CN106599337B - Power grid frequency simulation parameter identification method based on simplex method - Google Patents
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Abstract
The invention belongs to the technical field of power systems and automation thereof, and discloses a power grid frequency simulation parameter identification method based on a simplex method. The method comprises the steps of firstly determining a simulation parameter group to be identified by using a multi-parameter sensitivity analysis method, then intelligently guiding the parameter optimizing direction by using a simplex algorithm based on a numerical analysis method, and then calculating a target function value by using a simulation result of a power system electromechanical transient simulation program (BPA), thereby quickly and reliably finding the optimal parameter combination. The method can identify a set of parameters which enable the error between the simulation result and the actual response result to be minimum, improve the accuracy of power grid frequency simulation in the future, shorten the simulation time consumption and greatly improve the optimization efficiency of parameter identification.
Description
Technical Field
The invention belongs to the technical field of power systems and automation thereof, and particularly relates to a simplex method-based power grid frequency simulation parameter identification method.
Background
The power shortage of the east China power grid reaches 490 ten thousand watts when the voltage is switched on at 21:58:02 of 19 th of 9 th of 2015, the system frequency before the fault is 49.97Hz, the full grid frequency drops to 49.56Hz to the minimum after 12s, and the drop amplitude is 0.41 Hz. Under the same operation mode, the frequency dip amplitude calculated based on an electromechanical transient simulation program (BPA) under typical parameters is only 0.17 Hz. Although the accident has no serious consequences, the current frequency simulation precision of the power grid is reflected to be satisfactory and needs to be improved.
With the establishment of an extra-high voltage alternating current-direct current hybrid power grid, when a direct current transmission system has phase commutation failure and locking failure, the active power shortage is huge, and the problem of low frequency of the system after the loss of a large power supply directly depends on the response of devices such as a prime motor, a speed regulator and the like. For an electric power system, whether off-line calculation analysis or on-line real-time monitoring, the method must be established on the basis of an accurate and credible numerical model or equivalent parameters. Therefore, under the circumstance, relevant parameters with high sensitivity need to be found out based on a mathematical model of a frequency response system which accords with the reality, specific values of the parameters are obtained through identification, and the identified parameters are applied to simulation calculation, so that operation scheduling personnel can conveniently make emergency control measures.
At present, the dynamic modeling of a frequency response system by using an identification technology at home and abroad is slow, a standard model of related equipment of the frequency response system, especially a prime motor, a speed regulator and other devices for determining primary frequency modulation capability, provided by power system simulation software (such as BPA, PSS/E and the like) uses a typical model and a typical value which is not verified by tests, and the primary frequency modulation capability of a unit in an actual system is far from reaching the ideal value, so that the system frequency response simulation result under the high-power shortage is directly and optimistically obtained.
The disturbance identification and verification of frequency simulation related parameters by using an actual measurement system are important measures for improving the simulation reliability. The research means of parameter identification is mainly divided into an analytic analysis method and a numerical analysis method. The former emphasizes a method of mathematical analysis, lists equations to simulate an actual system, reduces the dimension of a system characteristic matrix through some mathematical methods, and finally solves the problem to obtain the corresponding relation between parameters and target quantities; the latter mainly solves the problem by utilizing the influence of each parameter in the system on the track, comparing the measured track and verifying the simulation. Since the influences of the primary frequency modulation capability, the load factors and the like of the unit are all nonlinear dynamic models, the complicated nonlinear dynamic behavior is difficult to express in an analytic mode, and the analytic analysis and design method is difficult to realize. The numerical analysis method has certain advantages in the aspect of identifying the parameters of the nonlinear dynamic model, and is a method which is used more frequently.
At present, the related parameter identification method of frequency simulation has fewer patents. The patent application 'a method for identifying parameters of a nuclear power unit prime mover and a speed regulator thereof based on a simplex method' (application number CN201210252089.1) provides a method for identifying parameters of a nuclear power unit prime mover and a speed regulator thereof based on a simplex method, but the method can only identify the parameters of a single unit, and is obviously huge in computation and too low in efficiency when applied to a large power grid in east China. The patent application 'speed regulator parameter identification method combining frequency track and particle swarm optimization' (application number CN201310236183.2) proposes that the speed regulator parameters are adjusted based on the particle swarm optimization algorithm, so that a simulation frequency curve is as close as possible to an actually measured frequency curve, the influence of load factors on the frequency in a large network is completely ignored, and the universal applicability is not available. The patent application 'modeling method for prime mover and speed regulator of electric power system' is based on field actual measurement (application number CN201010107126.0), and although the accuracy of model and parameters can be ensured, the simulation accuracy of the electric power system can be ensured, at present, for the east china power grid, the field actual measurement of all units can not be realized, and the accuracy of field test results under large frequency still needs to be testified.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects in the prior art, the invention provides a method for generating a new group of parameter sets by using a simplex method, which neglects the difference between each unit in the network and the load, fits the primary frequency modulation capability and the load level of the whole network unit into a unified model and unified parameters, and provides a method for determining the parameters to be identified and realizing the optimal identification of the parameters by taking the accuracy of the frequency simulation result as a target, thereby determining the related parameters of the online and offline frequency simulation of the power grid and greatly improving the accuracy of the frequency simulation of the power grid under the large disturbance fault.
The technical scheme is as follows: in order to achieve the above object, the invention provides a power grid frequency simulation parameter identification method based on a simplistic algorithm, which comprises the following steps:
1) collecting power grid simulation data and adjusting the power grid simulation data according to the operation condition when a power grid accident occurs, wherein the power grid simulation data comprise a tide file and a stable file, and the stable file comprises a unit and a load model related to frequency simulation;
acquiring specific large disturbance faults causing power grid frequency fluctuation and actual measurement frequency response wave recording data after the faults occur;
2) determining an n-dimensional parameter space X ═ X (X) affecting frequency simulation results1,x2,…,xn) The parameter space is the parameter to be identified, and the value range of the parameter space is set according to the actual condition of the power grid;
3) determining the initial simplex: typical values are adopted for frequency simulation related parameters of a power grid, the typical values of the parameters are used as a group of initial value parameter groups, another n groups of parameter groups are generated in an n-dimensional parameter space based on the initial value parameter groups, n +1 groups of parameter groups are provided in total, the n +1 groups of parameter groups form an initial simplex, and the iteration number k is set to be 0;
4) utilizing a BPA simulation program of the electric power system to obtain a frequency response result curve Y corresponding to each parameter group in n +1 groups of parameter groups under the same fault disturbance, calculating an objective function value J corresponding to each parameter group in n +1 groups of parameter groups, and finding out a maximum objective function value JHAnd a minimum objective function value JLAnd calculates the maximum objective function value JHAnd a minimum objective function value JLIf the difference value meets the convergence criterion, stopping identification; otherwise, entering step 5);
5) if the iteration number K does not reach the iteration number upper limit value K, generating a new set of parameter sets by using a simplex method, wherein the new set of parameter sets is used for replacing the maximum objective function value J in the step 4)HFinally forming a new simplex by the corresponding parameter group, and returning to the step 4); if the iteration number K reaches the iteration number upper limit value K, the parameter identification fails, and the method is ended.
Further, the n-dimensional parameter space X ═ (X) is determined in the step 2)1,x2,…,xn) The analysis method based on the multi-parameter sensitivity comprises the following steps:
2-a) determining the maximum value and the minimum value of the parameters of each unit and load model related to frequency simulation in the stable file according to the measured values of the parameters of each unit and load model in the power grid, and setting diParameters representing the ith unit or load model, dimaxRepresents diMaximum value of diminRepresents diThe value range of the parameter of the ith unit or the load model is [ d ]imin,dimax]Wherein i is 1,2, and t is the total number of parameters;
2-b) substituting the median of the parameters of each unit and the load model into the simulation model, and simulating to obtain an extreme value f of the frequency response after the fault disturbanceminAnd a steady state value f after 60 secondssThe median value of the parameters of the units and of the load model is equal to half the sum of the maximum and minimum values thereof, i.e. for diIn other words, the value thereof is equal to
2-c) for each parameter, m uniformly distributed independent random numbers are generated within the range of values, i.e. for diIn total generating m uniformly distributed independent random numbers dijWherein j is 1, 2.. multidot.m; then for all parameters, m is generated altogethertA set of group parameter values; respectively combine the mtSubstituting the group parameter value set into the simulation model, and simulating to obtain the extreme value f of the corresponding frequency response of each group parameter value set under the same fault disturbanceuminAnd a steady state value f after 60 secondsusWherein u is 1,2t；
2-d) generation of mtComparing the simulation result of the group parameter value set with the simulation result of the median in the step 2-b), calculating an index value Q according to a formula (1), simultaneously defining the median of all Q values as a base value theta, if Q is more than theta, determining that the group parameter value set is acceptable, and distributing the group parameter value set into an acceptable group; otherwise, considered unacceptable, the set of parameter values is distributed into unacceptable groups, thereby completing mtDistribution of group parameter value sets:
Q＝|fumin-fmin|+|fus-fsequation (1);
2-e) comparing the distribution of each parameter, and defining the number of the jth independent random number distributed in the acceptable group as p for any parameterjThe number of the groups distributed in the unacceptable group is qjAt the same timeDefining a sensitive parameter delta if
Then the acceptable and unacceptable groups are similarly distributed for the parameter, indicating that the parameter is not sensitive; otherwise, the parameter is indicated to be sensitive; if the total number of all sensitive parameters is n, an n-dimensional parameter space X (X) to be identified is formed1,x2,…,xn)。
Further, the step 3) of generating n additional sets of parameter sets based on the initial value parameter set specifically includes the following steps: dividing the n-dimensional parameter space X into (X)1,x2,…,xn) The typical values of the parameters are used as a group of initial value parameter groups, the typical values of the parameters in the initial value parameter groups are sequentially replaced by the measured values which deviate from the typical values of the parameters furthest from the typical values of the parameters in the parameters corresponding to the typical values of the parameters, and one parameter is replaced independently at a time, so that n groups of other parameter groups are formed.
Further, the objective function value J is calculated in step 4) according to formula (2):
wherein α is the number of sampling points, yiIs the value of the ith point of the frequency response result curve Y, aiThe value of the ith point of the PMU frequency response curve a after the fault is obtained;
let the maximum objective function value be JHThe minimum objective function value is JLTo obtain JHIs XHTo obtain JLIs set to XLWhile setting convergence criteria
If it is
Stopping the identification, displaying the successful parameter identification, and setting the parameter XLIs the optimal parameter set.
Further, the step 5)The method for generating a new group of parameter sets by utilizing a simplex method specifically comprises the following steps: the maximum objective function value JHCorresponding parameter set XHReflecting to obtain a reflection parameter set XRAnd calculating the reflection parameter set XRCorresponding objective function value JRReplacing the parameter set X by generating a compression parameter set or an expansion parameter setHAnd forms a new simplex with other sets of parameters.
Further, the set related to frequency simulation in the stable file comprises a prime mover, a speed regulator and a boiler main steam pressure model.
Has the advantages that: compared with the prior art, the invention has the advantages that:
1. aiming at the condition that the current power grid frequency simulation related parameters are not accurate enough, the invention provides a method for identifying the related parameters, firstly, a multi-parameter sensitivity analysis method is utilized to determine the simulation parameter group to be identified, then, a simplex algorithm based on a numerical analysis method is utilized to intelligently guide the parameter optimizing direction, and then, a simulation result of a power system electromechanical transient simulation program (BPA) is used for calculating a target function value, so that the optimal parameter combination is quickly and reliably found;
2. a reliable simulation model is provided for online and offline safety and stability calculation, the frequency simulation precision of the power grid under a large disturbance fault can be improved, support is provided for making frequency emergency control measures corresponding to the east China power grid, and online safety and stability analysis, early warning and control decision levels are improved;
3. the invention adopts a nonlinear parameter optimization method combining a simplex algorithm and an electromechanical transient simulation program to identify the parameters of the frequency simulation related model, thereby identifying a set of parameters which enable the error between the simulation result and the actual response result to be minimum, and improving the accuracy of the power grid frequency simulation in the future.
4. By utilizing the algorithm, the simulation time consumption can be shortened, and the optimization efficiency of parameter identification is greatly improved.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a schematic diagram of corresponding curve feature extraction applied to the 919 accident of the east China power grid by the method.
Detailed Description
The present invention will be further described with reference to the accompanying drawings.
Example (b):
referring to fig. 1, the method for identifying power grid frequency simulation parameters based on a simplistic algorithm provided by the invention firstly enters step S1 to start data preparation work, specifically collects power grid simulation data and adjusts the power grid simulation data according to the operation condition of a power grid accident, wherein the power grid simulation data comprises a tide file and a stabilization file, the stabilization file comprises a unit and a load model related to frequency simulation, and in addition, specific large disturbance fault causing power grid frequency fluctuation and actual measurement frequency response wave recording data after the fault occur need to be obtained;
then, the process proceeds to step S2, where n-dimensional parameter space X ═ X (X) affecting the frequency simulation result is determined1,x2,…,xn) The parameter space is a parameter to be identified (i.e., a parameter to be optimized), and the value range of the parameter space can be set according to the actual condition of the power grid. The method for determining the parameters to be identified adopts an analysis method based on multi-parameter sensitivity, and comprises the following steps:
s21, determining the maximum value and the minimum value of each set parameter related to frequency simulation and each parameter in the load model parameter in the stable file according to the measured values of each set and the load model in the power grid, and using di(i ═ 1, 2.. times, t) respectively represent each unit parameter and load model parameter, where t is the total number of parameters, and the maximum and minimum values of each parameter are respectively represented as dimaxAnd dimin，diHas a value range of [ dimin,dimax]；
S22 calculating the median value of each parameter
Substitution into simulation model, i.e. to be
Substituting the frequency response into a simulation model, and simulating to obtain an extreme value f of the frequency response after the fault disturbanceminAnd a steady state value f after 60 secondss；
S23, for each parameter, generating m independent random numbers d uniformly distributed in the value rangeij(j ═ 1, 2.. times, m), typically m has a value of 3 to 5, and is statistically represented as
Wherein
Expressed as parameter d1In the value range of [ d1min,d1max]M independent random numbers which are uniformly distributed are respectively generated in the random number generator, and so on,
expressed as parameter dtIn the value range of [ dtmin,dtmax]M uniformly distributed independent random numbers are respectively generated. Then for all parameters, m is generated altogethertSets of group parameter values, e.g. [ d ]11,d21,...,dt1]Represented as a set of parameter value sets. M to be generated respectivelytSubstituting the group parameter set into the simulation model, and obtaining the extreme value f of the corresponding frequency response of each group parameter set under the same fault disturbance by using BPA simulationuminAnd a steady state value f after 60 secondsusWherein (u ═ 1, 2.., mt)；
S24 generating mtComparing the simulation result of the group parameter value set with the simulation result of the median in the step S22, simultaneously taking the median of all Q values to define a base value theta, calculating an index value Q according to a formula (1), if Q is more than theta, considering the group parameter value set to be acceptable, and distributing the group parameter value set to an acceptable group; otherwise, considered unacceptable, the set of parameter value sets is distributed into an unacceptable group:
Q＝|fumin-fmin|+|fus-fsequation (1);
s25 comparing the parametersFor any parameter, the number of the jth independent random number distributed in the acceptable group is defined as pjThe number of the groups distributed in the unacceptable group is qjWhile defining the sensitive parameter delta if
Then the acceptable and unacceptable groups are similarly distributed for the parameter, indicating that the parameter is not sensitive; otherwise, it indicates that the parameter is sensitive. Setting the total number of all sensitive parameters as n to form n-dimensional parameter space X ═ X (X) to be identified1,x2,…,xn) (ii) a The sensitive parameter delta is an integer larger than zero, the value of the sensitive parameter delta depends on simulation precision required by actual research, and the higher the precision requirement is, the smaller the value of delta is.
And then, in step S3, determining an initial simplex, wherein typical values are adopted for the frequency simulation related parameters of the current power grid, and in the present invention, each parameter typical value is used as a set of initial value parameter sets, which can be expressed as (x'1,x′2,…,x′n) If n sets of parameter sets are generated based on the initial value parameter set, n +1 sets of parameter sets are shared, the n +1 sets of parameter sets form an initial simplex, and the iteration number k is set to be 0; generating n additional sets of parameters based on the initial value parameter set, specifically, generating the initial value parameter set (x'1,x′2,…,x′n) Each individual replacement of the typical values of the parameters in (a) forms a set of further parameter sets. When replacing, the typical parameter value is replaced with the actual measured value of the parameter corresponding to the typical parameter value, which is most far from the typical parameter value, so as to generate n +1 groups of parameter sets in total, for example, the parameter sets can be expressed as
Wherein x1Is the parameter x1Typical value x'1Corresponding parameter x1Actual measured deviation from the parametric typical value x'1The most distant actual measurement value is x'1Replacement with x ″)1And so on; then n +1 sets of parameters constitute the initial simplex;
the process then proceeds to step S4, where it is determined whether or notThe convergence criterion is reached, a frequency response result curve Y corresponding to each parameter group in n +1 groups of parameter groups under the same fault disturbance is obtained by utilizing a BPA simulation program of the power system, an objective function value J corresponding to each parameter group in n +1 groups of parameter groups is calculated, and the maximum objective function value J is found outHAnd a minimum objective function value JLAnd calculates the maximum objective function value JHAnd a minimum objective function value JLIf the difference meets the convergence criterion, stopping optimization; otherwise, step 5) is performed, wherein the method for judging whether the convergence criterion is reached is as follows, and the objective function value J is calculated according to the formula (2):
wherein α is the number of sampling points, yiIs the value of the ith point of the frequency response result curve Y, aiThe value of the ith point of the PMU frequency response curve a after the fault is obtained; if the finally obtained objective function value is smaller, the fitting error between the established model parameter simulation result and the actually measured curve is smaller, and the precision of the frequency simulation model is higher;
the maximum objective function value J is obtainedHThe corresponding parameter set is XHMinimum value of objective function JLThe corresponding parameter set is XLWhile setting convergence criteria
If it is
Stopping optimization, displaying successful parameter identification, and setting X of parameterLIf the parameter is the optimal parameter group, otherwise, the step goes to S5; wherein the convergence criterion
Depending on the simulation accuracy required for practical research, the higher the accuracy requirement, the convergence criterion
The smaller.
Step S5: making the iteration number K equal to K +1, and if the iteration number K does not reach the set iteration number upper limit value K, generating a new group of parameter sets by using a simplex method, wherein the new group of parameter sets is used for replacing the maximum objective function value J in the step 4)HCorresponding parameter set XHFinally forming a new simplex shape and returning to the step 4); if the iteration number K reaches the iteration number upper limit value K, the parameter optimization fails, and the method is ended, wherein a new group of parameter groups are generated by utilizing a simplex method, and the method specifically comprises the following steps: the maximum objective function value JHCorresponding parameter set XHReflecting to obtain a reflection parameter set XRAnd calculating the reflection parameter set XRCorresponding objective function value JRBy generating a set of compression parameters or a set of expansion parameters XNTo replace the parameter set XHNew simplices are formed with other sets of parameters, for example: in n +1 group parameter set
Wherein if [ x'1,x′2,…,x′n]Set to the maximum objective function value JHCorresponding parameter set XHThen x 'will be'1,x′2,…,x′n]Replacement is by a parameter set XNAnd regenerating a new n +1 group of parameter sets, namely a new simplex, and re-entering the step S4 to optimize the parameter sets to obtain an optimal parameter set, wherein the simulation frequency under the optimal parameter set has a better effect.
The set related to frequency simulation in the above-mentioned stable document in the present invention includes a prime mover, a speed regulator and a boiler main steam pressure model.
Referring to fig. 2, a schematic diagram of characteristic extraction of a response curve applied to a 919 accident of the east China power grid by the method of the present invention is shown, and it can be seen from the diagram that the actually measured response curve is basically fitted with the frequency response curve under the optimal parameters identified by the method of the present invention, while the response curve under typical parameters is not consistent with the actually measured response curve, and the error is large, further explaining that the accuracy of power grid frequency simulation is improved by using the method of the present invention.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.
Claims (5)
1. A power grid frequency simulation parameter identification method based on a simplex method is characterized by comprising the following steps:
1) collecting power grid simulation data and adjusting the power grid simulation data according to the operation condition when a power grid accident occurs, wherein the power grid simulation data comprise a tide file and a stable file, and the stable file comprises a unit and a load model related to frequency simulation;
acquiring specific large disturbance faults causing power grid frequency fluctuation and actual measurement frequency response wave recording data after the faults occur;
2) determining an n-dimensional parameter space X ═ X (X) affecting frequency simulation results1,x2,…,xn) The parameter space is the parameter to be identified, and the value range of the parameter space is set according to the actual condition of the power grid;
3) determining the initial simplex: typical values are adopted for frequency simulation related parameters of a power grid, the typical values of the parameters are used as a group of initial value parameter groups, another n groups of parameter groups are generated in an n-dimensional parameter space based on the initial value parameter groups, n +1 groups of parameter groups are provided in total, the n +1 groups of parameter groups form an initial simplex, and the iteration number k is set to be 0;
4) utilizing a BPA simulation program of the electric power system to obtain a frequency response result curve Y corresponding to each parameter group in n +1 groups of parameter groups under the same fault disturbance, calculating an objective function value J corresponding to each parameter group in n +1 groups of parameter groups, and finding out a maximum objective function value JHAnd a minimum objective function value JLAnd calculating the maximum objective function value JHAnd a minimum objective function value JLIf the difference value meets the convergence criterion, stopping identification; otherwise, entering step 5);
5) let k be k +1, if the iteration is repeatedIf the generation times K do not reach the upper limit value K of the iteration times, generating a new group of parameter sets by using a simplex method, wherein the new group of parameter sets is used for replacing the maximum objective function value J in the step 4)HFinally forming a new simplex by the corresponding parameter group, and returning to the step 4); if the iteration number K reaches the iteration number upper limit value K, the parameter identification fails, and the method is ended;
determining n-dimensional parameter space X ═ (X) in the step 2)1,x2,…,xn) The analysis method based on the multi-parameter sensitivity comprises the following steps:
2-a) determining the maximum value and the minimum value of the parameters of each unit and load model related to frequency simulation in the stable file according to the measured values of the parameters of each unit and load model in the power grid, and setting diParameters representing the ith unit or load model, dimaxRepresents diMaximum value of diminRepresents diThe value range of the parameter of the ith unit or the load model is [ d ]imin,dimax]Wherein i is 1,2, and t is the total number of parameters;
2-b) substituting the median of the parameters of each unit and the load model into the simulation model, and simulating to obtain an extreme value f of the frequency response after the fault disturbanceminAnd a steady state value f after 60 secondssThe median value of the parameters of the units and of the load model is equal to half the sum of the maximum and minimum values thereof, i.e. for diIn other words, the value thereof is equal to
2-c) for each parameter, m uniformly distributed independent random numbers are generated within the range of values, i.e. for diIn total generating m uniformly distributed independent random numbers dijWherein j is 1, 2.. multidot.m; then for all parameters, m is generated altogethertA set of group parameter values; respectively combine the mtSubstituting the group parameter value set into the simulation model, and simulating to obtain the extreme value f of the corresponding frequency response of each group parameter value set under the same fault disturbanceuminAnd a steady state value f after 60 secondsusWherein u ═1,2,...,mt；
2-d) generation of mtComparing the simulation result of the group parameter value set with the simulation result of the median in the step 2-b), calculating an index value Q according to a formula (1), simultaneously defining the median of all Q values as a base value theta, if Q is more than theta, determining that the group parameter value set is acceptable, and distributing the group parameter value set into an acceptable group; otherwise, considered unacceptable, the set of parameter values is distributed into unacceptable groups, thereby completing mtDistribution of group parameter value sets:
Q＝|fumin-fmin|+|fus-fsequation (1);
2-e) comparing the distribution of each parameter, and defining the number of the jth independent random number distributed in the acceptable group as p for any parameterjThe number of the groups distributed in the unacceptable group is qjWhile defining the sensitive parameter delta if
Then the acceptable and unacceptable groups are similarly distributed for the parameter, indicating that the parameter is not sensitive; otherwise, the parameter is indicated to be sensitive; if the total number of all sensitive parameters is n, an n-dimensional parameter space X (X) to be identified is formed1,x2,…,xn)。
2. The simplex method-based power grid frequency simulation parameter identification method according to claim 1, wherein: generating n additional sets of parameter sets based on the initial value parameter set in the step 3), specifically comprising the following steps: dividing the n-dimensional parameter space X into (X)1,x2,…,xn) The typical values of the parameters are used as a group of initial value parameter groups, the typical values of the parameters in the initial value parameter groups are sequentially replaced by the measured values which deviate from the typical values of the parameters furthest from the typical values of the parameters in the parameters corresponding to the typical values of the parameters, and one parameter is replaced independently at a time, so that n groups of other parameter groups are formed.
3. The simplex method-based power grid frequency simulation parameter identification method according to claim 1, wherein: in the step 4), the objective function value J is calculated according to the formula (2):
wherein α is the number of sampling points, yiIs the value of the ith point of the frequency response result curve Y, aiThe value of the ith point of the PMU frequency response curve a after the fault is obtained;
let the maximum objective function value be JHThe minimum objective function value is JLTo obtain JHIs XHTo obtain JLIs set to XLWhile setting convergence criteria
If it is
Stopping the identification, displaying the successful parameter identification, and setting the parameter XLIs the optimal parameter set.
4. The simplex method-based power grid frequency simulation parameter identification method according to claim 1, wherein: generating a new set of parameter sets by using a simplex method in the step 5), specifically comprising the following steps: the maximum objective function value JHCorresponding parameter set XHReflecting to obtain a reflection parameter set XRAnd calculating the reflection parameter set XRCorresponding objective function value JRReplacing the parameter set X by generating a compression parameter set or an expansion parameter setHAnd forms a new simplex with other sets of parameters.
5. The simplex method-based power grid frequency simulation parameter identification method according to claim 1 or 2, wherein: the set related to frequency simulation in the stable file comprises a prime motor, a speed regulator and a boiler main steam pressure model.
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