CN108398982A - A kind of maximum power tracking method of photovoltaic array under local shadow - Google Patents
A kind of maximum power tracking method of photovoltaic array under local shadow Download PDFInfo
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Abstract
Description
技术领域technical field
本发明涉及光伏发电技术领域,尤其是涉及一种局部阴影下光伏阵列的最大功率跟踪方法。The invention relates to the technical field of photovoltaic power generation, in particular to a method for maximum power tracking of a photovoltaic array under partial shadow.
背景技术Background technique
能源在创造新机遇和促进经济增长方面扮演着极其重要的角色,同时世界经济的发展和人口的增长反过来助长了世界能源需求量。我国能源结构的核心问题表现在:一是能源结构以煤为主;二是石油安全问题日趋显著;三是煤烟型污染已经给生态环境带来严重问题。由此可见,优化能源结构势在必行,缓慢增添绿色可再生能源的比例,减少化石能源的使用。Energy plays an extremely important role in creating new opportunities and promoting economic growth, while the development of the world economy and population growth in turn contribute to the world's energy demand. The core problems of my country's energy structure are as follows: first, the energy structure is dominated by coal; second, the problem of oil safety is becoming more and more prominent; third, soot pollution has brought serious problems to the ecological environment. It can be seen that it is imperative to optimize the energy structure, slowly increase the proportion of green and renewable energy, and reduce the use of fossil energy.
太阳能光伏发电被认为是当前世界上最具有发展前景的新能源技术,各发达国家均投入巨额资金竞相研究开发,并积极推进产业化进程,大力开拓市场应用。但是光伏发电产业在发展中也遇到了许多问题:光伏电池成本高昂、光电转化效率较低、局部遮挡的危害。Solar photovoltaic power generation is considered to be the most promising new energy technology in the world at present. All developed countries have invested huge sums of money to compete in research and development, actively promote the process of industrialization, and vigorously develop market applications. However, the photovoltaic power generation industry has also encountered many problems in the development: high cost of photovoltaic cells, low photoelectric conversion efficiency, and the harm of partial shading.
最大功率点跟踪是降低发电成本、提高发电效率最直接有效的方法。现有的大部分最大功率点跟踪方法的应用前提都是光伏阵列受到的光照均匀,而忽略了在现实生活中,光伏阵列被遮挡的概率很大。当光伏阵列被局部遮挡时,使得传统的最大功率点跟踪方法容易陷入局部最优难以搜寻到全局最优。Maximum power point tracking is the most direct and effective method to reduce power generation costs and improve power generation efficiency. Most of the existing maximum power point tracking methods are applied on the premise that the photovoltaic array is evenly illuminated, while ignoring that in real life, the photovoltaic array has a high probability of being blocked. When the photovoltaic array is partially shaded, the traditional maximum power point tracking method is easy to fall into the local optimum and difficult to search for the global optimum.
扰动观察法和电导增量法是较早应用在光伏发电系统中的最大功率跟踪方法,被称为传统最大功率跟踪方法。扰动观察法控制思路简单,实现较为方便,可实现对最大功率点的跟踪,提高系统的利用效率。但是由于扰动观察法仅以光伏电池前后两次的输出功率为对象进行研究,没有考虑外部环境条件变化对光伏阵列前后两次输出功率的影响,在使用的过程中容易出现方法的“误判”,“误判”增加了跟踪时间,降低了光伏阵列的输出效率,严重时导致跟踪的失效,使该方法不能准确地跟踪到最大输出功率。The perturbation and observation method and the incremental conductance method are the earlier maximum power tracking methods used in photovoltaic power generation systems, and are called traditional maximum power tracking methods. The perturbation and observation method has a simple control idea and is more convenient to implement. It can realize the tracking of the maximum power point and improve the utilization efficiency of the system. However, since the perturbation and observation method only studies the two output powers of the photovoltaic cell before and after, and does not consider the impact of changes in external environmental conditions on the output power of the photovoltaic array before and after the two times, it is prone to "misjudgment" in the process of use. , "Misjudgment" increases the tracking time, reduces the output efficiency of the photovoltaic array, and leads to tracking failure in severe cases, so that the method cannot accurately track the maximum output power.
电导增量法跟踪精度较高,控制效果好,不受功率时间曲线的影响。但该方法对传感器有较高的要求,同时步长的选取也将影响算法的性能,在外界环境条件变化较快的情况下也会出现“误判”。The conductance incremental method has high tracking accuracy and good control effect, and is not affected by the power-time curve. However, this method has higher requirements on the sensor, and the selection of the step size will also affect the performance of the algorithm, and "misjudgment" will also occur when the external environmental conditions change rapidly.
近年来,随着智能算法的不断完善,粒子群算法、遗传算法、模糊控制算法和神经网络算法等被引入到光伏发电系统的最大功率跟踪控制中。这些算法的使用,有效地提高了最大功率跟踪的精度,减少了能量损耗。但智能算法往往存在控制参数多,控制思想复杂,对硬件的要求高的缺点,这在一定程度上制约了这些算法的工程实践应用,并且随着光伏阵列的运行环境变得越来越复杂,由于建筑物、树木的遮挡或灰尘等造成光伏阵列表面受到的光照强度不均匀的情况时常发生,此时,光伏阵列的功率-电压(P-U)特性曲线将出现多个峰值。部分智能算法和传统最大功率跟踪方法一样,缺乏全局寻优的能力,仅仅适用于单峰值最大功率跟踪系统,当对多峰值系统进行跟踪时,会造成跟踪失效。因此,研究一种具有全局寻优特性的最大功率跟踪方法对于提高光伏发电效率十分关键。In recent years, with the continuous improvement of intelligent algorithms, particle swarm algorithm, genetic algorithm, fuzzy control algorithm and neural network algorithm have been introduced into the maximum power tracking control of photovoltaic power generation systems. The use of these algorithms effectively improves the accuracy of maximum power tracking and reduces energy loss. However, intelligent algorithms often have the disadvantages of many control parameters, complex control ideas, and high requirements for hardware, which restricts the engineering practice application of these algorithms to a certain extent, and as the operating environment of photovoltaic arrays becomes more and more complex, Due to the shading of buildings, trees or dust, the uneven light intensity on the surface of the photovoltaic array often occurs. At this time, the power-voltage (P-U) characteristic curve of the photovoltaic array will have multiple peaks. Like the traditional maximum power tracking method, some intelligent algorithms lack the ability of global optimization, and are only suitable for single-peak maximum power tracking systems. When tracking multi-peak systems, they will cause tracking failure. Therefore, it is very important to study a maximum power tracking method with global optimization characteristics to improve the efficiency of photovoltaic power generation.
在光伏阵列的应用中,PSO、QPSO和BQPSO算法是比较常用的算法,但三者因粒子多样性缺失易出现“早熟”和局部收敛问题,In the application of photovoltaic arrays, PSO, QPSO and BQPSO algorithms are commonly used algorithms, but the three are prone to "premature" and local convergence problems due to the lack of particle diversity.
发明内容Contents of the invention
本发明的目的就是为了克服上述现有技术存在的缺陷而提供一种基于Bloch球面的QPSO改进算法的、提高系统稳定性的局部阴影下光伏阵列的最大功率跟踪方法。The object of the present invention is to provide a method for maximum power tracking of photovoltaic arrays under local shadows based on the improved QPSO algorithm of the Bloch sphere and to improve system stability in order to overcome the above-mentioned defects in the prior art.
本发明的目的可以通过以下技术方案来实现:The purpose of the present invention can be achieved through the following technical solutions:
一种局部阴影下光伏阵列的最大功率跟踪方法,包括以下步骤:A method for maximum power tracking of a photovoltaic array under partial shading, comprising the following steps:
(一)根据光伏电池的特性,建立局部阴影条件下的光伏阵列模型;(1) According to the characteristics of photovoltaic cells, a photovoltaic array model under partial shadow conditions is established;
(二)采用基于Bloch球面的QPSO改进算法(IBQPSO算法)对光伏阵列模型求解,获取输出功率;(2) Using the improved QPSO algorithm (IBQPSO algorithm) based on the Bloch sphere to solve the photovoltaic array model to obtain the output power;
(三)以获取的输出功率作为适应度函数,通过迭代搜索,实现光伏阵列的最大功率点跟踪。(3) Using the obtained output power as the fitness function, through iterative search, the maximum power point tracking of the photovoltaic array is realized.
所述的步骤(二)中,基于Bloch球面的QPSO改进算法具体包括以下内容:In the described step (two), the QPSO improved algorithm based on the Bloch sphere specifically includes the following:
1)设置算法参数并初始化粒子种群,初始化粒子的相位,每个粒子包含三个位置的信息,具体步骤包括:1) Set the algorithm parameters and initialize the particle population, initialize the phase of the particles, each particle contains information of three positions, the specific steps include:
101)在Bloch球面坐标下,一个点可由两个角度θ和来确定,量子位用Bloch球面坐标表示为:101) In Bloch spherical coordinates, a point can be defined by two angles θ and To determine, the qubit is expressed in Bloch spherical coordinates as:
102)采用量子位的Bloch球面坐标编码,则种群中第i个粒子Pi的Bloch球面坐标为:102) Using the Bloch spherical coordinate encoding of the qubit, the Bloch spherical coordinate of the i-th particle P i in the population is:
式中,θij=π×rand,rand为[0,1]区间的随机数;i=1,2Λ,m,m为种群规模;n为优化变量的个数;在IBQPSO算法中,每个粒子同时占据空间三个位置,即同时代表三个优化解,分别为X解、Y解、Z解:In the formula, θ ij = π×rand, rand is a random number in the [0, 1] interval; i = 1, 2Λ, m, m is the population size; n is the number of optimization variables; in the IBQPSO algorithm, each particle simultaneously occupies Three positions in the space represent three optimized solutions at the same time, namely X solution, Y solution and Z solution:
Piz=(cosθi1,cosθi2,Λ,cosθin);P iz =(cosθ i1 ,cosθ i2 ,Λ,cosθ in );
103)记第i个粒子Pi上的第j个量子位的Bloch坐标为[xij,yij,zij]T,j=1,2,Λn,n为优化变量的个数;优化问题解空间的第j维的取值范围为则由单位空间In=[-1,1]n映射到优化问题解空间的变换公式为:103) Record the Bloch coordinates of the j qubit on the i particle P i as [x ij , y ij , z ij ] T , j=1, 2, Λn, n is the number of optimization variables; optimization problem The value range of the jth dimension of the solution space is Then the transformation formula for mapping the unit space I n = [-1,1] n to the optimization problem solution space is:
104)结束初始化,输出初始粒子信息。104) End initialization and output initial particle information.
2)计算各个粒子的适应度值;2) Calculate the fitness value of each particle;
3)根据粒子的适应度值更新自身和全局最优相位;3) Update itself and the global optimal phase according to the fitness value of the particle;
4)利用自适应量子旋转门,对全局最优相位的量子比特的两个相位参数θ和进行调整,实现粒子的位置更新,并将其映射到解空间;4) Using the adaptive quantum revolving gate, the two phase parameters θ and Make adjustments to update the particle's position and map it to the solution space;
自适应量子旋转门如下式所示:The adaptive quantum revolving door is shown in the following formula:
其中,当前迭代对应的旋转角αi的定义为:Among them, the definition of the rotation angle α i corresponding to the current iteration is:
式中,αmin是最小旋转角,取0.01×pi;αmax是最大旋转角,取0.5×pi;fi是指当前第i个粒子的适应值;fmin是当代粒子中的最小适应值;fmax是当代粒子中的最大适应值;gen是当前的迭代次数;maxgen是算法设置的最大迭代次数。In the formula, α min is the minimum rotation angle, which is 0.01×pi; α max is the maximum rotation angle, which is 0.5×pi; f i refers to the fitness value of the current i-th particle; f min is the minimum fitness value of contemporary particles ; f max is the maximum fitness value in the contemporary particle; gen is the current iteration number; maxgen is the maximum iteration number set by the algorithm.
更新公式如下式所示:The update formula is as follows:
5)计算各粒子适应度值并评价,根据粒子的适应度更新自身,选择个体最优相位,并获取全局最优相位;5) Calculate and evaluate the fitness value of each particle, update itself according to the fitness of the particle, select the individual optimal phase, and obtain the global optimal phase;
根据粒子的适应度值,判断粒子的初始位置,并将此次粒子的位置与其他粒子的位置进行比较;适应度值最高的粒子为个体最优粒子,其相位为个体最优相位;According to the fitness value of the particle, the initial position of the particle is judged, and the position of the particle is compared with the position of other particles; the particle with the highest fitness value is the individual optimal particle, and its phase is the individual optimal phase;
将个体最优粒子与上一级的四个粒子的适应度值进行比较,若适应度值大于上一级的最高适应度值,则该个体最优粒子为全局最优粒子,其相位为全局最优相位;若适应度值小于上一级的最高适应度值,则全局最优粒子为上一级最高适应度值粒子,其相位为全局最优相位。Compare the fitness value of the individual optimal particle with the four particles of the upper level. If the fitness value is greater than the highest fitness value of the upper level, the individual optimal particle is the global optimal particle, and its phase is the global optimal particle. Optimal phase; if the fitness value is less than the highest fitness value of the previous level, the global optimal particle is the particle with the highest fitness value of the previous level, and its phase is the global optimal phase.
6)保存个体最优相位并判断是否达到最大迭代次数,若未达到,转至步骤7),若达到,转至步骤8);6) Save the individual optimal phase and judge whether the maximum number of iterations is reached, if not reached, go to step 7), if reached, go to step 8);
7)以变异概率pa选择变异粒子,利用自适应量子旋转门调整量子比特的两个相位参数θ和实现粒子的变异,计算新种群的适应度值并评价,转至步骤4);7) Select the mutated particles with the mutation probability pa, and use the adaptive quantum revolving door to adjust the two phase parameters θ and Realize the variation of particles, calculate and evaluate the fitness value of the new population, go to step 4);
自适应量子旋转门如下式所示:The adaptive quantum revolving door is shown in the following formula:
其中,当前迭代对应的旋转角αi的定义为:Among them, the definition of the rotation angle α i corresponding to the current iteration is:
对每个粒子依变异概率,利用自适应量子旋转门调整量子比特的两个相位参数θ和实现粒子变异,粒子变异公式如下式所示:For each particle, according to the mutation probability, use the adaptive quantum revolving gate to adjust the two phase parameters θ and To achieve particle mutation, the particle variation formula is as follows:
更新公式如下式所示:The update formula is as follows:
8)输出最优解。8) Output the optimal solution.
在本发明局部阴影下光伏阵列的最大功率跟踪方法中,利用粒子的位置表示光伏阵列的电压,通过光伏阵列输出光伏阵列功率并作为粒子的适应度函数,通过不断地迭代搜索,寻找到光伏阵列最大功率,达到寻优目的。In the maximum power tracking method of the photovoltaic array under partial shadow of the present invention, the position of the particle is used to represent the voltage of the photovoltaic array, and the power of the photovoltaic array is output through the photovoltaic array as the fitness function of the particle, and the photovoltaic array is found through continuous iterative search Maximum power to achieve the purpose of optimization.
与现有技术相比,本发明具有以下优点:Compared with the prior art, the present invention has the following advantages:
1、本发明方法基于Bloch球面,对QPSO进行了改进,在应用于光伏阵列最大功率点跟踪时,对最大功率点具有更快的跟踪速度,改善了系统的动态响应,避免了在最大功率点附近的振荡,提高了系统的稳态性能;1. The method of the present invention is based on the Bloch spherical surface, and QPSO is improved. When applied to the maximum power point tracking of photovoltaic arrays, the maximum power point has a faster tracking speed, the dynamic response of the system is improved, and the maximum power point is avoided. nearby oscillations, improving the steady-state performance of the system;
2、IBQPSO算法利用Bloch球面进行粒子编码,每个粒子代表三个位置,在迭代时三个位置同步更新,同时在粒子变异中利用更具柔韧性的自适应量子旋转门,从而在迭代后期仍保持粒子的多样性,能够提高对解空间的遍历性,从而提高获得全局最优解的概率;2. The IBQPSO algorithm uses the Bloch sphere for particle encoding. Each particle represents three positions, and the three positions are updated synchronously during the iteration. Maintaining the diversity of particles can improve the ergodicity of the solution space, thereby increasing the probability of obtaining the global optimal solution;
3、本发明方法采用自适应量子旋转门实现粒子的更新和变异,在避免陷入局部最优的同时,实现了稳定的稳态功率输出,对于环境的变化,包括局部阴影和阴影突变情况下均能找到最大功率点,增强了系统的跟踪能力。3. The method of the present invention adopts the self-adaptive quantum revolving door to realize the update and variation of particles, and realizes a stable steady-state power output while avoiding falling into a local optimum. For changes in the environment, including local shadows and shadow mutations, it is stable The maximum power point can be found, which enhances the tracking ability of the system.
附图说明Description of drawings
图1为本发明方法的流程图;Fig. 1 is the flowchart of the inventive method;
图2为基于Bloch球面的QPSO改进算法的流程图;Fig. 2 is the flowchart of the QPSO improved algorithm based on the Bloch sphere;
图3为本实施例不同光照条件下的光伏阵列P-U特性曲线图,其中,图3(a)为标准光照下的光伏阵列的P-U特性曲线,图3(b)为阴影情况1、2、3下的光伏阵列的P-U特性曲线,图3(c)为阴影情况4、5、6下的光伏阵列的P-U特性曲线;Fig. 3 is the P-U characteristic curve of the photovoltaic array under different illumination conditions of the present embodiment, wherein, Fig. 3 (a) is the P-U characteristic curve of the photovoltaic array under the standard illumination, and Fig. 3 (b) is the shadow situation 1, 2, 3 The P-U characteristic curve of the photovoltaic array under , Fig. 3 (c) is the P-U characteristic curve of the photovoltaic array under the shadow conditions 4, 5 and 6;
图4为本发明实施例中光伏阵列在标准光照条件下应用IBQPSO、PSO、QPSO和BQPSO算法的P-T曲线仿真结果图;Fig. 4 is the P-T curve simulation result figure of applying IBQPSO, PSO, QPSO and BQPSO algorithm under the standard illumination condition of photovoltaic array in the embodiment of the present invention;
图5为本发明实施例中光伏阵列在六种阴影条件下应用IBQPSO、PSO、QPSO和BQPSO算法的P-T曲线结果图,其中,图5(a)为阴影情况1下的光伏阵列的P-T曲线,图5(b)为阴影情况2下的光伏阵列的P-T曲线,图5(c)为阴影情况3下的光伏阵列的P-T曲线,图5(d)为阴影情况4下的光伏阵列的P-T曲线,图5(e)为阴影情况5下的光伏阵列的P-T曲线,图5(f)为阴影情况6下的光伏阵列的P-T曲线;Fig. 5 is the P-T curve result figure of applying IBQPSO, PSO, QPSO and BQPSO algorithm to the photovoltaic array in the embodiment of the present invention under six kinds of shadow conditions, wherein, Fig. 5 (a) is the P-T curve of the photovoltaic array under shadow situation 1, Figure 5(b) is the P-T curve of the photovoltaic array under the shadow situation 2, Figure 5(c) is the P-T curve of the photovoltaic array under the shadow situation 3, and Figure 5(d) is the P-T curve of the photovoltaic array under the shadow situation 4 , Fig. 5 (e) is the P-T curve of the photovoltaic array under the shading situation 5, and Fig. 5 (f) is the P-T curve of the photovoltaic array under the shading situation 6;
图6为本发明实施例中光伏阵列在阴影突变情况下应用IBQPSO、PSO、QPSO和BQPSO算法的P-T曲线仿真结果图,其中,图6(a)为情况1下的光伏阵列的P-T曲线,图6(b)为情况2下的光伏阵列的P-T曲线,图6(c)为情况3下的光伏阵列的P-T曲线,图6(d)为情况4下的光伏阵列的P-T曲线。Fig. 6 is the P-T curve simulation result figure of applying IBQPSO, PSO, QPSO and BQPSO algorithm in the shadow sudden change situation of photovoltaic array in the embodiment of the present invention, wherein, Fig. 6 (a) is the P-T curve of the photovoltaic array under the situation 1, Fig. 6(b) is the P-T curve of the photovoltaic array in case 2, FIG. 6(c) is the P-T curve of the photovoltaic array in case 3, and FIG. 6(d) is the P-T curve of the photovoltaic array in case 4.
具体实施方式Detailed ways
下面结合附图和具体实施例对本发明进行详细说明。The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.
实施例Example
如图1所示,本发明涉及一种局部阴影下光伏阵列的最大功率跟踪方法,该方法包括以下步骤:As shown in Figure 1, the present invention relates to a method for maximum power tracking of a photovoltaic array under partial shading, the method comprising the following steps:
(一)根据光伏电池的特性,建立局部阴影条件下的光伏阵列模型;(1) According to the characteristics of photovoltaic cells, a photovoltaic array model under partial shadow conditions is established;
(二)采用基于Bloch球面的QPSO改进算法(IBQPSO算法)对光伏阵列模型求解,获取输出功率;(2) Using the improved QPSO algorithm (IBQPSO algorithm) based on the Bloch sphere to solve the photovoltaic array model to obtain the output power;
(三)以获取的输出功率作为适应度函数,通过迭代搜索,实现光伏阵列的最大功率点跟踪。(3) Using the obtained output power as the fitness function, through iterative search, the maximum power point tracking of the photovoltaic array is realized.
如图2所示,本发明中基于Bloch球面的QPSO改进算法具体包括以下步骤:As shown in Figure 2, the QPSO improved algorithm based on the Bloch sphere in the present invention specifically includes the following steps:
1)设置基于Bloch球面的QPSO改进算法的参数,初始化粒子种群,并对粒子的相位进行初始化;1) Set the parameters of the improved QPSO algorithm based on the Bloch sphere, initialize the particle population, and initialize the phase of the particles;
2)计算各个粒子的适应度值;2) Calculate the fitness value of each particle;
3)根据粒子的适应度值更新自身和全局最优相位;3) Update itself and the global optimal phase according to the fitness value of the particle;
4)利用自适应量子旋转门,对全局最优相位的量子比特的两个相位参数θ和进行调整,实现粒子的位置更新,并将其映射到解空间;4) Using the adaptive quantum revolving gate, the two phase parameters θ and Make adjustments to update the particle's position and map it to the solution space;
5)计算各粒子适应度值并评价,根据粒子的适应度更新自身,选出个体最优相位,并选出全局最优相位;5) Calculate and evaluate the fitness value of each particle, update itself according to the fitness of the particle, select the individual optimal phase, and select the global optimal phase;
6)保存个体最优相位并判断是否达到最大迭代次数,若未达到,转至步骤7),若达到,转至步骤8);6) Save the individual optimal phase and judge whether the maximum number of iterations is reached, if not reached, go to step 7), if reached, go to step 8);
7)选择变异粒子,计算自适应量子旋转门,利用自适应量子旋转门进行自适应粒子变异后,执行步骤4);7) Select the mutated particle, calculate the adaptive quantum revolving door, and perform step 4) after utilizing the adaptive quantum revolving door to carry out the adaptive particle variation;
8)输出最优解,根据变异后的粒子相位获取电压的变化,进一步获取输出功率。8) Output the optimal solution, obtain the voltage change according to the mutated particle phase, and further obtain the output power.
步骤1)的具体内容为:The specific content of step 1) is:
在Bloch球面坐标下的一个点可由两个角度θ和来确定,量子位用Bloch球面坐标表示为:A point in Bloch spherical coordinates can be defined by two angles θ and To determine, the qubit is expressed in Bloch spherical coordinates as:
BQPSO算法中采用量子位的Bloch球面坐标编码,本发明的IBQPSO算法采用相同的编码方式,即:The Bloch spherical coordinate encoding of qubit is adopted in the BQPSO algorithm, and the IBQPSO algorithm of the present invention adopts the same encoding method, namely:
式中,Pi为种群中第i个粒子的Bloch球面坐标;θij=π×rand,rand为[0,1]区间的随机数;i=1,2Λ,m,m为种群规模;n为优化变量的个数;在IBQPSO算法中,每个粒子同时占据空间三个位置,即同时代表三个优化解,分别为X解、Y解、Z解:In the formula, P i is the Bloch spherical coordinate of the i-th particle in the population; θ ij = π×rand, rand is a random number in the [0, 1] interval; i = 1, 2Λ, m, m is the population size; n is the number of optimization variables; in the IBQPSO algorithm, each particle simultaneously occupies Three positions in the space represent three optimized solutions at the same time, namely X solution, Y solution and Z solution:
Piz=(cosθi1,cosθi2,Λ,cosθin)P iz =(cosθ i1 ,cosθ i2 ,Λ,cosθ in )
记第i个粒子Pi上的第j个量子位的Bloch坐标为[xij,yij,zij]T,j=1,2,Λn,n为优化变量的个数;优化问题解空间的第j维的取值范围为则由单位空间In=[-1,1]n映射到优化问题解空间的变换公式为:Record the Bloch coordinates of the j qubit on the i particle P i as [x ij , y ij , z ij ] T , j=1, 2, Λn, n is the number of optimization variables; the optimization problem solution space The value range of the jth dimension of is Then the transformation formula for mapping from the unit space I n = [-1,1] n to the solution space of the optimization problem is:
结束初始化,输出初始粒子信息。End the initialization and output the initial particle information.
步骤4)的具体内容为:The specific content of step 4) is:
利用自适应量子旋转门,对全局最优相位的量子比特的两个相位参数θ和进行调整,实现粒子的位置更新,并将其映射到解空间;Using the adaptive quantum revolving gate, the two phase parameters θ and Make adjustments to update the particle's position and map it to the solution space;
自适应量子旋转门如下式所示:The adaptive quantum revolving door is shown in the following formula:
更新公式如下式所示:The update formula is as follows:
其中,αi为当前迭代对应的旋转角。Among them, α i is the rotation angle corresponding to the current iteration.
当前迭代对应的旋转角αi的定义为:The definition of the rotation angle α i corresponding to the current iteration is:
式中,αmin是最小旋转角,取0.01×pi;αmax是最大旋转角,取0.5×pi;fi是指当前第i个粒子的适应值;fmin是当代粒子中的最小适应值;fmax是当代粒子中的最大适应值;gen是当前的迭代次数;maxgen是算法设置的最大迭代次数。In the formula, α min is the minimum rotation angle, which is 0.01×pi; α max is the maximum rotation angle, which is 0.5×pi; f i refers to the fitness value of the current i-th particle; f min is the minimum fitness value of contemporary particles ; f max is the maximum fitness value in the contemporary particle; gen is the current iteration number; maxgen is the maximum iteration number set by the algorithm.
步骤5)的具体内容为:The specific content of step 5) is:
根据粒子的适应度值,判断粒子的初始位置;将粒子与其他所有粒子的位置进行适应度值比较,适应度值最高的粒子为个体最优粒子,其相位为个体最优相位;According to the fitness value of the particle, determine the initial position of the particle; compare the fitness value of the particle with the position of all other particles, the particle with the highest fitness value is the individual optimal particle, and its phase is the individual optimal phase;
将个体最优粒子与上一级的四个粒子的适应度值进行比较,若适应度值大于上一级的最高适应度值,则该个体最优粒子为全局最优粒子,其相位为全局最优相位;若适应度值小于上一级的最高适应度值,则全局最优粒子为上一级最高适应度值粒子,其相位为全局最优相位。Compare the fitness value of the individual optimal particle with the four particles of the upper level. If the fitness value is greater than the highest fitness value of the upper level, the individual optimal particle is the global optimal particle, and its phase is the global optimal particle. Optimal phase; if the fitness value is less than the highest fitness value of the previous level, the global optimal particle is the particle with the highest fitness value of the previous level, and its phase is the global optimal phase.
为证明本发明的有效性和优势性,本实施例对IBQPSO算法进行了光伏阵列在不同条件下的仿真,并与PSO、QPSO和BQPSO算法进行了仿真结果的比较。本实施例设计了六种阴影情况,针对六种阴影条件和标准光照条件的仿真结果进行了比较分析。In order to prove the effectiveness and advantages of the present invention, this embodiment simulates the IBQPSO algorithm for photovoltaic arrays under different conditions, and compares the simulation results with the PSO, QPSO and BQPSO algorithms. In this embodiment, six shadow conditions are designed, and the simulation results of the six shadow conditions and standard lighting conditions are compared and analyzed.
图3为在局部阴影条件下的光伏阵列功率-电压(P-U)特性曲线图,光伏阵列在标准情况,即指参考温度和参考光照强度下,P-U特性曲线如图3(a)所示。在现实生活中,光伏阵列被遮挡的概率很大。光伏阵列会因为周围树荫、乌云、房屋等的遮挡而产生局部阴影问题,本发明设计六种阴影情况,对局部阴影下光伏阵列的P-U特性曲线进行分析:Figure 3 is the power-voltage (P-U) characteristic curve of the photovoltaic array under partial shadow conditions. The photovoltaic array is in the standard situation, that is, the reference temperature and the reference light intensity, and the P-U characteristic curve is shown in Figure 3 (a). In real life, there is a high probability that the photovoltaic array will be shaded. Photovoltaic arrays will produce local shadow problems due to the surrounding shade of trees, dark clouds, houses, etc., the present invention designs six shadow situations, and analyzes the P-U characteristic curve of photovoltaic arrays under partial shadows:
1)阴影情况1:阴影分布为[3:2:1],其中1B、1C、1D辐照度为800W/m2,2C、2D辐照度为600W/m2,3D辐照度为200W/m2。1) Shadow situation 1: The shadow distribution is [3:2:1], where 1B, 1C, 1D irradiance is 800W/m 2 , 2C, 2D irradiance is 600W/m 2 , and 3D irradiance is 200W /m 2 .
2)阴影情况2:阴影分布为[2:1:0],其中1C、1D辐照度为800W/m2,2D辐照度为600W/m2。2) Shadow situation 2: The shadow distribution is [2:1:0], where the irradiance of 1C and 1D is 800W/m 2 , and the irradiance of 2D is 600W/m 2 .
3)阴影情况3:阴影分布为[2:0:0],其中1C、1D辐照度为800W/m2,阴影情况1、2和3下的P-U特性曲线如3图(b)所示:3) Shadow situation 3: The shadow distribution is [2:0:0], where the irradiance of 1C and 1D is 800W/m 2 , and the PU characteristic curves under shadow situations 1, 2 and 3 are shown in Figure 3(b) :
对图3(b)进行分析得出:在光伏阵列中光伏电池所处的光照强度不相同时,光伏阵列的P-U特性曲线呈现多峰值。阴影情况1、2和3下的P-U特性曲线分别呈现四峰值、三峰值和双峰值。Analysis of Fig. 3(b) shows that when the light intensity of the photovoltaic cells in the photovoltaic array is different, the P-U characteristic curve of the photovoltaic array presents multiple peaks. The P-U characteristic curves under shaded cases 1, 2, and 3 exhibit four peaks, three peaks, and double peaks, respectively.
4)阴影情况4:阴影分布为[3:2:1],其中1B、1C、1D温度为50℃,2C、2D温度为35℃,3D温度为15℃。4) Shadow situation 4: The shadow distribution is [3:2:1], where the temperature of 1B, 1C, and 1D is 50°C, the temperature of 2C, 2D is 35°C, and the temperature of 3D is 15°C.
5)阴影情况5:阴影分布为[2:1:0],其中1C、1D温度为50℃,2D温度为35℃。5) Shadow situation 5: The shadow distribution is [2:1:0], where the temperature of 1C and 1D is 50°C, and the temperature of 2D is 35°C.
6)阴影情况6:阴影分部为[2:0:0],其中1C、1D温度为50℃,阴影情况4、5和6下的P-U特性曲线如图3(c)所示:6) Shadow situation 6: The shadow division is [2:0:0], where the temperature of 1C and 1D is 50℃, and the P-U characteristic curves under shadow situations 4, 5 and 6 are shown in Figure 3(c):
对图3(c)进行分析得出:在光伏阵列中光伏电池所处的温度不相同时,光伏阵列的P-U特性曲线也会呈现多峰值。从而得出,在无论由光照强度不同还是由温度不同引起的局部阴影条件下都需要进行光伏阵列最大功率点跟踪。The analysis of Fig. 3(c) shows that when the temperature of the photovoltaic cells in the photovoltaic array is different, the P-U characteristic curve of the photovoltaic array will also show multiple peaks. Therefore, it is necessary to carry out the maximum power point tracking of photovoltaic array under the condition of partial shadow caused by different light intensity or different temperature.
图4为IBQPSO、PSO、QPSO和BQPSO算法在光伏阵列标准光照条件下的P-T曲线仿真结果图,对图4进行分析可得:PSO、QPSO和BQPSO算法收敛速度慢且前期振荡严重;IBQPSO算法能够实现快速稳定的稳态功率输出,显著地提高光伏发电效率。Figure 4 is the P-T curve simulation results of the IBQPSO, PSO, QPSO and BQPSO algorithms under the standard illumination conditions of the photovoltaic array. The analysis of Figure 4 shows that the convergence speed of the PSO, QPSO and BQPSO algorithms is slow and the early oscillation is serious; the IBQPSO algorithm can Realize fast and stable steady-state power output, and significantly improve the efficiency of photovoltaic power generation.
图5为IBQPSO、PSO、QPSO和BQPSO算法在光伏阵列六种阴影条件下的仿真结果图,在现实生活中,光伏阵列被遮挡的概率很大,当光伏阵列中的光伏电池处于不同的光照强度和温度条件下时,光伏阵列的P-U曲线呈现多峰值特性。Figure 5 is the simulation results of the IBQPSO, PSO, QPSO and BQPSO algorithms under six shadow conditions of the photovoltaic array. In real life, the probability of the photovoltaic array being blocked is very high. And temperature conditions, the P-U curve of the photovoltaic array presents a multi-peak characteristic.
标准光照条件和六种阴影条件下的四种方法的收敛时间如表1所示:The convergence times of the four methods under standard lighting conditions and six shade conditions are shown in Table 1:
表1四种方法仿真收敛时间Table 1 Simulation convergence time of four methods
结合标准光照条件、六种阴影情况下的仿真结果图及表1进行分析得出:PSO、QPSO和BQPSO算法收敛速度慢且在多峰情况时会陷入局部最优。IBQPSO算法收敛速度快,能够实现稳定的稳态功率输出,显著地提高光伏发电效率。Based on the analysis of the simulation results under standard lighting conditions, six shades and Table 1, it is concluded that the PSO, QPSO and BQPSO algorithms converge slowly and will fall into local optimum in multi-peak conditions. The IBQPSO algorithm has a fast convergence speed, can achieve stable steady-state power output, and significantly improve the efficiency of photovoltaic power generation.
针对阴影突变,本实施例设计了四种突变情况,具体如下:For shadow mutation, this embodiment designs four mutation situations, as follows:
1)标准光照→阴影情况1→阴影情况2→阴影情况31) Standard lighting → shadow case 1 → shadow case 2 → shadow case 3
2)阴影情况3→阴影情况2→阴影情况1→标准光照2) Shadow situation 3 → shadow situation 2 → shadow situation 1 → standard lighting
3)标准光照→阴影情况4→阴影情况5→阴影情况63) Standard Lighting→Shadow Situation 4→Shadow Situation 5→Shadow Situation 6
4)阴影情况6→阴影情况5→阴影情况4→标准光照4) Shadow situation 6 → Shadow situation 5 → Shadow situation 4 → Standard lighting
针对上述四种突变情况,分别利用IBQPSO、BQPSO、QPSO和PSO算法进行仿真,具体仿真结果如图6所示。由图6可知,PSO算法无论在光照强度还是温度突变时收敛速度慢且在突变处振荡严重,同时可能会陷入局部最优,使光伏发电效率得不到提高。QPSO和BQPSO算法虽然收敛速度快于PSO算法,在突变处的振荡小于PSO算法,但仍可能会陷入局部最优,使光伏发电效率得不到提高。IBQPSO算法收敛速度最快且在最大功率点附近不存在振荡,实现了稳定的稳态功率输出,显著地提高了光伏发电效率。Aiming at the above four mutation situations, IBQPSO, BQPSO, QPSO and PSO algorithms are used to simulate respectively, and the specific simulation results are shown in Figure 6. It can be seen from Figure 6 that the PSO algorithm has a slow convergence speed and severe oscillations at sudden changes in light intensity and temperature, and may fall into local optimum at the same time, so that the efficiency of photovoltaic power generation cannot be improved. Although the QPSO and BQPSO algorithms converge faster than the PSO algorithm, and the oscillation at the sudden change is smaller than the PSO algorithm, they may still fall into a local optimum, so that the efficiency of photovoltaic power generation cannot be improved. The IBQPSO algorithm has the fastest convergence speed and there is no oscillation near the maximum power point, which realizes a stable steady-state power output and significantly improves the efficiency of photovoltaic power generation.
综上所述,IBQPSO算法无论在光照强度突然增强还是减弱的情况下均能够快速高效地收敛到全局最大功率点。由于温度的变化也会导致光伏阵列P-U曲线呈现出多峰现象。通过仿真得出:无论温度突然升高还是降低的情况下也均能够快速高效地收敛到全局最大功率点。故IBQPSO算法能够在复杂遮挡条件下快速高效地实现全局最大功率点的跟踪,显著地提高光伏发电效率。In summary, the IBQPSO algorithm can quickly and efficiently converge to the global maximum power point no matter when the light intensity suddenly increases or decreases. Due to temperature changes, the P-U curve of the photovoltaic array will also show a multi-peak phenomenon. It is obtained through simulation that no matter the temperature suddenly rises or falls, it can quickly and efficiently converge to the global maximum power point. Therefore, the IBQPSO algorithm can quickly and efficiently track the global maximum power point under complex shading conditions, and significantly improve the efficiency of photovoltaic power generation.
以上所述,仅为本发明的具体实施方式,但本发明的保护范围并不局限于此,任何熟悉本技术领域的工作人员在本发明揭露的技术范围内,可轻易想到各种等效的修改或替换,这些修改或替换都应涵盖在本发明的保护范围之内。因此,本发明的保护范围应以权利要求的保护范围为准。The above is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any worker familiar with the technical field can easily think of various equivalents within the technical scope disclosed in the present invention. Modifications or replacements shall all fall within the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.
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CN110286708A (en) * | 2019-08-14 | 2019-09-27 | 青海民族大学 | A maximum power tracking control method and system for a photovoltaic array |
CN112083753A (en) * | 2020-09-28 | 2020-12-15 | 东莞市钜大电子有限公司 | Maximum power point tracking control method of photovoltaic grid-connected inverter |
CN112987838A (en) * | 2021-02-26 | 2021-06-18 | 大连海事大学 | Two-stage MPPT method for photovoltaic array under local shadow shielding |
CN113268931A (en) * | 2021-06-11 | 2021-08-17 | 云南电网有限责任公司电力科学研究院 | Method and system for reconstructing photovoltaic array based on multi-target slime optimization algorithm |
CN115276820A (en) * | 2022-07-29 | 2022-11-01 | 西安电子科技大学 | Method for setting power gradient of on-chip optical interconnection light source with mapping assistance |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103679271A (en) * | 2013-12-03 | 2014-03-26 | 大连大学 | Collision detection method based on Bloch spherical coordinates and quantum computing |
CN106505604A (en) * | 2016-12-23 | 2017-03-15 | 国网天津市电力公司 | Optimal configuration method of photovoltaic energy storage combined operation unit connected to regional distribution network |
CN106940742A (en) * | 2017-03-07 | 2017-07-11 | 西安石油大学 | Bad hole track optimizing method based on quick self-adapted quantum genetic algorithm |
CN106961117A (en) * | 2017-02-27 | 2017-07-18 | 南京邮电大学 | A kind of MPPT control method based on modified quanta particle swarm optimization |
-
2018
- 2018-01-30 CN CN201810092224.8A patent/CN108398982B/en active Active
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103679271A (en) * | 2013-12-03 | 2014-03-26 | 大连大学 | Collision detection method based on Bloch spherical coordinates and quantum computing |
CN106505604A (en) * | 2016-12-23 | 2017-03-15 | 国网天津市电力公司 | Optimal configuration method of photovoltaic energy storage combined operation unit connected to regional distribution network |
CN106961117A (en) * | 2017-02-27 | 2017-07-18 | 南京邮电大学 | A kind of MPPT control method based on modified quanta particle swarm optimization |
CN106940742A (en) * | 2017-03-07 | 2017-07-11 | 西安石油大学 | Bad hole track optimizing method based on quick self-adapted quantum genetic algorithm |
Cited By (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109144163A (en) * | 2018-09-06 | 2019-01-04 | 天津大学 | A kind of photovoltaic multimodal maximum power point tracking method based on manor population |
CN110286708A (en) * | 2019-08-14 | 2019-09-27 | 青海民族大学 | A maximum power tracking control method and system for a photovoltaic array |
CN112083753A (en) * | 2020-09-28 | 2020-12-15 | 东莞市钜大电子有限公司 | Maximum power point tracking control method of photovoltaic grid-connected inverter |
CN112987838A (en) * | 2021-02-26 | 2021-06-18 | 大连海事大学 | Two-stage MPPT method for photovoltaic array under local shadow shielding |
CN113268931A (en) * | 2021-06-11 | 2021-08-17 | 云南电网有限责任公司电力科学研究院 | Method and system for reconstructing photovoltaic array based on multi-target slime optimization algorithm |
CN113268931B (en) * | 2021-06-11 | 2022-11-29 | 云南电网有限责任公司电力科学研究院 | Method and system for reconstructing photovoltaic array based on multi-target slime optimization algorithm |
CN115276820A (en) * | 2022-07-29 | 2022-11-01 | 西安电子科技大学 | Method for setting power gradient of on-chip optical interconnection light source with mapping assistance |
CN115276820B (en) * | 2022-07-29 | 2023-09-01 | 西安电子科技大学 | On-chip optical interconnection light source power gradient setting method using mapping assistance |
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