CN115719975A - A wind farm equivalent virtual inertia constant online evaluation method, device and storage medium - Google Patents

Abstract

The invention discloses an on-line evaluation method, device and storage medium for equivalent virtual inertia constant of a wind power plant, belonging to the technical field of new energy power generation control, and the method comprises the following steps: preprocessing measured data of the wind power plant; establishing an equivalent oscillation equation of the wind power plant according to the oscillation equation of the synchronous unit; constructing a station-level inertia constant evaluation model according to the equivalent oscillation equation of the wind power plant by using a time-frequency transformation method; inputting the preprocessed wind power plant actual measurement data into the station-level inertia constant evaluation model to output an evaluation result; performing singular point removing operation on the evaluation result to obtain an equivalent virtual inertia constant of the wind power plant; the measured data of the wind power plant comprise a system frequency signal and an active power signal of the wind power plant. According to the method, the equivalent virtual inertia constant of the wind power plant is evaluated on line through a time-frequency transformation method, and the inertia contribution of the wind power plant to a power grid can be quantitatively expressed.

Description

A wind farm equivalent virtual inertia constant online evaluation method, device and storage medium

technical field

The invention relates to an online evaluation method, device and storage medium for an equivalent virtual inertia constant of a wind farm, and belongs to the technical field of new energy power generation control.

Background technique

In recent years, due to the excessive grid connection of new energy sources, the system inertia support capacity is insufficient, and power outages have occurred from time to time. Accurately assessing the inertia level of the system will help grid dispatchers grasp the weak stage of inertia, and then take inertia compensation measures in time to prevent frequency drop accidents in the grid.

The inertia constant of a synchronous generator in a power system is a fixed value, but for a wind farm with virtual inertia control applied, its equivalent virtual inertia constant is unknown due to the intermittent and uncertain output of wind turbines, and have fast time-varying characteristics. Most of the current inertia evaluation methods are offline identification of frequency events that have occurred, which cannot evaluate the inertia of wind farms online in real time and help grid dispatchers formulate inertia compensation strategies in a timely manner. The literature published by Q. Mengqi et al ["Inertial Response of Doubly-fed Induction Generator with the Phase-locked Loop," 2019IEEE Innovative Smart Grid Technologies-Asia (ISGTAsia), 2019, pp.1435-1439], using the control of wind turbines strategy, which defines the expression of the equivalent virtual inertia constant to realize the real-time evaluation of inertia, but this method is only suitable for wind turbines with grid-connected inertia control, so it is not universal. The document ["Online Identification of Power System EquivalentInertia Constant," in IEEE Transactions on Industrial Electronics, vol.64, no.10, pp.8098-8107, Oct.2017] published by J. Zhang et al. proposed a method using The online evaluation method of micro-disturbance by power electronic devices on the closed-loop system realizes the real-time identification of the time-varying nonlinear equivalent inertia constant, but the external micro-disturbance signal may affect the frequency response of the system, and then affect the safety of the system operation.

According to different evaluation levels, inertia evaluation can be divided into identification for single machine, multiple nodes and the whole system. The literature published by W.He et al ["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 Quantitative analytical method to characterize the time-varying wind turbine equivalent virtual inertia constant, but this method needs to obtain more wind turbine control parameters and state parameters, which is not suitable for system-level inertia evaluation and cannot be directly applied to the actual wind farm inertia Evaluate. 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 inertia constants based on the identification results. The literature published 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], using controlled The autoregressive model replaces the swing equation of the wind farm, and the high-order model replacement method is used to evaluate the inertia from the station level. However, the optimal order of the high-order model is not easy to confirm, and the accuracy of the evaluation results is related to the type of identification model. .

For the equivalent virtual inertia constant of wind farms, there is currently a lack of an online evaluation method at the system level, with easy access to data and high evaluation accuracy.

Contents of the invention

The purpose of the present invention is to provide an online evaluation method, device and storage medium for the equivalent virtual inertia constant of a wind farm. Through the time-frequency transformation method, the online evaluation of the equivalent virtual inertia constant of the wind farm can be quantitatively expressed. inertia contribution.

To achieve the above object, the present invention provides the following technical solutions:

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

Preprocessing the measured data of the wind farm;

According to the swing equation of the synchronous unit, the equivalent swing equation of the wind farm is established;

Using the time-frequency transformation method, according to the equivalent swing equation of the wind farm, a station-level inertia constant evaluation model is constructed;

Inputting the preprocessed wind farm measured data into the station-level inertia constant evaluation model to output evaluation results;

Performing a singular point removal operation on the evaluation result to obtain the equivalent virtual inertia constant of the wind farm;

Wherein, the measured data of the wind farm includes a system frequency signal and an active power signal of the wind farm.

With reference to the first aspect, further, the preprocessing includes: per unitization, detrending and pre-filtering.

In combination with the first aspect, further, the expression of the equivalent swing equation of the wind farm is shown in formula (1):

In formula (1), H _WF is the equivalent virtual inertia constant of the wind farm, D _WF is the damping coefficient of the wind farm, P _m is the mechanical power, _Pe is the electromagnetic power, ω _s is the system frequency, and ω _s0 is the system rated frequency.

In combination with the first aspect, further, using the time-frequency transformation method, according to the equivalent swing equation of the wind farm, constructing a station-level inertia constant evaluation model includes:

According to the Laplace transform and the inverse Laplace transform, the equivalent swing equation of the wind farm is converted from a differential form to an integral form to obtain an equivalent swing integral equation of the wind farm;

According to the gradient method of numerical integration, the equivalent swing integral equation of the wind farm is converted from the continuous domain to the discrete domain to obtain the station-level inertia constant evaluation model.

In combination with the first aspect, further, according to the Laplace transform and the inverse Laplace transform, converting the equivalent swing equation of the wind farm from a differential form to an integral form to obtain the equivalent swing integral equation of the wind farm includes:

Laplace transform is performed on the equivalent swing equation of the wind farm, the output variable is used to replace the system frequency variation, the input variable is used to replace the wind farm active power variation, and the wind farm mechanical power variation is ignored to obtain the wind farm, etc. effective swing algebraic equation;

According to the equivalent swing algebraic equation of the wind farm, derivate the operator, multiply both sides of the derived equation by s ^-2 , and perform Laplace inverse transformation to obtain the equivalent swing of the wind farm Integral equation;

Wherein, the expression of the equivalent swing algebraic equation of the wind farm is shown in formula (2):

In formula (2), s is the operation operator, Y is the output variable, U is the input variable, D _WF is the damping coefficient of the wind farm, and H _WF is the equivalent virtual inertia constant of the wind farm;

The expression of the equivalent swing integral equation of the wind farm is shown in formula (3):

In formula (3), H _WF is the equivalent virtual inertia constant of the wind farm, D _WF is the damping coefficient of the wind farm, T _F = n _F T, T _F is the time interval, n _F is the time window length, T is the sampling period, δ is the integral variable, y(δ) is the time-domain expression of the output variable Y, and u(δ) is the time-domain expression of the input variable U.

In combination with the first aspect, further, the expression of the station-level inertia constant evaluation model is shown in formula (4):

In formula (4), H _WF is the equivalent virtual inertia constant of the wind farm, D _WF is the damping coefficient of the wind farm, T is the sampling period, i is the i-th sampling point, n _F is the length of the time window, and k is the current sampling time point, y(i) is the output at the i-th sampling moment, y(i-1) is the output at the i-1th sampling moment, y(i)=Δψ(k-(n _F -i)) , Δω is the system frequency variation, u(i) is the input quantity at the i-th sampling moment, u(i)=ΔP _e (k-(n _F -i)), ΔP _e is the wind farm active power variation.

In combination with the first aspect, further, the formula used in the singular point removal operation is shown in formula (5):

T为采样周期，i为第i个采样点，n_F为时间窗长度，D_WF为风电场阻尼系数，y(i)为第i个采样时刻的输出量，u(i)为第i个采样时刻的输入量，

y(i-1)为第i-1个采样时刻的输出量，ε为防止出现数值误差的阈值，β为防止出现数值误差的比例系数，β＜＜1。In formula (5), H _WF (k) is the equivalent virtual inertia constant of the wind farm at the current sampling moment, H _WF (k-1) is the equivalent virtual inertia constant of the wind farm at the previous sampling moment, k is the sampling point at the current moment,

T is the sampling period, i is the ith sampling point, n _F is the length of the time window, D _WF is the damping coefficient of the wind farm, y(i) is the output at the i-th sampling time, u(i) is the i-th The input quantity at the sampling moment,

y(i-1) is the output at the i-1th sampling time, ε is the threshold to prevent numerical errors, β is the proportional coefficient to prevent numerical errors, β<<1.

In a second aspect, the present invention provides an online evaluation device for an equivalent virtual inertia constant of a wind farm, comprising:

Preprocessing module: used to preprocess the measured data of the wind farm;

Equation building module: used to establish the equivalent swing equation of the wind farm according to the swing equation of the synchronous unit;

Model building module: used to construct a site-level inertia constant evaluation model according to the equivalent swing equation of the wind farm by using the time-frequency transformation method;

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

Singular point removal module: used to perform singular point removal operation on the evaluation result to obtain the equivalent virtual inertia constant of the wind farm;

Wherein, the measured data of the wind farm includes a system frequency signal and an active power signal of the wind farm.

In a third aspect, the present invention provides an online evaluation device for an equivalent virtual inertia constant of a wind farm, including a processor and a storage medium;

The storage medium is used to store instructions;

The processor is configured to operate according to the instructions to perform the steps of any one of the methods according to the first aspect.

In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of any one of the methods described in the first aspect are implemented.

Compared with prior art, the beneficial effect of the present invention is:

The present invention uses the time-frequency transformation method to convert the equivalent swing equation of the wind farm from the differential form to the integral form, realizes the indirect processing of the frequency derivative term, and avoids the derivative peak problem; the time-frequency transformation method used in the present invention does not need an iterative process, and improves The evaluation speed is improved; the present invention realizes the online evaluation of the equivalent inertia constant at the station level, and has certain practical applicability; the evaluation method of the present invention can be applied to other new energy field groups or other virtual inertia control methods.

Description of drawings

Fig. 1 is a flow chart of an online evaluation method for an equivalent virtual inertia constant of a wind farm provided by an embodiment of the present invention;

Fig. 2 is the topological diagram of the wind farm of the sinmulink platform provided by the embodiment of the present invention;

Fig. 3 is the sinmulink platform wind farm provided by the embodiment of the present invention frequency disturbance waveform diagram when the load suddenly increases;

Fig. 4 is a waveform diagram of active power generated by the wind farm of the sinmulink platform provided by the embodiment of the present invention when the load suddenly increases;

Fig. 5 is the evaluation result of the equivalent virtual inertia constant of the wind farm under the sinmulink platform 10m/s wind speed provided by the embodiment of the present invention;

Fig. 6 is the evaluation result of the equivalent virtual inertia constant of the wind farm under the sinmulink platform 8m/s wind speed provided by the embodiment of the present invention;

Fig. 7 is the evaluation result of the equivalent virtual inertia constant of the wind farm under the sinmulink platform 12m/s wind speed provided by the embodiment of the present invention;

Fig. 8 is the evaluation result of the equivalent virtual inertia constant of Dafeng wind farm when the frequency is disturbed by the embodiment of the present invention;

Fig. 9 is the evaluation result of the equivalent virtual inertia constant of the Dafeng wind farm when the frequency is disturbed by the embodiment of the present invention.

Detailed ways

The technical solution of this patent will be further described in detail below in conjunction with specific embodiments.

Embodiments of the present patent are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals designate the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary and are only used for explaining the patent, and should not be construed as limiting the patent. In the case of no conflict, the embodiments of the present application and the technical features in the embodiments may be combined with each other.

Embodiment one:

Figure 1 is a flowchart of an online evaluation method for an equivalent virtual inertia constant of a wind farm provided by Embodiment 1 of the present invention. This flowchart only shows the logical sequence of the method in this embodiment. On the premise of not conflicting with each other, in this In other possible embodiments of the invention, the steps shown or described may be performed in an order different from that shown in FIG. 1 .

The wind farm equivalent virtual inertia constant online evaluation method provided in this embodiment can be applied to a terminal, and can be executed by a wind farm equivalent virtual inertia constant online evaluation device, which can be implemented by software and/or hardware. It can be integrated in a terminal, such as any tablet or computer device with communication functions. Referring to Fig. 1, the method of the present embodiment specifically includes the following steps:

Step 1: Preprocessing the measured data of the wind farm;

The measured data of the wind farm includes the system frequency signal and the active power signal of the wind farm; the preprocessing of the measured data of the wind farm includes the following steps:

Step A: Carry out per-unit processing on the measured data of the wind farm;

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

Step B: detrending the measured data of the wind farm after per-unit processing;

The detrending process of the measured data of the wind farm after the per-unit processing includes: respectively subtracting the optimal trend obtained by fitting the average value method from the system frequency signal and the active power signal of the wind farm after the per-unit processing. Straight line data to remove the DC component in the system frequency signal and the active power signal of the wind farm.

Step C: pre-filtering the measured wind farm data after detrending processing;

The pre-filtering of the detrended measured wind farm data includes: using a low-pass Butterworth filter with a cutoff frequency of 0.5 Hz to pre-filter the detrended system frequency signal and wind farm active power signal. In order to eliminate the high-frequency noise in the system frequency signal and the active power signal of the wind farm, the robustness of the evaluation model is improved.

Step 2: According to the swing equation of the synchronous unit, establish the equivalent swing equation of the wind farm;

The expression of the equivalent swing equation of the wind farm is shown in formula (1):

In formula (1), H _WF is the equivalent virtual inertia constant of the wind farm, D _WF is the damping coefficient of the wind farm, P _m is the mechanical power, _Pe is the electromagnetic power, ω _s is the system frequency, and ω _s0 is the system rated frequency.

According to the mechanism of virtual inertia, the calculation expression of the equivalent virtual inertia constant of the wind farm can be established; the wind turbine with virtual inertia control provides inertia support power to the grid by releasing the kinetic energy of the rotor, so the rotor speed of the fan is coupled with the system frequency, The change in kinetic energy of the rotor released by the fan can also be expressed by the system frequency and the equivalent virtual moment of inertia:

Among them, J _equ is the equivalent virtual moment of inertia of the wind turbine, J _inherent is the inherent moment of inertia of the wind turbine, E _k is the stored kinetic energy of the generator rotor at the rated speed of a single wind turbine, n is the number of pole pairs of the wind turbine, and S _N is the wind turbine Rated capacity, ω _r is the rotor speed of the fan, ω _r0 is the initial rotor speed of the fan, Δω _r is the variation of the fan rotor speed, and Δω _s is the variation of the system frequency.

Thus, the calculation expressions of equivalent virtual inertia constants of wind turbines and wind farms can be obtained:

Among them, H _inherent is the inherent inertia constant of the wind turbine, H _WF is the equivalent virtual inertia constant of the wind farm, and m is the number of wind turbines in the wind farm.

Calculating the equivalent virtual inertia constant of the wind farm by this method requires the inherent inertia constant H _inherent of each wind turbine, but in practical applications, the manufacturer usually does not give the inherent inertia constant H _inherent , which makes it difficult to evaluate the inertia level of the wind farm. To challenge, the method provided by the present invention can effectively solve this problem.

Step 3: Use the time-frequency transformation method to construct a station-level inertia constant evaluation model according to the equivalent swing equation of the wind farm;

Using the time-frequency transformation method and according to the equivalent swing equation of the wind farm, the construction of a station-level inertia constant evaluation model includes the following steps:

Step Ⅰ: According to the Laplace transform and Laplace inverse transform, the equivalent swing equation of the wind farm is converted from the differential form to the integral form to obtain the equivalent swing integral equation of the wind farm;

According to the Laplace transform and the inverse Laplace transform, the equivalent swing equation of the wind farm is converted from the differential form to the integral form to obtain the equivalent swing integral equation of the wind farm, which includes the following steps:

Step ①: Laplace transform the equivalent swing equation of the wind farm, replace the system frequency variation with the output variable, replace the active power variation of the wind farm with the input variable, and ignore the mechanical power variation of the wind farm to obtain the wind farm Equivalent swing algebraic equation;

Step ②: According to the equivalent swing algebraic equation of the wind farm, derive the derivative of the operator, multiply both sides of the derived equation by s ^-2 at the same time to reduce the noise, and perform Laplace inverse transform to obtain Equivalent swing integral equation of wind farm;

Among them, the expression of the equivalent swing algebraic equation of the wind farm is shown in formula (2):

In formula (2), s is the operation operator, Y is the output variable, U is the input variable, D _WF is the damping coefficient of the wind farm, and H _WF is the equivalent virtual inertia constant of the wind farm;

The expression of the wind farm equivalent swing integral equation is shown in formula (3):

In formula (3), H _WF is the equivalent virtual inertia constant of the wind farm, D _WF is the damping coefficient of the wind farm, T _F = n _F T, T _F is the time interval, n _F is the time window length, T is the sampling period, δ is the integral variable, y(δ) is the time-domain expression of the output variable Y, and u(δ) is the time-domain expression of the input variable U.

Step Ⅱ: According to the gradient method of numerical integration, the equivalent swing integral equation of the wind farm is converted from the continuous domain to the discrete domain to obtain the station-level inertia constant evaluation model;

The expression of the station-level inertia constant evaluation model is shown in formula (4):

In formula (4), H _WF is the equivalent virtual inertia constant of the wind farm, D _WF is the damping coefficient of the wind farm, T is the sampling period, i is the i-th sampling point, n _F is the length of the time window, and k is the current sampling time point, y(i) is the output at the i-th sampling moment, y(i-1) is the output at the i-1th sampling moment, y(i)=Δω(k-(n _F -i)) , Δω is the system frequency variation, u(i) is the input quantity at the i-th sampling moment, u(i)=ΔP _e (k-(n _F -i)), ΔP _e is the wind farm active power variation.

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

The preprocessed system frequency signal and wind farm active power signal are used as the input signals of the station-level inertia constant evaluation model, and the evaluation results are output through the station-level inertia constant evaluation model.

Step 5: Perform singular point removal operation on the evaluation results to obtain the equivalent virtual inertia constant of the wind farm;

The formula used for the singular point removal operation is shown in formula (5):

T为采样周期，i为第i个采样点，n_F为时间窗长度，D_WF为风电场阻尼系数，y(i)为第i个采样时刻的输出量，u(i)为第i个采样时刻的输入量，

y(i-1)为第i-1个采样时刻的输出量，ε为防止出现数值误差的阈值，β为防止出现数值误差的比例系数，β＜＜1。In formula (5), H _WF (k) is the equivalent virtual inertia constant of the wind farm at the current sampling moment, H _WF (k-1) is the equivalent virtual inertia constant of the wind farm at the previous sampling moment, k is the sampling point at the current moment,

T is the sampling period, i is the ith sampling point, n _F is the length of the time window, D _WF is the damping coefficient of the wind farm, y(i) is the output at the i-th sampling time, u(i) is the i-th The input quantity at the sampling moment,

y(i-1) is the output at the i-1th sampling time, ε is the threshold to prevent numerical errors, β is the proportional coefficient to prevent numerical errors, β<<1.

In order to verify the accuracy of the evaluation method provided by the present invention, a wind farm simulation system is established using Matlab/Sinmulink simulation software. As shown in Figure 2, the system model includes a wind farm with a capacity of 60MW and a synchronous generator with a capacity of 100MW, in which the wind farm with a capacity of 60MW (using the equivalent model of a single machine) is composed of 30 direct-drive wind power generators with a capacity of 2MW Crew composition. The parameters of the synchronous machine and fan are shown in Table 1 and Table 2, respectively. For the convenience of analysis, the simulation adopts the per unit value model.

Table 1 Synchronous Machine Parameters

parameter symbol value Grid side phase voltage rated amplitude U_base 20kV Active power rating P_nom 45.7MW Rated Capacity S_N 100MW Moment of inertia J 27000kg·m² damping factor K_d 5 Number of pole pairs no 2 Grid side angular frequency ω_g 100πrad/s Rotor mechanical angular frequency ω_r 50πrad/s

Table 2 Direct Drive Wind Motor Parameters

parameter symbol value Grid side phase voltage rated amplitude U_base 563V Active power rating P_nom 1.49MW Rated Capacity S_N 2MW Moment of inertia J_inherent 7·10⁶kg·m² damping factor D. 5 Number of pole pairs no 30 Grid side angular frequency ω_g 100πrad/s Rotor mechanical angular frequency ω_r 60rad/s

During the simulation process, it is assumed that the wind speed is constant at the rated wind speed of 10m/s. At 12s, the load at the 20kV bus increases suddenly by 2.5MW, causing the power grid frequency to drop. When the system frequency deviation is greater than 0.01Hz, the wind turbine starts the virtual inertia control strategy, and the simulation results are shown in Figure 3 and Figure 4. Figure 3 and Figure 4 respectively show the frequency response and power response of the wind farm to the sudden increase of load.

Using the data in Figure 3 and Figure 4 as the input of the proposed evaluation model, the identification value H _WF of the equivalent inertia constant of the wind farm is obtained; the extracted rotor speed change Δω _r , system frequency change Δω _s , and the intrinsic inertia constant H _inherent is combined with the number of wind turbines m, and the calculated value H _WF of the equivalent virtual inertia constant of the wind farm is obtained through comprehensive calculation. The identification value and calculated value of the equivalent virtual inertia constant of the wind farm are shown in Fig. 5 together.

From the comparison curve of _HWF in Fig. 5, the calculated value of the equivalent virtual inertia constant of the wind farm is in good agreement with the identified value, which proves the validity and accuracy of the evaluation model proposed by the present invention. In Figure 5, H _WF continues to decrease from a large initial value around 8s. At the initial moment of inertia response, the frequency change rate of the system is relatively large, resulting in the strongest frequency suppression effect of the wind farm, so the equivalent virtual inertia constant of the wind farm is the largest. The inertia response function of the wind farm is to provide dynamic active power support to the grid by releasing the kinetic energy of the rotor of the generator set. Therefore, when the virtual inertia starts, the rotor speed of the generator set continues to decrease. Since the wind turbine adopts maximum power point tracking (MPPT), the decrease degree of the rotor speed will affect the active power output by the wind farm. The variation of active power is not only related to the additional power of virtual inertia control, but also to the given value of output power in MPPT mode. Therefore, the _HWF obtained by identifying the variation of active power and the variation of system frequency is not a fixed value, but time-varying value. It can be seen from Fig. 5 that at about 12.5s, the active power generated by the wind farm begins to be affected by the rotor speed, and _HWF begins to decrease continuously. After about 17.7s, the value of H _WF <0 will appear in some time periods, which is mainly because the change of the rotor speed of the unit is not synchronized with the change of the system frequency. However, because the inertia response exists at the initial stage of the system frequency drop, the evaluation of _HWF only needs to focus on the frequency and active power data within a few seconds after the system frequency drops.

In order to fully verify the accuracy of the evaluation model proposed by the present invention, the present invention has carried out simulations under different wind speed conditions. Figure 6 and Figure 7 are the evaluation results of the proposed algorithm under the conditions of wind speed 8m/s and 12m/s respectively. It can be seen from Fig. 6 and Fig. 7 that the identification value of the proposed algorithm under different wind speeds is basically consistent with the trend of the calculated value. In the initial stage of inertia response, there are certain errors in the evaluation results, but this does not affect the overall inertia evaluation results.

The present invention is tested on-site at Tianrun Dafeng Wind Farm, and the test is divided into two types of frequency disturbance and frequency disturbance. The obtained Runlong second line wind power data - system frequency and active power are input into the evaluation model, and the evaluation results are shown in Figure 8 and Figure 9. Since the inertia response of Tianrun Dafeng Wind Farm is to block the MPPT mode of the wind turbine, the equivalent virtual inertia constant obtained from the evaluation should be a constant value. The evaluation results H _WF shown in Fig. 8 and Fig. 9 fluctuate around the set value of 5s, which proves the validity and accuracy of the evaluation algorithm proposed in the present invention.

In this embodiment, the equivalent swing equation of the wind farm is converted from a differential equation in the time domain to an algebraic equation in the frequency domain, algebraic operations are performed, and then the algebraic equation in the frequency domain is converted into a time domain by inverse Laplace transform. The identifiable integral equation is finally used to solve the expression of the equivalent inertia constant by using the gradient method of numerical integration to construct a station-level inertia constant evaluation model. This method converts the equivalent swing equation of the wind farm from a differential equation form to an integral equation form, realizes the indirect processing of the frequency derivative term in the inertia evaluation model equation, and avoids the derivative peak problem; and this method makes full use of the frequency deviation and power deviation. Historical data avoids the identification error caused by a single bad data point; the singular point removal operation on the evaluation results can prevent the sudden increase of H _WF in the evaluation results while maintaining the change trend of H _WF , and choosing an appropriate β can effectively eliminate Numerical errors improve the accuracy of the evaluation results and reduce the sensitivity of the evaluation model to noise.

Embodiment two:

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

Preprocessing module: used to preprocess the measured data of the wind farm;

Equation building module: used to establish the equivalent swing equation of the wind farm according to the swing equation of the synchronous unit;

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

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

Singularity removal module: used to remove the singularity of the evaluation results to obtain the equivalent virtual inertia constant of the wind farm;

Among them, the measured data of the wind farm includes the system frequency signal and the active power signal of the wind farm.

The wind farm equivalent virtual inertia constant online evaluation device provided by the embodiments of the present invention can execute the wind farm equivalent virtual inertia constant online evaluation method provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method.

Embodiment three:

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

The storage medium is used to store instructions;

The processor is configured to operate according to the instructions to execute the steps of the method in the first embodiment.

Embodiment four:

This embodiment provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the steps of the method in Embodiment 1 are implemented.

Those skilled in the art should understand that the embodiments of the present application may be provided as methods, systems, or computer program products. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may 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.) having computer-usable program code embodied therein.

The present application is described with reference to flowcharts and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each procedure and/or block in the flowchart and/or block diagram, and a combination of procedures and/or blocks in the flowchart and/or block diagram can be realized by computer program instructions. These computer program instructions may be provided to a general purpose computer, special purpose computer, embedded processor, or processor of other programmable data processing equipment to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing equipment produce a An apparatus for realizing the functions specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.

These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to operate in a specific manner, such that the instructions stored in the computer-readable memory produce an article of manufacture comprising instruction means, the instructions The device realizes the function specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.

These computer program instructions can also be loaded onto a computer or other programmable data processing device, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby The instructions provide steps for implementing the functions specified in the flow chart or blocks of the flowchart and/or the block or blocks of the block diagrams.

The above is only a preferred embodiment of the present invention, and it should be pointed out that for those of ordinary skill in the art, without departing from the technical principle of the present invention, some improvements and modifications can also be made. It should also be regarded as the protection scope of the present invention.

Claims (10)

1. An online evaluation method for equivalent virtual inertia constants of a wind power plant is characterized by comprising the following steps:

preprocessing measured data of the wind power plant;

establishing an equivalent swing equation of the wind power plant according to the swing equation of the synchronous unit;

constructing a station-level inertia constant evaluation model according to the equivalent oscillation equation of the wind power plant by using a time-frequency transformation method;

inputting the preprocessed wind power plant actual measurement data into the station-level inertia constant evaluation model to output an evaluation result;

performing singular point removing operation on the evaluation result to obtain an equivalent virtual inertia constant of the wind power plant;

the measured data of the wind power plant comprise a system frequency signal and an active power signal of the wind power plant.

2. The wind farm equivalent virtual inertia constant online evaluation method according to claim 1, wherein the preprocessing comprises: per unit, detrending, and pre-filtering.

3. The wind farm equivalent virtual inertia constant online evaluation method according to claim 1, wherein the wind farm equivalent oscillation equation has an expression shown in formula (1):

in the formula (1), H _WF Is equivalent virtual inertia constant of wind power plant, D _WF For damping coefficient of wind farm, P _m Is mechanical power, P _e Is electromagnetic power, omega _s Is the system frequency, ω _s0 The system nominal frequency.

4. The wind power plant equivalent virtual inertia constant online evaluation method according to claim 1, wherein constructing a plant-level inertia constant evaluation model according to the wind power plant equivalent oscillation equation by using a time-frequency transformation method comprises:

converting the equivalent oscillation equation of the wind power plant from a differential form to an integral form according to Laplace transform and Laplace inverse transform to obtain an equivalent oscillation integral equation of the wind power plant;

and converting the equivalent swing integral equation of the wind power plant from a continuous domain to a discrete domain according to a gradient method of numerical integration to obtain a station-level inertia constant evaluation model.

5. The wind farm equivalent virtual inertia constant online evaluation method according to claim 4, wherein converting the wind farm equivalent oscillation equation from a differential form to an integral form according to the Laplace transform and the Laplace inverse transform to obtain the wind farm equivalent oscillation integral equation comprises:

performing Laplace transformation on the wind power plant equivalent swing equation, replacing system frequency variation with an output variable, replacing active power variation of the wind power plant with an input variable, and neglecting mechanical power variation of the wind power plant to obtain a wind power plant equivalent swing algebraic equation;

according to the wind power plant equivalent swing algebraic equation, derivation is carried out on an arithmetic operator, and s is multiplied on two sides of the derived equation at the same time ^-2 Performing inverse Laplace transform to obtain an equivalent swing integral equation of the wind power plant;

the wind power plant equivalent swing algebraic equation has an expression shown in formula (2):

in formula (2), s is an operator, Y is an output variable, U is an input variable, and D _WF Damping coefficient for wind farms, H _WF The equivalent virtual inertia constant of the wind power plant is obtained;

the expression of the wind power plant equivalent swing integral equation is shown in formula (3):

in the formula (3), H _WF Is equivalent virtual inertia constant of wind power plant, D _WF Damping coefficient for wind farms, T _F ＝n _F T，T _F Is a time interval, n _F Is the length of the time windowDegree, T is the sampling period, δ is the integral variable, Y (δ) is the time domain expression of the output variable Y, and U (δ) is the time domain expression of the input variable U.

6. The wind farm equivalent virtual inertia constant online evaluation method according to claim 4, wherein the expression of the station-level inertia constant evaluation model is shown in formula (4):

in the formula (4), H _WF Is equivalent virtual inertia constant of wind power plant, D _WF Is damping coefficient of wind power plant, T is sampling period, i is ith sampling point, n _F For the length of the time window, k is a sampling point at the current moment, y (i) is an output quantity at the ith sampling moment, y (i-1) is an output quantity at the ith-1 sampling moment, and y (i) = delta omega (k- (n) = delta omega _F -i)), Δ ω is the system frequency variation, u (i) is the input at the i-th sampling time, u (i) = Δ P _e (k-(n _F -i))，ΔP _e The active power variation of the wind power plant.

7. The wind farm equivalent virtual inertia constant online evaluation method according to claim 1, wherein the formula used by the de-singular point operation is shown in formula (5):

in the formula (5), H _WF (k) Is the equivalent virtual inertia constant H of the wind power plant at the current sampling moment _WF (k-1) is an equivalent virtual inertia constant of the wind power plant at the previous sampling moment, k is a sampling point at the current moment,

t is the sampling period, i is the ith sampling point, n _F Is the length of the time window, D _WF As the damping coefficient of the wind power plant, y (i) is the output quantity of the ith sampling moment, n (i) is the input quantity of the ith sampling moment,

y (i-1) is the output quantity of the i-1 th sampling moment, epsilon is a threshold value for preventing numerical errors, beta is a proportionality coefficient for preventing numerical errors, and beta < 1.

8. The utility model provides an online evaluation device of wind-powered electricity generation field equivalent virtual inertia constant which characterized in that includes:

a preprocessing module: the method is used for preprocessing the measured data of the wind power plant;

an equation building module: the wind power plant equivalent oscillation equation is established according to the oscillation equation of the synchronous unit;

a model construction module: the system comprises a wind power plant equivalent oscillation equation, a station level inertia constant evaluation model, a time-frequency transformation method and a power station level inertia constant evaluation model, wherein the wind power plant equivalent oscillation equation is used for establishing the station level inertia constant evaluation model;

an evaluation module: the system comprises a wind power plant level inertia constant evaluation model, a wind power plant level inertia constant evaluation model and a wind power plant level inertia constant evaluation model, wherein the wind power plant level inertia constant evaluation model is used for inputting preprocessed wind power plant measured data to the wind power plant level inertia constant evaluation model so as to output an evaluation result;

a singular point removing module: the method is used for performing singular point removing operation on the evaluation result to obtain the equivalent virtual inertia constant of the wind power plant;

the measured data of the wind power plant comprise a system frequency signal and an active power signal of the wind power plant.

9. An online evaluation device for equivalent virtual inertia constants of a wind power plant is characterized by comprising a processor and a storage medium;

the storage medium is used for storing instructions;

the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.

Images