CN113759333B - Wind turbine multipath echo jiggle parameter estimation method based on whale optimization algorithm - Google Patents
Wind turbine multipath echo jiggle parameter estimation method based on whale optimization algorithm Download PDFInfo
- Publication number
- CN113759333B CN113759333B CN202110793231.2A CN202110793231A CN113759333B CN 113759333 B CN113759333 B CN 113759333B CN 202110793231 A CN202110793231 A CN 202110793231A CN 113759333 B CN113759333 B CN 113759333B
- Authority
- CN
- China
- Prior art keywords
- whale
- wind turbine
- parameters
- individual
- estimated
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 241000283153 Cetacea Species 0.000 title claims abstract description 70
- 238000004422 calculation algorithm Methods 0.000 title claims abstract description 50
- 238000000034 method Methods 0.000 title claims abstract description 35
- 238000005457 optimization Methods 0.000 title claims abstract description 34
- 230000007246 mechanism Effects 0.000 claims description 9
- 239000013598 vector Substances 0.000 claims description 8
- 230000008602 contraction Effects 0.000 claims description 5
- 230000010354 integration Effects 0.000 claims description 2
- 238000005516 engineering process Methods 0.000 abstract 1
- 238000004088 simulation Methods 0.000 description 10
- 230000006399 behavior Effects 0.000 description 8
- 238000004458 analytical method Methods 0.000 description 7
- 230000006870 function Effects 0.000 description 6
- 238000004364 calculation method Methods 0.000 description 5
- 239000003795 chemical substances by application Substances 0.000 description 5
- 238000002592 echocardiography Methods 0.000 description 5
- 230000000694 effects Effects 0.000 description 4
- 230000001629 suppression Effects 0.000 description 4
- 238000000354 decomposition reaction Methods 0.000 description 3
- 238000001514 detection method Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 238000012804 iterative process Methods 0.000 description 2
- 230000033001 locomotion Effects 0.000 description 2
- 230000000737 periodic effect Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000012795 verification Methods 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000003709 image segmentation Methods 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000000691 measurement method Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000003909 pattern recognition Methods 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
- 238000000513 principal component analysis Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/418—Theoretical aspects
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/411—Identification of targets based on measurements of radar reflectivity
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/72—Wind turbines with rotation axis in wind direction
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Computer Networks & Wireless Communication (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
The invention discloses a wind turbine multipath echo inching parameter estimation method based on a whale optimization algorithm, which comprises the following steps: s1, analyzing wind turbine echo signals to extract micro-motion parameters to be estimated, setting the number of the micro-motion parameters to be estimated as the dimension of a whale optimization algorithm, and setting the value range of each micro-motion parameter to be estimated; s2, initializing algorithm parameters, setting whale population scale and maximum iteration times, and randomly generating the position of each whale; s3, determining an individual fitness function according to the root mean square error, calculating individual fitness corresponding to each whale, and determining whale individuals and positions with optimal current fitness; s4, starting iteration, updating algorithm parameters, and updating individual positions according to the range of the generated random number; turning to step S3 until the maximum iteration number is reached; s5, outputting an optimal result. By the technology disclosed by the invention, the estimation precision of the inching parameters is improved, and meanwhile, the operation is simple and the calculated amount is reduced.
Description
Technical Field
The invention relates to the technical field of radar echo suppression, in particular to wind turbine multipath echo jiggle parameter estimation based on whale optimization algorithm.
Background
Along with the increasing severity of energy exhaustion, wind energy is used as a clean pollution-free green energy source to be the development focus of the power industry, and the construction of wind power stations is increased along with the rising of wind power generation demands. The main component of the wind power plant is a wind turbine, and the wind turbine blade can periodically modulate the frequency of radar echo when rotating, so that the radar echo spectrum is widened, the micro Doppler effect appears, and the false detection and the false tracking of the radar can be possibly caused. More importantly, the reflectors in the wind power plant can generate multipath effects on wind turbine echoes, and at the moment, the wind turbine echoes are dispersed in a plurality of Doppler filter banks with non-zero frequencies, so that a large number of false alarms are generated by the radar, and the information quality is reduced. It is therefore important to design an effective wind turbine multipath echo suppression means to reduce its impact on radar equipment.
One of the breaches that suppresses multipath echoes of wind turbines is the inching parameter estimation. The estimated micro-motion parameters can represent the rotation state of the wind turbine, further analyze the micro-Doppler effect characteristics of the multipath echoes of the wind turbine, and finally utilize the characteristics to inhibit the multipath echoes of the wind turbine. The micro-motion parameter estimation has important application in the fields of feature extraction, target identification, clutter suppression, synthetic aperture radar imaging and the like.
In the prior art, the solutions for wind turbine echo inching parameter estimation are mainly two types: the method is characterized in that each micro-motion component is separated from a mixed signal by utilizing the thought of signal decomposition, such as principal component analysis, wavelet decomposition, empirical mode decomposition and the like, but the method cannot effectively decompose and estimate parameters of the multi-component micro-motion signal due to the characteristic that each micro-motion component is overlapped in a time-frequency domain of the signal and the discontinuous condition such as fracture of a time-frequency curve caused by shielding effect. The other is to use a time-frequency analysis means and utilize the different energy distribution rules of each micro-motion component in the time-frequency domain to realize the instantaneous frequency estimation. The method can be divided into non-parameterized and parameterized time-frequency analysis, the thinking of the non-parameterized frequency estimation method is that the time-frequency analysis is firstly performed, then the instantaneous frequency estimation is performed by using methods such as peak detection or 1-order moment, and the like, and the methods have great errors under the multi-component inching target scene and have poor noise resistance. For parameter estimation of a multi-component micro Doppler non-stationary signal, time-frequency analysis and other methods have large limitations in terms of calculation accuracy, calculation efficiency and the like, and engineering requirements cannot be met, a new feature measurement method is needed to improve the degree of distinction among all micro components, so that accurate parameter estimation is realized.
The whale optimization algorithm (Whale Optimization Algorithm, WOA) is a novel swarm intelligent optimization algorithm for simulating the predation behavior of the whales in the ocean. The algorithm has the characteristics of simple structure, few parameters, strong searching capability, robustness, easiness in implementation and the like, and the research and application of WOA are still in a starting stage. In order to reduce the influence of the observation time of a periodic motion rule on the estimation of the wind turbine multipath echo inching parameters, the invention provides a method for estimating parameters by using a whale optimization algorithm.
Disclosure of Invention
The invention aims to provide a wind turbine multipath echo parameter estimation method based on a whale optimization algorithm, which solves the problems of large calculated amount, low calculation speed and low inching parameter estimation precision in the prior art.
The technical scheme for solving the technical problems is as follows:
the invention provides a wind turbine multipath echo inching parameter estimation method based on a whale optimization algorithm, which comprises the following steps: s1, analyzing wind turbine echo signals to extract micro-motion parameters to be estimated, setting the number of the micro-motion parameters to be estimated as the dimension of a whale optimization algorithm, and setting the value range of each micro-motion parameter to be estimated; s2, initializing algorithm parameters, setting whale population scale and maximum iteration times, and randomly generating the position of each whale; s3, determining an individual fitness function according to the root mean square error, calculating individual fitness corresponding to each whale, and determining whale individuals and positions with optimal current fitness; s4, starting iteration, updating algorithm parameters, generating a random number between [0,1], and updating the position of an individual by adopting a spiral mechanism if the random number is more than or equal to 0.5; if the random number is less than 0.5 and the absolute value of the convergence factor is less than 1, updating the individual position by adopting a contraction surrounding mechanism; turning to step S3 until the maximum iteration number is reached; s5, outputting a position vector corresponding to the optimal whale individual as an optimal value of the inching parameter.
The beneficial effects of the invention include: according to the method, the multipath echo inching parameters of the wind turbine are estimated based on the whale optimization algorithm, the fitness function is built by analyzing and extracting the inching parameters of the wind turbine echo signals, the optimal value is solved by introducing the whale optimization algorithm, and through simulation experiments, the method is verified to improve the multipath echo inching parameter estimation accuracy of the wind turbine, and meanwhile, the operation is simple and the calculated amount is reduced.
Drawings
The invention is further described below with reference to the accompanying drawings.
Fig. 1 is a graph of bubble predation behavior of whales provided by an embodiment of the present invention.
Fig. 2 is a flowchart of a wind turbine multipath echo jiggle parameter estimation method based on a whale optimization algorithm according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made more apparent and fully hereinafter with reference to the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment of the invention provides a wind turbine multipath echo micro-motion parameter estimation method based on a whale-change algorithm, which can be applied to the technical field of radar echo suppression. Firstly, dividing the wind turbine into a plurality of scattering points through a scattering point integration algorithm, and superposing echo vectors generated by all the scattering points to obtain a final wind turbine multipath echo signal model. The parameters affecting the echo characteristics of the wind turbine are mainly relative distance, blade length, blade rotation speed, initial rotation phase, pitch angle, azimuth angle and the like, wherein the time variability of the blade rotation speed and the initial rotation phase is large and is the main parameter affecting the clutter model of the wind turbine.
Referring to fig. 1, to estimate the blade rotation speed and initial rotation phase in a multi-path echo model of a wind turbine, the present invention introduces a whale optimization algorithm. The whale optimization algorithm is a population intelligent algorithm, is obtained by modeling by simulating the sound-net predation behavior of whale population in nature, is applied to the fields of turbine heat consumption, pattern recognition, image segmentation and the like, but is not applied to the aspect of radar signal processing. The whale optimization algorithm mainly comprises three major parts including surrounding prey, bubble-net attack and random search.
(1) Surrounding prey
The surrounding hunting mainly selects the individual position as the best searching agent of the hunting or the closest hunting in the process, and the positions of other individuals are correspondingly updated according to the positions of the best searching agents, and the behavior can be expressed by modeling as follows:
where t is the current number of iterations,representing the position vector of the individual->Position vector representing the current best search agent, +.>And->Are coefficient matrices>The value of (2) decreases linearly from 2 to 0 as the iteration proceeds,is a random number with the value range of 0-1. If a better solution occurs in the iterative process, the method is needed to be applied to +.>And updating.
(2) Bubble-net attack
The patent-net attack behavior of whales at the head is mainly characterized by a contraction surrounding mechanism and a spiral updating position in mathematical modeling. Shrink wrap mechanism by linear reductionSo that coefficient matrix->Is realized by random numbers ranging from-1 to 1. The spiral update position means that when an individual moves towards the position of the optimal search agent, the movement track of the individual also follows a logarithmic spiral, and the formula is shown as follows:
wherein the method comprises the steps ofRepresenting the distance between the ith individual and the best search agent, l is a random number ranging from-1 to 1, and b is a constant that determines the shape of the spiral curve.
In practice, the seesaw whale surrounds the prey and the spiral action are performed simultaneously, and when the behavior is mathematically simulated, the following formula can be used for supposing that the selection probabilities of the contraction surrounding mechanism and the spiral update position are equal:
wherein p is a random number ranging from 0 to 1.
(3) Random search
The whale performs analysis on the positions of other individuals in a random manner in the process of searching for a prey, and the behavior is expressed as a formula and a formula by using a mathematical formula.
Wherein the method comprises the steps ofIs a randomly selected individual position vector. Therefore, in the iterative process of the whale optimization algorithm, if the coefficient matrix |A| is more than or equal to 1, the population is shown to be searching more suitable hunting articles at the moment, random searching is carried out, the predation population deviates outwards, and the global searching capability of the algorithm is improved; if |A|<1, the position of the current random individual, namely the position of the prey, is described, and the population is subjected to hunting according to the established behavior mode.
Referring to fig. 2, the wind turbine multipath echo inching parameter estimation method based on whale optimization algorithm provided by the invention comprises the following steps:
s1, analyzing wind turbine echo signals to extract micro-motion parameters to be estimated, setting the number of the micro-motion parameters to be estimated as the dimension of a whale optimization algorithm, and setting the value range of each micro-motion parameter to be estimated.
Specifically, by combining scattering point integral calculation with wind turbine multipath echo characteristics, a wind turbine multipath echo model is established, parameters affecting wind turbine echo characteristics mainly comprise relative distance, blade length, blade rotation speed, rotation primary phase, pitch angle, azimuth angle and the like, and by adopting short-time Fourier transform to conduct time-frequency domain analysis on wind turbine echo, the time variability of the blade rotation speed and the rotation primary phase is obtained, and the time variability is large, and is the main parameter affecting the wind turbine clutter model, so that the blade rotation speed and the rotation primary phase are determined as micro-motion parameters to be estimated.
Further, the dimension of introducing a whale optimization algorithm is determined to be 2 according to the inching parameter to be estimated, the inching parameter to be estimated is the rotation speed of the blade and the rotation primary phase, the value range of the rotation speed is 0-pi rad/s, and the value range of the rotation primary phase is 0-2 pi/3 rad.
S2, initializing algorithm parameters, setting the population scale and the maximum iteration number of whales, and randomly generating the position of each whale.
Further, initializing algorithm parameters further includes: as the number of iterations increases, the number of iterations increases linearly from 2 to 0, the convergence factor a, the wobble factor C, the number of iterations l from-1 to 1, and the number of iterations p from 0 to 1.
Specifically, the rotation speed and the initial rotation phase of the blades are taken as position information X= [ X, y ] of the whale population, and the population position is randomly initialized.
S3, determining an individual fitness function according to the root mean square error, calculating the individual fitness corresponding to each whale, and determining whale individuals and positions with the optimal current fitness.
Specifically, the quality of an individual (solution) is evaluated by using the fitness function value, and the larger the fitness function value is, the better the quality of the solution is. Fitness evaluation in whale optimization algorithm is an indispensable step, and fitness evaluation is performed on each individual in each generation.
Further, the root mean square error is applied to individual optimal position selection, and the individual fitness function is as follows:
wherein X is i For the position of the ith whale, n represents the dimension of the whale individual in the search space, (x) n ,y n ) Represents the n-th dimension of the individual position of whale (x) n+1 ,y n+1 ) Whale representing dimension n+1Fish individual position, d, represents the dimension of the whale population.
Specifically, the basic thought of individual fitness value calculation is that firstly, determining the position of each dimension of whales, calculating the mean square value according to the positions of the selected n+1th and n-th dimension individuals, and obtaining the individual fitness f (X i ) The maximum value is the optimal individual. And when simulation verification is performed, selecting an average error for verification.
S4, starting iteration, updating algorithm parameters, generating a random number between [0,1], and updating the position of an individual by a spiral mechanism if the random number is more than or equal to 0.5; if the random number is less than 0.5 and the absolute value of the convergence factor is less than 1, updating the individual position by the contraction surrounding mechanism; turning to step S3 until the maximum iteration number is reached;
s5, outputting a position vector corresponding to the optimal whale individual as an optimal value of the inching parameter.
The effectiveness of the above method is verified by simulation experiments as follows.
Ten groups of data are set for simulation analysis in order to verify the effectiveness of the whale optimization algorithm on the estimation of different inching parameters. The rest of the simulation parameters except for the rotation speed and the initial phase set the simulation parameters as follows: the length of the blade is 20m, and the total number of the blades is 3; the azimuth angle of the radar beam and the rotation center of the wind turbine is pi/2 rad, and the pitch angle is pi/2 rad; the azimuth angle between the sight of the reflector and the rotation center of the wind turbine is pi/4 rad, and the pitch angle is pi/9 rad; the radar carrier frequency is 1GHz, and the simulation duration is 2s. According to the parameters, the multipath echo data are generated by combining the multipath echo model established previously, and on the basis, the inching parameters contained in the data are estimated by using a whale optimization algorithm, and the error comparison is carried out with the inching parameters of the generated data. Setting a dimension parameter range in a whale optimization algorithm: the rotation speed is in the range of 0-pi/s, and the rotation initial phase is in the range of 0-2 pi/3 rad. 10 experiments were performed on each set of data using the whale optimization algorithm, and the average error results for the estimated rotational speed and initial phase are shown in table 1.
TABLE 1 simulation results
As can be seen from table 1, the error of estimating the rotational speed and the initial phase using the whale optimization algorithm for different rotational speeds and initial phases is less than 1%, and the error of both are small, which means that the whale optimization algorithm can be used for estimating the rotational speed and the initial phase of the wind turbine.
In a real environment, the rotation speed of the wind turbine is not fixed, and one cycle of data cannot be accurately acquired. In order to analyze the effectiveness of the whale optimization algorithm on processing different data lengths, the initial rotation phase in the inching parameters is set to be 1.5rad, the rotation speed is set to be 2.4rad/s, and other simulation parameters are unchanged. The data lengths of 0.05s, 0.08s, 0.1s, 0.5s, 1s, 1.5s, 2s, 2.5s were selected and the mean error of 10 experiments per data length is shown in table 2.
TABLE 2 simulation results
As can be seen from table 2, the parameter estimation errors obtained by the values of different data lengths are very small. If the equivalent width of the radar antenna beam is 1.8 degrees, the rotating speed of the radar antenna is 6r/min, the residence time of the beam at the wind turbine is 0.05s when the radar scans, and under the scanning mode, the multipath echo inching parameters of the wind power plant can be estimated by using the method in one rotating period without acquiring the rotating periodic data of the wind turbine for observation for a long time, so that the value of the data length required by the method does not influence the effective extraction of the inching parameters, the flexibility is higher, the limitation is smaller, and the method has potential practical value.
Those of ordinary skill in the art will appreciate that all or a portion of the steps in the various methods of the above embodiments may be implemented by hardware associated with program instructions, where the program may be stored on a computer readable storage medium, where the storage medium may include: flash disk, read-only memory, random-access memory, magnetic or optical disk, or embedded device, etc.
The foregoing describes the method for estimating multipath echo micro-motion parameters of a wind turbine based on a whale optimization algorithm according to the embodiments of the present invention in detail, and specific examples are applied to illustrate the principles and embodiments of the present invention, and the foregoing examples are only for aiding in understanding the method and core ideas of the present invention, and the present specification should not be construed as limiting the invention, as long as the person skilled in the art can change the specific embodiments and application ranges according to the ideas of the present invention.
Claims (5)
1. The wind turbine multipath echo jiggle parameter estimation method based on whale optimization algorithm is characterized by comprising the following steps:
s1, dividing a wind turbine into a plurality of scattering points through a scattering point integration algorithm, and superposing echo vectors generated by all the scattering points to obtain a final wind turbine multipath echo signal model; analyzing wind turbine echo signals to extract micro-motion parameters to be estimated, setting the number of the micro-motion parameters to be estimated as the dimension of a whale optimization algorithm, and setting the value range of each micro-motion parameter to be estimated;
s2, initializing algorithm parameters, setting whale population scale and maximum iteration times, and randomly generating the position of each whale;
s3, determining an individual fitness function according to the root mean square error, calculating individual fitness corresponding to each whale, and determining whale individuals and positions with optimal current fitness;
s4, starting iteration, updating algorithm parameters, generating a random number between [0,1], and updating the position of an individual by a spiral mechanism if the random number is more than or equal to 0.5; if the random number is less than 0.5 and the absolute value of the convergence factor is less than 1, updating the individual position by the contraction surrounding mechanism; turning to step S3 until the maximum iteration number is reached;
s5, outputting a position vector corresponding to the optimal whale individual as an optimal value of the inching parameter.
2. The method according to claim 1, wherein the dimension of the whale optimization algorithm in step S1 is 2, and the inching parameters to be estimated are the blade rotation speed and the initial phase of rotation.
3. The method for estimating multipath echo inching parameters of wind turbine according to claim 2, wherein the rotation speed of the blade is in the range of 0-pi rad/s, and the initial phase of rotation is in the range of 0-2 pi 3rad.
4. The method of estimating multipath echo jiggle parameters of a wind turbine according to claim 1, wherein the initializing algorithm parameters in step S2 further comprises: as the number of iterations increases, the number of iterations increases linearly from 2 to 0, the convergence factor a, the wobble factor C, the number of iterations l from-1 to 1, and the number of iterations p from 0 to 1.
5. The method for estimating multipath echo jiggle parameters of a wind turbine according to claim 1, wherein the individual fitness function in step S3 is:
where Xi is the position of the ith whale, n represents the dimension of the ith whale individual in the search space, (xn, yn) represents the position of the whale individual in the nth dimension, (xn+1, yn+1) represents the position of the whale individual in the n+1 dimension, and d represents the dimension of the whale population.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110793231.2A CN113759333B (en) | 2021-07-14 | 2021-07-14 | Wind turbine multipath echo jiggle parameter estimation method based on whale optimization algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110793231.2A CN113759333B (en) | 2021-07-14 | 2021-07-14 | Wind turbine multipath echo jiggle parameter estimation method based on whale optimization algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113759333A CN113759333A (en) | 2021-12-07 |
| CN113759333B true CN113759333B (en) | 2024-04-02 |
Family
ID=78787707
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110793231.2A Active CN113759333B (en) | 2021-07-14 | 2021-07-14 | Wind turbine multipath echo jiggle parameter estimation method based on whale optimization algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113759333B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018072351A1 (en) * | 2016-10-20 | 2018-04-26 | 北京工业大学 | Method for optimizing support vector machine on basis of particle swarm optimization algorithm |
| CN109765893A (en) * | 2019-01-17 | 2019-05-17 | 重庆邮电大学 | Method for planning path for mobile robot based on whale optimization algorithm |
| CN110491141A (en) * | 2019-08-22 | 2019-11-22 | 长安大学 | A kind of information of vehicles identifying system and recognition methods |
| CN112197914A (en) * | 2020-09-16 | 2021-01-08 | 南京信息工程大学滨江学院 | Whale MUSIC algorithm-based gas leakage source estimation method |
| AU2020103826A4 (en) * | 2020-12-01 | 2021-02-11 | Dalian University | Whale dna sequence optimization method based on harmony search (hs) |
| AU2020103709A4 (en) * | 2020-11-26 | 2021-02-11 | Daqing Oilfield Design Institute Co., Ltd | A modified particle swarm intelligent optimization method for solving high-dimensional optimization problems of large oil and gas production systems |
-
2021
- 2021-07-14 CN CN202110793231.2A patent/CN113759333B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018072351A1 (en) * | 2016-10-20 | 2018-04-26 | 北京工业大学 | Method for optimizing support vector machine on basis of particle swarm optimization algorithm |
| CN109765893A (en) * | 2019-01-17 | 2019-05-17 | 重庆邮电大学 | Method for planning path for mobile robot based on whale optimization algorithm |
| CN110491141A (en) * | 2019-08-22 | 2019-11-22 | 长安大学 | A kind of information of vehicles identifying system and recognition methods |
| CN112197914A (en) * | 2020-09-16 | 2021-01-08 | 南京信息工程大学滨江学院 | Whale MUSIC algorithm-based gas leakage source estimation method |
| AU2020103709A4 (en) * | 2020-11-26 | 2021-02-11 | Daqing Oilfield Design Institute Co., Ltd | A modified particle swarm intelligent optimization method for solving high-dimensional optimization problems of large oil and gas production systems |
| AU2020103826A4 (en) * | 2020-12-01 | 2021-02-11 | Dalian University | Whale dna sequence optimization method based on harmony search (hs) |
Non-Patent Citations (3)
| Title |
|---|
| "Topology Design of an O shore Wind Farm with Multiple Types of Wind Turbines in a Circular Layout";Siyu Tao等;《energies》;20200123;1-16 * |
| "扫描工作模式的航管监视雷达风电场回波信号的微多普勒特征分析";何炜琨等;《信号处理》;第31卷(第10期);正文第2-4页 * |
| "风电机叶片微多普勒频率求解及其影响因子";唐波等;《太原理工大学学报》;第51卷(第1期);正文第2-8页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113759333A (en) | 2021-12-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Chen et al. | False-alarm-controllable radar detection for marine target based on multi features fusion via CNNs | |
| CN110501683B (en) | Sea-land clutter classification method based on four-dimensional data characteristics | |
| CN110940949B (en) | Method for estimating DOA of reciprocal array based on quantum penguin search mechanism in strong impact noise environment | |
| Du et al. | Noise reduction method based on principal component analysis with beta process for micro-Doppler radar signatures | |
| CN110647788B (en) | Human daily behavior classification method based on micro-Doppler characteristics | |
| CN114117912A (en) | Sea clutter modeling and inhibiting method under data model dual drive | |
| CN108387880A (en) | Detection method of small target under a kind of sea clutter background based on multiple dimensioned oriented Lyapunov indexes | |
| Luo et al. | Few-shot radar jamming recognition network via time-frequency self-attention and global knowledge distillation | |
| CN109669169B (en) | Method for detecting weak target signal under sea clutter background | |
| Domps et al. | High-frequency radar ocean current mapping at rapid scale with autoregressive modeling | |
| CN113759333B (en) | Wind turbine multipath echo jiggle parameter estimation method based on whale optimization algorithm | |
| CN111273251B (en) | Multi-core DSP-based particle swarm exchange long-time accumulation implementation method | |
| Kim et al. | Two-dimensional scattering center extraction based on multiple elastic modules network | |
| CN108896971B (en) | Simulation method for echoes of small targets floating on sea surface | |
| Choi et al. | ISAR motion compensation using evolutionary adaptive wavelet transform | |
| Heng et al. | Micro-movement Parameters Estimation of Wind Turbine Multipath Echo | |
| Bai et al. | Deep CNN for micromotion recognition of space targets | |
| CN112433215B (en) | Meteorological radar wind turbine clutter suppression method based on priori knowledge weighting | |
| Ning et al. | A novel method for ISAR imaging of space debris using improved genetic algorithm | |
| van Rossum et al. | Classification of human gaits using interrupted radar measurements | |
| Huang et al. | Frequency-diversity RCS based target recognition with ICA projection | |
| Tian et al. | Target detection in sea clutter with transformer neural network | |
| Jinfeng et al. | A TOPS Improved Algorithm Based on Eigenvalue Discrimination | |
| Zhu et al. | Convolutional Transformer for Radar Active Jamming Classification | |
| Xing et al. | A method for wind turbine clutter recognition and mitigation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |