CN115719975A - Wind power plant equivalent virtual inertia constant online evaluation method and device and storage medium - Google Patents
Wind power plant equivalent virtual inertia constant online evaluation method and device and storage medium Download PDFInfo
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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
Technical Field
The invention relates to an on-line evaluation method and device for equivalent virtual inertia constants of a wind power plant and a storage medium, and belongs to the technical field of new energy power generation control.
Background
In recent years, a phenomenon that a system inertia supporting capability is insufficient due to excessive new energy and grid connection, and a power failure accident is caused sometimes occurs. Accurate evaluation of the inertia level of the system is helpful for power grid dispatchers to master the weak stage of inertia, so that inertia compensation measures can be taken in time, and the frequency drop accidents of the power grid can be prevented.
Inertia constants of synchronous generators in a power system are fixed values, while for a wind power plant to which virtual inertia control is applied, an equivalent virtual inertia constant of the wind power plant is unknown due to intermittency and uncertainty of wind power plant output and has a rapid time-varying characteristic. Most of the existing inertia evaluation methods are offline identification of a frequency event, and cannot evaluate the inertia of a wind power plant on line in real time and help power grid scheduling personnel to make an inertia compensation strategy in time. Mengqi et al, with the control strategy of the wind turbine, define an expression of the equivalent virtual inertia constant to achieve real-time estimation of the inertia, but this method is only applicable to wind turbines with Grid-connected inertia control, and therefore it has no versatility. J.Zhang et al, the document of ' on line Identification of Power System Equivalent Inertia Constant, ' in IEEE Transactions on Industrial Electronics, vol.64, no.10, pp.8098-8107, oct.2017 ', proposes an on-line assessment method for micro-disturbance of a closed-loop System by using Power electronic devices, which realizes real-time Identification of time-varying nonlinear Equivalent Inertia Constant, but the additional micro-disturbance signal may influence the frequency response of the System, and further influence the safety of System operation.
Inertia estimation can be divided into identification for a single machine, multiple nodes and the whole system according to different estimation levels. A method for quantitatively analyzing and representing time-varying equivalent virtual Inertia constants of a Wind turbine generator set is researched in a document [ "Inertia Provision and Estimation of PLL-Based DFIG Wind Turbines," in IEEE Transactions on Power Systems, vol.32, no.1, pp.510-521, jan.2017] published by W.He et al, but the method needs to obtain more control parameters and state parameters of the Wind turbine generator set, is not suitable for system-level Inertia Estimation and cannot be directly applied to Inertia Estimation of an actual Wind Power plant. For system-level inertia identification, most scholars characterize a transfer function between frequency and power by means of a system parameter identification model (such as an input/output model or a state space model), and then extract inertia constants according to identification results. The controlled autoregressive model is used for replacing a swing equation of a wind Power plant, and a high-order model replacement method is used for realizing the evaluation of Inertia From a site level, but the optimal order of the high-order model is not easy to confirm, and the precision of an evaluation result is related to the type of an identification model.
For equivalent virtual inertia constants of a wind power plant, an online evaluation method with system level, easy data acquisition and high evaluation precision is lacked at present.
Disclosure of Invention
The invention aims to provide an on-line evaluation method, device and storage medium for wind power plant equivalent virtual inertia constants.
In order to achieve the purpose, the invention provides the following technical scheme:
in a first aspect, the invention provides an online evaluation method for an equivalent virtual inertia constant of a wind farm, which 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.
In combination with the first aspect, further, the preprocessing includes: per unit, detrending, and pre-filtering.
With reference to the first aspect, further, the expression of the wind farm equivalent oscillation equation is shown in formula (1):
in the formula (1), H WF Is equivalent virtual inertia constant, D, of wind farm 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.
With reference to the first aspect, further, the constructing a station-level inertia constant evaluation model according to the wind farm equivalent oscillation equation by using a time-frequency transform method includes:
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.
With reference to the first aspect, further, converting the wind farm equivalent oscillation equation from a differential form to an integral form according to the laplace transform and the inverse laplace transform to obtain a wind farm equivalent oscillation integral equation includes:
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 expression of the wind power plant equivalent swing algebraic equation is shown as a formula (2):
in formula (2), s is an operator, Y is an output variable, U is an input variable, and D WF Damping coefficient of wind farm, 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 For the time window length, T is the sampling period, δ is the integration 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.
With reference to the first aspect, further, 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 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 the sampling point at the current time, y (i) is the output quantity at the ith sampling time, and y (i-1) is the output quantity at the ith-1 sampling timeOutput quantity, y (i) = Δ ψ (k- (n) 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.
With reference to the first aspect, further, the formula used by the singularity removing operation is as 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, u (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.
In a second aspect, the present invention provides an online evaluation device for an equivalent virtual inertia constant of a wind farm, including:
a pretreatment 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.
In a third aspect, the invention provides an online evaluation device for equivalent virtual inertia constants of a wind power plant, which comprises a processor and a storage medium;
the storage medium is to store instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any one of the first aspect.
In a fourth aspect, the invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method of any one of the first aspect.
Compared with the prior art, the invention has the beneficial effects that:
according to the method, the wind power plant equivalent oscillation equation is converted from a differential form to an integral form by using a time-frequency transformation method, so that the frequency derivative item is indirectly processed, and the problem of derivative peak is avoided; the time-frequency transformation method does not need an iterative process, so that the evaluation speed is increased; the method realizes the on-line evaluation of the station-level equivalent inertia constant, and has certain practical applicability; the evaluation method can be applied to other new energy field groups or other virtual inertia control modes.
Drawings
FIG. 1 is a flow chart of an online evaluation method for equivalent virtual inertia constants of a wind farm, which is provided by the embodiment of the invention;
FIG. 2 is a wind farm topology diagram of a sinmulink platform provided by an embodiment of the invention;
FIG. 3 is a waveform diagram of frequency disturbance in a sinmulink platform wind farm during sudden load increase according to an embodiment of the present invention;
FIG. 4 is a waveform diagram of active power generated by a wind farm of a sinmulink platform during sudden load increase according to an embodiment of the present invention;
FIG. 5 shows the evaluation result of the equivalent virtual inertia constant of the wind farm at a wind speed of 10m/s for the sinmulink platform according to the embodiment of the invention;
FIG. 6 is an evaluation result of an equivalent virtual inertia constant of a wind farm at a wind speed of 8m/s for the sinmulink platform provided by the embodiment of the invention;
FIG. 7 shows the evaluation result of the equivalent virtual inertia constant of the wind farm at a wind speed of 12m/s for the sinmulink platform according to the embodiment of the invention;
FIG. 8 is an evaluation result of an equivalent virtual inertia constant of a wind farm of a Dafeng when disturbed at a frequency according to an embodiment of the present invention;
fig. 9 is an evaluation result of the equivalent virtual inertia constant of the wind farm when the frequency is disturbed according to the embodiment of the present invention.
Detailed Description
The technical solution of the present patent will be described in further detail with reference to the following embodiments.
Reference will now be made in detail to embodiments of the present patent, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present patent and are not to be construed as limiting the present patent. The embodiments of the present application and the technical features in the embodiments may be combined with each other without conflict.
The first embodiment is as follows:
fig. 1 is a flowchart of an online evaluation method for an equivalent virtual inertia constant of a wind farm according to an embodiment of the present invention, where the flowchart only shows a logical order of the method according to the embodiment, and on the premise of no conflict, in other possible embodiments of the present invention, the steps shown or described may be completed in an order different from that shown in fig. 1.
The online evaluation method for the equivalent virtual inertia constant of the wind farm provided by the embodiment can be applied to a terminal, and can be executed by an online evaluation device for the equivalent virtual inertia constant of the wind farm, which can be implemented by software and/or hardware, and can be integrated in the terminal, for example: any tablet computer or computer device with communication function. Referring to fig. 1, the method of the present embodiment specifically includes the following steps:
the method comprises the following steps: preprocessing measured data of the wind power plant;
the measured data of the wind power plant comprises a system frequency signal and an active power signal of the wind power plant; the method for preprocessing the measured data of the wind power plant comprises the following steps:
step A: per-unit processing is carried out on the measured data of the wind power plant;
the per-unit processing of the measured data of the wind power plant comprises the following steps: the system frequency signal and the wind farm active power signal are divided by their respective nominal values.
And B: trend removing processing is carried out on the per-unit processed measured data of the wind power plant;
the trend removing processing of the wind power plant measured data subjected to per unit processing comprises the following steps: and respectively subtracting the optimal trend straight line data obtained by fitting the system frequency signal and the wind power plant active power signal by adopting an average value method from the system frequency signal and the wind power plant active power signal subjected to per unit processing so as to remove direct current components in the system frequency signal and the wind power plant active power signal.
Step C: pre-filtering the actual measurement data of the wind power plant subjected to trend removing processing;
the pre-filtering processing of the actual measurement data of the wind power plant after the trend removing processing comprises the following steps: and a low-pass Butterworth filter with the cutoff frequency of 0.5Hz is adopted to pre-filter the system frequency signal and the wind power plant active power signal which are subjected to trend removing processing so as to eliminate high-frequency noise in the system frequency signal and the wind power plant active power signal and improve the robustness of the evaluation model.
Step two: establishing an equivalent oscillation equation of the wind power plant according to the oscillation equation of the synchronous unit;
the expression of the wind power plant equivalent swing equation is 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 Being electromagnetic power, ω s Is the system frequency, ω s0 The system nominal frequency.
According to the virtual inertia action mechanism, a calculation expression of the equivalent virtual inertia constant of the wind power plant can be established; the wind turbine generator set controlled by the virtual inertia provides inertia support power for a power grid by releasing rotor kinetic energy, so that the rotating speed of a rotor of a fan is coupled with the system frequency, and the variation of the rotor kinetic energy released by the fan can be expressed by the system frequency and the equivalent virtual inertia:
wherein, J equ Is equivalent virtual moment of inertia, J, of the wind turbine inherent For the inherent moment of inertia of the wind turbine, E k Storing kinetic energy for the generator rotor at rated speed of a single fan, n is the number of pole pairs of the wind turbine generator, S N Rated capacity of fan, omega r Is the rotational speed of the fan rotor, omega r0 Is the initial rotor speed, Δ ω, of the fan r Is the variation of the rotational speed of the fan rotor, delta omega s Is the system frequency variation.
Therefore, a calculation expression of the equivalent virtual inertia constant of the wind turbine generator and the wind power plant can be obtained:
wherein H inherent Is the inherent inertia constant H of the wind turbine WF The equivalent virtual inertia constant of the wind power plant is shown, and m is the number of wind power units in the wind power plant.
The method for calculating the equivalent virtual inertia constant of the wind power plant requires the inherent inertia constant H of each fan inherent However, in practical applications, the manufacturer usually does not give the inherent inertia constant H inherent The method provided by the invention can effectively solve the problem that the inertia level of the wind power plant is challenged to be evaluated.
Step three: constructing a station-level inertia constant evaluation model according to an equivalent swing equation of the wind power plant by using a time-frequency transformation method;
the method for constructing the station-level inertia constant evaluation model by using the time-frequency transformation method according to the equivalent swing equation of the wind power plant comprises the following steps of:
step I: converting the equivalent oscillation equation of the wind power plant from a differential form to an integral form according to Laplace transformation and Laplace inverse transformation so as to obtain the equivalent oscillation integral equation of the wind power plant;
according to the Laplace transform and the Laplace inverse transform, converting the wind power plant equivalent swing equation from a differential form to an integral form to obtain a wind power plant equivalent swing integral equation, and the method comprises the following steps:
step (1): performing Laplace transformation on the equivalent swing equation of the wind power plant, 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 an equivalent swing algebraic equation of the wind power plant;
step (2): according to the wind power plant equivalent swing algebraic equation, derivation is carried out on the arithmetic operator, and s is multiplied on two sides of the derived equation at the same time -2 Reducing noise and performing inverse Laplace transform to obtain an equivalent swing integral equation of the wind power plant;
the expression of the wind power plant equivalent swing algebraic equation is shown as a 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 Equivalent virtual inertia constant of the wind power plant;
the expression of the wind farm 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, T, of wind farms F ＝n F T，T F Is a time interval, n F For the time window length, T is the sampling period, δ is the integration 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.
And step II: converting an equivalent swing integral equation of the wind power plant from a continuous domain into a discrete domain according to a gradient method of numerical integration to obtain a station-level inertia constant evaluation model;
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 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.
Step four: inputting the preprocessed actual measurement data of the wind power plant into a station-level inertia constant evaluation model to output an evaluation result;
and taking the preprocessed system frequency signal and the wind power plant active power signal as input signals of the station-level inertia constant evaluation model, and outputting an evaluation result through the station-level inertia constant evaluation model.
Step five: performing singular point removing operation on the evaluation result to obtain an equivalent virtual inertia constant of the wind power plant;
the formula used for the de-singularity operation is shown in equation (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, u (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.
In order to verify the accuracy of the evaluation method provided by the invention, a wind power plant simulation system is established by utilizing Matlab/Sinmulink simulation software. As shown in fig. 2, the system model includes 1 wind farm with a capacity of 60MW and 1 synchronous generator with a capacity of 100MW, where the 60MW wind farm (using a single equivalent model) is composed of 30 direct-drive wind turbines with a capacity of 2 MW. The parameters of the synchronous machine and the fan are shown in table 1 and table 2, respectively. For convenient analysis, per unit value model is adopted for simulation.
TABLE 1 Sync machine parameters
| Parameter(s) | (symbol) | Numerical value | |
| Rated amplitude of network side phase voltage | U base | 20kV | |
| Rating of active power | P nom | 45.7MW | |
| Rated capacity | S N | 100MW | |
| Moment of inertia | J | 27000kg·m 2 | |
| Damping | K | d | 5 |
| Number of | n | 2 | |
| Net side angular frequency | ω g | 100πrad/s | |
| Mechanical angular frequency of rotor | ω r | 50πrad/s |
TABLE 2 direct drive wind turbine parameters
| Parameter(s) | (symbol) | Numerical value | |
| Rated amplitude of network side phase voltage | U base | 563V | |
| Rating of active power | P nom | 1.49MW | |
| Rated capacity | S N | 2MW | |
| Moment of | J | inherent | 7·10 6 kg·m 2 |
| Damping | D | 5 | |
| Number of pole pairs | n | 30 | |
| Net side angular frequency | ω g | 100πrad/s | |
| Mechanical angular frequency of rotor | ω r | 60rad/s |
In the simulation process, the wind speed is assumed to be constant at 10m/s of the rated wind speed, and when the wind speed is 12s, the load at a 20kV bus is suddenly increased by 2.5MW, so that the frequency of a power grid is reduced. When the frequency deviation of the system is greater than 0.01Hz, the wind turbine generator starts a virtual inertia control strategy, and simulation results are shown in fig. 3 and 4. Fig. 3 and 4 show the frequency response and the power response of the wind farm to a sudden load increase, respectively.
Taking the data of the graph 3 and the graph 4 as the input of the proposed evaluation model to obtain the identification value H of the equivalent inertia constant of the wind power plant WF (ii) a The extracted rotor speed variation delta omega is extracted r System frequency variation amount Δ ω s And constant of intrinsic inertia H inherent Combining with the number m of the fans, and comprehensively calculating to obtain a calculation value H of the equivalent virtual inertia constant of the wind power plant WF . Common table of identification value and calculation value of equivalent virtual inertia constant of wind power plantShown in fig. 5.
From H in FIG. 5 WF The comparison curve shows that the coincidence degree of the calculated value and the identification value of the equivalent virtual inertia constant of the wind power plant is higher, and the effectiveness and the accuracy of the evaluation model provided by the invention are proved. In FIG. 5, H WF The decrease continues from a larger initial value, which is around 8 s. At the initial moment of inertia response, the frequency change rate of the system is large, so that the wind power plant generates the strongest frequency suppression effect, and the equivalent virtual inertia constant of the wind power plant is the largest. The inertia response function of the wind power plant is to provide dynamic active power support for a power grid by releasing kinetic energy of a unit rotor, so that the rotating speed of the unit rotor continuously decreases when virtual inertia is started. Because the fan adopts Maximum Power Point Tracking (MPPT), the reduction degree of the rotor speed can influence the active power output by the wind power plant. The active power variation is not only related to the additional power of the virtual inertia control function, but also related to the given power value output in the MPPT mode, so that H is obtained by identifying the active power variation and the system frequency variation WF Not a fixed value but a time-varying value. It can be seen from fig. 5 that at approximately 12.5s, the active power emitted by the wind farm starts to be influenced by the rotor speed, H WF Begins to decrease continuously. After about 17.7s, H will appear for some period of time WF <0, mainly because the machine set rotor speed variation is not synchronous with the system frequency variation. But because the inertia response exists at the beginning of the system frequency drop, H WF The evaluation of (2) only needs to pay attention to the frequency and active power data within a few seconds after the system frequency drops.
In order to verify the accuracy of the evaluation model provided by the invention in all aspects, the invention carries out simulation under different wind speed conditions. FIG. 6 and FIG. 7 show the evaluation results of the proposed algorithm for wind speeds of 8m/s and 12m/s, respectively. As can be seen from FIGS. 6 and 7, the identification values of the algorithm are substantially consistent with the calculated value trends at different wind speeds. In the initial inertia response stage, the evaluation result has certain errors, but the integral inertia evaluation result is not influenced.
The method carries out field test on the Tianrun Dafeng wind power plant, and the test is divided into frequency up-disturbance and frequencyTwo types of downward perturbation. The obtained Runlong two-wire wind power data, i.e., the system frequency and the active power, is input into an evaluation model, and the evaluation result is shown in fig. 8 and 9. Because the inertia response of the Tianrunda Dafeng wind power plant is that the MPPT mode of the wind turbine generator is locked, the equivalent virtual inertia constant obtained through evaluation is a constant value. Evaluation results H shown in fig. 8 and 9 WF The method floats around the set value of 5s, and the effectiveness and the accuracy of the evaluation algorithm provided by the invention are proved.
In the embodiment, the wind power plant equivalent swing equation is converted into an algebraic equation form in a frequency domain from a differential equation form in the time domain, algebraic operation is performed, then the algebraic equation in the frequency domain is converted into an integral equation which can be identified in the time domain by using inverse Laplace transform, and finally an expression of an equivalent inertia constant is solved by using a gradient method of numerical integration to construct a station-level inertia constant evaluation model. According to the method, the wind power plant equivalent swing equation is converted from a differential equation form to an integral equation form, so that the frequency derivative term in the inertia evaluation model equation is indirectly processed, and the problem of derivative peak is avoided; in addition, the method fully utilizes the historical data of the frequency deviation and the power deviation, and avoids the identification error caused by a single bad data point; the singular point removing operation is carried out on the evaluation result, so that the evaluation result H can be prevented WF While suddenly increasing, maintaining H WF The numerical error can be effectively eliminated by selecting proper beta according to the variation trend of the model, the accuracy of the evaluation result is improved, and the sensitivity of the evaluation model to noise is reduced.
Example two:
the embodiment provides an online evaluation device for equivalent virtual inertia constants of a wind power plant, which comprises:
a pretreatment 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 method comprises the steps of constructing a station-level inertia constant evaluation model according to an equivalent swing equation of the wind power plant by using a time-frequency transformation method;
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 comprises the steps of performing singular point removing operation on an evaluation result to obtain an equivalent virtual inertia constant of the wind power plant;
the measured data of the wind power plant comprises a system frequency signal and an active power signal of the wind power plant.
The wind power plant equivalent virtual inertia constant online evaluation device provided by the embodiment of the invention can execute the wind power plant equivalent virtual inertia constant online evaluation method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
Example three:
the embodiment provides an online evaluation device for equivalent virtual inertia constants of a wind power plant, which comprises a processor and a storage medium;
a storage medium to store instructions;
the processor is configured to operate according to the instructions to perform the steps of the method of the first embodiment.
Example four:
the present embodiment provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps of the method of the first embodiment.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. 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, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may 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, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations 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.
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