Method and system for estimating probability of lowest point of frequency considering disturbance of power system

Abstract

The method comprises the following steps: acquiring a power disturbance quantity and a total inertia of a system, and establishing a deviation quantity model of the lowest frequency point based on a frequency response output model of a unit in the system according to the power disturbance quantity and the total inertia of the system; extracting probability characteristics of the power disturbance quantity; obtaining the probability characteristic of the deviation value of the frequency lowest point based on the deviation value model of the frequency lowest point by using the probability characteristic of the power disturbance quantity; and performing probability estimation on the lowest frequency point considering the disturbance of the power system according to the probability characteristic of the deviation value of the lowest frequency point. The probability distribution of the lowest point of the system frequency is calculated according to the probability characteristics of the power disturbance quantity, and a more reliable basis is provided for power system frequency stability analysis and control.

Description

Technical Field

[0001] The invention belongs to the field of power system frequency stability analysis, and in particular relates to a probability estimation method and system for a frequency minimum point taking into account power system disturbances. Background Art

[0002] The frequency of the power system is an important indicator for measuring the quality of electric energy and an important parameter affecting the stable operation of the power system. The new power system has low rotational inertia, insufficient frequency regulation reserve capacity, and increased disturbance power. Frequency stability has become the main factor threatening the safe operation of the power grid. The lowest point of the power system frequency includes obtaining the lowest point of the deterministic frequency. The essence of the stability of the power system frequency lies in the real-time balance of power generation and load demand. With the continuous increase in the proportion of new energy, the power system has shown many new characteristics in terms of source, grid, load, and storage: on the source side, a high proportion of highly random and volatile renewable energy sources are connected; on the grid side, a large number of power electronic equipment are connected, and electricity is closely coupled with various energy systems such as gas, heating, and transportation, and the interaction of multiple energy flows is complex; on the load side, various forms of energy use appear, and the "double peak" characteristics of summer and winter loads are increasingly prominent. The access of various distributed energy sources has led to the continuous enhancement of activeness, and the energy flow has changed from "one-way" to "multi-directional"; on the storage side, electrical energy storage represented by chemical batteries and traditional thermal energy storage cooperate with each other to provide more abundant and flexible resources for the power system. On the one hand, these new features significantly improve the flexibility and cleanliness of the power system, but on the other hand, they also make the power system face problems such as fast changes in operating status, many random influencing factors, and complex fault coupling. The changes in many aspects often lead to uncertain differences between generated power and consumed power, and the power difference will directly affect the changes in the frequency dynamic process.

[0003] In the prior art, frequency response analysis methods include: simulation method and analytical method. The simulation method can handle complex power system models, including nonlinear characteristics, dynamic behaviors and various disturbances, and can provide detailed reflection of the system's frequency response process under different conditions, and describe in detail the distribution of the lowest frequency points in each scenario. However, complex simulation models require a lot of computing resources and time, especially for long-term simulation of large-scale power systems, which brings challenges to real-time analysis and rapid decision-making. The analytical method uses mathematical formulas and theoretical analysis to quickly obtain the results of the system frequency response. Compared with the simulation method, the analytical method has lower computing resource requirements and is suitable for real-time analysis and online applications. For new power grids with complex scenarios, analytical methods with reliable calculation accuracy have greater advantages in efficiency. Summary of the invention

[0004] In order to solve the deficiencies in the prior art, the present invention provides a probability estimation method and system for the frequency minimum point taking into account the disturbance of the power system, simplifies the system frequency response model by means of de-looping and approximation, obtains the analytical expression of the system frequency minimum point after the disturbance, further analyzes the probability characteristics of the system disturbance, and calculates the probability distribution of the system frequency minimum point according to the probability characteristics of the power disturbance, so as to provide a more reliable basis for the frequency stability analysis and control of the power system.

[0005] The present invention adopts the following technical solution.

[0006] The present invention proposes a probability estimation method for the frequency minimum point of power system disturbance, comprising:

[0007] Obtain and establish a deviation model of the lowest frequency point based on the power disturbance and the total inertia of the system and the frequency response output model of the units in the system;

[0008] Extract the probability characteristics of the power disturbance; use the probability characteristics of the power disturbance and obtain the probability characteristics of the deviation at the lowest frequency point based on the deviation model at the lowest frequency point;

[0009] According to the probability characteristics of the deviation of the lowest frequency point, the probability of the lowest frequency point taking into account the power system disturbance is estimated.

[0010] Preferably, the power disturbance and the total inertia of the system are obtained, and a deviation model of the lowest frequency point is established based on the frequency response output model of the units in the system, including:

[0011] Obtain the power disturbance amount and disturbance duration, and establish a time-varying model of system frequency response output;

[0012] Obtain the total inertia of the system based on the system rotor motion equation;

[0013] The fitting method is used to establish the frequency response output fitting model of the units in the system;

[0014] Based on the simulated frequency difference and the frequency response output fitting model of the units in the system, the frequency response output model of the units in the system is obtained;

[0015] Based on the frequency response output model of the units in the system, the deviation model of the lowest frequency point is obtained.

[0016] Preferably, the system frequency response output time-varying model satisfies the following relationship:

[0017] ΔP(t)=K fit t

[0018] Where ΔP(t) is the system frequency response output at any time t during the disturbance process, K fit is the fitting coefficient.

[0019] Preferably, the fitting coefficient is the ratio of the lowest frequency difference at the lowest frequency point to the lowest time.

[0020] Preferably, the frequency response output of the i-th unit in the system is fitted with a quadratic polynomial, and the frequency response output fitting model of the units in the system satisfies the following relationship:

[0021] P i (t) = p Gi,0 +p Gi,1 t+p Gi,2 t 2

[0022] Where P i (t) is the frequency response output of the i-th unit in the system at time t, p Gi,0 、p Gi,1 、p Gi,2 are the constant, linear term coefficient and quadratic term coefficient in the quadratic fitting function of the speed governor curve of the i-th unit, i=1,2,…,N, N is the number of units in the system.

[0023] Preferably, in the simulated frequency difference Δf coi Under the action of , the frequency response output model of the units in the system satisfies the following relationship:

[0024]

[0025] Where P M is the power disturbance, H total is the total inertia of the system, and j is the power of time.

[0026] Preferably, the deviation model of the lowest frequency point satisfies the following relationship:

[0027]

[0028] in

[0029] Where Δf M is the deviation of the lowest frequency point; P M is the power disturbance; H total is the total inertia of the system, H i is the inertia of unit i in the system, S i is the capacity of unit i in the system; p 1,total is the linear coefficient p in the quadratic fitting function of the speed governor curves of all units in the system Gi,1 The sum, p 2,total is the quadratic term coefficient p in the quadratic fitting function of the speed governor curves of all units in the system Gi,2 sum.

[0030] Preferably, the probability characteristics of the power disturbance are extracted; the probability characteristics of the deviation at the lowest frequency point are obtained based on the deviation model at the lowest frequency point by using the probability characteristics of the power disturbance, including:

[0031] The probability density of power disturbance is used as the probability feature;

[0032] Based on the deviation model of the lowest frequency point, the probability density of the deviation at the lowest frequency point is determined according to the probability density of the power disturbance to serve as the probability feature of the deviation at the lowest frequency point.

[0033] Preferably, the result of the probability estimation of the frequency minimum point taking into account the power system disturbance includes: the frequency minimum point with the highest probability of occurrence under different disturbances and the corresponding frequency minimum value, and the frequency minimum value corresponding to each frequency minimum point under the same disturbance and its probability of occurrence;

[0034] The deviation of the lowest frequency point is calculated based on the deviation model of the lowest frequency point, and the lowest frequency value can be obtained by superimposing the deviation of the lowest frequency point with the initial value of the frequency before the disturbance.

[0035] The present invention also proposes a probability estimation system for the lowest frequency point of power system disturbances, comprising:

[0036] Model building module, probability feature extraction module, probability estimation module;

[0037] The model building module is used to obtain and build a deviation model of the lowest frequency point based on the frequency response output model of the units in the system according to the power disturbance and the total inertia of the system;

[0038] The probability feature extraction module is used to extract the probability feature of the power disturbance; using the probability feature of the power disturbance, based on the deviation model of the lowest frequency point, the probability feature of the deviation at the lowest frequency point is obtained;

[0039] The probability estimation module is used to estimate the probability of the lowest frequency point taking into account the power system disturbance according to the probability characteristics of the deviation amount of the lowest frequency point.

[0040] Preferably, the model building module includes: a first model building unit, a second model building unit, an inertia calculation unit, a third model building unit, and a fourth model building unit;

[0041] The first model building unit is used to obtain the power disturbance amount and the disturbance duration, and to build a system frequency response output time-varying model;

[0042] An inertia calculation unit, used to obtain the total inertia of the system according to the system rotor motion equation;

[0043] The second model building unit is used to establish a frequency response output fitting model of the units in the system by using a fitting method;

[0044] The third model building unit is used to obtain the frequency response output model of the units in the system based on the simulated frequency difference and the frequency response output fitting model of the units in the system;

[0045] The fourth model building unit is used to obtain a deviation model of the lowest frequency point based on the frequency response output model of the units in the system.

[0046] Preferably, the probability feature extraction module includes: a first probability feature extraction unit, a second probability feature extraction unit

[0047] A first probability feature extraction unit is used to use the probability density of the power disturbance as a probability feature;

[0048] The second probability feature extraction unit is used to determine the probability density of the deviation at the lowest frequency point based on the deviation model at the lowest frequency point and the probability density of the power disturbance as the probability feature of the deviation at the lowest frequency point.

[0049] A terminal includes a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the method.

[0050] A computer-readable storage medium stores a computer program, which implements the steps of the method when executed by a processor.

[0051] The beneficial effects of the present invention are that, compared with the prior art, the present invention at least includes: determining the lowest frequency point of the power system disturbance from a probabilistic perspective; extracting the probabilistic characteristics of the disturbance parameters that directly affect the frequency on the basis of the frequency response model, thereby obtaining an uncertainty model that can characterize the disturbance characteristics under the background of large-scale grid connection of new energy; based on the uncertainty model, establishing a mathematical model that characterizes the relationship between the power system disturbance and the lowest frequency point of the power system disturbance; and using a probabilistic method, converting the probability of the power system disturbance into the probability of the lowest frequency point of the power system disturbance.

[0052] The method proposed in the present invention can analyze the lowest frequency point of power system disturbance for various disturbance scenarios. The obtained probability characteristics of the lowest frequency point of power system disturbance have high accuracy and fast calculation speed compared with the simulation system results, and are more suitable for online analysis and decision-making in the current situation where a large number of new energy sources are connected to the grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 A flow chart of a probability estimation method for the frequency minimum point of a power system disturbance proposed by the present invention;

[0054] Figure 2 It is a fitting diagram of the unit output in the embodiment of the present invention;

[0055] Figure 3 A diagram showing the structure of the New England 39-node system used in an embodiment of the present invention;

[0056] Figure 4 A comparison diagram of probability density curves of power disturbance values ​​obtained by cubic Gaussian fitting in an embodiment of the present invention;

[0057] Figure 5 This is a comparison diagram of the probability density curves of the lowest frequency point obtained by the probability estimation method and the simulation method in the embodiment of the present invention. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only embodiments of a part of the present invention, rather than all embodiments. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the protection scope of the present invention.

[0059] After being disturbed, the frequency of the power system will first drop, and then gradually recover under the action of inertial response and primary frequency modulation. This process contains many parameters that can reflect the safety of the power system. Among them, the lowest frequency point of the power system refers to the state where the frequency reaches the lowest at a local moment before the frequency returns to a steady state after the system is disturbed (such as large-scale generators disconnecting from the grid, sudden increase in load, etc.). It reflects the most vulnerable state of the system when facing disturbances, and is also the key point for whether the power system dispatching and protection mechanisms can effectively respond. The lowest frequency point of the power system is an important indicator for evaluating the stability of the power system. By analyzing the value of the lowest frequency point and the time of its occurrence, the dynamic response and recovery capabilities of the system after the disturbance can be judged. If the lowest point can be predicted before the disturbance occurs, measures can be taken to suppress frequency changes and avoid low-frequency load shedding. At the same time, considering the complexity of the frequency dynamic process under the new situation, the characteristics of the frequency fluctuation of the power system should be analyzed, and the frequency changes of the power system should be further analyzed using the probability of disturbances. It is more appropriate to describe the lowest point from a probabilistic perspective because a deterministic frequency lowest point cannot fully reflect the system frequency status.

[0060] The probability characteristics of the system frequency dynamic process under the new situation are becoming more and more obvious, and the range of the system frequency minimum point is expanding. It is necessary to analyze the probability distribution of the system frequency minimum point based on the characteristics of system fluctuations.

[0061] In view of the above problems, the present invention proposes a probability estimation method taking into account the frequency minimum point of power system disturbance, such as Figure 1 As shown, including:

[0062] Step 1: Obtain and establish a deviation model of the lowest frequency point based on the frequency response output model of the units in the system according to the power disturbance and the total inertia of the system.

[0063] Specifically, step 1 includes:

[0064] Step 1.1, obtain the power disturbance amount and disturbance duration, and establish the system frequency response output time-varying model.

[0065] Specifically, Figure 2 As shown, the time period from the time the disturbance occurs to the time the frequency reaches the lowest point is the disturbance duration Δt M , during the duration of the disturbance, the system frequency response output will increase from 0 to the power disturbance. Based on the linear method, the system frequency response output is set to increase linearly with time after the disturbance occurs to establish a time-varying model of the system frequency response output, which satisfies the following relationship:

[0066] ΔP(t)=K fit t

[0067] In the formula, ΔP(t) is the system frequency response output at any time t during the disturbance process. In order to conveniently express the relationship between the output and time, this paper adopts a linear function to fit the output, and the fitting coefficient is K fit .

[0068] Figure 2 In the figure, the solid line is the actual power curve of the system output, and the dotted line is the fitted power curve based on the system frequency response output time-varying model.

[0069] Step 1.2, obtain the total inertia of the system according to the system rotor motion equation;

[0070] Specifically, the total inertia of the system H total Satisfies the following relationship:

[0071]

[0072] In the formula, ΔP e (t) is the change in electromagnetic power of the system.

[0073] When the system frequency response output increases linearly with time after the disturbance occurs, that is, a constant ramp rate is used to approximate the system frequency response output, then the decrease of the frequency offset presents a parabolic trajectory. The trajectory of the frequency offset is a quadratic function of any time t during the disturbance process. By unlooping the frequency offset from the model and expressing it as a function of time, the calculation efficiency is improved, and the nonlinear relationship between the system frequency response output ΔP(t) at any time t during the disturbance process and the system frequency offset Δf(t) at any time t during the disturbance process can be directly reflected.

[0074] Step 1.3, using the fitting method to establish the frequency response output fitting model of the units in the system.

[0075] Specifically, the frequency response output of the i-th unit in the system is fitted with a quadratic polynomial, and the frequency response output fitting model of the units in the system satisfies the following relationship:

[0076] P i (t) = p Gi,0 +p Gi,1 t+p Gi,2 t 2

[0077] Where P i (t) is the frequency response output of the i-th unit in the system, p Gi,0 、p Gi,1 、p Gi,2 are the constant, linear term coefficient and quadratic term coefficient in the quadratic fitting function of the speed governor curve of the i-th unit, i=1,2,…,N, N is the number of units in the system.

[0078] Step 1.4, based on the simulated frequency difference and the frequency response output fitting model of the units in the system, the frequency response output model of the units in the system is obtained.

[0079] The fitting coefficient K of the system frequency response output fit , according to different fitting requirements such as Figure 2 As shown, take any value from fitting curve 1 to fitting curve 3. In this paper, the value is the ratio of the lowest frequency difference to the lowest time at the lowest frequency point. Figure 3 As shown in the figure, the output of each unit in the system is decoupled from the system frequency offset, but is related to the simulated frequency difference Δf coi Coupling, after the output of each unit is processed by the per-unit value conversion coefficient and then summed with the disturbance, it is more convenient to add the change of the total frequency modulation output of the system over time.

[0080] In the simulated frequency difference Δf coi Under the action of , the frequency response output model of the units in the system satisfies the following relationship:

[0081]

[0082] Where P i (t) is the frequency response output of the i-th unit in the system, P M is the power disturbance, H total is the total inertia of the system, and j is the power of time.

[0083] Step 1.5, based on the frequency response output model of the units in the system, obtain the deviation model of the lowest frequency point, which satisfies the following relationship:

[0084]

[0085] in

[0086] Where Δf M is the deviation of the lowest frequency point; P M is the power disturbance; H total is the total inertia of the system; p 1,total is the sum of the linear coefficients in the quadratic fitting function of the speed governor curves of all units in the system, p 2,total It is the sum of the quadratic coefficients in the quadratic fitting function of the speed governor curve of all units in the system; N is the number of units in the system.

[0087] In the deviation model of the lowest frequency point, the deviation and power disturbance at the lowest frequency point are variables, and the deviation model of the lowest frequency point characterizes the relationship between the deviation and power disturbance at the lowest frequency point. The remaining items in the model are all numerical values ​​that can be calculated; according to the deviation model of the lowest frequency point, each parameter can be directly calculated to quickly obtain the offset of the lowest frequency point.

[0088] Step 2, extracting the probability characteristics of the power disturbance; using the probability characteristics of the power disturbance, based on the deviation model of the lowest frequency point, obtaining the probability characteristics of the deviation at the lowest frequency point.

[0089] The probability estimation method of the frequency minimum point considering the power system disturbance proposed in the present invention takes into account that the essence of power system frequency stability lies in the real-time balance of power generation and load demand, and the power difference will directly affect the change of the frequency dynamic process. In step 1, the analytical expression between the power system disturbance and the frequency minimum point has been given, and the frequency difference of the frequency minimum point can be directly obtained by a given disturbance value. Compared with the method of calculating the frequency minimum point by inputting a disturbance according to the simulation model, the efficiency can be greatly improved.

[0090] In the new situation where the frequency regulation resources of the power system are limited, ensuring the safety and stability of the power system requires describing the frequency dynamic process of the power system under various disturbances and understanding the corresponding lowest frequency points under all disturbances. If only the analytical formula in step 1 is used to solve, it is still necessary to solve the corresponding lowest point for each disturbance separately, and then sort and analyze it. Although its calculation efficiency is improved compared to the simulation method, the total calculation time is still long as the number of disturbances increases.

[0091] In summary, it is not appropriate to use a deterministic frequency minimum point to reflect the system frequency status, or to accumulate the minimum point status calculated under various conditions. Therefore, this paper proposes to describe the minimum point status under all disturbance types from a probabilistic perspective.

[0092] Specifically, the probability density of power disturbance f PM (x) as a probability feature; based on the deviation model at the lowest frequency point, according to the probability density f of the power disturbance PM (x), determine the probability density f of the deviation at the lowest frequency point Δ f M (y).

[0093] In a non-limiting preferred embodiment, using the probability density of the power disturbance as the probability feature is a non-limiting preferred choice, and those skilled in the art can use other probability functions to describe the probability feature of the power disturbance according to the calculation requirements.

[0094] Specifically, according to the data set of the power disturbance, the probability density of the power disturbance is fitted using a cubic Gaussian. Using a cubic Gaussian to fit the probability density of the power disturbance is a non-restrictive and preferred choice, and those skilled in the art can use other methods to fit the probability density of the power disturbance according to the calculation requirements.

[0095] The principle of high-order Gaussian fitting to describe probability density functions is to approximate a complex probability density function through a linear combination of multiple Gaussian functions, in which the mean, variance and weight of each Gaussian component are usually estimated by the expectation-maximization (EM) algorithm. The EM algorithm is an iterative optimization algorithm that includes the E step and the M step, as follows: the E step calculates the probability (posterior probability) that each data point belongs to each Gaussian component; the M step updates the parameters of the Gaussian component to maximize the log-likelihood of the data under the current model. High-order Gaussian fitting can handle complex distribution shapes, and by increasing the number of Gaussian components, it can better capture the multimodal characteristics or complex structures of the data.

[0096] Step 3: According to the probability characteristics of the deviation amount of the lowest frequency point, the probability of the lowest frequency point taking into account the power system disturbance is estimated.

[0097] The results of the probability estimation of the lowest frequency points taking into account the disturbances in the power system include: the lowest frequency points with the highest probability of occurrence under different disturbances and the corresponding lowest frequency values, and the lowest frequency values ​​corresponding to each lowest frequency point under the same disturbance and their probability of occurrence.

[0098] Specifically, the deviation of the lowest frequency point is calculated based on the deviation model of the lowest frequency point, and the lowest frequency value can be obtained by superimposing the deviation of the lowest frequency point and the initial value of the frequency before the disturbance.

[0099] In the embodiment, the introduction Figure 3 The following figure shows the daily operation scenario of the generator in the New England 39-bus system, and its parameters are shown in Table 1.

[0100] Table 1 Dynamic parameters of New England 39-node system

[0101]

[0102] The probability characteristics of the power disturbance are extracted and described by the probability density function. First, the probability density is obtained based on the data set of the power disturbance. High-order Gaussian fitting can handle complex distribution shapes. By increasing the number of Gaussian components, the multimodal characteristics or complex structures of the data can be better captured. Figure 4 As shown, the dotted line is the cubic Gaussian fitting curve of the power disturbance. Qualitatively, it can be seen that its fitting condition is ideal.

[0103] The probability density of the deviation at the lowest frequency point is as follows Figure 5 As shown in the embodiment, the results obtained by the probability estimation method proposed by the present invention are compared with the results obtained by the same model simulation method. The probability density curves of the deviation of the lowest frequency point calculated by the two methods are shown in FIG. Figure 5 The comparison of the calculation efficiency and accuracy of the two methods is shown in Table 2.

[0104] Table 2 Comparison between simulation method and analytical method

[0105] Method Type Mean(Hz) Divergence (max = 1) Calculation time(s) Simulation Method 0.18 0 582.6 Probability estimation method (analytical method) 0.22 0.0304 13.4 Method comparison +0.04 +0.0304 -569.2 Analysis method (single) / / 0.07

[0106] It can be seen from Table 2 that when the probability estimation method proposed in the present invention is compared with the simulation method, the mean difference between the two methods is only 0.04 and the divergence difference is 0.0304, taking the simulation method as the benchmark, and the probability estimation method proposed in the present invention significantly shortens the calculation time, and the calculation efficiency is greatly improved by 97.7%. It can be seen that the method proposed in the present invention can not only take into account the complexity of the frequency dynamic process under the new power system, and describe the state of the lowest frequency point under the disturbance from a probabilistic perspective, but also significantly improve the solution speed on the basis of ensuring the solution accuracy, thereby meeting the requirements of real-time analysis and control.

[0107] Therefore, the probability estimation method of the lowest frequency point considering the power system disturbance proposed in the present invention can consider the probability characteristics of the disturbance and analytically solve the probability density function of the lowest frequency point when the power system faces a disturbance, which can provide a reliable basis for the power system frequency stability analysis and control quickly and accurately.

[0108] The present invention also proposes a probability estimation system for the lowest frequency point of power system disturbances, comprising:

[0109] Model building module, probability feature extraction module, probability estimation module;

[0110] The model building module is used to obtain and build a deviation model of the lowest frequency point based on the frequency response output model of the units in the system according to the power disturbance and the total inertia of the system;

[0111] The probability feature extraction module is used to extract the probability feature of the power disturbance; using the probability feature of the power disturbance, based on the deviation model of the lowest frequency point, the probability feature of the deviation at the lowest frequency point is obtained;

[0112] The probability estimation module is used to estimate the probability of the lowest frequency point taking into account the power system disturbance according to the probability characteristics of the deviation amount of the lowest frequency point.

[0113] Preferably, the model building module includes: a first model building unit, a second model building unit, an inertia calculation unit, a third model building unit, and a fourth model building unit;

[0114] The first model building unit is used to obtain the power disturbance amount and the disturbance duration, and to build a system frequency response output time-varying model;

[0115] An inertia calculation unit, used to obtain the total inertia of the system according to the system rotor motion equation;

[0116] The second model building unit is used to establish a frequency response output fitting model of the units in the system by using a fitting method;

[0117] The third model building unit is used to obtain the frequency response output model of the units in the system based on the simulated frequency difference and the frequency response output fitting model of the units in the system;

[0118] The fourth model building unit is used to obtain a deviation model of the lowest frequency point based on the frequency response output model of the units in the system.

[0119] Preferably, the probability feature extraction module includes: a first probability feature extraction unit, a second probability feature extraction unit

[0120] A first probability feature extraction unit is used to use the probability density of the power disturbance as a probability feature;

[0121] The second probability feature extraction unit is used to determine the probability density of the deviation at the lowest frequency point based on the deviation model at the lowest frequency point and the probability density of the power disturbance as the probability feature of the deviation at the lowest frequency point.

[0122] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0123] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0124] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0125] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may 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 may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0126] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A probability estimation method for the frequency minimum point of a power system disturbance, characterized in that: include: Obtain and establish a deviation model of the lowest frequency point based on the power disturbance and the total inertia of the system and the frequency response output model of the units in the system; Extract the probability characteristics of the power disturbance; use the probability characteristics of the power disturbance and obtain the probability characteristics of the deviation at the lowest frequency point based on the deviation model at the lowest frequency point; According to the probability characteristics of the deviation of the lowest frequency point, the probability of the lowest frequency point taking into account the power system disturbance is estimated.

2. The probability estimation method of the frequency minimum point taking into account the power system disturbance according to claim 1 is characterized in that: Obtain the power disturbance and total system inertia, and establish a deviation model of the lowest frequency point based on the frequency response output model of the units in the system, including: Obtain the power disturbance amount and disturbance duration, and establish a time-varying model of system frequency response output; Obtain the total inertia of the system based on the system rotor motion equation; The fitting method is used to establish the frequency response output fitting model of the units in the system; Based on the simulated frequency difference and the frequency response output fitting model of the units in the system, the frequency response output model of the units in the system is obtained; Based on the frequency response output model of the units in the system, the deviation model of the lowest frequency point is obtained.

3. The probability estimation method of the frequency minimum point taking into account the power system disturbance according to claim 2 is characterized in that: The system frequency response output time-varying model satisfies the following relationship: ΔP(t)=K fit t Where ΔP(t) is the system frequency response output at any time t during the disturbance process, K fit is the fitting coefficient.

4. The probability estimation method of the frequency minimum point taking into account the power system disturbance according to claim 3 is characterized in that: The fitting coefficient is the ratio of the lowest frequency difference to the lowest time at the lowest frequency point.

5. The probability estimation method of the frequency minimum point taking into account the power system disturbance according to claim 3 is characterized in that: The frequency response output of the i-th unit in the system is fitted with a quadratic polynomial, and the frequency response output fitting model of the units in the system satisfies the following relationship: P i (t)=p Gi,0 +p Gi,1 t+p Gi,2 t 2 Where P i (t) is the frequency response output of the i-th unit in the system at time t, p Gi,0 、p Gi,1 、p Gi,2 are the constant, linear term coefficient and quadratic term coefficient in the quadratic fitting function of the speed governor curve of the i-th unit, i=1,2,…,N, N is the number of units in the system.

6. The probability estimation method of the frequency minimum point taking into account the power system disturbance according to claim 5 is characterized in that: In the simulated frequency difference Δf coi Under the action of , the frequency response output model of the units in the system satisfies the following relationship: Where P M is the power disturbance, H total is the total inertia of the system, and j is the power of time.

7. The probability estimation method of the frequency minimum point taking into account the power system disturbance according to claim 6 is characterized in that: The deviation model at the lowest frequency point satisfies the following relationship: Where Δf M is the deviation of the lowest frequency point; P M is the power disturbance; H total is the total inertia of the system, H i is the inertia of unit i in the system, S i is the capacity of unit i in the system; p 1,total is the linear coefficient p in the quadratic fitting function of the speed governor curves of all units in the system Gi,1 The sum, p 2,total is the quadratic term coefficient p in the quadratic fitting function of the speed governor curves of all units in the system Gi,2 sum.

8. The probability estimation method of the frequency minimum point taking into account the power system disturbance according to claim 1 is characterized in that: Extract the probability characteristics of the power disturbance; use the probability characteristics of the power disturbance and obtain the probability characteristics of the deviation at the lowest frequency point based on the deviation model at the lowest frequency point, including: The probability density of power disturbance is used as the probability feature; Based on the deviation model of the lowest frequency point, the probability density of the deviation at the lowest frequency point is determined according to the probability density of the power disturbance to serve as the probability feature of the deviation at the lowest frequency point.

9. The probability estimation method of the frequency minimum point taking into account the power system disturbance according to claim 1, characterized in that: The results of the probability estimation of the lowest frequency points taking into account the disturbance of the power system include: the lowest frequency points with the highest probability of occurrence under different disturbances and the corresponding lowest frequency values, the lowest frequency values ​​corresponding to each lowest frequency point under the same disturbance and their probability of occurrence; The deviation of the lowest frequency point is calculated based on the deviation model of the lowest frequency point, and the lowest frequency value can be obtained by superimposing the deviation of the lowest frequency point with the initial value of the frequency before the disturbance.

10. A probability estimation system for the frequency minimum point of power system disturbances, characterized in that: include: Model building module, probability feature extraction module, probability estimation module; A model building module is used to obtain and build a deviation model of the lowest frequency point based on the frequency response output model of the units in the system according to the power disturbance and the total inertia of the system; The probability feature extraction module is used to extract the probability feature of the power disturbance; using the probability feature of the power disturbance, based on the deviation model of the lowest frequency point, the probability feature of the deviation at the lowest frequency point is obtained; The probability estimation module is used to estimate the probability of the lowest frequency point taking into account the power system disturbance according to the probability characteristics of the deviation amount of the lowest frequency point.

11. The probability estimation system for the frequency minimum point of the power system disturbance according to claim 10, characterized in that: The model building module includes: a first model building unit, a second model building unit, an inertia calculation unit, a third model building unit, and a fourth model building unit; The first model building unit is used to obtain the power disturbance amount and the disturbance duration, and to build a system frequency response output time-varying model; An inertia calculation unit, used to obtain the total inertia of the system according to the system rotor motion equation; The second model building unit is used to establish a frequency response output fitting model of the units in the system by using a fitting method; The third model building unit is used to obtain the frequency response output model of the units in the system based on the simulated frequency difference and the frequency response output fitting model of the units in the system; The fourth model building unit is used to obtain a deviation model of the lowest frequency point based on the frequency response output model of the units in the system.

12. The probability estimation system for the frequency minimum point of the power system disturbance according to claim 10, characterized in that: The probability feature extraction module includes: a first probability feature extraction unit, a second probability feature extraction unit A first probability feature extraction unit is used to use the probability density of the power disturbance as a probability feature; The second probability feature extraction unit is used to determine the probability density of the deviation at the lowest frequency point based on the deviation model at the lowest frequency point and the probability density of the power disturbance as the probability feature of the deviation at the lowest frequency point.

13. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 9.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 9 are implemented.

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