CN109255361B - Tidal current energy power generation field unit layout method considering infeasible area - Google Patents

Tidal current energy power generation field unit layout method considering infeasible area Download PDF

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CN109255361B
CN109255361B CN201810557050.8A CN201810557050A CN109255361B CN 109255361 B CN109255361 B CN 109255361B CN 201810557050 A CN201810557050 A CN 201810557050A CN 109255361 B CN109255361 B CN 109255361B
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任洲洋
王元萌
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Abstract

The invention discloses a tidal current energy power generation field unit layout method considering infeasible areas, which mainly comprises the following steps: 1) acquiring basic data of the tidal current energy power generation field. 2) And extracting a typical change rule of the tidal flow velocity by adopting a k-means clustering method so as to obtain a typical curve of the tidal flow velocity. 3) An initial sample of a tidal current farm unit layout is generated and position coordinates of the tidal current energy generators in the initial sample are obtained. 4) And calculating the daily power generation amount of the tidal current energy power generation field by using the tidal current speed daily typical curve and the position coordinates of the tidal current energy power generator. 5) And optimizing the layout planning scheme of the tidal current energy power generation field unit by utilizing a particle swarm algorithm according to the daily output power of the tidal current energy power generation field. 6) It is determined whether the iteration is terminated. The method can be widely applied to the planning problem of the tidal current energy power generation field, and can provide beneficial reference for the planning and operation problem analysis of the tidal current energy power generation field.

Description

Tidal current energy power generation field unit layout method considering infeasible area
Technical Field
The invention relates to the field of power system planning, in particular to a tidal current energy power generation field unit layout method considering infeasible areas.
Background
Due to the environmental pressure caused by greenhouse effect and haze weather and the continuous increase of energy demand caused by the rapid development of human society, the development of renewable energy power generation technology is rapid in recent years. The tidal current energy power generation has the advantages of large power density, strong predictability and the like, is an environment-friendly resource and has great development potential. It is believed that tidal flow energy generation will certainly occupy an important position in the world's energy system in the near future.
The tidal current energy generator is a core device for converting tidal current energy into electric energy, and the layout of the tidal current energy generator is directly related to the output electric energy of a tidal current energy power generation field. Therefore, determining the layout of tidal flow energy generators would be an important issue facing tidal flow energy development. However, due to the limitations of the offshore conditions, many pre-defined areas cannot be installed with generators or are too expensive to install, and in tidal flow energy farm block layout planning, it is necessary to change the planning scheme to avoid infeasible areas. In the real sea, these infeasible areas are roughly divided into two categories:
1) geology is not feasible. The generator is difficult to install in the area due to soft or hard geology and the like.
2) The sea bed fluctuates. When the seabed is undulating too much, the area is difficult or expensive to install tidal current generators. Secondly, when the tide flows through the undulating region, a certain acceleration effect exists on the flow velocity of the tide, and the power output of the tidal current energy power generation field is influenced.
In terms of tidal current energy farm unit layout optimization, the prior art discloses methods of: firstly, randomly generating a population particle representation unit layout scheme according to the maximum target of the generated energy of the power generation field in a period of time. Secondly, for each layout scheme, the wake effect among the units is considered, and the power generation capacity of the tidal current energy power generation field is calculated. Then, calculating the fitness value of each layout scheme, updating the layout schemes according to the particle swarm algorithm, and calculating the target function again. And finally, comparing the objective function values of the previous and the next two times, if the objective function values are the same, stopping iterative computation, and otherwise, continuing the iterative computation. However, these methods are performed for ideal sea areas and cannot be directly applied to planning of the layout of tidal current energy farm units considering infeasible areas. The disadvantages of this method are: neglecting the influence of the infeasible area on the tidal flow velocity, the tidal flow energy power generation potential is difficult to be fully and fully excavated, and the fund utilization rate of the tidal flow energy power generation field is improved.
Disclosure of Invention
The present invention is directed to solving the problems of the prior art.
The technical scheme adopted for achieving the aim of the invention is that the tidal current energy power generation field unit layout method considering infeasible areas mainly comprises the following steps:
1) acquiring basic data of the tidal current energy power generation field.
Further, the basic data of the tidal current energy farm mainly comprises:
tidal flow energy power generation field within T days and 24 periods of each dayqt. q is the number of days. t is the period number. t is 1, 2 … 24.
Cut-in flow velocity V of tidal flow energy generatorinRated flow velocity VratedCut-out flow velocity VoutRated output power PratedCoefficient of energy gain CpCoefficient of thrust CTDiameter D of blade, radius r of blade0And the area a swept by the blade.
Sea water density rho and turbulence coefficient I0。
Planned area of tidal current energy farm and number of tidal current energy generators Nt。
2) And extracting a typical change rule of the tidal flow velocity by adopting a k-means clustering method so as to obtain a typical curve of the tidal flow velocity.
Further, the main steps to obtain the daily typical curve of tidal flow rate are as follows:
2.1) randomly selecting H days from the tidal flow rate measured data sample as an initial clustering center, wherein the clustering center is represented as Ce ═ ce1，ce2，...，ceT]Where e ═ 1, 2, …, ξ. ξ is the number of clusters.
2.2) calculating the distance from the flow data sample to the center of each cluster in turn:
distance d from day of flow data sample to e cluster centereqAs follows:
Figure GDA0001882239520000021
in the formula, vqtTidal flow rate at time t on day qth. c. CetThe t-th element representing the e-th class center Ce. e ═ 1, 2, …, ξ. q is the number of days. q is 1, 2, …, T. T is the number of daily samples of the measured tidal flow rate data. t is the period number. t is 1, 2 … 24. ξ is the number of clusters.
2.3) attributing the daily flow data samples to the category closest to the samples according to the distance of the flow data samples to the center of each category.
According to the clustering result, counting the number n of daily flow rate samples in each category of datae。
And after the reclassification of the flow speed data samples is completed, updating the clustering centers of all the categories.
Updated tth element c of e-th class center Cee tAs follows:
Figure GDA0001882239520000031
in the formula, vqtIs the data of the e category, and represents the tidal flow rate data of the qth period of the qth day of the category. n iseIs the number of samples belonging to the class e center. e ═ 1, 2, …, ξ. q is the number of days. q is 1, 2, …, T. T is the number of daily samples of the measured tidal flow rate data. t is the period number. t is 1, 2 … 24. ξ is the number of clusters.
2.4) repeating the steps 2.2 and 2.3 until the clustering center Ce produced by the two cycles is not changed any more.
The finally obtained clustering center is the daily typical curve of the tidal flow velocity. The daily typical curve of tidal flow rate characterizes the daily variation of tidal flow rate.
The number of tidal daily flow rate samples belonging to each category is counted, and the probability of the occurrence of the daily profile of each type of tidal flow rate is calculated based on the number of tidal daily flow rate samples belonging to each category. The probability of occurrence of the daily typical curve of tidal flow rates of class e is as follows:
Figure GDA0001882239520000032
in the formula, neRepresenting the number of samples that belong to class e centers. And n is the number of daily samples of the measured tidal flow rate data. e ═ 1, 2, …, ξ. ξ is the number of clusters.
3) An initial sample of a tidal current farm unit layout is generated and position coordinates of the tidal current energy generators in the initial sample are obtained.
Further, the main steps for generating an initial sample of a tidal flow farm unit layout are as follows:
3.1) initializing the maximum number of iterations of the particle swarm, the iteration count m being 1
3.2) computer random Generation of NpInitial individuals, each of which is 2N in lengtht。NpThe initial individuals constitute a real matrix Z. N is a radical ofpInitial individual representation NpDifferent tidal current energy power generation field unit layout schemes are provided.
Wherein the position coordinate of the ith generator in the kth tidal current energy farm unit layout scheme is expressed as (G)k，2i.1，Gk，2i)。i＝1，2…Nt。k＝1，2，…，Np。NpIs the initial number of individuals. N is a radical oftThe number of generators.
3.3) judging whether the ith generator in the kth tidal current energy power generation field unit layout scheme is positioned in the seabed fluctuation infeasible area or the geology infeasible area, and if the ith generator is positioned in the seabed fluctuation infeasible area or the geology infeasible area, regenerating the position coordinates (G) of the ith generatork，2i.1，Gk，2i) Until the ith generator is outside the seafloor fluctuation infeasible region and the geology infeasible region.
The geological non-feasible region is represented by a polygonal approximation method. The vertex coordinate of the geological infeasible area is Jχ。χ＝1，2…Nin. Wherein N isinThe number of polygon vertices.
The bottom surface of the seabed fluctuation infeasible area of the tidal current energy power generation field is an ellipse, and the cross section of the seabed fluctuation infeasible area is a cos-type curve.
3.4) solving the distance Z between any two generators in each initial individual, and judging whether the distance Z is greater than the minimum safe distance 5D. D is the tidal flow generator diameter.
If Z >5D, the initial individuals are recorded as initial samples of the tidal current energy farm unit layout.
And if Z is less than or equal to 5D, regenerating the initial individual and returning to the step 3.3.
4) And calculating the daily power generation amount of the tidal current energy power generation field by using the tidal current speed daily typical curve and the position coordinates of the tidal current energy power generator.
Further, the method for calculating the daily generated energy of the tidal current energy power generation field comprises the following main steps:
4.1) calculating the flow rate of the generator in the tidal current energy power generation field in each period based on the e type typical flow rate, and mainly comprising the following steps:
4.1.1) setting tidal flow velocity to flow in the positive direction of the x-axis, and based on the size of the generator abscissa, comparing N in the kth schemetThe table generators perform sequencing.
After sequencing, the flow velocity v of the 1 st generator in the kth schemek 1tAs follows:
Figure GDA0001882239520000041
in the formula, ce tIs the t-th element of the updated e-th class center Ce. k is 1, 2 … Np。t＝1，2…24。e＝1，2，…，ξ。NpIs the number of individuals. ξ denotes the number of clusters.
4.1.2) calculating the flow velocity of each generator independently affected by the wake of the ith upstream generator, except for the 1 st generator in the kth scheme. And if the distance between the generator and the direction of the coming tide is smaller than the distance between the target generator and the direction of the coming tide, the generator is an upstream generator. The upstream generator wake affects the target generator.
In the kth scheme, the jth generator is independently influenced by the wake flow u of the ith upstream generator in the period of tk ijtAs follows:
Figure GDA0001882239520000042
in the formula, mtIs the mean value of tidal flow rate over time t. k is 1, 2 … Np。NpIs the initial number of individuals. 1, 2, …, Nt。NtThe number of generators. t is the period number. t is 1, 2, …, 24. i is the number of the upstream generator. CTIs the thrust coefficient of the tidal flow energy generator. r is0Is the tidal flow energy generator blade radius. R (xi)ij) Is the wake radius of the upstream ith generator.
Wake radius R (xi) of upstream ith generatorij) As follows:
Figure GDA0001882239520000051
in the formula, r0Is the tidal flow energy generator blade radius. I is0Is the turbulence factor. XiijThe actual distance between the ith generator and the jth generator.
4.1.3) calculating the acceleration effect of the sea floor relief.
And establishing a model of the submarine undulating terrain of the tidal current energy power generation field.
In the region where the sea bottom heave is not feasible, a vertical section is made through observation point a along a specified flow rate. Observation point a is the ith generator position.
And recording the bottom surface projection point corresponding to the maximum height h 'on the vertical tangent plane as an O' point. And a projection point with the height h'/2 on the inflow surface of the vertical tangent plane is recorded as an L point.
And the point O is a projection point corresponding to the maximum height h. The O point is used as the flow velocity direction and the vertical flow velocity directionAnd (4) cutting the surface of the steel pipe. And the point B is a projection point with the corresponding height h/2 on the vertical flow velocity tangent plane. L is0Is a projection point with the corresponding height h/2 on the section along the direction of the flow velocity.
xx is the distance between observation points A and O'. L is the distance between the O' point and the L point. The length parameter B is the distance between points O and B. Length parameter l0Is O point and L0The distance between the points.
Calculating a global parameter Δ Smax. The global parameter Δ SmaxReflecting the maximum acceleration ratio that occurs under this flow direction condition. Global parameter Δ SmaxAs follows:
Figure GDA0001882239520000052
wherein Y is the corresponding acceleration parameter. L is the distance between the point O' and the point L. l0Is O point and L0The distance between the points.
The acceleration effect of tidal flow rate, Δ s (xx), is calculated. The acceleration effect of tidal flow rate Δ s (xx) is as follows:
Figure GDA0001882239520000061
where α and p are the corresponding acceleration parameters. xx is the distance between observation points A and O'. L is the distance between the O' point and the L point.
Calculating acceleration effect Ft of infeasible area of tidal current energy power generation field on flow velocityi. Acceleration effect Ft of the velocity of flow in infeasible areas of a tidal flow energy farmiAs follows:
Fti＝1+ΔS(xx) (9)
where Δ s (xx) is the acceleration effect of tidal flow velocity.
4.1.4) calculating the inflow flow rate of the tidal current energy farm generator under the multi-wake effect.
And (4) calculating the flow velocity of the power flow under the influence of the multiple tail flows, namely calculating the flow velocity of each generator except the 1 st generator. At typical flow rate in e, the j (th) line of the k (th) schemeActual flow rate of the motor at time t
Figure GDA0001882239520000064
As follows:
Figure GDA0001882239520000062
in the formula uk ijtIndicating the flow rate of the jth generator independently affected by the upstream ith generator wake at time t in the kth scheme. c. Ce tIs the t-th element of the updated e-th class center Ce. N is a radical ofk wiIs the number of units upstream of the jth generator of the kth scheme. FtiIs the tidal acceleration effect of the ith generator. k is 1, 2 … Np。i，j＝1，2…Nt。e＝1，2，…，ξ。NpIs the number of individuals. N is a radical oftThe number of generators. ξ is the number of clusters.
4.2) calculating the output power of the tidal current energy generator.
And calculating the output power of each generator according to the actual flow speed of each generator.
The output power of the kth individual jth tidal current energy Generator at time t at a typical flow rate in e
Figure GDA0001882239520000065
As follows:
Figure GDA0001882239520000063
in the formula (I), the compound is shown in the specification,
Figure GDA0001882239520000066
is the actual flow rate of the kth individual tidal flow energy generator at time t at the typical flow rate in e. k is 1, 2 … Np。j＝1，2…Nt。t＝1，2…24。e＝1，2，…，ξ。NpIs the number of individuals. N is a radical oftThe number of generators for the kth scheme. Xi isThe number of clusters. CpIs the coefficient of energy capture of the tidal flow energy generator. ρ is the density of seawater. And A is the area swept by the blades of the tidal current energy generator. VinIs the cut-in flow rate of the tidal flow energy generator. VratedIs the rated flow rate of the tidal flow energy generator. VoutIs the cut-out flow velocity of the tidal flow energy generator. PratedIs the rated output power of the tidal current energy generator.
4.3) calculating the daily generated energy of the tidal flow energy farm
Calculating the output power P of the tidal current energy power generation field according to the output power of each generatort k. Output power P of kth scheme at time tt kAs follows:
Figure GDA0001882239520000071
in the formula (I), the compound is shown in the specification,
Figure GDA0001882239520000073
the output power of the jth generator of the kth scheme at the time t is at the e-th typical flow rate. p is a radical ofeThe probability of the e-th typical flow rate, k-1, 2 … Np，j＝1，2…Nt，e＝1，2，…，ξ。NpThe number of individuals; n is a radical oftThe number of generators in the kth scheme. ξ denotes the number of clusters.
And calculating the daily power generation amount of the tidal current energy power generation field. Daily generated energy E of tidal flow field of kth schemekAs follows:
Figure GDA0001882239520000072
in the formula, Pt kThe output power at time t for the kth scheme. t is the period number. t is 1, 2, …, 24.
5) And optimizing the layout planning scheme of the tidal current energy power generation field unit by utilizing a particle swarm algorithm according to the daily output power of the tidal current energy power generation field.
Further, the method for optimizing the tidal current energy power generation field unit layout scheme by utilizing the particle swarm algorithm mainly comprises the following steps:
5.1) calculating the optimization target of each layout scheme. The optimization objective is the tidal current energy farm maximum output power.
When the kth scheme tidal current energy power generation field daily power generation amount FkDaily generated energy E equal to tidal flow field of kth schemekWhen the tidal current energy power generation field output power reaches the maximum, namely:
Fk＝Ek (14)
in the formula, FkThe daily power generation capacity of the tidal flow energy power farm for the kth scheme. EkThe daily power generation of tidal flow fields for the kth scenario. k is 1, 2 … Np。NpIs the number of individuals.
5.2) calculating the fitness value fitness of each layout scheme.
Fitness value fitness of k-th layout schemekAs follows:
Figure GDA0001882239520000081
wherein k is 1, 2, …, Np。NpIs the number of individuals. FkThe daily power generation capacity of the tidal flow energy power farm for the kth scheme.
6) It is determined whether the iteration is terminated. The judgment method is mainly as follows:
and judging whether the maximum iteration number is reached currently.
If so, the optimal tidal flow field planning scheme is the one with the minimum optimization objective in step 5.
If not, the iteration times m +1, updating the population according to the formula 1 and the formula 2, thereby generating a new scheme, and returning to the step 4.
The nth dimension component vv of the (m + 1) th iterative particle beta) flying velocity vectorβn m+1As follows:
Figure GDA0001882239520000082
in the formula, vvβn mIs the nth dimension component of the m-th iterative particle beta airspeed vector. x is the number ofβn mIs the nth dimension component of the m-th iterative particle beta airspeed vector. pbest is the best position the beta particle experiences. gbest is the best position that the population experiences. w is the inertial weight. c. C1And c2Are all learning factors. rand1And rand2Are all in [0, 1 ]]Uniformly distributed pseudo random numbers within the interval.
The nth dimension component x of the (m + 1) th iteration particle beta position vectorβn m+1As follows:
Figure GDA0001882239520000083
in the formula, vvβn m+1Is the nth dimension component of the (m + 1) th iterative particle beta flight velocity vector. x is the number ofβn mIs the nth dimension component of the m-th iterative particle beta airspeed vector.
The technical effect of the present invention is undoubted. The invention provides a tidal current energy power generation field coordination planning method considering reef influence, which not only considers the influence of an infeasible area on the installation of a generator and a cable, but also considers the influence of the infeasible area on the tidal current speed.
The planning scheme of the tidal current energy power generation field obtained based on the invention can reasonably avoid infeasible areas, fully excavate the tidal current energy power generation potential, and improve the fund utilization rate of the tidal current energy power generation field
The method can be widely applied to the planning problem of the tidal current energy power generation field, and can provide beneficial reference for the planning and operation problem analysis of the tidal current energy power generation field.
Drawings
FIG. 1 is a schematic view of a subsea heave model;
FIG. 2 is a block diagram of a method process flow;
FIG. 3 is a schematic diagram of the optimal layout of the machine set of the tidal current energy farm in the region X of China.
Detailed Description
The present invention is further illustrated by the following examples, but it should not be construed that the scope of the above-described subject matter is limited to the following examples. Various substitutions and alterations can be made without departing from the technical idea of the invention and the scope of the invention is covered by the present invention according to the common technical knowledge and the conventional means in the field.
Example 1:
referring to fig. 1 to 3, a tidal current energy farm unit layout method considering infeasible areas mainly includes the following steps:
1) acquiring basic data of the tidal current energy power generation field. The infeasible areas include primarily areas where sea floor heave is infeasible and areas where geology is infeasible. In fig. 3, the square frame is a geological infeasible area, the ellipse is an undulation infeasible area, the offshore substation is represented, and the round points represent the position of the generator.
Further, the basic data of the tidal current energy farm mainly comprises:
tidal flow energy power generation field within T days and 24 periods of each dayqt. q is the number of days. t is the period number. t is 1, 2 … 24.
Cut-in flow velocity V of tidal flow energy generatorinRated flow velocity VratedCut-out flow velocity VoutRated output power PratedCoefficient of energy gain CpCoefficient of thrust CTDiameter D of blade, radius r of blade0And the area a swept by the blade.
Sea water density rho and turbulence coefficient I0。
Planned area of tidal current energy farm and number of tidal current energy generators Nt。
2) And extracting a typical change rule of the tidal flow velocity by adopting a k-means clustering method so as to obtain a typical curve of the tidal flow velocity.
Further, the main steps to obtain the daily typical curve of tidal flow rate are as follows:
2.1) randomly selecting H days from the tidal flow rate measured data sample as an initial clustering center, wherein the clustering center is represented as Ce ═ ce1，ce2，...，ceT]Where e ═ 1, 2, …, ξ. ξ is the number of clusters.
2.2) calculating the distance from the flow data sample to the center of each cluster in turn:
distance d from day of flow data sample to e cluster centereqAs follows:
Figure GDA0001882239520000101
in the formula, vqtTidal flow rate at time t on day qth. c. CetThe t-th element representing the e-th class center Ce. e ═ 1, 2, …, ξ. q is the number of days. q is 1, 2, …, T. T is the number of daily samples of the measured tidal flow rate data. t is the period number. t is 1, 2 … 24. ξ is the number of clusters.
2.3) attributing the daily flow data samples to the category closest to the samples according to the distance of the flow data samples to the center of each category.
According to the clustering result, counting the number n of daily flow rate samples in each category of datae。
And after the reclassification of the flow speed data samples is completed, updating the clustering centers of all the categories.
Updated tth element c of e-th class center Cee tAs follows:
Figure GDA0001882239520000102
in the formula, vqtIs the data of the e category, and represents the tidal flow rate data of the qth period of the qth day of the category. n iseIs the number of samples belonging to the class e center. e ═ 1, 2, …, ξ. q is the number of days. q is 1, 2, …, T. T is the number of daily samples of the measured tidal flow rate data. t is a period of timeA serial number. t is 1, 2 … 24. ξ is the number of clusters.
2.4) repeating the steps 2.2 and 2.3 until the clustering center Ce produced by the two cycles is not changed any more.
The finally obtained clustering center is the daily typical curve of the tidal flow velocity. The daily typical curve of tidal flow rate characterizes the daily variation of tidal flow rate.
The number of tidal daily flow rate samples belonging to each category is counted, and the probability of the occurrence of the daily profile of each type of tidal flow rate is calculated based on the number of tidal daily flow rate samples belonging to each category. The probability of occurrence of the daily typical curve of tidal flow rates of class e is as follows:
Figure GDA0001882239520000111
in the formula, neRepresenting the number of samples that belong to class e centers. And n is the number of daily samples of the measured tidal flow rate data. e ═ 1, 2, …, ξ. ξ is the number of clusters.
3) An initial sample of a tidal current farm unit layout is generated and position coordinates of the tidal current energy generators in the initial sample are obtained.
Further, the main steps for generating an initial sample of a tidal flow farm unit layout are as follows:
3.1) initializing the maximum number of iterations of the particle swarm, the iteration count m being 1
3.2) computer random Generation of NpInitial individuals, each of which is 2N in lengtht。NpThe initial individuals constitute a real matrix Z. N is a radical ofpInitial individual representation NpDifferent tidal current energy power generation field unit layout schemes are provided.
Wherein the position coordinate of the ith generator in the kth tidal current energy farm unit layout scheme is expressed as (G)k，2i.1，Gk，2i)。i＝1，2…Nt。k＝1，2，…，Np。NpIs the initial number of individuals. N is a radical oftThe number of generators.
3.3) judgment of the k-thIn the tidal current energy power generation field unit layout scheme, whether the ith generator is located in the seabed fluctuation infeasible area or the geological infeasible area or not is judged, and if the ith generator is located in the seabed fluctuation infeasible area or the geological infeasible area, the position coordinates (G) of the ith generator are regeneratedk，2i.1，Gk，2i) Until the ith generator is outside the seafloor fluctuation infeasible region and the geology infeasible region.
The geological non-feasible region is represented by a polygonal approximation method. The vertex coordinate of the geological infeasible area is Jχ。χ＝1，2…Nin. Wherein N isinThe number of polygon vertices.
3.4) solving the distance Z between any two generators in each initial individual, and judging whether the distance Z is greater than the minimum safe distance 5D. D is the tidal flow generator diameter.
If Z >5D, the initial individuals are recorded as initial samples of the tidal current energy farm unit layout.
And if Z is less than or equal to 5D, regenerating the initial individual and returning to the step 3.3.
4) And calculating the daily power generation amount of the tidal current energy power generation field by using the tidal current speed daily typical curve and the position coordinates of the tidal current energy power generator.
Further, the method for calculating the daily generated energy of the tidal current energy power generation field comprises the following main steps:
4.1) calculating the flow rate of the generator in the tidal current energy power generation field in each period based on the e type typical flow rate, and mainly comprising the following steps:
4.1.1) setting tidal flow velocity to flow in the positive direction of the x-axis, and based on the size of the generator abscissa, comparing N in the kth schemetThe table generators perform sequencing.
After sequencing, the flow velocity v of the 1 st generator in the kth schemek 1tAs follows:
Figure GDA0001882239520000121
in the formula, ce tIs the t-th element of the updated e-th class center Ce。k＝1，2…Np。t＝1，2…24。e＝1，2，…，ξ。NpIs the number of individuals. ξ is the number of clusters.
4.1.2) calculating the flow velocity of each generator independently affected by the wake of the ith upstream generator, except for the 1 st generator in the kth scheme. And if the distance between the generator and the direction of the coming tide is smaller than the distance between the target generator and the direction of the coming tide, the generator is an upstream generator. The upstream generator wake affects the target generator.
In the kth scheme, the jth generator is independently influenced by the wake flow u of the ith upstream generator in the period of tk ijtAs follows:
Figure GDA0001882239520000122
where mt is the mean tidal flow rate over a period t. k is 1, 2 … Np。NpIs the initial number of individuals. 1, 2, …, Nt。NtThe number of generators. t is the period number. t is 1, 2, …, 24. i is the number of the upstream generator. CTIs the thrust coefficient of the tidal flow energy generator. r is0Is the tidal flow energy generator blade radius. R (xi)ij) Is the wake radius of the upstream ith generator.
Wake radius R (xi) of upstream ith generatorij) As follows:
Figure GDA0001882239520000123
in the formula, r0Is the tidal flow energy generator blade radius. I is0Is the turbulence factor. XiijThe actual distance between the ith generator and the jth generator.
4.1.3) calculating the acceleration effect of the sea floor relief.
And establishing a model of the submarine undulating terrain of the tidal current energy power generation field.
The bottom surface of the seabed fluctuating infeasible area of the tidal current energy power generation field is an ellipse, and the cross section of the seabed fluctuating infeasible area is a cos-type curve.
A vertical slice is made through observation point a along the specified flow rate. Observation point a is the ith generator position.
And recording the bottom surface projection point corresponding to the maximum height h 'on the tangent plane as an O' point. And a projection point with the height h'/2 on the inflow surface of the tangent plane is recorded as an L point.
And the point O is a projection point corresponding to the maximum height h. The cross section of the point O is made along the direction of flow velocity and perpendicular to the direction of flow velocity. And the point B is a projection point with the corresponding height h/2 on the vertical flow velocity tangent plane. L is0Is a projection point with the corresponding height h/2 on the section along the direction of the flow velocity.
xx is the distance between observation points A and O'. L is the distance between the O' point and the L point. The length parameter B is the distance between points O and B. Length parameter l0Is O point and L0The distance between the points.
Calculating a global parameter Δ Smax. The global parameter Δ SmaxReflecting the maximum acceleration ratio that occurs under this flow direction condition. Global parameter Δ SmaxAs follows:
Figure GDA0001882239520000131
wherein Y is the corresponding acceleration parameter. L is the distance between the point O' and the point L. l0Is O point and L0The distance between the points.
The acceleration effect of tidal flow rate, Δ s (xx), is calculated. The acceleration effect of tidal flow rate Δ s (xx) is as follows:
Figure GDA0001882239520000132
where α and p are the corresponding acceleration parameters. xx is the distance between observation points A and O'. L is the distance between the O' point and the L point.
Calculating acceleration effect Ft of infeasible area of tidal current energy power generation field on flow velocityi. Convection of infeasible area of tidal current energy power generation fieldAcceleration effect of speed FtiAs follows:
Fti＝1+ΔS(xx) (9)
where Δ s (xx) is the acceleration effect of tidal flow velocity.
4.1.4) calculating the inflow flow rate of the tidal current energy farm generator under the multi-wake effect.
And (4) calculating the flow velocity of the power flow under the influence of the multiple tail flows, namely calculating the flow velocity of each generator except the 1 st generator. Actual flow rate of jth generator of kth scheme at time t under typical flow rate of e
Figure GDA0001882239520000133
As follows:
Figure GDA0001882239520000141
in the formula uk ijtIndicating the flow rate of the jth generator independently affected by the upstream ith generator wake at time t in the kth scheme. c. Ce tIs the t-th element of the updated e-th class center Ce. N is a radical ofk wiIs the number of units upstream of the jth generator of the kth scheme. FtiIs the tidal acceleration effect of the ith generator. k is 1, 2 … Np。i，j＝1，2…Nt。e＝1，2，…，ξ。NpIs the number of individuals. N is a radical oftThe number of generators. ξ denotes the number of clusters.
4.2) calculating the output power of the tidal current energy generator.
And calculating the output power of each generator according to the actual flow speed of each generator.
The output power of the kth individual jth tidal current energy Generator at time t at a typical flow rate in e
Figure GDA0001882239520000144
As follows:
Figure GDA0001882239520000142
in the formula (I), the compound is shown in the specification,
Figure GDA0001882239520000145
is the actual flow rate of the kth individual tidal flow energy generator at time t at the typical flow rate in e. k is 1, 2 … Np。j＝1，2…Nt。t＝1，2…24。e＝1，2，…，ξ。NpIs the number of individuals. N is a radical oftThe number of generators for the kth scheme. ξ is the number of clusters. CpIs the coefficient of energy capture of the tidal flow energy generator. ρ is the density of seawater. And A is the area swept by the blades of the tidal current energy generator. VinIs the cut-in flow rate of the tidal flow energy generator. VratedIs the rated flow rate of the tidal flow energy generator. VoutIs the cut-out flow velocity of the tidal flow energy generator. PratedIs the rated output power of the tidal current energy generator.
4.3) calculating the daily generated energy of the tidal flow energy farm
Calculating the output power P of the tidal current energy power generation field according to the output power of each generatort k. Output power P of kth scheme at time tt kAs follows:
Figure GDA0001882239520000143
in the formula (I), the compound is shown in the specification,
Figure GDA0001882239520000146
the output power of the jth generator of the kth scheme at the time t is at the e-th typical flow rate. p is a radical ofeThe probability of the e-th typical flow rate, k-1, 2 … Np，j＝1，2…Nt，e＝1，2，…，ξ。NpIs the number of individuals; n is a radical oftThe number of generators in the kth scheme. ξ is the number of clusters.
And calculating the daily power generation amount of the tidal current energy power generation field. Daily generated energy E of tidal flow field of kth schemekAs follows:
Figure GDA0001882239520000151
in the formula, Pt kThe output power at time t for the kth scheme. t is the period number. t is 1, 2, …, 24.
5) And optimizing the layout planning scheme of the tidal current energy power generation field unit by utilizing a particle swarm algorithm according to the daily output power of the tidal current energy power generation field.
Further, the method for optimizing the tidal current energy power generation field unit layout scheme by utilizing the particle swarm algorithm mainly comprises the following steps:
5.1) calculating the optimization target of each layout scheme. The optimization objective is the tidal current energy farm maximum output power.
When the kth scheme tidal current energy power generation field daily power generation amount FkDaily generated energy E equal to tidal flow field of kth schemekWhen the tidal current energy power generation field output power reaches the maximum, namely:
Fk＝Ek (14)
in the formula, FkThe daily power generation capacity of the tidal flow energy power farm for the kth scheme. EkThe daily power generation of tidal flow fields for the kth scenario. k is 1, 2 … Np。NpIs the number of individuals.
5.2) calculating the fitness value fitness of each layout scheme.
Fitness value fitness of k-th layout schemekAs follows:
Figure GDA0001882239520000152
wherein k is 1, 2, …, Np。NpIs the number of individuals. FkThe daily power generation capacity of the tidal flow energy power farm for the kth scheme.
6) It is determined whether the iteration is terminated. The judgment method is mainly as follows:
and judging whether the maximum iteration number is reached currently.
If so, the optimal tidal flow field planning scheme is the one with the minimum optimization objective in step 5.
If not, the iteration times m +1, updating the population according to the formula 1 and the formula 2, thereby generating a new scheme, and returning to the step 4.
The nth dimension component vv of the (m + 1) th iterative particle beta) flying velocity vectorβn m+1As follows:
Figure GDA0001882239520000161
in the formula, vvβn mIs the nth dimension component of the m-th iterative particle beta airspeed vector. x is the number ofβn mIs the nth dimension component of the m-th iterative particle beta airspeed vector. pbest is the best position the beta particle experiences. gbest is the best position that the population experiences. w is the inertial weight. c. C1And c2Are all learning factors. rand1And rand2Are all in [0, 1 ]]Uniformly distributed pseudo random numbers within the interval.
The nth dimension component x of the (m + 1) th iteration particle beta position vectorβn m+1As follows:
Figure GDA0001882239520000162
in the formula, vvβn m+1Is the nth dimension component of the (m + 1) th iterative particle beta flight velocity vector. x is the number ofβn mIs the nth dimension component of the m-th iterative particle beta airspeed vector.
Example 2:
take the construction of a tidal current energy power generation field in the sea area near the coastal region of China as an example. The specific steps of the tidal current energy power generation field unit layout planning method for the infeasible area are as follows:
1) inputting basic data
Inputting tidal current energy power generation field tidal current velocity measured data samples v of 24 periods per day for 10 yearsqtMeasurement of tidal flow Rate on day qData samples are denoted Vq＝[vq1,vq2,...,vq24]And q is 1, 2 … n. t is 1, 2 … 24, n is 3656. Input tidal flow energy generator cut-in flow velocity VinRated flow velocity V of 0.7m/sratedCut-off flow velocity V of 3m/soutRated output power P of 3.5m/srated1.2MW, coefficient of energy gain CP0.45, thrust coefficient CT0.7, 18m blade diameter D and blade radius r09m, the area A swept by the blade is 254.34m2Sea water density rho is 1025kg/m3Coefficient of turbulence I00.07. The planning area of the input tidal current energy power generation field is 900m multiplied by 560m, and the number of the generators is Nt＝40。
The method of polygonal approximation is adopted in the tidal current energy power generation field to represent the geological infeasible area, in the embodiment, the simplified processing adopts a square to represent the infeasible area, wherein the vertex coordinates are [ 700200; 800200, respectively; 800300, respectively; 700300, respectively; 700200]. The bottom surface of the fluctuation infeasible area is an ellipse, the cross section is a cos-type curve, and as shown in figure 1, the major axis l of the ellipse is obtained1200, minor axis l2100 and 20, the maximum height h of the non-feasible region.
2) Calculating the daily mean curve of tidal flow
A typical change rule of tidal flow velocity is extracted by adopting a k-means clustering method to obtain a daily typical curve of the tidal flow velocity, and the method comprises the following specific steps:
2.1) randomly selecting 2 days of data from the tidal flow rate measured data sample as an initial clustering center, wherein the clustering center is represented as Ce ═ ce1，ce2，...，ceT]Wherein e is 1 or 2.
2.2) calculate the distance of each day's flow data sample to the center of each cluster in turn.
And 2.3) clustering according to the distance from each daily data sample to each type of clustering center, and attributing the daily flow velocity data sample to the category closest to the daily flow velocity data sample. According to the clustering result, counting the number of daily flow velocity samples in each type of data, and using neAnd (4) showing. Updating the clusters of each category after the reclassification of the flow rate data samples is completedA center.
2.4) repeating the calculations of steps 2.2 and 2.3 until the clustering center Ce produced by the two cycles is no longer changed.
The finally obtained clustering center is a daily typical curve of tidal flow velocity and represents the daily change rule of tidal flow velocity. Meanwhile, counting the number of tidal daily flow rate samples belonging to each category, and calculating the probability of daily typical curve occurrence of each type of tidal flow rate, wherein the calculation formula is as follows:
and (3) calculating the result: c1＝[2.18 2.09 1.59 1.22 1.23 1.67 2.19 2.16 1.71 1.22 1.17 1.56 2.09 2.21 1.84 1.32 1.16 1.45 2.07 2.24 1.94 1.35 1.13 1.37]，p1＝0.5651。
C2＝[1.15 1.39 2.17 2.68 2.58 1.91 1.24 1.35 2.01 2.61 2.54 1.94 1.27 1.23 1.83 2.54 2.66 2.20 1.39 1.25 1.71 2.43 2.59 2.19]，p2＝0.4349。
3) An initial sample of a tidal flow farm set layout is generated.
3.1) initializing the maximum number of iterations of the particle swarm, and setting the iteration count m to be 1.
3.2) randomly generating 100 initial individuals and speeds by the computer, wherein the length of each individual is 80, and forming a real number matrix Z, wherein the position coordinate of the ith generator of the kth scheme is expressed as (G)k，2i.1，Gk，2i)，i＝1，2…Nt，k＝1，2，…，Np，Np100 is the number of individuals, NtAnd (4) judging whether the ith generator is located in the infeasible area or not, if so, regenerating the position coordinates of the ith generator.
And solving the distance between any two generators in each individual, and judging whether the distance is greater than the minimum safe distance 5D, wherein D is 18m and is the diameter of the tidal current generator. If not, the individual is regenerated until the minimum safety distance requirement is met.
4) And calculating the daily power generation amount of the tidal current energy power generation field.
And (3) calculating the daily power generation amount of the tidal current energy power generation field by using the daily typical curve of the tidal current velocity calculated in the step (2) and the position coordinates of the tidal current energy power generator obtained in the step (3). The calculation steps are as follows:
4.1) calculating the flow rate of the generator in the tidal flow energy power generation field in each period based on the e type typical flow rate.
4.1.1) the 40 generators in the kth scenario were ranked based on the magnitude of the generator abscissa, assuming that the tidal flow velocity is flowing in the positive x-axis direction.
After sequencing, the flow rate of the 1 st generator in the kth scenario:
Figure GDA0001882239520000181
in the formula, vk 1tRepresenting the flow rate of the 1 st generator at time t in the kth variant, ce tIs the t-th element of the updated e-th class center Ce, k is 1, 2 … NpT 1, 2 … 24, e 1 or 2, Np100 is the number of individuals.
4.1.2) calculating the flow rate of each generator independently influenced by the wake flow of the ith upstream generator except for the 1 st generator in the kth scheme:
4.1.3) calculating the acceleration effect of the sea floor relief
And (3) establishing a model of the seabed undulating terrain of the tidal current energy power generation field, wherein the bottom surface of the infeasible area is an ellipse, and the cross section of the infeasible area is a cos-type curve, as shown in the attached figure 1. And (3) making a vertical section along the specified flow velocity through the observation point A, wherein the bottom surface projection point corresponding to the maximum height h ' on the vertical section is the point O ', and the projection point with the height h '/2 on the inflow surface is the point L. The point O is a projection point corresponding to the maximum height h, a tangent plane along the flow velocity direction and the vertical flow velocity direction is made after passing the point O, the point B is a projection point corresponding to the height h/2 on the vertical flow velocity tangent plane, L0Is a projection point with the corresponding height h/2 on the section along the direction of the flow velocity. Where xx denotes a distance between observation point a (i-th generator position) and O ', L denotes a distance between O' and L, and length parameter b is 33.33m and L016.67m means O and B and O and L, respectively0The distance between the points.
Calculating a global parameter Δ SmaxWhich reflects the maximum acceleration ratio that occurs under this flow direction condition.
Figure GDA0001882239520000182
Wherein Y is the corresponding acceleration parameter, detailed in Table 1, B33.33 m represents the distance between points O and B, l016.67m is O point and L0The distance between the points.
The formula for calculating the acceleration effect of tidal flow velocity is as follows:
Figure GDA0001882239520000191
in the formula, α and p are corresponding acceleration parameters, which are detailed in table 1, xx represents the distance between observation points a and O ', and L represents the distance between O' and L.
Figure GDA0001882239520000192
TABLE 1 acceleration parameters
Calculating the acceleration effect of the infeasible area of the tidal current energy power generation field on the flow velocity:
Fti＝1+ΔS(xx) (21)
in the formula, FtiIs the tidal acceleration effect of the ith generator.
4.1.4) calculating the inflow flow velocity of tidal flow energy farm generators under the multiple wake effect
And (4) calculating the flow velocity of the power flow under the influence of the multiple tail flows, namely calculating the flow velocity of each generator except the 1 st generator.
4.2) calculating the output Power of the tidal flow energy Generator
And calculating the output power of each generator according to the actual flow speed of each generator.
Figure GDA0001882239520000193
In the formula (I), the compound is shown in the specification,
Figure GDA0001882239520000194
is the actual flow rate of the kth individual tidal flow energy generator at time t at the typical flow rate in e. k is 1, 2 … Np。j＝1，2…Nt。t＝1，2…24。e＝1，2，…，ξ。NpIs the number of individuals. N is a radical oftThe number of generators for the kth scheme. ξ is the number of clusters. CpIs the coefficient of energy capture of the tidal flow energy generator. ρ is the density of seawater. And A is the area swept by the blades of the tidal current energy generator. VinIs the cut-in flow rate of the tidal flow energy generator. VratedIs the rated flow rate of the tidal flow energy generator. VoutIs the cut-out flow velocity of the tidal flow energy generator. PratedIs the rated output power of the tidal current energy generator.
4.3) calculating the daily generated energy of the tidal flow energy farm
Calculating the output power of each generator and the daily generated energy of the tidal current energy power generation field:
Figure GDA0001882239520000201
5) particle swarm optimization method for optimizing tidal current energy power generation field unit layout planning scheme
6) Iteration end condition
And judging whether the maximum iteration number is reached currently.
If so, the optimal tidal flow field planning scheme is the one with the minimum optimization objective in step 5.
If not, the iteration times m +1, updating the population according to the formula 1 and the formula 2, thereby generating a new scheme, and returning to the step 4.
The nth dimension component vv of the (m + 1) th iterative particle beta) flying velocity vectorβn m+1As follows:
Figure GDA0001882239520000202
Figure GDA0001882239520000203
in the formula, vvβn mFor the nth component, vv, of the m-th iterative particle beta vector of flight velocityβn m+1Is the nth dimension component, x, of the (m + 1) th iterative particle beta flight velocity vectorβn mAnd xβn m+1Respectively representing the nth dimension components of the beta position vectors of the m-th and m + 1-th iteration particles, pbest being the best position experienced by the beta-th particle, and gbest being the best position experienced by the group, wherein beta is 1, 2 … Np，n＝1，2…80，Np100 is the number of individuals. w is the inertial weight, c1、c2As a learning factor, rand1、rand2Is at [0, 1 ]]Uniformly distributed pseudo random numbers within the interval.
The layout results of the final optimization are shown in fig. 2.
Example 3:
a comparison of a tidal flow energy farm block layout approach that takes into account infeasible areas and one that does not take into account the effect of surge area acceleration on tidal flow velocity is as follows:
m0: a tidal flow energy farm unit layout method considering infeasible areas.
M1: the remaining optimization procedures are consistent with the present invention, without considering the acceleration effect of the undulating region on tidal flow velocity.
The test effect is as follows:
the daily power generation of the tidal current energy farm, the difference (M0-M1) between M0 and M1 and the percentage difference ((M0-M1)/M0) are calculated by respectively adopting methods M0 and M1, and are shown in Table 2.
As can be seen from Table 2, the tidal flow energy farm daily generated power of method M0 is 13.42% higher than that of method M1. Mainly, the method M1 does not consider the acceleration effect of the fluctuation area on the tidal flow speed in the optimization process, and the inflow speed of each generator is difficult to accurately describe, so that the daily power generation amount of the tidal flow energy power generation field is less than that of the method.
Figure GDA0001882239520000211
Table 3 method m0.m1 comparison of calculation results.

Claims (4)

1. A method of tidal flow energy farm block layout taking into account infeasible areas, comprising the steps of:
1) acquiring basic data of the tidal current energy power generation field;
2) extracting a typical change rule of the tidal flow rate by adopting a k-means clustering method so as to obtain a daily typical curve of the tidal flow rate;
3) generating an initial sample of the tidal current power generation field unit layout, and obtaining the position coordinates of the tidal current energy generator in the initial sample;
4) calculating the daily power generation capacity of the tidal current energy power generation field by utilizing the daily typical curve of tidal current velocity and the position coordinates of the tidal current energy power generator;
the steps for calculating the daily power generation amount of the tidal current energy power generation field are as follows:
4.1) calculating the flow velocity of the generator in the tidal current energy power generation field in each period based on the e type typical flow velocity, and the steps are as follows:
4.1.1) setting tidal flow velocity to flow in the positive direction of the x-axis, and based on the size of the generator abscissa, comparing N in the kth schemetSequencing by the platform generators;
after sequencing, the flow rate of the 1 st generator in the kth scheme
Figure FDA0003227623320000011
As follows:
Figure FDA0003227623320000012
in the formula, cetIs the t-th element of the updated e-th class center Ce; k is 1, 2 … Np；t＝1，2…24；e＝1，2，…，ξ；NpThe number of individuals; xi is the number of clusters;
4.1.2) calculating the flow velocity of each generator under the influence of the wake flow of the ith upstream generator in the kth scheme except the 1 st generator; if the distance between the generator and the direction of the coming tide is smaller than the distance between the target generator and the direction of the coming tide, the generator is an upstream generator; the upstream generator wake affects the target generator;
in the kth scheme, the jth generator is independently influenced by the wake flow of the ith upstream generator in the t period
Figure FDA0003227623320000013
As follows:
Figure FDA0003227623320000014
in the formula, mtIs the mean value of tidal flow rate in the period t; k is 1, 2 … Np；NpIs the initial number of individuals; 1, 2, …, Nt；NtThe number of generators; t is a time interval sequence number; t ═ 1, 2, …, 24; i is the number of the upstream generator; cTIs the thrust coefficient of the tidal flow energy generator; r is0Is the tidal flow energy generator blade radius; r (xi)ij) The wake radius of the ith upstream generator;
wake radius R (xi) of upstream ith generatorij) As follows:
Figure FDA0003227623320000021
in the formula, r0Is the tidal flow energy generator blade radius; i is0Is the turbulence factor; xiijThe actual distance between the ith generator and the jth generator is calculated;
4.1.3) calculating the acceleration effect of the sea floor topography fluctuation;
establishing a model of the sea floor undulating terrain of the tidal current energy power generation field;
in the seabed fluctuation infeasible area of the tidal current energy power generation field, a vertical tangent plane is made through an observation point A along a specified flow speed; the observation point A is the position of the ith generator; the bottom surface projection point corresponding to the maximum height h 'on the vertical tangent plane is marked as an O' point; the projection point with the height h'/2 on the inflow surface of the vertical tangent plane is marked as an L point;
the point O is a projection point corresponding to the maximum height h; making a tangent plane along the flow velocity direction and the vertical flow velocity direction through the O point; the point B is a projection point with the corresponding height h/2 on the vertical flow velocity tangent plane; l is0The projection point with the corresponding height of h/2 is arranged on the section along the flow direction;
xx is the distance between observation points A and O'; l is the distance between the O' point and the L point; the length parameter B is the distance between the point O and the point B; length parameter l0Is O point and L0The distance between the points;
calculating a global parameter Δ Smax(ii) a The global parameter Δ SmaxReflecting the maximum acceleration ratio generated under the condition of the flow velocity direction; global parameter Δ SmaxAs follows:
Figure FDA0003227623320000022
in the formula, Y is a corresponding acceleration parameter; l0Is O point and L0The distance between the points;
calculating the acceleration effect of tidal flow rate Δ s (xx); the acceleration effect of tidal flow rate Δ s (xx) is as follows:
Figure FDA0003227623320000023
wherein α and p are the corresponding acceleration parameters; xx is the distance between observation points A and O'; l is the distance between the O' point and the L point;
calculating acceleration effect Ft of infeasible area of tidal current energy power generation field on flow velocityi(ii) a Acceleration effect Ft of the velocity of flow in infeasible areas of a tidal flow energy farmiAs follows:
Fti＝1+ΔS(xx) (6)
wherein Δ s (xx) is the acceleration effect of tidal flow;
4.1.4) calculating the inflow flow rate of the tidal current energy power generation field generator under the multi-wake effect;
calculating the flow velocity of the power flow under the influence of multiple tail flows, namely calculating the flow velocity of each generator except the 1 st generator; actual flow rate of jth generator of kth scheme at time t under e type typical flow rate
Figure FDA0003227623320000031
As follows:
Figure FDA0003227623320000032
in the formula (I), the compound is shown in the specification,
Figure FDA0003227623320000033
the flow speed of the jth generator which is influenced by the wake flow of the ith generator at the upstream moment t is independently shown in the kth scheme; c. CetIs the t-th element of the updated e-th class center Ce;
Figure FDA0003227623320000034
the number of the units at the upstream of the jth generator in the kth scheme is shown; ftiThe tidal acceleration effect of the ith generator; k is 1, 2 … Np；i，j＝1，2…Nt；e＝1，2，…，ξ；NpThe number of individuals; n is a radical oftThe number of generators; ξ represents the number of clusters;
4.2) calculating the output power of the tidal current energy generator;
calculating the output power of each generator according to the actual flow velocity of each generator;
the output power of the jth tidal current energy Generator at the kth individual at the jth typical flow velocity
Figure FDA0003227623320000035
As follows:
Figure FDA0003227623320000036
wherein k is 1, 2 … Np；j＝1，2…Nt(ii) a t is 1, 2 … 24; e ═ 1, 2, …, ξ; xi is the number of clusters; n is a radical ofpIs the number of individuals; n is a radical oftNumber of generators for kth scheme; cpThe coefficient of energy capture for the tidal flow energy generator; rho is the density of the seawater; a is the area swept by the blades of the tidal current energy generator; vinIs the cut-in flow rate of the tidal flow energy generator; vratedIs the rated flow rate of the tidal flow energy generator; voutIs the cut-out flow velocity of the tidal current energy generator; pratedIs the rated output power of the tidal current energy generator;
4.3) calculating the daily generated energy of the tidal flow energy farm
Calculating the output power P of the tidal current energy power generation field according to the output power of each generatort k(ii) a Output power P of kth scheme at time tt kAs follows:
Figure FDA0003227623320000041
in the formula (I), the compound is shown in the specification,
Figure FDA0003227623320000042
the output power of the jth generator of the kth scheme at the time t under the e type typical flow rate; p is a radical ofeThe probability of the e-th typical flow rate, k-1, 2 … Np，j＝1，2…Nt，e＝1，2，…，ξ；NpThe number of individuals; n is a radical oftThe number of generators in the kth scheme; ξ represents the number of clusters;
calculating the daily power generation amount of the tidal current energy power generation field; daily generated energy E of tidal flow field of kth schemekAs followsThe following steps:
Figure FDA0003227623320000043
in the formula, Pt kThe output power at the moment t for the kth scheme; t is a time interval sequence number; t ═ 1, 2, …, 24;
5) optimizing a tidal current energy power generation field unit layout planning scheme by utilizing a particle swarm algorithm according to daily output power of the tidal current energy power generation field;
the method for optimizing the tidal current energy power generation field unit layout scheme by utilizing the particle swarm optimization comprises the following steps:
5.1) calculating the optimization target of each layout scheme; the optimization target is the maximum output power of the tidal current energy power plant;
when the kth scheme tidal current energy power generation field daily power generation amount FkDaily generated energy E equal to tidal flow field of kth schemekWhen the tidal current energy power generation field output power reaches the maximum, namely:
Fk＝Ek (11)
in the formula, FkGenerating daily power for the tidal flow energy farm of the kth scheme; ekThe daily power generation for the tidal flow field of scenario kth; k is 1, 2 … Np；NpIs the number of individuals;
5.2) calculating the fitness value fitness of each layout scheme;
fitness value fitness of k-th layout schemekAs follows:
Figure FDA0003227623320000044
wherein k is 1, 2, …, Np；NpThe number of individuals; fkGenerating daily power for the tidal flow energy farm of the kth scheme;
6) judging whether the iteration is terminated; the determination method is as follows:
judging whether the maximum iteration number is reached currently;
if so, the optimal tidal flow field planning scheme is the individual with the minimum optimization target in the step 5);
if not, the iteration times m +1, updating the population according to the formula (13) and the formula (14) so as to generate a new scheme, and returning to the step 4);
component of n' dimension of the m +1 th iterative particle beta airspeed vector
Figure FDA0003227623320000051
As follows:
Figure FDA0003227623320000052
in the formula (I), the compound is shown in the specification,
Figure FDA0003227623320000053
is the nth' dimension component of the flight velocity vector of the mth iteration particle beta;
Figure FDA0003227623320000054
is the nth' dimension component of the flight velocity vector of the mth iteration particle beta; pbestβThe best position the beta particle experiences; gbest is the best position for the population experience; w is the inertial weight; c. C1And c2Are all learning factors; rand1And rand2Are all in [0, 1 ]]Pseudo-random numbers uniformly distributed within the interval;
component of dimension n' of the (m + 1) th iterative particle beta position vector
Figure FDA0003227623320000055
As follows:
Figure FDA0003227623320000056
in the formula (I), the compound is shown in the specification,
Figure FDA0003227623320000057
is the nth' dimension component of the (m + 1) th iterative particle beta flight velocity vector;
Figure FDA0003227623320000058
is the nth' component of the flight velocity vector of the mth iteration particle beta.
2. A method of tidal flow energy farm block layout taking into account infeasible areas as claimed in claim 1, wherein: the basic data of the tidal current energy farm includes:
1) tidal flow energy power generation field within T days and 24 periods of each dayqt(ii) a q is the number of days; t is a time interval sequence number; t is 1, 2 … 24;
2) cut-in flow velocity V of tidal flow energy generatorinRated flow velocity VratedCut-out flow velocity VoutRated output power PratedCoefficient of energy gain CpCoefficient of thrust CTDiameter D of blade, radius r of blade0And the area A swept by the blade;
3) sea water density rho and turbulence coefficient I0；
4) Planned area of tidal current energy farm and number of tidal current energy generators Nt。
3. A method of tidal flow energy farm block layout taking into account infeasible areas as claimed in claim 1, wherein: the steps for obtaining a daily profile of tidal flow rate are as follows:
1) randomly selecting H days from the tidal flow rate measured data samples as an initial clustering center, wherein the clustering center is represented as Ce ═ ce1，ce2，...，ce24]Wherein e ═ 1, 2, …, ξ; xi is the number of clusters;
2) and sequentially calculating the distance from the flow speed data sample to the center of each category:
distance d from day of flow data sample to e cluster centereqAs follows:
Figure FDA0003227623320000061
in the formula, vqtTidal flow rate at time t on day qth; c. CetA tth element representing a class e center Ce; e ═ 1, 2, …, ξ; q is the number of days; q ═ 1, 2, …, T; t is the daily sample number of the measured tidal flow rate data; t is a time interval sequence number; t is 1, 2 … 24; xi is the number of clusters;
3) according to the distance from the flow speed data sample to the center of each category, the daily flow speed data sample is assigned to the category closest to the sample;
according to the clustering result, counting the number n of daily flow rate samples in each category of datae；
After the flow rate data samples are reclassified, updating the clustering centers of all categories;
updated tth element c of e-th class center CeetAs follows:
Figure FDA0003227623320000062
in the formula, vqt(ii) data of the e-th category representing tidal flow rate data for the qth interval of that category on day qth; n iseThe number of samples belonging to the e-type center; e ═ 1, 2, …, ξ; q is the number of days; q ═ 1, 2, …, T; t is the daily sample number of the measured tidal flow rate data; t is a time interval sequence number; t is 1, 2 … 24; xi is the number of clusters;
4) repeating the step 2) and the step 3) until the clustering center Ce generated by two times of circulation is not changed any more;
the finally obtained clustering center is a daily typical curve of tidal flow velocity; the daily typical curve of the tidal flow rate represents the daily change rule of the tidal flow rate;
counting the number of tidal daily flow rate samples belonging to each category, and calculating the probability of daily typical curve generation of each type of tidal flow rate according to the number of tidal daily flow rate samples belonging to each category; the probability of occurrence of the daily typical curve of tidal flow rates of class e is as follows:
Figure FDA0003227623320000071
in the formula, neRepresenting the number of samples belonging to the e-type class center; n is the number of daily samples of the measured tidal flow rate data; e ═ 1, 2, …, ξ; ξ is the number of clusters.
4. A method of tidal flow energy farm block layout taking into account infeasible areas as claimed in claim 1, wherein: the steps of generating an initial sample of a tidal flow farm unit layout are as follows:
1) initializing the maximum number of iterations of the particle swarm, wherein the iteration count m is 1
2) Computer random generation of NpInitial individuals, each of which is 2N in lengtht；NpThe initial individuals form a real number matrix Z; n is a radical ofpInitial individual representation NpDifferent tidal current energy power generation field unit layout schemes are adopted;
wherein the position coordinate of the ith generator in the kth tidal current energy farm unit layout scheme is expressed as (G)k，2i.1，Gk，2i)；i＝1，2…Nt；k＝1，2，…，Np；NpIs the initial number of individuals; n is a radical oftThe number of generators;
3) judging whether the ith generator in the kth tidal current energy power generation field unit layout scheme is positioned in the seabed fluctuation infeasible area or the geology infeasible area, and regenerating the position coordinates (G) of the ith generator if the ith generator is positioned in the seabed fluctuation infeasible area or the geology infeasible areak，2i.1，Gk，2i) Until the ith generator is positioned outside the seabed fluctuation infeasible area and the geology infeasible area;
the geological infeasible area is represented by a polygonal approximation method; geology infeasible areaThe vertex coordinate of the domain is Jχ；χ＝1，2…Nin；NinThe number of polygonal vertices;
the bottom surface of the seabed fluctuation infeasible area of the tidal current energy power generation field is an ellipse, and the cross section of the seabed fluctuation infeasible area is a cos-type curve;
4) solving the distance Z 'between any two generators in each initial individual, and judging whether the distance Z' is greater than the minimum safe distance 5D; d is the diameter of the tidal current generator;
if Z' >5D, recording the initial individuals as initial samples of the tidal current energy power plant unit layout;
if Z' is less than or equal to 5D, the original individual is regenerated and the procedure returns to step 3).
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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH11289663A (en) * 1998-04-06 1999-10-19 Kansai Electric Power Co Inc:The Distribution line control system
CN105512492A (en) * 2015-12-22 2016-04-20 重庆大学 Probability modeling method for output power of tide flow energy electric generator
CN106208046A (en) * 2016-08-09 2016-12-07 重庆大学 A kind of tidal current energy generating field unit layout method considering power generation settings cost
CN106204339A (en) * 2016-08-09 2016-12-07 重庆大学 A kind of generating reliability appraisal procedure containing tidal current energy generating field power system
CN106228468A (en) * 2016-08-09 2016-12-14 重庆大学 A kind of probability simulation method of tidal current energy generating field output
CN107681655A (en) * 2017-09-08 2018-02-09 重庆大学 A kind of tidal current energy generating field coordinated planning method

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH11289663A (en) * 1998-04-06 1999-10-19 Kansai Electric Power Co Inc:The Distribution line control system
CN105512492A (en) * 2015-12-22 2016-04-20 重庆大学 Probability modeling method for output power of tide flow energy electric generator
CN106208046A (en) * 2016-08-09 2016-12-07 重庆大学 A kind of tidal current energy generating field unit layout method considering power generation settings cost
CN106204339A (en) * 2016-08-09 2016-12-07 重庆大学 A kind of generating reliability appraisal procedure containing tidal current energy generating field power system
CN106228468A (en) * 2016-08-09 2016-12-14 重庆大学 A kind of probability simulation method of tidal current energy generating field output
CN107681655A (en) * 2017-09-08 2018-02-09 重庆大学 A kind of tidal current energy generating field coordinated planning method

Non-Patent Citations (7)

* Cited by examiner, † Cited by third party
Title
Integration of cost modelling within the micro-siting design optimisation of tidal turbine arrays;D.M. Culley et al.;《Renewable Energy 》;20150627;第215-227页 *
Optimal Sizing and Arrangement of Tidal Current Farm;Yi Dai et al.;《IEEE TRANSACTIONS ON SUSTAINABLE ENERGY》;20180131;第9卷(第1期);第168-177页 *
代溢.潮汐流能发电机组及集电系统的规划方法研究.《万方学位论文》.2018, *
基于粒子群算法的潮流发电机布局;刘丞 等;《浙江大学学报（工学版）》;20131231;第47卷(第12期);第2087-2093页 *
复杂地形风电场的机组布局优化;田琳琳 等;《南京航空航天大学学报》;20130831;第45卷(第4期);第503-509页 *
潮汐流能发电机组及集电系统的规划方法研究;代溢;《万方学位论文》;20180416;第二章 *
考虑障碍区域的潮汐流场集电系统规划方法;王元萌;《科技创新与应用》;20180430(第12期);第144-145页 *

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