Dimension reduction method, device and storage medium for model of grid-connected system of new energy

Abstract

The application relates to a dimension reduction method, device and storage medium of a model of a new energy grid-connected system, and the method comprises the following steps: S1, a linear dynamic model of a new energy unit grid-connected system is established; S2, an eigenvalue analysis method is used to analyze oscillation modes generated by the model, characteristic roots and participation factors existing in the system are calculated, and state variables mainly participated by the system are obtained; S3, the state variables are removed based on a time scale method, fast decay modes are omitted, dominant eigenvalues are reserved, a state equation of a dimension reduction system is established, and a dynamic response curve is analyzed based on the state equation of the dimension reduction system. Compared with the prior art, the application has the advantages of effectively realizing model dimension reduction of the new energy grid-connected system while guaranteeing model application effects and calculation accuracy, and reducing calculation amount.

Description

TECHNICAL FIELD

[0001] The present application relates to, in particular to a dimension reduction method, device and storage medium of a model of a new energy grid-connected system. BACKGROUND

[0002] With the gradual depletion of global fossil energy, renewable energy such as wind power and photovoltaic power has gradually become the preferred object due to its non-pollution and renewable characteristics. However, the rapid increase in new energy installed capacity leads to the need for a large number of power electronic converters to access the grid for the renewable energy contained in the source side, and the wide use of flexible DC transmission technology on the network side, resulting in a higher degree of power electronicization in each link of the power system. The development of new energy power generation and the access of power electronic equipment in the power system presents a high proportion development trend. However, in a high proportion of new energy system, dynamic interactions often occur between power electronic equipment and the network, which may even induce broadband oscillation phenomena, affecting the safe and stable operation of the power system.

[0003] To study the broadband oscillation of the system, a detailed model of the system needs to be established. Considering the fast change, multiple components and high complexity of the new energy system, if a detailed mathematical model is used for analysis, the complexity of the model will be greatly increased, resulting in an increase in the actual calculation time of the computer system and an increase in the hardware function requirements of the computer itself. Model dimension reduction refers to reducing the dimension of the high-dimensional system model established, replacing the original model with the low-dimensional model obtained, so that the dynamic response and stability characteristics of the entire system are basically the same as those of the original system, so that the calculation efficiency can be improved while the accuracy of the system is basically not lost.

[0004] The existing dimension reduction method is complex in calculation, and cannot reduce the calculation amount in the dimension reduction process while ensuring the application effect and calculation accuracy of the reduced model. SUMMARY

[0005] The purpose of the present application is to provide a dimension reduction method of a model of a new energy grid-connected system, which can effectively realize the dimension reduction of the model of the new energy grid-connected system while ensuring the application effect and calculation accuracy of the model and reducing the calculation amount.

[0006] The purpose of the present application can be achieved by the following technical solutions:

[0007] A dimension reduction method of a model of a new energy grid-connected system, the method comprising the following steps:

[0008] S1, a linear dynamic model of a new energy unit grid-connected system is established;

[0009] S2, eigenvalue analysis method is used to analyze the oscillation mode generated by the model, the characteristic roots and participation factors of the system are calculated, and the main participating state variables of the system are obtained;

[0010] S3, the main participating state variables are removed based on the time scale method, the main participating state variables corresponding to the rapidly decaying mode are omitted, the dominant eigenvalues are retained, the state equation of the reduced dimension system is established, and the dynamic response curve is analyzed based on the state equation of the reduced dimension system.

[0011] Further, the linear dynamic model of the new energy unit grid-connected system uses a double-fed wind turbine DFIG to represent a single-machine infinite grid-connected system of a double-fed wind farm, and all the state variables of the whole system in the dynamic model are:

[0012]

[0013] Where, Δi ds , Δi qs are the algebraic variables of the stator d, q axis currents of the double-fed wind turbine; Δi dr , Δi qr are the d, q axis currents of the double-fed wind turbine; Δu ds , Δu qs are the algebraic variables of the stator d, q axis voltages of the double-fed wind turbine; Δu dr , Δu qr are the algebraic variables of the rotor d, q axis voltages of the double-fed wind turbine; Δu s is the algebraic variable of the grid voltage.

[0014] Further, the linear dynamic model of the new energy unit grid-connected system uses a double-fed wind turbine DFIG to represent a single-machine infinite grid-connected system of a double-fed wind farm, and all the state variables of the whole system in the dynamic model are:

[0015]

[0016] Where, X DFIG , X DFIG-Ctrl are the state variables related to the double-fed wind turbine and its control system; X plls is the state variable of the phase-locked loop; X xc is the state variable of the transmission line part; Δω1, Δω2 are the inertia time constant and electrical speed state variables of the generator and wind turbine rotor; Δδ1, Δδ1 are the electrical angular displacement state variables of the wind turbine and generator rotor relative to the rated electrical speed synchronous rotation reference axis; Δψ ds , Δψ qs , Δψ dr , Δψ qr are the d, q axis flux linkage state variables of the double-fed wind turbine; Δudc is the DC voltage state variable of the back-to-back converter; Δi dg , Δi qg is the d, q axis current state variable of the grid side; Δi s1d , Δi s1q is the d, q axis current state variable of the double-fed wind turbine outlet; Δθ plls , ΔZ plls are the negative state variables of the output phase and the instantaneous change value of the angle of the phase-locked loop controller; Δx1-Δx8 are the state variables of the outer ring power control and the inner ring current control of the rotor side converter and the inner ring current control and the outer ring voltage control of the grid side converter; Δi xd , Δi xq is the d, q axis current state variable of the line current.

[0017] Further, the relationship between the system state variables and the algebraic variables corresponding to the dynamic model is:

[0018]

[0019] The state matrix corresponding to the dynamic model is specifically:

[0020]

[0021] Wherein, p is a differential operator; X and Y are system state variables and algebraic variables respectively; A1, B1 and C1 are state matrix, state and algebraic variable relationship matrix and algebraic and state variable relationship matrix respectively; A represents the state matrix of the whole system.

[0022] Further, the specific steps of S2 are: obtaining initial parameters of each variable by performing a power flow calculation on the new energy grid-connected system, the each variable being a state variable related to the double-fed wind turbine and its control system;

[0023] Establishing an initial state equation of the new energy grid-connected system based on the state matrix of the whole system;

[0024] Using the eigenvalue analysis method, eigenvalues and participation factors of a system eigenvalue matrix and a damping ratio corresponding to each eigenvalue are obtained based on the initial state equation, wherein one or more eigenvalues of the system eigenvalue matrix correspond to a specific oscillation mode, each eigenvalue corresponds to a main participating state variable, and the participation factor is used to measure the contribution degree of a state variable in a specific oscillation mode, the contribution degree of the kth state variable in the i th mode is the participation factor p ki , the participation factor p ki = u ki v ki , u kiis the kth column of the i th row of the left eigenvector; v ki is the kth column of the i th row of the right eigenvector;

[0025] The strong correlation state variables are screened by ranking the sizes of the participation factors, and the strong correlation state variables are state variables mainly participated by the system.

[0026] Further, the eigenvalue of the system eigenvalue matrix obtained based on the initial state equation is specifically:

[0027] Based on the initial state equation where Δx is a state variable, A is a state matrix of the whole system, B is an input matrix, and u is an input variable. A state space equation is constructed, the eigenvalue of the state space equation is solved, the system eigenvalue matrix is composed based on the eigenvalue of the state space equation, the system eigenvalue matrix is a diagonal matrix, and elements on the diagonal line are the eigenvalues of the state space equation.

[0028] The eigenvalue of the state space equation is:

[0029]

[0030] where α i and ω i are the real part and the imaginary part of the i th eigenvalue λ i respectively, and a, b and c represent the coefficients of the quadratic term, the linear term and the constant term of the eigenvalue determinant equation.

[0031] Further, the damping ratio is:

[0032]

[0033] where i represents the i th eigenvalue.

[0034] The mode of rapid decay is an oscillation mode with a damping ratio exceeding a threshold value.

[0035] Further, the state equation of the reduced dimension system is:

[0036]

[0037] where,

[0038]

[0039] Δx r is a strong correlation state variable after omitting the mode of rapid decay, that is, a variable corresponding to the dominant eigenvalue, and Δx z is a non-strong correlation state variable after screening.

[0040] Another aspect of the present application also provides a dimension reduction device of a model of a new energy grid-connected system, comprising a memory, a processor, and a program stored in the memory, and the processor implements the dimension reduction method of the model of the new energy grid-connected system when executing the program.

[0041] Another aspect of the present application also provides a storage medium having a program stored thereon, and the program implements the dimension reduction method of the model of the new energy grid-connected system when executed.

[0042] Compared with the prior art, the present application has the following beneficial effects:

[0043] The present application analyzes the oscillation mode generated by the model by using the eigenvalue analysis method, calculates the characteristic roots and participation factors existing in the system, obtains the state variables mainly participated by the system, and removes the state variables based on the time scale method, omits the rapidly decaying mode, retains the dominant eigenvalue, establishes the state equation of the dimension reduction system, realizes the dimension reduction of the model of the new energy grid-connected system, and omits the rapidly decaying mode to reduce the state variables and further reduce the calculation amount, and the application effect and calculation accuracy of the model after dimension reduction are as consistent as before dimension reduction. BRIEF DESCRIPTION OF DRAWINGS

[0044] Figure 1 The flowchart of the present application;

[0045] Figure 2 The topology structure diagram of the new energy unit grid-connected system of the present application;

[0046] Figure 3 The new energy unit shafting model structure diagram of the present application;

[0047] Figure 4 The new energy unit rotor side control structure diagram of the present application;

[0048] Figure 5 The new energy unit grid side control structure diagram of the present application;

[0049] Figure 6 The system dynamic response curve diagram before and after dimension reduction of the present application. DETAILED DESCRIPTION

[0050] The present application will be described in detail below in combination with the drawings and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present application, and gives a detailed implementation manner and specific operation process, but the protection scope of the present application is not limited to the following embodiments.

[0051] Embodiment 1:

[0052] The present application provides a dimension reduction method of a model of a new energy grid-connected system, and a flowchart of the method is asFigure 1 The method comprises the following steps:

[0053] S1, a linear dynamic model of a new energy unit grid-connected system is established;

[0054] S2, an eigenvalue analysis method is used to analyze the oscillation mode generated by the model, the characteristic roots and participation factors of the system are calculated, and the main participating state variables of the system are obtained;

[0055] S3, the main participating state variables are removed based on the time scale method, the main participating state variables corresponding to the rapidly decaying mode are omitted, the dominant eigenvalues are retained, the state equation of the reduced dimension system is established, and the dynamic response curve is analyzed based on the state equation of the reduced dimension system.

[0056] First, a linear dynamic model of a new energy unit grid-connected system is established. Taking a single machine infinite grid-connected system of a double-fed wind turbine (DFIG) as an example, the algebraic variables, i.e. non-state variables, of the whole system are:

[0057]

[0058] Among them, Δi ds , Δi qs are the algebraic variables of the d, q axis currents of the double-fed wind turbine stator; Δi dr , Δi qr are the d, q axis currents of the double-fed wind turbine rotor; Δu ds , Δu qs are the algebraic variables of the d, q axis voltages of the double-fed wind turbine stator; Δu dr , Δu qr are the algebraic variables of the d, q axis voltages of the double-fed wind turbine rotor; Δu s is the algebraic variable of the grid voltage.

[0059] All state variables of the whole system are:

[0060]

[0061] Among them, X DFIG , X DFIG-Ctrl are the state variables related to the double-fed wind turbine and its control system; X plls is the state variable of the phase-locked loop; X xc is the state variable of the transmission line part; Δω1, Δω2 are the inertia time constant and electrical speed state variables of the generator and wind turbine rotor; Δδ1, Δδ1 are the electrical angular displacement state variables of the wind turbine and generator rotor relative to the rated electrical speed synchronous rotation reference axis; Δψ ds , Δψ qs , Δψ dr, Δψ qr are d, q-axis flux linkage state variables of the doubly-fed wind turbine; Δu dc is a DC voltage state variable of the back-to-back converter; Δi dg , Δi qg are d, q-axis current state variables of the grid side; Δi s1d , Δi s1q are d, q-axis current state variables of the doubly-fed wind turbine outlet; Δθ plls , ΔZ plls are negative state variables of the output phase and the instantaneous change value of the angle of the phase-locked loop controller; Δx1-Δx8 are state variables of the outer ring power control and the inner ring current control of the rotor side converter and the inner ring current control and the outer ring voltage control of the grid side converter; Δi xd , Δi xq are d, q-axis current state variables of the line current.

[0062] As a preferred technical solution, according to the dynamic model built in the foregoing, the following can be obtained:

[0063]

[0064] wherein, p is a differential operator; X and Y are system state variables and algebraic variables respectively; A1, B1 and C1 are state matrix, state and algebraic variable relationship matrix and algebraic and state variable relationship matrix respectively.

[0065] Further, the state matrix A of the whole system can be obtained by eliminating the differential quantity Y of the operation variable of the system, and the state equation and the state matrix A of the whole system are as follows:

[0066]

[0067] The system matrix oscillation mode calculation method is as follows:

[0068] Taking the established state space matrix as an example, the following expression is obtained:

[0069] pX=AX

[0070] The eigenvalue expression of the state space equation |λI-A|=0 is as follows:

[0071]

[0072] wherein, α i and ω i are the real part and the imaginary part of the ith eigenvalue λ i respectively, and λ i =α i +jω i(i = 1, 2, …, n), and the damping ratio further embodies the speed of the oscillation mode, and the calculation formula is:

[0073]

[0074] Damping ratio ξ i The system damping is characterized as follows:

[0075] 1) ξ i ≥ 10%: strong system damping;

[0076] 2) ξ i ≤ 5%: weak system damping;

[0077] 3) ξ i ≤ 0%: the system is negative damping, and the amplitude oscillation phenomenon will occur.

[0078] As a preferred technical solution, the specific calculation steps of the oscillation mode generated by the model analyzed by the eigenvalue analysis method are as follows:

[0079] 1) The initial parameters of each variable are obtained by performing power flow calculation on the new energy grid-connected system;

[0080] 2) Establish the initial state equation of the new energy grid-connected system;

[0081] 3) Obtain the characteristic values and participation factors of the system characteristic value matrix;

[0082] 4) Compare and sort the size of the participation factor to screen the strongly correlated state variables.

[0083] As a preferred technical solution, the model dimension reduction method based on time scale is as follows:

[0084] Suppose a system can be described by the following formula after linearization:

[0085]

[0086] Where Δx is the state variable, A is the state space matrix, B is the input matrix, u is the input variable, all characteristic vectors of the system are listed, the fast decaying mode is omitted, and only the main characteristic value, i.e. the slow decaying mode, is retained, then the above formula can be written as:

[0087]

[0088] Where Δx r is the dominant characteristic value (slow decaying variable), and the dominant characteristic value is the variable remaining after the fast decaying variable is removed from the main participating state variable; Δx zThe non-dominant eigenvalue is all variables X excluding the dominant eigenvalue (ignoring the strongly correlated state variables after the fast-decaying mode).

[0089] Eliminate variables Δx z The state equation of the reduced dimension system is obtained:

[0090]

[0091] Wherein, s is a Laplace operator; A r (s) is the operation form of the reduced dimension coefficient matrix.

[0092] The present application has the following beneficial effects: in the small signal system analysis process, generally, the state variables can be divided into two categories through the change of the state variables: fast dynamic quantity and slow dynamic quantity, therefore, two or more scales can be used for asymptotic expansion solution, and the fast-decaying quantity is removed, so as to reduce the complexity of the model. The present application removes the state variables through the screening of the time scale, ignores the fast-decaying mode, retains the dominant eigenvalue, reduces the dimension of the model to a certain extent, and effectively reduces the calculation amount of the system while ensuring the application effect and calculation precision of the model.

[0093] Figure 1 is a flowchart of a dimension reduction method of a model of a new energy grid-connected system provided in the embodiments of the present application. The present application provides the method operation steps as described in the embodiments or the flowchart, but more or less operation steps can be included based on conventional or non-creative labor. The order of the steps listed in the embodiments is only one of the many new energy grid-connected system model dimension reduction method ways of the execution order of the steps, and does not represent the only execution order. The method can be realized by software and / or hardware. Please refer to Figure 1 , the method can include:

[0094] S1, a linear dynamic model of a new energy unit grid-connected system is established;

[0095] S2, the eigenvalue analysis method is used to analyze the oscillation mode generated by the model, the characteristic roots and participation factors of the system are calculated, and the main state variables participating in the system are obtained;

[0096] S3, the state variables are removed based on the time scale method, the fast-decaying mode is ignored, the dominant eigenvalue is retained, and the state equation of the reduced dimension system is established.

[0097] The topological structure of the new energy unit grid-connected system is shown in Figure 2 , according to Figure 2A linear dynamic model of a wind power unit grid-connected system is established. A doubly-fed wind turbine (DFIG) is taken as an example of a single-machine infinite-bus grid-connected system, in which the wind turbine shaft system, rotor-side converter and grid-side converter control system are shown in Fig. 1. The algebraic variables of the whole system, i.e. non-state variables, are as follows: Figures 3-5

[0098]

[0099] where Δi ds , Δi qs are the algebraic variables of the stator d, q-axis currents of the DFIG; Δi dr , Δi qr are the d, q-axis currents of the DFIG; Δu ds , Δu qs are the algebraic variables of the stator d, q-axis voltages of the DFIG; Δu dr , Δu qr are the algebraic variables of the rotor d, q-axis voltages of the DFIG; Δu s is the algebraic variable of the grid voltage.

[0100] All the state variables of the whole system are as follows:

[0101]

[0102] where X DFIG , X DFIG-Ctrl are the state variables related to the DFIG and its control system; X plls is the state variable of the phase-locked loop; X xc is the state variable of the transmission line part; Δω1, Δω2 are the inertia time constant and electrical speed state variables of the generator and wind turbine rotor; Δδ1, Δδ1 are the electrical angular displacement state variables of the wind turbine and generator rotor relative to the rated electrical speed synchronous rotating reference axis; Δψ ds , Δψ qs , Δψ dr , Δψ qr are the d, q-axis flux linkage state variables of the DFIG; Δu dc is the DC voltage state variable of the back-to-back converter; Δi dg , Δi qg are the grid-side d, q-axis current state variables; Δi s1d , Δi s1q are the DFIG outlet d, q-axis current state variables; Δθ plls , ΔZ plls ​Δθ is the negative state variable of the output phase and the instantaneous change value of the angle of the phase-locked loop controller; Δx1-Δx8 are state variables of the outer ring power control and the inner ring current control of the rotor side converter and the inner ring current control and the outer ring voltage control link of the grid side converter; Δi xd xq is the line current d, q-axis current state variable.

[0103] According to the dynamic model established in the foregoing, the following can be obtained:

[0104]

[0105] wherein p is a differential operator; X and Y are system state variables and algebraic variables respectively; A1, B1 and C1 are state matrix, state and algebraic variable relationship matrix and algebraic and state variable relationship matrix respectively.

[0106] Further, the state matrix A of the whole system can be obtained by eliminating the differential quantity Y of the operating variables of the system, and the state equation and the state matrix A of the whole system are as follows:

[0107]

[0108] According to the linear dynamic model established, the eigenvalue analysis method is used to analyze the oscillation mode generated by the model, the characteristic roots and participation factors of the system are calculated, and the main participating state variables of the system are obtained, as shown in Table 1. The variables whose participation factors exceed a certain threshold are strong correlation variables.

[0109] Table 1 Eigenvalue calculation results

[0110]

[0111]

[0112] wherein λ 14 , λ 15 represent the low frequency oscillation LFO-1; λ 16 , λ 17 , λ 18 , λ 19 represent the sub-synchronous oscillation SSO-1 and SSO-2 respectively; λ 20 , λ 21 , λ 22 , λ 23 are the super frequency oscillation modes SupSO-1 and SupSO-2; λ 24 , λ 25 are the high frequency oscillation modes HFO, and other modes have high damping and belong to fast decay modes, which can be ignored. Therefore, λ 14 , λ 15 , λ 16 ​, λ 17 , λ 18 , λ 19 , λ 20 , λ 21 , λ 22 , λ 23 , λ 24 , λ 25 is the strong correlation state variable after omitting the mode of rapid decay, and the remaining λ1~ λ7 13 is all variables after deleting the dominant eigenvalue (strong correlation state variable after omitting the mode of rapid decay) among all variables.

[0113] The damping ratio of the LFO-1 mode is 9.85%, which is higher than the threshold value of 5% required for stability; in the sub / supersynchronous frequency band, the damping ratio of the SSO-2 mode is less than the threshold value of 5%, and the damping ratio of the SupSO-1 mode is less than 0; the damping of the SSO-1 and SupSO-2 is higher and in a stable state; the damping ratio of the HFO mode is relatively small, but considering that the higher the oscillation frequency, the lower the damping ratio required for stability, it should be in a stable state.

[0114] To verify the effectiveness of the technical solution, the following examples are used to verify the dynamic response of the system before and after dimensionality reduction in this embodiment:

[0115] The selected observed oscillation modes are SSO-2 and SupSO-1 mode, and the participation factors of the oscillation modes are calculated, and the results are shown in Table 2.

[0116] Table 2: Strong correlation quantities corresponding to each oscillation mode

[0117]

[0118] The size of the parameters in Table 2 represents the correlation degree of the state variable with the oscillation mode, and the larger the participation factor, the stronger the relationship between the state variable and the oscillation mode. Through the calculation of the eigenvalue and the participation factor, the analysis result shows that the dominant state variables of the model are Δω2, Δψ qs , Δψ qr , Δx7, Δx8, Δi xd , Δi xq Therefore, the system model can be reduced to a 7th order model, and the calculation amount has been greatly reduced compared to the original matrix. The expression of the dominant state variable in the dimensionality reduction model is:

[0119] X r = [Δω2, Δψ qs , Δψ qr Δθ plls , Δx7, Δx8, Δi xd , Δi xq ]

[0120] pXr = A r (s)X r

[0121] The dynamic response curves of the dominant state variables before and after the dimension reduction of the system are shown in Fig. 2. Figure 6 As shown in Fig. 2, although the dynamic response curves of the state variables after the dimension reduction are slightly deviated from the original curves, the deviation is small, and the two curves still have high similarity, verifying that the dimension reduction method proposed in the application has high accuracy. Figure 6

[0122] In summary, the technical solution proposes a dimension reduction method for a model of a new energy grid-connected system. First, a linear dynamic model of a new energy unit grid-connected system is established. Then, an eigenvalue analysis method is used to analyze the oscillation mode generated by the model, to calculate the characteristic roots and participation factors of the system, and to obtain the main state variables participating in the system. Finally, a time scale-based method is used to eliminate the state variables, to omit the fast-decaying mode, to retain the dominant eigenvalues, and to establish the state equation of the dimension reduction system. The results show that the method proposed in the application can effectively realize the dimension reduction of the model of the new energy grid-connected system, and has application value in practical engineering.

[0123] The above detailed the preferred embodiments of the application. It should be understood that those skilled in the art can make many modifications and changes without creative labor based on the concept of the application. Therefore, any technical solution obtained by logical analysis, reasoning or limited experiment based on the existing technology according to the concept of the application shall be within the protection scope defined by the claims.

[0124] Example 2:

[0125] The embodiment provides a dimension reduction of a model of a new energy grid-connected system, which includes a memory and a processor. The memory stores a computer program. The processor implements the method of example 1 when executing the program.

[0126] The electronic device of the application includes a central processing unit (CPU) which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) or loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for device operation can also be stored. The CPU, the ROM and the RAM are connected to each other through a bus. An input / output (I / O) interface is also connected to the bus.

[0127] ​A number of components in the device are connected to the I / O interface, including: input units, such as a keyboard, a mouse, etc.; output units, such as various types of displays, speakers, etc.; storage units, such as a magnetic disk, an optical disk, etc.; and communication units, such as a network card, a modem, a wireless communication transceiver, etc. The communication units allow the device to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0128] The processing unit performs various methods and processes described above, such as the methods S1-S3. For example, in some embodiments, the methods S1-S3 can be implemented as a computer software program, which is tangibly embodied in a machine-readable medium, such as the storage unit. In some embodiments, part or all of the computer program can be loaded and / or installed on the device via the ROM and / or the communication unit. When the computer program is loaded into the RAM and executed by the CPU, one or more steps of the methods S1-S3 described above can be performed. Alternatively, in other embodiments, the CPU can be configured to perform the methods S1-S3 by any other suitable means, such as by means of firmware.

[0129] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, illustrative types of hardware logic components that can be used include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip systems (SOCs), complex programmable logic devices (CPLDs), etc.

[0130] Program code for carrying out methods of the present application can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program code, when executed by the processor or controller, causes the functions / operations specified in the flow charts and / or block diagrams to be implemented. The program code can execute entirely on a machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

[0131] For a technical improvement, it can be obvious whether the improvement is in hardware (e.g., improvement of circuit structures of diodes, transistors, switches, etc.) or in software (e.g., improvement of method flow). However, with the development of technology, many improvements of method flow today can be considered as direct improvements of hardware circuit structures. Designers almost always obtain the corresponding hardware circuit structures by programming the improved method flow into hardware circuits. Therefore, it cannot be said that an improvement of method flow cannot be implemented by hardware entity modules. For example, a programmable logic device (PLD) (e.g., a field programmable gate array (FPGA)) is an integrated circuit whose logic function is determined by the user programming the device. A designer programs a digital system "integrated" on a PLD by himself / herself, without having to ask a chip manufacturer to design and manufacture a special integrated circuit chip. Moreover, instead of manually manufacturing an integrated circuit chip, today, this programming is mostly implemented by using "logic compiler" software, which is similar to the software compiler used when developing programs, and the original code before compilation also needs to be written in a specific programming language, which is called a hardware description language (HDL), and there are many HDLs, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc., and the most commonly used are VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also be aware that only by logically programming the method flow into an integrated circuit using the above-mentioned hardware description languages can the hardware circuit implementing the logical method flow be easily obtained.

[0132] The controller can be implemented in any suitable way, for example the controller can take the form of a microprocessor or processor and a computer readable medium storing computer readable program code, such as software or firmware, executable by the (micro)processor, logic gates, switches, an application specific integrated circuit (ASIC), a programmable logic controller and an embedded microcontroller, examples of which include but are not limited to the following microcontrollers: ARC 625D, Atmel AT91 SAM, Microchip PIC18F26K20 and Silicone Labs C8051F320, the memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also know that in addition to being implemented in pure computer readable program code form, the controller can perfectly well be implemented by means of logic programmed into logic gates, switches, application specific integrated circuits, programmable logic controllers and embedded microcontrollers, etc. to perform the same functions. The controller can thus be considered as a hardware component, and the means comprised therein for performing the various functions can be considered as structures within the hardware component. Alternatively, or even, the means for performing the various functions can be considered as both a software module implementing the method and a structure within the hardware component.

[0133] The systems, apparatuses, modules or units illustrated by the above embodiments can be implemented by computer chips or entities, or products with certain functions. A typical implementation device is a computer. Specifically, the computer can be a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0134] For the sake of description, the above apparatuses are described in various units with functions respectively. Of course, the functions of the units can be implemented in one or more software and / or hardware in the implementation of the present application.

[0135] Those skilled in the art will understand that the embodiments of the present application can be provided as a method, a system or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

[0136] Embodiment 3

[0137] The application further provides a computer readable storage medium, having stored thereon a computer program, which when executed by a processor implements the method as proposed in embodiment 1.

[0138] Computer readable media includes permanent and non-permanent, removable and non-removable media, which can be implemented by any method or technology to store information. The information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device. According to the definition herein, computer readable media does not include transitory computer readable media, such as modulated data signals and carriers.

[0139] In the context of the present application, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or any suitable combination thereof. More specific examples of machine-readable storage media can include one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), fiber optics, compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0140] It should also be noted that the terms "comprising", "containing", or any other variant thereof are intended to cover non-exclusive inclusions, so that a process, method, article or apparatus that includes a list of elements not only includes those elements, but also includes other elements not explicitly listed, or inherent to such process, method, article or apparatus. Without more limitations, the element defined by the phrase "comprising a" does not exclude the presence of additional identical elements in the process, method, article or apparatus that includes the element.

[0141] Those skilled in the art will appreciate that embodiments of the present application can be devised for a method, a system, or a computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer readable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer readable program code.

[0142] The present application can be described in the general context of computer- executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The present application can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located in both local and remote computer storage media including memory storage devices.

[0143] Those skilled in the art will appreciate that embodiments of the present application can be devised for a method, a system, or a computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer readable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer readable program code.

[0144] The present application can be described in the general context of computer- executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The present application can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located in both local and remote computer storage media including memory storage devices.

[0145] Embodiments of the present application are described with reference to the attached figures. The same numbers are used throughout the drawings and description to reference like structures. The embodiments can take form in various components and arrangements of components, and in various steps and arrangements of steps. The figures are only for the purpose of illustrating preferred embodiments and are not intended to limit the present application, unless otherwise specified.

[0146] The above merely illustrates the embodiments of the present application but should not be taken as limitations. Various modifications and variations can be made to the present application based on the skilled in the art. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application should be included in the scope of claims of the present application.

Claims

1. A dimensionality reduction method for a model of a new energy grid-connected system, characterized in that, The method includes the following steps: S1. Establish a linear dynamic model of the grid-connected system for new energy generating units; S2. The oscillation mode generated by the model is analyzed by eigenvalue analysis, the characteristic roots and participation factors of the system are calculated, and the main state variables involved in the system are obtained. S3. The main participating state variables are eliminated using a time-scale-based method, omitting the main participating state variables corresponding to rapidly decaying modes, retaining the dominant eigenvalues, establishing the state equation of the dimensionality-reduced system, and analyzing the dynamic response curve based on the state equation of the dimensionality-reduced system; characterized in that the linear dynamic model of the new energy unit grid-connected system uses a single doubly-fed wind turbine (DFIG) to represent a single-unit infinite grid-connected system of a doubly-fed wind farm, and the algebraic variables, i.e., non-state variables, of the entire system in the dynamic model are: Where, Δi ds , Δi qs These are the algebraic variables of the stator d-axis and q-axis currents of the doubly-fed induction generator (DFIG); Δi dr , Δi qr Both are the d-axis and q-axis currents of the doubly-fed wind turbine rotor; Δu ds , Δu qs These are the algebraic variables of the stator d-axis and q-axis voltages of the doubly-fed induction generator (DFIG); Δu dr , Δu qr These are the algebraic variables of the d-axis and q-axis voltages of the doubly-fed induction generator rotor, respectively; Δu s The voltage is an algebraic variable of the power grid. The linear dynamic model of the new energy unit grid-connected system uses a single doubly-fed induction generator (DFIG) to represent the single-unit infinite grid-connected system of the doubly-fed induction generator wind farm. All state variables of the entire system in the dynamic model are: Among them, X DFIG X DFIG-Ctrl These are the state variables related to the doubly fed wind turbine and its control system; X plls X is the state variable of the phase-locked loop; xc Δω1 and Δω2 are the state variables of the transmission line section; Δω1 and Δω2 are the inertial time constant and electrical speed state variables of the generator and wind turbine rotors, respectively; Δδ1 and Δδ2 are the electrical angular displacement state variables of the wind turbine and generator rotors relative to the synchronous rotation reference axis at rated electrical speed, respectively; Δψ d s , Δψ qs , Δψ d r , Δψ qr For the doubly-fed wind turbine, the d-axis and q-axis flux linkage state variables are Δu. dc For the DC voltage state variable of the back-to-back converter; Δi dg , Δi qg For the d-axis and q-axis current state variables on the grid side; Δi s1d , Δi s1q Δθ represents the d-axis and q-axis current state variables at the outlet of the doubly-fed induction generator (DFIG). plls ΔZ plls Δx1 to Δx8 are the negative state variables representing the instantaneous changes in the output phase and angle of the phase-locked loop controller; Δx1 to Δx8 are the state variables of the outer loop power control and inner loop current control of the rotor-side converter, and the inner loop current control and outer loop voltage control of the grid-side converter; Δi xd , Δi xq These are the d-axis and q-axis current state variables of the line.

2. The dimensionality reduction method for a new energy grid-connected system model according to claim 1, characterized in that, The relationship between the system state variables and algebraic variables corresponding to the dynamic model is as follows: The state matrix corresponding to the dynamic model is specifically as follows: Where p is the differential operator; X and Y are the system state variables and algebraic variables, respectively; A1, B1, and C1 are the state matrix, the state-algebraic variable relation matrix, and the algebraic-state variable relation matrix, respectively; and A represents the state matrix of the entire system.

3. The dimensionality reduction method for a new energy grid-connected system model according to claim 2, characterized in that, The specific steps of S2 are as follows: the initial parameters of each variable are obtained by performing power flow calculation on the new energy grid-connected system, and the variables are the state variables related to the doubly fed wind turbine and its control system. The initial state equations of the new energy grid-connected system are established based on the state matrix of the entire system. Using eigenvalue analysis, the eigenvalues ​​and participation factors of the system eigenvalue matrix, as well as the damping ratio corresponding to each eigenvalue, are obtained based on the initial state equations. One or more eigenvalues ​​of the system eigenvalue matrix correspond to a specific oscillation mode, and each eigenvalue corresponds to a major participating state variable. The participation factor measures the contribution of a state variable in a specific oscillation mode; the contribution of the k-th state variable in the i-th mode is the participation factor p. ki The participation factor p is obtained by using the left and right eigenvectors of the state matrix A of the entire system. ki =u ki v ki u ki v is the i-th row and k-th column of the left eigenvector; ki The right eigenvector is in the k-th row and i-th column. Strongly correlated state variables are selected by ranking the participating factors according to their magnitude. These strongly correlated state variables are the main state variables involved in the system.

4. The dimensionality reduction method for a new energy grid-connected system model according to claim 3, characterized in that, The eigenvalues ​​of the system eigenvalue matrix obtained based on the initial state equation are specifically: Based on the initial state equation Where Δx is the state variable, A is the state matrix of the whole system, B is the input matrix, u is the input variable, the state space equation is constructed, the eigenvalues ​​of the state space equation are obtained by solving the state space equation, and the system eigenvalue matrix is ​​formed based on the eigenvalues ​​of the state space equation. The system eigenvalue matrix is ​​a diagonal matrix, and the elements on the diagonal are the eigenvalues ​​of the state space equation. The characteristic values ​​of the state-space equation are: Where, α i ω i The i-th eigenvalue λ i The corresponding real and imaginary parts, where a, b, and c represent the coefficients of the quadratic, linear, and constant terms of the eigenvalue determinant equation.

5. The dimensionality reduction method for a new energy grid-connected system model according to claim 4, characterized in that, The damping ratio is: Where i represents the i-th eigenvalue; The rapid decay mode is an oscillation mode in which the damping ratio exceeds a threshold.

6. The dimensionality reduction method for a new energy grid-connected system model according to claim 5, characterized in that, The state equation of the dimensionality reduction system is: in, Δx r For the strongly correlated state variables after neglecting the rapidly decaying patterns, which are the variables corresponding to the dominant eigenvalues, Δx z These are the filtered, non-strongly correlated state variables.

7. A dimensionality reduction device for a model of a new energy grid-connected system, comprising a memory, a processor, and a program stored in the memory, characterized in that, When the processor executes the program, it implements the dimensionality reduction method for the model of the new energy grid-connected system as described in any one of claims 1-6.

8. A storage medium having a program stored thereon, characterized in that, When the program is executed, it implements the dimensionality reduction method for the model of the new energy grid-connected system as described in any one of claims 1-6.

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