A Deterministic Particle Swarm Optimization Maximum Power Point Tracker for Photovoltaic System Under Partial Shading Condition

By Kashif Ishaque, Zainal Salam. IEEE transactions on industrial electronics (1982. Print), 2013

597 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/246876a3289091aeb4ffed5d615bf16b7c0232b7

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Citations in the corpus

A Maximum Power Point Tracking Method Based on Perturb-and-Observe Combined With Particle Swarm Optimization (2014, background): “Moreover, Ishaque and Salam [18] gave no guidelines on how to select this value.”
A Modified P&O Maximum Power Point Tracking Method With Reduced Steady-State Oscillation and Improved Tracking Efficiency (2016, methodology): “On the other hand, soft computing based MPPT such as artificial neural network [8], fuzzy logic [9], differential evolution [10], particle swarm optimization [11], [12] and cuckoo search [13] tend to be more versatile and flexible.”
An Overall Distribution Particle Swarm Optimization MPPT Algorithm for Photovoltaic System Under Partial Shading (2019, methodology): “2, the I–V characteristics of a PV panel can be expressed by (1) [28], [29], where A is the ideal diode factor,”
A Novel MPPT Algorithm Based on Particle Swarm Optimization for Photovoltaic Systems (2017, methodology): “In [9], the random numbers of the standard PSO acceleration factors were removed to reduce the search time.”
Simplified accelerated particle swarm optimisation algorithm for efficient maximum power point tracking in partially shaded photovoltaic systems (2016, methodology): “The random numbers in the PSO algorithm are eliminated and a deterministic PSO algorithm is proposed in [23].”
Enhanced Energy Output From a PV System Under Partial Shaded Conditions Through Artificial Bee Colony (2015, methodology): “Among these, particle swarm optimization (PSO) [12] has been extensively employed to track the global MPP (GMPP) in a PV system under PSC [8]–[11].”
Modified Perturb and Observe MPPT Algorithm for Drift Avoidance in Photovoltaic Systems (2015, methodology): “swarm optimization (PSO) [23], [24], and sliding mode [25],”
Simulation and Hardware Implementation of New Maximum Power Point Tracking Technique for Partially Shaded PV System Using Hybrid DEPSO Method (2015, methodology): “In the DPSO discussed in [33], the random coefficients of the PSO were removed to increase the convergence speed of the system.”
A Novel Flower Pollination Based Global Maximum Power Point Method for Solar Maximum Power Point Tracking (2017, methodology): “clouds, and 3) bird droppings [25], [26].”
A Novel MPPT Technique Based on an Image of PV Modules (2017, methodology): “1 s, which is less than the required time in the competitive methods available in the literature (optimization based methods) which is about 1-2 s using the same computing device (MATLAB) and the same computer [20].”
MPPT of PV Systems Under Partial Shaded Conditions Through a Colony of Flashing Fireflies (2014, background): “It is important to mention that while most of the works [23], [24] claim that the PSO-based approach is superior to conventional methods of tracking, a comparison between PSO and conventional methods in terms of tracking speed through simulated and/or measured results is not available under identica”
A social learning particle swarm optimization algorithm for scalable optimization (2015, background): “, water distribution network design [56], resource allocation [24], task assignment [30], maximum power point tracker for photovoltaic system [36], optimal control [67], DNA sequence compression [100] and reconstruction of gene regulatory network [60], image processing [69], text clustering [6] and ”