An online equivalent virtual inertia constant evaluation method and device for a wind farm and a storage medium

Abstract

The application discloses a kind of wind farm equivalent virtual inertia constant online evaluation method, device and storage medium, belong to new energy power generation control technical field, method includes: to wind farm measured data is preprocessed;According to the swing equation of synchronous unit, establish the equivalent swing equation of wind farm;Using time-frequency transform method, according to the equivalent swing equation of wind farm, construct station level inertia constant evaluation model;The wind farm measured data after preprocessing is input to the station level inertia constant evaluation model, to output evaluation result;The evaluation result is operated to remove singular point, to obtain the equivalent virtual inertia constant of wind farm;Wherein, the wind farm measured data includes system frequency signal and wind farm active power signal.The application is online evaluated to wind farm equivalent virtual inertia constant by time-frequency transform method, can quantitatively express the inertia contribution of wind farm to power grid.

Description

Technical Field

[0001] This invention relates to an online evaluation method, device, and storage medium for the equivalent virtual inertia constant of a wind farm, belonging to the field of new energy power generation control technology. Background Technology

[0002] In recent years, power outages have frequently occurred due to insufficient system inertia support caused by excessive grid connection of new energy sources. Accurately assessing the system's inertia level helps grid dispatchers identify weak points in inertia and take timely inertia compensation measures to prevent frequency drop accidents in the power grid.

[0003] The inertia constant of synchronous generators in a power system is a fixed value. However, for wind farms with virtual inertia control, the equivalent virtual inertia constant is unknown and exhibits rapid time-varying characteristics due to the intermittent and uncertain power output of wind turbines. Currently, most inertia assessment methods involve offline identification of frequency events that have already occurred, failing to provide real-time online assessment of wind farm inertia and hinder grid dispatchers from developing timely inertia compensation strategies. The paper by Q. Mengqi et al. ["Inertial Response of Doubly-fed Induction Generator with the Phase-locked Loop," 2019 IEEE Innovative Smart Grid Technologies-Asia (ISGTAsia), 2019, pp. 1435-1439] defines an expression for the equivalent virtual inertia constant using wind turbine control strategies to achieve real-time inertia assessment. However, this method is only applicable to grid-connected wind turbines with inertia control, thus lacking universality. The paper by J. Zhang et al. ["Online Identification of Power System Equivalent Inertia Constant," in IEEE Transactions on Industrial Electronics, vol. 64, no. 10, pp. 8098-8107, Oct. 2017] proposes an online evaluation method for micro-perturbations of closed-loop systems using power electronic devices, which realizes real-time identification of time-varying nonlinear equivalent inertia constants. However, the applied micro-perturbation signal may affect the frequency response of the system, thereby affecting the safety of system operation.

[0004] Depending on the assessment level, inertia assessment can be divided into identification for single units, multiple nodes, and the entire system. W. He et al.'s paper ["Inertia Provision and Estimation of PLL-Based DFIG WindTurbines," in IEEE Transactions on Power Systems, vol.32, no.1, pp.510-521, Jan.2017] studied a method for quantitatively analyzing the equivalent virtual inertia constant of time-varying wind turbines. However, this method requires obtaining a large number of wind turbine control and state parameters, making it unsuitable for system-level inertia assessment and inapplicable directly to the inertia assessment of actual wind farms. For system-level inertia identification, most scholars use system parameter identification models (such as input / output models or state-space models) to characterize the transfer function between frequency and power, and then extract the inertia constant based on the identification results. The paper by K. Tuttelberg et al. ["Estimation of Power System Inertia From Ambient Wide Area Measurements," in IEEE Transactions on Power Systems, vol.33, no.6, pp.7249-7257, Nov.2018] uses a controlled autoregressive model to replace the oscillation equation of a wind farm. It achieves the assessment of inertia at the site level using a high-order model replacement method. However, the optimal order of the high-order model is not easy to determine, and the accuracy of the assessment results is related to the type of model identified.

[0005] Currently, there is a lack of a system-level online evaluation method that is easy to obtain data for the equivalent virtual inertia constant of wind farms and has high evaluation accuracy. Summary of the Invention

[0006] The purpose of this invention is to provide an online evaluation method, device, and storage medium for the equivalent virtual inertia constant of a wind farm. By using the time-frequency transformation method, the equivalent virtual inertia constant of the wind farm can be evaluated online, which can quantitatively express the inertia contribution of the wind farm to the power grid.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] In a first aspect, the present invention provides an online evaluation method for the equivalent virtual inertia constant of a wind farm, comprising:

[0009] Preprocessing of measured data from wind farms;

[0010] Based on the swing equation of the synchronous generator unit, the equivalent swing equation of the wind farm is established;

[0011] Using the time-frequency transformation method, a site-level inertia constant evaluation model is constructed based on the equivalent oscillation equation of the wind farm.

[0012] The preprocessed measured data of the wind farm is input into the site-level inertia constant evaluation model to output the evaluation results;

[0013] The evaluation results are subjected to a singularity removal operation to obtain the equivalent virtual inertia constant of the wind farm;

[0014] The measured data of the wind farm includes system frequency signals and active power signals of the wind farm.

[0015] In conjunction with the first aspect, the preprocessing further includes: per-unitization, detrending, and pre-filtering.

[0016] In conjunction with the first aspect, the expression for the equivalent oscillation equation of the wind farm is shown in formula (1):

[0017]

[0018] In formula (1), H WF D is the equivalent virtual inertia constant of the wind farm. WF P is the damping coefficient of the wind farm. m For mechanical power, P e For electromagnetic power, ω s Let ω be the system frequency. s0 This is the system's rated frequency.

[0019] In conjunction with the first aspect, furthermore, using the time-frequency transformation method, based on the equivalent oscillation equation of the wind farm, a site-level inertia constant evaluation model is constructed, including:

[0020] Based on the Laplace transform and inverse Laplace transform, the equivalent oscillation equation of the wind farm is converted from differential form to integral form to obtain the equivalent oscillation integral equation of the wind farm.

[0021] Based on the gradient method of numerical integration, the equivalent oscillation integral equation of the wind farm is transformed from a continuous domain to a discrete domain to obtain an evaluation model of the station-level inertia constant.

[0022] In conjunction with the first aspect, further, based on the Laplace transform and inverse Laplace transform, the equivalent oscillation equation of the wind farm is converted from differential form to integral form to obtain the integral equation of the equivalent oscillation of the wind farm, which includes:

[0023] The equivalent oscillation equation of the wind farm is subjected to a Laplace transform. The output variable is used to replace the change in system frequency, the input variable is used to replace the change in active power of the wind farm, and the change in mechanical power of the wind farm is ignored, so as to obtain the equivalent oscillation algebraic equation of the wind farm.

[0024] Based on the equivalent oscillating algebraic equation of the wind farm, differentiate the operational operator, and multiply both sides of the differentiated equation by s. -2 And perform the inverse Laplace transform to obtain the equivalent oscillating integral equation of the wind farm;

[0025] The expression for the equivalent oscillating algebraic equation of the wind farm is shown in formula (2):

[0026]

[0027] In formula (2), s is the operator, Y is the output variable, U is the input variable, and D is the input variable. WF H is the damping coefficient of the wind farm. WF The equivalent virtual inertia constant of the wind farm;

[0028] The expression for the equivalent oscillating integral equation of the wind farm is shown in formula (3):

[0029]

[0030] In formula (3), H WF D is the equivalent virtual inertia constant of the wind farm. WF T is the damping coefficient of the wind farm. F =n F T, T F n is the time interval. F Let y(δ) be the time window length, T be the sampling period, δ be the integration variable, y(δ) be the time-domain expression of the output variable Y, and u(δ) be the time-domain expression of the input variable U.

[0031] In conjunction with the first aspect, the expression for the station-level inertia constant evaluation model is shown in formula (4):

[0032]

[0033] In formula (4), H WF D is the equivalent virtual inertia constant of the wind farm. WF Let T be the damping coefficient of the wind farm, T be the sampling period, i be the i-th sampling point, and n be the sampling point. F Let k be the current sampling point, y(i) be the output at the i-th sampling time, and y(i-1) be the output at the (i-1)-th sampling time. y(i) = Δψ(k-(n) F -i), where Δω is the system frequency change, u(i) is the input at the i-th sampling time, and u(i) = ΔP e (k-(n F -i)), ΔP e This represents the change in active power of the wind farm.

[0034] In conjunction with the first aspect, the formula used for the singularity removal operation is shown in formula (5):

[0035]

[0036] In formula (5), H WF (k) represents the equivalent virtual inertia constant of the wind farm at the current sampling time, H WF (k-1) represents the equivalent virtual inertia constant of the wind farm at the previous sampling time, and k represents the sampling point at the current time. T is the sampling period, i is the i-th sampling point, and n is the sampling period. F D is the length of the time window. WF Let be the wind farm damping coefficient, y(i) be the output at the i-th sampling time, and u(i) be the input at the i-th sampling time. y(i-1) is the output at the (i-1)th sampling time, ε is the threshold to prevent numerical errors, β is the proportional coefficient to prevent numerical errors, and β << 1.

[0037] Secondly, the present invention provides an online evaluation device for the equivalent virtual inertia constant of a wind farm, comprising:

[0038] Preprocessing module: Used to preprocess measured data from wind farms;

[0039] Equation building module: used to establish the equivalent swing equation of the wind farm based on the swing equation of the synchronous unit;

[0040] Model building module: used to construct a site-level inertia constant evaluation model based on the equivalent oscillation equation of the wind farm using the time-frequency transformation method;

[0041] Evaluation module: used to input preprocessed wind farm measured data into the site-level inertia constant evaluation model to output evaluation results;

[0042] Singularity removal module: Used to perform singularity removal operations on the evaluation results to obtain the equivalent virtual inertia constant of the wind farm;

[0043] The measured data of the wind farm includes system frequency signals and active power signals of the wind farm.

[0044] Thirdly, the present invention provides an online evaluation device for the equivalent virtual inertia constant of a wind farm, including a processor and a storage medium;

[0045] The storage medium is used to store instructions;

[0046] The processor is configured to operate according to the instructions to perform the steps of the method according to any one of the first aspects.

[0047] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any of the first aspects.

[0048] Compared with the prior art, the beneficial effects of the present invention are:

[0049] This invention utilizes the time-frequency transformation method to convert the equivalent oscillation equation of a wind farm from differential to integral form, thereby indirectly handling the frequency derivative term and avoiding derivative spikes. The time-frequency transformation method used in this invention eliminates the need for an iterative process, improving the evaluation speed. This invention enables online evaluation of the equivalent inertia constant at the wind farm level, demonstrating practical applicability. The evaluation method of this invention can be applied to other new energy power plants or other virtual inertia control methods. Attached Figure Description

[0050] Figure 1 This is a flowchart of an online evaluation method for the equivalent virtual inertia constant of a wind farm provided by an embodiment of the present invention;

[0051] Figure 2 This is a wind farm topology diagram of the sinmulink platform provided in this embodiment of the invention;

[0052] Figure 3 This is a waveform diagram of frequency disturbance in a wind farm on the sinmulink platform provided in an embodiment of the present invention during a sudden load increase;

[0053] Figure 4 This is a waveform diagram of the increased active power generated by a wind farm on the sinmulink platform during a sudden load surge, provided in an embodiment of the present invention.

[0054] Figure 5 This is the evaluation result of the equivalent virtual inertia constant of the wind farm at a wind speed of 10 m / s provided in the embodiment of the present invention using the sinmulink platform;

[0055] Figure 6 This is the evaluation result of the equivalent virtual inertia constant of the wind farm under a wind speed of 8 m / s provided in the embodiment of the present invention using the sinmulink platform;

[0056] Figure 7 This is the evaluation result of the equivalent virtual inertia constant of the wind farm at a wind speed of 12 m / s provided by the Sinmulink platform in this embodiment of the invention;

[0057] Figure 8 This is the evaluation result of the equivalent virtual inertia constant of the Dafeng wind farm under frequency perturbation provided in the embodiments of the present invention;

[0058] Figure 9This is the evaluation result of the equivalent virtual inertia constant of the Dafeng wind farm under frequency disturbance provided in the embodiments of the present invention. Detailed Implementation

[0059] The technical solution of this patent will be further described in detail below with reference to specific embodiments.

[0060] The embodiments of this patent are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this patent, and should not be construed as limiting this patent. Unless otherwise specified, the embodiments of this application and the technical features within them can be combined with each other.

[0061] Example 1:

[0062] Figure 1 This is a flowchart of an online evaluation method for the equivalent virtual inertia constant of a wind farm provided in Embodiment 1 of the present invention. This flowchart only shows the logical sequence of the method in this embodiment. Under the premise of no conflict, in other possible embodiments of the present invention, different methods may be used. Figure 1 Complete the steps shown or described in the order indicated.

[0063] The online evaluation method for the equivalent virtual inertia constant of a wind farm provided in this embodiment can be applied to a terminal and can be executed by an online evaluation device for the equivalent virtual inertia constant of a wind farm. This device can be implemented in software and / or hardware and can be integrated into the terminal, such as any tablet computer or computer device with communication capabilities. See also... Figure 1 The method in this embodiment specifically includes the following steps:

[0064] Step 1: Preprocess the measured data from the wind farm;

[0065] The measured data from the wind farm includes system frequency signals and active power signals from the wind farm; the preprocessing of the measured data from the wind farm includes the following steps:

[0066] Step A: Standardize the measured data from the wind farm;

[0067] The per-unit processing of measured data from wind farms includes dividing the system frequency signal and the active power signal of the wind farm by their respective rated values.

[0068] Step B: Detrend the measured wind farm data after it has been normalized;

[0069] Detrending processing of the measured wind farm data after per-unit processing includes subtracting the optimal trend line data obtained by fitting the average value method from the system frequency signal and the wind farm active power signal after per-unit processing, in order to remove the DC component from the system frequency signal and the wind farm active power signal.

[0070] Step C: Perform pre-filtering on the wind farm measured data after detrending processing;

[0071] The pre-filtering process for the detrended wind farm measured data includes: using a low-pass Butterworth filter with a cutoff frequency of 0.5Hz to pre-filter the detrended system frequency signal and the wind farm active power signal to eliminate high-frequency noise in the system frequency signal and the wind farm active power signal, thereby improving the robustness of the evaluation model.

[0072] Step 2: Based on the swing equation of the synchronous generator unit, establish the equivalent swing equation of the wind farm;

[0073] The expression for the equivalent oscillation equation of a wind farm is shown in Equation (1):

[0074]

[0075] In formula (1), H WF D is the equivalent virtual inertia constant of the wind farm. WF P is the damping coefficient of the wind farm. m For mechanical power, P e For electromagnetic power, ω s Let ω be the system frequency. s0 This is the system's rated frequency.

[0076] Based on the mechanism of virtual inertia, a calculation expression for the equivalent virtual inertia constant of a wind farm can be established. Wind turbines using virtual inertia control provide inertial support power to the grid by releasing rotor kinetic energy. Therefore, the rotor speed of the wind turbine is coupled with the system frequency, and the change in rotor kinetic energy released by the wind turbine can also be represented by the system frequency and the equivalent virtual moment of inertia.

[0077]

[0078]

[0079] Among them, J equ J is the equivalent virtual moment of inertia of the wind turbine. inherent E represents the inherent moment of inertia of the wind turbine. k The generator rotor stores kinetic energy at the rated speed of a single wind turbine, where n is the number of pole pairs in the wind turbine unit, and S is the energy stored in the generator rotor. N For the rated capacity of the fan, ω rω is the rotor speed of the fan. r0 Δω is the initial rotor speed of the fan. r Δω represents the change in the rotor speed of the wind turbine. s This represents the change in system frequency.

[0080] Therefore, we can obtain the calculation expression for the equivalent virtual inertia constant of wind turbine generators and wind farms:

[0081]

[0082]

[0083] Among them, H inherent H is the inherent inertia constant of the wind turbine. WF is the equivalent virtual inertia constant of the wind farm, and m is the number of wind turbines in the wind farm.

[0084] Calculating the equivalent virtual inertia constant of a wind farm using this method requires the intrinsic inertia constant H of each wind turbine. inherent However, in practical applications, manufacturers typically do not provide the inherent inertia constant H. inherent This poses a challenge to assessing the inertia level of wind farms, and the method provided by this invention can effectively solve this problem.

[0085] Step 3: Using the time-frequency transformation method, construct a site-level inertia constant evaluation model based on the equivalent oscillation equation of the wind farm;

[0086] Using the time-frequency transformation method and based on the equivalent oscillation equation of the wind farm, the following steps are taken to construct a site-level inertia constant evaluation model:

[0087] Step 1: Based on the Laplace transform and inverse Laplace transform, convert the equivalent oscillation equation of the wind farm from differential form to integral form to obtain the equivalent oscillation integral equation of the wind farm.

[0088] The equivalent oscillation equation of the wind farm is transformed from differential form to integral form using the Laplace transform and inverse Laplace transform. The integral equation of the equivalent oscillation of the wind farm is obtained by the following steps:

[0089] Step ①: Perform a Laplace transform on the equivalent oscillation equation of the wind farm, replace the system frequency change with the output variable, replace the wind farm active power change with the input variable, and ignore the wind farm mechanical power change to obtain the equivalent oscillation algebraic equation of the wind farm.

[0090] Step 2: Based on the equivalent oscillation algebraic equation of the wind farm, differentiate the operational operator, and multiply both sides of the differentiated equation by s. -2 To reduce noise, an inverse Laplace transform is performed to obtain the equivalent oscillating integral equation of the wind farm.

[0091] The expression for the equivalent oscillating algebraic equation of the wind farm is shown in formula (2):

[0092]

[0093] In formula (2), s is the operator, Y is the output variable, U is the input variable, and D is the input variable. WF H is the damping coefficient of the wind farm. WF The equivalent virtual inertia constant of the wind farm;

[0094] The expression for the equivalent oscillating integral equation of a wind farm is shown in equation (3):

[0095]

[0096] In formula (3), H WF D is the equivalent virtual inertia constant of the wind farm. WF T is the damping coefficient of the wind farm. F =n F T, T F n is the time interval. F Let y(δ) be the time window length, T be the sampling period, δ be the integration variable, y(δ) be the time-domain expression of the output variable Y, and u(δ) be the time-domain expression of the input variable U.

[0097] Step II: Based on the gradient method of numerical integration, the equivalent oscillation integral equation of the wind farm is transformed from the continuous domain to the discrete domain to obtain the evaluation model of the station-level inertia constant.

[0098] The expression for the evaluation model of the station-level inertia constant is shown in Equation (4):

[0099]

[0100] In formula (4), H WF D is the equivalent virtual inertia constant of the wind farm. WF Let T be the damping coefficient of the wind farm, T be the sampling period, i be the i-th sampling point, and n be the sampling point. F Let k be the current sampling point, y(i) be the output at the i-th sampling time, and y(i-1) be the output at the (i-1)-th sampling time. y(i) = Δω(k-(n) F -i), where Δω is the system frequency change, u(i) is the input at the i-th sampling time, and u(i) = ΔP e (k-(n F -i)), ΔP e This represents the change in active power of the wind farm.

[0101] Step 4: Input the preprocessed wind farm measured data into the site-level inertia constant evaluation model to output the evaluation results;

[0102] The preprocessed system frequency signal and wind farm active power signal are used as input signals for the station-level inertia constant evaluation model, which then outputs the evaluation results.

[0103] Step 5: Perform singularity removal on the evaluation results to obtain the equivalent virtual inertia constant of the wind farm;

[0104] The formula used for singularity removal is shown in formula (5):

[0105]

[0106] In formula (5), H WF (k) represents the equivalent virtual inertia constant of the wind farm at the current sampling time, H WF (k-1) represents the equivalent virtual inertia constant of the wind farm at the previous sampling time, and k represents the sampling point at the current time. T is the sampling period, i is the i-th sampling point, and n is the sampling period. F D is the length of the time window. WF Let be the wind farm damping coefficient, y(i) be the output at the i-th sampling time, and u(i) be the input at the i-th sampling time. y(i-1) is the output at the (i-1)th sampling time, ε is the threshold to prevent numerical errors, β is the proportional coefficient to prevent numerical errors, and β << 1.

[0107] To verify the accuracy of the evaluation method provided in this invention, a wind farm simulation system was established using Matlab / Sinmulink simulation software. For example... Figure 2 As shown, the system model includes a 60MW wind farm and a 100MW synchronous generator. The 60MW wind farm (using a single-unit equivalent model) consists of 30 direct-drive wind turbines with a capacity of 2MW each. The parameters of the synchronous generator and the wind turbines are shown in Tables 1 and 2, respectively. For ease of analysis, the simulation uses a per-unit model.

[0108] Table 1 Synchronizer Parameters

[0109] parameter symbol numerical values Rated amplitude of grid-side phase voltage <![CDATA[U base ]]> 20kV Active power rating <![CDATA[P nom ]]> 45.7MW Rated capacity <![CDATA[S N ]]> 100MW Moment of inertia J <![CDATA[27000kg·m 2 ]]> Damping factor <![CDATA[K d ]]> 5 Extreme logarithm n 2 Netside angular frequency <![CDATA[ω g ]]> 100πrad / s Rotor mechanical angular frequency <![CDATA[ω r ]]> 50πrad / s

[0110] Table 2 Parameters of Direct Drive Wind Motor

[0111] parameter symbol numerical values Rated amplitude of grid-side phase voltage <![CDATA[U base ]]> 563V Active power rating <![CDATA[P nom ]]> 1.49MW Rated capacity <![CDATA[S N ]]> 2MW Moment of inertia <![CDATA[J inherent ]]> <![CDATA[7·10 6 kg·m 2 ]]> Damping factor D 5 Extreme logarithm n 30 Netside angular frequency <![CDATA[ω g ]]> 100πrad / s Rotor mechanical angular frequency <![CDATA[ω r ]]> 60 rad / s

[0112] The simulation assumes a constant wind speed of 10 m / s (rated wind speed). At 12 seconds, the load at the 20kV bus suddenly increases by 2.5 MW, causing a drop in grid frequency. When the system frequency deviation exceeds 0.01 Hz, the wind turbine initiates a virtual inertia control strategy. The simulation results are as follows: Figure 3 , Figure 4 As shown. Figure 3 , Figure 4 These are the frequency response and power response of a wind farm to a sudden increase in load, respectively.

[0113] Will Figure 3 , Figure 4 The data is used as input to the proposed evaluation model to obtain the identification value H of the equivalent inertia constant of the wind farm. WF The extracted rotor speed change Δω r System frequency change Δω s With the inherent inertia constant H inherent Combined with the number of wind turbines m, the calculated value H of the equivalent virtual inertia constant of the wind farm is obtained through comprehensive calculation. WF The identified and calculated values ​​of the equivalent virtual inertia constant of a wind farm are jointly represented in... Figure 5 middle.

[0114] Depend on Figure 5 H WF The comparison curves show that the calculated value of the equivalent virtual inertia constant of the wind farm is in high agreement with the identified value, which proves the effectiveness and accuracy of the evaluation model proposed in this invention. Figure 5 In the middle, H WF The inertial response decreases continuously from a relatively large initial value, which is around 8 seconds. At the initial moment of the inertial response, the system frequency change rate is large, resulting in the strongest frequency suppression effect from the wind farm, thus maximizing the equivalent virtual inertial constant. The inertial response of the wind farm provides dynamic active power support to the grid by releasing the kinetic energy of the turbine rotor; therefore, the turbine rotor speed continuously decreases when virtual inertia starts. Since the wind turbine uses maximum power point tracking (MPPT), the degree of rotor speed decrease affects the active power output of the wind farm. The change in active power is related not only to the additional power of the virtual inertial control action but also to the power setpoint output in MPPT mode. Therefore, the H value identified by the change in active power and the change in system frequency is crucial. WF It's not a fixed value, but a time-varying value. From Figure 5 It can be seen that around 12.5s, the active power generated by the wind farm begins to be affected by the rotor speed, H WF It begins to decrease continuously. Around 17.7 seconds later, H appears for certain periods. WF The value <0 is mainly because the changes in the unit rotor speed and the system frequency are not synchronized. However, since the inertial response exists in the early stage of the system frequency drop, H...WF The assessment only needs to focus on the frequency and active power data within a few seconds after the system frequency drops.

[0115] To fully verify the accuracy of the evaluation model proposed in this invention, simulations were conducted under different wind speed conditions. Figure 6 , Figure 7 The evaluation results of the proposed algorithm are shown for wind speeds of 8 m / s and 12 m / s, respectively. Figure 6 , Figure 7 It can be seen that the trends of the identified and calculated values ​​of the proposed algorithm are basically consistent under different wind speeds. In the initial stage of inertial response, there is a certain error in the evaluation results, but it does not affect the overall inertial evaluation results.

[0116] This invention underwent on-site testing at the Tianrun Dafeng Wind Farm, with tests categorized into two types: frequency up-interference and frequency down-interference. The acquired wind power data from the Runlong Line 2—system frequency and active power—was input into the evaluation model, and the evaluation results are as follows: Figure 8 , Figure 9 As shown. Since the Tianrun Dafeng Wind Farm locks out the MPPT mode of the wind turbine in the inertial response, the equivalent virtual inertia constant obtained from the evaluation should be a constant value. Figure 8 , Figure 9 The evaluation result H shown WF The fact that the value fluctuated around 5 seconds proved the effectiveness and accuracy of the evaluation algorithm proposed in this invention.

[0117] In this embodiment, the equivalent oscillation equation of the wind farm is transformed from a differential equation in the time domain to an algebraic equation in the frequency domain. Algebraic operations are performed, and then the inverse Laplace transform is used to convert the frequency-domain algebraic equation into an identifiable integral equation in the time domain. Finally, the gradient method of numerical integration is used to solve for the expression of the equivalent inertia constant, thereby constructing a site-level inertia constant evaluation model. This method transforms the equivalent oscillation equation of the wind farm from a differential equation to an integral equation, indirectly handling the frequency derivative term in the inertia evaluation model equation and avoiding derivative spikes. Furthermore, this method fully utilizes historical data on frequency and power deviations, avoiding identification errors caused by single bad data points. The method also performs singularity removal on the evaluation results, preventing H-value errors in the evaluation results. WF While suddenly increasing, maintain H WF By observing the changing trend of β, choosing an appropriate β can effectively eliminate numerical errors, improve the accuracy of evaluation results, and reduce the sensitivity of the evaluation model to noise.

[0118] Example 2:

[0119] This embodiment provides an online evaluation device for the equivalent virtual inertia constant of a wind farm, including:

[0120] Preprocessing module: Used to preprocess measured data from wind farms;

[0121] Equation building module: used to establish the equivalent swing equation of the wind farm based on the swing equation of the synchronous unit;

[0122] Model building module: Used to construct a site-level inertia constant evaluation model based on the wind farm equivalent oscillation equation using the time-frequency transformation method;

[0123] Evaluation module: Used to input preprocessed wind farm measured data into the site-level inertia constant evaluation model to output evaluation results;

[0124] Singularity Removal Module: Used to remove singularities from the evaluation results in order to obtain the equivalent virtual inertia constant of the wind farm;

[0125] The measured data from the wind farm includes system frequency signals and active power signals from the wind farm.

[0126] The online evaluation device for the equivalent virtual inertia constant of a wind farm provided in this embodiment of the invention can execute the online evaluation method for the equivalent virtual inertia constant of a wind farm provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0127] Example 3:

[0128] This embodiment provides an online evaluation device for the equivalent virtual inertia constant of a wind farm, including a processor and a storage medium;

[0129] Storage media are used to store instructions;

[0130] The processor is used to perform operations according to instructions to execute the steps of the method in Embodiment 1.

[0131] Example 4:

[0132] This embodiment provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method in Embodiment 1.

[0133] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0134] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0135] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0136] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0137] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for online evaluation of the equivalent virtual inertia constant of a wind farm, characterized in that, include: Preprocessing of measured data from wind farms; Based on the swing equation of the synchronous generator unit, the equivalent swing equation of the wind farm is established; Using the time-frequency transformation method, a site-level inertia constant evaluation model is constructed based on the equivalent oscillation equation of the wind farm. The preprocessed measured data of the wind farm is input into the site-level inertia constant evaluation model to output the evaluation results; The evaluation results are subjected to a singularity removal operation to obtain the equivalent virtual inertia constant of the wind farm; The measured data of the wind farm includes system frequency signals and wind farm active power signals; The expression for the equivalent swing equation of the wind farm is shown in formula (1): (1); In formula (1), The equivalent virtual inertia constant of the wind farm. This represents the damping coefficient of the wind farm. For mechanical power, Electromagnetic power, For system frequency, The system's rated frequency; Using the time-frequency transformation method, based on the equivalent oscillation equation of the wind farm, a site-level inertia constant evaluation model is constructed, including: Based on the Laplace transform and inverse Laplace transform, the equivalent oscillation equation of the wind farm is converted from differential form to integral form to obtain the equivalent oscillation integral equation of the wind farm. Based on the gradient method of numerical integration, the equivalent oscillation integral equation of the wind farm is transformed from a continuous domain to a discrete domain to obtain an evaluation model of the station-level inertia constant. Based on the Laplace transform and inverse Laplace transform, the equivalent oscillation equation of the wind farm is converted from differential form to integral form to obtain the integral equation of the equivalent oscillation of the wind farm, which includes: The equivalent oscillation equation of the wind farm is subjected to a Laplace transform. The output variable is used to replace the change in system frequency, the input variable is used to replace the change in active power of the wind farm, and the change in mechanical power of the wind farm is ignored, so as to obtain the equivalent oscillation algebraic equation of the wind farm. Based on the equivalent oscillating algebraic equation of the wind farm, differentiate the operational operator, and multiply both sides of the differentiated equation by... And perform the inverse Laplace transform to obtain the equivalent oscillating integral equation of the wind farm; The expression for the equivalent oscillating algebraic equation of the wind farm is shown in formula (2): (2); In formula (2), For operation operators, For output variables, For input variables, This represents the damping coefficient of the wind farm. The equivalent virtual inertia constant of the wind farm; The expression for the equivalent oscillating integral equation of the wind farm is shown in formula (3): (3); In formula (3), The equivalent virtual inertia constant of the wind farm. This represents the damping coefficient of the wind farm. , For time intervals, The length of the time window. The sampling period is For integration variables, For output variables The time-domain expression, Input variables The time-domain expression; The expression for the evaluation model of the station-level inertia constant is shown in formula (4): (4); In formula (4), The equivalent virtual inertia constant of the wind farm. This represents the damping coefficient of the wind farm. The sampling period is For the first One sampling point, The length of the time window. The sampling point at the current moment, For the first The output at each sampling time For the first The output at each sampling time , This represents the change in system frequency. For the first The input quantity at each sampling time. , This represents the change in active power of the wind farm.

2. The online evaluation method for the equivalent virtual inertia constant of a wind farm according to claim 1, characterized in that, The preprocessing includes: per-unitization, detrending, and pre-filtering.

3. The online evaluation method for the equivalent virtual inertia constant of a wind farm according to claim 1, characterized in that, The formula used for the singularity removal operation is shown in formula (5): (5); In formula (5), The equivalent virtual inertia constant of the wind farm at the current sampling time. The equivalent virtual inertia constant of the wind farm at the previous sampling time. The sampling point at the current moment, , The sampling period is For the first One sampling point, The length of the time window. This represents the damping coefficient of the wind farm. For the first The output at each sampling time For the first The input quantity at each sampling time. , For the first The output at each sampling time A threshold to prevent numerical errors, To prevent numerical errors, a proportionality coefficient, .

4. An online evaluation device for the equivalent virtual inertia constant of a wind farm, characterized in that, include: Preprocessing module: Used to preprocess measured data from wind farms; Equation building module: used to establish the equivalent swing equation of the wind farm based on the swing equation of the synchronous unit; Model building module: used to construct a site-level inertia constant evaluation model based on the equivalent oscillation equation of the wind farm using the time-frequency transformation method; Evaluation module: used to input preprocessed wind farm measured data into the site-level inertia constant evaluation model to output evaluation results; Singularity removal module: Used to perform singularity removal operations on the evaluation results to obtain the equivalent virtual inertia constant of the wind farm; The measured data of the wind farm includes system frequency signals and wind farm active power signals; The expression for the equivalent swing equation of the wind farm is shown in formula (1): (1); In formula (1), The equivalent virtual inertia constant of the wind farm. This represents the damping coefficient of the wind farm. For mechanical power, Electromagnetic power, For system frequency, The system's rated frequency; Using the time-frequency transformation method, based on the equivalent oscillation equation of the wind farm, a site-level inertia constant evaluation model is constructed, including: Based on the Laplace transform and inverse Laplace transform, the equivalent oscillation equation of the wind farm is converted from differential form to integral form to obtain the equivalent oscillation integral equation of the wind farm. Based on the gradient method of numerical integration, the equivalent oscillation integral equation of the wind farm is transformed from a continuous domain to a discrete domain to obtain an evaluation model of the station-level inertia constant. Based on the Laplace transform and inverse Laplace transform, the equivalent oscillation equation of the wind farm is converted from differential form to integral form to obtain the integral equation of the equivalent oscillation of the wind farm, which includes: The equivalent oscillation equation of the wind farm is subjected to a Laplace transform. The output variable is used to replace the change in system frequency, the input variable is used to replace the change in active power of the wind farm, and the change in mechanical power of the wind farm is ignored, so as to obtain the equivalent oscillation algebraic equation of the wind farm. Based on the equivalent oscillating algebraic equation of the wind farm, differentiate the operational operator, and multiply both sides of the differentiated equation by... And perform the inverse Laplace transform to obtain the equivalent oscillating integral equation of the wind farm; The expression for the equivalent oscillating algebraic equation of the wind farm is shown in formula (2): (2); In formula (2), For operation operators, For output variables, For input variables, This represents the damping coefficient of the wind farm. The equivalent virtual inertia constant of the wind farm; The expression for the equivalent oscillating integral equation of the wind farm is shown in formula (3): (3); In formula (3), The equivalent virtual inertia constant of the wind farm. This represents the damping coefficient of the wind farm. , For time intervals, The length of the time window. The sampling period is For integration variables, For output variables The time-domain expression, Input variables The time-domain expression; The expression for the evaluation model of the station-level inertia constant is shown in formula (4): (4); In formula (4), The equivalent virtual inertia constant of the wind farm. This represents the damping coefficient of the wind farm. The sampling period is For the first One sampling point, The length of the time window. The sampling point at the current moment, For the first The output at each sampling time For the first The output at each sampling time , This represents the change in system frequency. For the first The input quantity at each sampling time. , This represents the change in active power of the wind farm.

5. An online evaluation device for the equivalent virtual inertia constant of a wind farm, characterized in that, Including processor and storage media; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1 to 3.

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