CN108438191B - A kind of fishing boat driving device and device selection method - Google Patents

A kind of fishing boat driving device and device selection method Download PDF

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CN108438191B
CN108438191B CN201810386103.4A CN201810386103A CN108438191B CN 108438191 B CN108438191 B CN 108438191B CN 201810386103 A CN201810386103 A CN 201810386103A CN 108438191 B CN108438191 B CN 108438191B
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高海波
熊留青
卢炳岐
杜康立
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Wuhan University of Technology WUT
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Abstract

本发明公开了一种诱鱼艇驱动装置及装置设备选型方法,诱鱼艇驱动装置主要由能量管理系统装置、柴油发电机组、蓄电池组、双向DC/DC变换器、变频器、电机和螺旋桨组成,其设计过程包括结构设计和设备选型两部分,系统结构设计主要采用以柴油发电机组为主、蓄电池组为辅的供电方案,有四种供电模式,可确保柴油发电机组不会欠负荷或过负荷。设备选型采用基于混沌运动的多目标粒子群算法来优化选型过程,可提供多种选型方案供决策者选择,最后决策者根据逼近理想解排序法将多种方案进行排序,从中选出最满意的方案。

Figure 201810386103

The invention discloses a fishing boat driving device and a device selection method. The fishing boat driving device mainly consists of an energy management system device, a diesel generator set, a battery pack, a bidirectional DC/DC converter, a frequency converter, a motor and a propeller. The design process includes two parts: structural design and equipment selection. The system structure design mainly adopts the power supply scheme mainly based on diesel generator sets and supplemented by battery packs. There are four power supply modes to ensure that diesel generator sets will not be under-loaded. or overload. The equipment selection adopts the multi-objective particle swarm algorithm based on chaotic motion to optimize the selection process, which can provide a variety of selection schemes for decision makers to choose. the most satisfactory solution.

Figure 201810386103

Description

一种诱鱼艇驱动装置及装置设备选型方法A kind of fishing boat driving device and device selection method

技术领域technical field

本发明属于诱鱼艇电力推进系统设计技术领域,尤其涉及一种诱鱼艇驱动装置及装置设备选型方法。The invention belongs to the technical field of design of electric propulsion systems for fishing boats, and in particular relates to a driving device for fishing boats and a method for selecting device equipment.

背景技术Background technique

诱鱼艇动力系统是其核心系统之一,设计方案的优劣直接关系到诱鱼艇的安全性、稳定性以及经济性。传统的诱鱼艇动力系统设计方案采用经验设计法,即根据诱鱼艇的航行工况选用功率大小合适的电机,再根据电机功率选用功率稍大的变频器,再根据变频器功率选用功率稍大的柴油发电机组,此设计方法简单易学,但动力系统各设备之间匹配性差、且由于诱鱼艇各个工作模式的负荷变化较大,柴油机难以处于最佳工况下,效率较差,油耗较高。The power system of the fishing boat is one of its core systems. The quality of the design scheme is directly related to the safety, stability and economy of the fishing boat. The traditional design scheme of the power system of the fishing boat adopts the empirical design method, that is, the motor with the appropriate power is selected according to the sailing conditions of the fishing boat, the inverter with a slightly larger power is selected according to the power of the motor, and the inverter with a slightly higher power is selected according to the power of the inverter. For large diesel generator sets, this design method is simple and easy to learn, but the matching between the various equipment of the power system is poor, and due to the large load changes in each working mode of the fishing boat, it is difficult for the diesel engine to be in the best working condition, the efficiency is poor, and the fuel consumption is low. higher.

智能优化算法如遗传算法、粒子群算法、蚁群算法等,其在飞机、汽车等方面应用越来越广泛,优化算法可以辅助系统选型设计,从多种方案中选取满意的方案。系统的选型设计需考虑多方面的因素,流行的智能优化算法大多是设置权重,将多目标问题转化为单目标问题求解,由于事先设定了偏好信息,因此将不可避免地遗漏更好的可行解。Intelligent optimization algorithms such as genetic algorithm, particle swarm algorithm, ant colony algorithm, etc., are more and more widely used in aircraft and automobiles. The selection and design of the system needs to consider many factors. Most of the popular intelligent optimization algorithms set weights to solve multi-objective problems into single-objective problems. Since the preference information is set in advance, better ones will inevitably be missed. Feasible solution.

发明内容SUMMARY OF THE INVENTION

本发明所要解决的技术问题是,提供一种诱鱼艇驱动装置及装置设备选型方法,提高诱鱼艇的安全性、稳定性以及经济性。The technical problem to be solved by the present invention is to provide a driving device for a fishing boat and a method for selecting the device equipment, so as to improve the safety, stability and economy of the fishing boat.

本发明解决其技术问题所采用的技术方案是:提供一种诱鱼艇驱动装置,主要由能量管理系统装置、柴油发电机组、蓄电池组、双向DC/DC变换器、变频器、电机和螺旋桨组成,能量管理系统装置分别与柴油发电机组、蓄电池组和双向DC/DC变换器连接。能量管理系统通过采用CAN总线通讯方式,将设置的柴油发电机组的目标功率值、蓄电池组的目标功率值以及切换双向DC/DC变换器能量传输模式的指令,采用RS485通讯,传递给PLC控制器,并通过PLC控制器来完成柴油发电机组启动、停止、加减油门功能、蓄电池组的充放电功能和双向DC/DC变换器升压和降压模式之间的切换功能;同时采用CAN总线通讯方式,用RS485通讯将蓄电池组的SoC值和电机的需求功率值传递给能量管理系统装置;柴油发电机组的三相电输出端接口通过电力电缆线连接变频器的三相电输入端接口;蓄电池组的直流电输出端接口通过电力电缆线连接双向DC/DC变换器的低压直流端接口,双向DC/DC变换器的高压直流端接口通过电力电缆线连接到变频器的直流母排接线输入端;变频器的三相电输出端接口通过电力电缆线连接电机的三相电输入端接口;电机的输出端通过联接轴连接螺旋桨。The technical scheme adopted by the present invention to solve the technical problem is to provide a driving device for fishing boat, which is mainly composed of an energy management system device, a diesel generator set, a battery pack, a bidirectional DC/DC converter, a frequency converter, a motor and a propeller , the energy management system device is respectively connected with the diesel generator set, the battery pack and the bidirectional DC/DC converter. The energy management system uses the CAN bus communication method to transmit the set target power value of the diesel generator set, the target power value of the battery pack and the command to switch the energy transmission mode of the bidirectional DC/DC converter to the PLC controller through RS485 communication. , and through the PLC controller to complete the diesel generator set start, stop, increase and decrease the throttle function, the charging and discharging function of the battery pack and the switching function between the boost and buck modes of the bidirectional DC/DC converter; at the same time, the CAN bus communication is used. By means of RS485 communication, the SoC value of the battery pack and the required power value of the motor are transmitted to the energy management system device; the three-phase electrical output port of the diesel generator set is connected to the three-phase electrical input port of the inverter through the power cable; the battery The DC output terminal interface of the group is connected to the low-voltage DC terminal interface of the bidirectional DC/DC converter through the power cable, and the high-voltage DC terminal interface of the bidirectional DC/DC converter is connected to the DC busbar wiring input terminal of the inverter through the power cable; The three-phase electrical output port of the inverter is connected to the three-phase electrical input port of the motor through a power cable; the output of the motor is connected to the propeller through a connecting shaft.

按上述技术方案,对能量管理系统装置的输入信号进行处理通过内部PLC程序对输入信号进行处理,根据处理后的信号通过PLC控制器来控制柴油发电机组、蓄电池组和双向DC/DC变换器,实现对四种供电模式的切换,模式一为蓄电池组通过双向DC/DC变换器为变频器和电机供电,柴油发电机组不工作,PLC控制器控制蓄电池组放电,双向DC/DC处于升压模式,柴油发电机组停机;模式二为柴油发电机组为变频器和电机供电,同时通过变频器的直流母线,经双向DC/DC变换器为蓄电池组充电,PLC控制器控制蓄电池组充电,双向DC/DC处于降压模式,柴油发电机组工作;模式三为柴油发电机组单独为变频器和电机供电,蓄电池组不工作,PLC控制器控制蓄电池组以及双向DC/DC停止工作,柴油发电机组工作;模式四为柴油发电机组和蓄电池组联合为变频器和电机供电,PLC控制器控制蓄电池组放电,双向DC/DC处于升压压模式,柴油发电机组工作。According to the above technical scheme, the input signal of the energy management system device is processed through the internal PLC program to process the input signal, and the diesel generator set, the battery pack and the bidirectional DC/DC converter are controlled by the PLC controller according to the processed signal, Realize the switching of four power supply modes. In mode one, the battery pack supplies power to the inverter and the motor through the bidirectional DC/DC converter. The diesel generator set does not work, the PLC controller controls the battery pack to discharge, and the bidirectional DC/DC is in boost mode. , the diesel generator set stops; mode 2 is for the diesel generator set to supply power to the inverter and the motor, and at the same time through the DC bus of the inverter, through the bidirectional DC/DC converter to charge the battery pack, the PLC controller controls the battery pack to charge, and the bidirectional DC/DC The DC is in the step-down mode, the diesel generator set works; the third mode is that the diesel generator set supplies power to the inverter and the motor alone, the battery pack does not work, the PLC controller controls the battery pack and the bidirectional DC/DC stops working, and the diesel generator set works; Mode The fourth is that the diesel generator set and the battery set are combined to supply power to the inverter and the motor. The PLC controller controls the discharge of the battery set. The two-way DC/DC is in the boost mode, and the diesel generator set works.

本发明还提供一种诱鱼艇驱动装置设备选型方法,该方法包括以下步骤,第一部分:多目标粒子群算法求Pareto解集过程。The invention also provides a method for selecting a type of driving device for a fishing boat, the method includes the following steps, the first part: the process of finding the Pareto solution set by the multi-objective particle swarm algorithm.

步骤1,混沌初始化粒子种群:Step 1, Chaos initializes the particle population:

以成本、油耗、排放、重量、布局5个因素作为多目标粒子群算法的求解目标,随机产生一个5维向量z1。然后根据式(1)的Logistic方程得到100个分量z1,z2,...,z100,其中μ的值取4,构成100行5列的混沌区间[z1;z2;...;z100],再根据式(2)将混沌区间[zl;z2;...;z100]映射到优化变量的取值范围(本发明中,多目标粒子群算法的优化变量为系统设备,即6种型号的柴油发电机组,6种型号的蓄电池组以及6种型号的电机;式中bj和aj分别为优化变量的上下限,则bj=6,aj=0;xij的值取整),构成100行5列的初始粒子种群(100个粒子),即设备选型方案,比如第一个方案(粒子)[x1,1,x1,2,x1,3,x1,4,x1,5]=[123564],第二个方案(粒子)[x2,1,x2,2,x2,3,x2,4,x2,5]=[654312],共100个方案(粒子)。Taking five factors of cost, fuel consumption, emission, weight and layout as the solution target of multi-objective particle swarm algorithm, a 5-dimensional vector z 1 is randomly generated. Then, 100 components z 1 , z 2 , . . . , z 100 are obtained according to the logistic equation of formula (1), where the value of μ is 4, forming a chaotic interval of 100 rows and 5 columns [z 1 ; z 2 ; .. .; z 100 ], and then map the chaotic interval [z l ; z 2 ;...; z 100 ] to the value range of the optimization variable (in the present invention, the optimization variable of the multi-objective particle swarm optimization is the system equipment, namely 6 types of diesel generator sets, 6 types of battery packs and 6 types of motors; where b j and a j are the upper and lower limits of the optimization variables, then b j =6, a j = 0; the value of x ij is rounded) to form an initial particle population (100 particles) with 100 rows and 5 columns, that is, the equipment selection scheme, such as the first scheme (particles) [x 1 , 1 , x 1, 2 , x 1,3 , x 1,4 , x 1,5 ]=[123564], the second scheme (particles)[x 2,1 ,x 2,2 ,x 2,3 ,x 2,4 ,x 2 , 5 ] = [654312], a total of 100 scenarios (particles).

Zη+1=μzη(1-zη)n=0,1,2,...;0<zη<1;μ∈[0,4] (1)Z η+1 = μz η (1−z η )n=0, 1, 2, ...; 0<z η <1; μ∈[0,4] (1)

Xij=αj+(bjj)ziji=1,2,...,N;j=1,2,...,D (2)X ijj +(b jj )z ij i=1,2,...,N; j=1,2,...,D (2)

步骤2,定义种群初始个体极值和全局极值:Step 2, define the initial individual extremum and global extremum of the population:

定义每个粒子i当前位置(即每个粒子初始化的值)为个体极值Pi,随机产生初始粒子群的速度vi(1)(vi(1)为5维向量),采用非支配评价思想(删除方案中最差的方案,比如其中一个方案比另外一个方案成本高、油耗高、排放高、重量大、布局困难,则该方案删除)得到第一代解集(除删除的方案外,其余方案构成第一代解集),随机选取其中一个解,定义为全局极值PgDefine the current position of each particle i (that is, the initialized value of each particle) as the individual extreme value P i , and randomly generate the initial particle swarm velocity vi (1) (vi (1) is a 5-dimensional vector), using non-dominated Evaluate the idea (delete the worst scheme among the schemes, such as one of the schemes has higher cost, higher fuel consumption, higher emission, larger weight, and difficulty in layout than the other scheme, then the scheme is deleted) to obtain the first generation solution set (except the deleted scheme) In addition, the other solutions constitute the first generation solution set), and one of the solutions is randomly selected, which is defined as the global extreme value P g .

步骤3,种群迭代:Step 3, population iteration:

根据粒子群的速度更新公式(3)和位置更新公式(4)更新粒子群的速度和位置(更新后,每个粒子速度和位置均为5维向量),将更新后的粒子群与上一代解集组合,构成当代种群,当代粒子位置为当代个体极值Pi,然后采用非支配评价思想,得到当代解集,在当代解集中随机选取一个粒子作为全局极值Pg,比如第一次迭代后更新的粒子群与第一代解集合并,构成第二代种群,第二代粒子位置为第二代个体极值Pi,然后采用非支配评价思想,得到第二代解集,在第二代解集中随机选取一个粒子作为第二代全局极值PgUpdate the velocity and position of the particle swarm according to the velocity update formula (3) and position update formula (4) of the particle swarm (after the update, the velocity and position of each particle are 5-dimensional vectors), and compare the updated particle swarm with the previous generation. The solution set is combined to form a contemporary population, and the position of the contemporary particle is the contemporary individual extreme value P i , and then the non-dominant evaluation idea is used to obtain the contemporary solution set, and a particle is randomly selected as the global extreme value P g in the contemporary solution set, such as the first time The particle swarm updated after iteration is merged with the first-generation solution set to form the second-generation swarm. The position of the second-generation particle is the second-generation individual extreme value P i , and then the non-dominated evaluation idea is adopted to obtain the second-generation solution set. A particle is randomly selected as the second-generation global extreme value P g in the second-generation solution set,

vi(t+1)=w×vi(t)+C1×r1(pi-xi(t))+C2×r2(pg-xi(t)) (3)v i (t+1)=w×v i (t)+C 1 ×r 1 ( pi -x i ( t))+C 2 ×r 2 (p g -xi (t)) (3)

xi(t+1)=xi(t)+vi(t+1) (4)x i (t+1)=x i (t)+v i (t+1) (4)

其中,pi和pg为个体极值和全局极值,vi(t)和vi(t+1)为第t次迭代和第t+1次迭代时的粒子速度,xi(t)和xi(t+1)为第t次迭代和第t+1次迭代时的粒子位置,w为惯性因子,C1和C2为认知学习因子和社会学习因子,r1,r2∈[0,1],是服从均匀分布的随机变量。Among them, p i and p g are the individual extreme values and global extreme values, v i (t) and v i (t+1) are the particle velocities at the t-th iteration and the t+1-th iteration, xi (t ) and x i (t+1) are the particle positions at the t-th iteration and the t+1-th iteration, w is the inertia factor, C 1 and C 2 are the cognitive learning factor and social learning factor, r1, r2∈ [0,1] is a random variable that obeys a uniform distribution.

步骤4,全局极值Pg局部混沌优化:Step 4, local chaotic optimization of global extreme value P g :

根据式(5)将Pg映射到Logistic方程的定义域[0,1],式中bj和aj分别为优化变量的上下限,bj=6,aj=0。再根据式(1)产生50个混沌变量z1,z2,…,z50,如步骤1,将其映射到优化变量的取值区间,得到50个粒子,根据非支配评价思想得到全局极值的解集,再从中随机选取其中一个粒子作为全局极值p'g,同时p'g替代群体中任意粒子的位置,According to formula (5), P g is mapped to the definition domain [0,1] of the logistic equation, where b j and a j are the upper and lower limits of the optimization variables, b j =6, a j =0. Then generate 50 chaotic variables z 1 , z 2 ,… , z 50 according to formula (1), as in step 1, map them to the value interval of the optimization variables, and obtain 50 particles, and obtain the global pole according to the non-dominated evaluation idea. value solution set, and then randomly select one of the particles as the global extreme value p' g , and at the same time p' g replaces the position of any particle in the population,

Figure BDA0001642273850000031
Figure BDA0001642273850000031

步骤5,返回步骤3,直到迭代次数结束(迭代次数为100),输出剩余的方案(多种不同的方案),即Pareto解集。Step 5, return to step 3, until the number of iterations ends (the number of iterations is 100), and output the remaining solutions (a variety of different solutions), that is, the Pareto solution set.

第二部分:决策过程。Part II: The decision-making process.

步骤6,逼近理想解排序方法对Pareto解集排序:Step 6, approximating the ideal solution sorting method to sort the Pareto solution set:

根据多目标粒子群算法可得到多种备用方案,这些方案对决策者而言都是可行的,但决策者如何从这些方案中选取最适合自己的方案。本发明采用逼近理想解排序方法将这些方案按决策者的要求排序,提供给决策者最适合的方案。对于备用方案,设定最优方案为S+,最劣方案为S,对于任意一个备用方案Si,根据式(6)和式(7)可求出该方案与最优和最劣方案之间的距离,然后根据式(8)求出该方案的相对贴近度Ci,即该方案在最优方案和最劣方案之间的贴近最优方案的程度,Ci=1,表示最优方案,Ci=0,表示最劣方案。According to the multi-objective particle swarm optimization algorithm, there are many alternative schemes, all of which are feasible for the decision makers, but how should the decision makers choose the most suitable scheme from these schemes. The present invention uses the approximation ideal solution sorting method to sort these schemes according to the requirements of the decision maker, and provides the most suitable scheme to the decision maker. For the backup scheme, the optimal scheme is set as S + , and the worst scheme is set as S . For any backup scheme S i , according to formula (6) and formula (7), the best and worst schemes can be calculated according to the formula Then, according to formula (8), the relative closeness degree C i of the scheme is obtained, that is, the degree of closeness of the scheme between the optimal scheme and the worst scheme to the optimal scheme, C i =1, indicating that the most The best solution, C i =0, represents the worst solution.

逼近理想解排序方法是根据相对贴近度Ci的大小来将所有Pareto解集方案从上到下进行排序,相对贴近度Ci值越大,表示备用方案Si越接近最优方案,更符合决策者的要求。The approach to the ideal solution sorting method is to sort all Pareto solution sets from top to bottom according to the relative closeness C i . requirements of decision makers.

Figure BDA0001642273850000041
Figure BDA0001642273850000041

Figure BDA0001642273850000042
Figure BDA0001642273850000042

Figure BDA0001642273850000043
Figure BDA0001642273850000043

式(6)、式(7)为评价方案Si与正负理想解之间的距离,记为di +和di ,式(8)为方案Si相对贴近度Ci的计算公式。Equations (6) and (7) are the distances between the evaluation scheme Si and the positive and negative ideal solutions, denoted as d i + and d i , and formula (8) is the calculation formula for the relative closeness C i of the scheme Si .

步骤7,选择最优选型方案:根据步骤1排序结果选择相对贴近度Ci值最大的方案。Step 7, select the most preferred solution: according to the sorting result of step 1, select the solution with the largest value of relative closeness C i .

本发明产生的有益效果是:本发明中诱鱼艇采用电力推进系统结构,其设计过程包括结构设计和设备选型两部分,系统结构设计主要采用以柴油发电机组为主、蓄电池组为辅的供电方案,有四种供电模式,可确保柴油发电机组不会欠负荷或过负荷。设备选型采用基于混沌运动的多目标粒子群算法来优化选型过程,可提供多种选型方案供决策者选择,最后决策者根据逼近理想解排序法将多种方案进行排序,从中选出最满意的方案。The beneficial effects of the present invention are: the fishing boat in the present invention adopts an electric propulsion system structure, and its design process includes two parts: structural design and equipment selection. The power supply scheme, with four power supply modes, ensures that the diesel generator set will not be under-loaded or overloaded. The equipment selection adopts the multi-objective particle swarm algorithm based on chaotic motion to optimize the selection process, which can provide a variety of selection schemes for decision makers to choose. the most satisfactory solution.

附图说明Description of drawings

下面将结合附图及实施例对本发明作进一步说明,附图中:The present invention will be further described below in conjunction with the accompanying drawings and embodiments, in which:

图1是诱鱼艇驱动装置示意图;Fig. 1 is a schematic diagram of a fishing boat driving device;

图2诱鱼艇动力系统选型设计过程。Fig. 2 The selection and design process of the power system of the lure boat.

具体实施方式Detailed ways

为了使本发明的目的、技术方案及优点更加清楚明白,以下结合附图及实施例,对本发明进行进一步详细说明。应当理解,此处所描述的具体实施例仅用以解释本发明,并不用于限定本发明。In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.

本发明实施例中,提供一种诱鱼艇驱动装置,本发明设计的诱鱼艇结构方案由控制系统、供电系统和推进系统三部分组成。如图1所示,控制系统主要设备是能量管理系统装置7;供电系统主要设备有柴油发电机组1、蓄电池组6和双向DC/DC变换器5;推进系统主要设备有变频器2、电机3和螺旋桨4。提供一种诱鱼艇驱动装置,主要由能量管理系统装置、柴油发电机组、蓄电池组、双向DC/DC变换器、变频器、电机和螺旋桨组成,能量管理系统装置分别与柴油发电机组、蓄电池组和双向DC/DC变换器连接。能量管理系统通过采用CAN总线通讯方式,将设置的柴油发电机组的目标功率值、蓄电池组的目标功率值以及切换双向DC/DC变换器能量传输模式的指令,采用RS485通讯,传递给PLC控制器,并通过PLC控制器来完成柴油发电机组启动、停止、加减油门功能、蓄电池组的充放电功能和双向DC/DC变换器升压和降压模式之间的切换功能;同时采用CAN总线通讯方式,用RS485通讯将蓄电池组的SOC值和电机的需求功率值传递给能量管理系统装置;柴油发电机组的三相电输出端接口通过电力电缆线连接变频器的三相电输入端接口;蓄电池组的直流电输出端接口通过电力电缆线连接双向DC/DC变换器的低压直流端接口,双向DC/DC变换器的高压直流端接口通过电力电缆线连接到变频器的直流母排接线输入端;变频器的三相电输出端接口通过电力电缆线连接电机的三相电输入端接口;电机的输出端通过联接轴连接螺旋桨。In the embodiment of the present invention, a driving device for a fishing boat is provided, and the structure scheme of the fishing boat designed by the present invention is composed of three parts: a control system, a power supply system and a propulsion system. As shown in Figure 1, the main equipment of the control system is the energy management system device 7; the main equipment of the power supply system is a diesel generator set 1, a battery pack 6 and a bidirectional DC/DC converter 5; the main equipment of the propulsion system is a frequency converter 2, a motor 3 and propeller 4. Provided is a fishing boat driving device, which is mainly composed of an energy management system device, a diesel generator set, a battery pack, a bidirectional DC/DC converter, a frequency converter, a motor and a propeller. The energy management system device is respectively connected with the diesel generator set and the battery pack. Connect to a bidirectional DC/DC converter. The energy management system uses the CAN bus communication method to transmit the set target power value of the diesel generator set, the target power value of the battery pack and the command to switch the energy transmission mode of the bidirectional DC/DC converter to the PLC controller through RS485 communication. , and through the PLC controller to complete the diesel generator set start, stop, increase and decrease the throttle function, the charging and discharging function of the battery pack and the switching function between the boost and buck modes of the bidirectional DC/DC converter; at the same time, the CAN bus communication is used. By means of RS485 communication, the SOC value of the battery pack and the required power value of the motor are transmitted to the energy management system device; the three-phase electrical output port of the diesel generator set is connected to the three-phase electrical input port of the inverter through the power cable; the battery The DC output terminal interface of the group is connected to the low-voltage DC terminal interface of the bidirectional DC/DC converter through the power cable, and the high-voltage DC terminal interface of the bidirectional DC/DC converter is connected to the DC busbar wiring input terminal of the inverter through the power cable; The three-phase electrical output port of the inverter is connected to the three-phase electrical input port of the motor through a power cable; the output of the motor is connected to the propeller through a connecting shaft.

所述供电系统主要通过柴油发电机组、蓄电池组为变频器和电机供电。柴油发电机组只能作为能量源提供电能,而蓄电池组即可以作为能量源提供电能以外,还可以作为储能装置,回收电能。The power supply system mainly supplies power to the frequency converter and the motor through a diesel generator set and a battery pack. Diesel generator sets can only provide electrical energy as an energy source, while battery packs can not only provide electrical energy as an energy source, but also serve as an energy storage device to recover electrical energy.

所述推进系统主要通过变频器控制电机的启停以及加减速,然后电机通过连接轴带动螺旋桨转动,从而推动船舶前进。The propulsion system mainly controls the start, stop, acceleration and deceleration of the motor through the frequency converter, and then the motor drives the propeller to rotate through the connecting shaft, thereby propelling the ship forward.

进一步地,对能量管理系统装置的输入信号进行处理通过内部PLC程序对输入信号进行处理,根据处理后的信号通过PLC控制器来控制柴油发电机组、蓄电池组和双向DC/DC变换器,实现对四种供电模式的切换,模式一为蓄电池组通过双向DC/DC变换器为变频器和电机供电,柴油发电机组不工作,PLC控制器控制蓄电池组放电,双向DC/DC处于升压模式,柴油发电机组停机;模式二为柴油发电机组为变频器和电机供电,同时通过变频器的直流母线,经双向DC/DC变换器为蓄电池组充电,PLC控制器控制蓄电池组充电,双向DC/DC处于降压模式,柴油发电机组工作;模式三为柴油发电机组单独为变频器和电机供电,蓄电池组不工作,PLC控制器控制蓄电池组以及双向DC/DC停止工作,柴油发电机组工作;模式四为柴油发电机组和蓄电池组联合为变频器和电机供电,PLC控制器控制蓄电池组放电,双向DC/DC处于升压压模式,柴油发电机组工作。Further, the input signal of the energy management system device is processed through the internal PLC program to process the input signal, and the diesel generator set, the battery pack and the two-way DC/DC converter are controlled by the PLC controller according to the processed signal, so as to realize the Switching between four power supply modes, mode 1 is that the battery pack supplies power to the inverter and the motor through the bidirectional DC/DC converter, the diesel generator set does not work, the PLC controller controls the discharge of the battery pack, the bidirectional DC/DC is in boost mode, and the diesel generator set is in boost mode. The generator set is stopped; in mode 2, the diesel generator set supplies power to the inverter and the motor, and at the same time, the DC bus of the inverter is used to charge the battery pack through the bidirectional DC/DC converter. The PLC controller controls the battery pack to charge, and the bidirectional DC/DC is in Step-down mode, the diesel generator set works; mode three is that the diesel generator set supplies power to the inverter and the motor alone, the battery pack does not work, the PLC controller controls the battery pack and bidirectional DC/DC to stop working, and the diesel generator set works; mode four: The diesel generator set and the battery set are combined to supply power to the inverter and the motor, the PLC controller controls the discharge of the battery set, the bidirectional DC/DC is in the boost mode, and the diesel generator set works.

本发明设计的诱鱼艇动力系统主要设备选型设计过程由两部分组成,第一部分是多目标粒子群算法求Pareto解集过程,第二部分是决策过程。第一部分根据决策者(比如设计者、造船厂、船东等)在建造和营运过程中需考虑的因素(比如成本、油耗、排放、重量、布局等),负责从大量的方案中筛选出部分合适的方案(这些方案没有绝对的最优或最劣方案)。第二部分是根据某个决策者的具体意见来筛选出最优的方案。The main equipment selection and design process of the power system of the fishing boat designed by the invention consists of two parts. The first part is responsible for selecting parts from a large number of schemes according to the factors (such as cost, fuel consumption, emissions, weight, layout, etc.) that decision makers (such as designers, shipyards, ship owners, etc.) need to consider during construction and operation. Appropriate solutions (there is no absolute best or worst solution for these solutions). The second part is to screen out the optimal solution according to the specific opinion of a decision maker.

本发明提供一种诱鱼艇驱动装置设备选型方法,如图2所示,该方法包括以下步骤,The present invention provides a type selection method for a fishing boat driving device, as shown in FIG. 2 , the method includes the following steps:

第一部分:多目标粒子群算法求Pareto解集过程。The first part: the multi-objective particle swarm algorithm to find the Pareto solution set process.

步骤1,混沌初始化粒子种群:Step 1, Chaos initializes the particle population:

以成本、油耗、排放、重量、布局5个因素(由决策者决定具体哪些考虑因素)作为多目标粒子群算法的求解目标,随机产生一个5维向量z1。然后根据式(1)的Logistic方程得到100个分量z1,z2,...,z100,其中μ的值取4,构成100行5列的混沌区间[zl;z2;...;zl00],再根据式(2)将混沌区间[z1;z2;...;zl00]映射到优化变量的取值范围(本发明中,多目标粒子群算法的优化变量为系统设备,即6种型号的柴油发电机组,6种型号的蓄电池组以及6种型号的电机;式中bj和aj分别为优化变量的上下限,则bj=6,aj=0;xij的值取整),构成100行5列的初始粒子种群(100个粒子),即设备选型方案,比如第一个方案(粒子)[x1,1,x1,2,x1,3,x1,4,xl,5]=[123564],第二个方案(粒子)[x2,1,x2,2,x2,3,x2,4,x2,5]=[654312],共100个方案(粒子)。Taking five factors of cost, fuel consumption, emission, weight and layout (decision-makers decide which factors to consider) as the solution target of multi-objective particle swarm algorithm, a 5-dimensional vector z 1 is randomly generated. Then, 100 components z 1 , z 2 , . .; z l00 ], and then according to formula (2), the chaotic interval [z 1 ; z 2 ; ... ; is the system equipment, namely 6 types of diesel generator sets, 6 types of battery packs and 6 types of motors; where b j and a j are the upper and lower limits of the optimization variables, then b j =6, a j = 0; the value of x ij is rounded) to form an initial particle population (100 particles) with 100 rows and 5 columns, that is, the equipment selection scheme, such as the first scheme (particles) [x 1 , 1 , x 1, 2 , x 1,3 , x 1,4 , x 1,5 ]=[ 123564 ], the second scheme (particles)[x 2,1 ,x 2,2 ,x 2,3 ,x 2,4 ,x 2 , 5 ] = [654312], a total of 100 scenarios (particles).

Zη+1=μzη(1-zη)a=0,1,2,...;0<Zη<1;μ∈[0,4] (1)Z η+1 = μz η (1−z η )a=0,1,2,...; 0<Z η <1; μ∈[0,4] (1)

Xij=αj+(bjj)ziji=1,2,...,N;j=1,2,...,D (2)X ijj +(b jj )z ij i=1,2,...,N; j=1,2,...,D (2)

步骤2,定义种群初始个体极值和全局极值:Step 2, define the initial individual extremum and global extremum of the population:

定义每个粒子i当前位置(即每个粒子初始化的值)为个体极值Pi,随机产生初始粒子群的速度vi(1)(vi(1)为5维向量),采用非支配评价思想(删除方案中最差的方案,比如其中一个方案比另外一个方案成本高、油耗高、排放高、重量大、布局困难,则该方案删除)得到第一代解集(除删除的方案外,其余方案构成第一代解集),随机选取其中一个解,定义为全局极值PgDefine the current position of each particle i (that is, the initialized value of each particle) as the individual extreme value P i , and randomly generate the initial particle swarm velocity vi (1) (vi (1) is a 5-dimensional vector), using non-dominated Evaluate the idea (delete the worst scheme among the schemes, such as one of the schemes has higher cost, higher fuel consumption, higher emission, larger weight, and difficulty in layout than the other scheme, then the scheme is deleted) to obtain the first generation solution set (except the deleted scheme) In addition, the other solutions constitute the first generation solution set), and one of the solutions is randomly selected, which is defined as the global extreme value P g .

步骤3,种群迭代:Step 3, population iteration:

根据粒子群的速度更新公式(3)和位置更新公式(4)更新粒子群的速度和位置(更新后,每个粒子速度和位置均为5维向量),将更新后的粒子群与上一代解集组合,构成当代种群,当代粒子位置为当代个体极值Pi,然后采用非支配评价思想,得到当代解集,在当代解集中随机选取一个粒子作为全局极值Pg,比如第一次迭代后更新的粒子群与第一代解集合并,构成第二代种群,第二代粒子位置为第二代个体极值Pi,然后采用非支配评价思想,得到第二代解集,在第二代解集中随机选取一个粒子作为第二代全局极值PgUpdate the velocity and position of the particle swarm according to the velocity update formula (3) and position update formula (4) of the particle swarm (after the update, the velocity and position of each particle are 5-dimensional vectors), and compare the updated particle swarm with the previous generation. The solution set is combined to form a contemporary population, and the position of the contemporary particle is the contemporary individual extreme value P i , and then the non-dominant evaluation idea is used to obtain the contemporary solution set, and a particle is randomly selected as the global extreme value P g in the contemporary solution set, such as the first time The particle swarm updated after iteration is merged with the first-generation solution set to form the second-generation swarm. The position of the second-generation particle is the second-generation individual extreme value P i , and then the non-dominated evaluation idea is adopted to obtain the second-generation solution set. A particle is randomly selected as the second-generation global extreme value P g in the second-generation solution set,

vi(t+1)=w×vi(t)+C1×r1(pi-xi(t))+C2×r2(pg-xi(t)) (3)v i (t+1)=w×v i (t)+C 1 ×r 1 ( pi -x i ( t))+C 2 ×r 2 (p g -xi (t)) (3)

xi(t+1)=xi(t)+vi(t+1) (4)x i (t+1)=x i (t)+v i (t+1) (4)

其中,pi和pg为个体极值和全局极值,vi(t)和vi(t+1)为第t次迭代和第t+1次迭代时的粒子速度,xi(t)和xi(t+1)为第t次迭代和第t+1次迭代时的粒子位置,w为惯性因子,C1和C2为认知学习因子和社会学习因子,r1,r2∈[0,1],是服从均匀分布的随机变量。Among them, p i and p g are the individual extreme values and global extreme values, v i (t) and v i (t+1) are the particle velocities at the t-th iteration and the t+1-th iteration, xi (t ) and x i (t+1) are the particle positions at the t-th iteration and the t+1-th iteration, w is the inertia factor, C 1 and C 2 are the cognitive learning factor and social learning factor, r1, r2∈ [0,1] is a random variable that obeys a uniform distribution.

步骤4,全局极值Pg局部混沌优化:Step 4, local chaotic optimization of global extreme value P g :

根据式(5)将Pg映射到Logistic方程的定义域[0,1],式中bj和aj分别为优化变量的上下限,bj=6,aj=0。再根据式(1)产生50个混沌变量z1,z2,…,z50,如步骤1,将其映射到优化变量的取值区间,得到50个粒子,根据非支配评价思想得到全局极值的解集,再从中随机选取其中一个粒子作为全局极值p'g,同时p'g替代群体中任意粒子的位置,According to formula (5), P g is mapped to the definition domain [0,1] of the logistic equation, where b j and a j are the upper and lower limits of the optimization variables, b j =6, a j =0. Then generate 50 chaotic variables z 1 , z 2 ,… , z 50 according to formula (1), as in step 1, map them to the value range of the optimized variables to obtain 50 particles, and obtain the global pole according to the non-dominated evaluation idea. value solution set, and then randomly select one of the particles as the global extreme value p' g , and at the same time p' g replaces the position of any particle in the population,

Figure BDA0001642273850000071
Figure BDA0001642273850000071

步骤5,返回步骤3,直到迭代次数结束(迭代次数为100),输出剩余的方案(多种不同的方案),即Pareto解集。Step 5, return to step 3, until the number of iterations ends (the number of iterations is 100), and output the remaining solutions (a variety of different solutions), that is, the Pareto solution set.

第二部分:决策过程。Part II: The decision-making process.

步骤6,逼近理想解排序方法对Pareto解集排序。Step 6, approximating the ideal solution sorting method to sort the Pareto solution set.

根据多目标粒子群算法可得到多种备用方案,这些方案对决策者而言都是可行的,但决策者如何从这些方案中选取最适合自己的方案。本发明采用逼近理想解排序方法将这些方案按决策者的要求排序,提供给决策者最适合的方案。对于备用方案,设定最优方案为S+(决策者对选型需考虑的因素最理想的结果,比如成本为a万,油耗为b升,排放为c克,重量为d吨,布局为e立方米),最劣方案为S(决策者对选型需考虑的因素最不理想的结果,比如成本为A万,油耗为B升,排放为C克,重量为D吨,布局为E立方米),对于任意一个备用方案Si,根据式(6)和式(7)可求出该方案与最优和最劣方案之间的距离,然后根据式(8)求出该方案的相对贴近度Ci,即该方案在最优方案和最劣方案之间的贴近最优方案的程度,Ci=1,表示最优方案,Ci=0,表示最劣方案。According to the multi-objective particle swarm optimization algorithm, there are many alternative schemes, all of which are feasible for the decision makers, but how should the decision makers choose the most suitable scheme from these schemes. The present invention uses the approximation ideal solution sorting method to sort these schemes according to the requirements of the decision maker, and provides the most suitable scheme to the decision maker. For the backup plan, set the optimal plan as S + (the most ideal result for decision makers to consider the factors that need to be considered in the model selection, such as the cost of a million, the fuel consumption of b liters, the emission of c grams, the weight of d tons, and the layout of e cubic meters), the worst solution is S (the most unsatisfactory result of the factors that decision makers need to consider in the selection, such as the cost of A million, the fuel consumption of B liters, the emission of C grams, the weight of D tons, and the layout of E cubic meters), for any alternative S i , the distance between the scheme and the optimal and worst schemes can be obtained according to formula (6) and formula (7), and then the scheme can be obtained according to formula (8) The relative closeness C i of , that is, the degree of closeness of the solution to the optimal solution between the optimal solution and the worst solution, C i =1, represents the optimal solution, and C i =0, represents the worst solution.

逼近理想解排序方法是根据相对贴近度Ci的大小来将所有Pareto解集方案从上到下进行排序,相对贴近度Ci值越大,表示备用方案Si越接近最优方案,更符合决策者的要求。The approach to the ideal solution sorting method is to sort all Pareto solution sets from top to bottom according to the relative closeness C i . requirements of decision makers.

Figure BDA0001642273850000081
Figure BDA0001642273850000081

Figure BDA0001642273850000082
Figure BDA0001642273850000082

Figure BDA0001642273850000083
Figure BDA0001642273850000083

式(6)、式(7)为评价方案Si与正负理想解之间的距离,记为di +和di ,式(8)为方案Si相对贴近度Ci的计算公式。Equations (6) and (7) are the distances between the evaluation scheme Si and the positive and negative ideal solutions, denoted as d i + and d i , and formula (8) is the calculation formula for the relative closeness C i of the scheme Si .

步骤7,选择最优选型方案:根据步骤1排序结果选择相对贴近度Ci值最大的方案。Step 7, select the most preferred solution: according to the sorting result of step 1, select the solution with the largest value of relative closeness C i .

应当理解的是,对本领域普通技术人员来说,可以根据上述说明加以改进或变换,而所有这些改进和变换都应属于本发明所附权利要求的保护范围。It should be understood that, for those skilled in the art, improvements or changes can be made according to the above description, and all these improvements and changes should fall within the protection scope of the appended claims of the present invention.

Claims (1)

1.一种诱鱼艇驱动装置设备选型方法,其特征在于,该方法包括以下步骤,1. a method for selecting type of fishing boat driving device equipment, is characterized in that, the method comprises the following steps, 步骤1,混沌初始化粒子种群:Step 1, Chaos initializes the particle population: 以成本、油耗、排放、重量、布局5个因素作为多目标粒子群算法的求解目标,随机产生一个5维向量z1,然后根据式(1)的Logistic方程得到100个分量z1,z2,…,z100,其中μ的值取4,构成100行5列的混沌区间[z1;z2;…;z100],再根据式(2)将混沌区间[z1;z2;…;z100]映射到优化变量的取值范围,即设备选型方案,Taking the five factors of cost, fuel consumption, emission, weight and layout as the solution target of the multi-objective particle swarm algorithm, a 5-dimensional vector z 1 is randomly generated, and then 100 components z 1 , z 2 are obtained according to the logistic equation of formula (1). ,...,z 100 , where the value of μ is 4 to form a chaotic interval [z 1 ; z 2 ; ... ; ...; z 100 ] is mapped to the value range of the optimization variable, that is, the equipment selection scheme, zn+1=μzn(1-zn) n=0,1,2,...;0<zn<1;μ∈[0,4] (1)z n+1 = μz n (1−z n ) n=0, 1, 2, . . . ; 0<z n <1; μ∈[0,4] (1) xij=aj+(bj-aj)zij i=1,2,...,N;j=1,2,...,D (2)x ij =a j +(b j -a j )z ij i=1,2,...,N; j=1,2,...,D (2) 步骤2,定义种群初始个体极值和全局极值:Step 2, define the initial individual extremum and global extremum of the population: 定义每个粒子i当前位置为个体极值Pi,随机产生初始粒子群的速度vi(1),采用非支配评价思想得到第一代解集,随机选取其中一个解,定义为全局极值PgDefine the current position of each particle i as the individual extreme value P i , randomly generate the initial particle swarm velocity v i(1) , use the non-dominated evaluation idea to obtain the first generation solution set, randomly select one of the solutions, and define it as the global extreme value P g ; 步骤3,种群迭代:Step 3, population iteration: 根据粒子群的速度更新公式(3)和位置更新公式(4)更新粒子群的速度和位置,将更新后的粒子群与上一代解集组合,构成当代种群,当代粒子位置为当代个体极值Pi,然后采用非支配评价思想,得到当代解集,在当代解集中随机选取一个粒子作为全局极值Pg,然后采用非支配评价思想,得到第二代解集,在第二代解集中随机选取一个粒子作为第二代全局极值PgUpdate the speed and position of the particle swarm according to the velocity update formula (3) and the position update formula (4) of the particle swarm, and combine the updated particle swarm with the previous generation solution set to form a contemporary population, and the contemporary particle position is the contemporary individual extreme value P i , and then adopt the non-dominated evaluation idea to obtain the contemporary solution set, randomly select a particle in the contemporary solution set as the global extreme value P g , and then adopt the non-dominated evaluation idea to obtain the second generation solution set, in the second generation solution set Randomly select a particle as the second generation global extreme value P g , vi(t+1)=w×vi(t)+C1×r1(pi-xi(t))+C2×r2(pg-xi(t)) (3)v i (t+1)=w×v i (t)+C 1 ×r 1 ( pi -x i ( t))+C 2 ×r 2 (p g -xi (t)) (3) xi(t+1)=xi(t)+vi(t+1) (4)x i (t+1)=x i (t)+v i (t+1) (4) 其中,pi和pg为个体极值和全局极值,vi(t)和vi(t+1)为第t次迭代和第t+1次迭代时的粒子速度,xi(t)和xi(t+1)为第t次迭代和第t+1次迭代时的粒子位置,w为惯性因子,C1和C2为认知学习因子和社会学习因子,r1,r2∈[0,1],是服从均匀分布的随机变量;Among them, p i and pg are the individual extreme values and global extreme values, v i (t) and v i (t+1) are the particle velocities at the t-th iteration and the t+1-th iteration, and x i (t) and x i (t+1) are the particle positions at the t-th iteration and the t+1-th iteration, w is the inertia factor, C 1 and C 2 are the cognitive learning factor and social learning factor, r1,r2∈[ 0,1], is a random variable obeying a uniform distribution; 步骤4,全局极值Pg局部混沌优化:Step 4, local chaotic optimization of global extreme value P g : 根据式(5)将Pg映射到Logistic方程的定义域[0,1],式中bj和aj分别为优化变量的上下限,bj=6,aj=0,再根据式(1)产生50个混沌变量z1,z2,…,z50,如步骤1,将其映射到优化变量的取值区间,得到50个粒子,根据非支配评价思想得到全局极值的解集,再从中随机选取其中一个粒子作为全局极值p'g,同时p'g替代群体中任意粒子的位置,According to the formula (5), P g is mapped to the definition domain [0,1] of the Logistic equation, where b j and a j are the upper and lower limits of the optimization variables, respectively, b j =6, a j =0, and then according to the formula ( 1) Generate 50 chaotic variables z 1 , z 2 ,..., z 50 , as in step 1, map them to the value interval of the optimization variables, get 50 particles, and obtain the solution set of the global extremum according to the non-dominated evaluation idea , and then randomly select one of the particles as the global extreme value p' g , and at the same time p' g replaces the position of any particle in the population,
Figure FDA0002485639480000021
Figure FDA0002485639480000021
步骤5,返回步骤3,直到迭代次数结束,输出剩余的方案,即Pareto解集;Step 5, return to step 3, until the number of iterations ends, output the remaining solutions, that is, the Pareto solution set; 步骤6,逼近理想解排序方法对Pareto解集排序:Step 6, approximating the ideal solution sorting method to sort the Pareto solution set: 对于备用方案,设定最优方案为S+,最劣方案为S,对于任意一个备用方案Si,根据式(6)和式(7)可求出该方案与最优和最劣方案之间的距离,然后根据式(8)求出该方案的相对贴近度Ci,即该方案在最优方案和最劣方案之间的贴近最优方案的程度,Ci=1,表示最优方案,Ci=0,表示最劣方案;For the backup scheme, the optimal scheme is set as S + , and the worst scheme is set as S . For any backup scheme S i , according to formula (6) and formula (7), the best and worst schemes can be calculated according to the formula Then, according to formula (8), the relative closeness degree C i of the scheme is obtained, that is, the degree of closeness of the scheme between the optimal scheme and the worst scheme to the optimal scheme, C i =1, indicating that the most The optimal solution, C i =0, represents the worst solution;
Figure FDA0002485639480000022
Figure FDA0002485639480000022
Figure FDA0002485639480000023
Figure FDA0002485639480000023
Figure FDA0002485639480000024
Figure FDA0002485639480000024
式(6)、式(7)为评价方案Si与正负理想解之间的距离,记为di +和di ,式(8)为方案Si相对贴近度Ci的计算公式;Equations (6) and (7) are the distances between the evaluation scheme Si and the positive and negative ideal solutions, denoted as d i + and d i , and formula (8) is the calculation formula for the relative closeness C i of the scheme Si ; 步骤7,选择最优选型方案:根据步骤1排序结果选择相对贴近度Ci值最大的方案。Step 7, select the most preferred solution: according to the sorting result of step 1, select the solution with the largest value of relative closeness C i .
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