CN112949188A - Optimizing system and method for wave energy device parameter configuration - Google Patents
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Abstract
The invention provides an optimizing system and method for wave energy device parameter configuration, which is compiled by MATLAB language and Windows batch processing commands and comprises the following steps: the MA-System is used for establishing a simulation model, reading and outputting data, calculating hydrodynamic force and energy obtaining data, and calculating the average power generation power of the wave energy device; and the optimizing operation System is used for combining the calculation result of the MA-System with an intelligent optimizing algorithm to calculate the parameter corresponding to the wave energy device with the maximum average generating power. According to the wave energy device structure parameter configuration method, full-automatic optimization operation is carried out by adopting an intelligent optimization algorithm on the basis of mass data analysis, and the wave energy device structure parameter configuration with the maximum electric power under specific sea area conditions is simply and efficiently found out by carrying out energy obtaining power influence parameter analysis on a single wave energy device or a plurality of array wave energy devices.
Description
Technical Field
The invention belongs to the field of wave energy power generation, and particularly relates to an optimization system and method for wave energy device parameter configuration.
Background
Wave energy is a specific form of ocean energy and is one of the most important energy sources in ocean energy, and the development and utilization of the wave energy are very important for relieving the energy crisis and reducing the environmental pollution. At present, wave energy is increasingly concerned by countries in the world as a green renewable energy source, and has a wide development and utilization prospect. However, wave motion is irregular and unstable, and how to fully utilize wave energy is increasingly receiving wide attention from researchers.
The wave energy device captures and collects wave energy and converts the wave energy into electric energy, and wave power generation is realized. However, the power generation power of the wave energy devices with different structural parameters under different sea area conditions is different, and how to find the structural parameter configuration of the wave energy device with the maximum power generation power under specific sea area conditions, that is, the parameter optimization configuration of the wave energy device, becomes a key technology for wave energy power generation research.
In practical research, when a wave energy device is optimized, the relation between the energy-obtaining power and the structural parameters is usually invisible and complex, and with the increase of design variables, the matching combination of different variables is multiplied. For example, the energy capturing performance of the wave energy device arranged in an actual sea area is often influenced by factors such as the shape and size of a floating body, PTO damping, array arrangement mode and the like, so that the energy capturing power influence parameter analysis of the wave energy device can be carried out by relying on a large amount of data, and the optimal matching of the device influence parameters is obtained. However, it is obviously difficult to satisfy the above-mentioned large amount of data requirements by means of a physical model test means, and by means of a numerical simulation method, that is, by using numerical simulation software to manually establish wave energy device models matched with different parameters, and process and obtain related energy obtaining data, there are problems of low efficiency, long time consumption, difficulty in obtaining enough data in a short time, and the like.
Disclosure of Invention
The problems to be solved by the invention are as follows:
in view of the above problems, the present invention provides an optimizing system and method for wave energy device parameter configuration, which can efficiently find out the wave energy device structural parameter configuration with the maximum electric power under specific sea area conditions by a simple and fast means.
The technical means for solving the problems are as follows:
the invention provides an optimizing system for parameter configuration of a wave energy device, which is compiled by MATLAB language and Windows batch processing commands and comprises the following steps:
the MA-System is a combined simulation System of MATLAB, AQWA and APDL, which is constructed according to the specific form of the wave energy device, and is used for carrying out simulation model establishment, data read-in output, hydrodynamic calculation and energy obtaining data output and calculating the average generating power of the wave energy device; and
and the optimizing operation System combines the calculation result of the MA-System with an intelligent optimizing algorithm to calculate the parameter corresponding to the wave energy device with the maximum average generating power.
In the invention, MATLAB is used as a pivot platform to summarize input and output data, software is called through batch processing commands, a data transmission channel between the software is established, and modules in a system are sequentially connected in series to run so as to realize the joint simulation of MATLAB, AQWA and APDL. According to the wave energy device optimization method, full-automatic optimization operation is carried out by adopting an intelligent optimization algorithm on the basis of analysis of a large amount of data, and energy obtaining power influence parameters are analyzed on a single wave energy device or a plurality of wave energy devices arranged in an array, so that the optimal matching of the device influence parameters is obtained, and the structural parameter configuration of the wave energy device with the maximum electric power under a specific sea area condition is found. Compared with exhaustive optimization, the method greatly saves the optimization time, improves the optimization efficiency, and can simply and efficiently realize the full utilization of the wave energy.
In the present invention, the MA-System may include:
a parameter input module, which defines and inputs parameter variables for control model and simulation calculation through MATLAB according to the specific form of the wave energy device, wherein the parameter variables at least comprise: wave conditions, calculation step length and step number, load size and application mode, floating body structure parameters, floating body number and array arrangement, gravity acceleration, sea water density and sea water depth;
the model generation module writes floating body structure parameters, floating body numbers and array arrangement in the parameter variables into a first data document in the form of an APDL software modeling command stream, completes model creation after the APDL software reads the modeling command stream in the first data document, and at least stores model grid node data and hydrodynamic calculation unit data into a second data document; an AQWA solving module comprising: the device comprises a frequency domain calculating unit for calculating a frequency domain calculating result based on AQWA software and a time domain calculating unit for calculating a time domain calculating result based on the AQWA software and the frequency domain calculating result; and
and the data processing module is used for reading time-course data of the movement displacement and the movement speed of the floating body contained in the data file in which the time-domain calculation result is stored, calculating the instantaneous power time course and the average power of the floating body according to the time-course data and the PTO damping force and outputting the instantaneous power time course and the average power.
In the present invention, the frequency domain calculating unit writes the frequency domain analysis data into a third data document in a format of an AQWA software frequency domain calculation input document, so that the AQWA software reads the third data document for frequency domain analysis, and stores the analysis result in a fourth data document; the frequency domain analysis data includes: the gravity acceleration, the seawater density and the sea depth in the parameter variables; automatically calculating the weight, the moment of inertia and the gravity center position of the floating body according to the structural parameters of the floating body; model mesh node data and hydrodynamic calculation unit data in the second data document;
in the time domain calculating unit, at least wave conditions, calculating step length and step number, load size and application mode are written into a fifth data document in a format of an input document calculated by an AQWA software time domain, on the basis of reading in the fourth data document, the AQWA software reads the fifth data document for time domain analysis, and an analysis result is stored in a sixth data document.
In the present invention, the intelligent optimization algorithm may be a genetic optimization algorithm.
In the present invention, the optimizing operation system may include:
the parameter variable selection module selects at least one parameter variable from the parameter variables input in the MA-System as an optimization parameter variable and keeps other parameter variables unchanged;
the initial population generation module is used for coding the optimization parameter variables, determining corresponding coding lengths according to the value ranges of different variables, integrating the coding lengths into the same individual coding string, generating a plurality of different individual coding strings and forming an initial population of a genetic optimization algorithm;
the target function generation module is used for calling the MA-System to calculate the average power generation power of different individuals in the initial population as the target function of the individual, and outputting the related capacitation data of each individual to a seventh data document;
a fitness function determining module that determines a fitness function by maximizing the objective function;
the genetic optimization operation module selects high-quality individuals from the population through the fitness function to eliminate poor-quality individuals, and performs circular screening until reaching a genetic algebra, and finally takes the individuals in the genetic population as optimal individuals; and
and the optimal parameter output module is used for outputting the optimal individuals as the structural parameters with the maximum power generation power of the wave energy device.
The invention provides an optimizing method for parameter configuration of a wave energy device, which comprises the following steps:
step 1), constructing an MA-System serving as a combined simulation System of MATLAB, AQWA and APDL according to the specific form of the wave energy device, and calculating the average generated power of the wave energy device;
and 2) combining the MA-System and an intelligent optimization algorithm to calculate structural parameters corresponding to the wave energy device with the maximum average generated power.
In the present invention, the step 1) may include:
step 1-1), defining and inputting parameter variables for control models and simulation calculation through MATLAB according to the specific form of the wave energy device, wherein the parameter variables at least comprise: wave conditions, calculation step length and step number, load size and application mode, floating body structure parameters, floating body number and array arrangement, gravity acceleration, sea water density and sea water depth;
step 1-2), floating body structure parameters, floating body numbers and array arrangement in the parameter variables are written into a first data document in the form of an APDL software modeling command stream, the APDL software finishes model creation after reading the modeling command stream in the first data document, and at least model grid node data and hydrodynamic calculation unit data are stored into a second data document;
step 1-3), comprising: the device comprises a frequency domain calculating unit for calculating a frequency domain calculating result based on AQWA software and a time domain calculating unit for calculating a time domain calculating result based on the AQWA software and the frequency domain calculating result; and
and 1-4) reading time-course data of the movement displacement and the movement speed of the floating body contained in the data file in which the time-domain calculation result is stored, calculating the instantaneous power time course and the average power of the floating body according to the time-course data and the PTO damping force, and outputting the instantaneous power time course and the average power.
In the present invention, the step 1-3) may further include:
step 1-3-1), writing frequency domain analysis data into a third data document in a format of an input document calculated by AQWA software frequency domain, enabling the AQWA software to read the third data document for frequency domain analysis, and storing an analysis result in a fourth data document; the frequency domain analysis data includes: the gravity acceleration, the seawater density and the sea depth in the parameter variables; automatically calculating the weight, the moment of inertia and the gravity center position of the floating body according to the structural parameters of the floating body; model mesh node data and hydrodynamic calculation unit data in the second data document; and
step 1-3-2), at least writing wave conditions, calculation step length and step number, load size and application mode into a fifth data document in a format of an input document for AQWA software time domain calculation, enabling the AQWA software to read the fifth data document for time domain analysis on the basis of reading in the fourth data document, and storing an analysis result into a sixth data document.
In the present invention, the step 2) may include:
step 2-1), selecting at least one item from parameter variables input in the MA-System as an optimization parameter variable, and keeping other parameter variables unchanged;
step 2-2), encoding the optimization parameter variables, determining corresponding encoding lengths according to the value ranges of different variables, integrating the encoding lengths into the same individual encoding string, generating a plurality of different individual encoding strings, and forming an initial population of a genetic optimization algorithm;
step 2-3), the MA-System is called to calculate the average power generation power of different individuals in the initial population as the target function of the individuals, and the related capacitation data of each individual is output to a seventh data document;
step 2-4), determining a fitness function by maximizing the objective function;
step 2-5), selecting high-quality individuals from the population through the fitness function to eliminate poor-quality individuals, performing circular screening until reaching a genetic algebra, and taking the individuals in the final genetic population as optimal individuals;
and 2-6), outputting the optimal individual as the structural parameter with the maximum power generation power of the wave energy device.
The invention has the following effects:
the invention aims to provide an optimization System and method for wave energy device parameter configuration, which are based on the ingenious combination of an intelligent optimization algorithm and an MA-System serving as combined simulation software of MATLAB, AQWA and APDL, and can be used for efficiently finding the wave energy device optimization method for wave energy device structural parameter configuration with maximum electric power under specific sea area conditions by a simple and rapid means, thereby greatly saving the optimization time and improving the optimization efficiency.
Drawings
FIG. 1 is a schematic diagram showing the architecture of the optimization system of the present invention;
FIG. 2 is a diagram showing the communication relationship between respective software in the MA-System of the present invention;
FIG. 3 is a framework flow diagram illustrating the MA-System of the present invention;
FIG. 4 is a flow chart illustrating the genetic optimization algorithm optimization in the optimization algorithm system of the present invention;
fig. 5 is a graph showing the average value of the average generated power and the optimum value of the single-floater population as a function of the genetic algebra.
Detailed Description
The present invention is further described below in conjunction with the following embodiments, which are to be understood as merely illustrative, and not restrictive, of the invention. The same or corresponding reference numerals denote the same components in the respective drawings, and redundant description is omitted.
The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. In the description of the present invention, it should be noted that the terms "first", "second", "third", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The optimizing System for parameter configuration of the wave energy device is written in MATLAB language and Windows batch processing commands, and comprises an MA-System and an optimizing operation System as shown in FIG. 1. The MA-System is a combined simulation System of MATLAB, AQWA and APDL, which is constructed according to the specific form of the wave energy device, and is used for carrying out simulation model establishment, data read-in output, hydrodynamic calculation and energy obtaining data output and calculating the average generating power of the wave energy device. And in the optimizing operation System, the calculation result of the MA-System is combined with an intelligent optimizing algorithm, and the parameter corresponding to the wave energy device with the maximum average generating power is calculated.
[MA-System]
In the invention, the MA-System is the joint simulation of MATLAB, AQWA and APDL, and comprises a parameter input module, a model generation module, an AQWA solving module and a data processing module. MATLAB is data analysis software, AQWA is hydrodynamic force numerical simulation software, APDL is modeling software, in the field, APDL generally establishes a model with AQWA in a matching way, and corresponding grid node data documents and the like are derived for AQWA calculation. As shown in fig. 2, the MATLAB is used as a pivot platform to summarize input and output data, calls software through a batch processing command, establishes a data transmission channel between the software, and sequentially runs modules in the system in series. The optimization system of the present invention will be described below in conjunction with a specific method.
As shown in fig. 3, the MATLAB control parameter input module is activated, and according to the specific form of the wave energy device, parameter variables for control model and simulation calculation are defined and input by the MATLAB, and the parameter variables at least include: wave conditions, calculation step length and step number, load size and application mode, floating body structure parameters, floating body number and array arrangement, gravity acceleration, sea water density, sea water depth and the like.
And starting the MATLAB control model generation module, and writing the floating body structure parameters, the floating body number and the array arrangement in the defined parameter variables into a first data document in the APDL software modeling command stream format. The first data document is a command stream file used to create the model, and may be, but is not limited to, a text document. In the process of establishing a model, APDL software reads a modeling command stream in a text document of 'txt' as a first data document, creates model nodes, generates lines by the nodes, generates model surface elements by the lines, cuts a model waterline according to the water height, merges the model surface elements into shell63 surface element entities, divides a grid, and then stores grid related data into a second data document and exports the second data document. The second data document includes at least model mesh node data and hydrodynamic calculation unit data, and may be, but is not limited to, an aqwa document. In addition, in actual operation, the modeling command flow needs to be adjusted according to the shape of the floating body.
And the MATLAB controls the AQWA solving module to be started to perform analysis and calculation of a frequency domain and a time domain. The AQWA solving module comprises a frequency domain calculating unit and a time domain calculating unit. The frequency domain calculating unit calculates a frequency domain calculating result based on the AQWA software. The time domain calculation unit calculates a time domain calculation result based on the AQWA software and the frequency domain calculation result.
Specifically, in the frequency domain calculating unit, the frequency domain analysis data is written into the third data document in a format of an input document calculated by AQWA software frequency domain by MATLAB, and the frequency domain analysis data at least includes: the gravity acceleration, the seawater density and the sea depth in the parameter variables; automatically calculating the weight, the moment of inertia and the gravity center position of the floating body according to the structural parameters of the floating body; model mesh node data and hydrodynamic calculation cell data in the aqwa document as a second data document. The third data document may be, but is not limited to, a dat document.
Next, MATLAB creates a Windows batch file with suffix.com and bat, each containing a batch command statement as follows:
com file:
run line Buoy
bat document:
IF exists \ bin \ winx64 rem "here written into AQWA software installation path" std
IF EXIST..\bin\winx64..\bin\winx64\aqwa std/NOWIND
And calling the Windows batch files with the suffix of com and bat by the MATLAB, starting a line module (namely, a frequency domain analysis module) in the AQWA software based on the Windows batch commands to read in previously generated x, dat documents serving as third data documents, starting frequency domain analysis, and storing frequency domain analysis results in a fourth data document serving as a result document. The fourth data document may be, but is not limited to, an anbuoy.
In the time domain calculating unit, at least wave conditions, calculating step length and step number, load size, application mode and the like are written into a fifth data document in a format of an input document calculated by an AQWA software time domain through MATLAB. The fifth data document may be, but is not limited to, a dat document. If the linear load is selected to be applied, load data can be directly written into the Category9 of the dot-dat document as a fifth data document according to a corresponding format, but if the fixed load is selected, the AQWA software needs to be developed again by using Fortran language. For example, a damping torque is added to the floating body through programming, a dynamic connection × dll file is generated, placed in the computation path of the AQWA software, and invoked in the time domain computation through an FDLL command. Therefore, dynamic connection of different loads needs to be prepared in advance, and the files are called by an AQWA solving module of the MA-System. More specifically, dynamic links with different loads prepared in advance may be placed in the same folder, and the dynamic links may be named according to the size of the load. When a certain fixed load is used, FDLL is added to the command control field of the dat document as the fifth data document, so that the MATLAB copies the dynamic link corresponding to the fixed load under the AQWA software computation path folder and renames it to user _ force64.dll, by which the addition of the fixed load can be realized.
As above, MATLAB created the x.dat document as the fifth data document, and then created again the Windows batch files with suffix of.com and.bat, each of which contained the following batch command statements:
com file:
copy ALBuoy.res ANBuoy.res
run naut Buoy
bat document:
IF exists \ bin \ winx64 rem "here written into AQWA software installation path" std
IF EXIST..\bin\winx64..\bin\winx64\aqwa std/NOWIND
The Windows batch processing command is used for calling the Windows batch processing files of com and bat finally by MATLAB to realize the operation and end the time domain analysis.
Then, the Windows batch file with the suffix of com and bat is called by the MATLAB, based on the Windows batch command, on the basis of reading in the fourth data document (i.e., anbuoy. res) as the frequency domain analysis result, the naut module (i.e., the time domain analysis module) in the AQWA software is started to read in the previously generated data document of dat, which is the fifth data document, to start time domain analysis, and the time domain analysis result is stored in the sixth data document as the result document. The sixth data file at least contains time course data of the movement displacement and the movement speed of the floating body, and can be, but is not limited to, an anbuoy.
And the MATLAB control data processing module reads an ANBuoy. Lis document serving as a sixth data document, calculates the instantaneous power time course and the average power of the floating body according to the movement speed of the floating body, the PTO damping force and the like, and then outputs the calculated data of the displacement, the speed, the power time course, the average power and the like of the floating body to an external file for later use. The external files can be selected as different types of files such as txt, dat, xlsx and the like, and related graphs and the like can be drawn based on the files and requirements.
[ optimizing calculation System ]
In the invention, the optimization operation System combines the calculation result of the MA-System with the intelligent optimization algorithm to calculate the optimal parameter corresponding to the wave energy device with the maximum average generating power. Various intelligent optimization algorithms are used as the existing mature technologies and can be theoretically combined with the settlement result of the MA-System of the invention so as to solve the optimization problem of the wave energy device, but the principles of various intelligent optimization algorithms are different and are not practical in exhaustion, so that the invention only takes the genetic optimization algorithm as an example to describe the specific operation process in detail, but the invention is not limited to the above, and the optimization process of combining other intelligent optimization algorithms with the MA-System of the invention is supposed to be within the protection scope of the invention.
As shown in fig. 1, the optimizing operation system includes: the system comprises a parameter variable selection module, an initial population generation module, an objective function generation module, a fitness function determination module, a genetic optimization operation module and an optimal parameter output module. The parameter variable selection module selects at least one item from parameter variables input in the MA-System as an optimization parameter variable, and keeps other parameter variables unchanged, wherein the parameter variable can be selected according to an optimization purpose, for example, if the maximum generating power of the device under different drafts and heights is to be obtained, other parameters are kept unchanged, the draft and the height of the floater are used as the optimization variables, and the optimal draft and the height of the floater corresponding to the maximum generating power are obtained through calculation. The initial population generation module encodes the optimization parameter variables, determines corresponding encoding lengths according to the value ranges of the selected different optimization parameter variables, integrates the encoding lengths into the same individual encoding string, generates at least more than two different individual encoding strings and forms an initial population of the genetic optimization algorithm. The target function generation module calls the MA-System to calculate the average generating power of different individuals in the initial population as the target function of the individuals, and outputs the related energy obtaining data of each individual to a seventh data document, wherein the energy obtaining data is, for example, a document (namely, a sixth data document) obtained by reading an AQWA time domain analysis result by MATLAB, and data such as the displacement of the floating body, the speed time course, the power time course and the average power obtained by calculating the speed time course and the PTO damping force are obtained. The fitness function determination module determines a fitness function by maximizing an objective function. And the genetic optimization operation module selects high-quality individuals in the population through a fitness function to eliminate poor-quality individuals, and performs circular screening until reaching a genetic algebra, and finally takes the individuals in the genetic population as optimal individuals. And the optimal parameter output module outputs the optimal individuals as the maximum structural parameters of the power generation power of the wave energy device.
Specifically, the following description will be made in detail with reference to the embodiment and fig. 4. It is also to be understood that the following examples are illustrative of the present invention and are not to be construed as limiting the scope of the invention, and that certain insubstantial modifications and adaptations of the invention by those skilled in the art may be made in light of the above teachings. The specific process parameters and the like of the following examples are also only one example of suitable ranges, i.e., those skilled in the art can select the appropriate ranges through the description herein, and are not limited to the specific values exemplified below.
Firstly, the parameter variable selection module selects an optimization parameter variable, other parameters are kept unchanged, for example, the height of a floater is selected as the optimization parameter variable, and other parameters such as the radius of the floater, wave conditions, the depth of water in the sea area and the like are not changed. In addition, the genetic algorithm needs to set genetic algebra, crossover probability, and mutation probability.
Then, the initial population generation module encodes the changed structural parameter variables (including binary codes, real number codes, integer or letter arrangement codes, general data structure codes and the like), determines corresponding code lengths according to the value ranges of different variables, integrates the corresponding code lengths into the same individual code string, generates N different individual code strings and forms an initial population of the genetic algorithm. After genetic optimization selection, the individual code strings are decoded into optimized target values. For example, the cylindrical heaving float wave energy device takes the height H and the radius R of a floating body as design variables, binary coding is adopted, the variation ranges of H and R are 0.20-0.51 m and 0.2-0.34 m respectively, the variation intervals are 0.01m and 0.02m respectively, so that H and R have 32 variation values and 8 variation values respectively, the length of a coding string adopts 5 bits and 3 bits respectively, and the following coding relations are established:
H＝[h1h2h3h4h5]decimal value*0.01+0.2 h1,h2,h3,h4,h5＝0,1
R＝[r1r2r3]Decimal value*0.02+0.2 r1,r2,r3＝0,1。
Then, the target function generation module calls the MA-System to calculate the average power generation power of the individuals in different populations, the average power generation power is used as the target function of the individuals, and the related energy obtaining data of each individual are output to an external file.
Then, a fitness function determining module determines a fitness function in the genetic process, the adaptive value of an individual is the capability of measuring self ' adapting to the natural environment ', and the size of the adaptive value determines the result of the individual's excellence or disadvantage. In the wave energy device optimization method, the final result expected to be the individual with the maximum generated power is that the objective function is maximized, and the fitness function is as follows:
then, the genetic optimization operation module carries out genetic operation on the genetic population, and the genetic operation comprises three basic operators: selection, crossover and variation form the core of strong search ability of genetic algorithm, and are the main carriers for simulating propagation, hybridization and mutation phenomena in natural selection and genetic process. The method is characterized in that high-quality individuals are selected in the population through fitness, poor-quality individuals are eliminated, and the principle of survival of the fittest is embodied, so that the good-quality individuals are reserved to be directly inherited to the next generation population or new individuals are generated through pairing and crossing and then are inherited to the next generation population.
As for the selection, common selection methods are roulette wheel selection, sorting selection, optimal individual selection, random tournament selection, and the like. In the invention, a roulette wheel selection method is taken as an example, the size of a population is assumed to be N, and the selection process of the method comprises the following steps:
a. calculating fitness ratio, i.e. the probability of each individual being selected
b. And calculating the cumulative probability of each individual, wherein the cumulative probability is equivalent to the 'arc span area' on the turntable, the larger the area is, the higher the probability of being selected is, and the sum of the selection probabilities of all the individuals is 1.
c. Randomly generating r ∈ [0,1 ]]If q isiIf r is greater than r, selecting individual xi. r is equivalent to a rotating pointer in the turntable, and if the pointer randomly stays within the range of the arc span area of a certain individual, the individual is selected.
Individuals are selected through different selection probabilities, individuals with high and low adaptation values are selected with an opportunity, and only individuals with high adaptation values are selected more easily.
Regarding crossover, two individuals exchange their partial genes with each other in some way to form two new individuals, and the search ability of the algorithm is dramatically improved by crossover. According to the cross point selection and gene exchange modes, the crossing method is divided into single point crossing, two point crossing, uniform crossing, arithmetic crossing and the like. Taking the single-point crossing as an example, the specific operation method is as follows: randomly setting a cross point in the individual string, and interchanging partial structures on any side of the two individual cross points to generate two new individuals, for example:
individual A:1001 ↓111 → 1001000, new individual
Individual B:0011 ↓ → 0011111, new individual
The ratio of the number of exchanged individuals to the number of populations in a population is called the crossover probability pcReflecting the execution efficiency of the interleaving operation.
With respect to mutation, it is an aid to create a new individual by replacing some gene values in the individual code string with other gene values. The mutation operation enables the genetic algorithm to have local random search capability and accelerates the convergence of the optimal solution. Taking the basic bit variation method as an example, the variation method is to use the variation probability p for the individual code stringmRandomly appointing a certain gene or genes to change, and the mutation operation of the binary code string is as follows:
and judging whether the genetic algebra is reached, if not, sequentially circulating the target function generation module, the fitness function determination module and the genetic optimization operation module, and if so, ending the circulation, and taking the individuals in the final genetic population as the optimal individuals.
And finally, outputting the structural parameters of the optimal individuals by an optimal parameter output module, namely the wave energy device with the maximum generating power found by the optimization method.
In the specific implementation process of the embodiment, the method is used for optimizing the single floater of the cylindrical heaving floater wave energy device, the load size of the floater draft depth D, PTO is taken as a design variable, and other parameters are kept unchanged, such as
Table 1 shows:
| parameter(s) | Numerical value | Parameter(s) | Numerical value |
| Float radius (m) | 0.4 | Wave spectrum type | JONSWAP |
| Float height (m) | 0.4 | Spectral peak increasing factor | 3.3 |
| Working depth (m) | 10 | Effective wave height (m) | 0.12 |
| Sea water density (kg/(m/s2)) | 1025 | Average period(s) | 1.48 |
Table 1 shows the float parameter settings.
The PTO damping variable range is 0-630N/(m/s), 64 different damping working conditions are selected at uniform intervals, the draft variable range is 0.06-0.36 m, and 16 different draft working conditions are selected in the same way. The relationship between the optimum individual energy obtaining power in the population and the variation of the population average energy obtaining power along with the genetic algebra is shown in fig. 5, and it can be known from fig. 5 that the population optimum individual energy obtaining power curve is gradually stabilized at a certain specific value, which means that the algorithm is continuously approaching to the optimum solution. The best individual's energetic power at passage 20 was 2.435w, corresponding to a draft of 0.32m, and PTO damping was 360N/(m/s).
In order to verify the optimization result of the single floater, the energy obtaining power of the floater under 1024 matching combination working conditions of PTO damping and draft variables is calculated by adopting an exhaustive algorithm, wherein the first 1% of the optimal working conditions are shown in Table 3, the optimal individual obtained by genetic algorithm optimization is consistent with the optimal working condition obtained by exhaustion, and the high efficiency of the optimization process of the single floater genetic algorithm is fully proved;
table 3 shows the optimum condition for the first 1% of the single floats.
In the specific implementation process, the method is applied to optimizing the double floats of the cylindrical heaving float wave energy device array, the load size of the draft depth D, PTO of the floats is taken as a design variable, and the rest parameters are the same as the optimizing setting of the single floats. Each floater considers 8 groups of draught (0.22-0.34 m) and 8 groups of PTO damping (300- & lt 580N/(m/s)), and the floaters are arranged in the wave direction with the distance being half of the effective wavelength. Heritable to the 20 th generation, the optimum individual's capacitated power was 5.038w, with the draft and PTO damping for float 1 and float 2 being 0.3m, 420N/(m/s), 300N/(m/s), respectively.
Table 4 shows the first 3% optimal partial working conditions obtained by exhausting the double-float 4096 groups of working conditions, and the results of the comparative genetic optimization and the exhaustive calculation show that the optimal individuals can fall within the first 3% optimal working conditions of the whole situation, and the optimization results are very reliable;
table 4 array front 3% optimal partial conditions for dual floats.
In addition, wave energy devices in different forms can be only based on specific modification programs when models are built and AQWA input documents are written, and the cylindrical oscillating floater is used for example in the invention, but the invention is not limited to the wave energy devices.
In conclusion, the wave energy device optimization method based on the genetic algorithm and the MA-System can be used in the wave energy device structure parameter optimization process, the optimization result has high reliability, and compared with exhaustive optimization, the method saves optimization time and improves optimization efficiency. In addition, the whole optimizing process of the method realizes automation, and after necessary parameters are defined in advance, the optimal structure parameters of the wave energy device with the maximum electric power under the specific sea area condition are found out automatically by utilizing a genetic algorithm and calling an MA-System. According to the wave energy device optimization method based on the ocean power, full-automatic optimization operation is carried out by adopting an intelligent optimization algorithm on the basis of analysis of a large amount of data, and energy obtaining power influence parameters are analyzed on a single wave energy device or a plurality of wave energy devices arranged in an array, so that the optimal matching of the device influence parameters is obtained, and the structural parameter configuration of the wave energy device with the maximum electric power under a specific sea area condition is found. Compared with exhaustive optimization, the method greatly saves the optimization time, improves the optimization efficiency, and can simply and efficiently realize the full utilization of the wave energy.
The above embodiments are intended to illustrate and not to limit the scope of the invention, which is defined by the claims, but rather by the claims, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.
Claims (9)
1. An optimizing system for parameter configuration of a wave energy device is written by MATLAB language and Windows batch processing commands and comprises:
the MA-System is a combined simulation System of MATLAB, AQWA and APDL, which is constructed according to the specific form of the wave energy device, and is used for carrying out simulation model establishment, data read-in output, hydrodynamic calculation and energy obtaining data output and calculating the average generating power of the wave energy device; and
and the optimizing operation System combines the calculation result of the MA-System with an intelligent optimizing algorithm to calculate the parameter corresponding to the wave energy device with the maximum average generating power.
2. The wave energy device parameter configuration optimization system of claim 1,
the MA-System includes:
a parameter input module, which defines and inputs parameter variables for control model and simulation calculation through MATLAB according to the specific form of the wave energy device, wherein the parameter variables at least comprise: wave conditions, calculation step length and step number, load size and application mode, floating body structure parameters, floating body number and array arrangement, gravity acceleration, sea water density and sea water depth;
the model generation module writes floating body structure parameters, floating body numbers and array arrangement in the parameter variables into a first data document in the form of an APDL software modeling command stream, completes model creation after the APDL software reads the modeling command stream in the first data document, and at least stores model grid node data and hydrodynamic calculation unit data into a second data document;
an AQWA solving module comprising: the device comprises a frequency domain calculating unit for calculating a frequency domain calculating result based on AQWA software and a time domain calculating unit for calculating a time domain calculating result based on the AQWA software and the frequency domain calculating result; and
and the data processing module is used for reading time-course data of the movement displacement and the movement speed of the floating body contained in the data file in which the time-domain calculation result is stored, calculating the instantaneous power time course and the average power of the floating body according to the time-course data and the PTO damping force and outputting the instantaneous power time course and the average power.
3. The wave energy device parameter configuration optimization system of claim 2,
in the frequency domain calculating unit, writing frequency domain analysis data into a third data document in a format of an input document calculated by an AQWA software frequency domain, enabling the AQWA software to read the third data document for frequency domain analysis, and storing an analysis result in a fourth data document; the frequency domain analysis data includes: the gravity acceleration, the seawater density and the sea depth in the parameter variables; automatically calculating the weight, the moment of inertia and the gravity center position of the floating body according to the structural parameters of the floating body; model mesh node data and hydrodynamic calculation unit data in the second data document;
in the time domain calculating unit, at least wave conditions, calculating step length and step number, load size and application mode are written into a fifth data document in a format of an input document calculated by an AQWA software time domain, on the basis of reading in the fourth data document, the AQWA software reads the fifth data document for time domain analysis, and an analysis result is stored in a sixth data document.
4. The wave energy device parameter configuration optimizing system according to any one of claims 1 to 3, wherein the intelligent optimization algorithm is a genetic optimization algorithm.
5. The wave energy device parameter configuration optimization system of claim 4,
the optimizing operation system comprises:
the optimizing parameter variable selection module selects at least one item from the parameter variables input in the MA-System as an optimizing parameter variable and keeps other parameter variables unchanged;
the initial population generation module is used for coding the optimization parameter variables, determining corresponding coding lengths according to the value ranges of different variables, integrating the coding lengths into the same individual coding string, generating a plurality of different individual coding strings and forming an initial population of a genetic optimization algorithm;
the target function generation module is used for calling the MA-System to calculate the average power generation power of different individuals in the initial population as the target function of the individual, and outputting the related capacitation data of each individual to a seventh data document;
a fitness function determining module that determines a fitness function by maximizing the objective function;
the genetic optimization operation module selects high-quality individuals from the population through the fitness function to eliminate poor-quality individuals, and performs circular screening until reaching a genetic algebra, and finally takes the individuals in the genetic population as optimal individuals; and
and the optimal parameter output module is used for outputting the optimal individuals as the structural parameters with the maximum power generation power of the wave energy device.
6. An optimization method for wave energy device parameter configuration comprises the following steps:
step 1), constructing an MA-System serving as a combined simulation System of MATLAB, AQWA and APDL according to the specific form of the wave energy device, and calculating the average generated power of the wave energy device;
and 2) combining the MA-System and an intelligent optimization algorithm to calculate structural parameters corresponding to the wave energy device with the maximum average generated power.
7. The method of optimizing wave energy device parameter configuration according to claim 6,
the step 1) comprises the following steps:
step 1-1), defining and inputting parameter variables for control models and simulation calculation through MATLAB according to the specific form of the wave energy device, wherein the parameter variables at least comprise: wave conditions, calculation step length and step number, load size and application mode, floating body structure parameters, floating body number and array arrangement, gravity acceleration, sea water density and sea water depth;
step 1-2), floating body structure parameters, floating body numbers and array arrangement in the parameter variables are written into a first data document in the form of an APDL software modeling command stream, the APDL software finishes model creation after reading the modeling command stream in the first data document, and at least model grid node data and hydrodynamic calculation unit data are stored into a second data document;
step 1-3), comprising: the device comprises a frequency domain calculating unit for calculating a frequency domain calculating result based on AQWA software and a time domain calculating unit for calculating a time domain calculating result based on the AQWA software and the frequency domain calculating result; and
and 1-4) reading time-course data of the movement displacement and the movement speed of the floating body contained in the data file in which the time-domain calculation result is stored, calculating the instantaneous power time course and the average power of the floating body according to the time-course data and the PTO damping force, and outputting the instantaneous power time course and the average power.
8. The method of optimizing wave energy device parameter configuration according to claim 7,
step 1-3-1), writing frequency domain analysis data into a third data document in a format of an input document calculated by AQWA software frequency domain, enabling the AQWA software to read the third data document for frequency domain analysis, and storing an analysis result in a fourth data document; the frequency domain analysis data includes: the gravity acceleration, the seawater density and the sea depth in the parameter variables; automatically calculating the weight, the moment of inertia and the gravity center position of the floating body according to the structural parameters of the floating body; model mesh node data and hydrodynamic calculation unit data in the second data document; and
step 1-3-2), at least writing wave conditions, calculation step length and step number, load size and application mode into a fifth data document in a format of an input document for AQWA software time domain calculation, enabling the AQWA software to read the fifth data document for time domain analysis on the basis of reading in the fourth data document, and storing an analysis result into a sixth data document.
9. The method of optimizing wave energy device parameter configuration according to claim 6,
the step 2) comprises the following steps:
step 2-1), selecting at least one item from parameter variables input in the MA-System as an optimization parameter variable, and keeping other parameter variables unchanged;
step 2-2), encoding the optimization parameter variables, determining corresponding encoding lengths according to the value ranges of different variables, integrating the encoding lengths into the same individual encoding string, generating a plurality of different individual encoding strings, and forming an initial population of a genetic optimization algorithm;
step 2-3), the MA-System is called to calculate the average power generation power of different individuals in the initial population as the target function of the individuals, and the related capacitation data of each individual is output to a seventh data document;
step 2-4), determining a fitness function by maximizing the objective function;
step 2-5), selecting high-quality individuals from the population through the fitness function to eliminate poor-quality individuals, performing circular screening until reaching a genetic algebra, and taking the individuals in the final genetic population as optimal individuals;
and 2-6), outputting the optimal individual as the structural parameter with the maximum power generation power of the wave energy device.
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