An oscillation stability analytical method and device, electronic equipment and storage medium

Abstract

The application discloses an oscillation stability analytical method and device, electronic equipment and storage medium. The method and device are applied to the electronic equipment, specifically, impedance values of elements in a new energy power system are calculated at a plurality of predetermined frequency sampling points, and a system aggregate impedance determinant and impedance sampling values from an observation node are calculated based on all the impedance values; on the basis of determining an upper limit and a lower limit, an s-domain rational function with a known analytical expression is used to perform approximate fitting on the system aggregate impedance determinant and the impedance sampling values by using a VF algorithm, and AIC criterion indexes at the upper limit and the lower limit are calculated respectively; a minimum point of the AIC criterion indexes is found by using a dichotomy method, and the minimum point is taken as an estimated order; and the s-domain rational function is calculated by using the VF algorithm under the estimated order, so that a zero point set of the s-domain rational function is obtained, the zero point set comprises quantitative information under an oscillation mode of the new energy power system, and therefore the application purposes are achieved.

Description

Technical Field

[0001] This application relates to the field of power technology, and more specifically, to an oscillation-stabilized analytical method, apparatus, electronic device, and storage medium. Background Technology

[0002] With the development of power technology and the vigorous construction of new energy sources, high proportions of new energy and high proportions of power electronic equipment are becoming important characteristics of power systems. New energy equipment with power electronics as the interface has significantly changed the characteristics of power systems that were traditionally dominated by synchronous machines. At the same time, the interaction between the addition of a large number of new energy equipment and the power grid will cause the power grid to oscillate and become unstable.

[0003] Impedance analysis in frequency domain theory is the most commonly used method for analyzing the oscillation stability of new energy power systems. A typical technical approach involves: establishing impedance models for each component in the complex frequency domain based on the characteristics of new energy equipment and various power grid components, using mechanistic modeling or external measurement methods; constructing an impedance network for the new energy power system by combining the power grid topology; splitting the impedance network into two independent subsystems at a certain observation node (usually the port of the new energy equipment), and calculating the aggregate impedance of each subsystem as seen from the observation node; and analyzing the oscillation stability of the new energy power system based on the Nyquist curve of the ratio of the aggregate impedances of the two subsystems. However, the Nyquist criterion only yields qualitative stability conclusions and is insufficient to obtain quantitative information such as the damping coefficient and oscillation frequency of the power system oscillation modes. Summary of the Invention

[0004] In view of this, this application provides an oscillation stabilization analysis method, apparatus, electronic device, and storage medium for obtaining quantitative information on the oscillation modes of a new energy power system.

[0005] To achieve the above objectives, the following solution is proposed:

[0006] An oscillation-stabilized analytical method, applied to electronic equipment, is used to calculate multiple quantitative information in a new energy power system. The analytical method includes the following steps:

[0007] At multiple predetermined frequency sampling points, the impedance values ​​of each component in the new energy power system are calculated, and the system aggregate impedance determinant and its impedance sampling values ​​as seen from the observation node are calculated based on all the impedance values.

[0008] Based on the determination of the upper and lower bounds, the VF algorithm approximates the aggregate impedance determinant of the system and its impedance sample values ​​by using the rational function in the s-domain with known analytical expressions, and calculates the AIC criterion index at the upper and lower bounds respectively.

[0009] The minimum point of the AIC criterion index is found using the binary search method, and the minimum point is used as the estimation order.

[0010] Under the estimated order, the VF algorithm is used to calculate the rational function in the s-domain to obtain the set of zeros of the rational function in the s-domain. The set of zeros includes quantitative information on the oscillation mode of the new energy power system.

[0011] Optionally, the step of calculating the impedance value of each component in the new energy power system at multiple predetermined frequency sampling points, and calculating the system aggregate impedance determinant and its impedance sampling value from the observation node based on all the impedance values, includes the following steps:

[0012] Select multiple discrete frequency sampling points within the selected frequency band range;

[0013] Based on the current / voltage input and output characteristics of multiple external grid components in the new energy power system, an impedance model for each external grid component is established, and the impedance value of each external grid component at each frequency sampling point is calculated based on the impedance model.

[0014] Based on the selected observation nodes, multiple external power grid components are connected to form an impedance network model, and the aggregated impedance determinant of the system is obtained.

[0015] The impedance sample value is obtained by performing series and parallel calculations on the aggregate impedance determinant of the system.

[0016] Optionally, the upper bound is N. U The lower bound is N L The upper bound and the lower bound must satisfy ΔAIC(N) L )<0 and ΔAIC(N) U )>0, and N U -N L >>1.

[0017] Optionally, the real part of the zero represents the attenuation coefficient of the corresponding oscillation mode, and the imaginary part of the zero represents the oscillation angular frequency.

[0018] An oscillation-stabilized analytical device, applied in electronic equipment, is used to calculate multiple quantitative information in a new energy power system. The analytical device includes:

[0019] The first calculation module is configured to calculate the impedance value of each component in the new energy power system at multiple predetermined frequency sampling points, and calculate the system aggregate impedance determinant and its impedance sampling value as seen from the observation node based on all the impedance values.

[0020] The fitting processing module is configured to, based on the determination of the upper and lower bounds, use the VF algorithm to approximate the aggregate impedance determinant of the system and its impedance sample values ​​using a rational function in the s-domain with a known analytical expression, and to calculate the AIC criterion index at the upper and lower bounds respectively.

[0021] The second calculation module is configured to use the bisection method to find the minimum point of the AIC criterion index and use the minimum point as the estimation order.

[0022] The analytical processing module is configured to use the VF algorithm to calculate the rational function in the s-domain under the estimated order, and obtain the set of zeros of the rational function in the s-domain, wherein the set of zeros includes quantitative information on the oscillation mode of the new energy power system.

[0023] Optionally, the first computing module includes:

[0024] The sampling point selection unit is configured to select multiple discrete frequency sampling points within a selected frequency band range;

[0025] The first calculation unit is configured to establish an impedance model for each external grid element based on the current / voltage input and output characteristics of multiple external grid elements in the new energy power system, and to calculate the impedance value of each external grid element at each frequency sampling point based on the impedance model.

[0026] The model building unit is configured to connect multiple external power grid components based on selected observation nodes to form an impedance network model, thereby obtaining the aggregate impedance determinant of the system.

[0027] The second calculation unit is configured to perform series and parallel calculations on the aggregate impedance determinant of the system to obtain the impedance sample value.

[0028] Optionally, the upper bound is N. U The lower bound is N L The upper bound and the lower bound must satisfy ΔAIC(N) L )<0 and ΔAIC(N) U )>0, and N U -N L >>1.

[0029] Optionally, the real part of the zero represents the attenuation coefficient of the corresponding oscillation mode, and the imaginary part of the zero represents the oscillation angular frequency.

[0030] An electronic device includes at least one processor and a memory connected to the processor, wherein:

[0031] The memory is used to store computer programs or instructions;

[0032] The processor is used to execute the computer program or instructions to enable the electronic device to implement the vibration stabilization analytical method as described above.

[0033] A storage medium for use in an electronic device, the storage medium carrying one or more computer programs that can be executed by the electronic device to enable the electronic device to implement the oscillation-stabilized analytical method as described above.

[0034] As can be seen from the above technical solution, this application discloses an analytical method, apparatus, electronic device, and storage medium for oscillation stabilization. The method and apparatus are applied to electronic devices, specifically by calculating the impedance values ​​of each component in a new energy power system at multiple predetermined frequency sampling points, and calculating the system aggregate impedance determinant and its impedance sampling values ​​from the observation node based on all impedance values. After determining the upper and lower bounds, the VF algorithm is used to approximate the system aggregate impedance determinant and its impedance sampling values ​​using a rational function in the s-domain with a known analytical expression, and the AIC criterion index is calculated at the upper and lower bounds respectively. The minimum point of the AIC criterion index is found using the bisection method, and the minimum point is used as the estimation order. Under the estimated order, the VF algorithm is used to calculate the rational function in the s-domain, obtaining the set of zeros of the rational function in the s-domain. This set of zeros includes quantitative information on the oscillation mode of the new energy power system, thereby achieving the inventive objective of this application. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1a This is a typical structural diagram of the new energy power system involved in the embodiments of this application;

[0037] Figure 1b This refers to the impedance network of the new energy power system involved in the embodiments of this application;

[0038] Figure 2 This is a flowchart illustrating an oscillation-stabilized analytical method according to an embodiment of this application;

[0039] Figure 3 This is a schematic diagram illustrating a specific computational example of an embodiment of this application;

[0040] Figure 4 This is a diagram showing the relationship between the AIC criterion index and the order in an embodiment of this application.

[0041] Figure 5a This is a graph showing the amplitude-frequency response of an embodiment of this application.

[0042] Figure 5b This is a phase-frequency characteristic curve of an embodiment of this application;

[0043] Figure 6 This is a block diagram of an oscillation-stabilized analytical device according to an embodiment of this application;

[0044] Figure 7 This is a block diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0045] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0046] The typical structure of the new energy power system involved in this application is as follows: Figure 1a As shown. The new energy converter uses a step-up transformer R... T +jL T , collector line R l +jL l Components, etc., are connected in parallel to the ideal voltage source E. g With series impedance R g +jL g A simplified representation of the external power grid.

[0047] Figure 1a The impedance network of the new energy power system shown is as follows: Figure 1b As shown, Z VSC Z C Z T Z l Z g These represent the impedance models of the new energy converter, filter capacitor, transformer, collector line, and external power grid, respectively. These models can be obtained through mechanistic modeling or external measurement methods, depending on the specific circumstances. In practical engineering, it is often difficult to write the system aggregate impedance determinant |Z due to reasons such as excessively high overall order or unknown controller structures or parameters of some components. LIM The zeros of the s-domain analytic expression for (s)| are even more difficult to find directly. However, |Z LIM The sampled values ​​of (s) at each discrete frequency point are easily obtained. Let |Z LIMThe discrete sampled values ​​of (s)| are fitted with a rational function whose analytical expression is known, and the zeros of the rational function are used as |Z|. LIM Approximating the zero point (s) is a feasible technical approach.

[0048] This application considers determining a sufficiently large number of discrete sampling points within the frequency band of interest, calculating the sampled impedance value of each component at each sampling point, and then connecting the impedances according to the electrical network topology to form an impedance network. Finally, it calculates the system aggregate impedance determinant |Z| as seen from the observation node. LIM The sampled value of (s)|. This application proposes that the VF algorithm can be used to obtain the sampled value of |Z. LIM (s)| using the rational expression 1 / F of the following form fit (s) Approximate fit:

[0049]

[0050] Let F(s) = 1 / |Z LIM (s)|, the above formula can be rewritten as:

[0051]

[0052] Obviously, {a1 a2 L a N} is a rational expression 1 / F fit The set of zeros of (s), i.e., the aggregate impedance determinant |Z LIM The zeros of (s)| are approximations of the system's oscillation modes. Parameters {a1 a2 L a N}、{c1 c2 L c N The values ​​of} and d can be calculated by the VF algorithm based on the least squares principle, so that F fit (s) Within the sampling frequency band, at a given order N, it is related to F(s)=1 / |Z LIM (s)|Minimizes the mean square error between sampled values.

[0053] A major bottleneck in the application of the VF algorithm in oscillation stability analysis lies in determining a suitable order N. If the order N is chosen too small, it may lead to problems with F... fit (s) and F(s) = 1 / |Z LIM The large fitting error between (s)| affects the accuracy of identifying the system's oscillation modes, resulting in "underfitting." If the order N is chosen too large, it will lead to redundant identification results for oscillation modes that do not actually exist in the system under study, potentially causing misjudgments of oscillation stability. Therefore, this invention proposes an order estimation method based on the AIC criterion index. This invention defines the AIC criterion index as:

[0054] AIC(N)=ln(ε(θ))+N (3)

[0055]

[0056] It is evident that the AIC criterion index is related to the order N and the fitting error ε(θ) of the rational function. θ represents F fit The parameter set of (s), i.e., θ = [{a i},{c i},d],N s ω is the number of sampling points. k Let N be the angular frequency corresponding to the k-th sampling point. This invention considers using the minimum value of the AIC criterion index as a reasonable estimate of the order N. * .

[0057] To improve the efficiency of the above methods, this invention proposes a method using a bisection approach to quickly retrieve the minimum point of the AIC criterion index. The difference ΔAIC(N) of the AIC criterion index is defined as follows:

[0058] ΔAIC(N)=AIC(N+1)-AIC(N) (5)

[0059] The specific steps of the bisection method are summarized as follows:

[0060] (1) Given an upper bound N on the order of a rational function. U With lower bound N L , respectively satisfying ΔAIC(N) L )<0 and ΔAIC(N) U )>0, and N U -N L >>1;

[0061] (2) Let N D =ceil[(N L +N U [) / 2](ceil(x) function represents taking the smallest integer not less than x), consider ΔAIC(N) D The sign of ) if ΔAIC(N) D If ) > 0, then the upper bound N will be set. U Updated to N D If ΔAIC(N) D If ) < 0, then the lower bound N is set. L Updated to N D ;

[0062] (3) Repeat step (2) until N. U -N L =1, N U The final value of the iteration is the minimum point of the AIC criterion index, which serves as a reasonable estimate of the order N. * .

[0063] Based on the above, this application proposes the following specific embodiments to enable the analysis of multiple quantitative information of new energy power systems.

[0064] Example 1

[0065] Figure 2 This is a flowchart of an oscillation stabilization analysis method according to an embodiment of this application.

[0066] The analytical method provided in this embodiment is used to analyze the oscillation stability of a new energy power system. The new energy power system in this embodiment includes a grid-connected new energy converter based on phase-locked loop control, and a filter inductor L... f Filter capacitor C f Transformer R T +jL T , collector line R l +jL l Then, at point PCC, the ideal voltage source E is connected in parallel. g With impedance R g +jL g The equivalent external power grid formed by series connection, such as Figure 3 As shown in the table below, the new energy converter controller mainly consists of a DC voltage outer loop, an AC current inner loop, and a phase-locked loop.

[0067]

[0068] Combination Figure 3 The oscillation stabilization analysis method provided in this embodiment, as shown in the figure, includes the following steps, as detailed below. Figure 2 As shown:

[0069] S1. Determine several discrete frequency sampling points within the frequency band of interest, calculate the impedance value of each component in the new energy power system at each sampling point, and further calculate the system aggregate impedance determinant and its impedance sampling value from the observation node.

[0070] First, based on the modeling accuracy of the converter in the example, the system oscillation mode was analyzed in the range of 0-500Hz, and 5000 frequency sampling points were set up with linear uniform distribution in this frequency band.

[0071] Then, based on the voltage / current input and output characteristics of components such as converters, filter capacitors, transformers, and equivalent external power grids, impedance models of the above components are established, and impedance values ​​at each frequency sampling point are calculated.

[0072] Next, select an observation point, such as the PCC point, as the observation node, and connect the impedance models according to the power topology to obtain the impedance network model, thereby obtaining the aggregate impedance determinant of the system.

[0073] Finally, the series and parallel operations are performed on the aggregate impedance determinant of the system to obtain the impedance sample value condensed to the PCC point.

[0074] S2. Determine the upper and lower bounds. When the order is equal to the upper and lower bounds, use the VF algorithm to approximate the system aggregate impedance determinant with a rational function in the s-domain whose analytical expression is known, and calculate the AIC criterion index for each of the two cases.

[0075] Let N denote the upper and lower bounds of the order. U With N L The upper and lower bounds must satisfy ΔAIC(N) L )<0 and ΔAIC(N) U )>0, and N U -N L >>1. In this example, the lower bound is considered to be the smallest order N that makes ΔAIC(N) < 0. L =5, and the upper bound is taken as the order value N that makes the fitting error ε(θ) much smaller than the engineering accuracy requirement. U =30.

[0076] Using the VF algorithm, respectively with N L =5th order and N U = 30th order rational function 1 / F as shown in equation (2) fit (s) Fitting the polymer impedance determinant |Z LIM (s)|, calculate the fitting error ε(θ) using equation (4), and calculate the AIC criterion index using equation (3).

[0077] S3. Treat the AIC criterion index as a function of the order, use the bisection method to find its minimum point, and use the minimum point as the estimated order.

[0078] Let the midpoint N D =ceil[(N L +N U [) / 2](ceil(x) function represents taking the smallest integer not less than x), consider ΔAIC(N) D The sign of ) if ΔAIC(N) D If ) > 0, then the upper bound N will be set. U Iteratively update to N D If ΔAIC(N) D If ) < 0, then the lower bound N is set. L Iteratively update to N D Repeat this step until N. U-N L =1, N U The final value of the iteration is the minimum point of the AIC criterion index, which serves as a reasonable estimate of the order N. * That is, to use it as the estimated order.

[0079] For this example, we first calculate the midpoint N between the upper and lower bounds of the order. D There are N D =18. From equation (5), note that ΔAIC(N) D Since ) > 0, the upper bound is modified to N for iteration. U =N D =18. Repeat this step.

[0080] After 5 rounds of iteration, the final order estimate N was determined. * =N U =12. The calculation results of each iteration are recorded in the table below:

[0081]

[0082] The relationship between the AIC criterion index and the order is as follows: Figure 4 As shown.

[0083] S4. Under the estimated order, calculate the rational function in the s-domain to obtain the set of zeros of the rational function in the s-domain.

[0084] The zero-point set includes quantitative information on the oscillation modes of new energy power systems. Specifically, it utilizes the VF algorithm, using N... * =12th order rational function F as shown in equation (2) fit (s) Fitting polymerization impedance. Rational function F fit (s) and the reciprocal of the polymer impedance determinant F(s) = 1 / |Z LIM The amplitude-frequency response curves of (s)| are compared as follows: Figure 5a and Figure 5b As shown. The obtained parameters {a1 a2 L a N} is a rational function 1 / F fit The set of zeros of (s), i.e., |Z LIM An approximation of the set of zeros (each oscillation mode of the system) of (s)|. If all zeros are distributed in the left half of the complex plane, the system has good oscillation stability; otherwise, the system is unstable. The real part of each zero represents the attenuation coefficient of the corresponding oscillation mode, and the imaginary part represents the oscillation angular frequency, which can be used for further quantitative analysis of oscillation stability. The actual oscillation modes of the system in this example, the oscillation mode identification results obtained by approximating the port impedance function of the equivalent second-order series RLC circuit, and the oscillation mode identification results obtained by applying this invention are compared in the following table:

[0085]

[0086] It is evident that the identification results obtained by this invention have relatively ideal accuracy, and the order estimation results are consistent with the actual number of oscillation modes, effectively reducing the impact of underfitting and overfitting on oscillation stability analysis.

[0087] As can be seen from the above technical solution, this embodiment provides an analytical method for oscillation stabilization. This method is applied to electronic equipment, specifically by calculating the impedance values ​​of each component in the new energy power system at multiple predetermined frequency sampling points, and calculating the system aggregate impedance determinant and its impedance sampling values ​​from the observation node based on all impedance values. After determining the upper and lower bounds, the VF algorithm is used to approximate the system aggregate impedance determinant and its impedance sampling values ​​using a rational function in the s-domain with a known analytical expression, and the AIC criterion index is calculated at the upper and lower bounds respectively. The minimum point of the AIC criterion index is found using the bisection method, and the minimum point is used as the estimation order. Under the estimated order, the VF algorithm is used to calculate the rational function in the s-domain, obtaining the set of zeros of the rational function in the s-domain. This set of zeros includes quantitative information on the oscillation mode of the new energy power system, thereby achieving the objective of this application.

[0088] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0089] Although the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. In certain environments, multitasking and parallel processing may be advantageous.

[0090] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0091] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including but not limited to object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer.

[0092] Example 2

[0093] Figure 6 This is a block diagram of an oscillation-stabilized analytical device according to an embodiment of this application.

[0094] The analytical device provided in this embodiment is used to analyze the oscillation stability of a new energy power system. The new energy power system in this embodiment includes a grid-connected new energy converter based on phase-locked loop control, and a filter inductor L... f Filter capacitor C f Transformer R T +jL T , collector line R l +jL l Then, at point PCC, the ideal voltage source E is connected in parallel. g With impedance R g +jL g The equivalent external power grid formed by series connection, such as Figure 3 As shown in the table below, the new energy converter controller mainly consists of a DC voltage outer loop, an AC current inner loop, and a phase-locked loop.

[0095]

[0096] Combination Figure 3 As shown, the oscillation-stabilized analytical device provided in this embodiment includes a first calculation module 10, a fitting processing module 20, a second calculation module 30, and an analytical processing module 40, as detailed below. Figure 6 As shown:

[0097] The first calculation module is used to determine several discrete frequency sampling points within the frequency band of interest, calculate the impedance value of each component in the new energy power system at each sampling point, and further calculate the system aggregate impedance determinant and its impedance sampling value as seen from the observation node. This module includes a sampling point selection unit, a first calculation unit, a model construction unit, and a second calculation unit.

[0098] The sampling point selection unit is used to analyze the system oscillation mode in the range of 0-500Hz, taking into account the modeling accuracy of the converter in the example. 5000 frequency sampling points are set up with linear uniform distribution in this frequency band.

[0099] The first calculation unit is used to establish the impedance model of the above components, such as converters, filter capacitors, transformers, and equivalent external power grids, based on their voltage / current input and output characteristics, and to calculate the impedance value at each frequency sampling point.

[0100] The model building unit is used to select an observation point, such as the PCC point, as the observation node, and connect each impedance model according to the power topology to obtain the impedance network model, thereby obtaining the aggregate impedance determinant of the system.

[0101] The second calculation unit is used to perform series and parallel operations on the aggregate impedance determinant of the system to obtain the impedance sample value condensed to the PCC point.

[0102] The fitting module is used to determine the upper and lower bounds. When the order is equal to the upper and lower bounds, the VF algorithm is used to approximate the system aggregate impedance determinant with a rational function in the s-domain whose analytical expression is known, and the AIC criterion index is calculated for the two cases respectively.

[0103] Let N denote the upper and lower bounds of the order. U With N L The upper and lower bounds must satisfy ΔAIC(N) L )<0 and ΔAIC(N) U )>0, and N U -N L >>1. In this example, the lower bound is considered to be the smallest order N that makes ΔAIC(N) < 0. L =5, and the upper bound is taken as the order value N that makes the fitting error ε(θ) much smaller than the engineering accuracy requirement. U =30.

[0104] Using the VF algorithm, respectively with N L =5th order and N U = 30th order rational function 1 / F as shown in equation (2) fit (s) Fitting the polymer impedance determinant |Z LIM(s)|, calculate the fitting error ε(θ) using equation (4), and calculate the AIC criterion index using equation (3).

[0105] The second calculation module is used to treat the AIC criterion index as a function of the order, use the bisection method to find its minimum point, and use the minimum point as the estimated order.

[0106] Let the midpoint N D =ceil[(N L +N U [) / 2](ceil(x) function represents taking the smallest integer not less than x), consider ΔAIC(N) D The sign of ) if ΔAIC(N) D If ) > 0, then the upper bound N will be set. U Iteratively update to N D If ΔAIC(N) D If ) < 0, then the lower bound N is set. L Iteratively update to N D Repeat this step until N. U -N L =1, N U The final value of the iteration is the minimum point of the AIC criterion index, which serves as a reasonable estimate of the order N. * That is, to use it as the estimated order.

[0107] For this example, we first calculate the midpoint N between the upper and lower bounds of the order. D There are N D =18. From equation (5), note that ΔAIC(N) D Since ) > 0, the upper bound is modified to N for iteration. U =N D =18. Repeat this step 5 times to finally determine the order estimate N. * =N U =12. The calculation results of each iteration are recorded in the table below:

[0108]

[0109] The relationship between the AIC criterion index and the order is shown in the following figure. Figure 4 As shown.

[0110] The analytical processing module is used to calculate the rational function in the s-domain under the estimated order, and obtain the set of zeros of the rational function in the s-domain.

[0111] The zero-point set includes quantitative information on the oscillation modes of new energy power systems. Specifically, it utilizes the VF algorithm, using N... * =12th order rational function F as shown in equation (2) fit (s) Fitting polymerization impedance. Rational function Ffit (s) and the reciprocal of the polymer impedance determinant F(s) = 1 / |Z LIM The amplitude frequency response curve and phase frequency response curve of (s)| are respectively as follows: Figure 5a and Figure 5b As shown. The obtained parameters {a1 a2 L a N} is a rational function 1 / F fit The set of zeros of (s), i.e., |Z LIM An approximation of the set of zeros (each oscillation mode of the system) of (s)|. If all zeros are distributed in the left half of the complex plane, the system has good oscillation stability; otherwise, the system is unstable. The real part of each zero represents the attenuation coefficient of the corresponding oscillation mode, and the imaginary part represents the oscillation angular frequency, which can be used for further quantitative analysis of oscillation stability. The actual oscillation modes of the system in this example, the oscillation mode identification results obtained by approximating the port impedance function of the equivalent second-order series RLC circuit, and the oscillation mode identification results obtained by applying this invention are compared in the following table:

[0112]

[0113] It is evident that the identification results obtained by this invention have relatively ideal accuracy, and the order estimation results are consistent with the actual number of oscillation modes, effectively reducing the impact of underfitting and overfitting on oscillation stability analysis.

[0114] As can be seen from the above technical solution, this embodiment provides an analytical device for oscillation stabilization. This device is applied to electronic equipment, specifically calculating the impedance values ​​of each component in the new energy power system at multiple predetermined frequency sampling points, and calculating the system aggregate impedance determinant and its impedance sampling values ​​from the observation node based on all impedance values. After determining the upper and lower bounds, the VF algorithm is used to approximate the system aggregate impedance determinant and its impedance sampling values ​​using a rational function in the s-domain with a known analytical expression, and the AIC criterion index is calculated at the upper and lower bounds respectively. The minimum point of the AIC criterion index is found using the bisection method, and the minimum point is used as the estimation order. Under the estimated order, the VF algorithm is used to calculate the rational function in the s-domain, obtaining the set of zeros of the rational function in the s-domain. This set of zeros includes quantitative information on the oscillation mode of the new energy power system, thereby achieving the inventive objective of this application.

[0115] The units described in the embodiments of this disclosure can be implemented in software or in hardware. The name of a unit does not necessarily limit the unit itself; for example, the first acquisition unit can also be described as "a unit that acquires at least two Internet Protocol addresses".

[0116] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0117] Example 3

[0118] Figure 7 This is a block diagram of an electronic device according to an embodiment of this application.

[0119] refer to Figure 7 The diagram illustrates a structural schematic suitable for implementing the electronic device in the embodiments of this disclosure. The terminal device in the embodiments of this disclosure may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 7 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0120] The electronic device may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage device 608 into a random access memory (RAM) 603. The RAM 603 also stores various programs and data required for the operation of the electronic device 600. The processing unit 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0121] Typically, the following devices can be connected to I / O interface 605: input devices 606 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 607 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 608 including, for example, magnetic tapes, hard disks, etc.; and communication devices 609. Communication device 609 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 6 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or have alternatively.

[0122] Example 4

[0123] This embodiment provides a computer-readable storage medium carrying one or more computer programs. When these programs are executed by an electronic device, the device calculates the impedance values ​​of each component in a new energy power system at multiple predetermined frequency sampling points. Based on all impedance values, it calculates the system aggregate impedance determinant and its impedance sample values ​​as seen from the observation node. After determining upper and lower bounds, the VF algorithm approximates the system aggregate impedance determinant and its impedance sample values ​​using a rational function in the s-domain with a known analytical expression, and calculates the AIC criterion index at the upper and lower bounds respectively. A bisection method is used to find the minimum point of the AIC criterion index, and this minimum point is used as the estimation order. Under the estimated order, the VF algorithm is used to calculate the rational function in the s-domain, obtaining the set of zeros of the rational function in the s-domain. This set of zeros includes quantitative information on the oscillation modes of the new energy power system, thereby achieving the objective of this application.

[0124] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0125] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0126] Although preferred embodiments of the present invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present invention.

[0127] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0128] The technical solution provided by the present invention has been described in detail above. Specific examples have been used to illustrate the principle and implementation of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation and application scope based on the idea of ​​the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. An oscillatory-stabilized analytical method, applied to electronic equipment, for calculating multiple quantitative information in a new energy power system, characterized in that, The parsing method includes the following steps: At multiple predetermined frequency sampling points, the impedance values ​​of each component in the new energy power system are calculated, and the system aggregate impedance determinant and its impedance sampling values ​​as seen from the observation node are calculated based on all the impedance values. Based on the determined upper and lower bounds, the VF algorithm approximates the aggregate impedance determinant and its sampled impedance values ​​of the system using a rational function in the s-domain with a known analytical expression, and calculates the AIC criterion index at the upper and lower bounds respectively, where the upper bound is N. U The lower bound is N L The upper bound and the lower bound must satisfy ΔAIC(N) L )<0 and ΔAIC(N) U )>0, and N U -N L >>1; The minimum point of the AIC criterion index is found by using the difference and bisection method of the AIC criterion index. The minimum point is used as the estimated order. The difference of the AIC criterion index ΔAIC(N) = AIC(N+1) - AIC(N), where AIC(N+1) and AIC(N) are the AIC criterion indexes and N is the order. Under the estimated order, the VF algorithm is used to calculate the rational function in the s-domain to obtain the set of zeros of the rational function in the s-domain. The set of zeros includes quantitative information on the oscillation mode of the new energy power system.

2. The analytical method as described in claim 1, characterized in that, The step of calculating the impedance values ​​of each component in the new energy power system at multiple predetermined frequency sampling points, and calculating the system aggregate impedance determinant and its impedance sampling values ​​from the observation node based on all the impedance values, includes the following steps: Select multiple discrete frequency sampling points within the selected frequency band range; Based on the current / voltage input and output characteristics of multiple external grid components in the new energy power system, an impedance model for each external grid component is established, and the impedance value of each external grid component at each frequency sampling point is calculated based on the impedance model. Based on the selected observation nodes, multiple external power grid components are connected to form an impedance network model, and the aggregated impedance determinant of the system is obtained. The impedance sample value is obtained by performing series and parallel calculations on the aggregate impedance determinant of the system.

3. The analytical method as described in claim 1, characterized in that, The real part of the zero represents the attenuation coefficient of the corresponding oscillation mode, and the imaginary part of the zero represents the oscillation angular frequency.

4. An oscillation-stabilized analytical device, applied in electronic equipment, for calculating multiple quantitative information in a new energy power system, characterized in that, The analytical device includes: The first calculation module is configured to calculate the impedance value of each component in the new energy power system at multiple predetermined frequency sampling points, and calculate the system aggregate impedance determinant and its impedance sampling value as seen from the observation node based on all the impedance values. The fitting module is configured to, based on the determined upper and lower bounds, use the VF algorithm to approximate the aggregate impedance determinant and the impedance sample values ​​of the system using a rational function in the s-domain with a known analytical expression, and to calculate the AIC criterion index at the upper and lower bounds, respectively, where the upper bound is N. U The lower bound is N L The upper bound and the lower bound must satisfy ΔAIC(N) L )<0 and ΔAIC(N) U )>0, and N U -N L >>1; The second calculation module is configured to use the difference and bisection method of the AIC criterion index to find the minimum point of the AIC criterion index, and use the minimum point as the estimation order. The difference of the AIC criterion index is ΔAIC(N) = AIC(N+1) - AIC(N), where AIC(N+1) and AIC(N) are the AIC criterion indexes, and N is the order. The analytical processing module is configured to use the VF algorithm to calculate the rational function in the s-domain under the estimated order, and obtain the set of zeros of the rational function in the s-domain, wherein the set of zeros includes quantitative information on the oscillation mode of the new energy power system.

5. The analytical apparatus as described in claim 4, characterized in that, The first computing module includes: The sampling point selection unit is configured to select multiple discrete frequency sampling points within a selected frequency band range; The first calculation unit is configured to establish an impedance model for each external grid element based on the current / voltage input and output characteristics of multiple external grid elements in the new energy power system, and to calculate the impedance value of each external grid element at each frequency sampling point based on the impedance model. The model building unit is configured to connect multiple external power grid components based on selected observation nodes to form an impedance network model, thereby obtaining the aggregate impedance determinant of the system. The second calculation unit is configured to perform series and parallel calculations on the aggregate impedance determinant of the system to obtain the impedance sample value.

6. The analytical apparatus as described in claim 4, characterized in that, The real part of the zero represents the attenuation coefficient of the corresponding oscillation mode, and the imaginary part of the zero represents the oscillation angular frequency.

7. An electronic device, characterized in that, It includes at least one processor and a memory connected to the processor, wherein: The memory is used to store computer programs or instructions; The processor is used to execute the computer program or instructions to enable the electronic device to implement the oscillation-stabilized analytical method as described in any one of claims 1 to 3.

8. A storage medium used in electronic devices, characterized in that, The storage medium carries one or more computer programs that can be executed by the electronic device to enable the electronic device to implement the oscillation-stabilized analytical method as described in any one of claims 1 to 3.

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