MAXIMUM POWER POINT TRACKING CONTROL OF CENTRALIZED THERMOELECTRIC POWER GENERATION SYSTEMS BASED ON ENN-AOSMA ALGORITHM

Feng Lulu; Chen Yan; He Xinying; Zhang Yibo; Cheng Yuan

doi:10.19912/j.0254-0096.tynxb.2023-2000

PDF(1490 KB)

Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (4) : 125-132. DOI: 10.19912/j.0254-0096.tynxb.2023-2000

MAXIMUM POWER POINT TRACKING CONTROL OF CENTRALIZED THERMOELECTRIC POWER GENERATION SYSTEMS BASED ON ENN-AOSMA ALGORITHM

Feng Lulu, Chen Yan, He Xinying, Zhang Yibo, Cheng Yuan

Author information +

History +

Abstract

A novel maximum power point tracking （MPPT） algorithm based on an improved slime mould algorithm （SMA） combined with Elman neural network is proposed to address the nonlinear multi-peak P-V curves exhibited by centralized thermoelectric generation systems under nonuniform temperature distribution （NTD） condition. The feedforward neural network is established with duty cycle as input and system power as output, and the input-output fitting curve is obtained. Based on the fitting curve, the boundary conditions are updated, and the local search formula of the slime mold algorithm （SMA） is updated using arithmetic optimization algorithm （AOA）, achieving a fast approximation to the global maximum power point （GMPP）. The effectiveness and superiority of the proposed algorithm are verified through examples including start-up tests, temperature step changes, time-varying temperature tests, and accuracy analysis. Simulation results demonstrate that the proposed MPPT method not only achieves fast tracking speed, but also attains a tracking accuracy of 99.94%.

Key words

maximum power point tracking / thermoelectric generation / neural network / nonuniform temperature distribution

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Feng Lulu, Chen Yan, He Xinying, Zhang Yibo, Cheng Yuan. MAXIMUM POWER POINT TRACKING CONTROL OF CENTRALIZED THERMOELECTRIC POWER GENERATION SYSTEMS BASED ON ENN-AOSMA ALGORITHM[J]. Acta Energiae Solaris Sinica. 2025, 46(4): 125-132 https://doi.org/10.19912/j.0254-0096.tynxb.2023-2000

References

[1] ZHANG X S, YANG B, YU T, 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.
[2] UCHIDA K I, ADACHI H, KIKKAWA T, et al.Thermoelectric generation based on spin seebeck effects[J]. Proceedings of the IEEE, 2016, 104(10): 1946-1973.
[3] 孙立明, 杨博. 基于自适应罗盘搜索的集中式温差发电系统MPPT设计[J]. 电力系统保护与控制, 2020, 48(21): 140-148.
SUN L M, YANG B.MPPT design of centralized thermoelectric generation system using adaptive compass search[J]. Power system protection and control, 2020, 48(21): 140-148.
[4] BIJUKUMAR B, KAUSHIK RAAM A G, GANESAN S I, et al. A linear extrapolation-based MPPT algorithm for thermoelectric generators under dynamically varying temperature conditions[J]. IEEE transactions on energy conversion, 2018, 33(4): 1641-1649.
[5] SUN K, QIU Z X, WU H F, et al.Evaluation on high-efficiency thermoelectric generation systems based on differential power processing[J]. IEEE transactions on industrial electronics, 2018, 65(1): 699-708.
[6] 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 J, YAN T S, CHEN L, 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.
[7] WANG Z Y, LEONOV V, FIORINI P, et al.Realization of a wearable miniaturized thermoelectric generator for human body applications[J]. Sensors and actuators A: physical, 2009, 156(1): 95-102.
[8] 解宝, 李萍宇, 苏绎仁, 等. 局部阴影下光伏阵列的最大功率点跟踪算法研究[J]. 太阳能学报, 2023, 44(12): 47-52.
XIE B, LI P Y, SU Y R, et al.Research on maximum power point tracking algorithm of PV array under local shadow[J]. Acta energiae solaris sinica, 2023, 44(12): 47-52.
[9] XUE J K, SHEN B.A novel swarm intelligence optimization approach: sparrow search algorithm[J]. Systems science & control engineering, 2020, 8(1): 22-34.
[10] 葛传九, 武鹏, 董祥祥, 等. 基于布谷鸟算法的光伏MPPT改进[J]. 太阳能学报, 2022, 43(10): 59-64.
GE C J, WU P, DONG X X, et al.Improved photovoltaic maximum power point tracking based on cuckoo search algorithm[J]. Acta energiae solaris sinica, 2022, 43(10): 59-64.
[11] MOHANTY S, SUBUDHI B, RAY P K.A new MPPT design using grey wolf optimization technique for photovoltaic system under partial shading conditions[J]. IEEE transactions on sustainable energy, 2016, 7(1): 181-188.
[12] 王冉冉, 高慧敏, 张昕宇. 基于GA-GRU神经网络的光伏MPPT算法[J]. 太阳能学报, 2023, 44(9): 212-219.
WANG R R, GAO H M, ZHANG X Y.MPPT algorithm for photovoltaics based on GA-GRU neural network[J]. Acta energiae solaris sinica, 2023, 44(9): 212-219.
[13] LIN W M, HONG C M, CHEN C H.Neural-network-based MPPT control of a stand-alone hybrid power generation system[J]. IEEE transactions on power electronics, 2011, 26(12): 3571-3581.
[14] THANKAKAN R, SAMUEL NADAR E R. Investigation of the double input power converter with N stages of voltage multiplier using PSO-based MPPT technique for the thermoelectric energy harvesting system[J]. International journal of circuit theory and applications, 2020, 48(3): 435-448.
[15] MIAO H, QIU Z R, ZENG C B.Multi-strategy improved slime mould algorithm and its application in optimal operation of cascade reservoirs[J]. Water resources management, 2022, 36(9): 3029-3048.
[16] 杨博, 王俊婷, 钟林恩, 等. 基于贪婪神经网络的集中式温差发电系统最大功率跟踪[J]. 电工技术学报, 2020, 35(11): 2349-2359.
YANG B, WANG J T, ZHONG L E, et al.Maximum power point tracking of centralized thermoelectric generation system using greedy neural network[J]. Transactions of China Electrotechnical Society, 2020, 35(11): 2349-2359.
[17] 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.
[18] CHANDRA S, GAUR P.Radial basis function neural network technique for efficient maximum power point tracking in solar photo-voltaic system[J]. Procedia computer science, 2020, 167: 2354-2363.