CN111525590A - Dynamic reactive power compensation device modeling method and device - Google Patents
Dynamic reactive power compensation device modeling method and device Download PDFInfo
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Abstract
The invention provides a modeling method and a device of a dynamic reactive power compensation device, wherein the modeling method of the dynamic reactive power compensation device comprises the following steps: acquiring test data of a dynamic reactive power compensation device to be modeled; selecting a reactive power compensation device model matched with the dynamic reactive power compensation device to be modeled from a preset dynamic reactive power compensation device model library according to the test data; and establishing a model of the dynamic reactive power compensation device to be modeled by utilizing a genetic algorithm and the matched reactive power compensation device model. The modeling method and the device for the dynamic reactive power compensation device can realize accurate modeling of the dynamic reactive power compensation device.
Description
Technical Field
The invention relates to the field of power industry, belongs to the technical field of reactive power compensation of transformer substations, and particularly relates to a modeling method and device of a dynamic reactive power compensation device.
Background
With the gradual depletion of fossil energy and the increasing pressure of environmental protection, people are increasingly keen on the desire for clean and sustainable energy. Wind power is in a priority position in the development of clean energy due to the advantages of mature technology and abundant resources. With the leap-type development of wind power, the contradiction between the explosive growth of the installed scale of the wind power and the safety and stability of the voltage of a power grid is increasingly prominent. In order to deal with the voltage safety problem of large-scale wind power access, each wind power station is simultaneously provided with a dynamic reactive power compensation device so as to improve and coordinate the stability of voltage. In order to perform stable calculation of the power system after new energy is accessed, a proper and accurate mathematical and simulation model of the wind turbine generator and the dynamic reactive power compensation device needs to be established. At present, international IEC-TC88-WG27 wind power generation modeling working group, American WECC modeling working group and other research organizations or organizations develop modeling work of wind turbine generators/wind power plants, the energy industry standard 'wind turbine generator low voltage ride through modeling and verification method' written by China academy of electric sciences has been reported, and related technologies of related wind turbine generator simulation modeling are successively proposed and continuously improved. However, the simulation modeling of the dynamic reactive power compensation device just starts, and the new energy industry standard, namely the grid-connected performance test specification of the reactive power compensation device of the wind power plant, is compiled by the organization of the China academy of electric sciences at present, and instructive opinions are provided for the simulation modeling test of the dynamic reactive power compensation device of the wind power plant.
At present, research aiming at modeling of the dynamic reactive power compensation device is gradually developed, a dynamic reactive power compensation device model based on various simulation platforms has been introduced by documents, and research on aspects such as grid-connected control performance, voltage regulation performance and the like is developed. However, models of the dynamic reactive power compensation device in the mainstream research at present are all based on a general theoretical model, low voltage ride through, high voltage ride through and the like of the dynamic reactive power compensation device are not developed, the established models are not verified by actual operation data, the accuracy and the applicability of the models are low, and the actual operation characteristics of the dynamic reactive power compensation device cannot be completely and accurately simulated.
Disclosure of Invention
The dynamic reactive power compensation device modeling method is used for modeling the dynamic reactive power compensation device based on the low voltage ride through and high voltage ride through test data, and can realize accurate modeling of the dynamic reactive power compensation device.
In order to achieve the above object, there is provided a dynamic reactive power compensation device modeling method, including:
acquiring test data of a dynamic reactive power compensation device to be modeled;
selecting a reactive power compensation device model matched with the dynamic reactive power compensation device to be modeled from a preset dynamic reactive power compensation device model library according to the test data;
and establishing a model of the dynamic reactive power compensation device to be modeled by utilizing a genetic algorithm and the matched reactive power compensation device model.
In an embodiment, the obtaining test data of the dynamic reactive power compensation device to be modeled includes:
acquiring test data of the dynamic reactive power compensation device to be modeled by using a low voltage ride through and high voltage ride through method, wherein the test data comprises: and the boost high-voltage side voltage instantaneous value and the current instantaneous value of the dynamic reactive power compensation device to be modeled are obtained.
In an embodiment, the selecting, according to the test data, a reactive power compensation device model matching the dynamic reactive power compensation device to be modeled from a preset dynamic reactive power compensation device model library includes:
calculating a reactive power characteristic curve and a reactive current characteristic curve according to instantaneous values of the voltage and the current of the boost transformer high-voltage side;
dividing the reactive power characteristic curve and the reactive current characteristic curve into three sections of before-fault, fault and after-fault;
determining the type and the characteristic of the dynamic reactive power compensation device to be modeled according to the respective characteristics of the reactive power characteristic curve and the reactive current characteristic curve in the three intervals;
and selecting a reactive power compensation device model matched with the dynamic reactive power compensation device to be modeled from a preset dynamic reactive power compensation device model library according to the type and the characteristics.
In one embodiment, the modeling the dynamic reactive power compensation device to be modeled by using a genetic algorithm and the matched reactive power compensation device model includes:
determining a parameter object to be identified of the dynamic reactive power compensation device to be modeled according to the matched reactive power compensation device model;
and establishing a model of the dynamic reactive power compensation device to be modeled according to the parameter object to be identified by utilizing a genetic algorithm.
In one embodiment, establishing a model of the dynamic reactive power compensation device to be modeled according to the parameter object to be identified by using a genetic algorithm includes:
taking the parameters of the matched reactive power compensation device model as initial parameters of a genetic training model;
training a genetic training model by using the parameters to generate the parameter object to be identified;
and establishing a model of the dynamic reactive power compensation device to be modeled according to the parameter object to be identified.
In a second aspect, the present invention provides a dynamic reactive power compensation modeling apparatus, including:
the test data acquisition unit is used for acquiring test data of the dynamic reactive power compensation device to be modeled;
the model selection unit is used for selecting a reactive power compensation device model matched with the dynamic reactive power compensation device to be modeled from a preset dynamic reactive power compensation device model library according to the test data;
and the model establishing unit is used for establishing the model of the dynamic reactive power compensation device to be modeled by utilizing a genetic algorithm and the matched reactive power compensation device model.
In an embodiment, the test data obtaining unit is specifically configured to obtain test data of the dynamic reactive power compensation device to be modeled by using a low voltage ride through and high voltage ride through method, where the test data includes: and the boost high-voltage side voltage instantaneous value and the current instantaneous value of the dynamic reactive power compensation device to be modeled are obtained.
In one embodiment, the model selecting unit includes:
the curve calculation module is used for calculating a reactive power characteristic curve and a reactive current characteristic curve according to instantaneous values of the voltage and the current of the boost transformer high-voltage side;
the curve partitioning module is used for dividing the reactive power characteristic curve and the reactive current characteristic curve into three sections, namely a section before a fault, a section before the fault and a section after the fault;
the model type determining module is used for determining the type and the characteristics of the dynamic reactive power compensation device to be modeled according to the characteristics of the reactive power characteristic curve and the reactive current characteristic curve in the three intervals;
and the model matching module is used for selecting a reactive power compensation device model matched with the dynamic reactive power compensation device to be modeled from a preset dynamic reactive power compensation device model library according to the type and the characteristics.
In one embodiment, the model building unit comprises:
the parameter object determining module is used for determining a parameter object to be identified of the dynamic reactive power compensation device to be modeled according to the matched reactive power compensation device model;
and the model establishing first module is used for establishing a model of the dynamic reactive power compensation device to be modeled according to the parameter object to be identified by utilizing a genetic algorithm.
In one embodiment, the model building first module comprises:
the initial parameter determining module is used for taking the parameters of the matched reactive compensation device model as initial parameters of a genetic training model;
the model training module is used for training a genetic training model by using the parameters to generate the parameter object to be identified;
and the model establishing second module is used for establishing a model of the dynamic reactive power compensation device to be modeled according to the parameter object to be identified.
In a third aspect, the present invention provides an electronic device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the dynamic reactive power compensation device modeling method when executing the program.
In a fourth aspect, the invention provides a computer readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the dynamic reactive compensation apparatus modeling method.
As can be seen from the above description, embodiments of the present invention provide a dynamic reactive power compensation device modeling method and apparatus, first obtain rich measured data obtained by a low voltage and high voltage ride through test of a dynamic reactive power compensation device to be modeled; selecting a reactive power compensation device model matched with the dynamic reactive power compensation device to be modeled from a preset dynamic reactive power compensation device model base based on the measured data; and finally, establishing a model of the dynamic reactive power compensation device to be modeled by utilizing a genetic algorithm and the matched reactive power compensation device model. Specifically, the beneficial effects of the invention are as follows:
(1) the characteristics of the reactive compensation device during low voltage ride through are classified and summarized, part of transient processes are ignored, only the most important characteristics are paid attention to, the most approximate original model can be matched for a new reactive compensation device test curve, the blindness of subsequent identification is reduced, and therefore the identification efficiency is improved.
(2) Aiming at the problem of slow PSCAD simulation, the genetic algorithm is improved, a gene library is established, the genes, fitness functions and evaluation function values of each generation of population are stored through the gene library, when a new population is generated, repeated genes are screened out through comparison and inquiry, the fitness functions in the library are directly read, the function calculation process is skipped, the calculated amount is reduced, and therefore the identification efficiency is improved.
(3) And establishing a reactive compensation device model library, and continuously updating and enriching the reactive compensation device model and the parameter library by a newly identified reactive compensation device warehousing mode.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a dynamic reactive power compensation device modeling method provided in an embodiment of the present invention;
fig. 2 is a flow chart illustrating a step 100 of a dynamic reactive power compensation device modeling method according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a low-voltage and high-voltage ride through field test of the dynamic reactive power compensation device according to the embodiment of the invention;
fig. 4 is a flow chart of a low voltage and high voltage ride through test of the dynamic reactive power compensation device according to an embodiment of the present invention;
FIG. 5 is a flowchart illustrating a method step 200 for modeling a dynamic reactive power compensation device according to an embodiment of the invention;
fig. 6 is a sectional view of a low-voltage and high-voltage ride through curve of the dynamic reactive power compensation device according to an embodiment of the present invention;
FIG. 7 is a flowchart illustrating a method step 300 for modeling a dynamic reactive power compensation device according to an embodiment of the invention;
FIG. 8 is a schematic flow chart of a dynamic reactive power compensation device modeling method 302 according to an embodiment of the present invention;
FIG. 9 is a schematic flow chart of a dynamic reactive power compensation device modeling method in an embodiment of the present invention;
FIG. 10 is a graph of the measured low-pass voltage of the reactive power compensation device model TKSVG-8/10 in the embodiment of the invention;
FIG. 11 is a graph of the low penetration reactive power measured curve of the reactive power compensation device of the Taikai TKSVG-8/10 type in the embodiment of the invention;
FIG. 12 is a graph of the measured high-voltage penetration voltage of a reactive power compensation device of the Taikai TKSVG-8/10 type in an embodiment of the present invention;
fig. 13 is a graph of the actual measurement of the high penetration reactive power of the tai kai TKSVG-8/10 type reactive power compensation device in the specific application example of the invention;
FIG. 14 is a schematic diagram of a simulation model obtained after modeling the dynamic reactive power compensation device in an embodiment of the present invention;
FIG. 15 is a diagram showing an example of genetic algorithm thinking in a specific application example of the present invention;
FIG. 16 is a flow chart of a genetic algorithm in an embodiment of the present invention;
FIG. 17 is a graph showing the actual measurement and simulation of the reactive power effective value of the low voltage ride through TKSVG-8/10 in the embodiment of the present invention;
FIG. 18 is a graph showing the actual measurement and simulation of the reactive power effective value of the Taikai TKSVG-8/10 high voltage ride through in the embodiment of the present invention;
fig. 19 is a schematic structural diagram of a dynamic reactive power compensation device modeling apparatus according to an embodiment of the present invention;
FIG. 20 is a diagram illustrating a structure of a model selecting unit according to an embodiment of the present invention;
FIG. 21 is a schematic structural diagram of a model building unit according to an embodiment of the present invention;
FIG. 22 is a block diagram of a first module for modeling according to an embodiment of the present invention;
fig. 23 is a schematic structural diagram of an electronic device in an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the present invention further provides a specific implementation manner of a dynamic reactive power compensation device modeling method, and referring to fig. 1, the method specifically includes the following steps:
step 100: and acquiring test data of the dynamic reactive power compensation device to be modeled.
It is understood that reactive power compensation (reactive power compensation) is a technique that plays a role in increasing the power factor of the grid, reducing the loss of the power supply transformer and the transmission line, increasing the power supply efficiency, and improving the power supply environment in the electric power supply system. Reactive power compensation devices are in an indispensable and very important place in power supply systems. The compensation device is reasonably selected, so that the loss of the power grid can be reduced to the maximum extent, and the quality of the power grid is improved.
When the step 100 is implemented, the method specifically comprises the following steps: and recording the voltage, the current and the power of the high-voltage side of the dynamic reactive power compensation device by using a high-precision wave recorder as basic data for model verification of the dynamic reactive power compensation device.
Step 200: and selecting a reactive power compensation device model matched with the dynamic reactive power compensation device to be modeled from a preset dynamic reactive power compensation device model library according to the test data.
It is to be understood that various dynamic reactive compensation devices in a dynamic reactive compensation device model library are known, in particular, the dynamic reactive compensation device model library comprises: the model module of the reactive power compensation device, the low voltage ride through type module, the high voltage ride through type module, the actual measurement curve module of the reactive power compensation device to be modeled, the parameter module of the reactive power compensation device model and the like. In addition, the model and the parameter library of the reactive power compensation device are continuously updated and enriched in a mode of warehousing the newly identified reactive power compensation device.
Step 300: and establishing a model of the dynamic reactive power compensation device to be modeled by utilizing a genetic algorithm and the matched reactive power compensation device model.
Specifically, the test data obtained in step 100 is used, and the parameters to be identified are determined through a genetic algorithm, so as to obtain parameters meeting the error requirement; and obtaining a model of the dynamic reactive power compensation device to be modeled by using the parameters meeting the error requirements.
As can be seen from the above description, the embodiment of the present invention provides a dynamic reactive power compensation device modeling method, which includes obtaining rich measured data obtained by a low voltage and high voltage ride through test of a dynamic reactive power compensation device to be modeled; selecting a reactive power compensation device model matched with the dynamic reactive power compensation device to be modeled from a preset dynamic reactive power compensation device model base based on the measured data; and finally, establishing a model of the dynamic reactive power compensation device to be modeled by utilizing a genetic algorithm and the matched reactive power compensation device model. Specifically, the beneficial effects of the invention are as follows:
(1) the characteristics of the reactive compensation device during low voltage ride through are classified and summarized, part of transient processes are ignored, only the most important characteristics are paid attention to, the most approximate original model can be matched for a new reactive compensation device test curve, the blindness of subsequent identification is reduced, and therefore the identification efficiency is improved.
(2) Aiming at the problem of slow PSCAD simulation, the genetic algorithm is improved, a gene library is established, the genes, fitness functions and evaluation function values of each generation of population are stored through the gene library, when a new population is generated, repeated genes are screened out through comparison and inquiry, the fitness functions in the library are directly read, the function calculation process is skipped, the calculated amount is reduced, and therefore the identification efficiency is improved.
(3) And establishing a reactive compensation device model library, and continuously updating and enriching the reactive compensation device model and the parameter library by a newly identified reactive compensation device warehousing mode.
In one embodiment, referring to fig. 2, step 100 comprises:
step 101: acquiring test data of the dynamic reactive power compensation device to be modeled by using a low voltage ride through and high voltage ride through method, wherein the test data comprises: and the boost high-voltage side voltage instantaneous value and the current instantaneous value of the dynamic reactive power compensation device to be modeled are obtained.
Specifically, a low-voltage and high-voltage ride through actual measurement characteristic curve and other test data of the dynamic reactive power compensation device to be modeled are obtained through a low-voltage and high-voltage ride through field test. Fig. 3 is a schematic diagram of simulation of low-voltage and high-voltage ride through of a dynamic reactive power compensation device in the technical solution of the embodiment of the present invention. Wherein, MP1 is a system side node, which can be a grid-connected point at the low-voltage side of a wind farm, MP2 is a test point, and MP3 is a side where the voltage rises and goes low, that is, a reactive power compensation device end. Z1For limiting impedance, the influence of voltage drop on other running equipment in a power grid and a wind power plant is limited during short-circuit fault, and according to the requirements of IEC61400-21, the voltage fluctuation of the MP1 on the system side is within 5% Un during voltage drop. Z2For short-circuit impedance, adjusting Z2Can change the depth of the voltage dip. S1As a bypass switch, S2For short-circuitingAnd off. Z3Adjusting Z for abruptly raising capacitive reactance3May change the amplitude of the voltage swell. S1As a bypass switch, S3Is a voltage swell analog switch.
The switch S is controlled in sequence according to the sequence shown in FIG. 41And S2、S3The specified voltage drop and shock rise are generated in a simulation mode at the end of the dynamic reactive power compensation device, and the low-voltage ride through capability and the high-voltage ride through capability of the dynamic reactive power compensation device are sequentially verified. And recording the voltage, the current and the power of the high-voltage side of the dynamic reactive power compensation device by using a high-precision wave recorder as basic data for model verification of the dynamic reactive power compensation device.
In one embodiment, referring to fig. 5, step 200 comprises:
step 201: and calculating a reactive power characteristic curve and a reactive current characteristic curve according to the instantaneous values of the voltage and the current of the boost transformer high-voltage side.
Specifically, according to a high-precision recorder, the instantaneous voltage value and the instantaneous current value of the boost-up transformer high-voltage side of the dynamic reactive power compensation device are recorded, the positive sequence effective values of the voltage and the current are calculated by adopting a sliding window calculation method, and then the reactive power and reactive current characteristic curves of the dynamic reactive power compensation device are obtained and are used as references for subsequent parameter identification.
Step 202: and dividing the reactive power characteristic curve and the reactive current characteristic curve into three sections of before-fault, fault and after-fault.
The dynamic reactive power compensation device curves (reactive power characteristic curve and reactive current characteristic curve) are divided into three areas of before-fault, in-fault and after-fault, and then are further divided according to transient and steady-state processes.
As shown in fig. 6, in the measured curve, the transient power changes are severe at the voltage drop and recovery moments (periods B1_ r and C1_ r) during the low-voltage and high-voltage ride-through test, but the trends of the curve are basically consistent, so that the electrical characteristics of the periods are not taken as the classification characteristics. Therefore, the low-voltage and high-voltage ride-through classification features of the dynamic reactive power compensation device include the following two points:
-steady state (B2 — r period) reactive power characteristics during a fault;
secondly, transient state (C2_ r period) reactive power characteristic after fault;
the above two classification features are described as follows: in the period B2_ r, the reactive power change condition is divided into three working conditions of power generation, zero generation and absorption of the power grid; and in the period C2_ r, the reactive power change condition is divided into three working conditions of power generation, zero emission and suction to the power grid.
Step 203: and determining the type and the characteristics of the dynamic reactive power compensation device to be modeled according to the characteristics of the reactive power characteristic curve and the reactive current characteristic curve in the three intervals.
Step 204: and selecting a reactive power compensation device model matched with the dynamic reactive power compensation device to be modeled from a preset dynamic reactive power compensation device model library according to the type and the characteristics.
In step 203 and step 204, specifically, comparing the low voltage ride through and high voltage ride through characteristics of the dynamic reactive power compensation device to be modeled with the information stored in the dynamic reactive power compensation device model base, completing the type matching and characteristic matching of the dynamic reactive power compensation device to find out the known model most similar to the dynamic reactive power compensation device to be modeled from the dynamic reactive power compensation device model base.
In one embodiment, referring to fig. 7, step 300 further comprises:
step 301: and determining a parameter object to be identified of the dynamic reactive power compensation device to be modeled according to the matched reactive power compensation device model.
The parameter object to be identified in step 301 includes: the parameters to be identified comprise: proportional gain K of DC voltage outer loop controllervdcTime constant T of DC voltage outer loop controllervdcProportional gain K of reactive power outer loop controllerQTime constant T of reactive power outer loop controllerQMaximum value i of reactive current of low-voltage ride-through controllermaxLDC upper limit value U of low voltage ride through controllerdc_maxLDC voltage lower limit U of low voltage ride through controllerdc_minLLow voltage ride through controller gridVoltage outer loop control proportional gain KVsLOuter ring control time constant T of grid voltage of low voltage ride through controllerVsLMaximum value of reactive current i of high voltage ride through controllermaxHDC upper limit value U of high voltage ride through controllerdc_maxHDC voltage lower limit U of high voltage ride through controllerdc_minHOuter loop control proportional gain K of grid voltage of high voltage ride through controllerVsHOuter ring control time constant T of grid voltage of high voltage ride through controllerVsH。
After determining the low voltage ride through realization type and the characteristic type of the dynamic reactive power compensation device, finding out a known dynamic reactive power compensation device model which is most similar to the dynamic reactive power compensation device to be modeled from the dynamic reactive power compensation device model base, wherein the key parameter of the known dynamic reactive power compensation device model is the parameter to be identified by the dynamic reactive power compensation device. Typical dynamic reactive power compensation device parameters are shown in table 1.
TABLE 1
Step 302: and establishing a model of the dynamic reactive power compensation device to be modeled according to the parameter object to be identified by utilizing a genetic algorithm.
In steps 301 and 302, determining parameters to be identified by using initial parameters and actually measured characteristic curves of the dynamic reactive power compensation device through a genetic algorithm to obtain parameters meeting error requirements; and obtaining a model of the dynamic reactive power compensation device to be modeled by using the parameters meeting the error requirements.
In one embodiment, referring to fig. 8, step 302 further comprises:
step 3021: and taking the parameters of the matched reactive compensation device model as initial parameters of a genetic training model.
And determining a parameter object to be identified of the dynamic reactive power compensation device to be modeled according to the parameters of the known dynamic reactive power compensation device model, and taking the parameters of the known dynamic reactive power compensation device model as initial parameters of the parameter object to be identified of the dynamic reactive power compensation device to be modeled. According to the fan characteristic subarea shown in fig. 6, the characteristics of the reactive power compensation device in the low-penetration process are (sending, sending and zero). The known model of the type is selected from the dynamic reactive power compensation device model library, the parameter type of the known model is used as the parameter object to be identified of the dynamic reactive power compensation device to be modeled in this embodiment, and the parameter of the known model is used as the initial data of the parameter object to be identified of the dynamic reactive power compensation device to be modeled in this embodiment, as shown in table 2. In addition, it can be understood that, by reading the reserved typical parameters of the known dynamic reactive power compensation device model from the dynamic reactive power compensation device model library as the starting point (i.e. initial parameters) of the parameter identification, the efficiency of the parameter identification can be greatly improved.
TABLE 2
Step 3022: and training a genetic training model by using the parameters to generate the parameter object to be identified.
Step 3023: and establishing a model of the dynamic reactive power compensation device to be modeled according to the parameter object to be identified.
In step 3022 and step 3023, determining a parameter to be identified by a genetic algorithm to obtain a parameter that meets an error requirement; and obtaining a model of the dynamic reactive power compensation device to be modeled by using the parameters meeting the error requirements.
As can be seen from the above description, the embodiment of the present invention provides a dynamic reactive power compensation device modeling method, which includes obtaining rich measured data obtained by a low voltage and high voltage ride through test of a dynamic reactive power compensation device to be modeled; selecting a reactive power compensation device model matched with the dynamic reactive power compensation device to be modeled from a preset dynamic reactive power compensation device model base based on the measured data; and finally, establishing a model of the dynamic reactive power compensation device to be modeled by utilizing a genetic algorithm and the matched reactive power compensation device model. Specifically, the beneficial effects of the invention are as follows:
(1) the characteristics of the reactive compensation device during low voltage ride through are classified and summarized, part of transient processes are ignored, only the most important characteristics are paid attention to, the most approximate original model can be matched for a new reactive compensation device test curve, the blindness of subsequent identification is reduced, and therefore the identification efficiency is improved.
(2) Aiming at the problem of slow PSCAD simulation, the genetic algorithm is improved, a gene library is established, the genes, fitness functions and evaluation function values of each generation of population are stored through the gene library, when a new population is generated, repeated genes are screened out through comparison and inquiry, the fitness functions in the library are directly read, the function calculation process is skipped, the calculated amount is reduced, and therefore the identification efficiency is improved.
(3) And establishing a reactive compensation device model library, and continuously updating and enriching the reactive compensation device model and the parameter library by a newly identified reactive compensation device warehousing mode.
To further explain the scheme, an 8MW Takara TKSVG-8/10 type reactive power compensation device of a certain wind power plant is selected as a tested object. To provide a specific application example of the dynamic reactive power compensation device modeling method, which specifically includes the following contents, see fig. 9.
This wind-powered electricity generation field totally 6 returns a fan and collects circuit and a set of dynamic reactive power compensator, all inserts booster station 1 number owner and becomes, sends out the circuit through 220kV and merges the electric wire netting into. No. 1 main transformer capacity 100MVA of booster station, model: SZ11-100000/220, rated voltage: 230 ± 8 × 1.25%/36.75 kV, coupling group designation: YN, d 11; short-circuit impedance: 14 percent. The tested dynamic reactive power compensation device is connected to a 1# main transformer through a No. 037 35kV switch. The information related to the dynamic reactive power compensation device is shown in table 3.
TABLE 3
S1: test data is acquired.
Specifically, a low-voltage ride through actual measurement characteristic curve and test data of the dynamic reactive power compensation device to be modeled are obtained through a low-voltage ride through field test and a high-voltage ride through field test; and selecting the terminal voltage of the reactive compensation device to drop to 20% p.u. as a basic working condition for testing, wherein the minimum voltage drop of the three-phase line in the test process is 0.21pu, and the duration is 625 ms. The reactive power of the dynamic reactive power compensation device before the test is 0.99 Qn; from the time when the fault voltage is recovered to be normal, the reactive power of the reactive power compensation device is recovered to 0.99Qn after about 0.04s, and then the reactive power compensation device is horizontally and stably operated before the fault is recovered. The voltage and reactive power changes of the grid-connected point of the reactive power compensation device are shown in fig. 10 to 13.
S2: and carrying out model matching.
According to the fan characteristic subarea shown in fig. 6, the characteristics of the reactive power compensation device in the low-penetration process are (sending, sending and zero). Selecting a known model of the type from a model library of the dynamic reactive power compensation device, taking the parameter type of the known model as a parameter object to be identified of the dynamic reactive power compensation device to be modeled in the embodiment, and taking the parameter of the known model as initial data of the parameter object to be identified of the dynamic reactive power compensation device to be modeled in the embodiment.
S3: and performing parameter identification.
In the aspect of parameter identification, a simulation system is built in the PSCAD, parameters of each electrical device are set according to the measured parameters, and the simulation system is shown in FIG. 14. PSCAD is a professional simulation software of a power electronic system, and is mostly used for new energy simulation modeling at present. It is understood that the simulation model in fig. 14 is expected to be obtained here, but many control parameters in the controller of the model are not determined, and the parameters directly influence the power characteristics of the dynamic reactive power compensation device in the low-voltage and high-voltage ride-through process.
It was found that for the dynamic var compensation apparatus, the parameters that most affect the low voltage, high voltage ride through control characteristics are the parameter sets described in table 2.
S4: modeling is performed by using a genetic algorithm.
The role of the genetic algorithm is: and adjusting the initial data of the parameter object to be identified of the dynamic reactive power compensation device to be modeled of the specific application example according to the actual operation condition to determine the parameter object to be identified of the dynamic reactive power compensation device to be modeled. The identified parameters are applied to the model shown in fig. 14, then the differences between the simulated low-penetration characteristics of the reactive power compensation device and the tested low-penetration and high-penetration characteristics are compared by performing simulation in the PSCAD which is the same as the actual test working condition, when the errors between the simulated low-penetration characteristics and the tested low-penetration and high-penetration characteristics are smaller than a preset threshold value, the obtained model is considered to be accurate enough, and the corresponding parameter group is the parameter group to be identified. Referring to fig. 15 and fig. 16, the genetic algorithm process adopted by the specific application example specifically includes:
s41: and generating a new population according to the parameter object to be identified and the initial parameter of the dynamic reactive power compensation device obtained by matching.
S42: and comparing the population members with population members in a population library, and rejecting repeated population members to reduce the calculated amount.
S43: and calling a simulation model in the PSCAD program, assigning parameters of each population to the simulation model, and performing simulation calculation to obtain a simulation curve corresponding to each quasi-population member.
S44: and calling the actual measurement curve according to a method required by the standard, and calculating the error between the simulation result and the actual measurement result.
S45: and (4) comparing and screening the most excellent gene individuals, and simultaneously storing all population members into a population library to enrich the population library.
S46: if the best individual meets the error requirement, the cycle identification is finished, and if the best individual does not meet the error requirement, a new generation of population members are generated through replication, crossover and mutation, and the identification cycle is restarted.
S47: and for the genes meeting the error requirement, outputting an identification result as a final identification result, and storing the identification result into a model library.
The genetic algorithm is called, the key parameters of the finally obtained model are shown in table 4, and the comparison between the measured curve and the simulation curve is shown in fig. 17 and fig. 18. The average deviation, the average absolute deviation, the maximum deviation of the steady-state interval and the weighted average absolute deviation of the reactive power in each time partition are calculated, and compared with the maximum allowable value of each deviation specified in the standard wind turbine generator low voltage ride through modeling and verification method, and the result is shown in table 5. Therefore, the model specified in the standard of wind turbine generator low voltage ride through modeling and verification method verifies three assessed electrical quantities: the deviation of the test and simulation data of the active power P, the reactive power Q and the reactive current IQ is within the maximum deviation range allowed by the standard.
TABLE 4
TABLE 5
As can be seen from the above description, the embodiment of the present invention provides a dynamic reactive power compensation device modeling method, which includes obtaining rich measured data obtained by a low voltage and high voltage ride through test of a dynamic reactive power compensation device to be modeled; selecting a reactive power compensation device model matched with the dynamic reactive power compensation device to be modeled from a preset dynamic reactive power compensation device model base based on the measured data; and finally, establishing a model of the dynamic reactive power compensation device to be modeled by utilizing a genetic algorithm and the matched reactive power compensation device model. Specifically, the beneficial effects of the invention are as follows:
(1) the characteristics of the reactive compensation device during low voltage ride through are classified and summarized, part of transient processes are ignored, only the most important characteristics are paid attention to, the most approximate original model can be matched for a new reactive compensation device test curve, the blindness of subsequent identification is reduced, and therefore the identification efficiency is improved.
(2) Aiming at the problem of slow PSCAD simulation, the genetic algorithm is improved, a gene library is established, the genes, fitness functions and evaluation function values of each generation of population are stored through the gene library, when a new population is generated, repeated genes are screened out through comparison and inquiry, the fitness functions in the library are directly read, the function calculation process is skipped, the calculated amount is reduced, and therefore the identification efficiency is improved.
(3) And establishing a reactive compensation device model library, and continuously updating and enriching the reactive compensation device model and the parameter library by a newly identified reactive compensation device warehousing mode.
Based on the same inventive concept, the embodiment of the present application further provides a dynamic reactive power compensation device modeling apparatus, which can be used to implement the method described in the above embodiment, such as the following embodiments. Because the principle of solving the problems of the dynamic reactive power compensation device modeling device is similar to that of the dynamic reactive power compensation device modeling method, the implementation of the dynamic reactive power compensation device modeling device can be referred to the implementation of the dynamic reactive power compensation device modeling method, and repeated parts are not repeated. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. While the system described in the embodiments below is preferably implemented in software, implementations in hardware, or a combination of software and hardware are also possible and contemplated.
An embodiment of the present invention provides a specific implementation manner of a dynamic reactive power compensation device modeling apparatus capable of implementing a dynamic reactive power compensation device modeling method, and referring to fig. 19, the dynamic reactive power compensation device modeling apparatus specifically includes the following contents:
the test data acquisition unit 10 is used for acquiring test data of the dynamic reactive power compensation device to be modeled;
the model selecting unit 20 is configured to select a reactive power compensation device model matched with the dynamic reactive power compensation device to be modeled from a preset dynamic reactive power compensation device model library according to the test data;
and the model establishing unit 30 is used for establishing the model of the dynamic reactive power compensation device to be modeled by utilizing a genetic algorithm and the matched reactive power compensation device model.
In an embodiment, the test data obtaining unit is specifically configured to obtain test data of the dynamic reactive power compensation device to be modeled by using a low voltage ride through and high voltage ride through method, where the test data includes: and the boost high-voltage side voltage instantaneous value and the current instantaneous value of the dynamic reactive power compensation device to be modeled are obtained.
In one embodiment, referring to fig. 20, the model selecting unit 20 includes:
a curve calculation module 201, configured to calculate a reactive power characteristic curve and a reactive current characteristic curve according to instantaneous values of the voltage and the current at the high-voltage side of the step-up transformer;
the curve partitioning module 202 is configured to divide the reactive power characteristic curve and the reactive current characteristic curve into three sections, namely, a pre-fault section, a fault section and a post-fault section;
the model type determining module 203 is configured to determine the type and the characteristic of the dynamic reactive power compensation device to be modeled according to respective characteristics of the reactive power characteristic curve and the reactive current characteristic curve in the three intervals;
and the model matching module 204 is configured to select, according to the type and the characteristic, a reactive power compensation device model matched with the dynamic reactive power compensation device to be modeled from a preset dynamic reactive power compensation device model library.
In one embodiment, referring to fig. 21, the model building unit 30 includes:
a parameter object determining module 301, configured to determine a parameter object to be identified of the dynamic reactive power compensation device to be modeled according to the matched reactive power compensation device model;
a first model establishing module 302, configured to establish a model of the dynamic reactive power compensation device to be modeled according to the parameter object to be identified by using a genetic algorithm.
In one embodiment, referring to fig. 22, the model building first module 302 includes:
an initial parameter determining module 3021, configured to use the parameters of the matched reactive power compensation device model as initial parameters of a genetic training model;
a model training module 3022, configured to train a genetic training model using the parameters to generate the parameter object to be identified;
a second model establishing module 3023, configured to establish a model of the dynamic reactive power compensation device to be modeled according to the parameter object to be identified.
As can be seen from the above description, the embodiment of the present invention provides a dynamic reactive power compensation device modeling device, which first obtains rich measured data obtained by a low voltage and high voltage ride through test of a dynamic reactive power compensation device to be modeled; selecting a reactive power compensation device model matched with the dynamic reactive power compensation device to be modeled from a preset dynamic reactive power compensation device model base based on the measured data; and finally, establishing a model of the dynamic reactive power compensation device to be modeled by utilizing a genetic algorithm and the matched reactive power compensation device model. Specifically, the beneficial effects of the invention are as follows:
(1) the characteristics of the reactive compensation device during low voltage ride through are classified and summarized, part of transient processes are ignored, only the most important characteristics are paid attention to, the most approximate original model can be matched for a new reactive compensation device test curve, the blindness of subsequent identification is reduced, and therefore the identification efficiency is improved.
(2) Aiming at the problem of slow PSCAD simulation, the genetic algorithm is improved, a gene library is established, the genes, fitness functions and evaluation function values of each generation of population are stored through the gene library, when a new population is generated, repeated genes are screened out through comparison and inquiry, the fitness functions in the library are directly read, the function calculation process is skipped, the calculated amount is reduced, and therefore the identification efficiency is improved.
(3) And establishing a reactive compensation device model library, and continuously updating and enriching the reactive compensation device model and the parameter library by a newly identified reactive compensation device warehousing mode.
The embodiment of the present application further provides a specific implementation manner of an electronic device, which is capable of implementing all steps in the dynamic reactive power compensation device modeling method in the foregoing embodiment, and referring to fig. 23, the electronic device specifically includes the following contents:
a processor (processor)1201, a memory (memory)1202, a communication interface 1203, and a bus 1204;
the processor 1201, the memory 1202 and the communication interface 1203 complete communication with each other through the bus 1204; the communication interface 1203 is configured to implement information transmission between related devices, such as a server-side device, a power measurement device, and a client device.
The processor 1201 is configured to call a computer program in the memory 1202, and the processor executes the computer program to implement all the steps in the dynamic reactive power compensation device modeling method in the above embodiments, for example, the processor executes the computer program to implement the following steps:
step 100: and acquiring test data of the dynamic reactive power compensation device to be modeled.
Step 200: and selecting a reactive power compensation device model matched with the dynamic reactive power compensation device to be modeled from a preset dynamic reactive power compensation device model library according to the test data.
Step 300: and establishing a model of the dynamic reactive power compensation device to be modeled by utilizing a genetic algorithm and the matched reactive power compensation device model.
As can be seen from the above description, in the electronic device in the embodiment of the present application, first, rich actual measurement data obtained by a low voltage and high voltage ride through test of a dynamic reactive power compensation device to be modeled is obtained; selecting a reactive power compensation device model matched with the dynamic reactive power compensation device to be modeled from a preset dynamic reactive power compensation device model base based on the measured data; and finally, establishing a model of the dynamic reactive power compensation device to be modeled by utilizing a genetic algorithm and the matched reactive power compensation device model. Specifically, the beneficial effects of the invention are as follows:
(1) the characteristics of the reactive compensation device during low voltage ride through are classified and summarized, part of transient processes are ignored, only the most important characteristics are paid attention to, the most approximate original model can be matched for a new reactive compensation device test curve, the blindness of subsequent identification is reduced, and therefore the identification efficiency is improved.
(2) Aiming at the problem of slow PSCAD simulation, the genetic algorithm is improved, a gene library is established, the genes, fitness functions and evaluation function values of each generation of population are stored through the gene library, when a new population is generated, repeated genes are screened out through comparison and inquiry, the fitness functions in the library are directly read, the function calculation process is skipped, the calculated amount is reduced, and therefore the identification efficiency is improved.
(3) And establishing a reactive compensation device model library, and continuously updating and enriching the reactive compensation device model and the parameter library by a newly identified reactive compensation device warehousing mode.
Embodiments of the present application also provide a computer-readable storage medium capable of implementing all steps in the dynamic reactive power compensation device modeling method in the above embodiments, where the computer-readable storage medium stores thereon a computer program, and when the computer program is executed by a processor, the computer program implements all steps of the dynamic reactive power compensation device modeling method in the above embodiments, for example, when the processor executes the computer program, the processor implements the following steps:
step 100: and acquiring test data of the dynamic reactive power compensation device to be modeled.
Step 200: and selecting a reactive power compensation device model matched with the dynamic reactive power compensation device to be modeled from a preset dynamic reactive power compensation device model library according to the test data.
Step 300: and establishing a model of the dynamic reactive power compensation device to be modeled by utilizing a genetic algorithm and the matched reactive power compensation device model.
As can be seen from the above description, the computer-readable storage medium in the embodiment of the present application first obtains rich measured data obtained by a low voltage and high voltage ride through test of the dynamic reactive power compensation device to be modeled; selecting a reactive power compensation device model matched with the dynamic reactive power compensation device to be modeled from a preset dynamic reactive power compensation device model base based on the measured data; and finally, establishing a model of the dynamic reactive power compensation device to be modeled by utilizing a genetic algorithm and the matched reactive power compensation device model. Specifically, the beneficial effects of the invention are as follows:
(1) the characteristics of the reactive compensation device during low voltage ride through are classified and summarized, part of transient processes are ignored, only the most important characteristics are paid attention to, the most approximate original model can be matched for a new reactive compensation device test curve, the blindness of subsequent identification is reduced, and therefore the identification efficiency is improved.
(2) Aiming at the problem of slow PSCAD simulation, the genetic algorithm is improved, a gene library is established, the genes, fitness functions and evaluation function values of each generation of population are stored through the gene library, when a new population is generated, repeated genes are screened out through comparison and inquiry, the fitness functions in the library are directly read, the function calculation process is skipped, the calculated amount is reduced, and therefore the identification efficiency is improved.
(3) And establishing a reactive compensation device model library, and continuously updating and enriching the reactive compensation device model and the parameter library by a newly identified reactive compensation device warehousing mode.
To sum up, the computer-readable storage medium provided by the embodiment of the present invention can support a service provider to perform adaptive offline and online of services according to the availability of its own software and hardware resources, thereby implementing the self-isolation capability of the service provider and ensuring the success rate of the service provider in responding to a service request.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the hardware + program class embodiment, since it is substantially similar to the method embodiment, the description is simple, and the relevant points can be referred to the partial description of the method embodiment.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
Although the present application provides method steps as in an embodiment or a flowchart, more or fewer steps may be included based on conventional or non-inventive labor. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an actual apparatus or client product executes, it may execute sequentially or in parallel (e.g., in the context of parallel processors or multi-threaded processing) according to the embodiments or methods shown in the figures.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (12)
1. A dynamic reactive power compensation device modeling method is characterized by comprising the following steps:
acquiring test data of a dynamic reactive power compensation device to be modeled;
selecting a reactive power compensation device model matched with the dynamic reactive power compensation device to be modeled from a preset dynamic reactive power compensation device model library according to the test data;
and establishing a model of the dynamic reactive power compensation device to be modeled by utilizing a genetic algorithm and the matched reactive power compensation device model.
2. The modeling method for the dynamic reactive power compensation device according to claim 1, wherein the obtaining test data of the dynamic reactive power compensation device to be modeled comprises:
acquiring test data of the dynamic reactive power compensation device to be modeled by using a low voltage ride through and high voltage ride through method, wherein the test data comprises: and the boost high-voltage side voltage instantaneous value and the current instantaneous value of the dynamic reactive power compensation device to be modeled are obtained.
3. The dynamic reactive power compensation device modeling method according to claim 2, wherein the selecting a reactive power compensation device model matching the dynamic reactive power compensation device to be modeled from a preset dynamic reactive power compensation device model library according to the test data comprises:
calculating a reactive power characteristic curve and a reactive current characteristic curve according to instantaneous values of the voltage and the current of the boost transformer high-voltage side;
dividing the reactive power characteristic curve and the reactive current characteristic curve into three sections of before-fault, fault and after-fault;
determining the type and the characteristic of the dynamic reactive power compensation device to be modeled according to the respective characteristics of the reactive power characteristic curve and the reactive current characteristic curve in the three intervals;
and selecting a reactive power compensation device model matched with the dynamic reactive power compensation device to be modeled from a preset dynamic reactive power compensation device model library according to the type and the characteristics.
4. The dynamic reactive compensation device modeling method according to claim 1, wherein the modeling the dynamic reactive compensation device to be modeled using a genetic algorithm and the matched reactive compensation device model comprises:
determining a parameter object to be identified of the dynamic reactive power compensation device to be modeled according to the matched reactive power compensation device model;
and establishing a model of the dynamic reactive power compensation device to be modeled according to the parameter object to be identified by utilizing a genetic algorithm.
5. The dynamic reactive power compensation device modeling method according to claim 4, wherein the modeling of the dynamic reactive power compensation device to be modeled according to the parameter object to be identified by using a genetic algorithm comprises:
taking the parameters of the matched reactive power compensation device model as initial parameters of a genetic training model;
training a genetic training model by using the parameters to generate the parameter object to be identified;
and establishing a model of the dynamic reactive power compensation device to be modeled according to the parameter object to be identified.
6. A dynamic reactive power compensation device modeling apparatus, comprising:
the test data acquisition unit is used for acquiring test data of the dynamic reactive power compensation device to be modeled;
the model selection unit is used for selecting a reactive power compensation device model matched with the dynamic reactive power compensation device to be modeled from a preset dynamic reactive power compensation device model library according to the test data;
and the model establishing unit is used for establishing the model of the dynamic reactive power compensation device to be modeled by utilizing a genetic algorithm and the matched reactive power compensation device model.
7. The dynamic reactive power compensation device modeling apparatus according to claim 6, wherein the test data obtaining unit is specifically configured to obtain test data of the dynamic reactive power compensation device to be modeled by using a low voltage ride through and high voltage ride through method, and the test data includes: and the boost high-voltage side voltage instantaneous value and the current instantaneous value of the dynamic reactive power compensation device to be modeled are obtained.
8. The dynamic reactive power compensation device modeling apparatus of claim 7, wherein the model selection unit comprises:
the curve calculation module is used for calculating a reactive power characteristic curve and a reactive current characteristic curve according to instantaneous values of the voltage and the current of the boost transformer high-voltage side;
the curve partitioning module is used for dividing the reactive power characteristic curve and the reactive current characteristic curve into three sections, namely a section before a fault, a section before the fault and a section after the fault;
the model type determining module is used for determining the type and the characteristics of the dynamic reactive power compensation device to be modeled according to the characteristics of the reactive power characteristic curve and the reactive current characteristic curve in the three intervals;
and the model matching module is used for selecting a reactive power compensation device model matched with the dynamic reactive power compensation device to be modeled from a preset dynamic reactive power compensation device model library according to the type and the characteristics.
9. The dynamic reactive compensation device modeling apparatus of claim 6, wherein the model building unit comprises:
the parameter object determining module is used for determining a parameter object to be identified of the dynamic reactive power compensation device to be modeled according to the matched reactive power compensation device model;
and the model establishing first module is used for establishing a model of the dynamic reactive power compensation device to be modeled according to the parameter object to be identified by utilizing a genetic algorithm.
10. The dynamic reactive compensation device modeling apparatus of claim 9, wherein the model building first module comprises:
the initial parameter determining module is used for taking the parameters of the matched reactive compensation device model as initial parameters of a genetic training model;
the model training module is used for training a genetic training model by using the parameters to generate the parameter object to be identified;
and the model establishing second module is used for establishing a model of the dynamic reactive power compensation device to be modeled according to the parameter object to be identified.
11. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the dynamic reactive compensation apparatus modeling method of any of claims 1 to 5.
12. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the dynamic var compensation apparatus modeling method according to any one of claims 1 to 5.
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