这项研究解决了光伏（PV）系统中部分阴影的众所周知的问题。由于云，建筑物，树木或任何其他遮挡物，与未遮挡的模块相比，光伏阵列的遮挡的模块受到的阳光照射较少。这会导致在电源电压特性曲线（P-VCC）中出现多个峰值，从而导致PV系统中出现热点。因此，这导致输出功率的极大降低和太阳能电池组件寿命的快速降低。因此，为了减轻局部阴影效应，已经开发了许多最大功率点跟踪（MPPT）方法。最近，诸如元启发式和确定性算法之类的高级控制算法（ACA）已被严格地用于解决此问题。
最近开发的智能的，自然启发式的基于元启发式的优化算法（称为乌鸦搜索算法（CSA））已针对MPPT进行了研究和实现。由于CSA是基于人群的技术，它模仿了食品存储和检索的智能行为。尽管CSA在寻找全球最大功率点（GMPP）的准确性方面提供了可喜的结果，但由于其元启发法，其跟踪速度受到了影响性质。在这项研究中，另一种基于确定性优化的算法称为扰动和观测（P＆O）算法。尽管P＆O的跟踪速度非常快，但是可以确定的是它有陷入局部最优的趋势。
为了利用元启发式和确定性特性的优势来获得快速准确的MPPT GMPP。本文通过将改进的CSA与P＆O相结合，提出了一种用于部分阴影光伏系统的新方法。在各种局部阴影情况下分析了该算法。通过将所提方法与原始CSA的性能进行比较，已验证了仿真和实验结果。所提方法的结果表明，该方法在跟踪精度和跟踪速度方面都有较好的表现。

This study addresses, the well-known problem of partial shading in the photovoltaic (PV) system. Due to clouds, buildings, trees or any other blocking object, the shaded modules of the PV array receives less solar irradiation compared to unshaded modules. This causes occurrence of multiple peaks in the power-voltage characteristic curves (P-VCC), thereby causing hotspots in the PV system. Hence, it results in the great reduction in output power and fast rate of deterioration of life of solar modules. Thus, to alleviate partial shading effects, copious maximum power point tracking (MPPT) methods have been developed. Recently, the advance control algorithms (ACA), such as metaheuristic and deterministic algorithms have been rigorously used to approach this issue.
The recently developed intelligent, nature inspired metaheuristic-based optimization algorithm, known as, crow search algorithm (CSA) has been studied and implemented for MPPT. As, CSA is a population-based technique, it mimics their intelligent behaviour of food storing and retrieving. Although, CSA provides promising results in terms of accuracy for finding global maximum power point (GMPP), it compromises with its tracking speed due to its metaheuristic nature. Another algorithm, based on deterministic optimization, known as perturb and observe (P&O) algorithm, has been implemented for MPPT in this study. Though the tracking speed of P&O is remarkably fast but it is well established that it has the tendency of getting stuck in local optimum.
To take the advantage of both metaheuristic and deterministic characteristics for obtaining fast and accurate GMPP for MPPT this thesis has been established. This thesis formulates a novel method by combining modified CSA with P&O for partially shaded PV system. The proposed algorithm is analysed under various partial shading cases. The simulation and experimental result have been validated by comparing the performance of the proposed method with original CSA. The results of the proposed method reveal that it has better performance in terms of tracking accuracy and tracking speed.

M. Rabaia, M. Abdelkareem, E. Sayed, “Environmental impacts of solar energy systems: A review,” Science of The Total Environment, Vol. 754, 1 February 2021, 141989.
[2] R. Aghamolaei, M. H. Shamsi, J. O’Donnell, “Feasibility analysis of community-based PV systems for residential districts: A comparison of on-site centralized and distributed PV installations,” Renewable Energy, Vol. 157, September 2020, Pages 793-808.
[3] H.Ding, D.Q.Zhou, G.Q.Liu, P.Zhou, “Cost reduction or electricity penetration: Government R&D-induced PV development and future policy schemes,” Renewable and Sustainable Energy Reviews, Vol. 124, May 2020, 109752.
[4] B.Yang, T. Zhu, J. Wang, H. Shu, “Comprehensive overview of maximum power point tracking algorithms of PV systems under partial shading condition,” Journal of Cleaner Production, Vol. 268, 20 September 2020, 121983.
[5] N. Femia, G. Lisi, G. Petrone, G. Spagnuolo, & M. Vietelli, “Distributed Mximum Power Point Tracking of Photovoltaic Arrays: Novel approach system analysis,” IEEE Transactions on Industrial Electronics, Vol. 55, no. 7, pp 2610-2612, July, 2008.
[6] J. Jiahui, Q. Yanhui, & C. Daolian, “A distributed maximum power point tracking flyback pv grid-connected inverter,” in IECON 2017-43rd Annual Conference of IEEE Industrial Electronics Society, pp. 7713-7717, Oct 2017. [7] V. Andrean, P.C. Chang, K. L. Lian, “A Review and New Problems Discovery of Four Simple Decentralized Maximum Power Point Tracking Algorithms—Perturb and Observe, Incremental Conductance, Golden Section Search, and Newton’s Quadratic Interpolation,” Energies 2018, 11, 2966.
[8] K. Amine, “Multiobjective Simulated Annealing: Principle and Algorithm Variants,” Advances in Operation Research, Vol. 2019, 2019. [9] M. M. Hasan, A. Abu-Siada and M. S. A. Dahidah, "A Three-Phase Symmetrical DC-Link Multilevel Inverter with Reduced Number of DC Sources," IEEE Transactions on Power Electronics, Vol. 33, no. 10, pp. 8331-8340, Oct. 2018.
51
[10] M. Uno and A. Kukita, "Single-Switch Single-Magnetic PWM Converter Integrating Voltage Equalizer for Partially Shaded Photovoltaic Modules in Standalone Applications," IEEE Transactions on Power Electronics, Vol. 33, no. 2, pp. 1259-1270, Feb. 2018. [11] R. López-Erauskin, A. González, G. Petrone, G. Spagnuolo and J. Gyselinck, "Multi-Variable Perturb and Observe Algorithm for Grid-Tied PV Systems with Joint Central and Distributed MPPT Configuration," IEEE Transactions on Sustainable Energy, Vol. 12, no. 1, pp. 360-367, Jan. 2021. [12] Zeyu Wang, Ravi S. Srinivasan, “A review of artificial intelligence-based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models,” Renewable and Sustainable Energy Reviews, Vol. 75, Pages 796-808, 2017. [13] Robles Algarín, C., Taborda Giraldo, J, Rodríguez Álvarez, O., “Fuzzy Logic Based MPPT Controller for a PV System,” Energies 2017. [14] Elsheikh, A. H., Abd Elaziz, M., “Review on applications of particle swarm optimization in solar energy systems,” International Journal of Environmental Science and Technology, Pages 1159-1170, 2019. [15] C. Y. Liao, R. K. Subroto, I. S. Millah, K. L. Lian and W. Huang, "An Improved Bat Algorithm for More Efficient and Faster Maximum Power Point Tracking for a Photovoltaic System Under Partial Shading Conditions," IEEE Access, vol. 8, pp. 96378-96390, 2020. [16] Wijaya, B.H., Subroto, R.K.; Lian, K.L., Hariyanto, N., “A Maximum Power Point Tracking Method Based on a Modified Grasshopper Algorithm Combined with Incremental Conductance,” Energies, 2020. [17] Alireza Askarzadeh, “A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm,” Computers & Structures, Vol. 169, Pages 1-12, 2016. [18] H. A. B. Siddique, P. Xu and R. W. De Doncker, "Parameter extraction algorithm for one-diode model of PV panels based on datasheet values," International Conference on Clean Electrical Power (ICCEP), pp. 7-13, 2013. [19] M. Abdel-Basset, L. Abdel-Fatah, A. Sangaiah, “Metaheuristic Algorithms: A Comprehensive Review,” Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications, Pages 185-231, 2018.
52
[20] Meraihi, Yassine, Gabis, Asma Benmessaoud, Ramdane-Cherif, Amar, Acheli, Dalila, “A comprehensive survey of Crow Search Algorithm and its applications,” Artificial Intelligence Review, Pages 1573-7462, 2020. [21] Houam, Yehya, Terki, Amel,Bouarroudj, Noureddine, “An Efficient Metaheuristic Technique to Control the Maximum Power Point of a Partially Shaded Photovoltaic System Using Crow Search Algorithm (CSA),” Journal of Electrical Engineering & Technology, Vol.- 16, Pages 381-402, 2021. [22] Alivarani Mohapatra, Byamakesh Nayak, Priti Das, Kanungo Barada Mohanty, “A review on MPPT techniques of PV system under partial shading condition,” Renewable and Sustainable Energy Reviews, Vol. 80, Pages 854-867, 2017. [23] D. A. Nugraha, K. L. Lian and Suwarno, "A Novel MPPT Method Based on Cuckoo Search Algorithm and Golden Section Search Algorithm for Partially Shaded PV System," Canadian Journal of Electrical and Computer Engineering, vol. 42, no. 3, pp. 173-182, 2019.