Optimal Reconfiguration Method for Thermoelectric Power Array Based on Artificial Bee Colony Algorithm

doi:10.16183/j.cnki.jsjtu.2022.284

Abstract

Abstract:

With the rapid development of new energy generation technology, the thermoelectric generation technology (TEG) can make good use of the waste heat generated in new energy generation. However, the change of temperature distribution will worsen the output characteristics and reduce the power generation efficiency of the TEG system. In this paper, a TEG array reconfiguration method based on the artificial bee colony (ABC) algorithm is proposed. In three different temperature distributions, ABC is used for dynamic reconfiguration of symmetric 9×9 and unsymmetric 10×15 TEG arrays. Three meta-heuristic algorithms, the genetic algorithm, the particle swarm optimization algorithm, and the bald eagle search are compared with the proposed method, and the temperature distribution of the TEG array reconfiguration by ABC is given. The results show that ABC can improve the output power of the TEG array, and the output power-voltage curves tend to show a single peak value. In addition, real-time hardware-in-the-loop (HIL) experiment based on the RTLAB platform is undertaken to verify the implementation feasibility.

Key words: thermoelectric generation, artificial bee colony algorithm (ABC), dynamic reconfiguration, hardware-in-the-loop (HIL)

CLC Number:

TM913

YANG Bo, HU Yuanweiji, GUO Zhengxun, SHU Hongchun, CAO Pulin, LI Zilin. Optimal Reconfiguration Method for Thermoelectric Power Array Based on Artificial Bee Colony Algorithm[J]. Journal of Shanghai Jiao Tong University, 2024, 58(1): 111-126.

Figures/Tables 20

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References 26

[1]	蒋新强. 汽车尾气半导体温差发电系统研究[D]. 广州: 华南理工大学, 2010.
	JIANG Xinqiang. Research on semiconductor thermoelectric generating system of vehicle exhaust gases[D]. Guangzhou: South China University of Technology, 2010.
[2]	林比宏, 陈晓航, 陈金灿. 太阳能驱动半导体温差发电器性能参数的优化设计[J]. 太阳能学报, 2006, 27(10): 1021-1026.
	LIN Bihong, CHEN Xiaohang, CHEN Jincan. Optimum design of the performance parameters of a solar-driven semiconductor thermoelectric generator[J]. Acta Energiae Solaris Sinica, 2006, 27(10): 1021-1026.
[3]	WANG Z Y, LEONOV V, FIORINI P, et al. Realization of a wearable miniaturized thermoelectric generator for human body applications[J]. Sensors & Actuators A: Physical, 2009, 156(1): 95-102.
[4]	YU C, CHAU K T. Thermoelectric automotive waste heat energy recovery using maximum power point tracking[J]. Energy Conversion & Management, 2009, 50(6): 1506-1512.
[5]	CHATZIDAKIS P G, CHRISTIDIS G C, TATAKIS E C. Comparative study of MPPT algorithms for thermoelectric generators[C]// 2013 15th European Conference on Power Electronics & Applications. Lille, France: IEEE, 2013: 1-8.
[6]	刘永刚, 张辉. 半导体温差发电特性分析及MPPT控制算法研究[C]// 中国电工技术学会电力电子学会第十四届学术年会论文集. 福州, 2014: 280-283.
	LIU Yonggang, ZHANG Hui. Semiconductor thermoelectric generation characteristics analysis and MPPT control algorithm research[C]// The 14th Annual Conference of Power Electronics Society of China Electrotechnical Society. Fuzhou, China: 2014: 280-283.
[7]	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, 2014, 30(2): 828-839. doi: 10.1109/TPEL.63 URL
[8]	王军, 阎铁生, 陈亮, 等. 基于短路电流法的温差发电最大功率点跟踪控制[J]. 电工技术学报, 2020, 35(2): 318-329.
	WANG Jun, YAN Tiesheng, CHEN Liang, et al. Maximum power point tracking scheme for thermoelectric generators based on short-circuit current method[J]. Transactions of China Electrotechnical Society, 2020, 35(2): 318-329.
[9]	王立舒, 白龙, 房俊龙, 等. 基于双曲正切函数的光伏/温差自适应MPPT控制策略研究[J]. 农业工程学报, 2021, 37(16): 184-191.
	WANG Lishu, BAI Long, FANG Junlong, et al. Self-adaptive photovoltaic/temperature difference MPPT control strategy based on hyperbolic tangent function[J]. Transactions of the Chinese Society of Agricultural Engineering, 2021, 37(16): 184-191.
[10]	徐建国, 王海新, 沈建新. 基于电导增量法与改进粒子群算法混合控制的最大功率点跟踪策略[J]. 可再生能源, 2019, 37(6): 824-831.
	XU Jianguo, WANG Haixin, SHEN Jianxin. The hybrid control maximum power point tracking(MPPT) strategy based on incremental conductance method and improved particle swarm optimization algorithm[J]. Renewable Energy Resources, 2019, 37(6): 824-831.
[11]	邵瑞凝, 杨博, 束洪春, 等. 基于改进蜉蝣算法的光伏阵列最优重构方法[J]. 电力系统自动化, 2022, 46(11): 142-150.
	SHAO Ruining, YANG Bo, SHU Hongchun, et al. Optimal reconfiguration method for photovoltaic arrays based on improved mayfly algorithm[J]. Automation of Electric Power Systems, 2022, 46(11): 142-150.
[12]	YOUSRI D, BABU T S, BESHR E, et al. A robust strategy based on marine predators algorithm for large scale photovoltaic array reconfiguration to mitigate the partial shading effect on the performance of PV system[J]. IEEE Access, 2020, 8: 112407-112426. doi: 10.1109/Access.6287639 URL
[13]	ALAHMAD M, CHAABAN M A, LAU S K, et al. An adaptive utility interactive photovoltaic system based on a flexible switch matrix to optimize performance in real-time[J]. Solar Energy, 2012, 86(3): 951-963. doi: 10.1016/j.solener.2011.12.028 URL
[14]	王龙, 陈卓, 黄文力, 等. 基于秃鹰搜索算法的部分遮蔽条件下光伏阵列重构方法[J]. 电力建设, 2022, 43(3): 22-30. doi: 10.12204/j.issn.1000-7229.2022.03.003
	WANG Long, CHEN Zhuo, HUANG Wenli, et al. Bald eagle search based PV array reconfiguration technique under partial shading condition[J]. Electric Power Construction, 2022, 43(3): 22-30. doi: 10.12204/j.issn.1000-7229.2022.03.003
[15]	姜建国, 郭晓丽, 陈鹏, 等. 优化的GA算法在大型配电网络重构中的应用[J]. 吉林大学学报(信息科学版), 2022, 40(3): 400-407.
	JIANG Jianguo, GUO Xiaoli, CHEN Peng, et al. Application of optimized GA algorithm in large distribution network reconfiguration[J]. Journal of Jilin University (Information Science Edition), 2022, 40(3): 400-407.
[16]	包丽梅. 人工蜂群算法研究综述[J]. 电脑知识与技术, 2016, 12(22): 159-160.
	BAO Limei. Artificial colony algorithm research were reviewed[J]. Computer Knowledge & Technology, 2016, 12(22): 159-160.
[17]	COMPADRE TORRECILLA M, MONTECUCCO A, SIVITER J, et al. Novel model and maximum power tracking algorithm for thermoelectric generators operated under constant heat flux[J]. Applied Energy, 2019, 256: 113930. doi: 10.1016/j.apenergy.2019.113930 URL
[18]	LI L, GAO X, ZHANG G, et al. Combined solar concentration and carbon nanotube absorber for high performance solar thermoelectric generators[J]. Energy Conversion & Management, 2019, 183: 109-115.
[19]	LAIRD I, LU D D C. High step-up DC/DC topology and MPPT algorithm for use with a thermoelectric generator[J]. IEEE Transactions on Power Electronics, 2013, 28(7): 3147-3157. doi: 10.1109/TPEL.2012.2219393 URL
[20]	LIU Y H, CHIU Y H, HUANG J W, et al. A novel maximum power point tracker for thermoelectric generation system[J]. Renewable Energy, 2016, 97: 306-318. doi: 10.1016/j.renene.2016.05.001 URL
[21]	AOUCHICHE N, AITCHEIKH M S, BECHERIF M, et al. AI-based global MPPT for partial shaded grid connected PV plant via MFO approach[J]. Solar Energy, 2018, 171: 593-603. doi: 10.1016/j.solener.2018.06.109 URL
[22]	WU S J, WANG S, YANG C J, et al. Energy management for thermoelectric generators based on maximum power point and load power tracking[J]. Energy Conversion & Management, 2018, 177: 55-63.
[23]	YANG B, YE H Y, WANG J B, et al. PV arrays reconfiguration for partial shading mitigation: Recent advances, challenges and perspectives[J]. Energy Conversion & Management, 2021, 247: 114738
[24]	RAJAN N A, SHRIKANT K D, DHANALAKSHMI B, et al. Solar PV array reconfiguration using the concept of standard deviation and genetic algorithm[J]. Energy Procedia, 2017, 117: 1062-1069. doi: 10.1016/j.egypro.2017.05.229 URL
[25]	BABU T S, RAM J P, DRAGIČEVIĆ T, et al. Particle swarm optimization based solar PV array reconfiguration of the maximum power extraction under partial shading conditions[J]. IEEE Transactions on Sustainable Energy, 2018, 9(1): 74-85. doi: 10.1109/TSTE.2017.2714905 URL
[26]	ALSATTAR H A, ZAIDAN A A, ZAIDAN B B. Novel meta-heuristic bald eagle search optimisation algorithm[J]. Artificial Intelligence Review, 2020, 53(3): 2237-2264. doi: 10.1007/s10462-019-09732-5

算法	算例结果	不均匀温度分布类型
算法	算例结果	对角线	外部	内部
未优化状态	P_max/W	532.4	670.9	519.8
ABC	P_max/W	585.8	694.4	533.2
	P_ave/W	579.0	693.7	533.1
GA	P_max/W	585.8	694.4	533.2
	P_ave/W	565.5	694.4	532.8
PSO	P_max/W	585.8	686.9	533.2
	P_ave/W	570.4	675.1	532.8
BES	P_max/W	565.0	647.0	532.6
	P_ave/W	558.7	623.3	532.4

算法	运行时间/s
算法	对角线	外部	内部
ABC	6.26	6.36	6.39
GA	6.39	6.28	6.26
PSO	12.41	12.35	12.97
BES	15.96	15.66	15.00

算法	算例结果	不均匀温度分布类型
算法	算例结果	对角线	外部	内部
未优化状态	P_max/W	1048.5	1143.8	1017.5
ABC	P_max/W	1191.2	1193.3	1036.9
	P_ave/W	1161.4	1192.4	1036.8
GA	P_max/W	1162.3	1193.4	1036.9
	P_ave/W	1130.1	1192.0	1036.7
PSO	P_max/W	1156.6	1193.0	1036.8
	P_ave/W	1133.0	1180.3	1036.5
BES	P_max/W	1103.5	1155.5	1036.1
	P_ave/W	1091.2	1150.9	1035.8

算法	运行时间/s
算法	对角线	外部	内部
ABC	7.82	7.79	7.82
GA	7.59	7.20	7.42
PSO	18.48	18.45	18.47
BES	16.41	16.67	16.78