CN113849975B - Low-voltage ride through characteristic identification method and system for 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. Firstly, constructing a time domain simulation model of the doubly-fed wind turbine, selecting observed quantity data through calculating track sensitivity between identification parameters and output external characteristics of the time domain simulation model of the doubly-fed wind turbine, optimizing an objective function through the observed quantity data and actual measurement data, specifically solving a preliminary identification result of control parameters of the doubly-fed wind turbine during normal operation of a power grid and a preliminary identification result of the control parameters of the doubly-fed wind turbine during failure of the power grid by utilizing the optimized objective function multi-working condition-step identification output characteristics, and solving final identification results of the control parameters of the doubly-fed wind turbine during normal operation of the power grid and during failure of the power grid based on the two preliminary identification results. The invention can obviously improve the accuracy of the time domain simulation model, and ensure 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 doubly-fed wind power generation, in particular to a doubly-fed wind turbine generator low voltage ride through characteristic identification method and a doubly-fed wind turbine generator low voltage ride through characteristic identification system.
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 reliable operation of the doubly-fed wind turbine, stability of the grid-connected doubly-fed wind power generation system during low voltage ride through needs to be analyzed and calculated, and establishment of an accurate model is a key point.
The selection of motor parameters in the doubly-fed wind turbine generator model can be obtained from an accurate model provided by a fan manufacturer and adjusted by means of modeling experience. However, for some parameters in the wind turbine control system, manufacturers cannot directly provide or directly measure the parameters, so that deviation exists between an established model and the running characteristics of an actual wind turbine, the requirements of stability analysis of the wind turbine access power system cannot be met, and parameter identification needs to be performed on the wind turbine model in order to improve the accuracy of the wind turbine model. At present, the students at home and abroad have developed related researches, 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] pan Xueping, ju Ping, xu Qian, etc. doubly-fed wind generator parameters step-by-step identification and choice of observables [ J ]. Chinese motor engineering journal, 2013, 33 (13): 116-126.
Document [1] proposes a parameter identification method of a permanent magnet synchronous motor driving system, which is to take nonlinear characteristics of an inverter into consideration according to a permanent magnet motor mathematical model, establish a parameter estimation model, and track electrical parameters, mechanical parameters and inverter parameters by using a dynamic learning estimator based on a dynamic self-learning particle swarm algorithm. The literature [2] adopts a step-by-step identification method for the doubly-fed wind turbine generator, firstly identifies electrical part parameters under the working condition of power grid faults, and then identifies mechanical part parameters by utilizing the working condition of wind speed change of an input end. However, the above documents only identify the parameters of the motor, and because the number of the parameters of the motor is small and the parameters can be obtained from the nameplate or obtained through measurement, the identification difficulty is low, the number of the control parameters is large, the variation range is wide, the measurement is difficult, the identification difficulty is high, and the method of the above documents has limitations.
Disclosure of Invention
The invention aims at: aiming at the problems, the method and the system for identifying the low-voltage ride through characteristics of the doubly-fed wind turbine are provided, so that a solution capable of improving the identification accuracy of the doubly-fed wind turbine model is provided.
The technical scheme adopted by the invention is as follows:
a low voltage ride through characteristic identification method of a doubly-fed wind turbine generator comprises the following steps:
s1, constructing a time domain simulation model of a doubly-fed wind turbine;
s2, taking control parameters (control system parameters) of the doubly-fed wind turbine generator as parameters to be identified, calculating 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 an objective function of the simulation model based on observed quantity data and actual measurement data;
s4, solving a preliminary identification result of control parameters of the doubly-fed wind turbine generator set during normal operation of the power grid by utilizing the objective function in the S3;
s5, solving a preliminary identification result of control parameters of the doubly-fed wind turbine during the power grid fault by utilizing the objective function in the S3;
s6, based on the identification results of the steps S4 and S5, utilizing the objective function in the step S3 to solve the final identification results of the control parameters of the doubly-fed wind turbine generator during the normal operation of the power grid and the power grid fault.
Further, the method for 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 comprises the following steps:
wherein y is the output external characteristic, θ is the parameter to be identified,the track sensitivity of y to theta, y 0 For theta equal to theta 0 The output external characteristic of the system, delta theta is the variation quantity of the parameter to be identified, theta 0 And the initial value of theta, and L is the number of discrete data sampling points.
Further, the selecting observed quantity data based on the trajectory sensitivity includes:
and comparing the calculated track sensitivities, and selecting the output external characteristic corresponding to the highest track sensitivity value as observed quantity data.
Further, in the step S3, the objective function is optimized by the following method:
wherein f is an objective function value in an optimization algorithm, y u Representing the (u) th actual measurement data,represents the (u) th observed quantity data, p u Weight representing the ith observed quantity data, i represents the ith data sampling point, t mk1 And t mk2 The data sampling start time point and the data sampling end time point are respectively.
The invention also provides a system for identifying the low-voltage ride through characteristics of the doubly-fed wind turbine, 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 constructs a time domain simulation model of the doubly-fed wind turbine generator;
the data screening module calculates track sensitivity between parameters to be identified and the output external characteristics of the time domain simulation model of the doubly-fed wind turbine, and selects observation data according to the track sensitivity, wherein the parameters to be identified are control parameters of the doubly-fed wind turbine;
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 primary identification result of the control parameters of the doubly-fed wind turbine generator during the normal operation of the power grid, the primary identification result of the control parameters of the doubly-fed wind turbine generator during the power grid fault, and 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 power grid fault.
Further, the data filtering module is configured with a program, and the program is run to execute the following method for calculating the track sensitivity:
wherein y is the output external characteristic, θ is the parameter to be identified,the track sensitivity of y to theta, y 0 For theta equal to theta 0 The output external characteristic of the system, delta theta is the variation quantity of the parameter to be identified, theta 0 And the initial value of theta, and L is the number of discrete data sampling points.
Further, the data screening module compares the calculated plurality of track sensitivities, and selects the output external characteristic corresponding to the highest track sensitivity value as observed quantity data.
Further, the model optimization module is configured with a program, and the program is run to execute the following method for optimizing the objective function:
wherein f is an objective function value in an optimization algorithm, y u Representing the (u) th actual measurement data,represents the (u) th observed quantity data, p u Weight representing the ith observed quantity data, i represents the ith data sampling point, t mk1 And t mk2 The data sampling start time point and the data sampling end time point are respectively.
In summary, due to the adoption of the technical scheme, the beneficial effects of the invention are as follows:
according to the low-voltage ride through characteristic identification scheme of the doubly-fed wind turbine generator, on the basis of not increasing hardware equipment, the intelligent optimization algorithm is utilized, a multi-working condition-distribution identification strategy is provided, control parameters of the doubly-fed wind turbine generator are identified, accuracy of a time domain simulation model can be remarkably improved, and output characteristics of a simulation system can be accurately fitted with actual system output characteristics.
Drawings
The invention will now be described by way of example and with reference to the accompanying drawings in which:
FIG. 1 is a flow chart for identifying low voltage ride through characteristics of a doubly-fed wind turbine.
FIG. 2 is an output waveform diagram of a time domain simulation model of the doubly-fed wind turbine after the identification scheme of the invention is adopted and an actual system output waveform diagram of the doubly-fed wind turbine after the grid voltage drops to 90% rated value.
FIG. 3 is an output waveform diagram of a time domain simulation model of the doubly-fed wind turbine after the identification scheme of the invention is adopted and an actual system output waveform diagram of the doubly-fed wind turbine after the grid voltage drops to 50% rated value.
Detailed Description
All of the features disclosed in this specification, or all of the steps in a method or process disclosed, may be combined in any combination, except for mutually exclusive features and/or steps.
Any feature disclosed in this specification (including any accompanying claims, abstract) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise. That is, each feature is one example only of a generic series of equivalent or similar features, unless expressly stated otherwise.
Example 1
As shown in fig. 1, this embodiment discloses a method for identifying low voltage ride through characteristics of a doubly-fed wind turbine, including:
s1: and constructing a time domain simulation model of the doubly-fed wind turbine.
And solving an initial objective function of the control parameter by using the time domain simulation model of the doubly-fed wind turbine. The time domain simulation model is a model which is obtained by adding a control link during low voltage ride through on the basis of the existing typical physical link.
S2: and 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 by taking the control parameters of the doubly-fed wind turbine as the parameters to be identified, and selecting observed quantity data based on the track sensitivity.
In the step, the external output characteristics of the doubly-fed wind turbine generator comprise active power, reactive power, voltage, current and the like. After the simulation is finished, the track sensitivity is obtained by taking all sampling points into the following formula 1:
wherein y is the output external characteristic, θ is the parameter to be identified,the track sensitivity of y to theta, y 0 Representing that the parameter theta to be identified is equal to theta 0 The output external characteristic of the system, delta theta is the variation quantity of the parameter to be identified, theta 0 And the initial value of theta, and L is the number of discrete data sampling points.
After track sensitivity under a plurality of sampling points is calculated, comparing the sizes of the track sensitivity, and selecting the output external characteristic corresponding to the highest value of the track sensitivity as observed quantity data.
S3: optimizing the simulation model objective function based on the observed quantity data and the actual measurement data.
And (3) observing the output external characteristics selected in the step (S2), wherein the actual measurement data is the output external characteristics 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 an objective function value in an optimization algorithm, y u Representing the (u) th actual measurement data,represents the (u) th observed quantity data, p u Weight representing the ith observed quantity data, i represents the ith data sampling point, t mk1 And t mk2 The data sampling start time point and the data sampling end time point are respectively.
S4: and setting an initial value of a parameter to be solved (namely a control parameter) according to actual measurement data of the shallow short-circuit fault working condition by using the objective function, and solving a preliminary identification result of the control parameter of the doubly-fed wind turbine generator during normal operation of the power grid.
The objective function is the objective function obtained by optimization in the step S3. And solving based on an objective function by utilizing actual measurement data of the shallow short-circuit fault condition, wherein an empirical value is adopted as an initial value of a parameter to be solved, and the solved variable value is used as a primary identification result of the control parameter of the doubly-fed wind turbine generator during normal operation of the power grid.
S5: and setting the initial identification result of the S4 as an initial value of a control parameter in the normal operation period of the power grid based on the actual measurement data of the deep short-circuit fault working condition by utilizing the objective function, and solving the initial identification result of the control parameter of the doubly-fed wind turbine generator in the power grid fault period.
And (3) setting the primary identification result of the S4 as an initial value of the control parameter in the normal operation period of the power grid by using the actual measurement data of the deep short circuit fault condition, solving by using an objective function, and identifying the primary identification result of the control parameter of the doubly-fed wind turbine generator in the power grid fault period.
S6: and based on the actual measurement data of the deep fault condition, setting the initial identification result of the S4 as an initial value of the control parameter during the normal operation of the power grid, using the initial identification result of the S5 as the initial value of the control parameter during the fault operation of the power grid, and solving the final identification result of the control parameter of the doubly-fed wind turbine generator during the normal operation of the power grid and the fault period of the power grid.
And setting the primary identification result of the S4 as a control parameter in the normal operation period of the power grid by using the actual data of the deep fault working condition, setting the primary identification result of the S5 as a control parameter in the fault operation period of the power grid, carrying out global identification by using an objective 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 the normal operation period and the fault period of the power grid.
Example two
The embodiment discloses a doubly-fed wind turbine generator low voltage ride through characteristic identification system, including simulation model construction module, data screening module, model optimization module and identification module, wherein:
the simulation model construction module constructs a time domain simulation model of the doubly-fed wind turbine. As in the previous embodiment, the time domain simulation model of the doubly-fed wind turbine generator is in the prior art, and what model is constructed is not limited in the application embodiment.
The data screening module calculates track sensitivity between parameters to be identified and the output external characteristics of the time domain simulation model of the doubly-fed wind turbine, and selects observation data according to the track sensitivity, wherein the parameters to be identified are control parameters of the doubly-fed wind turbine.
The calculation program of the track sensitivity is configured in the data screening module, 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, θ is the parameter to be identified,the track sensitivity of y to theta, y 0 Representing that the parameter theta to be identified is equal to theta 0 The output external characteristic of the system, delta theta is the variation quantity of the parameter to be identified, theta 0 And the initial value of theta, and L is the number of discrete data sampling points.
The data screening module compares the calculated track sensitivities, and selects the output external characteristic corresponding to the highest track sensitivity value as observed quantity data.
The model optimization module optimizes the 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, and optimizing an objective function by the method:
wherein f is an objective function value in an optimization algorithm, y u Representing the (u) th actual measurement data,represents the (u) th observed quantity data, p u Weight representing the ith observed quantity data, i represents the ith data sampling point, t mk1 And t mk2 The data sampling start time point and the data sampling end time point are respectively.
The optimization method is implemented 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 primary identification result of the control parameters of the doubly-fed wind turbine generator during the normal operation of the power grid, the primary identification result of the control parameters of the doubly-fed wind turbine generator during the power grid fault, and 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 power grid fault.
Specifically, the identification module utilizes actual measurement data of the shallow short-circuit fault working condition, solves based on an objective function, and the solved variable value is used as a preliminary identification result of control parameters of the doubly-fed wind turbine generator during normal operation of the power grid. Further, the objective function is utilized, based on actual measurement data of the deep fault condition, the variable value (namely, the primary identification result of the control parameter of the doubly-fed wind turbine generator during the normal operation of the power grid) is used as an initial value of a variable to be solved (namely, the primary 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 primary identification result of the control parameter of the doubly-fed wind turbine generator during the power grid fault is identified. And performing global identification by using the objective function by taking the two preliminary identification results as initial values of variables to be solved (namely taking the preliminary identification results during the normal operation of the power grid as control parameters during the normal operation of the power grid and taking the preliminary identification results during the fault operation of the power grid as control parameters during the fault operation of the power grid), thereby obtaining final identification results of the control parameters of the doubly-fed wind turbine generator during the normal operation of the power grid and during the fault operation of the power grid.
The invention also performs verification of the effect, as shown in fig. 2, the power grid voltage drops to 90% rated value, and the output waveform diagram of the doubly-fed wind turbine generator time domain simulation model and the output waveform diagram of the actual system are obtained by adopting the identification method. The shallow short circuit fault occurs in the corresponding power grid of the graph 2, at this time, the control structure and control parameters of the control system are unchanged, the state during normal operation is still maintained, 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, after the identification scheme of the present invention is adopted during the shallow short-circuit fault of the power grid, 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 to the output waveform of the actual system. FIG. 3 is an output waveform diagram of a time domain simulation model of the doubly-fed wind turbine after the identification method of the invention is adopted and an output waveform diagram of an actual system when the power grid voltage drops to 50% rated value. In fig. 3, the power grid has a deep short circuit fault, at this time, the control structure and control parameters of the control system are changed, and the state is switched to a state in the fault period, and as can be seen from fig. 3, after the identification method of the invention is adopted in the power grid deep short circuit fault period, the control parameters in the fault period can be identified more accurately, and the output waveform of the time domain simulation model can be better fitted with the output waveform of the actual system.
Therefore, the control parameters in the normal operation period and the fault period can be determined by identifying the low voltage ride-through characteristic of the doubly-fed wind turbine generator during the grid short-circuit fault period, and further an accurate doubly-fed wind turbine generator time domain simulation model is established, so that the output waveform of the time domain simulation can be more accurately fitted with the output waveform of the actual system.
The invention is not limited to the specific embodiments described above. The invention extends to any novel one, or any novel combination, of the features disclosed in this specification, as well as to any novel one, or any novel combination, of the steps of the method or process disclosed.
Claims (2)
1. The low-voltage ride through characteristic identification method of the doubly-fed wind turbine generator is characterized by comprising the following steps of:
s1, constructing a time domain simulation model of a doubly-fed wind turbine;
s2, calculating 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 by taking the control parameters of the doubly-fed wind turbine as the parameters to be identified, and selecting observed quantity data based on the track sensitivity;
the method for calculating the track sensitivity between the parameters to be identified and the output external characteristics of the doubly-fed wind turbine time domain simulation model comprises the following steps:
in the method, in the process of the invention,yis an external characteristic of the output and,θin order for the parameters to be identified,is thatyFor a pair ofθIs used for the trajectory sensitivity of the (c),y 0 is thatθEqual toθ 0 Output external characteristics of time system, deltaθFor the amount of change in the parameter to be identified,θ 0 is thatθIs used for the initial value of (a),Lthe number of the discrete data sampling points is the number;
the selecting observed quantity data based on the track sensitivity includes: comparing the calculated track sensitivities, and selecting the output external characteristic corresponding to the highest track sensitivity value as observed quantity data;
s3, optimizing an objective function of the simulation model based on observed quantity data and actual measurement data; the objective function is optimized by the following method:
……（2）
in the method, in the process of the invention,fin order to optimize the objective function value in the algorithm,y u represent the firstuThe actual measurement data of the individual pieces of data,represent the firstuThe number of observed quantity data is calculated,p u represent the firstuThe weight of the individual observables is determined,irepresent the firstiA number of data sampling points are used,t mk1 andt mk2 respectively a data sampling start time point and a data sampling end time point;
s4, solving a preliminary identification result of control parameters of the doubly-fed wind turbine generator set during normal operation of the power grid by utilizing the objective function in the S3;
s5, solving a preliminary identification result of control parameters of the doubly-fed wind turbine during the power grid fault by utilizing the objective function in the S3;
s6, based on the identification results of the steps S4 and S5, utilizing the objective function in the step S3 to solve the final identification results of the control parameters of the doubly-fed wind turbine generator during the normal operation of the power grid and the power grid fault.
2. The low-voltage ride through characteristic identification system of the doubly-fed wind turbine generator is characterized by comprising a simulation model construction module, a data screening module, a model optimization module and an identification module, wherein:
the simulation model construction module constructs a time domain simulation model of the doubly-fed wind turbine generator;
the data screening module calculates track sensitivity between parameters to be identified and the output external characteristics of the time domain simulation model of the doubly-fed wind turbine, and selects observation data according to the track sensitivity, wherein the parameters to be identified are control parameters of the doubly-fed wind turbine; the data screening module is provided with a program, and the program is operated to execute the following method for calculating the track sensitivity:
in the method, in the process of the invention,yis an external characteristic of the output and,θin order for the parameters to be identified,is thatyFor a pair ofθIs used for the trajectory sensitivity of the (c),y 0 is thatyInitial value of deltaθFor the amount of change in the parameter to be identified,θ 0 is thatθIs used for the initial value of (a),Lthe number of the discrete data sampling points is the number;
the data screening module compares the calculated track sensitivities, and selects the output external characteristic corresponding to the highest track sensitivity value as observed quantity data;
the model optimization module optimizes a simulation model objective function according to the obtained observed quantity data and the actual measurement data; the model optimization module is provided with a program, and the program is operated to execute the following method for optimizing the objective function:
……（2）
in the method, in the process of the invention,fin order to optimize the objective function value in the algorithm,y u represent the firstuThe actual measurement data of the individual pieces of data,represent the firstuThe number of observed quantity data is calculated,p u represent the firstuThe weight of the individual observables is determined,irepresent the firstiA number of data sampling points are used,t mk1 andt mk2 respectively a data sampling start time point and a data sampling end time point;
the identification module utilizes the objective function optimized by the model optimization module to solve the primary identification result of the control parameters of the doubly-fed wind turbine generator during the normal operation of the power grid, the primary identification result of the control parameters of the doubly-fed wind turbine generator during the power grid fault, and 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 power grid fault.
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| CN106169747A (en) * | 2016-07-20 | 2016-11-30 | 河海大学 | A kind of double fed induction generators parameter identification method |
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