CN105354346A - Wind power unit parameter identification method - Google Patents
Wind power unit parameter identification method Download PDFInfo
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- CN105354346A CN105354346A CN201410415543.XA CN201410415543A CN105354346A CN 105354346 A CN105354346 A CN 105354346A CN 201410415543 A CN201410415543 A CN 201410415543A CN 105354346 A CN105354346 A CN 105354346A
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Abstract
The invention provides a wind power unit parameter identification method. The method comprises: step A, selecting a wind power unit model in time domain simulation software; step B, performing binary coding on various parameters; step C, initializing a population; step D, calculating fitness and evaluation function values of each individual in the current population, storing the fitness and evaluation function values in a first storage unit, selecting out an optimal individual with the maximum fitness, and storing the optimal individual in a second storage unit; performing genetic operation to update the current population and repeatedly performing the step until a stop condition is met; and step E, determining a gene sequence of the optimal individual in the second storage unit as a parameter identification result, wherein in the step D, when the fitness and evaluation function values of each individual are calculated, for each repeated individual, the fitness of the repeated individual is directly read from the first storage unit. According to the method, the fitness of the repeated individual does not need to be repeatedly calculated, so that the redundancy of calculation can be remarkably reduced and the parameter identification efficiency is greatly improved.
Description
Technical field
The present invention relates to technical field of wind power generation, particularly, relate to a kind of Wind turbines parameter identification method.
Background technology
Along with developing rapidly of wind power technology, the installed capacity of China's wind-powered electricity generation increases sharply, and nowadays leaps to No. 1 in the world.The increase of wind-powered electricity generation proportion in China's generating total amount, also more and more outstanding on the impact of power system safety and stability operation and the quality of power supply after making wind-electricity integration.In order to improve the reliability of Wind turbines, ensure the reliability service of the grid-connected rear electrical network of Wind turbines, study large-scale wind power access to the concrete impact of electrical network and solution, just must there be blower fan and wind energy turbine set model and parameter accurately, therefore just propose requirement wind turbine model being carried out to parameter identification.
Build wind turbine model, first need to carry out identification to every design parameter of Wind turbines.Wind turbines partial parameters is provided by producer, but does not clearly provide for other parameter producers.According to the demand that electric system bulk power grid is built, need to set up wind turbine model, therefore need to carry out parameter identification to Wind turbines.
The parameter identification process of Wind turbines refers to selects suitable model to describe various element characteristic and the parameters of Wind turbines in blower fan simulation software, wherein the initial value of parameters is any determined value, then simulate the model selected to this by experiment and apply disturbance, obtain the emulated data curve under disturbance, recorder data curve after the low voltage crossing of Wind turbines tests recorder data curve or grid disturbance in this emulated data curve and reality is contrasted, after numerical value by constantly revising parameters in whole process makes emulated data curve test recorder data curve or grid disturbance with actual low voltage crossing, recorder data curve reaches and overlaps to greatest extent, the numerical value of final parameters is parameter identification result.
The parameter identification process of above-mentioned Wind turbines generally adopts genetic algorithm to carry out the amendment of parameters numerical value.Genetic algorithm is the method adopting similar nature biological heredity, by the selection of simulating nature bound pair biology, produces the effect of the survival of the fittest, the survival of the fittest, and then realizes optimizing.
Traditional operatings of genetic algorithm flow process first carries out binary coding to Wind turbines parameter values to form gene, utilizes gene to form individuality subsequently, then form population by individuality; Formulate corresponding fitness function afterwards and evaluation function carries out functional value calculating to the gene of population; Calculated the highest individuality of fitness in rear selection present age to save as optimum individual, the later successive dynasties all can have corresponding optimum individual to screen preservation mechanism, contemporary optimal base because of with previous generation's optimal base because contrasting, in both, more the superior is saved; Constantly contemporary genes of individuals intersected in process, copy, that the genetic manipulation such as variation produces a new generation is individual, until reach optimization aim or end condition, after iteration terminates, export the optimum individual stored.Because genetic algorithm can avoid nonlinear element on the impact of Wind turbines parameter identification process well, be therefore widely applied.
But the population number of genetic algorithm is general huger, the complexity of fitness function and evaluation function has direct relation with the calculating execution time, when population at individual quantity reaches certain value, the fitness function value of generation population and the calculating of evaluation function value may be promoted to level second even minute level, so repeatedly carry out hundreds of the generation even functional value in several thousand generations to calculate, flow performing process can make the computation process of function consuming time huge because fitness function and evaluation function are complicated and number of parameters is numerous, in addition respectively occur that identical possibility is very large for gene in genetic algorithm, traditional genetic algorithm does not have screening devices to the individuality repeated, the mode of double counting still can be taked to obtain its fitness and evaluation function value, thus cause computation process redundance high, the efficiency of parameter identification is low.
Summary of the invention
Fundamental purpose of the present invention is to provide a kind of Wind turbines parameter identification method, has that computation process redundance is high, the inefficient problem of parameter identification to solve the existing method utilizing genetic algorithm to carry out Wind turbines parameter identification.
To achieve these goals, the invention provides a kind of Wind turbines parameter identification method, comprising:
Steps A, chooses the wind turbine model of element characteristic with Wind turbines to be measured and match parameters in time-domain-simulation software;
Step B, carries out binary coding to parameters, obtains gene order corresponding to described parameters interval;
Step C, based on the multiple individuality of the interval stochastic generation of described gene order as initial population;
Step D, calculate fitness and the evaluation function value of each individuality in current population, by all individualities of current population and fitness thereof and evaluation function value stored in the first storage unit, and the fitness of the maximum adaptation degree of current population and the optimum individual of the second cell stores is compared, if the maximum adaptation degree of current population is greater than the fitness of the optimum individual of described second cell stores, then the optimum individual of described second cell stores is updated to the individuality corresponding to maximum adaptation degree of current population, execution copies, crossover and mutation three kinds of genetic manipulations upgrade current population after repeated execution of steps D, until meet end condition, wherein, when step D first time performs, current population is described initial population, the individuality of optimum individual corresponding to the maximum adaptation degree of described initial population of described second cell stores,
Step e, is defined as the parameter identification result of described Wind turbines to be measured by the gene order of the optimum individual of described second cell stores;
The fitness of each individuality and evaluation function value in the current population of calculating described in described step D, specifically comprise:
Individual for each in current population, search the individuality whether existing in described first storage unit and there is with this individuality homologous genes sequence; If have, then this individuality is defined as repetition individuality, otherwise, this individuality is defined as new individuality;
Each is repeated individual, from described first storage unit, directly read the fitness of this repetition individuality;
For each new individuality, the gene order of this new individuality is inputed in described time-domain-simulation software as the parameter value of wind turbine model, utilize described time-domain-simulation software to apply fault disturbance to obtain emulated data curve to described wind turbine model simultaneously, the deviation of the physical fault recorder data curve of described emulated data curve and described Wind turbines to be measured is defined as the evaluation function value of this new individuality; In conjunction with reading all evaluation function values repeating individuality from described first storage unit, obtaining the evaluation function value of all individualities in current population, then determining the maximal value of the evaluation function value of all individualities in current population; The fitness of this new individuality is defined as the absolute value of the difference of the maximal value of described evaluation function value and the evaluation function value of this new individuality.
By means of technique scheme, the present invention carries out in the process of Wind turbines parameter identification utilizing genetic algorithm, by the gene order of each individuality that calculates and fitness thereof stored in the first storage unit, and first judge whether stored the repetition with homologous genes sequence in the first storage unit when the fitness of subsequent calculations individuality individual, if have, then directly read fitness, need not calculate again, otherwise calculate again, compared to prior art, the present invention adds and screens the individual mechanism whether repeated in genetic algorithm, due to need not double counting fitness again to the individuality repeated, therefore computing redundancy degree can significantly be reduced, greatly improve parameter identification efficiency, save the parameter identification time.
Accordingly, the present invention also provides a kind of Wind turbines parameter identification method, comprising:
The wind turbine model of element characteristic with Wind turbines to be measured and match parameters is chosen in time-domain-simulation software;
Respectively parameter identification is carried out to electric network fault stage of described Wind turbines to be measured, power Restoration stage and recovering state stage; Wherein, described electric network fault stage, power Restoration stage and recovering state stage are under time domain, carry out division to the physical fault recorder data curve of described Wind turbines to be measured in advance to obtain; The described electric network fault stage originates in the moment that in described physical fault recorder data curve, voltage step formula reduces, and ends at the moment that voltage step formula is gone up; Described power Restoration stage originates in the moment that in described physical fault recorder data curve, voltage step formula is gone up, and ends at the moment of power first time rise to original power value; The described recovering state stage originates in the moment that in described physical fault recorder data curve, voltage step formula is gone up, and ends at power stability in the moment of original power value;
Described electric network fault stage, power Restoration stage and the parameter identification result in recovering state stage are combined, is defined as the parameter identification result of described Wind turbines to be measured;
Wherein, respectively parameter identification is carried out to electric network fault stage of described Wind turbines to be measured, power Restoration stage and recovering state stage, specifically comprise described electric network fault stage, power Restoration stage and the every one-phase in the recovering state stage, all perform following steps:
Steps A, determines the parameters that this stage relates to, for this stage distributes one first storage unit and one second storage unit;
Step B, carries out binary coding to the described parameters that this stage relates to, and obtains gene order corresponding to described parameters interval;
Step C, based on the multiple individuality of the interval stochastic generation of described gene order as initial population;
Step D, calculate fitness and the evaluation function value of each individuality in current population, by all individualities of current population and fitness thereof and evaluation function value stored in the first storage unit corresponding to this stage, and the fitness of the optimum individual of the second corresponding with this stage for the maximum adaptation degree of current population cell stores is compared, if the maximum adaptation degree of current population is greater than the fitness of the optimum individual of described second cell stores, then the optimum individual of described second cell stores is updated to the individuality corresponding to maximum adaptation degree of current population, execution copies, crossover and mutation three kinds of genetic manipulations repeat this step D after upgrading current population, until meet end condition, wherein, when step D first time performs, current population is described initial population, the individuality of optimum individual corresponding to the maximum adaptation degree of described initial population of described second cell stores,
Step e, is defined as the parameter identification result in this stage by the gene order of the optimum individual of described second cell stores;
The fitness of each individuality and evaluation function value in the current population of calculating described in described step D, specifically comprise:
Individual for each in current population, search in the first storage unit corresponding to this stage whether there is the individuality with this individuality with homologous genes sequence; If have, then this individuality is defined as repetition individuality, otherwise, this individuality is defined as new individuality;
Each is repeated individual, from described first storage unit, directly read the fitness of this repetition individuality;
For each new individuality, the gene order of this new individuality is inputed in described time-domain-simulation software as the parameter value of wind turbine model, utilize described time-domain-simulation software to apply fault disturbance to obtain emulated data curve to described wind turbine model simultaneously, the deviation of described physical fault recorder data curved portion corresponding with this stage for described emulated data curve is defined as the evaluation function value of this new individuality; In conjunction with reading all evaluation function values repeating individuality from described first storage unit, obtaining the evaluation function value of all individualities in current population, then determining the maximal value of the evaluation function value of all individualities in current population; The fitness of this new individuality is defined as the absolute value of the difference of the maximal value of described evaluation function value and the evaluation function value of this new individuality.
By means of such scheme, the present invention not only adds and screens the individual mechanism whether repeated in genetic algorithm, and by physical fault recorder data curve is divided into different phase, genetic algorithm is adopted to carry out parameter identification to different phase respectively, comprehensively determine the univers parameter identification result of Wind turbines again, compared to prior art, the present invention has taken into full account the difference condition that physical fault different phase Wind turbines parameter shows, respectively identification has been carried out to the parameter that different phase relates to, improve identification precision, make identification result more credible.
Accompanying drawing explanation
In order to be illustrated more clearly in the embodiment of the present invention or technical scheme of the prior art, below the accompanying drawing used required in describing embodiment is briefly described, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skill in the art, under the prerequisite not paying creative work, other accompanying drawing can also be obtained according to these accompanying drawings.
Fig. 1 is the Wind turbines parameter identification method schematic flow sheet that the embodiment of the present invention one provides;
Fig. 2 is the fitness of each individuality of calculating and the schematic flow sheet of evaluation function value that the embodiment of the present invention one provides;
Fig. 3 is the Wind turbines parameter identification method schematic flow sheet that the embodiment of the present invention two provides;
Fig. 4 be the embodiment of the present invention two provide carry out the schematic flow sheet of parameter identification respectively for the different faults stage;
Fig. 5 is the fitness of each individuality of calculating and the schematic flow sheet of evaluation function value that the embodiment of the present invention two provides;
Fig. 6 be the embodiment of the present invention two provide low voltage crossing is tested the diagram that recorder data curve is divided into electric network fault stage, power Restoration stage and recovering state stage three phases.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, be clearly and completely described the technical scheme in the embodiment of the present invention, obviously, described embodiment is only the present invention's part embodiment, instead of whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art, not making the every other embodiment obtained under creative work prerequisite, belong to the scope of protection of the invention.
Embodiment one
The present embodiment provides a kind of Wind turbines parameter identification method, and as shown in Figure 1, the method comprises:
Step S11, chooses the wind turbine model of element characteristic with Wind turbines to be measured and match parameters in time-domain-simulation software.
Concrete, the wind turbine model selected by this step should be able to characterize various element characteristic and the parameters relation of Wind turbines to be measured.
Preferably, this step can choose suitable wind turbine model in BPA power system computation analysis software or DigSILENT electric power analysis software.
Step S12, carries out binary coding to parameters, obtains gene order corresponding to described parameters interval.
Concrete, this step can adopt traditional genetic algorithm, carry out binary coding according to the empirical value variation range of the every parameter of Wind turbines, obtain gene order corresponding to parameters interval, each gene order in this interval represents an occurrence of this parameter.
Such as, Wind turbines has n parameter, is designated as K1 respectively, K2, K3 ... Kn, each parameter adopts the binary number representation of different length according to the difference of its empirical value variation range, and the empirical value variation range of such as parameter K1 is [0,3], burst length is 3, if require that parameters precision to be accurate to after radix point six, so need with 22 binary representations, circular is as follows:
[0,3] need to be divided into 3 × 1000000=3000000 isometric interval, so due to 2097152=2
21<3000000<2
22=4194304, therefore adopt binary representation, just must can contain whole empirical value variation range with 22 scale-of-two.
According to said method, carry out binary numeral conversion to each parameter, suppose that the n-th parameter needs Mn binary number representation, body needs to represent with M1+M2+M3+M4+...+Mn bit so one by one.The front M1 bit representation parameter K1 of the binary number of such individuality, M2 bit representation parameter K2 afterwards, completes the gene code of body one by one by that analogy.
Step S13, based on the multiple individuality of the interval stochastic generation of described gene order as initial population.
Concrete, this step is random selecting gene order (being equivalent to, in the empirical value variation range of parameter, determine the occurrence of parameter at random) in the gene order interval that each parameter is corresponding; After gene order (being equivalent to determine at random the occurrence of each parameter) is chosen to all stochastic parameters, can form body (being equivalent to the occurrence of each parameter determined at random to combine) one by one by the gene order of these random selecting of combination again, the gene order of each individuality is the set all stochastic parameters being chosen to occurrence.
The individual amount of initial population and the individual amount of initial stochastic generation, such as, the individual amount of initial population is 100, i.e. initial stochastic generation 100 individualities.The individual amount of initial population should be arranged according to actual needs, excessive, and calculated amount can be caused to increase, too small, and may need to produce more population algebraically could meet end condition.
Step S14, calculate fitness and the evaluation function value of each individuality in current population, by all individualities of current population and fitness thereof and evaluation function value stored in the first storage unit, and the fitness of the maximum adaptation degree of current population and the optimum individual of the second cell stores is compared, if the maximum adaptation degree of current population is greater than the fitness of the optimum individual of described second cell stores, then the optimum individual of described second cell stores is updated to the individuality corresponding to maximum adaptation degree of current population, execution copies, crossover and mutation three kinds of genetic manipulations upgrade current population after repeated execution of steps S14, until meet end condition, wherein, when step S14 first time performs, current population is described initial population, the individuality of optimum individual corresponding to the maximum adaptation degree of described initial population of described second cell stores.
Concrete, end condition can be designed as population recruitment algebraically and reaches a certain preset value, and such as, end condition is for stop when generation the 500th generation population; In addition, the fitness that end condition also can be designed as optimum individual reaches a certain preset value.
Perform in genetic manipulation process, the realization of reproduction process can in the following way: whole population regards a disk entirety as, press its fitness size to each individuality is its allocation space on disk, random rotary disk afterwards, the individuality of pointed is and is replicated and enters follow-on individuality.
The realization of crossover process is preset value based on crossover probability PC, PC, and before intersecting, first generate the floating number between a 0-1 to each individuality, the individuality that this floating number is less than PC intersects.Carry out forked working to the individual random pair that will intersect chosen, the concrete gene location of intersection is determined by generating a random number.
The realization of mutation process is based on mutation probability Pm, Pm is preset value, due to variation be for gene position, each gene likely makes a variation, based on this to the floating number between each gene stochastic generation 0-1 of each individuality, the gene position that this floating number is less than Pm makes a variation.
Step S15, is defined as the parameter identification result of described Wind turbines to be measured by the gene order of the optimum individual of described second cell stores.
As shown in Figure 2, the fitness of each individuality and evaluation function value in the current population of the calculating described in described step S14, specifically comprise:
Step S141, individual for each in current population, search the individuality whether existing in described first storage unit and there is with this individuality homologous genes sequence; If have, then this individuality is defined as repetition individuality, otherwise, this individuality is defined as new individuality;
Step S142, repeats for each individual, from described first storage unit, directly reads the fitness of this repetition individuality; For each new individuality, the gene order of this new individuality is inputed in described time-domain-simulation software as the parameter value of wind turbine model, utilize described time-domain-simulation software to apply fault disturbance to obtain emulated data curve to described wind turbine model simultaneously, the deviation of the physical fault recorder data curve of described emulated data curve and described Wind turbines to be measured is defined as the evaluation function value of this new individuality; In conjunction with reading all evaluation function values repeating individuality from described first storage unit, obtaining the evaluation function value of all individualities in current population, then determining the maximal value of the evaluation function value of all individualities in current population; The fitness of this new individuality is defined as the absolute value of the difference of the maximal value of described evaluation function value and the evaluation function value of this new individuality.
Above-mentioned steps S14-S15 adopts genetic algorithm to carry out in the process of parameter identification, the gene order of each individuality is inputed in time-domain-simulation software by the occurrence set as all parameters of wind turbine model, utilize time-domain-simulation software to apply fault disturbance to wind turbine model simultaneously, obtain emulated data curve; Whole process constantly produces new individuality by genetic algorithm, be equivalent to the occurrence constantly revising each parameter of wind turbine model, thus obtaining the emulated data curve of constantly correction, overall goals is that the matching degree of emulated data curve and the physical fault recorder data curve making to obtain is become better and better.
In order to reach this target, the present embodiment is using the deviation of emulated data curve and physical fault recorder data curve as evaluation function value, and evaluation function value is larger, illustrates that the matching degree of emulated data curve and physical fault recorder data curve is poorer, calculate the evaluation function value of all individualities for every generation population after, determine maximal value wherein, the individuality (hereinafter referred to as the poorest individuality) that in the physical significance of this maximal value to be corresponding individuality be this population, the matching degree of emulated data curve and physical fault recorder data curve is the poorest in generation, using the absolute value of the evaluation function value of other individualities and the difference of this maximal value as fitness, fitness is less, illustrate with the evaluation function value of the poorest individuality more close, the matching degree of the emulated data curve obtained and physical fault recorder data curve is also poorer, fitness is larger, illustrate and differ larger with the evaluation function value of the poorest individuality, the matching degree of emulated data curve and physical fault recorder data curve is better, that is, fitness embodies the matching degree of emulated data curve and physical fault recorder data curve, and fitness is larger, and emulated data curve is described more close to physical fault recorder data curve, matching degree is better.For every generation population, select the individuality that wherein fitness is maximum, itself and the optimum individual stored are compared, using individuality larger for fitness in both as new optimum individual, thus obtain the individuality that in all generations population, fitness is maximum, the physical significance of this optimum individual is that the matching degree of emulated data curve and the physical fault recorder data curve its gene order obtained as parameter occurrence input time-domain-simulation software is the highest, that is, the gene order of optimum individual is closest to the parameter actual value of Wind turbines to be measured.The gene order of the optimum individual obtained when meeting end condition can be defined as the parameter identification result of Wind turbines to be measured.
Owing to having copy type in genetic manipulation, therefore some cognition directly copies becomes of future generation, make different from the individuality (namely repeating individuality) occurring having homologous genes sequence in population, and crossover and mutation also likely produces the individuality (namely repeating individuality) with former generation with homologous genes sequence, because the fitness repeating individuality is identical, if therefore still carry out fitness calculating again to repetition individuality, computing redundancy degree will be there is high, the inefficient problem of identification.Consider these, each can individual and fitness and evaluation function value preserve by the present embodiment in step S14, when spending for subsequent calculations ideal adaptation, whether inquiry has repetition individuality to occur, individual for repetition, can directly call the fitness preserved, and need not double counting again, thus decrease the computing redundancy degree of Wind turbines parameter identification process, improve parameter identification efficiency.
In order to carry out comprehensively identification to the parameters involved by Wind turbines to be measured, when implementing the present embodiment, should select and can cover parameters, and the physical fault recorder data curve of energy well-characterized parameters, preferably, the present embodiment can adopt low voltage crossing to test recorder data curve as physical fault recorder data curve, or, grid disturbance rear fan recorder data curve also can be adopted as physical fault recorder data curve.
It should be noted that; when implementing of the present invention; can go to select suitable physical fault recorder data curve according to actual conditions; to reach the object of comprehensive identification Wind turbines parameter; the present invention does not do concrete restriction to adopted physical fault recorder data curve type; namely these are only specific embodiments of the invention; the protection domain be not intended to limit the present invention; within the spirit and principles in the present invention all, select the physical fault recorder data curve of other any type all should be included within protection scope of the present invention.Such as, except above-mentioned low voltage crossing test recorder data curve and grid disturbance rear fan recorder data curve, can also select to adopt wind energy turbine set near-end power network line (three-phase, two-phase, single-phase) short trouble recorder data curve.
Method according to Fig. 1, the present embodiment has added to screen and has repeated individual and new individual step in the middle of the calculating of ideal adaptation degree, improve parameter identification efficiency to a certain extent, but Calculation Estimation functional value and fitness are still needed for new individuality, when individual number new in population is larger, the calculated amount of evaluation function value and fitness and computing velocity will become the key factor affecting identification efficiency, consider this point, in a kind of preferred embodiment, the present embodiment can adopt the new individual fitness of parallel computation mode computation, namely to the new individuality in every generation population, the calculation task of its evaluation function value and fitness is distributed to fifty-fifty the multi-process executed in parallel of multiple stage computing machine or multi-core computer, such as, adopt the parallel computation pattern under the Master-Client management mode of multiple stage computing machine, management is performed as Master by a machine, the calculation task of new individual evaluation functional value and fitness is distributed to multiple stage Client computing machine executed in parallel fifty-fifty, result of calculation sends Master to again.Owing to have employed parallel computation pattern to calculate new individual fitness and evaluation function value, compared to prior art, the present invention substantially increases computing velocity, thus further increases parameter identification efficiency.
Embodiment two
When Wind turbines is in physical fault process, its operating voltage and electric current may occur very significantly to change in time, the duty change that this different timetable reveals is actual is that failure phase is in the performance not relating to different parameters in the same time, in this case, the parameter identification carried out due to method shown in Fig. 1 is with whole piece physical fault recorder data curve as a reference, its identification result will be no longer accurate, consider this point, the present embodiment provides another kind of Wind turbines parameter identification method, as shown in Figure 3, comprising:
Step S31, chooses the wind turbine model of element characteristic with Wind turbines to be measured and match parameters in time-domain-simulation software.
Concrete, the wind turbine model selected by this step should be able to characterize various element characteristic and the parameters relation of Wind turbines to be measured.
Step S32, carries out parameter identification respectively to electric network fault stage, power Restoration stage and recovering state stage.
Wherein, described electric network fault stage, power Restoration stage and recovering state stage are under time domain, carry out division to the physical fault recorder data curve of described Wind turbines to be measured in advance to obtain; The described electric network fault stage originates in the moment that in described physical fault recorder data curve, voltage step formula reduces, and ends at the moment that voltage step formula is gone up; Described power Restoration stage originates in the moment that in described physical fault recorder data curve, voltage step formula is gone up, and ends at the moment of power first time rise to original power value (performance number namely before fault generation); The described recovering state stage originates in the moment that in described physical fault recorder data curve, voltage step formula is gone up, and ends at power stability in the moment of original power value.
When this step has taken into full account that Wind turbines is in physical fault may there is the situation of larger change in its duty in time, according to the feature that the Different periods Wind turbines duty of fault changes, physical fault recorder data curve is become the electric network fault stage according to temporal partitioning, power Restoration stage and recovering state stage three phases, wherein, the feature in electric network fault stage is that blower voltage can reduce from raw voltage values (magnitude of voltage namely before fault generation) in phase step type, continue for some time after being down to minimum (in such as low voltage crossing test, minimum can be down to 20% of rated voltage), while blower voltage reduces, power of fan also reduces in same trend, and continue for some time at minimum, the feature of power Restoration stage be power of fan from minimum bottom out, and whole power Restoration stage is in rise state always, until go up to original power value, the feature in recovering state stage is that blower voltage gos up to after raw voltage values, a wave process may be had, fluctuating range reduces until stable gradually, this stage power of fan returns to original power value gradually from minimum, and through the wave process that an amplitude reduces gradually, be finally stable at original power value.
Each stage all has the duty feature being different from other stages above, this different duty feature causes just because of the parameter difference involved by each stage, based on this, the present embodiment carries out independently parameter identification respectively to three phases, identification process is no longer with whole piece physical fault recorder data curve for reference data, but with the curved portion of each stage difference correspondence for reference data, therefore, identification result tallies with the actual situation more, and accuracy is higher.
Low voltage crossing is tested the diagram that recorder data curve is divided into electric network fault stage, power Restoration stage and recovering state stage three phases by Fig. 6: electric network fault stage initial time is fault start time t
0, in the moment of also i.e. voltage step formula reduction, the end time in electric network fault stage is the moment t that voltage step formula is gone up
1; Voltage step rise moment t
1being the initial time of power Restoration stage, is also simultaneously the initial time in recovering state stage, and the performance number p first time after fault in rejuvenation reaches original power value p before fault
0moment t
2for the finish time of power Restoration stage; The recovering state stage comprises the stage that whole power Restoration stage and power tend towards stability after fluctuation, fixed integer (as 20) the individual industrial-frequency alternating current cycle (0.02 second) is got from the end time of power Restoration stage, a fixed integer end cycle point regards as recovering state procedure ends, is also recovering state period expires moment t
3.
Step S33, combines described electric network fault stage, power Restoration stage and the parameter identification result in recovering state stage, is defined as the parameter identification result of described Wind turbines to be measured.
As shown in Figure 4, parameter identification is carried out for electric network fault stage, power Restoration stage and recovering state stage respectively in above-mentioned steps S32, specifically comprise described electric network fault stage, power Restoration stage and the every one-phase in the recovering state stage, all perform following steps:
Step S321, determines the parameters that this stage relates to, for this stage distributes one first storage unit and one second storage unit.
Concrete, parameter involved by each stage is different, therefore first need to determine the parameters involved by the current identification stage, and because needs carry out independently parameter identification process to each stage, in order to be independent of each other, the present embodiment when carrying out parameter identification to every one-phase, for this stage distributes separately one first storage unit and one second storage unit.
Step S322, carries out binary coding to the parameters that this stage relates to, and obtains gene order corresponding to described parameters interval.
Step S323, based on the multiple individuality of the interval stochastic generation of described gene order as initial population.
Step S324, calculate fitness and the evaluation function value of each individuality in current population, by all individualities of current population and fitness thereof and evaluation function value stored in the first storage unit corresponding to this stage, and the fitness of the optimum individual of the second corresponding with this stage for the maximum adaptation degree of current population cell stores is compared, if the maximum adaptation degree of current population is greater than the fitness of the optimum individual of described second cell stores, then the optimum individual of described second cell stores is updated to the individuality corresponding to maximum adaptation degree of current population, execution copies, crossover and mutation three kinds of genetic manipulations repeat this step S324 after upgrading current population, until meet end condition, wherein, when step S324 first time performs, current population is described initial population, the individuality of optimum individual corresponding to the maximum adaptation degree of described initial population of described second cell stores.
Step S325, is defined as the parameter identification result in this stage by the gene order of the optimum individual of described second cell stores.
As shown in Figure 5, the fitness of each individuality and evaluation function value in the current population of the calculating described in described step S324, specifically comprise:
Step S3241, individual for each in current population, search in the first storage unit corresponding to this stage whether there is the individuality with this individuality with homologous genes sequence; If have, then this individuality is defined as repetition individuality, otherwise, this individuality is defined as new individuality.
Step S3242, repeats for each individual, from described first storage unit, directly reads the fitness of this repetition individuality; For each new individuality, the gene order of this new individuality is inputed in described time-domain-simulation software as the parameter value of wind turbine model, utilize described time-domain-simulation software to apply fault disturbance to obtain emulated data curve to described wind turbine model simultaneously, the deviation of described physical fault recorder data curved portion corresponding with this stage for described emulated data curve is defined as the evaluation function value of this new individuality; In conjunction with reading all evaluation function values repeating individuality from described first storage unit, obtaining the evaluation function value of all individualities in current population, then determining the maximal value of the evaluation function value of all individualities in current population; The fitness of this new individuality is defined as the absolute value of the difference of the maximal value of described evaluation function value and the evaluation function value of this new individuality.
Concrete, step S12-S15 in the embodiment of above step S322-S325 and embodiment one is similar, difference is, for every one-phase, individual and the evaluation function value of first cell stores that this stage need be adopted corresponding and fitness, and the second cell stores optimum individual adopting this stage corresponding, and, adopt the reference of deviation as Calculation Estimation functional value of this stage curved portion of correspondence in physical fault recorder data curve.
Compared to the method that embodiment one provides, the present embodiment not only adds in genetic algorithm screens the individual mechanism whether repeated, and by physical fault recorder data curve is divided into different phase, genetic algorithm is adopted to carry out parameter identification to different phase respectively, comprehensively determine the univers parameter identification result of Wind turbines again, compared to prior art, the method has taken into full account the difference condition that physical fault different phase Wind turbines parameter shows, respectively identification has been carried out to the parameter that different phase relates to, improve identification precision, make identification result more credible.
In order to carry out comprehensively identification to the parameters involved by Wind turbines to be measured, when implementing the present embodiment, should select and can cover parameters, and the physical fault recorder data curve of energy well-characterized parameters, preferably, the method that the present embodiment provides can adopt low voltage crossing to test recorder data curve as physical fault recorder data curve, also grid disturbance rear fan recorder data curve can be adopted as physical fault recorder data curve, during concrete execution, the electric network fault stage need be become according to temporal partitioning to physical fault recorder data curve, power Restoration stage and power system restoration stage three phases.
It should be noted that; when implementing of the present invention; can go to select suitable physical fault recorder data curve according to actual conditions; to reach the object of comprehensive identification Wind turbines parameter; the present invention does not do concrete restriction to adopted physical fault recorder data curve type; namely these are only specific embodiments of the invention; the protection domain be not intended to limit the present invention; within the spirit and principles in the present invention all, select the physical fault recorder data curve of other any type all should be included within protection scope of the present invention.Such as, except above-mentioned low voltage crossing test recorder data curve and grid disturbance rear fan recorder data curve, can also select to adopt wind energy turbine set near-end power network line (three-phase, two-phase, single-phase) short trouble recorder data curve.
The method that the present embodiment provides has added to screen and has repeated individual and new individual step in the middle of the calculating of ideal adaptation degree, improve parameter identification efficiency to a certain extent, but Calculation Estimation functional value and fitness are still needed for new individuality, when individual number new in population is larger, the calculated amount of evaluation function value and fitness and computing velocity will become the key factor affecting identification efficiency, consider this point, in a kind of preferred embodiment, the method that the present embodiment provides can adopt the new individual fitness of parallel computation mode computation, namely to the new individuality in every generation population, the calculation task of its evaluation function value and fitness is distributed to fifty-fifty the multi-process executed in parallel of multiple stage computing machine or multi-core computer, accordingly, improve the computing velocity of new ideal adaptation degree and evaluation function value, thus further increase parameter identification efficiency.
Above-described specific embodiment; object of the present invention, technical scheme and beneficial effect are further described; be understood that; the foregoing is only specific embodiments of the invention; the protection domain be not intended to limit the present invention; within the spirit and principles in the present invention all, any amendment made, equivalent replacement, improvement etc., all should be included within protection scope of the present invention.
Claims (6)
1. a Wind turbines parameter identification method, is characterized in that, comprising:
Steps A, chooses the wind turbine model of element characteristic with Wind turbines to be measured and match parameters in time-domain-simulation software;
Step B, carries out binary coding to parameters, obtains gene order corresponding to described parameters interval;
Step C, based on the multiple individuality of the interval stochastic generation of described gene order as initial population;
Step D, calculate fitness and the evaluation function value of each individuality in current population, by all individualities of current population and fitness thereof and evaluation function value stored in the first storage unit, and the fitness of the maximum adaptation degree of current population and the optimum individual of the second cell stores is compared, if the maximum adaptation degree of current population is greater than the fitness of the optimum individual of described second cell stores, then the optimum individual of described second cell stores is updated to the individuality corresponding to maximum adaptation degree of current population, execution copies, crossover and mutation three kinds of genetic manipulations upgrade current population after repeated execution of steps D, until meet end condition, wherein, when step D first time performs, current population is described initial population, the individuality of optimum individual corresponding to the maximum adaptation degree of described initial population of described second cell stores,
Step e, is defined as the parameter identification result of described Wind turbines to be measured by the gene order of the optimum individual of described second cell stores;
The fitness of each individuality and evaluation function value in the current population of calculating described in described step D, specifically comprise:
Individual for each in current population, search the individuality whether existing in described first storage unit and there is with this individuality homologous genes sequence; If have, then this individuality is defined as repetition individuality, otherwise, this individuality is defined as new individuality;
Each is repeated individual, from described first storage unit, directly read the fitness of this repetition individuality;
For each new individuality, the gene order of this new individuality is inputed in described time-domain-simulation software as the parameter value of wind turbine model, utilize described time-domain-simulation software to apply fault disturbance to obtain emulated data curve to described wind turbine model simultaneously, the deviation of the physical fault recorder data curve of described emulated data curve and described Wind turbines to be measured is defined as the evaluation function value of this new individuality; In conjunction with reading all evaluation function values repeating individuality from described first storage unit, obtaining the evaluation function value of all individualities in current population, then determining the maximal value of the evaluation function value of all individualities in current population; The fitness of this new individuality is defined as the absolute value of the difference of the maximal value of described evaluation function value and the evaluation function value of this new individuality.
2. Wind turbines parameter identification method according to claim 1, is characterized in that, described physical fault recorder data curve is low voltage crossing test recorder data curve, or, grid disturbance rear fan recorder data curve.
3. Wind turbines parameter identification method according to claim 1, is characterized in that, adopts new individual fitness described in parallel computation mode computation.
4. a Wind turbines parameter identification method, is characterized in that, comprising:
The wind turbine model of element characteristic with Wind turbines to be measured and match parameters is chosen in time-domain-simulation software;
Respectively parameter identification is carried out to electric network fault stage of described Wind turbines to be measured, power Restoration stage and recovering state stage; Wherein, described electric network fault stage, power Restoration stage and recovering state stage are under time domain, carry out division to the physical fault recorder data curve of described Wind turbines to be measured in advance to obtain; The described electric network fault stage originates in the moment that in described physical fault recorder data curve, voltage step formula reduces, and ends at the moment that voltage step formula is gone up; Described power Restoration stage originates in the moment that in described physical fault recorder data curve, voltage step formula is gone up, and ends at the moment of power first time rise to original power value; The described recovering state stage originates in the moment that in described physical fault recorder data curve, voltage step formula is gone up, and ends at power stability in the moment of original power value;
Described electric network fault stage, power Restoration stage and the parameter identification result in recovering state stage are combined, is defined as the parameter identification result of described Wind turbines to be measured;
Wherein, respectively parameter identification is carried out to electric network fault stage of described Wind turbines to be measured, power Restoration stage and recovering state stage, specifically comprise described electric network fault stage, power Restoration stage and the every one-phase in the recovering state stage, all perform following steps:
Steps A, determines the parameters that this stage relates to, for this stage distributes one first storage unit and one second storage unit;
Step B, carries out binary coding to the described parameters that this stage relates to, and obtains gene order corresponding to described parameters interval;
Step C, based on the multiple individuality of the interval stochastic generation of described gene order as initial population;
Step D, calculate fitness and the evaluation function value of each individuality in current population, by all individualities of current population and fitness thereof and evaluation function value stored in the first storage unit corresponding to this stage, and the fitness of the optimum individual of the second corresponding with this stage for the maximum adaptation degree of current population cell stores is compared, if the maximum adaptation degree of current population is greater than the fitness of the optimum individual of described second cell stores, then the optimum individual of described second cell stores is updated to the individuality corresponding to maximum adaptation degree of current population, execution copies, crossover and mutation three kinds of genetic manipulations repeat this step D after upgrading current population, until meet end condition, wherein, when step D first time performs, current population is described initial population, the individuality of optimum individual corresponding to the maximum adaptation degree of described initial population of described second cell stores,
Step e, is defined as the parameter identification result in this stage by the gene order of the optimum individual of described second cell stores;
The fitness of each individuality and evaluation function value in the current population of calculating described in described step D, specifically comprise:
Individual for each in current population, search in the first storage unit corresponding to this stage whether there is the individuality with this individuality with homologous genes sequence; If have, then this individuality is defined as repetition individuality, otherwise, this individuality is defined as new individuality;
Each is repeated individual, from described first storage unit, directly read the fitness of this repetition individuality;
For each new individuality, the gene order of this new individuality is inputed in described time-domain-simulation software as the parameter value of wind turbine model, utilize described time-domain-simulation software to apply fault disturbance to obtain emulated data curve to described wind turbine model simultaneously, the deviation of described physical fault recorder data curved portion corresponding with this stage for described emulated data curve is defined as the evaluation function value of this new individuality; In conjunction with reading all evaluation function values repeating individuality from described first storage unit, obtaining the evaluation function value of all individualities in current population, then determining the maximal value of the evaluation function value of all individualities in current population; The fitness of this new individuality is defined as the absolute value of the difference of the maximal value of described evaluation function value and the evaluation function value of this new individuality.
5. Wind turbines parameter identification method according to claim 4, is characterized in that, described physical fault recorder data curve is low voltage crossing test recorder data curve, or, grid disturbance rear fan recorder data curve.
6. Wind turbines parameter identification method according to claim 4, is characterized in that, adopts new individual fitness described in parallel computation mode computation.
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