Bald Eagle Search Based PV Array ReconfigurationTechnique under Partial Shading Condition

doi:10.12204/j.issn.1000-7229.2022.03.003

Abstract

Abstract:

Partial shading (PS) is one of the most severe factors affecting the efficiency of photovoltaic (PV) generation, which usually causes mismatch losses and reduces the output power of PV arrays. To solve this problem, PV reconfiguration technology is widely used in PV systems. This paper proposes a PV array reconfiguration method based on bald eagle search (BES) algorithm, which can effectively reduce power losses and improve power generation efficiency. To mitigate the malignant effect of PS and obtain satisfactory output characteristics, BES is designed to reconfigure the electrical connection of PV arrays. To verify the effectiveness of BES, shadows of slowly moving clouds in ten minutes are simulated, upon which a comprehensive comparison of three typical metaheuristic algorithms is undertaken. Simulation results show that compared with total cross tied (TCT), BES can increase the output power by 15.10%, reduce the mismatch loss by 57.90%, and increase the filling factor from 0.6254 to 0.7143 under shadow of slowly moving clouds. Compared with ant colony optimization (ACO) and tabu search (TS), BES also obtains lower mismatch loss, higher power enhancement, and higher filling factor.

Key words: photovoltaic array, partial shading, photovoltaic reconfiguration, bald eagle search

CLC Number:

TM615

WANG Long, CHEN Zhuo, HUANG Wenli, GUO Yinyuan, LI Xiang, CHANG Xucheng, YANG Bo. Bald Eagle Search Based PV Array ReconfigurationTechnique under Partial Shading Condition[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(3): 22-30.

Figures/Tables 15

Fig.1 N×N TCT connected array

Fig.2 Three optimization steps of BES

Fig.3 Structure of a switching matrix

Fig.4 Flowchart of BES for PV reconfiguration

Table 1 Pseudo code of BES for PV reconfiguration

Table 2 Main parameters of PV module

Fig.5 Irradiation distribution of slowly moving clouds

Fig.6 I-V curves and P-V curves with PV reconfiguration

Fig.7 I-V curves and P-V curves at the 5th minute with and without PV reconfiguration

Fig.8 The optimal irradiation distribution of reconfigured PV array

Table 3 The maximum output power, average output power and standard deviation ofPV array obtained by BES, ACO and TS in 30 independent runs

Fig.9 Mismatch loss, power enhancement andfilling factor of TCT, BES, ACO, and TS

Fig.10 The RTLAB platform-based real-timehardware-in-loop experiment

Fig.11 Output characteristics curves of PV array underdifferent irradiances when temperature is constant at 25℃

Fig.12 Output characteristics curves of PV array underdifferent temperatures when irradiance is constant at 1 000 W/m2

References 27

[1]	MURTY V V S N, KUMAR A. Multi-objective energy management in microgrids with hybrid energy sources and battery energy storage systems[J]. Protection and Control of Modern Power Systems, 2020, 5(1):1-20. doi: 10.1186/s41601-019-0145-1 URL
[2]	张颖梓, 李华强, 李旭翔, 等. 基于用户需求行为的综合能源服务产品定价策略研究[J]. 电力系统保护与控制, 2021, 49(17):1-9.
	ZHANG Yingzi, LI Huaqiang, LI Xuxiang, et al. Pricing strategy of integrated energy service products based on user demand behavior[J]. Power System Protection and Control, 2021, 49(17):1-9.
[3]	李峰, 孟少飞. 局部阴影条件下的光伏阵列插空列循环静态重构方法[J]. 电力自动化设备, 2021, 41(8):82-88.
	LI Feng, MENG Shaofei. ICL static reconstruction method of photovoltaic array under partial shadow condition[J]. Electric Power Automation Equipment, 2021, 41(8):82-88.
[4]	YANG B, YU T, ZHANG X S, et al. Dynamic leader based collective intelligence for maximum power point tracking of PV systems affected by partial shading condition[J]. Energy Conversion and Management, 2019, 179:286-303. doi: 10.1016/j.enconman.2018.10.074 URL
[5]	邬炜, 赵腾, 李隽, 等. 考虑碳预算与碳循环的能源规划方法及建议[J]. 电力建设, 2021, 42(10):1-8.
	WU Wei, ZHAO Teng, LI, Juan, et al. Energy planning methods and proposals considering carbon budget and carbon cycle[J]. Electric Power Construction, 2021, 42(10):1-8.
[6]	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. doi: 10.1109/TEC.2020.2967511 URL
[7]	YANG B, WANG J B, ZHANG X S, et al. Comprehensive overview of meta-heuristic algorithm applications on PV cell parameter identification[J]. Energy Conversion and Management, 2020, 208:112595. doi: 10.1016/j.enconman.2020.112595 URL
[8]	KARMAKAR B K, KARMAKAR G. A current supported PV array reconfiguration technique to mitigate partial shading[J]. IEEE Transactions on Sustainable Energy, 2021, 12(2):1449-1460. doi: 10.1109/TSTE.2021.3049720 URL
[9]	SHANG L Q, GUO H C, ZHU W W. An improved MPPT control strategy based on incremental conductance algorithm[J]. Protection and Control of Modern Power Systems, 2020, 5(2):176-184.
[10]	BONTHAGORLA P K, MIKKILI S. A novel fixed PV array configuration for harvesting maximum power from shaded modules by reducing the number of cross-ties[J]. IEEE Journal of Emerging and Selected Topics in Power Electronics, 2021, 9(2):2109-2121. doi: 10.1109/JESTPE.2020.2979632 URL
[11]	张明锐, 陈喆旸. 一种基于最小均衡差的光伏阵列重构方案[J]. 电力自动化设备, 2021, 41(2):33-38.
	ZHANG Mingrui, CHEN Zheyang. Reconfiguration scheme of photovoltaic array based on minimum equalization difference[J]. Electric Power Automation Equipment, 2021, 41(2):33-38.
[12]	夏永洪, 李梦茹, 曾繁鹏, 等. 基于TCT结构及开关控制的光伏阵列重构[J]. 太阳能学报, 2018, 39(10):2797-2802.
	XIA Yonghong, LI Mengru, ZENG Fanpeng, et al. Reconstruction of PV arrays based on TCT structure and switch control[J]. Acta Energiae Solaris Sinica, 2018, 39(10):2797-2802.
[13]	丁坤, 王祥, 徐俊伟, 等. 常见光伏阵列拓扑结构分析[J]. 电网与清洁能源, 2014, 30(3):114-118.
	DING Kun, WANG Xiang, XU Junwei, et al. Topological analysis of common PV arrays[J]. Power System and Clean Energy, 2014, 30(3):114-118.
[14]	张丹, 陈华. 光伏阵列拓扑结构对系统输出的影响[J]. 计算机仿真, 2014, 31(5):125-129.
	ZHANG Dan, CHEN Hua. Effects of topology structure of photovoltaic array on output of system[J]. Computer Simulation, 2014, 31(5):125-129.
[15]	AJMAL A M, SUDHAKAR BABU T, RAMACHANDARAMURTHY V K, et al. Static and dynamic reconfiguration approaches for mitigation of partial shading influence in photovoltaic arrays[J]. Sustainable Energy Technologies and Assessments, 2020, 40:100738. doi: 10.1016/j.seta.2020.100738 URL
[16]	DESHKAR S N, DHALE S B, MUKHERJEE J S, et al. Solar PV array reconfiguration under partial shading conditions for maximum power extraction using genetic algorithm[J]. Renewable and Sustainable Energy Reviews, 2015, 43:102-110. doi: 10.1016/j.rser.2014.10.098 URL
[17]	HASANIEN H M, AL-DURRA A, MUYEEN S M. Gravitational search algorithm-based photovoltaic array reconfiguration for partial shading losses reduction[C]// 5th IET International Conference on Renewable Power Generation (RPG) 2016. London, UK. Institution of Engineering and Technology, 2016: 1-6.
[18]	FATHY A. Recent meta-heuristic grasshopper optimization algorithm for optimal reconfiguration of partially shaded PV array[J]. Solar Energy, 2018, 171:638-651. doi: 10.1016/j.solener.2018.07.014 URL
[19]	BABU T S, RAM J P, DRAGIČ EVIĊ 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
[20]	MAHMOUD A, SHAMSELDEIN M, HASANIEN H, et al. Photovoltaic array reconfiguration to reduce partial shading losses using water cycle algorithm[C]// 2019 IEEE Electrical Power and Energy Conference. October 16-18, 2019, Montreal, QC, Canada. IEEE, 2019: 1-6.
[21]	KRISHNAN G S, KINATTINGAL S, SIMON S P, et al. MPPT in PV systems using ant colony optimisation with dwindling population[J]. IET Renewable Power Generation, 2020, 14(7):1105-1112. doi: 10.1049/iet-rpg.2019.0875 URL
[22]	BI Z Q, MA J M, WANG K S, et al. Identification of partial shading conditions for photovoltaic strings[J]. IEEE Access, 2020, 8:75491-75502. doi: 10.1109/ACCESS.2020.2988017 URL
[23]	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 URL
[24]	YOUSRI D, ALLAM D, ETEIBA M B. Optimal photovoltaic array reconfiguration for alleviating the partial shading influence based on a modified Harris Hawks optimizer[J]. Energy Conversion and Management, 2020, 206:112470. doi: 10.1016/j.enconman.2020.112470 URL
[25]	FATHY A. Butterfly optimization algorithm based methodology for enhancing the shaded photovoltaic array extracted power via reconfiguration process[J]. Energy Conversion and Management, 2020, 220:113115. doi: 10.1016/j.enconman.2020.113115 URL
[26]	YOUSRI D, BABU T S, MIRJALILI S, et al. A novel objective function with artificial ecosystem-based optimization for relieving the mismatching power loss of large-scale photovoltaic array[J]. Energy Conversion and Management, 2020, 225:113385. doi: 10.1016/j.enconman.2020.113385 URL
[27]	ALKAHTANI M, ZHOU J F, HU Y H, et al. An experimental investigation on output power enhancement with offline reconfiguration for non-uniform aging photovoltaic array to maximise economic benefit[J]. IEEE Access, 2021, 9:87510-87519. doi: 10.1109/ACCESS.2021.3088386 URL