Generator virtual inertia estimation method and device, equipment, storage medium and product

Abstract

The invention discloses a generator virtual inertia estimation method, device and equipment, a storage medium and a product, and the method comprises the steps: obtaining the generator active power outputted by a simulation model according to the simulation model of a to-be-estimated generator; wherein the to-be-estimated generator comprises a grid-following type converter interface generator; determining a virtual inertia parameter posterior probability density function of the generator to be estimated according to the generator active power output by the simulation model; virtual inertia parameter posterior distribution is determined according to the virtual inertia parameter posterior probability density function; and performing screening in the virtual inertia parameter posterior distribution according to the posterior probability density value of each sample, and determining a virtual inertia estimation result of the to-be-estimated generator. According to the generator virtual inertia estimation method disclosed by the invention, by establishing the simulation model, normal operation of the system is not influenced, the requirement of real-time estimation of the virtual inertia can be met, and accurate estimation of the virtual inertia of the generator of the grid-following type converter interface is realized.

Description

Technical Field

[0001] The present invention relates to the field of power generation technology, and in particular to a method, device, equipment, storage medium and product for estimating virtual inertia of a generator. Background Art

[0002] In new power systems, grid-connected converter interface generators have replaced a large number of traditional synchronous generators to promote the integration of renewable energy. While reducing the system inertia, virtual inertia control also provides a certain amount of virtual inertia, which cannot be ignored. Therefore, it is necessary to accurately estimate the virtual inertia to ensure that the converter interface generator can provide sufficient virtual inertia support to balance the grid frequency fluctuations.

[0003] As an important part of system inertia, various model-driven and data-driven methods have been widely studied by power researchers for estimating virtual inertia. In the prior art, most model-driven methods use disturbed data to estimate inertia, but such work will affect the normal operation of the system and cannot estimate virtual inertia in real time. Some data-driven methods require that all state variables of the system are known, which is difficult to apply in practice. Summary of the invention

[0004] The present invention provides a method, device, equipment, storage medium and product for estimating virtual inertia of a generator, so as to realize accurate estimation of the virtual inertia of a generator with a grid-following converter interface.

[0005] According to one aspect of the present invention, a method for estimating virtual inertia of a generator is provided, comprising:

[0006] According to the simulation model of the generator to be estimated, the active power of the generator output by the simulation model is obtained; wherein the generator to be estimated includes a grid-connected converter interface generator;

[0007] Determining a posterior probability density function of a virtual inertia parameter of the generator to be estimated according to the active power of the generator output by the simulation model;

[0008] Determining a posterior distribution of a virtual inertia parameter according to the posterior probability density function of the virtual inertia parameter;

[0009] Screening is performed according to the posterior probability density value of each sample in the posterior distribution of the virtual inertia parameter to determine the virtual inertia estimation result of the generator to be estimated.

[0010] Furthermore, in the simulation model, the external circuit of the generator to be estimated is simulated as an infinite bus, and the bus voltage amplitude and bus voltage phase angle corresponding to the infinite bus are used as inputs of the simulation model.

[0011] Further, determining the posterior probability density function of the virtual inertia parameter of the generator to be estimated according to the active power of the generator output by the simulation model includes:

[0012] Obtaining an active power measurement value of the generator to be estimated, and determining an active power likelihood function according to the active power measurement value and the active power of the generator output by the simulation model;

[0013] Determine the virtual inertia prior density function;

[0014] A virtual inertia parameter posterior probability density function is determined according to the active power likelihood function and the virtual inertia prior density function.

[0015] Further, determining the posterior distribution of the virtual inertia parameter according to the posterior probability density function of the virtual inertia parameter includes:

[0016] Determine a target sampling algorithm, use the virtual inertia parameter posterior probability density function as a target distribution function of the target sampling algorithm, and determine a target proposal function of the target sampling algorithm;

[0017] Extracting an initial sample according to the target proposal function and determining an acceptance rate of the initial sample;

[0018] Determine a random number corresponding to the initial sample, and determine a new sample corresponding to the initial sample according to the acceptance rate of the initial sample and the random number;

[0019] Return to the step of extracting an initial sample according to the target proposal function and determining the acceptance rate of the initial sample until the set number of extractions is reached;

[0020] The new samples obtained by the set number of extractions are screened to obtain the posterior distribution of the virtual inertia parameter.

[0021] Further, determining a new sample corresponding to the initial sample according to the acceptance rate of the initial sample and the random number includes:

[0022] If the acceptance rate of the initial sample is greater than or equal to the corresponding random number, the initial sample is used as the corresponding new sample;

[0023] If the acceptance rate of the initial sample is less than the corresponding random number, the new sample corresponding to the initial sample is determined as the new sample corresponding to the initial sample drawn last time.

[0024] Furthermore, screening is performed according to the posterior probability density value of each sample in the posterior distribution of the virtual inertia parameter to determine the virtual inertia estimation result of the generator to be estimated, including:

[0025] Respectively determine the posterior probability density value corresponding to each sample in the posterior distribution of the virtual inertia parameter;

[0026] The sample with the largest posterior probability density value in the posterior distribution of the virtual inertia parameter is determined as the virtual inertia estimation result of the generator to be estimated.

[0027] According to another aspect of the present invention, there is provided a generator virtual inertia estimation device, comprising:

[0028] A generator active power acquisition module, used to acquire the generator active power output by the simulation model according to the simulation model of the generator to be estimated; wherein the generator to be estimated includes a grid-connected converter interface generator;

[0029] A virtual inertia parameter posterior probability density function determination module, used to determine the virtual inertia parameter posterior probability density function of the generator to be estimated according to the generator active power output by the simulation model;

[0030] A virtual inertia parameter posterior distribution determination module, used to determine the virtual inertia parameter posterior distribution according to the virtual inertia parameter posterior probability density function;

[0031] The virtual inertia estimation result determination module is used to screen the virtual inertia parameter posterior distribution according to the posterior probability density value of each sample to determine the virtual inertia estimation result of the generator to be estimated.

[0032] Optionally, in the simulation model, the external circuit of the generator to be estimated is simulated as an infinite bus, and the bus voltage amplitude and bus voltage phase angle corresponding to the infinite bus are used as inputs of the simulation model.

[0033] Optionally, the module for determining the posterior probability density function of virtual inertia parameters is further used for:

[0034] Obtaining an active power measurement value of the generator to be estimated, and determining an active power likelihood function according to the active power measurement value and the active power of the generator output by the simulation model;

[0035] Determine the virtual inertia prior density function;

[0036] A virtual inertia parameter posterior probability density function is determined according to the active power likelihood function and the virtual inertia prior density function.

[0037] Optionally, the module for determining the posterior distribution of virtual inertia parameters is also used for:

[0038] Determine a target sampling algorithm, use the virtual inertia parameter posterior probability density function as a target distribution function of the target sampling algorithm, and determine a target proposal function of the target sampling algorithm;

[0039] Extracting an initial sample according to the target proposal function and determining an acceptance rate of the initial sample;

[0040] Determine a random number corresponding to the initial sample, and determine a new sample corresponding to the initial sample according to the acceptance rate of the initial sample and the random number;

[0041] Return to the step of extracting an initial sample according to the target proposal function and determining the acceptance rate of the initial sample until the set number of extractions is reached;

[0042] The new samples obtained by the set number of extractions are screened to obtain the posterior distribution of the virtual inertia parameter.

[0043] Optionally, the module for determining the posterior distribution of virtual inertia parameters is also used for:

[0044] If the acceptance rate of the initial sample is greater than or equal to the corresponding random number, the initial sample is used as the corresponding new sample;

[0045] If the acceptance rate of the initial sample is less than the corresponding random number, the new sample corresponding to the initial sample is determined as the new sample corresponding to the initial sample drawn last time.

[0046] Optionally, the virtual inertia estimation result determination module is further used for:

[0047] Respectively determine the posterior probability density value corresponding to each sample in the posterior distribution of the virtual inertia parameter;

[0048] The sample with the largest posterior probability density value in the posterior distribution of the virtual inertia parameter is determined as the virtual inertia estimation result of the generator to be estimated.

[0049] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0050] at least one processor; and

[0051] a memory communicatively connected to the at least one processor; wherein,

[0052] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the generator virtual inertia estimation method described in any embodiment of the present invention.

[0053] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the generator virtual inertia estimation method described in any embodiment of the present invention when executed.

[0054] According to another aspect of the present invention, a computer program product is provided, the computer program product comprising a computer program / instruction, and the computer program / instruction, when executed by a processor, implements the steps of the method for estimating virtual inertia of a generator according to any embodiment of the present invention.

[0055] The method for estimating the virtual inertia of a generator disclosed in the present invention first obtains the active power of the generator output by the simulation model according to the simulation model of the generator to be estimated; wherein the generator to be estimated includes a grid-type converter interface generator; then the posterior probability density function of the virtual inertia parameter of the generator to be estimated is determined according to the active power of the generator output by the simulation model; then the posterior distribution of the virtual inertia parameter is determined according to the posterior probability density function of the virtual inertia parameter; finally, the posterior probability density value of each sample is screened in the posterior distribution of the virtual inertia parameter to determine the virtual inertia estimation result of the generator to be estimated. The method for estimating the virtual inertia of a generator disclosed in the present invention, by establishing a simulation model, will not affect the normal operation of the system, and can also meet the demand for real-time estimation of virtual inertia, thereby realizing accurate estimation of the virtual inertia of the grid-type converter interface generator.

[0056] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0058] Figure 1 is a flow chart of a method for estimating virtual inertia of a generator provided according to Embodiment 1 of the present invention;

[0059] Figure 2 is a schematic diagram of a model of a grid-connected converter interface generator provided according to Embodiment 1 of the present invention;

[0060] Figure 3 is a flow chart of a method for estimating virtual inertia of a generator provided according to a second embodiment of the present invention;

[0061] Figure 4 is a schematic structural diagram of a generator virtual inertia estimation device provided according to a third embodiment of the present invention;

[0062] Figure 5 It is a structural schematic diagram of an electronic device for implementing the generator virtual inertia estimation method of the fourth embodiment of the present invention. DETAILED DESCRIPTION

[0063] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0064] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0065] Embodiment 1

[0066] Figure 1 This is a flow chart of a method for estimating the virtual inertia of a generator provided in the first embodiment of the present invention. This embodiment is applicable to the case of estimating the virtual inertia of a generator with a grid-connected converter interface. The method can be executed by a generator virtual inertia estimation device, which can be implemented in the form of hardware and / or software, and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0067] S110. According to the simulation model of the generator to be estimated, the active power of the generator output by the simulation model is obtained.

[0068] The generator to be estimated includes a grid-connected converter interface generator.

[0069] Specifically, a grid following converter (GF) is a converter that depends on the voltage and frequency of the grid to operate. Its control strategy is mainly to achieve energy conversion and transmission by tracking the voltage and frequency of the grid. A grid following converter interface generator refers to a mechanical device that converts mechanical energy into electrical energy, and the electrical energy is converted into a voltage and frequency suitable for the grid through a grid following converter. The simulation model of the generator to be estimated is a mathematical simulation model of a grid following converter interface generator for virtual inertia estimation according to current needs.

[0070] In this embodiment, in the simulation model, the external circuit of the generator to be estimated is simulated as an infinite bus, and the bus voltage amplitude and bus voltage phase angle corresponding to the infinite bus are used as inputs of the simulation model.

[0071] Specifically, Figure 2 : is a schematic diagram of a model of a grid-type converter interface generator provided by an embodiment of the present invention. As shown in the figure, the external circuit of the grid-type converter interface generator is simulated as an infinite bus, and the bus voltage amplitude V and phase angle θ are constantly changing. The bus voltage amplitude, phase angle and active power can be measured by using a PMU (phasor measurement unit) placed at the bus. v and P v They are respectively the generator active power output by the generator interfaced with the grid-connected converter and the generator active power reference value.

[0072] The mathematical simulation model of the grid-connected converter interface generator can be expressed as:

[0073]

[0074] ω PLL =K p v q +K i x PLL

[0075] Among them, P v and P vs They are the active power of the generator output by the grid-connected converter interface generator and the reference value of the active power of the generator, K v Used to introduce virtual inertia H v , and K v =2H v , T v and T p are the time constant of virtual inertia control and the equivalent time constant of power control dynamics, x v and x PL are all intermediate variables, ω PLLis the difference between the frequency of the generator connection point of the grid-type converter interface and the standard grid frequency ω0, V and θ are the bus voltage amplitude and bus voltage phase angle respectively, θ PLL is the phase-locked loop output angle, v q is the q-axis voltage, K p and K i They are the proportional gain and integral gain of the phase-locked loop controller respectively.

[0076] In the mathematical simulation model of the grid-type converter interface generator provided in this embodiment, the external circuit of the grid-type converter interface generator is simulated as an infinite bus, and the bus voltage amplitude V and bus voltage phase angle θ corresponding to the infinite bus are constantly changing and can be measured by PMU (phasor measurement unit). The voltage amplitude V and bus voltage phase angle θ are used as inputs of the simulation model for simulation, and the model output is the generator active power P output by the simulation model. v .

[0077] Furthermore, in the simulation of the grid-type converter interface generator as the generator to be estimated, the simulation step size can be set according to the sampling frequency of the PMU (phasor measurement unit), the total simulation time can be set according to the measured values ​​of the bus voltage amplitude V and the bus voltage phase angle θ obtained, and the steady-state initial value of the grid-type converter interface generator is calculated. The voltage amplitude V and the phase angle θ are used as input variables, and the fourth-order Runge-Kutta method is used in the model to integrate the fourth-order differential equation of the grid-type converter interface generator to solve the generator active power P output by the model. V .

[0078] S120. Determine a posterior probability density function of a virtual inertia parameter of the generator to be estimated according to the active power of the generator output by the simulation model.

[0079] Among them, the posterior probability is one of the basic concepts of information theory. In a communication system, after receiving a message, the probability of the message being sent known by the receiver is called the posterior probability. The posterior probability density function is a concept used in the Bayesian method to estimate model parameters. It treats the unknown parameters to be estimated in the model as random variables rather than constants.

[0080] In this embodiment, the active power P of the generator to be estimated is obtained by simulating the generator to be estimated. V After that, the likelihood function of the active power of the generator to be estimated can be established, and then the virtual inertia H can be selected. v Prior distribution of the virtual inertia H v The prior density function of the virtual inertia parameter is calculated, and then the posterior probability density function of the virtual inertia parameter is calculated, that is, the virtual inertia H v The posterior probability density function of .

[0081] S130. Determine a posterior distribution of the virtual inertia parameter according to a posterior probability density function of the virtual inertia parameter.

[0082] The posterior distribution refers to the distribution obtained by calculating the conditional distribution of the parameter when the sample data is known.

[0083] In this embodiment, after determining the posterior probability density function of the virtual inertia parameter of the generator to be estimated, a sampling algorithm may be used to characterize the posterior distribution of the virtual inertia parameter.

[0084] Preferably, the sampling algorithm may be an MH sampling algorithm, which is the abbreviation of the Metropolis-Hastings sampling algorithm. This algorithm was first proposed by Metropolis and improved by Hastings, so it is called the Metropolis-Hastings sampling algorithm or the MH sampling algorithm. When characterizing the posterior distribution of the virtual inertia parameter, the posterior probability density function of the virtual inertia parameter can be used as the target distribution of the MH sampling algorithm, and then samples are extracted according to the proposed function of the MH sampling algorithm. After multiple sample extraction and screening processes, a certain number of samples are finally retained to form a sample chain as the posterior distribution of the virtual inertia parameter.

[0085] S140 , screening is performed according to the posterior probability density value of each sample in the posterior distribution of the virtual inertia parameter to determine the virtual inertia estimation result of the generator to be estimated.

[0086] In this embodiment, after obtaining the posterior distribution of the virtual inertia parameter, the samples can be screened according to the posterior probability density value of each sample, and the samples that meet the requirements are used as the virtual inertia estimation results of the generator to be estimated.

[0087] Preferably, the sample with the largest posterior probability density value in the posterior distribution of the virtual inertia parameter can be determined as the virtual inertia estimation result of the generator to be estimated.

[0088] The method for estimating the virtual inertia of a generator disclosed in the present invention first obtains the active power of the generator output by the simulation model according to the simulation model of the generator to be estimated; wherein the generator to be estimated includes a grid-type converter interface generator; then the posterior probability density function of the virtual inertia parameter of the generator to be estimated is determined according to the active power of the generator output by the simulation model; then the posterior distribution of the virtual inertia parameter is determined according to the posterior probability density function of the virtual inertia parameter; finally, the posterior probability density value of each sample is screened in the posterior distribution of the virtual inertia parameter to determine the virtual inertia estimation result of the generator to be estimated. The method for estimating the virtual inertia of a generator disclosed in the present invention, by establishing a simulation model, does not need to use the disturbed data, which will not affect the normal operation of the system, and can also meet the demand for real-time estimation of virtual inertia, thereby realizing accurate estimation of the virtual inertia of the grid-type converter interface generator.

[0089] Embodiment 2

[0090] Figure 3 Flow chart of a method for estimating virtual inertia of a generator provided in the second embodiment of the present invention. This embodiment is a refinement of the above embodiment. Figure 3 As shown, the method includes:

[0091] S210. According to the simulation model of the generator to be estimated, the active power of the generator output by the simulation model is obtained.

[0092] The generator to be estimated includes a grid-connected converter interface generator. In the simulation model, the external circuit of the generator to be estimated is simulated as an infinite bus, and the bus voltage amplitude and bus voltage phase angle corresponding to the infinite bus are used as inputs of the simulation model.

[0093] Specifically, the mathematical simulation model of the grid-connected converter interface generator can be expressed as:

[0094]

[0095] ω PLL =K p v q +K i x PLL

[0096] Among them, P v and P vs They are the active power of the generator output by the grid-connected converter interface generator and the reference value of the active power of the generator, K v Used to introduce virtual inertia H v , and K v =2H v , T v and T pare the time constant of virtual inertia control and the equivalent time constant of power control dynamics, x v and x PL are all intermediate variables, ω PLL is the difference between the frequency of the generator connection point of the grid-type converter interface and the standard grid frequency ω0, V and θ are the bus voltage amplitude and bus voltage phase angle respectively, θ PLL is the phase-locked loop output angle, v q is the q-axis voltage, K p and K i They are the proportional gain and integral gain of the phase-locked loop controller respectively.

[0097] In the mathematical simulation model of the grid-type converter interface generator provided in this embodiment, the external circuit of the grid-type converter interface generator is simulated as an infinite bus, and the bus voltage amplitude V and bus voltage phase angle θ corresponding to the infinite bus are constantly changing and can be measured by PMU (phasor measurement unit). The voltage amplitude V and bus voltage phase angle θ are used as inputs of the simulation model for simulation, and the model output is the generator active power P output by the simulation model. v .

[0098] S220, obtaining an active power measurement value of the generator to be estimated, and determining an active power likelihood function according to the active power measurement value and the active power of the generator output by the simulation model.

[0099] The measured value of the active power of the generator can be measured by a PMU (Phasor Measurement Unit) placed at the bus. The likelihood function is a function of the statistical model parameters. In this embodiment, the active power likelihood function is used to describe the possible values ​​of the active power of the generator to be estimated.

[0100] Specifically, based on the simulation model output of the generator to be estimated, the generator active power P can be obtained. v , according to P v The active power likelihood function can be established by combining the active power measurement value P0 obtained by PMU measurement:

[0101]

[0102] Among them, σ noise is the standard deviation of the PMU measurement error.

[0103] S230, determining a virtual inertia prior density function, and determining a virtual inertia parameter posterior probability density function according to the active power likelihood function and the virtual inertia prior density function.

[0104] The virtual inertia prior density function represents the assumption about the probability distribution of the virtual inertia before the data is observed.

[0105] In this embodiment, the virtual inertia prior density function can be expressed as:

[0106]

[0107] Among them, H v is the virtual inertia, and σ pr are the prior mean and standard deviation of the virtual inertia, respectively.

[0108] Then, the posterior probability density function of the virtual inertia parameter can be calculated based on the active power likelihood function and the virtual inertia prior density function, which is expressed as:

[0109] π post (H v )∝π like (P v )π prior (H v )

[0110] S240, determining a target sampling algorithm, using the virtual inertia parameter posterior probability density function as a target distribution function of the target sampling algorithm, and determining a target proposal function of the target sampling algorithm.

[0111] In this embodiment, the sampling algorithm may be the MH sampling algorithm. When describing the posterior distribution of the virtual inertia parameter based on the MH sampling algorithm, the posterior probability density function of the virtual inertia parameter π may be post (H v ) as the target distribution function of the target sampling algorithm, and set the target proposal function Q of the MH sampling algorithm.

[0112] Among them, the initial value of the target proposal function is is the known initial value of virtual inertia.

[0113] S250. Draw an initial sample according to the target proposal function, determine the acceptance rate of the initial sample, determine the random number corresponding to the initial sample, determine the new sample corresponding to the initial sample according to the acceptance rate and the random number of the initial sample, and return to execute the step of drawing an initial sample according to the target proposal function and determining the acceptance rate of the initial sample until the set number of extractions is reached.

[0114] In this embodiment, the target proposal function can be expressed as Where i represents the i-th sample extraction, i = 1, 2, ..., N, and the number of extractions is set to N. In the i-th extraction, firstly, the target proposal function Draw an initial sample from And calculate the initial sample The target distribution function value of Then based on the target distribution function πpost (H v ) and the target proposal function Calculate the initial sample The acceptance rate α is:

[0115]

[0116] Then we can randomly draw from a uniform distribution in [0,1] to determine the initial sample The corresponding random number u is used to select the new sample corresponding to the initial sample according to the acceptance rate α and the corresponding random number u Repeat the above process N times to get a sample chain

[0117] Optionally, the method for determining the new sample corresponding to the initial sample based on the acceptance rate of the initial sample and the random number may be: if the acceptance rate of the initial sample is greater than or equal to the corresponding random number, the initial sample is used as the corresponding new sample; if the acceptance rate of the initial sample is less than the corresponding random number, the new sample corresponding to the initial sample is determined as the new sample corresponding to the initial sample obtained by the last extraction.

[0118] Specifically, in the i-th extraction, the initial sample is The corresponding acceptance rate and random number are α and u respectively, and the corresponding new sample is Then we have:

[0119]

[0120] S260, screening is performed among new samples obtained by setting the number of extractions to obtain a posterior distribution of virtual inertia parameters.

[0121] In this embodiment, the MH sampling algorithm is used to characterize the posterior distribution of the virtual inertia parameter. The MH sampling algorithm has a burning period. The burning period of the MH sampling algorithm refers to the period during the sampling process when the algorithm starts to extract samples from the target distribution. The distribution of the initial samples is not stable. This stage is called the burning period. During the burning period, the distribution of the samples gradually converges to the target distribution. This process takes a certain amount of time to complete. Therefore, in order to obtain stable samples, it is necessary to extract N samples from a sample chain containing N new samples. Screening is performed and then the stable sample is used as the posterior distribution of the virtual inertia parameter.

[0122] Preferably, based on the convergence property of the MH sampling algorithm, the early samples in the sample chain in the convergence process of the MH algorithm can be deleted, and the later samples can be retained as the posterior distribution of the virtual inertia parameter. The first 30% of the samples, the last 70% of the samples as the posterior distribution of the virtual inertia parameter.

[0123] S270, respectively determine the posterior probability density value corresponding to each sample in the posterior distribution of the virtual inertia parameter, and determine the sample with the largest posterior probability density value in the posterior distribution of the virtual inertia parameter as the virtual inertia estimation result of the generator to be estimated.

[0124] In this embodiment, after obtaining the posterior distribution of the virtual inertia parameter, the posterior probability density value corresponding to each sample in the posterior distribution of the virtual inertia parameter can be calculated respectively:

[0125]

[0126] Then the sample with the largest posterior probability density value in the posterior distribution of the virtual inertia parameter is determined as the virtual inertia estimation result of the generator to be estimated.

[0127]

[0128] The method for estimating the virtual inertia of a generator disclosed in the present invention, by establishing a simulation model, does not need to use the disturbed data, and only needs to measure the bus voltage and active power. It will not affect the normal operation of the system, and can also meet the demand for real-time estimation of the virtual inertia, thereby realizing accurate estimation of the virtual inertia of the generator of the grid-following converter interface.

[0129] Embodiment 3

[0130] Figure 4 A schematic diagram of the structure of a generator virtual inertia estimation device provided in the third embodiment of the present invention is shown in FIG. Figure 4 As shown, the device includes: a generator active power acquisition module 310, a virtual inertia parameter posterior probability density function determination module 320, a virtual inertia parameter posterior distribution determination module 330 and a virtual inertia estimation result determination module 340.

[0131] The generator active power acquisition module 310 is used to acquire the generator active power output by the simulation model according to the simulation model of the generator to be estimated.

[0132] The generator to be estimated includes a grid-connected converter interface generator.

[0133] The virtual inertia parameter posterior probability density function determination module 320 is used to determine the virtual inertia parameter posterior probability density function of the generator to be estimated according to the generator active power output by the simulation model.

[0134] The virtual inertia parameter posterior distribution determination module 330 is used to determine the virtual inertia parameter posterior distribution according to the virtual inertia parameter posterior probability density function.

[0135] The virtual inertia estimation result determination module 340 is used to screen the virtual inertia parameter posterior distribution according to the posterior probability density value of each sample to determine the virtual inertia estimation result of the generator to be estimated.

[0136] Optionally, in the simulation model, the external circuit of the generator to be estimated is simulated as an infinite bus, and the bus voltage amplitude and bus voltage phase angle corresponding to the infinite bus are used as inputs of the simulation model.

[0137] Optionally, the virtual inertia parameter posterior probability density function determination module 320 is further used for:

[0138] The active power measurement value of the generator to be estimated is obtained, and the active power likelihood function is determined according to the active power measurement value and the active power of the generator output by the simulation model; the virtual inertia prior density function is determined; and the virtual inertia parameter posterior probability density function is determined according to the active power likelihood function and the virtual inertia prior density function.

[0139] Optionally, the virtual inertia parameter posterior distribution determination module 330 is further used for:

[0140] Determine the target sampling algorithm, use the posterior probability density function of the virtual inertia parameter as the target distribution function of the target sampling algorithm, and determine the target proposal function of the target sampling algorithm; extract an initial sample according to the target proposal function, and determine the acceptance rate of the initial sample; determine the random number corresponding to the initial sample, and determine the new sample corresponding to the initial sample according to the acceptance rate of the initial sample and the random number; return to execute the steps of extracting an initial sample according to the target proposal function and determining the acceptance rate of the initial sample until the set number of extractions is reached; screen the new samples obtained by the set number of extractions to obtain the posterior distribution of the virtual inertia parameter.

[0141] Optionally, the virtual inertia parameter posterior distribution determination module 330 is further used for:

[0142] If the acceptance rate of the initial sample is greater than or equal to the corresponding random number, the initial sample is used as the corresponding new sample; if the acceptance rate of the initial sample is less than the corresponding random number, the new sample corresponding to the initial sample is determined as the new sample corresponding to the initial sample obtained in the last draw.

[0143] Optionally, the virtual inertia estimation result determination module 340 is further used for:

[0144] The posterior probability density value corresponding to each sample in the posterior distribution of the virtual inertia parameter is determined respectively; and the sample with the largest posterior probability density value in the posterior distribution of the virtual inertia parameter is determined as the virtual inertia estimation result of the generator to be estimated.

[0145] The generator virtual inertia estimation device provided in the embodiment of the present invention can execute the generator virtual inertia estimation method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0146] Embodiment 4

[0147] Figure 5 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0148] like Figure 5 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0149] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0150] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a generator virtual inertia estimation method.

[0151] In some embodiments, the generator virtual inertia estimation method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the generator virtual inertia estimation described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the generator virtual inertia estimation method in any other appropriate manner (e.g., by means of firmware).

[0152] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0153] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0154] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, 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), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0155] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0156] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0157] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0158] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0159] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for estimating virtual inertia of a generator, characterized in that: include: According to the simulation model of the generator to be estimated, the active power of the generator output by the simulation model is obtained; wherein the generator to be estimated includes a grid-connected converter interface generator; Determining a posterior probability density function of a virtual inertia parameter of the generator to be estimated according to the active power of the generator output by the simulation model; Determining a posterior distribution of a virtual inertia parameter according to the posterior probability density function of the virtual inertia parameter; Screening is performed according to the posterior probability density value of each sample in the posterior distribution of the virtual inertia parameter to determine the virtual inertia estimation result of the generator to be estimated.

2. The method according to claim 1, characterized in that In the simulation model, the external circuit of the generator to be estimated is simulated as an infinite bus, and the bus voltage amplitude and bus voltage phase angle corresponding to the infinite bus are used as inputs of the simulation model.

3. The method according to claim 1, characterized in that Determining a posterior probability density function of a virtual inertia parameter of the generator to be estimated according to the active power of the generator output by the simulation model includes: Obtaining an active power measurement value of the generator to be estimated, and determining an active power likelihood function according to the active power measurement value and the active power of the generator output by the simulation model; Determine the virtual inertia prior density function; A virtual inertia parameter posterior probability density function is determined according to the active power likelihood function and the virtual inertia prior density function.

4. The method according to claim 1, characterized in that: Determining the posterior distribution of the virtual inertia parameter according to the posterior probability density function of the virtual inertia parameter includes: Determine a target sampling algorithm, use the virtual inertia parameter posterior probability density function as a target distribution function of the target sampling algorithm, and determine a target proposal function of the target sampling algorithm; Extracting an initial sample according to the target proposal function and determining an acceptance rate of the initial sample; Determine a random number corresponding to the initial sample, and determine a new sample corresponding to the initial sample according to the acceptance rate of the initial sample and the random number; Return to the step of extracting an initial sample according to the target proposal function and determining the acceptance rate of the initial sample until the set number of extractions is reached; The new samples obtained by the set number of extractions are screened to obtain the posterior distribution of the virtual inertia parameter.

5. The method according to claim 4, characterized in that Determining a new sample corresponding to the initial sample according to the acceptance rate of the initial sample and the random number includes: If the acceptance rate of the initial sample is greater than or equal to the corresponding random number, the initial sample is used as the corresponding new sample; If the acceptance rate of the initial sample is less than the corresponding random number, the new sample corresponding to the initial sample is determined as the new sample corresponding to the initial sample drawn last time.

6. The method according to claim 1, characterized in that Screening according to the posterior probability density value of each sample in the posterior distribution of the virtual inertia parameter to determine the virtual inertia estimation result of the generator to be estimated includes: Respectively determine the posterior probability density value corresponding to each sample in the posterior distribution of the virtual inertia parameter; The sample with the largest posterior probability density value in the posterior distribution of the virtual inertia parameter is determined as the virtual inertia estimation result of the generator to be estimated.

7. A generator virtual inertia estimation device, characterized in that: include: A generator active power acquisition module, used to acquire the generator active power output by the simulation model according to the simulation model of the generator to be estimated; wherein the generator to be estimated includes a grid-connected converter interface generator; A virtual inertia parameter posterior probability density function determination module, used to determine the virtual inertia parameter posterior probability density function of the generator to be estimated according to the generator active power output by the simulation model; A virtual inertia parameter posterior distribution determination module, used to determine the virtual inertia parameter posterior distribution according to the virtual inertia parameter posterior probability density function; The virtual inertia estimation result determination module is used to screen the virtual inertia parameter posterior distribution according to the posterior probability density value of each sample to determine the virtual inertia estimation result of the generator to be estimated.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the generator virtual inertia estimation method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the generator virtual inertia estimation method according to any one of claims 1 to 6 when executed.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method for estimating virtual inertia of a generator according to any one of claims 1 to 6 are implemented.

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