CN113759333A - Wind turbine multipath echo micromotion parameter estimation method based on whale optimization algorithm - Google Patents
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Abstract
The invention discloses a wind turbine multipath echo micromotion parameter estimation method based on whale optimization algorithm, which comprises the following steps: s1, analyzing the wind turbine echo signals, extracting micromotion parameters to be estimated, setting the number of the micromotion parameters to be estimated as the dimensionality of a whale optimization algorithm, and setting the value range of each micromotion 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 the individual fitness corresponding to each whale, and determining the whale individual with the optimal current fitness and the position of the whale; s4, starting iteration, updating algorithm parameters, and updating the individual position according to the range of the generated random number; step S3 is carried out until the maximum iteration number is reached; and S5, outputting the optimal result. By the technology disclosed by the invention, the estimation precision of the micromotion parameters is improved, 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 micromotion parameter estimation based on a whale optimization algorithm.
Background
With the increasingly severe form of energy exhaustion, wind energy becomes the development focus of the power industry as a clean and pollution-free green energy, and the construction of wind power plants is increasing along with the rising of the demand of wind power generation. The wind turbine is a main component of the wind power plant, and when blades of the wind turbine rotate, radar echoes can be subjected to periodic frequency modulation, so that the radar echo frequency spectrum is widened, the micro Doppler effect occurs, and false detection and false tracking of the radar can be caused. More importantly, the reflectors in the wind power plant can generate multipath effect on wind turbine echoes, and the wind turbine echoes are dispersed in a plurality of non-zero frequency Doppler filter banks, so that a large amount of false alarms are generated by the radar, and the quality of information is reduced. It is therefore important to design effective wind turbine multipath echo suppression means to reduce its impact on radar equipment.
One of the breakthrough points for suppressing wind turbine multipath echoes is the estimation of the micromovement parameters. The estimated micro-motion parameters can represent the rotation state of the wind turbine, so that the micro-Doppler effect characteristic of the wind turbine multi-path echo is analyzed, and finally the wind turbine multi-path echo is restrained by utilizing the characteristic. The estimation of the micro-motion parameters 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 estimating the wind turbine echo micromotion parameters mainly fall into two categories: one is to separate each micromotion component from a mixed signal by using a signal decomposition idea, such as principal component analysis, Warblet wavelet decomposition, empirical mode decomposition and the like, but due to the cross superposition characteristic of each micromotion component in a signal time-frequency domain and due to the shielding effect, a time-frequency curve is broken and other discontinuous conditions, and for an empirical mode decomposition algorithm, when the frequencies of two components are too close, the two components cannot be separated, so that the method cannot effectively carry out decomposition and parameter estimation on a multi-component micromotion signal. And the other type adopts a time-frequency analysis means and utilizes different energy distribution rules of each micromotion component in a time-frequency domain to realize instantaneous frequency estimation. The method can be divided into non-parametric and parametric time-frequency analysis, and the idea of the non-parametric frequency estimation method is to perform time-frequency analysis first and then perform instantaneous frequency estimation by using methods such as peak detection or 1-order moment, and the methods have large errors in a multi-component micro-motion target scene and poor anti-noise performance. For the parameter estimation of the non-stationary signal such as the multi-component micro-doppler, the time-frequency analysis and other methods have great limitations in the aspects of calculation accuracy, calculation efficiency and the like, and cannot meet the engineering requirements, a new characteristic measurement method is urgently needed to improve the discrimination between the micro-motion components, so that the accurate parameter estimation is realized.
Whale Optimization Algorithm (WOA) is a novel swarm intelligence Optimization Algorithm for simulating the predation behavior of the Whale in the ocean. The WOA 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 the WOA are still in a starting stage. In order to reduce the influence of observation time of a periodic motion law on the estimation of the multi-path echo micromotion parameters of the wind turbine, the invention provides a method for estimating the 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 whale optimization algorithm, and solves the problems of large calculation amount, low calculation speed and low estimation precision of micro-motion parameters in the prior art.
The technical scheme provided by the invention for solving the technical problem is as follows:
the invention provides a wind turbine multipath echo micromotion parameter estimation method based on a whale optimization algorithm, which comprises the following steps of: s1, analyzing the wind turbine echo signals, extracting micromotion parameters to be estimated, setting the number of the micromotion parameters to be estimated as the dimensionality of a whale optimization algorithm, and setting the value range of each micromotion 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 the individual fitness corresponding to each whale, and determining the whale individual with the optimal current fitness and the position of the whale; s4, starting iteration, updating algorithm parameters, generating a random number between [0,1], and if the random number is more than or equal to 0.5, updating the position of the individual by adopting a spiral mechanism; 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 shrink wrapping mechanism; step S3 is carried out until the maximum iteration number is reached; and S5, outputting the position vector corresponding to the optimal whale individual as the optimal value of the micromotion parameter.
The beneficial effects of the invention include: the wind turbine multi-path echo micro-motion parameter estimation method is used for estimating wind turbine multi-path echo micro-motion parameters based on a whale optimization algorithm, firstly, a fitness function is established by analyzing and extracting micro-motion parameters from wind turbine echo signals, then, the whale optimization algorithm is introduced to solve an optimal value, and through simulation experiments, the method is verified to improve the wind turbine multi-path echo micro-motion parameter estimation precision, and meanwhile, the operation is simple, and the calculated amount is reduced.
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The invention will be further explained with reference to the drawings.
FIG. 1 is a graph of bubble predation behavior of whales according to an embodiment of the invention.
FIG. 2 is a flow chart of a wind turbine multi-path echo micromotion parameter estimation method based on whale optimization algorithm according to an embodiment of the invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it is to be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention provides a wind turbine multipath echo micromotion parameter estimation method based on a whale chemical algorithm, and can be applied to the technical field of radar echo suppression. Firstly, a wind turbine is divided into a plurality of scattering points through a scattering point integration algorithm, echo vectors generated by all the scattering points are superposed, and a final wind turbine multipath echo signal model is obtained. The method is characterized in that parameters influencing the wind turbine echo characteristics are mainly relative distance, blade length, blade rotating speed, rotating initial phase, pitch angle, azimuth angle and the like, wherein the time variability of the blade rotating speed and the rotating initial phase is large and is a main parameter influencing a wind turbine clutter model.
Referring to FIG. 1, to estimate blade rotational speed and rotational onset in a wind turbine multipath echo model, the present invention introduces a whale optimization algorithm. The whale optimization algorithm is a population intelligent algorithm, is obtained by modeling through simulating the bunble-net predation behavior of the 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. Whale optimization algorithms mainly comprise three major parts, namely prey surrounding, bump-net attack and random search.
(1) Surrounding prey
The hunting objects are mainly surrounded by selecting the best search agent with the position as the hunting object or closest to the hunting object in the process, and the positions of other objects are correspondingly updated according to the position of the best search agent, and the behavior can be expressed by modeling as follows:
where t is the current number of iterations,
a position vector representing the individual is determined,
a location vector representing the best current search agent,
and
are all a matrix of coefficients, and are,
as the iteration progresses, the value of (c) decreases linearly from 2 to 0,
is a random number with a value range of 0-1. If a better solution appears in the iterative process, the method needs to be applied to
And (6) updating.
(2) Bump-net attack
The Bubble-net attack behavior of the whale-whale is mainly modeled mathematically as a contraction and wrapping mechanism and a spiral updating position. Shrink wrap mechanism by linear reduction
So that the coefficient matrix
Is a random number in the range of-1 to 1. The spiral update position means that when the individual moves to the position of the best search agent, the motion track of the individual also follows a logarithmic spiral, as shown in the following formula:
wherein
Represents the distance between the ith individual and the optimal search agent, l is a random number in the range of-1 to 1,b is a constant that determines the shape of the spiral curve.
In practice, the action of embracing a prey and spiraling by whale is performed simultaneously, and the probability of selecting both the contraction embracing mechanism and the location of spiral updating is assumed to be equal when mathematically modeling this action, and can be described by the following formula:
wherein p is a random number ranging from 0 to 1.
(3) Random search
The whale with whale head analyzes the positions of other individuals in a random mode in the process of searching for prey, and the behavior is expressed as a formula and an equation by a mathematical formula.
Wherein
Is a randomly selected individual position vector. Therefore, in the iteration process of the whale optimization algorithm, if the coefficient matrix | A | ≧ 1, the population is found to be a more appropriate prey at the moment, random search is carried out, the predatory population deviates outwards, and the global search capability of the algorithm is improved; if | A |<The time 1 indicates the current position of the random individual, namely the position of the prey, and the population is subjected to hunting according to a set behavior mode.
Referring to fig. 2, the wind turbine multi-path echo micromotion parameter estimation method based on whale optimization algorithm provided by the invention comprises the following steps:
and S1, analyzing the wind turbine echo signals, extracting micromotion parameters to be estimated, setting the number of the micromotion parameters to be estimated as the dimensionality of the whale optimization algorithm, and setting the value range of each micromotion parameter to be estimated.
Specifically, the wind turbine multipath echo model is established by combining scattering point integral calculation and division with wind turbine multipath echo characteristics, parameters influencing wind turbine echo characteristics mainly comprise relative distance, blade length, blade rotating speed, rotating initial phase, pitch angle, azimuth angle and the like, and the wind turbine echo is analyzed in time-frequency domain by adopting short-time Fourier transform to obtain the wind turbine multipath echo model, wherein the blade rotating speed and the rotating initial phase have larger time variability and are main parameters influencing the wind turbine clutter model, so that the blade rotating speed and the rotating initial phase are determined to be micro-motion parameters to be estimated.
Furthermore, the dimension of the whale optimization algorithm is determined to be 2 according to the micromotion parameters to be estimated, the micromotion parameters to be estimated are the blade rotating speed and the rotating initial phase, the rotating speed range is 0-pi rad/s, and the rotating initial phase range is 0-2 pi/3 rad.
S2, initializing algorithm parameters, setting the whale population size and the maximum iteration number, and randomly generating the position of each whale.
Further, initializing algorithm parameters further comprises: as the number of iterations increases the random number a, which decreases linearly from 2 to 0, the convergence factor a, the wobble factor C, the random number l, which ranges from-1 to 1, and the random number p, which ranges from 0 to 1.
Specifically, the rotating speed and the initial phase of rotation of the blades are used as position information X ═ X, y ] of whale population, and the population position is initialized randomly.
And S3, determining an individual fitness function according to the root mean square error, calculating the individual fitness corresponding to each whale, and determining the individual and the position of the whale with the optimal current fitness.
Specifically, the quality of an individual (solution) is evaluated by using a fitness function value, and the larger the fitness function value is, the better the quality of the solution is. Fitness evaluation in the whale optimization algorithm is an indispensable step, and fitness evaluation is carried out on each individual in each generation.
Further, the root mean square error is applied to the individual optimal position selection, and the individual fitness function is as follows:
wherein, XiFor the location of the ith whale, n represents the dimension of the whale individual in the search space, (x)n,yn) Represents the individual position of whale in the nth dimension, (x)n+1,yn+1) Represents the position of the whale individual with the (n + 1) th dimension, and d represents the dimension of the whale population.
Specifically, the basic idea of calculating the individual fitness value is to firstly determine the position of each dimension of whale, calculate the mean square deviation value according to the selected (n + 1) th dimension and the position of the nth dimension individual, and calculate the individual fitness f (X)i) The maximum value is the optimal individual. And when simulation verification is carried out, selecting an average error for verification.
S4, starting iteration, updating algorithm parameters, generating a random number between [0,1], and if the random number is more than or equal to 0.5, updating the position of the individual by the spiral mechanism; if the random number is less than 0.5 and the absolute value of the convergence factor is less than 1, the shrink wrapping mechanism updates the individual position; step S3 is carried out until the maximum iteration number is reached;
and S5, outputting the position vector corresponding to the optimal whale individual as the optimal value of the micromotion parameter.
The effectiveness of the above method is verified by simulation experiments.
In order to verify the effectiveness of the whale optimization algorithm in estimating different micromotion parameters, ten groups of data are set for simulation analysis. The rest of the simulation parameters, except for the rotation speed and the initial phase, set the simulation parameters as follows: the length of each blade is 20m, and the total number of the blades is 3; the azimuth angle between the radar wave beam and the rotating center of the wind turbine is pi/2 rad, and the pitch angle is pi/2 rad; the azimuth angle between the reflector sight line and the rotating 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 2 s. And generating multipath echo data according to the parameters by combining the established multipath echo model, estimating the micromotion parameters contained in the data by using a whale optimization algorithm on the basis, and carrying out error comparison on the micromotion parameters and the micromotion parameters of the generated data. Setting a dimension parameter range in a whale optimization algorithm: the range of the rotation speed is 0-pi rad/s, and the range of the rotation initial phase is 0-2 pi/3 rad. The average error results for the estimated rotational speed and initial phase are shown in table 1 using whale optimization algorithm for 10 experiments per group of data.
TABLE 1 simulation results
As can be seen from Table 1, for different rotational speeds and initial phases, the errors in the estimation of the rotational speed and the initial phase using the whale optimization algorithm are both below 1%, and are small, indicating that the whale optimization algorithm can be used for the estimation of the rotational speed and the initial phase of the wind turbine.
In a real environment, the rotating speed of the wind turbine is not fixed, and data of one period cannot be accurately acquired. In order to analyze the effectiveness of whale optimization algorithm on processing different data lengths, the rotation initial phase in the micromotion parameters is set to be 1.5rad, the rotation speed is set to be 2.4rad/s, and other simulation parameters are unchanged. Data lengths of 0.05s, 0.08s, 0.1s, 0.5s, 1s, 1.5s, 2s, 2.5s were selected and the mean error for 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 taking values of different data lengths are all very small. If the equivalent width of the radar antenna beam is 1.8 degrees and the rotating speed of the radar antenna is 6r/min, the residence time of the beam at the wind turbine during radar scanning is 0.05s, the wind turbine multi-path echo micro-motion parameter can be estimated by using the method in one rotating period under the scanning mode, long-time acquisition of wind turbine rotating periodic data for observation is not needed, and the fact that the effective extraction of the micro-motion parameter is not influenced by the value of the data length required by the method is shown, so that the method is high in flexibility, small in limitation and has potential practical value.
Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be implemented by hardware associated with program instructions, and the program may be stored in a computer-readable storage medium, which may include: flash memory disks, read-only memory, random access memory, magnetic or optical disks, or embedded devices, etc.
The wind turbine multipath echo micro-motion parameter estimation method based on whale optimization algorithm provided by the embodiment of the invention is described in detail above, and the principle and the implementation of the invention are explained in this document by applying specific examples, and the description of the above embodiment is only used to help understanding the method of the invention and the core idea thereof, and for those skilled in the art, there may be changes in the specific implementation and the application scope according to the idea of the invention, and the description should not be construed as limiting the invention.
Claims (5)
1. A wind turbine multipath echo micromotion parameter estimation method based on whale optimization algorithm is characterized by comprising the following steps:
s1, analyzing the wind turbine echo signals, extracting micromotion parameters to be estimated, setting the number of the micromotion parameters to be estimated as the dimensionality of a whale optimization algorithm, and setting the value range of each micromotion 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 the individual fitness corresponding to each whale, and determining the whale individual with the optimal current fitness and the position of the whale;
s4, starting iteration, updating algorithm parameters, generating a random number between [0,1], and if the random number is more than or equal to 0.5, updating the position of the individual by the spiral mechanism; if the random number is less than 0.5 and the absolute value of the convergence factor is less than 1, the shrink wrapping mechanism updates the individual position; step S3 is carried out until the maximum iteration number is reached;
and S5, outputting the position vector corresponding to the optimal whale individual as the optimal value of the micromotion parameter.
2. The wind turbine multi-path echo micromotion parameter estimation method of claim 1, wherein the dimension of the whale optimization algorithm in step S1 is 2, and the micromotion parameters to be estimated are blade rotation speed and initial phase of rotation.
3. The wind turbine multi-path echo micromotion parameter estimation method according to claim 2, wherein said rotation speed ranges from 0 to pi rad/s and said initial phase of rotation ranges from 0 to 2 pi 3 rad.
4. The wind turbine multi-path echo micromotion parameter estimation method of claim 1, wherein said initializing algorithm parameters in step S2 further comprises: as the number of iterations increases the random number a, which decreases linearly from 2 to 0, the convergence factor a, the wobble factor C, the random number l, which ranges from-1 to 1, and the random number p, which ranges from 0 to 1.
5. The wind turbine multi-path echo micromotion parameter estimation method according to claim 1, wherein said individual fitness function in step S3 is:
wherein, XiFor the location of the ith whale, n represents the dimension of the ith whale in the search space, (x)n,yn) Represents the individual position of whale in the nth dimension, (x)n+1,yn+1) Represents the position of the whale individual with the (n + 1) th dimension, and d represents the dimension of the whale population.
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