CN108875251B - Wave period prediction method, device and equipment - Google Patents
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Abstract
The invention discloses a wave period prediction method, which can acquire displacement information of a motor rotor in real time, determine a dominant frequency displacement curve according to the displacement information, further determine a dominant frequency speed curve and a dominant frequency acceleration curve, finally automatically detect zero crossing points of the dominant frequency speed curve and the dominant frequency acceleration curve by using a dsp chip, and take two times of difference values of adjacent zero crossing points as a wave period prediction result. Therefore, the method predicts the wave period in real time according to the displacement information, has high real-time performance, does not need to set complex parameters, utilizes the automatic zero-crossing detection of the dsp chip, has a very simple prediction process and realizes the aim of simply and effectively predicting the wave period. The invention also provides a wave period prediction device, equipment and a computer readable storage medium, and the action of the wave period prediction device corresponds to the action of the method.
Description
Technical Field
The present invention relates to the field of wave power generation, and in particular, to a wave period prediction method, device, apparatus, and computer-readable storage medium.
Background
With the development of science and technology, wave power generation is more and more popular. The conventional direct-drive wave power generation system for wave power generation can provide electric energy for electric equipment of an island or an onshore user or transmit the electric energy in a grid-connected mode by connecting a submarine cable by absorbing wave energy by utilizing the heave motion of a permanent magnet linear motor and then converting the wave energy into electric energy for output.
At present, wave energy has been paid attention to by many developed countries in the field of new energy power generation, how to obtain better economic benefit with lower cost as much as possible becomes a great hot spot of wave energy power generation research, namely maximum power capture of a wave power generation device.
The main means for improving the output power of the traditional direct drive type wave power generation system is to control the rotor to move by controlling the electromagnetic force of the linear motor by utilizing the resonance principle in the mechanical oscillation theory, so that the running frequency of the wave power generation device is equal to the wave frequency. Therefore, the power optimization problem of the wave energy is directly related to the wave period, the wave period in a future period of time is effectively predicted, and the method plays an important role in controlling the output power of the wave power generation system.
Common schemes for predicting the wave period include the following three schemes:
according to the disturbance observation method, the scheme can avoid over dependence on a mathematical model of the wave power generation system, however, the disturbance step length of power increase and decrease adjustment is difficult to select, the oscillation is caused near the maximum power point due to too large step length, the steady-state performance of the system is influenced, and the convergence speed is too low due to too small step length, and the dynamic performance of the system is influenced.
According to the scheme, a neural network model is introduced into an internal model controller of the oscillating floater device, so that a large amount of experimental data can be used for training a neural network. However, the trained neural network may cause poor control effect due to seasonal changes, which is particularly obvious in summer.
The method can enable the wave power generation device and the wave operating frequency to achieve resonance under the sea condition of irregular waves by repeatedly releasing or latching the floater, but the control of the latching and releasing time is difficult to grasp, the realization is difficult, and the actual sea test effect is not good.
Therefore, how to provide a simple and effective wave period prediction scheme has great research significance.
Disclosure of Invention
The invention aims to provide a wave period prediction method, a wave period prediction device, wave period prediction equipment and a computer readable storage medium, which are used for solving the problems that the traditional wave period prediction scheme is high in complexity and not ideal in prediction effect.
In order to solve the technical problem, the invention provides a wave period prediction method, which comprises the following steps:
acquiring displacement information of a motor rotor positioned on irregular waves at the current time period;
determining a dominant frequency displacement curve of the irregular waves according to the displacement information;
determining a dominant frequency speed curve and a dominant frequency acceleration curve corresponding to the dominant frequency displacement curve;
determining zero crossing points on the dominant frequency speed curve and the dominant frequency acceleration curve by using the dsp chip;
and calculating the difference value of two adjacent zero-crossing points on the dominant frequency speed curve and the dominant frequency acceleration curve, and taking two times of the difference value as a wave period prediction result of the next time period.
Wherein the determining a dominant frequency displacement curve of the irregular waves according to the displacement information comprises:
determining model parameters of Kalman filtering according to the displacement information by using an AMRA model, wherein the model parameters comprise a transfer matrix A, a noise matrix B and a system error matrix Q;
determining a Kalman filtering model according to the model parameters;
and determining a dominant frequency displacement curve of the irregular wave by using the Kalman filtering model.
Determining Kalman filtering model parameters by using an AMRA model according to the displacement information, wherein the model parameters comprise a transfer matrix A, a noise matrix B and a system error matrix Q, and the method comprises the following steps:
determining a fitting residual error and a residual error variance according to the displacement information by using an AMRA model;
determining the transfer matrix A and the noise matrix B of the Kalman filtering model according to the fitting residual error;
and determining the system error matrix Q according to the residual variance.
The invention also provides a wave period prediction device, comprising:
an acquisition module: the displacement information of the motor rotor positioned on the irregular waves in the current time period is acquired;
a dominant frequency displacement curve determination module: the displacement information is used for determining a dominant frequency displacement curve of the irregular waves;
a curve determination module: the main frequency displacement curve is used for determining a main frequency speed curve and a main frequency acceleration curve corresponding to the main frequency displacement curve;
a zero crossing point determination module: the zero crossing points on the main frequency speed curve and the main frequency acceleration curve are determined by the dsp chip;
a prediction module: and the wave period prediction method is used for calculating the difference value of two adjacent zero-crossing points on the dominant frequency speed curve and the dominant frequency acceleration curve, and taking two times of the difference value as the wave period prediction result of the next time period.
Wherein the dominant frequency displacement curve determining module comprises:
a model parameter determination unit: the model parameters are used for determining Kalman filtering according to the displacement information by utilizing an AMRA model, and the model parameters comprise a transfer matrix A, a noise matrix B and a system error matrix Q;
a model determination unit: the Kalman filtering model is determined according to the model parameters;
dominant frequency displacement curve determination unit: for determining a dominant frequency displacement curve of the irregular wave using the kalman filter model.
Wherein the model parameter determination unit includes:
a first subunit: the AMRA model is used for determining a fitting residual error and a residual error variance according to the displacement information;
a second subunit: the transfer matrix A and the noise matrix B of the Kalman filtering model are determined according to the fitting residual errors;
a third subunit: for determining the systematic error matrix Q from the residual variance.
In addition, the present invention also provides a wave period predicting apparatus comprising:
a memory: for storing a computer program;
a processor: for executing said computer program for implementing the steps of the wave period prediction method as described above.
Finally, the invention also provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the wave period prediction method as described above.
The wave period prediction method provided by the invention can acquire the displacement information of the motor rotor in real time, determine the dominant frequency displacement curve according to the displacement information, further determine the dominant frequency speed curve and the dominant frequency acceleration curve, finally automatically detect the zero crossing point of the dominant frequency speed curve and the dominant frequency acceleration curve by using a dsp chip, and take two times of the difference value of adjacent zero crossing points as the wave period prediction result. Therefore, the method predicts the wave period in real time according to the displacement information, has high real-time performance, does not need to set complex parameters, utilizes the automatic zero-crossing detection of the dsp chip, has a very simple prediction process and realizes the aim of simply and effectively predicting the wave period.
The invention also provides a wave period prediction device, equipment and a computer readable storage medium, the function of which corresponds to the function of the method, and the description is omitted here.
Drawings
In order to more clearly illustrate the embodiments or technical solutions of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a flow chart of an embodiment of a wave period prediction method provided by the present invention;
FIG. 2 is a schematic diagram of a dominant frequency velocity curve and a dominant frequency acceleration curve provided by the present invention;
FIG. 3 is a schematic diagram illustrating the principle of the optimization cycle prediction results provided by the present invention;
FIG. 4 is a diagram of a simulation experiment result of extracting a dominant frequency displacement curve based on an AMRA model according to the present invention;
FIG. 5 is a diagram of a simulation experiment result of extracting a dominant frequency displacement curve based on an AMRA model and a Kalman filtering model according to the present invention;
fig. 6 is a block diagram of an embodiment of a wave period prediction device provided in the present invention.
Detailed Description
The core of the invention is to provide a wave period prediction method, a wave period prediction device, wave period prediction equipment and a computer readable storage medium, and the purpose of simply and effectively predicting the wave period is achieved.
In order that those skilled in the art will better understand the disclosure, reference will now be made in detail to the embodiments of the disclosure as illustrated in the accompanying drawings. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
An embodiment of a wave period prediction method provided by the present invention is described below, and referring to fig. 1, the embodiment includes:
step S101: and acquiring displacement information of the motor rotor positioned on the irregular waves in the current time period.
Specifically, the displacement information of the motor rotor on the irregular waves can be acquired in real time by using the position sensor, and the displacement information reflects the motion track of the irregular waves. The sampling frequency is generally not lower than 20Hz, and in this embodiment, the sampling frequency may be set to 1000Hz, and obviously, the discrete sequence of the displacement information is acquired.
In addition, in an actual application scenario, the displacement information is interfered by various factors, so that the displacement information includes a lot of interference information, and therefore, in order to better predict the wave period, the present embodiment extracts dominant frequency displacement information from the displacement information, and a specific extraction process refers to step S102.
Step S102: and determining a dominant frequency displacement curve of the irregular waves according to the displacement information.
Specifically, as a preferred implementation, a combination of the AMRA model and the kalman filter is used in the present embodiment to determine the dominant frequency displacement curve according to the displacement information. The steps of extracting the dominant frequency curve can be summarized as follows, and the specific extraction and calculation process is described in detail at the end of this embodiment:
firstly, determining a fitting residual error and a residual error variance according to the displacement information by utilizing an AMRA model; determining the transfer matrix A and the noise matrix B of the Kalman filtering model according to the fitting residual error; and determining the system error matrix Q of the Kalman filtering model according to the residual variance.
Secondly, determining a Kalman filtering model according to a transfer matrix A, a noise matrix B and a system error matrix Q of the Kalman filtering model; and determining a dominant frequency displacement curve of the irregular wave by using the Kalman filtering model.
Step S103: and determining a dominant frequency speed curve and a dominant frequency acceleration curve corresponding to the dominant frequency displacement curve.
It should be noted that, from a physical point of view, the speed is a derivative of the displacement, and therefore the parameter of the speed has a property of leading, and can reflect the movement trend of the displacement curve. Similarly, the acceleration can reflect the change speed of the motion trend of the displacement curve. Therefore, compared with a displacement curve, the speed curve and the acceleration curve can reflect the movement trend of the waves and the change speed of the movement trend. In view of this, the wave period is predicted in this embodiment from the dominant frequency velocity profile and the dominant frequency acceleration profile.
In addition, the present embodiment utilizes the dominant frequency velocity curve and the dominant frequency acceleration curve at the same time, because of the following considerations:
if a single dominant frequency velocity curve or dominant frequency acceleration curve has only two or three zero-crossing points in a period, then the dominant frequency acceleration curve and dominant frequency velocity curve have four or five zero-crossing points in a period, then according to the idea of predicting the wave period based on the zero-crossing points in this embodiment, only one or two period predictions can be performed in a period through a single curve, and at least three period predictions can be performed through two curves. In this case, whether the present embodiment predicts the wave period based on the wave period closest to the current period in the sampling time period as the period prediction result or by comprehensively considering the variation trend of a plurality of wave periods in the short sampling time, the accuracy of prediction can be improved to a certain extent compared with prediction based on a single curve. In addition, the sampling time can be shortened, so that the real-time performance of wave period prediction is further improved.
Step S104: and determining zero crossing points on the main frequency speed curve and the main frequency acceleration curve by using the dsp chip.
Since the general dsp chip has the function of the zero-crossing detector, the zero-crossing point of the dominant frequency velocity curve and the dominant frequency acceleration curve is determined by using the characteristic of the dsp chip, so that the period is determined by complicated calculation, and the calculation amount and time are saved.
Step S105: and calculating the difference value of two adjacent zero-crossing points on the dominant frequency speed curve and the dominant frequency acceleration curve, and taking two times of the difference value as a wave period prediction result of the next time period.
Referring to fig. 2, fig. 2 is a schematic diagram of a dominant frequency velocity curve and a dominant frequency acceleration curve in a cycle, and as shown in fig. 2, there are five zero-crossing points in the dominant frequency velocity curve and the dominant frequency acceleration curve, so that the dominant frequency velocity curve and the dominant frequency acceleration curve can be divided into four parts, and cycle prediction can be performed in each part.
In summary, the wave period prediction method provided in this embodiment can acquire displacement information of the motor mover in real time, determine a dominant frequency displacement curve according to the displacement information, further determine a dominant frequency velocity curve and a dominant frequency acceleration curve, finally automatically detect zero-crossing points of the dominant frequency velocity curve and the dominant frequency acceleration curve by using a dsp chip, and use twice of a difference value of adjacent zero-crossing points as a wave period prediction result. Therefore, the method predicts the wave period in real time according to the displacement information, has high real-time performance, does not need to set complex parameters, utilizes the automatic zero-crossing detection of the dsp chip, has a very simple prediction process and realizes the aim of simply and effectively predicting the wave period.
In addition, besides the motor rotor, in the embodiment, a great number of additional expensive devices are not required to be added, only the displacement information of the motor rotor needs to be acquired, and the wave period is predicted according to the displacement information through the processor, so that the cost is low, and the implementation mode is simple.
Specifically, the formula for calculating the wave period may be as follows:
T(k)＝2*|Ts(k-1)-Ta(k-1)|
wherein, Ts(k-1) and TaAnd (k-1) is respectively the main frequency speed curve and two adjacent zero-crossing points of the main frequency speed curve in the current time period.
Finally, it is worth mentioning that, as shown in fig. 3, the wave cycle prediction method provided in this embodiment can be used to predict the wave cycle of the next time period according to the displacement information of the current time period, and then the actual wave cycle of the "next time period" can be measured when the time enters the next time period, so as to check the actual wave cycle according to the predicted wave cycle, calculate the prediction error, and consider the prediction error in the calculation process of the next wave cycle prediction, thereby continuously optimizing the wave cycle prediction process and improving the accuracy of the wave cycle prediction.
In the following embodiment, the calculation process of step S102 is described in detail, and it should be noted that the following calculation process is only one implementation manner of the present embodiment, and the present embodiment does not limit how the calculation process is implemented.
As for the displacement information, specifically, it can be expressed by the following formula (1),
x＝0.8*cos(2*pi*t*3)+randn(1，N) (1)
where randn is a function in matlab, and randn (1, N) means N random numbers between 0 and 1. Specifically, the sampling time may be 1s, the number of times of acquisition N may be 1000, and the displacement information obtained by sampling may be stored by using the matrix DATA.
In this embodiment, after obtaining the discrete sequence of the displacement information, the differential operation and normalization processing may be performed on the discrete sequence. Then, the AQMA model parameters can be obtained through the AR model parameters. Specifically, the order p of the AR model may be set to 3, which means that the displacement of the first 3 moments is used to fit the displacement of the next moment.
Firstly, the parameter FAI of the AR model is calculated by using the least square method, and the formula is as follows:
FAI＝inv((X'*X))*X'*Y (2)
wherein inv is inversion, and the matrix X is a matrix of N × 3 obtained by sorting after the displacement information matrix obtained by collection is inverted. Specifically, the order of the arrangement of X is from the fourth element, assuming that the fourth element is a and the fifth element is b, and the sixth, seventh and eighth elements are listed as c, d and e in the same way, then the matrix of X is:
and the matrix Y is a matrix of N x1 for storing the collected unique information, and the sequence of the matrix Y is also from the fourth element.
Then, the fitting value of the AR model can be represented by equation (4):
AR_OUT＝X*FAI (4)
the fitted residual of the AR model can be represented by equation (5):
Ae＝Y-AR_OUT (5)
the ARMA parameter is calculated by finding the AR parameter, which also requires the least square method to find the ARMA parameter FAI1, wherein the specific formula (6) is
FAI1＝inv((X1'*X1))*X1'*Y (6)
The matrix X1 is composed of the collected original displacement information and the fitting residual of the AR model, and the specific form is formula (7):
in the matrix X1, A, B, C, D are the first four elements of the original displacement information, respectively, and the numbers 2, 3, 4 are the second to fourth elements in the residual matrix of the AR model.
In the same way, the fitting value and the fitting residual error of the ARMA model can be obtained.
Then, the prediction error of the AMRA model, the variance and the mean of the AMRA model are obtained, and the variance and the mean can be realized by specifically calling a matlab function, wherein a specific formula is not given. And the prediction error of the ARMA model can be represented by the formula (8)
W＝DATA-DATA1 (8)
The transpose of the X1 matrix is then multiplied by ARMA parameters FAI1, and the resulting DATA is stored in DATA 1.
After the prediction error, the variance and the error mean are solved, various parameter matrixes of Kalman filtering can be constructed, and initialization setting is carried out, wherein the method specifically comprises the following three matrix parameters:
1. the systematic error matrix Q is
Wherein a1_ var in equation (9) is the residual variance of the ARMA model;
2. the transfer matrix A is represented by formula R
The FAI1(1) and the FAI1(2) in the transition matrix a represent the first and second elements in the ARMA parameter.
3. The noise matrix B can be represented by formula (11)
The Kalman filter equation can be solved according to a space model of a discrete state, and then a filter is designed. The state equation in the Kalman filter does not contain input, because the wave state equation does not contain input, the time updating equation is as follows;
the error variance of the state can be predicted using equation (12)
Prior covariance
The kalman filter can be obtained from equation (13);
wherein, R is a measurement error variance W _ var;
since the prediction process by the preceding time update formula (12), the measurement update state equation, i.e., the correction process, represented by formula (14) can be calculated from the kalman filter gain
Wherein,
the state covariance and estimate of the a posteriori are shown.
In summary, as can be seen from the analysis of (12), (13), and (14), the kalman filter estimates the value at the current time by using the estimated value at the previous time and a similar observation data, and performs continuous prediction and correction, so that the real-time performance and the flexibility are both very good.
In addition, in the embodiment, a method combining an ARMA model and a kalman filtering model is adopted to extract displacement signals of irregular waves, in order to verify the effectiveness of the displacement signals, a simulation comparison test is performed based on a matlab platform, as shown in fig. 4 and 5, the ARMA model and the ARMA-kalman filtering model are respectively adopted for signal extraction in simulation, and the result shows that the effect of adopting the ARMA-kalman filtering model is better.
In the following, an embodiment of a wave period prediction device provided by an embodiment of the present invention is described, and the wave period prediction device described below and the wave period prediction method described above may be referred to correspondingly.
Referring to fig. 6, the embodiment includes:
the obtaining module 601: the method is used for acquiring displacement information of the motor rotor positioned on the irregular waves in the current time period.
Dominant frequency shift curve determination module 602: and the displacement information is used for determining a dominant frequency displacement curve of the irregular waves according to the displacement information.
The curve determination module 603: and the method is used for determining a dominant frequency speed curve and a dominant frequency acceleration curve corresponding to the dominant frequency displacement curve.
Zero crossing determination module 604: and the zero-crossing points on the main frequency speed curve and the main frequency acceleration curve are determined by utilizing the dsp chip.
The prediction module 605: and the wave period prediction method is used for calculating the difference value of two adjacent zero-crossing points on the dominant frequency speed curve and the dominant frequency acceleration curve, and taking two times of the difference value as the wave period prediction result of the next time period.
Wherein the dominant frequency displacement curve determining module 602 comprises:
model parameter determination unit 6021: and determining model parameters of Kalman filtering according to the displacement information by utilizing an AMRA model, wherein the model parameters comprise a transfer matrix A, a noise matrix B and a system error matrix Q.
Model determination unit 6022: and the Kalman filtering model is determined according to the model parameters.
Main frequency shift curve determination unit 6023: for determining a dominant frequency displacement curve of the irregular wave using the kalman filter model.
Wherein the model parameter determination unit 6021 comprises:
first sub-unit 60211: for determining a fitting residual and a residual variance from the displacement information using an AMRA model.
Second subunit 60212: and the method is used for determining the transfer matrix A and the noise matrix B of the Kalman filtering model according to the fitting residual errors.
Third subunit 60213: and the system error matrix Q is determined according to the residual variance.
The wave period predicting device of this embodiment is used to implement the wave period predicting method, and therefore a specific implementation manner of the device can be seen in the embodiment parts of the wave period predicting method in the foregoing, for example, the obtaining module 601, the dominant frequency displacement curve determining module 602, the curve determining module 603, the zero crossing point determining module 604, and the predicting module 605 are respectively used to implement steps S101, S102, S103, S104, and S105 in the wave period predicting method. Therefore, the detailed description thereof may refer to the description of the respective partial embodiments, which will not be presented herein.
In addition, since the wave period prediction device of this embodiment is used to implement the wave period prediction method, its function corresponds to that of the wave period prediction method, and is not described herein again.
In addition, the present invention also provides a wave period predicting apparatus comprising:
a memory: for storing a computer program;
a processor: for executing said computer program for implementing the steps of the wave period prediction method as described above.
Finally, the invention also provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the wave period prediction method as described above.
Since the wave period prediction device of the present embodiment and a computer-readable storage medium are used to implement the wave period prediction method, the implementation manner thereof can be referred to the description of the wave period prediction method embodiment, and will not be described here. In addition, the actions correspond to those of the above-described method, and are not described in detail herein.
The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The wave period prediction method, device, equipment and computer readable storage medium provided by the invention are described in detail above. The principles and embodiments of the present invention are explained herein using specific examples, which are presented only to assist in understanding the method and its core concepts. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.
Claims (6)
1. A wave period prediction method, comprising:
acquiring displacement information of a motor rotor positioned on irregular waves at the current time period;
determining a dominant frequency displacement curve of the irregular waves according to the displacement information; wherein the determining a dominant frequency displacement curve of the irregular waves according to the displacement information comprises: determining model parameters of Kalman filtering according to the displacement information by using an AMRA model, wherein the model parameters comprise a transfer matrix A, a noise matrix B and a system error matrix Q; determining a Kalman filtering model according to the model parameters; determining a dominant frequency displacement curve of the irregular waves by using the Kalman filtering model;
determining a dominant frequency speed curve and a dominant frequency acceleration curve corresponding to the dominant frequency displacement curve;
determining zero crossing points on the main frequency speed curve and the main frequency acceleration curve by using a dsp chip;
and calculating the difference value of two adjacent zero crossing points on the dominant frequency speed curve and the dominant frequency acceleration curve, and taking two times of the difference value as a wave period prediction result of the next time period.
2. The method of claim 1, wherein the determining model parameters for kalman filtering from the displacement information using the AMRA model, the model parameters including a transition matrix a, a noise matrix B, and a system error matrix Q, comprises:
determining a fitting residual error and a residual error variance according to the displacement information by using an AMRA model;
determining the transfer matrix A and the noise matrix B of the Kalman filtering model according to the fitting residual error;
and determining the system error matrix Q according to the residual variance.
3. A wave period prediction device, comprising:
an acquisition module: the displacement information of the motor rotor positioned on the irregular waves in the current time period is acquired;
a dominant frequency displacement curve determination module: the displacement information is used for determining a dominant frequency displacement curve of the irregular waves; the dominant frequency displacement curve determination module comprises: a model parameter determination unit: the model parameters are used for determining Kalman filtering according to the displacement information by utilizing an AMRA model, and the model parameters comprise a transfer matrix A, a noise matrix B and a system error matrix Q; a model determination unit: the Kalman filtering model is determined according to the model parameters; dominant frequency displacement curve determination unit: for determining a dominant frequency displacement curve of the irregular wave using the Kalman filtering model;
a curve determination module: the main frequency displacement curve is used for determining a main frequency speed curve and a main frequency acceleration curve corresponding to the main frequency displacement curve;
a zero crossing point determination module: the device is used for determining zero crossing points on the dominant frequency speed curve and the dominant frequency acceleration curve by using a dsp chip;
a prediction module: and the wave period prediction method is used for calculating the difference value of two adjacent zero-crossing points on the dominant frequency speed curve and the dominant frequency acceleration curve, and taking two times of the difference value as the wave period prediction result of the next time period.
4. The apparatus of claim 3, wherein the model parameter determination unit comprises:
a first subunit: the AMRA module is used for determining a fitting residual error and a residual error variance according to the displacement information;
a second subunit: the transfer matrix A and the noise matrix B of the Kalman filtering model are determined according to the fitting residual errors;
a third subunit: for determining the systematic error matrix Q from the residual variance.
5. A wave period prediction apparatus, characterized by comprising:
a memory: for storing a computer program;
a processor: for executing the computer program for carrying out the steps of the wave period prediction method according to any one of claims 1-2.
6. A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, which computer program, when being executed by a processor, carries out the steps of the wave period prediction method according to any one of claims 1-2.
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| CN101706275A (en) * | 2009-11-09 | 2010-05-12 | 哈尔滨工程大学 | Measuring device and method of dynamic ocean waves |
| JP2013061768A (en) * | 2011-09-13 | 2013-04-04 | Nippon Telegr & Teleph Corp <Ntt> | Optimal model estimation device, method, and program |
| CN107357170A (en) * | 2017-07-14 | 2017-11-17 | 山东大学 | A kind of Wave Model Forecasting Methodology based on active disturbance rejection state observer |
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| CN101706275A (en) * | 2009-11-09 | 2010-05-12 | 哈尔滨工程大学 | Measuring device and method of dynamic ocean waves |
| JP2013061768A (en) * | 2011-09-13 | 2013-04-04 | Nippon Telegr & Teleph Corp <Ntt> | Optimal model estimation device, method, and program |
| CN107357170A (en) * | 2017-07-14 | 2017-11-17 | 山东大学 | A kind of Wave Model Forecasting Methodology based on active disturbance rejection state observer |
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