Tunicate swarm algorithm based maximum power point tracking for photovoltaic system under non-uniform irradiation

Abstract

<span>A new maximum power point tracking (MPPT) technique based on the bio-inspired metaheuristic algorithm for photovoltaic system (PV system) is proposed, namely tunicate swarm algorithm-based MPPT (TSA-MPPT). The proposed algorithm is implemented on the PV system with five PV modules arranged in series and integrated with DC-DC buck converter. Then, the PV system is tested in a simulation using PowerSim (PSIM) software. TSA-MPPT is tested under varying irradiation conditions both uniform irradiation and non-uniform irradiation. Furthermore, to evaluate the performance, TSA-MPPT is compared with perturb & observe-based MPPT (P&O-MPPT) and particle swarm optimization-based MPPT (PSO-MPPT). The TSA-MPPT has an accuracy of 99% and has a reasonably practical capability compared to the MPPT technique, which already existed before.</span>

A missing data imputation method based on salp swarm algorithm for diabetes disease

“…The ability of nature-inspired metaheuristics to provide solutions to modern optimization problems has attracted much research interest, especially their performance on, nomadic people (NP-hard) optimization problems, such as the travelling salesman problem and feature selection [10]- [13]. One of the nature-inspired metaheuristics commonly used in solving difficult optimizations tasks is the particle swarm optimization (PSO) which was first developed in 1995 by Eberhard and Kennedy [14].…”

Modernisation of DC-DC converter topologies for solar energy harvesting applications: A review

“…Solar tracking is the traditional way that is utilized to get the most out of the energy that is collected [156] [157]. One other approach that may be taken to get maximum power is called MPPT [6]- [8], [11], [12], [15], [19], [27], [28], [35], [41], [42], [48], [51], [52], [57], [62], [64]- [66], [69]- [71], [74], [77], [80], [82], [83], [85], [89], [90], [93], [95], [96], [98], [106], [108], [112], [113], [158]- [245]. The MPP can be detected with the use of an MPPT algorithm that is placed on the microcontroller [246].…”

“…Numerous methods are utilized on a regular basis to accomplish the task of tracking maximum power point in PV systems [185]. These methods include hill-climbing, perturbation and observation (P&O), look-up table, incremental conductance (Inc-Cond), linearization-based, DC-link capacitor droop control, curve fitting, extremum seeking control, feedback voltage or current, feedback of power variation with voltage, feedback of power variation with current, firefly algorithm, fractional open-circuit voltage (FOCV), fractional short-circuit current (FSCI), artificial neural network (ANN), fuzzy logic, genetic algorithm, particle swarm optimization (PSO), ant colony based optimization, one cycle control (OCC), ripple correlation control (RCC), parasitic capacitance, and slidingmode [6]- [8], [11], [12], [15], [19], [27], [28], [35], [41], [42], [48], [51], [52], [57], [62], [64]- [66], [69]- [71], [74], [77], [80], [82], [83], [85], [89], [90], [93], [95], [96], [98], [106], [108], [112], [113], [158]- [245].…”