Enhanced Energy Output From a PV System Under Partial Shaded Conditions Through Artificial Bee Colony
By Sundareswaran, K. Sankar, P. Nayak, P. Simon, V. K. Sundareswaran, S. Vignesh Kumar, Palani, Krishnasamy Vijayakumar, N. Kumaresan, Nanjappagounder Ammasaigounden and 6 more. IEEE Transactions on Sustainable Energy, 2015
360 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Renewable Energy, Sustainability and the Environment
View Paper: https://www.semanticscholar.org/paper/44d214aece5c992244530e6be6deda5e06d26fd2
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In Artificial Intelligence
- Adel El-Shahat
- Zainal Salam
- Frede Blaabjerg
- Yousef A. Mahmoud
- N. Rajasekar
- Jubaer Ahmed
- J. Prasanth Ram
- Ehab F. El-Saadany
In Renewable Energy, Sustainability and the Environment
- Jubaer Ahmed
- Yousef A. Mahmoud
- Ehab F. El-Saadany
- Zainal Salam
- N. Rajasekar
- J. Prasanth Ram
- Ahmed Faheem Zobaa
- Adel El-Shahat
Citations in the corpus
- A Novel Flower Pollination Based Global Maximum Power Point Method for Solar Maximum Power Point Tracking (2017, methodology): “Cuckoo search [27], firefly algorithm [28], random search method (RSM) [29], artificial bee colony methods [30], ant colony optimization [31], and grey wolf optimization [32] are some of the important methods proposed in the field of MPPT.”
- An Experimental Estimation of Hybrid ANFIS–PSO-Based MPPT for PV Grid Integration Under Fluctuating Sun Irradiance (2020, methodology): “The recent artificial intelligent algorithms, such as particle swarm optimization (PSO), firefly algorithm, artificial bee colony (ABC), and ant colony optimization (ACO), are used to solve optimized problems [12]–[15].”
- An Enhanced Adaptive P&O MPPT for Fast and Efficient Tracking Under Varying Environmental Conditions (2018, methodology): “The performance of the proposed EA-P&O is evaluated against four recent MPPT techniques, namely the modified incremental conductance (MIC) [33], artificial bee colony (ABC) [5], cuckoo search (CS) [8] and a hybrid scheme that combines P&O with the ant colony optimization (ACO-P&O) [4].”
- A Novel MPPT Algorithm Based on Particle Swarm Optimization for Photovoltaic Systems (2017, methodology): “Although such methods are simple to implement [7], they are unable to track the MPP accurately in circumstances”
- A Novel MPPT Technique Based on an Image of PV Modules (2017, methodology): “Biological optimization algorithms such as Artificial Bee Colony [19] and particle swarm [11], [20], [21] have been extensively utilized for maximum point tracking.”