It is one of the topics that have been studied extensively on maximum power point tracking(MPPT)recently.Traditional or soft computing methods are used for MPPT.Since soft computing approaches are more effective than ...It is one of the topics that have been studied extensively on maximum power point tracking(MPPT)recently.Traditional or soft computing methods are used for MPPT.Since soft computing approaches are more effective than traditional approaches,studies on MPPT have shifted in this direction.This study aims comparison of performance of seven meta-heuristic training algorithms in the neuro-fuzzy training for MPPT.The meta-heuristic training algorithms used are particle swarm optimization(PSO),harmony search(HS),cuckoo search(CS),artificial bee colony(ABC)algorithm,bee algorithm(BA),differential evolution(DE)and flower pollination algorithm(FPA).The antecedent and conclusion parameters of neuro-fuzzy are determined by these algorithms.The data of a 250 W photovoltaic(PV)is used in the applications.For effective MPPT,different neuro-fuzzy structures,different membership functions and different control parameter values are evaluated in detail.Related training algorithms are compared in terms of solution quality and convergence speed.The strengths and weaknesses of these algorithms are revealed.It is seen that the type and number of membership function,colony size,number of generations affect the solution quality and convergence speed of the training algorithms.As a result,it has been observed that CS and ABC algorithm are more effective than other algorithms in terms of solution quality and convergence in solving the related problem.展开更多
Photovoltaic(PV)systems utilize maximum power point tracking(MPPT)controllers to optimize power output amidst varying environmental conditions.However,the presence of multiple peaks resulting from partial shading pose...Photovoltaic(PV)systems utilize maximum power point tracking(MPPT)controllers to optimize power output amidst varying environmental conditions.However,the presence of multiple peaks resulting from partial shading poses a challenge to the tracking operation.Under partial shade conditions,the global maximum power point(GMPP)may be missed by most traditional maximum power point tracker.The flower pollination algorithm(FPA)and particle swarm optimization(PSO)are two examples of metaheuristic techniques that can be used to solve the issue of failing to track the GMPP.This paper discusses and resolves all issues associated with using the standard FPA method as the MPPT for PV systems.The first issue is that the initial values of pollen are determined randomly at first,which can lead to premature convergence.To minimize the convergence time and enhance the possibility of detecting the GMPP,the initial pollen values were modified so that they were near the expected peak positions.Secondly,in the modified FPA,population fitness and switch probability values both influence swapping between two-mode optimization,which may improve the flower pollination algorithm’s tracking speed.The performance of the modified flower pollination algorithm(MFPA)is assessed through a comparison with the perturb and observe(P&O)method and the standard FPA method.The simulation results reveal that under different partial shading conditions,the tracking time for MFPA is 0.24,0.24,0.22,and 0.23 s,while for FPA,it is 0.4,0.35,0.45,and 0.37 s.Additionally,the simulation results demonstrate that MFPA achieves higher MPPT efficiency in the same four partial shading conditions,with values of 99.98%,99.90%,99.93%,and 99.26%,compared to FPA with MPPT efficiencies of 99.93%,99.88%,99.91%,and 99.18%.Based on the findings from simulations,the proposed method effectively and accurately tracks the GMPP across a diverse set of environmental conditions.展开更多
由于光伏组件的P-V特性是单峰非线性曲线,由它组成的光伏阵列的发电功率易受温度、光照强度等外界环境因素的影响,导致实际工程中光伏发电效率大大降低,因此追踪最大功率点(Maximum Power Point Tracking,MPPT)使光伏发电系统的发电功...由于光伏组件的P-V特性是单峰非线性曲线,由它组成的光伏阵列的发电功率易受温度、光照强度等外界环境因素的影响,导致实际工程中光伏发电效率大大降低,因此追踪最大功率点(Maximum Power Point Tracking,MPPT)使光伏发电系统的发电功率一直处于最大功率点,对提高系统整体的发电效率有着十分重要的意义。引用进化差分算法对传统的粒子群算法(PSO)的寻优过程进行差分进化选择,并将非线性策略与改进后的PSO算法相结合。通过仿真计算分析,改进后的PSO算法相较于传统的PSO算法能够更快速、更准确地找到部分阴影情况下的最大功率点,进而提升了光伏系统的发电效率。展开更多
光伏阵列在局部遮荫的情况下会导致光伏系统输出的功率-电压曲线出现多个极值点,具有全局寻优能力的传统群体智能优化算法进行最大功率追踪(maximum power point tracking,MPPT),普遍存在收敛速度慢、易陷入局部最优等问题。该文提出了...光伏阵列在局部遮荫的情况下会导致光伏系统输出的功率-电压曲线出现多个极值点,具有全局寻优能力的传统群体智能优化算法进行最大功率追踪(maximum power point tracking,MPPT),普遍存在收敛速度慢、易陷入局部最优等问题。该文提出了一种基于改进黏菌优化算法的控制方法。先采用正态分布初始化种群,增加种群的多样性。然后应用莱维飞行策略和螺旋搜索策略,提高算法的全局搜索能力,避免陷入局部最优。经仿真验证,与粒子群算法和普通黏菌算法相比,改进黏菌算法在跟踪速度、稳定性等方面均有更显著的效果。展开更多
在局部阴影条件下,常规的最大功率点跟踪MPPT(maximum power point tracking)算法因含有容易陷入局部极值、跟踪精度低等弊端,使其无法及时、精确地跟踪光伏发电系统的最大功率点,因此,提出了一种基于改进型鲸鱼优化算法的光伏发电系统M...在局部阴影条件下,常规的最大功率点跟踪MPPT(maximum power point tracking)算法因含有容易陷入局部极值、跟踪精度低等弊端,使其无法及时、精确地跟踪光伏发电系统的最大功率点,因此,提出了一种基于改进型鲸鱼优化算法的光伏发电系统MPPT控制策略。首先,采用混沌映射初始化种群,增加种群的多样性。其次,通过引入非线性收敛因子使局部寻优能力和全局搜索能力达到均衡。最后,通过引入非线性时变的自适应权重使系统及时跳出局部最优解,并提高搜索的精度。经仿真验证,与粒子群优化算法、狮群优化算法、传统的鲸鱼优化算法等相比,改进的鲸鱼算法在跟踪速度、精度、稳定性等方面均有更显著的效果。展开更多
当光伏组件受到局部阴影遮挡时,其最大功率点跟踪(maximum power point tracking,MPPT)过程常出现误跟踪、跟踪速度慢、光伏组件输出功率低等问题.为使局部阴影下光伏组件保持快速、稳定、准确地最大功率输出,基于电导增量法(incrementa...当光伏组件受到局部阴影遮挡时,其最大功率点跟踪(maximum power point tracking,MPPT)过程常出现误跟踪、跟踪速度慢、光伏组件输出功率低等问题.为使局部阴影下光伏组件保持快速、稳定、准确地最大功率输出,基于电导增量法(incremental conductance algorithm,INC),结合全局比较算法(global comparison algorithm,GCA),提出一种基于电导增量/全局比较的复合MPPT算法(INC-GCA),并通过搭建Simulink仿真模型和设计光伏试验平台,验证该算法的可行性.结果表明:基于电导增量/全局比较的复合MPPT算法在光伏组件受到局部阴影遮挡时可准确追踪到最大功率,且跟踪速度快、可靠性高,完全避免了误跟踪问题;相较于电导增量法,该算法可有效提高光伏系统的发电效率,提升光伏电站的经济效益.展开更多
Renewable energy sources like solar,wind,and hydro are becoming increasingly popular due to the fewer negative impacts they have on the environment.Because,Since the production of renewable energy sources is still in ...Renewable energy sources like solar,wind,and hydro are becoming increasingly popular due to the fewer negative impacts they have on the environment.Because,Since the production of renewable energy sources is still in the process of being created,photovoltaic(PV)systems are commonly utilized for installation situations that are acceptable,clean,and simple.This study presents an adaptive artificial intelligence approach that can be used for maximum power point tracking(MPPT)in solar systems with the help of an embedded controller.The adaptive method incorporates both the Whale Optimization Algorithm(WOA)and the Artificial Neural Network(ANN).The WOA was implemented to enhance the process of the ANN model’s training,and the ANN model was developed using the WOA.In addition to this,the inverter circuit is connected to the smart grid system,and the strengthening of the smart grid is achieved through the implementation of the CMCMAC protocol.This protocol prevents interference between customers and the organizations that provide their utilities.Using a protocol known as Cross-Layer Multi-Channel MAC(CMCMAC),the effect of interference is removed using the way that was suggested.Also,with the utilization of the ZIGBEE communication technology,bidirectional communication is made possible.The strategy that was suggested has been put into practice,and the results have shown that the PV system produces an output power of 73.32 KW and an efficiency of 98.72%.In addition to this,a built-in regulator is utilized to validate the proposed model.In this paper,the results of various experiments are analyzed,and a comparison is made between the suggested WOA with the ANN controller approach and others,such as the Particle Swarm Optimization(PSO)based MPPT and the Cuckoo Search(CS)based MPPT.By examining the comparison findings,it was determined that the adaptive AI-based embedded controller was superior to the other alternatives.展开更多
局部遮阴情况下光伏阵列的输出功率呈现多峰现象,导致传统MPPT控制算法失效,而基于元启发式算法的MPPT控制功率追踪速度慢,输出功率振荡大。针对上述问题,提出一种基于改进型灰狼优化算法(improved grey wolf optimization algorithm,IG...局部遮阴情况下光伏阵列的输出功率呈现多峰现象,导致传统MPPT控制算法失效,而基于元启发式算法的MPPT控制功率追踪速度慢,输出功率振荡大。针对上述问题,提出一种基于改进型灰狼优化算法(improved grey wolf optimization algorithm,IGWO)与改进型扰动观察法(improved perturbation and observation method,IP&O)相结合的光伏MPPT控制算法。IGWO采用非线性收敛因子调整策略提高算法适应性,并通过使用改进型莱维飞行与增强型醉汉漫步结合的搜索策略平衡全局搜索与局部寻优的关系。利用IGWO追踪至最大功率点附近,再与可调节扰动步长变化速率的IP&O结合实现最大功率的稳定输出。算法测试实验数据和仿真结果表明,所提出的MPPT控制算法具有快速的追踪速度和高输出精度,且在功率追踪过程中输出振荡小。展开更多
针对局部遮阴环境下传统灰狼优化(Gray wolf optimization,GWO)算法在跟踪最大功率点时P-U特性曲线出现多峰值、后期收敛速度慢、稳态精度低等问题,结合灰狼优化算法和扰动观察法(Perturbation and observation,P&O)各自的优势,提...针对局部遮阴环境下传统灰狼优化(Gray wolf optimization,GWO)算法在跟踪最大功率点时P-U特性曲线出现多峰值、后期收敛速度慢、稳态精度低等问题,结合灰狼优化算法和扰动观察法(Perturbation and observation,P&O)各自的优势,提出了基于GWO-P&O的混合优化最大功率点跟踪(Maximum power point tracking,MPPT)算法。首先,采用灰狼优化算法逐渐向光伏的全局最大功率点靠近。其次,在灰狼优化算法收敛后期引入P&O法,既保持了灰狼优化算法较高的稳态精度,又能以较快速度寻找到局部最大功率点。最后,在不同环境工况下,将所提出的GWO-P&O方法与传统GWO算法进行对比。结果表明,改进的GWO-P&O算法在保证良好稳态性能的同时,一定程度上提高了GWO算法后期跟踪最大功率时的收敛速度。展开更多
为实现水培营养液水质参数的高效、精确控制,减少设备供能产生的碳排量,构建了一个基于粒子群优化(Particle Swarm Optimization,PSO)算法和最大功率点跟踪(Maximum Power Point Tracking,MPPT)算法的水培智能控制系统。用PSO算法优化...为实现水培营养液水质参数的高效、精确控制,减少设备供能产生的碳排量,构建了一个基于粒子群优化(Particle Swarm Optimization,PSO)算法和最大功率点跟踪(Maximum Power Point Tracking,MPPT)算法的水培智能控制系统。用PSO算法优化模糊控制器的量化、比例因子,加入Smith预估器补偿反馈时延,对pH为4.5、电导率(Electrical Conductivity,EC)为0 mS/cm的营养液进行精确调控。经过优化,分别在44 s和43 s后达到预设值,并能维持稳定状态。建立光伏发电模块,引入MPPT算法,缩短跟踪时长至0.04 s。结果表明,该系统能提高营养液水质参数的调节精度,缩短控制时长,增强水培环境的稳定性;同时,能提升发电效率,实现节能减排。展开更多
文摘It is one of the topics that have been studied extensively on maximum power point tracking(MPPT)recently.Traditional or soft computing methods are used for MPPT.Since soft computing approaches are more effective than traditional approaches,studies on MPPT have shifted in this direction.This study aims comparison of performance of seven meta-heuristic training algorithms in the neuro-fuzzy training for MPPT.The meta-heuristic training algorithms used are particle swarm optimization(PSO),harmony search(HS),cuckoo search(CS),artificial bee colony(ABC)algorithm,bee algorithm(BA),differential evolution(DE)and flower pollination algorithm(FPA).The antecedent and conclusion parameters of neuro-fuzzy are determined by these algorithms.The data of a 250 W photovoltaic(PV)is used in the applications.For effective MPPT,different neuro-fuzzy structures,different membership functions and different control parameter values are evaluated in detail.Related training algorithms are compared in terms of solution quality and convergence speed.The strengths and weaknesses of these algorithms are revealed.It is seen that the type and number of membership function,colony size,number of generations affect the solution quality and convergence speed of the training algorithms.As a result,it has been observed that CS and ABC algorithm are more effective than other algorithms in terms of solution quality and convergence in solving the related problem.
文摘Photovoltaic(PV)systems utilize maximum power point tracking(MPPT)controllers to optimize power output amidst varying environmental conditions.However,the presence of multiple peaks resulting from partial shading poses a challenge to the tracking operation.Under partial shade conditions,the global maximum power point(GMPP)may be missed by most traditional maximum power point tracker.The flower pollination algorithm(FPA)and particle swarm optimization(PSO)are two examples of metaheuristic techniques that can be used to solve the issue of failing to track the GMPP.This paper discusses and resolves all issues associated with using the standard FPA method as the MPPT for PV systems.The first issue is that the initial values of pollen are determined randomly at first,which can lead to premature convergence.To minimize the convergence time and enhance the possibility of detecting the GMPP,the initial pollen values were modified so that they were near the expected peak positions.Secondly,in the modified FPA,population fitness and switch probability values both influence swapping between two-mode optimization,which may improve the flower pollination algorithm’s tracking speed.The performance of the modified flower pollination algorithm(MFPA)is assessed through a comparison with the perturb and observe(P&O)method and the standard FPA method.The simulation results reveal that under different partial shading conditions,the tracking time for MFPA is 0.24,0.24,0.22,and 0.23 s,while for FPA,it is 0.4,0.35,0.45,and 0.37 s.Additionally,the simulation results demonstrate that MFPA achieves higher MPPT efficiency in the same four partial shading conditions,with values of 99.98%,99.90%,99.93%,and 99.26%,compared to FPA with MPPT efficiencies of 99.93%,99.88%,99.91%,and 99.18%.Based on the findings from simulations,the proposed method effectively and accurately tracks the GMPP across a diverse set of environmental conditions.
文摘由于光伏组件的P-V特性是单峰非线性曲线,由它组成的光伏阵列的发电功率易受温度、光照强度等外界环境因素的影响,导致实际工程中光伏发电效率大大降低,因此追踪最大功率点(Maximum Power Point Tracking,MPPT)使光伏发电系统的发电功率一直处于最大功率点,对提高系统整体的发电效率有着十分重要的意义。引用进化差分算法对传统的粒子群算法(PSO)的寻优过程进行差分进化选择,并将非线性策略与改进后的PSO算法相结合。通过仿真计算分析,改进后的PSO算法相较于传统的PSO算法能够更快速、更准确地找到部分阴影情况下的最大功率点,进而提升了光伏系统的发电效率。
文摘光伏阵列在局部遮荫的情况下会导致光伏系统输出的功率-电压曲线出现多个极值点,具有全局寻优能力的传统群体智能优化算法进行最大功率追踪(maximum power point tracking,MPPT),普遍存在收敛速度慢、易陷入局部最优等问题。该文提出了一种基于改进黏菌优化算法的控制方法。先采用正态分布初始化种群,增加种群的多样性。然后应用莱维飞行策略和螺旋搜索策略,提高算法的全局搜索能力,避免陷入局部最优。经仿真验证,与粒子群算法和普通黏菌算法相比,改进黏菌算法在跟踪速度、稳定性等方面均有更显著的效果。
文摘在局部阴影条件下,常规的最大功率点跟踪MPPT(maximum power point tracking)算法因含有容易陷入局部极值、跟踪精度低等弊端,使其无法及时、精确地跟踪光伏发电系统的最大功率点,因此,提出了一种基于改进型鲸鱼优化算法的光伏发电系统MPPT控制策略。首先,采用混沌映射初始化种群,增加种群的多样性。其次,通过引入非线性收敛因子使局部寻优能力和全局搜索能力达到均衡。最后,通过引入非线性时变的自适应权重使系统及时跳出局部最优解,并提高搜索的精度。经仿真验证,与粒子群优化算法、狮群优化算法、传统的鲸鱼优化算法等相比,改进的鲸鱼算法在跟踪速度、精度、稳定性等方面均有更显著的效果。
文摘当光伏组件受到局部阴影遮挡时,其最大功率点跟踪(maximum power point tracking,MPPT)过程常出现误跟踪、跟踪速度慢、光伏组件输出功率低等问题.为使局部阴影下光伏组件保持快速、稳定、准确地最大功率输出,基于电导增量法(incremental conductance algorithm,INC),结合全局比较算法(global comparison algorithm,GCA),提出一种基于电导增量/全局比较的复合MPPT算法(INC-GCA),并通过搭建Simulink仿真模型和设计光伏试验平台,验证该算法的可行性.结果表明:基于电导增量/全局比较的复合MPPT算法在光伏组件受到局部阴影遮挡时可准确追踪到最大功率,且跟踪速度快、可靠性高,完全避免了误跟踪问题;相较于电导增量法,该算法可有效提高光伏系统的发电效率,提升光伏电站的经济效益.
基金funding this research work through the Small Group Research Project under Grant Number RGP1/70/44.
文摘Renewable energy sources like solar,wind,and hydro are becoming increasingly popular due to the fewer negative impacts they have on the environment.Because,Since the production of renewable energy sources is still in the process of being created,photovoltaic(PV)systems are commonly utilized for installation situations that are acceptable,clean,and simple.This study presents an adaptive artificial intelligence approach that can be used for maximum power point tracking(MPPT)in solar systems with the help of an embedded controller.The adaptive method incorporates both the Whale Optimization Algorithm(WOA)and the Artificial Neural Network(ANN).The WOA was implemented to enhance the process of the ANN model’s training,and the ANN model was developed using the WOA.In addition to this,the inverter circuit is connected to the smart grid system,and the strengthening of the smart grid is achieved through the implementation of the CMCMAC protocol.This protocol prevents interference between customers and the organizations that provide their utilities.Using a protocol known as Cross-Layer Multi-Channel MAC(CMCMAC),the effect of interference is removed using the way that was suggested.Also,with the utilization of the ZIGBEE communication technology,bidirectional communication is made possible.The strategy that was suggested has been put into practice,and the results have shown that the PV system produces an output power of 73.32 KW and an efficiency of 98.72%.In addition to this,a built-in regulator is utilized to validate the proposed model.In this paper,the results of various experiments are analyzed,and a comparison is made between the suggested WOA with the ANN controller approach and others,such as the Particle Swarm Optimization(PSO)based MPPT and the Cuckoo Search(CS)based MPPT.By examining the comparison findings,it was determined that the adaptive AI-based embedded controller was superior to the other alternatives.
文摘局部遮阴情况下光伏阵列的输出功率呈现多峰现象,导致传统MPPT控制算法失效,而基于元启发式算法的MPPT控制功率追踪速度慢,输出功率振荡大。针对上述问题,提出一种基于改进型灰狼优化算法(improved grey wolf optimization algorithm,IGWO)与改进型扰动观察法(improved perturbation and observation method,IP&O)相结合的光伏MPPT控制算法。IGWO采用非线性收敛因子调整策略提高算法适应性,并通过使用改进型莱维飞行与增强型醉汉漫步结合的搜索策略平衡全局搜索与局部寻优的关系。利用IGWO追踪至最大功率点附近,再与可调节扰动步长变化速率的IP&O结合实现最大功率的稳定输出。算法测试实验数据和仿真结果表明,所提出的MPPT控制算法具有快速的追踪速度和高输出精度,且在功率追踪过程中输出振荡小。
基金supported by National Natural Science Foundation of China(No.52067013)Natural Science Foundation of Gansu Province(No.21JR7RA280)。
文摘针对局部遮阴环境下传统灰狼优化(Gray wolf optimization,GWO)算法在跟踪最大功率点时P-U特性曲线出现多峰值、后期收敛速度慢、稳态精度低等问题,结合灰狼优化算法和扰动观察法(Perturbation and observation,P&O)各自的优势,提出了基于GWO-P&O的混合优化最大功率点跟踪(Maximum power point tracking,MPPT)算法。首先,采用灰狼优化算法逐渐向光伏的全局最大功率点靠近。其次,在灰狼优化算法收敛后期引入P&O法,既保持了灰狼优化算法较高的稳态精度,又能以较快速度寻找到局部最大功率点。最后,在不同环境工况下,将所提出的GWO-P&O方法与传统GWO算法进行对比。结果表明,改进的GWO-P&O算法在保证良好稳态性能的同时,一定程度上提高了GWO算法后期跟踪最大功率时的收敛速度。