电源学报

多峰值热电系统下改进启发式MPPT方法研究

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李鹏 ¹ , 李浩 ¹ , 朱文超 ¹^,²^,³ , 谢长君 ¹^,³

电源学报 | 新能源发电与储能 2026,24(5): 168-179

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电源学报 |新能源发电与储能 2026 , 24 (5) : 168 -179

多峰值热电系统下改进启发式MPPT方法研究

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李鹏¹, 李浩¹, 朱文超¹^,²^,³, 谢长君¹^,³

作者信息

^1.武汉理工大学自动化学院，武汉　430070

^2.材料复合新技术国家重点实验室，武汉　430070

^3.现代汽车零部件技术湖北省重点实验室（武汉理工大学），武汉　430070

通讯作者:

朱文超（1993—　），男，通信作者，博士。研究方向：温差发电技术。E-mail：zhuwenchao@whut.edu.cn。

作者简介:

李鹏（1999—　），男，硕士研究生。研究方向：温差发电技术。E-mail：1160042032@qq.com。

李浩（1971—　），男，博士，副教授。研究方向：智能控制理论与应用。E-mail：lihao@whut.edu.cn。

谢长君（1983—　），男，博士，教授。研究方向：高效热电发电及应用。E-mail：jackxie@mail.whut.edu.cn。

Research on Improved Heuristic MPPT Method in Multi-peak Thermoelectric System

Peng LI¹, Hao LI¹, Wenchao ZHU¹^,²^,³, Changjun XIE¹^,³

Affiliations

^1.School of Automation, Wuhan University of Technology, Wuhan 430070, China

^2.State Key Laboratory of Advanced Technology for Materials Synthesis and Processing, Wuhan 430070, China

^3.Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan 430070, China

出版时间: 2026-05-30 doi: 10.13234/j.issn.2095-2805.2026.5.168

文章导航

摘要

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非均匀温度分布NUTD（non-uniform temperature distribution）情况下集中式热电系统呈现出多峰值特性，基于传统最大功率点跟踪MPPT（maximum power point tracking）方法易陷入局部最大功率点LMPP（local maximum power point），以及现有的元启发式算法因探索和开发阶段的搜索能力不均衡导致搜索时间较长所造成的系统能量损失的问题，提出了一种改进的猎人猎物优化IHPO（improved hunter-prey optimization）算法。该算法依托于标准猎人猎物优化HPO（hunter-prey optimization）算法所具有的明确的探索和开发阶段的搜索能力机制，对原有机制进行了适用于多峰值特性的改进，在减弱算法本身频繁随机跃动导致的大幅振荡的同时，能够快速追踪到全局最大功率点GMPP（global maximum power point）。基于集中式热电系统模型，将IHPO算法与扰动观察法P&O（perturbation and observation）、粒子群优化PSO（particle swarm optimization）算法、改进的粒子群优化IPSO（improved PSO）算法和标准HPO算法在不同工况下进行仿真对比。结果表明，所提算法可以在非均匀温度分布的情况下快速且准确地追踪到GMPP，并大幅减小追踪过程的振荡，能量产出的提升最高可达21.8%，IHPO算法能够有效解决集中式热电系统的多峰值问题。

关键词

热电发电机 / 最大功率跟踪 / 猎人猎物优化算法 / 终止机制和重启机制

Abstract

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The centralized thermoelectric system exhibits multi-peak characteristics under non-uniform temperature distribution (NUTD). Based on the problem that traditional maximum power point tracking (MPPT) methods are prone to falling into local maximum power point (LMPP) and existing metaheuristic algorithms suffer from energy loss due to long search times caused by imbalanced search capabilities during exploration and exploitation phases, an improved hunter-prey optimization (IHPO) algorithm is proposed. This algorithm relies on the clear exploration and exploitation phases search capability mechanism of standard hunter-prey optimization (HPO) algorithm, and improves the original mechanism for multi-peak characteristics, reducing the large amplitude oscillation caused by frequent random jumps of the algorithm itself while quickly tracking the global maximum power point (GMPP). Based on the centralized thermoelectric system model, IHPO algorithm is simulated and compared with perturbation and observation (P&O) method, particle swarm optimization algorithm (PSO), improved PSO (IPSO) and standard HPO under different operating conditions. The results show that the proposed algorithm can quickly and accurately track GMPP under NUTD, significantly reduce the oscillation of the tracking process, and improve energy output by up to 21.8%. The IHPO algorithm can effectively solve the multi-peak problem of centralized thermoelectric systems.

Key words

Thermoelectric generator / maximum power point tracking / hunter-prey optimization algorithm / termination mechanism and restart mechanism

引用本文

李鹏, 李浩, 朱文超, 谢长君. 多峰值热电系统下改进启发式MPPT方法研究. 电源学报, 2026 , 24 (5) : 168 -179 . DOI: 10.13234/j.issn.2095-2805.2026.5.168

Peng LI, Hao LI, Wenchao ZHU, Changjun XIE. Research on Improved Heuristic MPPT Method in Multi-peak Thermoelectric System[J]. Journal of Power Supply, 2026 , 24 (5) : 168 -179 . DOI: 10.13234/j.issn.2095-2805.2026.5.168

基金

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国家自然科学基金面上项目(51977164)

参考文献

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文献

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参考文献引证文献

排序方式：

[1]

Wang Jizhe, Liu Shuang, Li Liangliang. Experiments and modeling on thermoelectric power generators used for waste heat recovery from hot water pipes[J]. Energy Procedia, 2019, 158: 1052-1058.

[2]

Wang Jun, Li Yilin, Zhao Chuang, et al. An optimization study of structural size of parameterized thermoelectric generator module on performance[J]. Energy Conversion and Management, 2018, 160: 176-181.

[3]

Zhang Xiaoshun, Tan Tian, Yang Bo, et al. Greedy search based data-driven algorithm of centralized thermoelectric generation system under non-uniform temperature distribution[J]. Applied Energy, 2020, 260: 114232.

[4]

Gopinath M, Marimuthu R. PV-TEG output: Comparison with heat sink and graphite sheet as heat dissipators[J]. Case Studies in Thermal Engineering, 2023, 45: 102935.

[5]

Khan N M, Mansoor M, Mirza A F, et al. Energy harvesting and stability analysis of centralized TEG system under non-uniform temperature distribution[J]. Sustainable Energy Technologies and Assessments, 2022, 52: 102028.

[6]

罗宇强, 王江伟, 谢锡锋. 基于改进扰动观察法的光伏MPPT控制[J]. 红水河, 2023, 42(6): 42-46, 51. Luo Yuqiang, Wang Jiangwei, Xie Xifeng. Photovoltaic MPPT control based on improved perturbation and observation method[J]. Hongshui River, 2023, 42(6): 42-46, 51. (in Chinese)

[7]

刘聘凭, 程若发, 许立斌, 等. 基于光伏MPPT采样电流的自适应变步长INC算法[J]. 电源学报, 2023, 21(5): 58-66. Liu Pinping, Cheng Ruofa, Xu Libin, et al. Adaptive variable-step INC algorithm based on PV MPPT sampling current[J]. Journal of Power Supply, 2023, 21(5): 58-66. (in Chinese)

[8]

王思敏. 基于改进型变步长扰动观察法的光伏最大功率控制策略[J]. 集成电路应用, 2023, 40(6): 382-385. Wang Simin. Analysis of photovoltaic maximum power control strategy based on improved variable step disturbance observation method[J]. Application of IC, 2023, 40(6): 382-385. (in Chinese)

[9]

Montecucco A, Knox A R. Maximum power point tracking converter based on the open-circuit voltage method for thermoelectric generators[J]. IEEE Transactions on Power Electronics, 2015, 30(2): 828-839.

[10]

王喆伟, 朱文超, 黄亮, 等. 基于线性外推的汽车尾气热电发电系统最大功率跟踪算法[J]. 汽车技术, 2021(8): 14-19. Wang Zhewei, Zhu Wenchao, Huang Liang, et al. MPPT algorithm of vehicle exhaust thermoelectric power generation system based on linear extrapolation[J]. Auto-mobile Technology, 2021(8): 14-19. (in Chinese)

[11]

郭轩成, 陈逸峰, 朱文超, 等. 启发式热电发电多峰值MPPT方法研究[J]. 计算机仿真, 2022, 39(12): 84-92. Guo Xuancheng, Chen Yifeng, Zhu Wenchao, et al. Heuristic multi-peak MPPT method for thermoelectric power generation[J]. Computer Simulation, 2022, 39(12): 84-92. (in Chinese)

[12]

郭伟欣, 张志东. 基于Jaya算法的遮阴条件下光伏MPPT控制研究[J]. 电源学报, 2025, 23(5): 105-111. Guo Weixin, Zhang Zhidong. Research on photovoltaic MPPT control under shading conditions based on Jaya algorithm[J]. Journal of Power Supply, 2025, 23(5): 105-111. (in Chinese)

[13]

肖义平, 赵云峰. 基于混沌映射和高斯扰动的改进粒子群优化算法MPPT控制策略研究[J]. 电源学报, 2025, 23(5): 96-104. Xiao Yiping, Zhao Yunfeng. Research on MPPT control strategy based on improved PSO algorithm using chaotic mapping and gaussian perturbation[J]. Journal of Power Supply, 2025, 23(5): 96-104. (in Chinese)

[14]

赵玫, 赵君, 左思承, 等. 横向磁通永磁直线电机电磁振动特性分析[J]. 电工技术学报, 2024, 39(1): 168-181. Zhao Mei, Zhao Jun, Zuo Sicheng, et al. Analysis of electromagnetic vibration characteristics of transverse flux permanent magnet linear motor[J]. Transactions of China Electrotechnical Society, 2024, 39(1): 168-181. (in Chinese)

[15]

Aguila-Leon J, Vargas-Salgado C, Chiñas-Palacios C, et al. Solar photovoltaic maximum power point tracking controller optimization using grey wolf optimizer: A performance comparison between bio-inspired and traditional algorithms[J]. Expert Systems with Applications, 2023, 211: 118700.

[16]

刘帼巾, 刘达明, 缪建华, 等. 基于变分模态分解和改进灰狼算法优化深度置信网络的自动转换开关故障识别[J]. 电工技术学报, 2024, 39(4): 1221-1233. Liu Guojin, Liu Daming, Miao Jianhua, et al. Fault identification of automatic transfer switching equipment based on VMD-WPE and IGWO optimized DBN[J]. Transactions of China Electrotechnical Society, 2024, 39(4): 1221-1233. (in Chinese)

[17]

Yang Bo, Wang Junting, Zhang Xiaoshun, et al. MPPT design of centralized thermoelectric generation system using adaptive compass search under non-uniform temperature distribution condition[J]. Energy Conversion and Management, 2019, 199: 111991.

[18]

Zhang Rui, Yang Bo, Chen Nuo. Arithmetic optimization algorithm based MPPT technique for centralized TEG systems under different temperature gradients[J]. Energy Reports, 2022, 8: 2424-2433.

[19]

Zhang Xiaoshun, Yang Bo, Yu Tao, et al. Dynamic surrogate model based optimization for MPPT of centralized thermoelectric generation systems under heterogeneous temperature difference[J]. IEEE Transactions on Energy Conversion, 2020, 35(2): 966-976.

[20]

Li Fusheng, Lin Dan, Yu Tao, et al. Adaptive rapid neural optimization: A data-driven approach to MPPT for centralized TEG systems[J]. Electric Power Systems Research, 2021, 199: 107426.

[21]

陈逸峰, 朱文超, 谢长君, 等. 基于动态温变特性的全局温差发电MPPT方法[J]. 电源技术, 2023, 47(3): 377-381. Chen Yifeng, Zhu Wenchao, Xie Changjun, et al. Global thermoelectric power generation MPPT method based on dynamic temperature difference characteristics[J]. Chinese Journal of Power Sources, 2023, 47(3): 377-381. (in Chinese)

[22]

Montecucco A, Siviter J, Knox A R. The effect of temperature mismatch on thermoelectric generators electrically connected in series and parallel[J]. Applied Energy, 2014, 123: 47-54.

[23]

Naruei I, Keynia F, Sabbagh Molahosseini A. Hunterprey optimization: Algorithm and applications[J]. Soft Computing, 2022, 26(3): 1279-1314.

[24]

陈璇. 动态热源下温差发电器响应特征与功率跟踪研究[D]. 镇江: 江苏大学, 2021. Chen Xuan. Research on response characteristics and power tracking of thermoelectric generator under dynamic heat source[D]. Zhenjiang: Jiangsu University, 2021. (in Chinese)

2026年第24卷第5期

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文章信息

doi: 10.13234/j.issn.2095-2805.2026.5.168

接收时间：2024-01-04
首发时间：2026-10-29
出版时间：2026-05-30

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出版历史

收稿日期：2024-01-04
修回日期：2024-04-24
录用日期：2024-05-01

基金

General Program of National Natural Science Foundation of China(51977164)

国家自然科学基金面上项目(51977164)

作者信息

^1.武汉理工大学自动化学院，武汉　430070

^2.材料复合新技术国家重点实验室，武汉　430070

^3.现代汽车零部件技术湖北省重点实验室（武汉理工大学），武汉　430070

通讯作者:

朱文超（1993—　），男，通信作者，博士。研究方向：温差发电技术。E-mail：zhuwenchao@whut.edu.cn。

参考文献

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https://castjournals.cast.org.cn/joweb/dyxb/CN/10.13234/j.issn.2095-2805.2026.5.168

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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