A Novel Bat Algorithm Strategy for Maximum Power Point Tracker of Photovoltaic Energy Systems Under Dynamic Partial Shading

Abstract

Power versus voltage curves of partial shading photovoltaic (PV) systems contain several local peaks (LPs) and one global peak (GP). Most conventional maximum power point tracker (MPPT) techniques may not follow the GP under partial shading conditions (PSC). The use of metaheuristic techniques such as the bat algorithm (BA) and particle swarm optimization (PSO) can overcome these obstacles. All problems inherent in the using of BA as MPPT of PV systems has been discussed and solved in this paper. The first problem is the random initial values of bats that may cause premature convergence. Therefore, the initial values of bats were modified to be close to the anticipated positions of peaks to reduce the convergence time and improve the chance of capturing the GP. The second problem occurs when shading pattern changes the value and position of the GP which is not configurable because all bats are concentrated at the previous GP; this can be resolved by BA re-initialization. The the third problem is the GP memorized in the execution of the BA code forces the PV system to work at the duty ratio of the highest GP ever seen, which may not be the real GP. This problem is solved by updating the memorized GP. This paper also proposes a new criterion for selecting the optimal swarm size against number of peaks to reduce the convergence time and improve the chance of capturing the GP. To the authors' knowledge, most of these problems inherent in the BA have hitherto not been addressed in the literature. The simulation and experimental results obtained from the proposed modified BA (MBA) with re-initialization have been compared to the PSO and grey wolf optimization (GWO) techniques which show the superiority of using MBA strategy in the MPPT of partial shading PV systems. INDEX TERMS Bat algorithm, boost converter, dynamic global peak, maximum power point tracker, partial shading conditions, PV system.

IBDA: Improved Binary Dragonfly Algorithm With Evolutionary Population Dynamics and Adaptive Crossover for Feature Selection

“…Heuristic algorithms can solve various optimization problems including the feature selection. Due to their effectiveness and simplicity, many heuristic algorithms have been proposed for solving the feature selection problems, e.g., GA [15], PSO [16], GWO [17], flower pollination algorithm (FPA) [18], artificial bee colony (ABC) [19], bacterial foraging optimization (BFO) [20], BA [21], cuckoo search (CS) [22], firefly algorithm (FA) [23], whale optimization algorithm (WOA) [24], grasshopper optimization algorithm (GOA) [25]. Recently, more and more heuristic algorithms are proposed to deal with many kinds of optimization problems.…”

Performance Improvement of PV Systems’ Maximum Power Point Tracker Based on a Scanning PSO Particle Strategy

“…Metaheuristic techniques have the ability to track the GP in uniformly distributed irradiance as well as in case of PSCs. Many metaheuristic techniques have been used as MPPT, including particle swarm optimization (PSO) [4], bat algorithm (BA) [5], cuckoo search (CS) [6], grey wolf optimization (GWO) [7], crow search algorithm (CSA) [8], ant colony optimization (ACO) [9], colony of flashing fireflies [10], artificial bee colony (ABC) [11], gravitational search algorithm (GSA) [12], Cauchy and Gaussian sine cosine optimization [13], moth-flame optimization (MFO) [14], etc. Some of these techniques have been integrated with one of the conventional techniques to gain the benefits of both of these techniques.…”

MPPT for a PV Grid-Connected System to Improve Efficiency under Partial Shading Conditions

“…Thus, to solve this problem, control strategies as well as converter topologies have been adopted in most applications of PV systems connected to the network. MPPT techniques can be classified into four categories [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] One of the model-based, or what is sometimes called fixed-step, methods is the open circuit voltage fraction method, which estimates the maximum power point value using the Voc value as a basis, considering it to be linearly proportional to Vmax, which is equal at a fraction of the Voc value. This method, which is very simple to implement, requires a voltage sensor to be installed.…”

“…These methods include particle swarm optimization (PSO) [19], which is attractive due to the compromise between performance and complexity, as well as maturity in relation to optimization methods that implement totally numerical or heuristic procedures [20,21]. Thus, unlike traditional MPPT techniques that, in most cases, achieve the LMPP, the BAT-based MPPT method is able to minimize the effects caused by partial shading, due to the fact that it always reaches the GMPP, hence improving the performance of the PV system [22,23].…”