New energy power system frequency stability characteristic analysis method and device, equipment and storage medium

Abstract

The invention provides a new energy power system frequency stability characteristic analysis method and device, equipment and a storage medium. The invention relates to the technical field of high-proportion new energy power system frequency stabilization. According to the method, a response characteristic curve based on key frequency characteristic quantity and frequency can be drawn in a new frequency characteristic distribution scene, firstly, a set of inertia center frequency and related characteristic quantity of a system is constructed, and after time sequence characteristic quantity is extracted, a thermodynamic diagram is drawn through Pearson correlation analysis; a hierarchical clustering graph is drawn through clustering analysis, so that the hierarchical clustering graph and the hierarchical clustering graph jointly present characteristic quantities with high correlation degree with the frequency; drawing a response characteristic curve of the system center frequency and the characteristic quantity with high correlation degree to form a criterion, wherein the response characteristic curve converges when the system frequency is stable; and the curve is divergent during instability. In addition, the influence of different new energy proportions on the frequency characteristic can be analyzed through the response characteristic curve.

Description

Technical Field

[0001] This application relates to the field of frequency stability technology for high-proportion new energy power systems, and in particular to a method, apparatus, equipment and storage medium for analyzing the frequency stability characteristics of new energy power systems. Background Technology

[0002] The future power grid will exhibit the characteristics of "dual highs"—high proportion of renewable energy and high proportion of power electronic equipment—and the dynamic behavior of frequencies after disturbances will become increasingly complex. Currently, typical global frequency response modeling methods for power grids after disturbances mainly fall into categories such as average system frequency and system frequency response models, as well as fusion models based on both. Node frequency response modeling and analysis methods include time-domain simulation based on mechanistic models and data-driven methods based on black-box models, but these methods have many problems and limitations. With the rapid development and improvement of information measurement technology, transient stability analysis methods based on response information can effectively avoid dependence on system operating modes, models, and parameters. Compared to traditional system fault identification through monitoring devices, which identifies disturbance sources and types, response-driven methods can analyze disturbance occurrence based on changes in detection data, more easily meeting the needs of modern power grids for "real-time decision-making and real-time control." Summary of the Invention

[0003] This application provides a method, apparatus, equipment, and storage medium for analyzing the frequency stability characteristics of a new energy power system, in order to plot response characteristic curves based on key frequency characteristic quantities and frequency under new frequency characteristic distribution scenarios, and to obtain the changes in frequency and its characteristic quantities under stable and unstable states of the system and under different proportions of new energy.

[0004] Firstly, this application provides a method for analyzing the frequency stability characteristics of a new energy power system, including:

[0005] Construct the system's inertial center frequency and its set of frequency-related characteristic quantities;

[0006] Based on the real-time nature of response-driven information, a set of variables reflecting frequency time-series characteristics is initially selected from the set of frequency-related feature quantities;

[0007] Based on the set of variables reflecting frequency time-series characteristics, the corresponding characteristic variables of n generators, n loads, and n buses, as well as the port energy of the generator with the largest power, are randomly selected as characteristic quantities. Based on the characteristic quantities, a heat map is drawn through Pearson correlation analysis, and a hierarchical clustering diagram is drawn through cluster analysis to intuitively present the characteristic quantities with a high degree of correlation with frequency. Based on the degree of correlation, several key characteristic quantities are selected from the set of variables reflecting frequency time-series characteristics.

[0008] Based on the aforementioned key features, a response feature curve of the system center frequency and each key feature is plotted, and frequency-specific analysis is performed based on the end points and curve characteristics of the response feature curves.

[0009] In one possible design, the system's center frequency of inertia is calculated using the following formula:

[0010]

[0011] In the formula, f COI (t) is the inertial center frequency of the power system, M i Let m be the inertial time constant of generator i; m be the number of generators; M be the inertial time constant of generator i. T Let m be the sum of the inertia of the m generators in the power system.

[0012] X (i+m) (t) represents the dynamic frequency of generator i.

[0013] In one possible design, the set of frequency-related features is represented as:

[0014] {TZ1,TZ2,TZ3,TZ4,TZ5,TZ6,TZ7,TZ8,TZ9,TZ 10 TZ 11 TZ 12 TZ 13 TZ 14 TZ 15 TZ 16}(3)

[0015] In the formula, TZ1 is the sum of active power of all generators in the system before the fault, TZ2 is the rotor speed of all generators in the system before the fault, TZ3 is the maximum rate of change of frequency of all generators after the fault, TZ4 is the increase in active power generated by all generators in the system after the fault, TZ5 is the time of the lowest frequency of all generators in the system after the fault, TZ6 is the generalized inertia of the power system, TZ7 is the frequency change of each node when the system reaches steady state after the fault, TZ8 is the system frequency change, TZ9 is the load level, and TZ 10 For generator active power, TZ 11 TZ represents the instantaneous voltage of each bus after the disturbance. 12 TZ is the generator terminal current. 13 For generator phase angle, TZ 14 TZ represents the mechanical power of each generator. 15 For reactive power, TZ 16 For port energy.

[0016] In one possible design, the port energy TZ is calculated using the following formula. 16 :

[0017]

[0018] In the formula, x s Let θ and V be the values ​​of θ and V at the equilibrium point, x be the values ​​of θ and V at the integration cutoff time, P be the active power at the new energy port, θ be the phase angle of the new energy grid-connected node voltage, Q be the reactive power at the new energy port, and V be the voltage at the new energy grid-connected node.

[0019] In one possible design, the set of variables reflecting frequency timing characteristics is represented as:

[0020] {TZ8,TZ9,TZ 10 TZ 11 TZ 12 TZ 13 TZ 14 TZ 15 TZ 16}(4). In one possible design, the multiple key feature quantities are represented as:

[0021] {TZ 12 TZ 13 TZ 16}(5).

[0022] In one possible design, frequency-specific analysis is performed based on the endpoints and characteristics of the response characteristic curve using the following formula:

[0023]

[0024] In the formula, f end This is the end of the response characteristic curve.

[0025] Secondly, this application provides a frequency stability characteristic analysis device for a new energy power system, the device comprising:

[0026] The set construction module is configured to construct the system's inertial center frequency and its frequency-related characteristic quantities set;

[0027] The initial screening module is configured to initially select a set of variables reflecting frequency time-series characteristics from the set of frequency-related features based on the real-time nature of response-driven information.

[0028] The feature filtering module is configured to randomly select the corresponding feature variables of n generators, n loads, and n buses, as well as the port energy of the generator with the largest power, as feature quantities based on the set of variables reflecting frequency time-series characteristics. Based on the feature quantities, a heat map is drawn through Pearson correlation analysis, and a hierarchical clustering diagram is drawn through cluster analysis to intuitively present the feature quantities with a high degree of correlation with frequency. Multiple key feature quantities are filtered out from the set of variables reflecting frequency time-series characteristics according to the degree of correlation.

[0029] The feature analysis module is configured to plot the response feature curves of the system center frequency and each key feature quantity based on the multiple key feature quantities, and to perform frequency-specific analysis based on the end of the response feature curves and the curve characteristics.

[0030] Thirdly, embodiments of this application provide an electronic device, including: at least one processor and a memory; the memory stores computer execution instructions; the at least one processor executes the computer execution instructions stored in the memory, causing the at least one processor to execute the frequency stability characteristic analysis method for new energy power systems as described in the first aspect and various possible designs of the first aspect.

[0031] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions. When a processor executes the computer-executable instructions, it implements the method for analyzing the frequency stability characteristics of a new energy power system as described in the first aspect and various possible designs of the first aspect.

[0032] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the method for analyzing the frequency stability characteristics of a new energy power system as described in the first aspect and various possible designs of the first aspect.

[0033] The method, apparatus, equipment, and storage medium for analyzing the frequency stability characteristics of new energy power systems provided in this application have at least the following beneficial effects:

[0034] This application enables the plotting of response characteristic curves based on key frequency features and frequency in new frequency characteristic distribution scenarios. First, it constructs a set of the system's inertial center frequency and its related features. After extracting time-series features, a heatmap is generated using Pearson correlation analysis. Then, a hierarchical clustering diagram is generated using cluster analysis, ensuring that both exhibit features highly correlated with frequency. Next, response characteristic curves are plotted between the system's center frequency and its highly correlated features to form a criterion. The response characteristic curves converge when the system frequency is stable and diverge when it is unstable. Furthermore, the impact of different renewable energy proportions on frequency characteristics can be analyzed using the response characteristic curves. Attached Figure Description

[0035] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0036] Figure 1 A flowchart illustrating a method for analyzing the frequency stability characteristics of a new energy power system, provided in an embodiment of this application;

[0037] Figure 2 Correlation heatmaps of frequency-related feature quantities provided in embodiments of this application;

[0038] Figure 3 This is a diagram illustrating the hierarchical clustering effect of frequency-related features provided in the embodiments of this application.

[0039] Figure 4 A comparison diagram of the Ef response characteristic curves of the faulty unit G3 under frequency stability and instability conditions provided in the embodiments of this application;

[0040] Figure 5 A comparison diagram of the If response characteristic curves of the faulty unit G3 under stable and unstable conditions provided in the embodiments of this application;

[0041] Figure 6 A comparison chart of If response characteristic curves of non-faulty unit G6 under different renewable energy ratios in the system provided in this application embodiment;

[0042] Figure 7 A structural diagram of the frequency stability characteristic analysis device for a new energy power system provided in this application embodiment.

[0043] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0044] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0045] The collection, storage, use, processing, transmission, provision, and disclosure of financial data or user data involved in the technical solution of this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0046] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0047] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0048] Based on this, embodiments of this application provide a method for analyzing the frequency stability characteristics of a new energy power system, such as... Figure 1 The flowchart shown is a method for analyzing the frequency stability characteristics of a new energy power system provided in an embodiment of this application. The method for analyzing the frequency stability characteristics of a new energy power system includes the following steps S100-S400.

[0049] S100: Construct the system's inertial center frequency and its set of frequency-related characteristic quantities.

[0050] In some embodiments, the expression for the inertial center frequency derived from wide-area frequency dynamic data is as follows:

[0051]

[0052] In the formula, f COI (t) is the inertial center frequency of the power system, M i Let m be the inertial time constant of generator i; m be the number of generators; M be the inertial time constant of generator i. T Let m be the sum of the inertia of the m generators in the power system.

[0053] X (i+m) (t) represents the dynamic frequency of generator i.

[0054] In some embodiments, the set of frequency-related features includes conventional frequency features and frequency time-series features.

[0055] Traditional frequency characteristic quantities include the sum of active power of each generator in the system before the fault (TZ1), the rotor speed of each generator in the system before the fault (TZ2), the maximum rate of change of frequency of each generator after the fault (TZ3), the increased active power generated by each generator in the system after the fault (TZ4), the time of the lowest frequency of each generator in the system after the fault (TZ5), the generalized inertia of the power system (TZ6), and the frequency change value of each node when each frequency of the system reaches steady state after the fault (TZ7), etc.

[0056] The selection principle for frequency timing characteristics should be that they have a certain correlation with the dynamic frequency and can reflect the frequency change after a disturbance, such as the system frequency change TZ8. Power system frequency usually reflects the balance between generator active power and load. When the system's reserve power is insufficient after a disturbance, it will lead to system frequency instability. Therefore, load level TZ9 and generator active power TZ9 can be considered as selection criteria. 10 These are used as frequency-dependent characteristic quantities. Simultaneously, the node's voltage and current, phase angle, and the injected active and reactive power are coupled. The selection of frequency characteristic quantities can consider the instantaneous bus voltage TZ after the disturbance. 11 Generator terminal current TZ 12 Generator phase angle TZ 13 and the mechanical power TZ of each generator 14 Reactive power TZ 15 The generator port energy after the disturbance can be considered as an accumulation of transient energy related to power, voltage, and power angle. Therefore, the port energy can be considered as a frequency-dependent characteristic quantity.

[0057]

[0058] In the formula, x s Let θ and V be the values ​​of θ and V at the equilibrium point, x be the values ​​of θ and V at the integration cutoff time, P be the active power at the new energy port, θ be the phase angle of the new energy grid-connected node voltage, Q be the reactive power at the new energy port, and V be the voltage at the new energy grid-connected node.

[0059] Considering the above 16 frequency-related features, construct a set:

[0060] {TZ1,TZ2,TZ3,TZ4,TZ5,TZ6,TZ7,TZ8,TZ9,TZ 10 TZ 11 TZ 12 TZ 13 TZ 14 TZ 15 TZ 16}(3)

[0061] S200: Based on the real-time nature of response-driven information, a set of variables reflecting frequency time-series characteristics is initially selected from the set of frequency-related feature quantities;

[0062] Step S200 extracts a set of key time-series features. In some embodiments, to find features that consistently exhibit high correlation with frequency changes under response-driven conditions within a certain timeframe after a transient disturbance, a set of frequency-related features is constructed based on the real-time nature of the response-driven information, and variables that can initially reflect frequency time-series characteristics are selected:

[0063] {TZ8,TZ9,TZ 10 TZ 11 TZ 12 TZ 13 TZ 14 TZ 15 TZ 16}(4).

[0064] S300: Based on the set of variables reflecting frequency time-series characteristics, the corresponding characteristic variables of n generators, n loads, and n buses, as well as the port energy of the generator with the largest power, are randomly selected as characteristic quantities. Based on the characteristic quantities, a heat map is drawn through Pearson correlation analysis, and a hierarchical clustering diagram is drawn through cluster analysis to intuitively present the characteristic quantities with a high degree of correlation with frequency. Based on the degree of correlation, multiple key characteristic quantities are selected from the set of variables reflecting frequency time-series characteristics.

[0065] Obtain the set of time-series feature quantities after dimensionality reduction mapping. To reduce redundancy of feature quantities with the same name for similar equipment, feature quantities of the same type of equipment are randomly selected: the corresponding feature variables of n generators, n loads, n buses, and the port energy of the generator with the largest power are randomly selected as input quantities for feature quantity correlation research.

[0066] First, data on these frequency-related time-series features are acquired. Then, Pearson correlation analysis is used to create a heatmap. The heatmap is used to display the density, distribution, or correlation of two-dimensional data. By using color coding to represent the values ​​of data points, the heatmap can visually display data patterns and trends. The degree of correlation is reflected by the intensity of the intersecting color blocks between features; lighter colors indicate a higher degree of correlation. An example correlation heatmap for frequency-related features is shown below. Figure 2 As shown.

[0067] Following cluster analysis, a hierarchical clustering diagram is generated to visually represent features with high frequency correlation. Each horizontal line in the hierarchical clustering diagram corresponds to the distance or similarity value between two variables or clusters; lower distance values ​​indicate higher similarity. An example hierarchical clustering diagram of frequency-related features is shown below. Figure 3 As shown. Therefore, the lower the horizontal line, the higher the similarity. Finally, based on the degree of correlation, time-series features such as generator terminal current, generator port energy, and generator power angle can be selected as key features:

[0068] {TZ 12 TZ 13 TZ 16}(5).

[0069] In one possible design, frequency-specific analysis is performed based on the endpoints and characteristics of the response characteristic curve using the following formula:

[0070] S400: Based on multiple key characteristic quantities, plot the response characteristic curves of the system center frequency and each key characteristic quantity, and perform frequency-specific analysis based on the end of the response characteristic curves and the curve characteristics.

[0071] In some embodiments, the response characteristic curves of the system center frequency versus generator current, power angle, and port energy are used to form the criteria:

[0072]

[0073] In the formula, f end This is the end of the response characteristic curve.

[0074] The response characteristic curve converges when the system frequency is stable and diverges when the system is unstable. The characteristic response curve can then be used to analyze the frequency stability characteristics of the renewable energy power system and examine the impact of different renewable energy proportions on frequency characteristics.

[0075] To verify the effectiveness of the proposed method, different tripping ratios were set in the low-frequency example of the CSEE-FS example to obtain corresponding response drive data, and Pearson coefficient correlation analysis was performed between frequency and its related characteristic quantities. Since the number of characteristic quantities is large and difficult to display in a single graph, and the characteristic quantities of similar equipment represent almost identical features, the corresponding characteristic variables of four generators, three loads, and three buses were randomly selected as inputs for the characteristic quantity correlation study. The inputs are the time-series quantities of each characteristic quantity after the system is disturbed. To make the obtained heatmap and hierarchical clustering diagram clearer, only the correlation analysis results of 1-2 representative characteristic quantities of generators, loads, and buses are shown, such as... Figure 2 and Figure 3 As shown. Then, taking current and energy as examples, the differences in frequency characteristics of the Ef and If response characteristic curves under stable and unstable conditions were studied, as shown in the figure. Figure 4 and Figure 5 As shown, the differences in the frequency characteristics of the If response characteristic curve under different proportions of new energy sources were then studied. Figure 6 As shown. In summary, this frequency stability characteristic analysis of the new energy power system can effectively establish a relationship between frequency and its related characteristic quantities, and perform frequency stability determination and characteristic analysis of the new energy power system.

[0076] This application also provides a device for analyzing the frequency stability characteristics of a new energy power system, such as... Figure 7 As shown, the frequency stability characteristic analysis device for the new energy power system includes:

[0077] The set construction module 701 is configured to construct the system inertial center frequency and its frequency-related characteristic quantity set;

[0078] The initial screening module 702 is configured to initially select a set of variables reflecting frequency time-series characteristics from the set of frequency-related feature quantities based on the real-time nature of response-driven information.

[0079] The feature filtering module 703 is configured to randomly select the corresponding feature variables of n generators, n loads, n buses, and the port energy of the generator with the largest power as feature quantities based on the set of variables reflecting frequency time-series characteristics. Based on the feature quantities, a heat map is drawn through Pearson correlation analysis, and a hierarchical clustering diagram is drawn through cluster analysis to intuitively present the feature quantities with a high degree of correlation with frequency. Multiple key feature quantities are filtered from the set of variables reflecting frequency time-series characteristics according to the degree of correlation.

[0080] The feature analysis module 704 is configured to plot the response feature curves of the system center frequency and each key feature quantity based on the multiple key feature quantities, and to perform frequency-specific analysis based on the end of the response feature curves and the curve characteristics.

[0081] In some embodiments, the set construction module is further configured to calculate the system inertial center frequency using the following formula:

[0082]

[0083] In the formula, f COI (t) is the inertial center frequency of the power system, M i Let m be the inertial time constant of generator i; m be the number of generators; M be the inertial time constant of generator i. T Let m be the sum of the inertia of the m generators in the power system.

[0084] X (i+m) (t) represents the dynamic frequency of generator i.

[0085] In some embodiments, the set of frequency-related features is represented as:

[0086] {TZ1,TZ2,TZ3,TZ4,TZ5,TZ6,TZ7,TZ8,TZ9,TZ 10 TZ 11 TZ 12 TZ 13 TZ 14 TZ 15 TZ 16}(3)

[0087] In the formula, TZ1 is the sum of active power of all generators in the system before the fault, TZ2 is the rotor speed of all generators in the system before the fault, TZ3 is the maximum rate of change of frequency of all generators after the fault, TZ4 is the increase in active power generated by all generators in the system after the fault, TZ5 is the time of the lowest frequency of all generators in the system after the fault, TZ6 is the generalized inertia of the power system, TZ7 is the frequency change of each node when the system reaches steady state after the fault, TZ8 is the system frequency change, TZ9 is the load level, and TZ 10 For generator active power, TZ 11 TZ represents the instantaneous voltage of each bus after the disturbance. 12 TZ is the generator terminal current. 13 For generator phase angle, TZ 14 TZ represents the mechanical power of each generator. 15 For reactive power, TZ 16 For port energy.

[0088] In some embodiments, the set building module is further configured to calculate the port energy TZ using the following formula. 16 :

[0089]

[0090] In the formula, x s Let θ and V be the values ​​of θ and V at the equilibrium point, x be the values ​​of θ and V at the integration cutoff time, P be the active power at the new energy port, θ be the phase angle of the new energy grid-connected node voltage, Q be the reactive power at the new energy port, and V be the voltage at the new energy grid-connected node.

[0091] In some embodiments, the set of variables reflecting frequency-time characteristics is represented as:

[0092] {TZ8,TZ9,TZ 10 TZ 11 TZ 12 TZ 13 TZ 14 TZ 15 TZ 16}(4). In some embodiments, the plurality of key feature quantities are represented as:

[0093] {TZ 12 TZ 13 TZ 16}(5).

[0094] In some embodiments, the feature analysis module is further configured to perform frequency-specific analysis based on the endpoints and curve characteristics of the response feature curve using the following formula:

[0095]

[0096] In the formula, f end This is the end of the response characteristic curve.

[0097] This application provides an electronic device. The electronic device may include a processor and a memory, wherein the processor and the memory can communicate; exemplarily, the processor and the memory communicate via a communication bus.

[0098] The processor executes computer execution instructions stored in memory, causing the processor to perform the schemes in the above embodiments. The processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0099] The communication bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The system bus can be divided into address bus, data bus, control bus, etc. Transceivers are used to enable communication between database access devices and other computers (e.g., clients, read-write libraries, and read-only libraries). Memory may include random access memory (RAM) and may also include non-volatile memory.

[0100] The electronic device provided in this application embodiment can be the terminal device described in the above embodiments.

[0101] This application also provides a computer-readable storage medium storing computer instructions. When the computer instructions are executed on a computer, the computer performs the technical solution of the above-described method for analyzing the frequency stability characteristics of a new energy power system.

[0102] This application also provides a computer program product, which includes a computer program stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium. When the at least one processor executes the computer program, it can implement the technical solution of the frequency stability characteristic analysis method of the new energy power system in the above embodiments.

[0103] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or modules, and may be electrical, mechanical, or other forms.

[0104] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to implement the solution of this embodiment according to actual needs.

[0105] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The unit composed of the above modules can be implemented in hardware or in the form of hardware plus software functional units.

[0106] The integrated modules described above, implemented as software functional modules, can be stored in a computer-readable storage medium. These software functional modules, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods of the various embodiments of this application.

[0107] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly manifested as being executed by a hardware processor, or executed by a combination of hardware and software modules within the processor.

[0108] The memory may include high-speed RAM, and may also include non-volatile storage (NVM), such as at least one disk storage device, and may also be a USB flash drive, external hard drive, read-only memory, disk or optical disc, etc.

[0109] Buses can be Industry Standard Architecture (ISA) buses, Peripheral Component Interconnect (PCI) buses, or Extended Industry Standard Architecture (EISA) buses, etc. Buses can be categorized into address buses, data buses, control buses, etc.

[0110] The aforementioned storage medium can be implemented from any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The storage medium can be any available medium accessible to general-purpose or special-purpose computers.

[0111] An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Alternatively, the storage medium can be an integral part of the processor. The processor and storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and storage medium can exist as discrete components in an electronic control unit or main control device.

[0112] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0113] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for analyzing the frequency stability characteristics of a new energy power system, characterized in that, The method includes: Construct the system's inertial center frequency and its set of frequency-related characteristic quantities; Based on the real-time nature of response-driven information, a set of variables reflecting frequency time-series characteristics is initially selected from the set of frequency-related feature quantities; Based on the set of variables reflecting frequency time-series characteristics, the corresponding characteristic variables of n generators, n loads, and n buses, as well as the port energy of the generator with the largest power, are randomly selected as characteristic quantities. Based on the characteristic quantities, a heat map is drawn through Pearson correlation analysis, and a hierarchical clustering diagram is drawn through cluster analysis to intuitively present the characteristic quantities with a high degree of correlation with frequency. Based on the degree of correlation, several key characteristic quantities are selected from the set of variables reflecting frequency time-series characteristics. Based on the aforementioned key features, a response feature curve of the system center frequency and each key feature is plotted, and frequency-specific analysis is performed based on the end points and curve characteristics of the response feature curves.

2. The method for analyzing the frequency stability characteristics of a new energy power system according to claim 1, characterized in that, The system's center of inertial frequency is calculated using the following formula: In the formula, f COI (t) is the inertial center frequency of the power system, M i Let m be the inertial time constant of generator i; m be the number of generators; M be the inertial time constant of generator i. T Let m be the sum of the inertia of the m generators in the power system. X (i+m) (t) represents the dynamic frequency of generator i.

3. The method for analyzing the frequency stability characteristics of a new energy power system according to claim 1, characterized in that, The set of frequency-related features is represented as follows: {TZ1,TZ2,TZ3,TZ4,TZ5,TZ6,TZ7,TZ8,TZ9,TZ 10 ,TZ 11 ,TZ 12 ,TZ 13 ,TZ 14 ,TZ 15 ,TZ 16 }(3) In the formula, TZ1 is the sum of active power of all generators in the system before the fault, TZ2 is the rotor speed of all generators in the system before the fault, TZ3 is the maximum rate of change of frequency of all generators after the fault, TZ4 is the increase in active power generated by all generators in the system after the fault, TZ5 is the time of the lowest frequency of all generators in the system after the fault, TZ6 is the generalized inertia of the power system, TZ7 is the frequency change of each node when the system reaches steady state after the fault, TZ8 is the system frequency change, TZ9 is the load level, and TZ 10 For generator active power, TZ 11 TZ represents the instantaneous voltage of each bus after the disturbance. 12 TZ is the generator terminal current. 13 For generator phase angle, TZ 14 TZ represents the mechanical power of each generator. 15 For reactive power, TZ 16 For port energy.

4. The method for analyzing the frequency stability characteristics of a new energy power system according to claim 3, characterized in that, The port energy TZ is calculated using the following formula. 16 : In the formula, x s Let θ and V be the values ​​of θ and V at the equilibrium point, x be the values ​​of θ and V at the integration cutoff time, P be the active power at the new energy port, θ be the phase angle of the new energy grid-connected node voltage, Q be the reactive power at the new energy port, and V be the voltage at the new energy grid-connected node.

5. The method for analyzing the frequency stability characteristics of a new energy power system according to claim 3, characterized in that, The set of variables reflecting frequency-time characteristics is represented as follows: {TZ8,TZ9,TZ 10 ,TZ 11 ,TZ 12 ,TZ 13 ,TZ 14 ,TZ 15 ,TZ 16 }(4).

6. The method for analyzing the frequency stability characteristics of a new energy power system according to claim 5, characterized in that, The multiple key feature quantities are represented as follows: {TZ 12 ,TZ 13 ,TZ 16 }(5).

7. The method for analyzing the frequency stability characteristics of a new energy power system according to any one of claims 1 to 6, characterized in that, Based on the endpoints and characteristics of the response characteristic curve, frequency-specific analysis is performed using the following formula: In the formula, f end This is the end of the response characteristic curve.

8. A frequency stability characteristic analysis device for a new energy power system, characterized in that, The device includes: The set construction module is configured to construct the system's inertial center frequency and its frequency-related characteristic quantities set; The initial screening module is configured to initially select a set of variables reflecting frequency time-series characteristics from the set of frequency-related features based on the real-time nature of response-driven information. The feature filtering module is configured to randomly select the corresponding feature variables of n generators, n loads, and n buses, as well as the port energy of the generator with the largest power, as feature quantities based on the set of variables reflecting frequency time-series characteristics. Based on the feature quantities, a heat map is drawn through Pearson correlation analysis, and a hierarchical clustering diagram is drawn through cluster analysis to intuitively present the feature quantities with a high degree of correlation with frequency. Multiple key feature quantities are filtered out from the set of variables reflecting frequency time-series characteristics according to the degree of correlation. The feature analysis module is configured to plot the response feature curves of the system center frequency and each key feature quantity based on the multiple key feature quantities, and to perform frequency-specific analysis based on the end of the response feature curves and the curve characteristics.

9. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes the computer execution instructions stored in the memory to implement the method for analyzing the frequency stability characteristics of a new energy power system as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method for analyzing the frequency stability characteristics of a new energy power system as described in any one of claims 1-7.

Images