CN107565867B - Synchronous generator parameter identification method based on track sensitivity - Google Patents
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Abstract
The invention relates to a synchronous generator parameter identification method based on track sensitivity. The parameter identification problem of the synchronous generator is that through track sensitivity analysis, a data interval which is beneficial to improving identification precision is selected, and on the basis of solving the problem of selecting the identification data interval, a complete parameter identification model is also provided. The method has good poor resistance, and promotes the practical process of generator parameter identification.
Description
Technical Field
The invention relates to a synchronous generator parameter identification method based on track sensitivity.
Background
The synchronous generator is one of the most core devices in the power system, and the accuracy of a mathematical model of the synchronous generator has great influence on the dynamic simulation result of the power system. Because of lack of actual parameters, most of the synchronous generator parameters used for the analysis, calculation and simulation of the current power system adopt data or typical values provided by manufacturers or have to adopt simplified models, and most of the dynamic parameters of the synchronous generator are equivalent model parameters and can change along with different operating conditions of the system. Meanwhile, due to the fact that data are incomplete and influences of actual operation conditions such as eddy current, magnetic hysteresis and saturation are not taken into account, the result obtained by simulating by using the parameters provided by a manufacturer is greatly different from the actual dynamic process, and accuracy and reliability of dynamic calculation of the power system are seriously influenced. Identification of parameters of synchronous generators has been an important issue for research in power systems.
The existing synchronous generator parameter identification method mainly comprises two types: offline identification and online identification. The off-line identification is carried out by carrying out short-circuit test, load rejection test and other disturbance tests in the shutdown period of the generator and carrying out parameter identification according to test data. However, the field test is complicated and may bring potential safety hazards to the generator, and the offline identification work is difficult to implement. And the online identification avoids the complexity of the test, and the identification result based on the actual operation data is closer to the operation condition, so the method is more suitable for the identification of the generator parameters.
When the generator operates in a steady state, the steady state data only reflects the steady state parameter Xd,XqThe size of the transient parameter is irrelevant to the transient parameter, and the change of the electric quantity at the generator end can reflect the size of the transient parameter of the generator only when the generator is disturbed, so that the on-line parameter identification of the generator must be carried out by combining with the disturbance data of the generator. However, in practical applications, how to separate the effective disturbance data of the generator, i.e. how to select the identification data interval from the longer measurement data, has not been proposed in the literature so far. The difficulty of identifying the data interval selection problem is that too much steady-state data is contained in an overlong data segment, so that the proportion of transient and sub-transient information in the data is reduced, and the identification accuracy of transient and sub-transient parameters is reduced; the excessively short data segment is likely to lack the required sub-transient state and sub-transient state information, and is more unfavorable for parameter identification. Therefore, how to select the identification data interval is a problem closely related to the parameter identification precision.
Disclosure of Invention
The invention aims to provide a synchronous generator parameter identification method based on track sensitivity, which has good tolerance performance and promotes the practical process of generator parameter identification.
In order to achieve the purpose, the technical scheme of the invention is as follows: a synchronous generator parameter identification method based on track sensitivity is characterized in that: comprises the following steps of (a) carrying out,
step S1, selecting the identification data interval based on the track sensitivity:
s11, solving absolute value | S of track sensitivity in complete time periodd(t)|,|Sq(t)|；
S12, taking the steady state sampling point before disturbance as the starting point of the identification data interval, namely from t0Starting at 0, every Δ T4T "d0The data segment of (2) calculates the average of the sensitivity of the trajectory once from the starting point to the point
SΔt,avg,S2Δt,avg,S3Δt,avg…：
Wherein N isnΔtIs t0number of sampling points in n · Δ t time period, SnΔt,avgCalculating the average value of the sensitivity of the track for the nth time;
s13, when the track sensitivity average value is attenuated to SnΔt,avg≤SnΔt/10,avgThe calculation can be stopped when/4 or n delta t is more than or equal to 30s, and t is used0The n & delta t time period is used as a d-axis parameter identification data interval; in the same way, the q-axis parameter identification data interval can be solved;
step S2, adopting the robust optimization model as a parameter identification model identification parameter:
a six-order practical model of the synchronous generator:
wherein, TJThe damping power coefficient is the inertia time constant of the generator set, and D is the damping power coefficient of the generator;
in a clear view of the above, it is known that,
the d-axis to-be-identified parameter has Xd,X'd,X'd',Td'0,Td”0The q-axis parameter to be identified has Xq,Xq',Xq”,Tq'0,Tq”0；
Using the identification of q-axis parameters as an example, assume that the measured value is x, based on the parameter estimationAnd solving the obtained q-axis parameter identification data interval, wherein x is ud、iq、ω，The model was optimized by the following tolerance:
the q-axis parameter can be identified by solving the above formula optimization problem by adopting an interior point method; wherein, tiAt the ith time step in the interval, tiThe q-axis parameter identification data interval obtained in step S1 is the interval of (1). And similarly, the identification of the d-axis parameters can be realized.
Further, the calculation formula of the absolute value of the track sensitivity in step S11 is as follows:
compared with the prior art, the invention has the following beneficial effects:
the invention provides a parameter identification data interval selection method, which solves the problem that parameter identification data are difficult to select. The method is based on the track sensitivity analysis, selects the data interval which is beneficial to improving the identification precision, and provides a theoretical basis for solving the practical engineering problem. On the basis of solving the problem of identification data interval selection, the invention also provides a complete parameter identification model, has good tolerance performance and promotes the practical process of generator parameter identification.
Drawings
FIG. 1 is a diagram of a MATLAB load rejection test simulation system of the present invention.
FIG. 2 is a graph of trace sensitivity according to the present invention.
FIG. 3 is a graph of trace sensitivity according to the present invention.
FIG. 4 is a graph of recognition errors of different recognition data intervals.
FIG. 5 is a graph of recognition errors of different recognition data intervals.
Detailed Description
The technical scheme of the invention is specifically explained below with reference to the accompanying drawings.
A synchronous generator parameter identification method based on track sensitivity is characterized in that: comprises the following steps of (a) carrying out,
step S1, selecting the identification data interval based on the track sensitivity:
s11, solving absolute value | S of track sensitivity in complete time periodd(t)|,|Sq(t)|；
S12, taking the steady state sampling point before disturbance as the starting point of the identification data interval, namely from t0Starting at 0, every Δ T4T "d0The data segment of (2) calculates the average of the sensitivity of the trajectory once from the starting point to the point
SΔt,avg,S2Δt,avg,S3Δt,avg…：
Wherein N isnΔtIs t0The number of sampling points in the n & delta t time period;
s13, when the track sensitivity average value is attenuated to SnΔt,avg≤SnΔt/10,avgThe calculation can be stopped when/4 or n delta t is more than or equal to 30s, and t is used0The n & delta t time period is used as a d-axis parameter identification data interval; in the same way, the q-axis parameter identification data interval can be solved;
step S2, adopting the robust optimization model as a parameter identification model identification parameter:
a six-order practical model of the synchronous generator:
in a clear view of the above, it is known that,
the d-axis to-be-identified parameter has Xd,X'd,X'd',Td'0,Td”0The q-axis parameter to be identified has Xq,Xq'q,Xq”,Tq'0,Tq”0；
Using the identification of q-axis parameters as an example, assume that the measured value is x, based on the parameter estimationAnd solving the obtained q-axis parameter identification data interval, wherein x is ud、iq、ω，The model was optimized by the following tolerance:
the q-axis parameter can be identified; similarly, the identification of d-axis parameters can be realized; wherein, tiThe obtained q-axis parameter identification data interval is the interval of (1).
Further, the calculation formula of the absolute value of the track sensitivity in step S11 is as follows:
the following is a specific implementation of the present invention.
The invention mainly comprises a data interval selection method for synchronous generator parameter identification; the parameter identification method based on the robust optimization model works in two aspects.
Identification data interval selection
In the actual operation process of the generator, the steady-state operation state is a normal state, and the disturbance state only accounts for a small part, so the measurement data which can be used for parameter identification generally comprise long-time steady-state data, and the disturbance data comprising transient state and sub-transient state information are shorter. When the identification data is selected from the measured data, the disturbance data should be used as the identification data as much as possible, because the steady-state data can only reflect the steady-state parameter Xd,XqThe size is not related to the transient parameter, and the change of the electric quantity at the generator end can reflect the size of the transient parameter of the generator only when the generator is disturbed. In the process of selecting the disturbance data from the measured data, the starting point of the disturbance data, namely the initial moment of the disturbance occurrence, is easy to identify, but the end point of the disturbance data is difficult to define. The overlong data segment contains excessive steady-state data, so that the proportion of transient and sub-transient information in the data is reduced, and the identification accuracy of transient and sub-transient parameters is reduced; the excessively short data segment is likely to lack the required sub-transient state and sub-transient state information, and is more unfavorable for parameter identification. Therefore, the identification data interval selection problem is very important for the parameter identification algorithm.
According to the method, the identification data interval is selected based on the track sensitivity, and the track sensitivity and the calculation method thereof need to be analyzed firstly. The dynamic behavior of the generator can be described using the following system of differential algebraic equations:
in the formula: x is a state vector, Y is an algebraic vector, and theta is a parameter vector. The trajectory sensitivity is the ratio of the trajectory change amount of the state variable or the output variable to the parameter change amount, and is defined as:
in the formula: y isiThe locus of the ith variable in the system; thetajIs the jth parameter in the system; m is the total number of parameters; k is timeAnd (4) sampling points. In order to improve the calculation precision of the track sensitivity, the track needs to be calculated twice: y isi(θ1,θ2,…θj+Δθj,…θm,k)，yi(θ1,θ2,…θj-Δθj,…θmK), then the trajectory sensitivity is calculated as:
the trajectory sensitivity reflects the change degree of the dynamic trajectory of the output quantity of the model when the parameters slightly change, and is widely applied to a parameter identification method of a power system at present. If the generator state quantity has larger track sensitivity to a certain parameter in a specific interval, the smaller identification error of the parameter in the interval can cause great deviation of fitting and actually measured data. Based on the meaning of the track sensitivity, the invention provides that the identification data interval is selected according to the track sensitivity, and because the parameter identification method is d-axis and q-axis decoupling, reasonable identification data intervals can be respectively selected for d-axis and q-axis parameters.
When the external network of the generator breaks down or operates, the current at the motor end in each electrical quantity fluctuates most severely, so the selected track sensitivity should be d and q axis current id,iqThe state variable is used as the state variable, and the dynamic fluctuation time of the generator is directly related to the transient state and sub-transient state time constants, so that the trajectory sensitivity is selected as follows:
because the track sensitivity is compared with two measuring points, the identification data interval is selected according to the attenuation degree of the track sensitivity, and the specific algorithm steps are as follows:
1) solving for trajectory sensitivity over a complete time periodAbsolute value | Sd(t)|,|Sq(t)|。
2) Taking the steady sampling point before disturbance as the starting point (t) of the identification data interval00), every Δ T4T ″.d0The data segment of (a) calculates a track sensitivity average S once from the starting point to the pointΔt,avg,S2Δt,avg,S3Δt,avg…：
Wherein N isnΔtIs t0The number of sampling points in the n · Δ t time period.
3) When the track sensitivity average value is attenuated to SnΔt,avg≤SnΔt/10,avgThe calculation can be stopped when/4 or n delta t is more than or equal to 30s, and t is used0The n · Δ t time period is used as a d-axis parameter identification data interval. The q-axis parameter identification data interval can be solved in the same way.
Parameter identification method based on robust optimization model
The practical model of the synchronous generator is the most widely applied generator model, so the practical model of the synchronous generator with six orders is selected for parameter identification, and the practical model of the synchronous generator with six orders is as shown in the formula:
wherein the d-axis parameter to be identified has Xd,X'd,X'd',Td'0,T”d0The q-axis parameter to be identified has Xq,X'q,X”q,Tq'0,”q0(ignoring armature resistance Ra). All parameters and variables are per unit values herein, except for special labels.
The essence of the parameter identification problem is an optimization problem, taking q-axis parameters as an example (d-axis and q-axis parameters are identified separately and decoupled), and assuming that the measurement value is x (including u)d,iqω), the optimization process for the following equation is identified based on the parameter estimates:
i.e. solving a set of parameter estimation values which can make the fitting degree of the fitting current and the measured current reach the highestHowever, in the actual measurement, there is a measurement error inevitably, and even a few sampling points may have data that deviates from the actual value seriously, which is called bad data. The bad data can be caused to occupy the dominant position in the optimization objective function, so that the optimization result is more prone to fit the bad data. Aiming at the situation, the invention adopts an robust optimization model as a parameter identification model:
in the objective function of the model, if bad data i existsq(ti) The ratio of which in the whole objective function isBecause the difference between the bad data item and the fitting current value is large, the proportion of the bad data item in the objective function is reduced to be within an allowable error, and therefore the parameter identification model has the tolerance performance.
The following is an embodiment of the present invention
According to the identification data interval selection method based on the track sensitivity, MATLAB simulation data is adopted for parameter identification, and the parameter identification result of the identification data interval selected based on the method is compared with the parameter identification result of other data intervals. The advantage of using MATLAB simulation data for parameter identification is that the true values of the generator parameters are known, so that the quality of the identification result can be judged.
And (3) adopting MATLAB to build a load rejection test simulation system of the synchronous generator, as shown in figure 1. The synchronous generator adopts a non-salient pole synchronous generator model in MATLAB-SIMULINK, the capacity is 555MVA, and the rated terminal voltage is 24 kV. The system sets two loads, 150MW +15MVar (initial Load) and 100MW (Load Step off), respectively, and the Load Step off Load is disconnected with the synchronous generator when the simulation reaches 6 s.
First calculate the trajectory sensitivity over a complete time periodAs shown in fig. 2.
As can be seen from FIGS. 2 and 3, the trajectory sensitivity Sd(t),Sq(t) values are all very small at initial steady state, the trajectory sensitivity starts to fluctuate after the load rejection disturbance occurs for 6S, and the trajectory sensitivity S is changed to a new steady state along with the transition of the generatord(t),Sq(t) decays again to a smaller value. Selecting the identification data interval according to the steps of the method provided by the invention by taking the moment 5.96S before disturbance as the starting point of the identification data interval, Sd(t),Sq(t) the attenuation conditions are satisfied at 15.2s and 15.5s, respectively, so that the d-axis parameter identification data interval is selected to be [5.96s,15.2s ]]The q-axis parameter identification data interval is [5.96s,15.5s ]]。
Fig. 4 and 5 compare parameter identification errors obtained according to different identification data intervals, where the starting points of the data intervals are all fixed to the time (5.96s) before the disturbance occurs, the horizontal axis coordinate of each point is the end point value of the data interval, and different interval end points are selected to correspond to different identification data intervals, where the parameter identification error refers to the sum of absolute values of relative errors of all parameters of the d-axis or the q-axis.
As can be seen from fig. 4 and 5, the too large or too small identification data interval causes the parameter identification error to be larger, while the parameter identification error obtained from the identification data interval selected by the method provided by the present invention is smaller and very close to the minimum identification error that can be achieved, which proves the effectiveness of the method for selecting the identification data interval based on the trajectory sensitivity provided by the present invention.
The above are preferred embodiments of the present invention, and all changes made according to the technical scheme of the present invention that produce functional effects do not exceed the scope of the technical scheme of the present invention belong to the protection scope of the present invention.
Claims (1)
1. A synchronous generator parameter identification method based on track sensitivity is characterized in that: comprises the following steps of (a) carrying out,
step S1, selecting the identification data interval based on the track sensitivity:
s11, solving absolute value | S of track sensitivity in complete time periodd(t)|,|Sq(t) |; the absolute value of the track sensitivity is calculated as follows:
wherein Sd(t) track sensitivity with d-axis current as state variable; sq(t) track sensitivity with q-axis current as state variable;
s12, taking the steady state sampling point before disturbance as the starting point of the identification data interval, namely from t0Start at 0 and every Δ T4T ″d0The data segment of (a) calculates a track sensitivity average S once from the starting point to the pointΔt,avg,S2Δt,avg,S3Δt,avg…：
Wherein S isnΔt,avgFor the nth calculated average of the sensitivity of the track, NnΔtIs t0The number of sampling points in the time period of-n.delta t; t'd0One of the parameters to be identified of the d axis of the six-order practical model of the synchronous generator;
s13, when the track sensitivity average value is attenuated to SnΔt,avg≤SnΔt/10,avgThe calculation can be stopped when/4 or n delta t is more than or equal to 30s, and t is used0～n·Δthe time period t is used as a d-axis parameter identification data interval; in the same way, the q-axis parameter identification data interval can be solved; snΔt/10，avgAverage of track sensitivity calculated for the n/10 th time
Step S2, adopting the robust optimization model as a parameter identification model identification parameter:
a six-order practical model of the synchronous generator:
wherein, TJThe damping power coefficient is the inertia time constant of the generator set, and D is the damping power coefficient of the generator;
in a clear view of the above, it is known that,
the d-axis to-be-identified parameter has Xd,X'd,X″d,T′d0,T″d0The q-axis parameter to be identified has Xq,X'q,X″q,T′q0,T″q0；
Using the identification of q-axis parameters as an example, assume that the measured value is x, based on the parameter estimationAnd solving the obtained q-axis parameter identification data interval, wherein x is ud、iq、ω，X'q、X″q、T′q0、T″q0By the following robust optimization model:
the q-axis parameter can be identified by solving the above formula optimization problem by adopting an interior point method; wherein, tiIs the ith in the intervalTime of step, tiThe q-axis parameter identification data interval obtained in step S1 is the same as the q-axis parameter identification data interval, and d-axis parameters can be identified.
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