Artificial bee colony based algorithm for maximum power point tracking (MPPT) for PV systems operating under partial shaded conditions

Abstract

Abstract:Optimal energy harvesting is a key point in any photovoltaic system where economic and efficiency aspects are strongly interrelated. In this paper a novel artificial bee colony optimization-based MPPT is proposed. The proposed Bee's algorithm allows the tracking of the maximal available power from a PV array under uniform and nonuniform illuminating conditions. A co-simulation methodology, combining Matlab/Simulink TM and Cadence/Pspice TM , has been used to verify the effectiveness of Bee's algorithm to track the MPP of serially connected PV modules subject to various shading patterns. In addition, a performance comparison with Particle Swarm Optimization (PSO) based MPPT algorithm is also presented. The experimental resuls have shown the validity of the developed heuristic algorithm and its good tracking capabilities under shading conditions.

Analysis of MPPT Failure and Development of an Augmented Nonlinear Controller for MPPT of Photovoltaic Systems under Partial Shading Conditions

“…voltage) [36]. This voltage-source model is widely used to represent the closed-loop grid-connected inverters owing to its satisfactory balance between accuracy and simplicity, which is confirmed in [30][31][32][33][34][35][36][37][38][39][40]. In the topological structure, Pm is the ANN output, namely, the reference power.…”

“…In the last decade, several researchers have compared various MPPT techniques [9,10], focusing on the P-V characteristics [11][12][13][14][15], models [16][17][18][19], and methods [20][21][22][23][24][25][26][27][28][29][30][31][32][33] to track the maximum power of PV modules/arrays under partial shading conditions (PSC). The research on the PV output characteristics is mainly focused on the analysis of failure, power loss, and voltage variations in the MPPT method under the PSC.…”

“…In the third category, using a heuristic and intelligent optimization algorithm, the global maximum power of the PV system is directly calculated using an iterative method [27][28][29][30][31][32][33], to achieve MPPT. The advantages of these methods are that they do not depend on the model or PV characteristics, and can even update the algorithm through online follow-ups and self-learning.…”

Fuzzy Logic Based MPPT Controller for a PV System

“…Also, algorithms based on artificial intelligence techniques such as fuzzy logic [13][14][15][16][17][18][19] and neural networks [20][21][22] have been used, as well as the implementation of optimization algorithms such as glowworm swarm [23], ant colony [24,25] and bee colony [26][27][28]. These algorithms are part of soft computing techniques and have the advantage of being easily implemented using embedded systems.…”

Performance Analysis of Maximum Power Point Tracking Algorithms Under Varying Irradiation

“…Artificial neural network (ANN) and fuzzy logic controller (FLC) based MPPT algorithms are considered to be part of artificial intelligent (AI) techniques (Lin et al 2011, Khateb et al 2014. The MPPT algorithms based on nature inspired optimization techniques are genetic algorithm (Larbes et al 2009), particle swarm optimization technique (Liu et al 2012), ant colony optimization (Jianga et al 2013), artificial bee colony (Benyoucef et al 2015), and grey wolf optimization technique (Mohanty et al 2016). The P&O method is easier to implement, but this algorithm fails to track MPP and will result in oscillation at steady state point.…”