CN113849975A - Method and system for identifying low-voltage ride through characteristics of doubly-fed wind turbine generator - Google Patents
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Abstract
The invention discloses a method and a system for identifying low-voltage ride through characteristics of a doubly-fed wind turbine generator. Firstly, a time domain simulation model of the doubly-fed wind turbine generator is constructed, observed quantity data are selected through calculating track sensitivity between identification parameters and output external characteristics of the time domain simulation model of the doubly-fed wind turbine generator, then an objective function is optimized through the observed quantity data and actual measurement data, the output characteristics are identified step by utilizing the optimized objective function under multiple working conditions, a preliminary identification result of control parameters of the doubly-fed wind turbine generator during normal operation of a power grid is specifically solved, a preliminary identification result of the control parameters of the doubly-fed wind turbine generator during fault of the power grid is solved, and then final identification results of the control parameters of the doubly-fed wind turbine generator during normal operation of the power grid and fault of the power grid are solved based on the two preliminary identification results. The invention can obviously improve the accuracy of the time domain simulation model, so that the output characteristic of the simulation system can be accurately fitted with the output characteristic of the actual system.
Description
Technical Field
The invention relates to the field of double-fed wind power generation, in particular to a method and a system for identifying low-voltage ride through characteristics of a double-fed wind turbine generator.
Background
In recent years, the installed capacity of wind power is gradually increased, and the influence of large-scale wind power integration on a power system is more and more prominent. In order to ensure the reliable operation of the doubly-fed wind turbine generator, the stability of the grid-connected doubly-fed wind power generation system during the low voltage ride through period needs to be analyzed and calculated, and the key point is to establish an accurate model.
The selection of the motor parameters in the doubly-fed wind turbine generator model can be obtained from an accurate model provided by a fan manufacturer, and the motor parameters are adjusted by means of modeling experience. However, for some parameters in the wind turbine generator control system, manufacturers cannot directly provide or directly measure the parameters, so that the established model has deviation from the actual wind turbine generator operating characteristics, the requirement of stability analysis of the wind turbine generator connected to the power system cannot be met, and the wind turbine generator model needs to be subjected to parameter identification for improving the accuracy of the wind turbine generator model. Relevant studies have been carried out by scholars at home and abroad, such as the following published documents:
[1]Z Liu，H Wei，X Li，et al.Global Identification ofElectrical and Mechanical Parameters in PMSM Drive Based on Dynamic Self-Learning PSO[J].IEEE Transactions on Power Electronics，2018，33(12)：10858-10871.
[2] step-by-step identification of parameters of the doubly-fed wind generator and selection of observed quantities [ J ]. China Motor engineering newspaper, 2013, 33 (13): 116-126.
Document [1] proposes a parameter identification method for a permanent magnet synchronous motor driving system, which comprises the steps of establishing a parameter estimation model according to a mathematical model of a permanent magnet motor and by considering the nonlinear characteristic of an inverter, and tracking electrical parameters, mechanical parameters and inverter parameters by using a dynamic learning estimator based on a dynamic self-learning particle swarm algorithm. Document [2] adopts a step-by-step identification method for a doubly-fed wind turbine generator, and firstly identifies electrical part parameters under a power grid fault working condition, and then identifies mechanical part parameters by using an input end wind speed change working condition. However, the above documents only identify the parameters of the motor, and since the number of the parameters of the motor is small and can be obtained from a nameplate or obtained through measurement, the identification difficulty is low, the number of the control parameters is large, the variation range is wide, and the measurement is not easy, the identification difficulty is high, and the method of the above documents has limitations.
Disclosure of Invention
The invention aims to: aiming at the existing problems, a method and a system for identifying the low-voltage ride through characteristic of the doubly-fed wind turbine generator are provided, so that a solution for improving the identification accuracy of a doubly-fed wind turbine generator model is provided.
The technical scheme adopted by the invention is as follows:
a method for identifying low voltage ride through characteristics of a doubly-fed wind turbine generator set comprises the following steps:
s1, constructing a doubly-fed wind turbine generator time domain simulation model;
s2, taking the control parameters (control system parameters) of the doubly-fed wind turbine generator as parameters to be identified, calculating the track sensitivity between the parameters to be identified and the output external characteristics of the doubly-fed wind turbine generator time domain simulation model, and selecting observed quantity data based on the track sensitivity;
s3, optimizing a simulation model objective function based on the observed quantity data and the actual measurement data;
s4, solving a preliminary identification result of the control parameters of the doubly-fed wind turbine generator during the normal operation of the power grid by using the objective function in the S3;
s5, solving a preliminary identification result of the control parameters of the doubly-fed wind turbine generator during the grid fault period by using the objective function in the S3;
and S6, based on the identification results of the steps S4 and S5, solving the final identification result of the control parameters of the doubly-fed wind turbine generator during the normal operation of the power grid and the fault of the power grid by using the objective function in the step S3.
Further, the calculation method of the track sensitivity between the parameter to be identified and the output external characteristic of the doubly-fed wind turbine generator time domain simulation model comprises the following steps:
wherein y is the output external characteristic, theta is the parameter to be identified,
is y to thetaTrack sensitivity of y0Theta is equal to theta0The output external characteristic of the time system, delta theta is the variation of the parameter to be identified, theta0Is the initial value of theta and L is the number of discrete data samples.
Further, the selecting observation data based on trajectory sensitivity includes:
and comparing the calculated track sensitivities, and selecting the output external characteristic corresponding to the track sensitivity highest value as the observed quantity data.
Further, in step S3, the objective function is optimized by the following method:
wherein f is the objective function value in the optimization algorithm, yuRepresents the data of the u-th actual measurement,
representing the u-th observed quantity data, puWeight of u-th observed quantity data, i represents i-th data sampling point, tmk1And tmk2Respectively, a data sampling start time point and an end time point.
The invention also provides a low voltage ride through characteristic identification system of the doubly-fed wind turbine generator, which comprises a simulation model construction module, a data screening module, a model optimization module and an identification module, wherein the simulation model construction module comprises:
the simulation model building module builds a time domain simulation model of the doubly-fed wind turbine generator;
the data screening module calculates the track sensitivity between the parameter to be identified and the output external characteristic of the time domain simulation model of the doubly-fed wind turbine generator, and selects observation data according to the track sensitivity, wherein the parameter to be identified is a control parameter of the doubly-fed wind turbine generator;
the model optimization module optimizes a simulation model objective function according to the obtained observed quantity data and the actual measurement data;
the identification module utilizes the objective function optimized by the model optimization module to solve the preliminary identification result of the control parameters of the doubly-fed wind turbine generator during the normal operation of the power grid, the preliminary identification result of the control parameters of the doubly-fed wind turbine generator during the fault of the power grid and utilize the two preliminary identification results to solve the final identification result of the control parameters of the doubly-fed wind turbine generator during the normal operation of the power grid and the fault of the power grid.
Further, a program is configured in the data filtering module, and the program is executed to perform the following method for calculating the trajectory sensitivity:
wherein y is the output external characteristic, theta is the parameter to be identified,
track sensitivity of y to theta, y0Theta is equal to theta0The output external characteristic of the time system, delta theta is the variation of the parameter to be identified, theta0Is the initial value of theta and L is the number of discrete data samples.
Further, the data screening module compares the calculated magnitudes of the plurality of track sensitivities, and selects the output external characteristic corresponding to the track sensitivity maximum value as the observed quantity data.
Further, a program is configured in the model optimization module, and the program is executed to perform the following method for optimizing the objective function:
wherein f is the objective function value in the optimization algorithm, yuRepresents the data of the u-th actual measurement,
representing the u-th observed quantity data, puWeight of u-th observed quantity data, i represents i-th data sampling point, tmk1And tmk2Respectively, a data sampling start time point and an end time point.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
according to the identification scheme for the low-voltage ride-through characteristic of the doubly-fed wind turbine generator, on the basis of not increasing hardware equipment, a multi-working-condition-distribution identification strategy is provided by using an intelligent optimization algorithm, the control parameters of the doubly-fed wind turbine generator are identified, the accuracy of a time domain simulation model can be obviously improved, and the output characteristic of a simulation system can be accurately fitted with the output characteristic of an actual system.
Drawings
The invention will now be described, by way of example, with reference to the accompanying drawings, in which:
FIG. 1 is a flow chart of identification of low voltage ride through characteristics of a doubly-fed wind turbine.
FIG. 2 is a diagram of an output waveform of a time domain simulation model of the doubly-fed wind turbine generator and an output waveform of an actual system after the voltage of a power grid drops to a rated value of 90 percent by adopting the identification scheme of the invention.
FIG. 3 is a diagram of an output waveform of a time domain simulation model of the doubly-fed wind turbine generator and an output waveform of an actual system after the voltage of a power grid drops to a rated value of 50% and the identification scheme is adopted.
Detailed Description
All of the features disclosed in this specification, or all of the steps in any method or process so disclosed, may be combined in any combination, except combinations of features and/or steps that are mutually exclusive.
Any feature disclosed in this specification (including any accompanying claims, abstract) may be replaced by alternative features serving equivalent or similar purposes, unless expressly stated otherwise. That is, unless expressly stated otherwise, each feature is only an example of a generic series of equivalent or similar features.
Example one
As shown in fig. 1, the embodiment discloses a method for identifying low voltage ride through characteristics of a doubly-fed wind turbine generator, which includes:
s1: and constructing a time domain simulation model of the doubly-fed wind turbine generator.
And solving the initial objective function of the control parameter by the double-fed wind turbine generator time domain simulation model. The time domain simulation model is a model of adding a control link in a low-voltage ride through period on the basis of the existing typical physical link.
S2: and taking the control parameters of the doubly-fed wind turbine generator as the parameters to be identified, calculating the track sensitivity between the parameters to be identified and the output external characteristics of the time domain simulation model of the doubly-fed wind turbine generator, and selecting the observed quantity data based on the track sensitivity.
In the step, the output external characteristics of the double-fed wind turbine generator set comprise active power, reactive power, voltage, current and the like. After the simulation is finished, the track sensitivity is calculated by taking all sampling points into the following formula 1:
wherein y is the output external characteristic, theta is the parameter to be identified,
track sensitivity of y to theta, y0Indicates that the parameter theta to be identified is equal to theta0The output external characteristic of the time system, delta theta is the variation of the parameter to be identified, theta0Is the initial value of theta and L is the number of discrete data samples.
After calculating the track sensitivity of a plurality of sampling points, comparing the track sensitivity, and selecting the output external characteristic corresponding to the highest track sensitivity value as observed quantity data.
S3: and optimizing a simulation model objective function based on the observed quantity data and the actual measurement data.
The observed quantity data is the output external characteristic selected in S2, and the actual measurement data is the output external characteristic of the doubly-fed wind turbine generator in the actual system. After the simulation is finished, all sampling points are brought into the following formula 2 to realize the optimization objective function:
wherein f is the objective function value in the optimization algorithm, yuRepresents the data of the u-th actual measurement,
representing the u-th observed quantity data, puWeight of u-th observed quantity data, i represents i-th data sampling point, tmk1And tmk2Respectively, a data sampling start time point and an end time point.
S4: and setting an initial value of a parameter to be solved (namely a control parameter) by using the objective function based on actual measurement data of the shallow short circuit fault working condition and an empirical value, and solving an initial identification result of the control parameter of the doubly-fed wind turbine generator during the normal operation of the power grid.
The objective function is the optimized objective function in S3. And solving by using the actual measurement data of the shallow short-circuit fault working condition based on an objective function, wherein the initial value of the parameter to be solved adopts an empirical value, and the solved variable value is used as an initial identification result of the control parameter of the doubly-fed wind turbine generator during the normal operation of the power grid.
S5: and setting the preliminary identification result of the S4 as the initial value of the control parameter during the normal operation of the power grid based on the actual measurement data of the deep short circuit fault working condition by using the target function, and solving the preliminary identification result of the control parameter of the doubly-fed wind turbine generator during the fault of the power grid.
And (3) setting the preliminary identification result of the S4 as an initial value of the control parameter during the normal operation of the power grid by using the actual measurement data of the deep short circuit fault working condition, solving by using a target function, and identifying the preliminary identification result of the control parameter of the doubly-fed wind turbine generator during the fault of the power grid.
S6: and by utilizing the target function, based on actual measurement data of deep fault conditions, setting the initial identification result of S4 as the initial value of the control parameter during the normal operation of the power grid, taking the initial identification result of S5 as the initial value of the control parameter during the fault operation of the power grid, and solving the final identification results of the control parameter of the doubly-fed wind turbine generator during the normal operation of the power grid and the fault operation of the power grid.
And (3) setting the initial identification result of S4 as a control parameter during normal operation of the power grid by using the actual data of the deep fault working condition, taking the initial identification result of S5 as the control parameter during fault operation of the power grid, performing global identification by using a target function, and solving all parameters to be identified to obtain the final identification results of the control parameters of the doubly-fed wind turbine generator during normal operation of the power grid and fault of the power grid.
Example two
The embodiment discloses a double-fed wind turbine generator system low-voltage ride through characteristic identification system, which comprises a simulation model building module, a data screening module, a model optimization module and an identification module, wherein:
and the simulation model building module builds a time domain simulation model of the doubly-fed wind turbine generator. As shown in the foregoing embodiment, the doubly-fed wind turbine generator time domain simulation model is the prior art, and what kind of model is constructed is not limited in the application embodiment.
The data screening module calculates the track sensitivity between the parameter to be identified and the output external characteristic of the time domain simulation model of the doubly-fed wind turbine generator, and selects observation data according to the track sensitivity, wherein the parameter to be identified is a control parameter of the doubly-fed wind turbine generator.
The above-mentioned calculation program of the track sensitivity is configured in the data screening module, and after the simulation is finished, all sampling points are substituted, and the following method for calculating the track sensitivity is executed:
wherein y is the output external characteristic, theta is the parameter to be identified,
track sensitivity of y to theta, y0Indicates that the parameter theta to be identified is equal to theta0The output external characteristic of the time system, delta theta is the variation of the parameter to be identified, theta0Is an initial value of theta, L is offAnd the number of scattered data sampling points.
And the data screening module compares the calculated track sensitivities and selects the output external characteristic corresponding to the highest track sensitivity value as the observed quantity data.
And the model optimization module optimizes a simulation model objective function according to the obtained observed quantity data and the actual measurement data.
After each simulation is finished, substituting all sampling points for optimization once, wherein the method for optimizing the objective function comprises the following steps:
wherein f is the objective function value in the optimization algorithm, yuRepresents the data of the u-th actual measurement,
representing the u-th observed quantity data, puWeight of u-th observed quantity data, i represents i-th data sampling point, tmk1And tmk2Respectively, a data sampling start time point and an end time point.
The optimization method is executed by a computer program configured in an execution model optimization module.
The identification module utilizes the objective function optimized by the model optimization module to solve the preliminary identification result of the control parameters of the doubly-fed wind turbine generator during the normal operation of the power grid and the preliminary identification result of the control parameters of the doubly-fed wind turbine generator during the fault of the power grid, and utilizes the two preliminary identification results to solve the final identification result of the control parameters of the doubly-fed wind turbine generator during the normal operation of the power grid and the fault of the power grid.
Specifically, the identification module utilizes actual measurement data of shallow short-circuit fault working conditions to solve based on a target function, and the solved variable value is used as an initial identification result of the control parameters of the doubly-fed wind turbine generator during the normal operation of the power grid. Further, the objective function is utilized, based on actual measurement data of deep fault conditions, the variable value (namely, the initial identification result of the control parameter of the doubly-fed wind turbine generator during the normal operation of the power grid) is used as the initial value of the variable to be solved (namely, the initial identification result during the normal operation of the power grid is used as the control parameter during the normal operation of the power grid), the objective function is utilized for solving, and the initial identification result of the control parameter of the doubly-fed wind turbine generator during the fault of the power grid is identified. And thirdly, by using actual data of the deep fault working condition, taking the two preliminary identification results as initial values of variables to be solved (namely, taking the preliminary identification result during the normal operation of the power grid as a control parameter during the normal operation of the power grid, and taking the preliminary identification result during the fault operation of the power grid as a control parameter during the fault operation of the power grid), and performing global identification by using a target function to obtain final identification results of the control parameters of the doubly-fed wind turbine generator during the normal operation of the power grid and the fault operation of the power grid.
The effect of the invention is also verified, for example, fig. 2 shows that the grid voltage drops to 90% rated value, and the output oscillogram of the doubly-fed wind turbine time domain simulation model and the output oscillogram of the actual system are obtained after the identification method is adopted. When a shallow short circuit fault occurs in a power grid corresponding to the power grid shown in fig. 2, the control structure and control parameters of the control system are unchanged and the control system still keeps the state during normal operation, wherein P represents the active power output by the grid-connected point of the doubly-fed wind turbine generator, Q represents the reactive power output, U represents the voltage amplitude at the grid-connected point, and I represents the current amplitude at the grid-connected point. As can be seen from FIG. 2, during the short-circuit fault of the shallow degree of the power grid, after the identification scheme of the invention is adopted, the control parameters during the normal operation can be accurately identified, and the output waveform of the time domain simulation model can be better fitted with the output waveform of the actual system. FIG. 3 is an output waveform diagram of a time domain simulation model of a doubly-fed wind turbine generator and an output waveform diagram of an actual system after the voltage of a power grid drops to a rated value of 50%, and the identification method is adopted. In fig. 3, when a deep short-circuit fault occurs in the power grid, the control structure and the control parameters of the control system are changed and switched to the state during the fault period, as can be seen from fig. 3, after the identification method of the present invention is adopted during the deep short-circuit fault of the power grid, the control parameters during the fault period can be identified more accurately, and the output waveform of the time domain simulation model can better fit the output waveform of the actual system.
Therefore, the method can determine the control parameters during normal operation and fault by identifying the low voltage ride through characteristic of the doubly-fed wind turbine during the short-circuit fault of the power grid, and further establish an accurate time domain simulation model of the doubly-fed wind turbine, so that the output waveform of time domain simulation can be more accurately fitted with the output waveform of an actual system.
The invention is not limited to the foregoing embodiments. The invention extends to any novel feature or any novel combination of features disclosed in this specification and any novel method or process steps or any novel combination of features disclosed.
Claims (8)
1. A method for identifying low voltage ride through characteristics of a doubly-fed wind turbine generator is characterized by comprising the following steps:
s1, constructing a doubly-fed wind turbine generator time domain simulation model;
s2, taking the control parameters of the doubly-fed wind turbine generator as parameters to be identified, calculating the track sensitivity between the parameters to be identified and the output external characteristics of the time domain simulation model of the doubly-fed wind turbine generator, and selecting observed quantity data based on the track sensitivity;
s3, optimizing a simulation model objective function based on the observed quantity data and the actual measurement data;
s4, solving a preliminary identification result of the control parameters of the doubly-fed wind turbine generator during the normal operation of the power grid by using the objective function in the S3;
s5, solving a preliminary identification result of the control parameters of the doubly-fed wind turbine generator during the grid fault period by using the objective function in the S3;
and S6, based on the identification results of the steps S4 and S5, solving the final identification result of the control parameters of the doubly-fed wind turbine generator during the normal operation of the power grid and the fault of the power grid by using the objective function in the step S3.
2. The identification method for the low voltage ride through characteristics of the doubly-fed wind turbine generator set according to claim 1, wherein the calculation method for the trajectory sensitivity between the parameter to be identified and the output external characteristics of the time domain simulation model of the doubly-fed wind turbine generator set comprises the following steps:
wherein y is the output external characteristic, theta is the parameter to be identified,
track sensitivity of y to theta, y0Theta is equal to theta0The output external characteristic of the time system, delta theta is the variation of the parameter to be identified, theta0Is the initial value of theta and L is the number of discrete data samples.
3. The identification method for the low voltage ride through characteristics of the doubly-fed wind turbine generator set according to claim 1, wherein the selecting observation quantity data based on the trajectory sensitivity comprises:
and comparing the calculated track sensitivities, and selecting the output external characteristic corresponding to the track sensitivity highest value as the observed quantity data.
4. The method for identifying the low voltage ride through characteristics of the doubly-fed wind turbine generator set of claim 1, wherein in the step S3, the objective function is optimized by the following method:
wherein f is the objective function value in the optimization algorithm, yuRepresents the data of the u-th actual measurement,
representing the u-th observed quantity data, puWeight of u-th observed quantity data, i represents i-th data sampling point, tmk1And tmk2Respectively a data sampling start time point and an end timeAnd (4) point.
5. The utility model provides a doubly-fed wind turbine generator system low-voltage ride through characteristic identification system which characterized in that, includes simulation model construction module, data screening module, model optimization module and identification module, wherein:
the simulation model building module builds a time domain simulation model of the doubly-fed wind turbine generator;
the data screening module calculates the track sensitivity between the parameters to be identified and the configuration and the output external characteristics of the time domain simulation model of the doubly-fed wind turbine generator, and selects observation data according to the track sensitivity, wherein the parameters to be identified are control parameters of the doubly-fed wind turbine generator;
the model optimization module optimizes a simulation model objective function according to the obtained observed quantity data and the actual measurement data;
the identification module utilizes the objective function optimized by the model optimization module to solve the preliminary identification result of the control parameters of the doubly-fed wind turbine generator during the normal operation of the power grid, the preliminary identification result of the control parameters of the doubly-fed wind turbine generator during the fault of the power grid and utilize the two preliminary identification results to solve the final identification result of the control parameters of the doubly-fed wind turbine generator during the normal operation of the power grid and the fault of the power grid.
6. The identification system for the low voltage ride through characteristics of the doubly-fed wind turbine generator set according to claim 5, wherein a program is configured in the data screening module, and the program is run to execute the following method for calculating the trajectory sensitivity:
wherein y is the output external characteristic, theta is the parameter to be identified,
track sensitivity of y to theta, y0Is the initial value of y, delta theta is the variation of the parameter to be identified, theta0Is the initial value of theta and L is the number of discrete data samples.
7. The identification system for the low voltage ride through characteristics of the doubly-fed wind turbine generator set according to claim 5, wherein the data screening module compares the calculated magnitudes of the plurality of trajectory sensitivities and selects the output external characteristic corresponding to the highest value of the trajectory sensitivities as the observed quantity data.
8. The identification system for the low voltage ride through characteristics of the doubly-fed wind turbine generator set according to claim 5, wherein a program is configured in the model optimization module, and the program is run to execute the following method for optimizing the objective function:
wherein f is the objective function value in the optimization algorithm, yuRepresents the data of the u-th actual measurement,
representing the u-th observed quantity data, puWeight of u-th observed quantity data, i represents i-th data sampling point, tmk1And tmk2Respectively, a data sampling start time point and an end time point.
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