基于多元宇宙优化算法的混合光伏-热电系统MPPT设计

doi:10.11930/j.issn.1004-9649.202210127

中国电力 ›› 2023, Vol. 56 ›› Issue (11): 197-205.DOI: 10.11930/j.issn.1004-9649.202210127

基于多元宇宙优化算法的混合光伏-热电系统MPPT设计

李大虎¹(), 周泓宇²(), 周悦¹, 饶渝泽¹, 姚伟²()

1. 国网湖北省电力有限公司，湖北武汉　430077
2. 强电磁技术全国重点实验室（华中科技大学），湖北武汉　430074

收稿日期:2022-10-31 出版日期:2023-11-28 发布日期:2023-11-28
作者简介:李大虎（1978—），男，博士，高级工程师（教授级），从事电力系统稳定与控制、电力系统安全稳定管理，E-mail： 6517562@qq.com
周泓宇（1997—），男，通信作者，博士研究生，从事新能源电力系统稳定与控制研究，E-mail： ee.henry_zhou@foxmail.com
姚伟（1983—），男，博士，教授，博士生导师，从事交直流混联电网稳定性分析与控制、新能源电力系统稳定分析与控制研究，E-mail： w.yao@hust.edu.cn
基金资助:
国网湖北省电力有限公司科技项目（面向多特高压直流馈入和新能源基地发展的湖北新型电力系统运行与控制关键技术研究，52150521000W）。

Multi-verse Optimization-based MPPT Design for PV-TEG System

Dahu LI¹(), Hongyu ZHOU²(), Yue ZHOU¹, Yuze RAO¹, Wei YAO²()

1. State Grid Hubei Electric Power Co., Ltd., Wuhan 430077, China
2. State Key Laboratory of Advanced Electromagnetic Technology, Huazhong University of Science and Technology, Wuhan 430074, China

Received:2022-10-31 Online:2023-11-28 Published:2023-11-28
Supported by:
This work is supported by Science and Technology Project of State Grid Hubei Electric Power Co., Ltd. (Research on Key Technologies of Operation and Control of Hubei New Power System for Multi-UHVDC Feed in and New Energy Base Development, No.52150521000W).

摘要/Abstract

摘要：

混合光伏-热电（centralized hybrid photovoltaic thermoelectric generator，PV-TEG）系统在部分遮蔽（partial shading condition，PSC）条件下呈现多个局部最大功率点（local maximum power point，LMPP）。采用多元宇宙优化算法（multi-verse optimization，MVO），用于PV-TEG系统在PSC下的最大功率点跟踪（maximum power point tracking，MPPT）。MVO通过平衡全局搜索和局部搜索，有效识别多个LMPPs中唯一的全局最大功率点（global maximum power point，GMPP），避免搜索结果陷入LMPP，以提高发电效率和能源利用率。算例仿真结果表明：基于MVO的MPPT可以在更短的时间内收集到更高的功率，实现功率波动最小。

关键词: 混合光伏-热电系统, 最大功率点跟踪, 多元宇宙优化算法, 部分遮蔽

Abstract:

A centralized hybrid photovoltaic thermoelectric generator (PV-TEG) system shows multiple local maximum power points (LMPPs) under a partial shading condition (PSC). The multi-verse optimization (MVO) is used for the maximum power point tracking (MPPT) of the PV-TEG system under PSC. MVO can effectively identify the unique global maximum power point (GMPP) among multiple LMPPs by balancing global search and local search, avoiding search results trapped in LMPP and improving generation efficiency and energy utilization. The simulation results of the example show that the MPPT based on the MVO algorithm can collect higher power in a shorter time and achieve the minimum power fluctuation.

Key words: centralized photovoltaic thermoelectric generator system, maximum power point tracking, multi-verse optimization, partial shading

李大虎, 周泓宇, 周悦, 饶渝泽, 姚伟. 基于多元宇宙优化算法的混合光伏-热电系统MPPT设计[J]. 中国电力, 2023, 56(11): 197-205.

Dahu LI, Hongyu ZHOU, Yue ZHOU, Yuze RAO, Wei YAO. Multi-verse Optimization-based MPPT Design for PV-TEG System[J]. Electric Power, 2023, 56(11): 197-205.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202210127

https://www.electricpower.com.cn/CN/Y2023/V56/I11/197

图/表 12

图 1 TEG结构和等效电路

Fig.1 Structure and simplified circuit of TEG

图 2 集中式TEG拓扑结构

Fig.2 Centralized TEG topology

图 3 混合PV-TEG系统物理结构

Fig.3 Physical structure of PV-TEG system

图 4 PV-TEG系统单个模块电气连接

Fig.4 Electrical connection of single module of PV-TEG system

图 5 PV系统和PV-TEG混合系统的P-V曲线对比

Fig.5 Comparison of P-V curves of PV system and PV-TEG system

图 6 MVO示意

Fig.6 MVO Graph

图 7 PSC条件下基于MVO算法的集中式PV-TEG混合系统的MPPT设计总体框架

Fig.7 General framework of MPPT design of PV-TEG system based on MVO algorithm under PSC

表 1 混合PV-TEG系统和升压转换器的主要参数

Table 1 Main parameters of PV-TEG system and boost converter

PV系统
模型		模块数量	最大功率/W		开路电压/V	短路电流/A	电压最大值/V	电流最大值/A
A10 Green Technology A10 J-M60-225		60	224.9856		36.24	8.04	30.24	7.44

TEG系统			升压转换器
塞贝克系数		温度 T₀/K	转换式	开关频率 f_s/kHz	负载 R/Ω	电感 L/mH	电容/μF
α₀/(μV·K^–1)	变化率 α₁/(μV·(sK)^–1)	温度 T₀/K	转换式	开关频率 f_s/kHz	负载 R/Ω	电感 L/mH	C₁	C₂
210	120	300	V_out=V_in/(1–D_C)	20	3	250	66	200

表 2 5种算法的参数设置

Table 2 Parameter settings of five algorithms

算法	上界	下界	种群数量	初始值	最大迭代次数	参数1	参数2	参数3
MVO	1	0	5	0.5	5	WEP_max=1	WEP_min=0.2	膨胀率 c=0.6
MFO	1	0	5	0.5	5	选择参数 t=0.6	路径数量 k=–1	火焰螺旋参数 f=0.5
GWO	1	0	5	0.5	5	α=1	β=1.5	δ=1.5
FA	1	0	5	0.5	5	步长因子 α=0.25	吸引度 β=0.2	光强度吸收系数 γ=1
PSO	1	0	5	0.5	5	加速常数 c₁, c₂=2	惯性因子 w=0.6	速度最大值 V_max=0.8

图 8 集中式混合PV-TEG系统在固定光照条件下通过5种启发式算法的MPPT结果对比

Fig.8 Comparison of MPPT results of PV-TEG system under fixed lighting conditions by five heuristic algorithms

图 9 集中式混合PV-TEG系统在阶跃光照条件下通过5种启发式算法的MPPT结果对比

Fig.9 Comparison of MPPT results of PV-TEG system under step lighting conditions by five heuristic algorithms

表 3 5种算法在2种情况下得到的结果

Table 3 Results obtained under two cases by five algorithms

算法	温度的阶跃变化				启动测试
算法	功率/ W	电流/ A	电压/ V	能量/ (W·s)	功率/ W	电流/ A	电压/ V	能量/ (W·s)
MVO	130.0	1.89	68.8	236.5	195.6	2.62	74.7	92.6
GWO	114.5	1.78	64.0	225.6	195.1	2.62	74.5	90.5
FA	92.1	1.71	53.9	186.3	194.9	2.61	74.6	89.2
MFO	110.8	1.81	61.2	214.7	194.7	2.61	74.6	88.7
PSO	110.7	1.80	61.5	220.1	195.0	2.62	74.6	91.2

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基于多元宇宙优化算法的混合光伏-热电系统MPPT设计

Multi-verse Optimization-based MPPT Design for PV-TEG System

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基于多元宇宙优化算法的混合光伏-热电系统MPPT设计

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