neglects the influence of the damping coefficient on system stability. An excessively large virtual damping coefficient will prolong the time required for the system to recover stability and cannot solve the problem of mismatch between rotational inertia and damping, leading to a decrease in the stability and reliability of the self-synchronizing voltage source connected to the grid.

[0004] In summary, the selection of inertia coefficient and damping coefficient has a significant impact on the damping inertia characteristics of self-synchronizing voltage sources. Existing self-synchronizing voltage source technologies struggle to simultaneously achieve optimal control of virtual inertia and virtual damping coefficient under both dynamic and static characteristics, which is detrimental to improving the stability of self-synchronizing voltage source systems under complex power grid conditions. Summary of the Invention

[0005] Purpose of the invention: To overcome the shortcomings of existing self-synchronizing voltage source technologies in achieving optimal control of virtual inertia and virtual damping coefficients under both dynamic and static conditions, this invention provides a coordinated control method for multiple self-synchronizing voltage sources based on ridge estimation damping inertia. This method achieves optimal matching of inertia and damping under both dynamic and static conditions of the self-synchronizing voltage source in new energy power plants, ensuring the stability of the self-synchronizing voltage source system under complex power grid conditions.

[0006] Technical Solution: To achieve the above objectives, this invention provides a coordinated control method for multiple self-synchronizing voltage sources based on ridge estimation damped inertia, comprising the following steps:

[0007] S1: Based on the inherent characteristics and system mechanism of the multiple self-synchronizing voltage sources on the source side of the new energy power station, establish the system transfer function and the self-synchronizing voltage source model;

[0008] S2: Linearize and differentiate the established self-synchronizing voltage source model, decouple the virtual damping and virtual inertia through ridge estimation and LASSO regression, and solve for the optimal virtual damping coefficient and virtual rotational inertia of the system under dynamic and static conditions;

[0009] S3: Based on dynamic performance and optimal second-order system criteria, transient compensation is introduced to achieve optimal control of the system's damped inertia;

[0010] S4: Set the virtual inertia and virtual damping coefficient of each synchronous voltage source according to the reasonable matching principle of virtual damping inertia, keep the transition time of the multi-machine system consistent, and perform dynamic and static coordination control of multiple self-synchronous voltage sources.

[0011] Furthermore, the process of establishing the system transfer function and the self-synchronizing voltage source model in step S1 is as follows:

[0012] Based on the inherent characteristics of the self-synchronizing voltage source, virtual inertia and virtual damping coefficient are introduced. The second-order rotor motion equation of the self-synchronizing voltage source is determined by the second-order model of the synchronous motor as follows:

[0013]

[0014] The system angular frequency is , It is the system power angle; and These are the mechanical torque and electromagnetic torque of the generator, respectively; J is the moment of inertia; and D is the damping coefficient. This is the actual angular frequency of the system; Electromagnetic power; and These represent the output voltage and current of the self-synchronizing voltage source in three-phase coordinates, respectively. For virtual mechanical power, For simplified synchronous power, Zself is the sum of system impedances; where

[0015]

[0016] The closed-loop transfer function, settling time within the system error band, and maximum overshoot are obtained from the mechanism of the self-synchronizing voltage source system. The closed-loop transfer function of the system is:

[0017]

[0018] The maximum overshoot σ% and settling time corresponding to the second-order model for:

[0019]

[0020] Maximum overshoot σ% and settling time Its main function is to serve as an evaluation parameter for system regulation.

[0021] Furthermore, the specific process of step S2 is as follows:

[0022] The linearized transfer function of the self-synchronized voltage source is shown below, where c is the linear variation coefficient:

[0023]

[0024]

[0025] The differential transformation of the transfer function of the self-synchronized voltage source is shown below:

[0026]

[0027] Based on the linearized and differential self-synchronizing voltage source control model described above, the following matrix transformation is obtained: ;

[0028]

[0029]

[0030] The least squares solution corresponding to the difference equation matrix of the system model is: The ridge regression estimate, after Tikhonov regularization, yields: ,in Here, E represents the ridge parameter, and E is the system's corresponding order identity matrix. Ridge estimation regression can better reduce singular values ​​and correlations between variables in the model. The ridge parameter value in ridge estimation can be determined using the curve method, specifically:

[0031]

[0032] in For regression estimates, To represent the 2-norm, with x-axis Using the ordinate as the coordinate, a series of points are obtained in the plane coordinate system. The point with the maximum curvature on the fitted curve for each data point corresponds to the ridge parameter value.

[0033] In this invention, , By fitting a curve to discrete points, the curvature of the curve can effectively reflect the degree of influence of the singular values ​​of variables on the regression estimation. Since virtual damping and virtual inertia have a certain coupling relationship, using the curve method to make ridge estimation regression can maximize the improvement of regression accuracy.

[0034] By using the LASSO regression model, the penalty term in the ridge estimation is changed from the L2 norm to the L1 norm, reducing the regression coefficients of low-correlation variables to 0, thereby decoupling the virtual damping and virtual inertia characteristics under the L2 norm, reducing the complexity of the model, reducing the mean square error, and improving the regression accuracy.

[0035]

[0036] In this invention, the norm is a function that measures the degree of change of a vector. In the vector space, it is used to characterize the degree or magnitude of non-zero growth. The L1 norm refers to the sum of the absolute values ​​of each element in the vector and can be used for feature selection due to feature sparsity. The L2 norm is the square root of the sum of the squares of each element in the vector. The L2 norm can prevent overfitting and improve the generalization ability of the model. Ridge regression is a modified least squares estimation method. By reducing the unbiased estimation of least squares, it obtains regression coefficients by losing some low correlation information. It can handle multicollinearity and overfitting problems and is suitable for data fitting of outliers. However, some coefficients in ridge regression do not shrink to 0, resulting in a decrease in the overall coefficients and a reduction in the interpretability of the ridge regression model. Therefore, the LASSO regression model is used to change the penalty term from the L2 norm to the L1 norm, reducing the low correlation regression coefficients to 0 to eliminate variables and improve regression accuracy.

[0037] Represents the sum of squared errors. This represents a penalty term, which is an absolute value and not differentiable at zero. Using the coordinate descent method, we set the derivative of each component to 0 to obtain the objective function that achieves a global minimum. ;

[0038]

[0039] by x-axis Given a series of points on the ordinate in a planar coordinate system, a curve is obtained by fitting the data points. The required ridge parameter is the k value corresponding to the point of maximum curvature on the curve. Based on the above, the virtual damping coefficient and virtual moment of inertia can be solved by ridge estimation.

[0040]

[0041] Based on the above, the self-synchronizing voltage source control model and its corresponding linear time-domain transfer function established in this invention according to the inherent characteristics of the self-synchronizing voltage source need to undergo linearization and differential preprocessing in step S2 in order to achieve the application conditions of ridge estimation LASSO regression. Therefore, the establishment of the self-synchronizing voltage source control model and the linearization and differential preprocessing are the foundation for realizing ridge estimation LASSO regression and are related to each other.

[0042] Furthermore, the specific process of step S3 is as follows:

[0043] Based on dynamic performance and the optimal second-order system criterion, transient compensation is introduced to improve the system's inertia damping characteristics, and the virtual rotational inertia during stable operation of the self-synchronizing voltage source is... and virtual damping coefficient All systems are tuned using the "optimal second-order system" method, enabling the system to achieve a faster response speed and smaller overshoot; because,

[0044]

[0045] This is the coordination control coefficient for rotational inertia and damping coefficient. These represent the virtual moment of inertia and virtual damping coefficient during stable operation, respectively. Given the speed deviation threshold and frequency deviation threshold, the range of values ​​for the virtual moment of inertia and virtual damping coefficient is as follows:

[0046]

[0047] In the inertial damping control stage, a differential term is introduced into the forward channel, and the input signal is compensated for through transient response. After correction, the corrected transfer function is: , For the transition frequency, Here are the differential term coefficients; the steady-state output active power after the differential term compensation system's inertia damping characteristics is: , This is the system's active power command.

[0048] It should be noted that when the output power of the self-synchronizing voltage source jumps, a differential correction stage is introduced for transient compensation. This avoids the regulation lag and power oscillation caused by changes in virtual moment of inertia and virtual damping coefficient in the amplitude-frequency characteristics, improves the transient gain of the system, and reduces the impact of the two parameters on the steady-state error.

[0049] Furthermore, the specific process of step S4 is as follows:

[0050] To eliminate the difference in transition time between multiple self-synchronizing voltage sources and maintain the dynamic and coordinated operation of the system under parallel operation, due to: When the self-synchronizing voltage source is disturbed, the operating point changes from point S to point D:

[0051]

[0052] The transition time of multiple self-synchronizing voltage sources

[0053] To eliminate the difference in transition time between machines when multiple self-synchronizing voltage sources are operating in parallel, the following must be met:

[0054]

[0055] When the self-synchronizing voltage sources have the same capacity, they are set with equal virtual inertia and damping coefficients. When the self-synchronizing voltage sources have different capacities, the virtual inertia is set inversely proportional to the ratio of the active power coefficient to the rated capacity, and the damping coefficient is set in direct proportion to the active power capacity of the self-synchronizing voltage source. This allows multiple self-synchronizing voltage sources to participate in the distribution and regulation of disturbance power during the process from the occurrence to the end of the disturbance, enabling the system to reach a new steady state after the disturbance, and achieving consistency in the transition time of multiple self-synchronizing voltage sources and coordinated control under dynamic and static conditions.

[0056] This invention achieves optimal matching of inertia and damping under both dynamic and static conditions of the self-synchronizing voltage source in new energy power plants. The specific implementation principle is as follows: using ridge estimation and LASSO regression, when the output power of the self-synchronizing voltage source jumps, the coupling between rotational inertia and damping coefficient is fully considered to identify the virtual inertia and damping control parameters of the system. Due to the reduction of ill-conditioned effects of the model, the regression accuracy and the identification of inertia and damping parameters are better, thereby improving the synchronization regulation capability. Therefore, it is possible to achieve optimal matching of inertia and damping under both dynamic and static conditions of the self-synchronizing voltage source in new energy power plants.

[0057] Beneficial Effects: This invention employs a multi-self-synchronized voltage source coordinated control method using ridge estimation of damped inertia. Compared to traditional self-synchronized voltage source adaptive control methods, it not only better reduces system singular values ​​and inter-variable correlations through ridge estimation regression, but also, through the LASSO regression model, transforms the ridge estimation penalty term from the L2 norm to the L1 norm, reducing the regression coefficient between virtual inertia and damping coefficient to 0. This fully considers the coupling characteristics of virtual inertia and virtual damping coefficient under the L2 norm, reducing mean square error and improving regression accuracy. Furthermore, it facilitates stable output of the self-synchronized voltage source and reduces frequency offset. In multi-machine parallel systems, the reasonable matching of virtual inertia and virtual damping coefficients is considered, effectively improving the system's dynamic characteristics and static stability, resulting in higher power quality from the source-side self-synchronized voltage source in new energy power plants and better system adaptability. Attached Figure Description

[0058] Figure 1 This is a system flowchart of the present invention;

[0059] Figure 2 This is a block diagram of the self-synchronizing voltage source control for a new energy power station;

[0060] Figure 3 This is a flowchart of the LASSO regression estimation process.

[0061] Figure 4 This is the equivalent diagram of a parallel system of multiple self-synchronizing voltage sources. Detailed Implementation

[0062] The present invention will be further illustrated below with reference to the accompanying drawings and specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. After reading this invention, any modifications of the invention in various equivalent forms by those skilled in the art will fall within the scope defined by the appended claims.

[0063] This invention provides a coordinated control method for multiple self-synchronizing voltage sources based on ridge estimation damped inertia, such as... Figure 1 As shown, it includes the following steps:

[0064] S1: Based on the inherent characteristics and system mechanism of the multiple self-synchronizing voltage sources on the source side of the new energy power station, establish the system transfer function and the self-synchronizing voltage source model;

[0065] S2: Linearize and differentiate the established self-synchronizing voltage source model, decouple the virtual damping and virtual inertia through ridge estimation and LASSO regression, and solve for the optimal virtual damping coefficient and virtual rotational inertia of the system under dynamic and static conditions;

[0066] S3: Based on dynamic performance and optimal second-order system criteria, transient compensation is introduced to achieve optimal control of the system's damped inertia;

[0067] S4: Set the virtual inertia and virtual damping coefficient of each synchronous voltage source according to the reasonable matching principle of virtual damping inertia, keep the transition time of the multi-machine system consistent, and perform dynamic and static coordination control of multiple self-synchronous voltage sources.

[0068] Based on the above scheme, this embodiment applies the above method to a specific case, and the control process is as follows:

[0069] Step S1: Based on the inherent characteristics of the self-synchronizing voltage source, and from the second-order model of the synchronous motor, we know that the generator has 1 pole pair. Assuming the electrical angular velocity is equal to the mechanical angular velocity, the second-order rotor motion equation of the self-synchronizing voltage source is:

[0070]

[0071] The system angular frequency is , It is the system power angle; and These are the mechanical torque and electromagnetic torque of the generator, respectively; J is the moment of inertia; and D is the damping coefficient. This is the actual angular frequency of the system;

[0072]

[0073]

[0074] Electromagnetic power; and These represent the output voltage and current of the self-synchronizing voltage source in three-phase coordinates, respectively. For virtual mechanical power, For simplified synchronous power, Zself is the sum of system impedances;

[0075] like Figure 2 As shown, the structure of the self-synchronizing voltage source system of the new energy power station can be seen...

[0076] The open-loop transfer function is:

[0077]

[0078] The closed-loop transfer function is:

[0079]

[0080] The natural oscillating angular frequency corresponding to the second-order model Damping ratio :

[0081]

[0082] 0< <1, error band ±5% (8)

[0083] The maximum overshoot σ% and settling time corresponding to the second-order model :

[0084]

[0085]

[0086] Maximum overshoot σ% and settling time Its main function is to serve as an evaluation parameter for system regulation;

[0087] Inertial time constant :

[0088]

[0089] Given the rated capacity of the self-synchronizing voltage source, and with both active and reactive power specified, the dynamic performance of the second-order model corresponding to the active power loop is determined by the moment of inertia J and the damping coefficient D.

[0090] Step S2: As Figure 2As shown, based on the established self-synchronizing voltage source control model and the control block diagram of the self-synchronizing voltage source, the closed-loop transfer function and the dynamic performance of the corresponding second-order model are established. The virtual damping coefficient and virtual moment of inertia of the self-synchronizing voltage source are introduced. The closed-loop transfer function is determined by tuning the active power, reactive power, settling time within the system error band, and maximum overshoot.

[0091] From the transfer function and Laplace transform, the step response of the system is:

[0092]

[0093] The damped oscillation angular frequency is:

[0094] From step S1, we can know

[0095]

[0096]

[0097] like Figure 3 As shown, the virtual damping coefficient D and virtual moment of inertia J of the self-synchronous voltage source are analyzed by using the ridge estimation LASSO regression model. First, the closed-loop transfer function is linearized and differentiated. Then, the correlation between variables is reduced by matrix transformation to determine the ridge parameters. Then, the penalty term of the ridge estimation is changed from L2 norm to L1 norm by LASSO regression, reducing the regression coefficient of low correlation variables to 0. This decouples the virtual damping and virtual inertia characteristics under L2 norm, reduces the complexity of the model, reduces the mean square error, and improves the regression accuracy.

[0098] Linearization of the closed-loop transfer function:

[0099]

[0100] Where: c is the linear variation coefficient;

[0101]

[0102]

[0103] The difference equation for the transfer function can be obtained from the above:

[0104]

[0105] The difference in the transfer function is as follows:

[0106]

[0107] Preferably, the linearized and differential self-synchronizing voltage source control model is obtained through matrix transformation:

[0108]

[0109]

[0110] The least squares solution corresponding to the difference equation matrix of the system model is:

[0111]

[0112] Ridge regression, after Tikhonov regularization, yields: ,in Here, E represents the ridge parameter, and E is the system's corresponding order identity matrix. Ridge estimation regression can better reduce outliers and correlations between variables in the data. The key to ridge estimation is determining the ridge parameter. Here, the curve method is used to determine suitable ridge parameter values, specifically:

[0113]

[0114] in, For regression estimates, To represent the 2-norm, with x-axis Using the ordinate as the coordinate, a series of points are obtained in the plane coordinate system. The point with the maximum curvature on the fitted curve for each data point corresponds to the ridge parameter value.

[0115] Preferably, by using the LASSO regression model, the ridge estimation penalty term is changed from the L2 norm to the L1 norm, reducing the regression coefficients of low-correlation variables to 0, thereby decoupling the virtual damping and virtual inertia characteristics under the L2 norm, reducing the complexity of the model, reducing the mean square error, and improving the regression accuracy.

[0116]

[0117] Represents the sum of squared errors. This represents the penalty term. The penalty term is an absolute value and not differentiable at zero. Using the coordinate descent method, we set the derivative of each component to 0 to obtain the objective function that achieves a global minimum. .

[0118]

[0119] by x-axis Given a series of points on the ordinate in a planar coordinate system, a curve is obtained by fitting the data points. The required ridge parameter is the k value corresponding to the point of maximum curvature on the curve. Based on the above, the virtual damping coefficient and virtual moment of inertia can be solved by ridge estimation regression.

[0120]

[0121]

[0122] Step S3: Based on dynamic performance and the optimal second-order system criterion, coordinate the control of the virtual damping coefficient and the virtual system inertia. As can be seen from the above, This is the coordination control coefficient for rotational inertia and damping coefficient. These represent the virtual moment of inertia and virtual damping coefficient during stable operation of the self-synchronizing voltage source, respectively. Let the rate deviation threshold and frequency deviation threshold be, then:

[0123] when and (31)

[0124] , (32)

[0125] when and (33)

[0126] , (34)

[0127] Virtual rotational inertia during stable operation of a self-synchronizing voltage source and virtual damping coefficient Both are tuned using the "optimal second-order system" method to achieve a faster response speed and smaller overshoot. Therefore, the range of values ​​for the virtual moment of inertia and virtual damping coefficient is:

[0128]

[0129]

[0130] Step S4: Introduce a differential term into the forward channel of the inertia damping control loop, and apply transient compensation to the input signal. After correction, the corrected transfer function is:

[0131] For the transition frequency, For the differential term coefficient (37)

[0132] The steady-state output active power after the differential term compensation system's inertia damping characteristics is:

[0133] , The system active power command (38)

[0134] like Figure 4 As shown, based on the principle of reasonable matching of virtual damping and virtual inertia, the virtual inertia and virtual damping coefficients of each synchronous voltage source are set. To eliminate the difference in transition time between machines and maintain the dynamic coordinated operation of the system under parallel operation of multiple synchronous voltage sources, as can be seen from the preceding content:

[0135]

[0136] When the self-synchronizing voltage source is disturbed, the operating point changes from point S to point D: then

[0137]

[0138] The transition time of multiple self-synchronizing voltage sources

[0139] To eliminate the difference in transition time between machines when multiple self-synchronizing voltage sources are operating in parallel, then:

[0140]

[0141] When the self-synchronizing voltage sources have the same capacity, they are set with equal virtual inertia and damping coefficients. When the self-synchronizing voltage sources have different capacities, the virtual inertia is set inversely proportional to the ratio of the active power coefficient to the rated capacity, and the damping coefficient is set in direct proportion to the active power capacity of the self-synchronizing voltage source. This allows multiple self-synchronizing voltage sources to participate in the distribution and regulation of disturbance power during the process from the occurrence to the end of the disturbance, enabling the system to reach a new steady state after the disturbance, and achieving consistency in the transition time of multiple self-synchronizing voltage sources and coordinated control under dynamic and static conditions.

[0142] This invention achieves optimal matching of inertia and damping under both dynamic and static conditions of the self-synchronizing voltage source in new energy power plants. The specific implementation principle is as follows: using ridge estimation and LASSO regression, when the output power of the self-synchronizing voltage source jumps, the coupling between rotational inertia and damping coefficient is fully considered to identify the virtual inertia and damping control parameters of the system. Due to the reduction of ill-conditioned effects of the model, the regression accuracy and the identification of inertia and damping parameters are better, thereby improving the synchronization regulation capability. Therefore, it is possible to achieve optimal matching of inertia and damping under both dynamic and static conditions of the self-synchronizing voltage source in new energy power plants.

[0143] This embodiment also provides a multi-self-synchronous voltage source coordinated control system based on ridge estimation damped inertia. The system includes a network interface, a memory, and a processor. The network interface is used to receive and send signals during the process of sending and receiving information with other external network elements. The memory is used to store computer program instructions that can run on the processor. The processor is used to execute the steps of the consensus method described above when running the computer program instructions.

[0144] This embodiment also provides a computer storage medium storing a computer program that, when executed by a processor, can implement the methods described above. The computer-readable medium can be considered tangible and non-transitory. Non-limiting examples of non-transitory tangible computer-readable media include non-volatile memory circuitry (e.g., flash memory circuitry, erasable programmable read-only memory circuitry, or masked read-only memory circuitry), volatile memory circuitry (e.g., static random access memory circuitry or dynamic random access memory circuitry), magnetic storage media (e.g., analog or digital magnetic tape or hard disk drive), and optical storage media (e.g., CD, DVD, or Blu-ray disc). The computer program includes processor-executable instructions stored on at least one non-transitory tangible computer-readable medium. The computer program may also include or depend on stored data. The computer program may include a basic input / output system (BIOS) for interacting with the hardware of a dedicated computer, device drivers for interacting with specific devices of the dedicated computer, one or more operating systems, user applications, background services, background applications, etc.

[0145] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0146] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0147] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0148] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

Claims

1. A method for coordinated control of multi-autonomous voltage sources based on ridge estimation damping inertia, characterized in that, Comprise the following steps: S1: according to the new energy station source side multi self synchronization voltage source ontology characteristics and system mechanism, the system transfer function and self synchronization voltage source model are established; S2: the established self synchronization voltage source model is linearized and differentiated, the virtual damping and virtual inertia are decoupled by ridge estimation LASSO regression, the optimal virtual damping coefficient and virtual rotational inertia under system dynamic and static are solved; S3: according to the dynamic performance and optimal second-order system criterion, the transient compensation is introduced to carry out optimal control to system damping inertia; S4: according to the reasonable matching principle of virtual damping inertia, the virtual inertia and virtual damping coefficient of each self synchronization voltage source are set, the transition time of multi-machine system is kept consistent, and dynamic and static coordinated control of multi self synchronization voltage source is carried out; In order to eliminate the difference of transition time of each machine and maintain the dynamic coordinated operation of the system, since: When the self-synchronous voltage source is stable after being disturbed, the working point changes from S point to D point: ； Then the multi-self-synchronous voltage source transition time ; In order to eliminate the transition time difference of each machine under the parallel operation of multi self synchronization voltage source, the following conditions need to be met: ； J in the above equation represents the virtual moment of inertia; is the system angular frequency; is the electromagnetic power; represents the virtual mechanical power; is the simplified synchronous power coefficient.

2. The method of claim 1, wherein the method is characterized by, The establishment process of system transfer function and self synchronization voltage source model in the step S1 is as follows: According to the ontology characteristics of self synchronization voltage source, virtual inertia and virtual damping coefficient are introduced, and the second-order rotor motion equation of self synchronization voltage source is determined by the second-order model of synchronous motor as follows: ； System angular frequency is , is the system power angle; and are the mechanical and electromagnetic torque of the generator respectively; J is the virtual moment of inertia; D is the virtual damping coefficient; is the actual angular frequency of the system; is the electromagnetic power; and are the output voltage and current of the self-synchronous voltage source in three-phase coordinates respectively, is the virtual mechanical power, is the simplified synchronous power, is the sum of the system impedance; wherein ； The closed-loop transfer function of self synchronization voltage source system mechanism is obtained, and the regulation time and maximum overshoot in the system error band are obtained, and the system closed-loop transfer function is as follows: ； The maximum overshoot corresponding to the second order model and the settling time is: 。 3. The method of claim 2, wherein, The specific process of the step S2 is as follows: The linearization of self synchronization voltage source transfer function is as follows, wherein c is the linear change coefficient: ； ； The differentiation of self synchronization voltage source transfer function is as follows: ； According to the linearized, differentiated self-synchronous voltage source control model described above, a matrix transformation is performed to obtain: ; ； ； The least square solution of the system model difference equation matrix is: The ridge estimation regression is obtained by Tikhonov regularization: Wherein is the ridge parameter, E is the corresponding order unit matrix of the system, and the determination of the value of the ridge parameter in the ridge estimation adopts the curve method, specifically: ； wherein is a regression estimate value, denotes a 2-norm, to is the horizontal coordinate, is the vertical coordinate a series of points are obtained in a plane coordinate system, each data point is fitted on a curve, and a point with a maximum curvature is taken as a corresponding ridge parameter value; Through LASSO regression model, the penalty term in ridge estimation is changed from L2 norm to L1 norm, and the regression coefficient of low correlation variable is reduced to 0, so as to decouple the virtual damping and virtual inertia characteristics under L2 norm; ； represents a sum of error squares, represents a penalty term, the penalty term is an absolute value and is not derivable at zero, a coordinate descent method is adopted, each component derivative function is set to 0, and the global minimum of the target function is obtained ; ； by x-axis Given a series of points on the ordinate in a planar coordinate system, a curve is obtained by fitting the data points. The required ridge parameter is the k value corresponding to the point of maximum curvature on the curve. Based on the above, the virtual damping coefficient and virtual moment of inertia can be solved by ridge estimation. 。 4. The method of claim 2, wherein the method is characterized by, The specific process of the step S3 is as follows: According to dynamic performance and optimal second-order system criterion, the transient compensation is introduced to improve the inertia damping characteristics of the system, and the virtual inertia and virtual damping coefficient of the self-synchronous voltage source when it is in stable operation are set by the method of "optimal second-order system". and virtual damping coefficient Since, ； is a coordination control coefficient of the moment of inertia and the damping coefficient, is a virtual moment of inertia and is a virtual damping coefficient when the system is in stable operation, respectively, is a rate deviation threshold value and a frequency deviation threshold value, and the virtual moment of inertia and the virtual damping coefficient have a range of values of: ； The differential term is introduced in the forward channel of the inertia damping control link, and the input signal is corrected in the form of transient state compensation The corrected transfer function is: , is the turning frequency, is the differential term coefficient; the steady-state output active power after the inertia damping characteristic of the system compensated by the differential term is: , is the system active power instruction, When the capacity of self synchronization voltage source is equal, the equal virtual inertia and damping coefficient are set; When the capacity of self synchronization voltage source is not equal, the virtual inertia is set according to the inverse ratio of active coefficient and rated capacity ratio, and the damping coefficient is set according to the positive ratio of self synchronization voltage source active capacity, so that the multi self synchronization voltage source participates in the distribution and regulation of disturbance power during the process from disturbance to end, so that the system after disturbance reaches new steady state, and the consistency of transition time and coordinated control under dynamic and static of multi self synchronization voltage source is realized.

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