PV Maximum Power-point Tracking Using Modified Particle Swarm Optimization under Partial Shading Conditions*

doi:10.23919/CJEE.2020.000035

Chinese Journal of Electrical Engineering ›› 2020, Vol. 6 ›› Issue (4): 106-121.doi: 10.23919/CJEE.2020.000035

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PV Maximum Power-point Tracking Using Modified Particle Swarm Optimization under Partial Shading Conditions^*

Al-wesabi Ibrahim^1,2, M.B. Shafik^3,*, Min Ding^1,2, Mohammad Abu Sarhan^1,2, Zhijian Fang^1,2, Ahmed. G. Alareqi⁴, Tariq Al'moqri⁵, Ayman. M. Al-Rassas⁶

1. School of Automation, China University of Geoscience, Wuhan 430074, China;
2. Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems,Wuhan 430074, China;
3. School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China;
4. School of Oil and Natural Gas Engineering, China University of Geosciences, Wuhan 430074, China;
5. School of Mathematics and Physics, China University of Geoscience, Wuhan 430074, China;
6. School of Petroleum Engineering, China University of Petroleum, East China, Qingdao 266580, China

Received:2020-06-06 Revised:2020-07-29 Accepted:2020-11-19 Online:2020-12-25 Published:2021-01-15
Contact: *E-mail: muhammedshafiq@yahoo.com
About author:Al-wesabi Ibrahim was born in IBB, Yemen, in 1988. He received the B.S. degree in electrical power and machines engineering from Sana’a University; Yemen, in 2014, and the master’s degree in control science and engineering from the China University of Geoscience, Wuhan, China, in 2020. He is currently a Ph.D. candidate at the School of Electrical Engineering and Automation, China University of Geoscience, Wuhan, China. His research interests include smart electric grids, MPPT controllers for PV systems, machine learning, automation systems, and optimization.
M.B. Shafik was born in Kafrelsheikh, Egypt, in 1986. He received a B.S. degree in electrical power and machines engineering from Kafrelsheikh University, Egypt, in 2008, and master’s degree in electrical power engineering from Tanta University, Egypt, in 2013. He is currently a Ph.D. candidate at the School of Electrical Engineering and Automation, Wuhan University, Wuhan, China. His research interests include smart electric grids, active distribution networks, planning, Internet of Things, automation systems, and optimization.
Min Ding received the B.S degree in automation in 2009, and the M.S. in control science and control engineering in 2011 from Central South University, Changsha, China. She received the Ph.D. degree from the Graduate School of Environment and Energy Engineering, Waseda University, Tokyo, Japan. She is currently a lecturer with the School of Automation at the China University of Geosciences, Wuhan, China. Her research interests include renewable energy generation and control, microgrid control and operation optimization, wind power prediction, and user behavior analysis, including distributed generation.
Mohammad Abu Sarhan received an M.S. degree in control science and engineering from China University of Geosciences, Wuhan, China, in 2019. He is currently working in the sector of electrical power engineering and renewable energy resources in the MENA region. His research interests include the areas of smart grids, renewable energy, and electrical power systems.
Zhijian Fang (M’13) was born in Nanzhang, Hubei, China, in 1988. He received the B.S. and Ph.D. degrees in electrical engineering from Huazhong University of Science and Technology, Wuhan, China, in 2010 and 2015, respectively. Since 2018, he has been a professor with the School of Automation, China University of Geosciences, Wuhan, China. From July 2015 to November 2018, he was a lecturer with the School of Electrical Engineering and Automation, Wuhan University, China. From February 2016 to January 2017, he was a post-doctoral research fellow with the Department of Electrical and Computer Engineering, Ryerson University, Canada. His research interests include high-performance DC/DC converters, battery chargers, renewable energy applications, and wireless power transfer.
Ahmed. G. Alareqi was born in Taiz, Yemen, in 1989. He received the B.S. degree in 2012 from Aden University, Faculty of Oil and Menials, Yemen, in Oil and Natural Gas Engineering, and a master’s degree in Oil and Natural Gas Engineering from the China University of Geosciences Wuhan, China, in 2019, and he is now a Ph.D. scholar at the School of Oil and Natural Gas Engineering, China University of Geosciences, Wuhan, China. His research interests include drilling and applied technology, to optimize and enhance drilling efficiency to achieve the optimal rate of penetration, especially in basement rock drilling.
Tariq Al’moqri received a B.S. degree in Mathematics from Thamar University, Yemen, in 2010. He is currently pursuing an M.S degree in applied mathematics at China University of Geosciences, Wuhan, China. His research interests are around data mining, machine learning, and optimization.
Ayman. M. Al-Rassas was born in Ibb, Yemen, in 1989. He received his B.S in petroleum engineering, in 2015, from USCI University, Malaysia. He received his master’s degree in Oil and Natural Gas Engineering, from Faculty of Earth Resources, China University of Geosciences, Wuhan, China. Currently, he is doing his Ph.D. in oil-gas field development engineering at China University of Petroleum (East China), and his current research is about CO2-EOR, unconventional resources.
Supported by:
*Hubei Provincial Natural Science Foundation of China (2015CFA010), the Technology Project of State Grid Company “Soft Connection Mechanism and Modeling of Smart Grid Adapting to the Development of Global Energy Interconnection,” and the 111 Projects (B17040)

Abstract

Abstract: A novel maximum power-point tracking approach is proposed based on studies investigating the output characteristics of photovoltaic (PV) systems under partial shading conditions. The existence of partially shaded conditions leads to the presence of several peaks on PV curves, which decrease the efficiency of conventional techniques. Hence, the proposed algorithm, which is based on the modified particle-swarm optimization (MPSO) technique, increases the output power of PV systems under such abnormal conditions and has a better performance compared to other methods. The proposed method is examined under several scenarios for partial shading condition and non-uniform irradiation levels using Matlab, and to investigate its effectiveness adequately, the results of the proposed method are compared with those of the neural network technique. The experimental results show that the proposed method can decrease the interference of the local maximum power-point to cause the PV system to operate at a global maximum power-point. The efficiency of the MPSO is achieved with the least number of steady-state oscillations under partial shading conditions compared with the neural network method.

Key words: Photovoltaic(PV), maximum power point tracking(MPPT), step-up converter, artificial neural networks (ANNs), particle-swarm optimization (PSO)

Al-wesabi Ibrahim, M.B. Shafik, Min Ding, Mohammad Abu Sarhan, Zhijian Fang, Ahmed. G. Alareqi, Tariq Al'moqri, Ayman. M. Al-Rassas. PV Maximum Power-point Tracking Using Modified Particle Swarm Optimization under Partial Shading Conditions^*[J]. Chinese Journal of Electrical Engineering, 2020, 6(4): 106-121.

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PV Maximum Power-point Tracking Using Modified Particle Swarm Optimization under Partial Shading Conditions^*

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