Global Maximum Power Point Tracking of Photovoltaic Module Arrays Based on Improved Artificial Bee Colony Algorithm

Abstract

In this paper, an improved artificial bee colony (I-ABC) algorithm for the maximum power point tracking (MPPT) of a photovoltaic module array (PVMA) is presented. Even though the P-V output characteristic curve with multi-peak was generated due to any damages or shading discovered on the PVMA, the I-ABC algorithm could get rid of stuck on tracking the local maximum power point (LMPP), but quickly and stably track the global maximum power point (GMPP), thereby improving the power generation efficiency. This proposed I-ABC algorithm could search for the higher power point of a PVMA by a small bee colony, determine the next tracking direction through the perturb and observe (P&O) method, and keep tracking until the GMPP is obtained. This method could prevent tracking the GMPP for too long due to applying a small bee colony. First, in this study, the photovoltaic modules produced by Sunworld Co., Ltd. were used and were configured as a PVMA with four series and three parallel connections under different numbers of shaded modules and different shading ratios, so that corresponding P-V output characteristic curves with multi-peak values were generated. Then, the GMPP was tracked by the proposed MPPT method. The simulation and experimental results showed that the proposed method performed better both in dynamic response and steady-state performance than the traditional artificial bee colony (ABC) algorithm. According to the experimental results, it showed that the tracking accuracy for the GMPP based on the proposed MPPT with 100 iterations under 5 different shading ratios was about 100%; on the other hand, that of the traditional ABC algorithm was 70%, and that of the P&O method was lower at about 30%.

Nature‐ınspired algorithms for optimizing fractional order PID controllers in time‐delayed systems

“…The initial attractiveness at $$$ r=0 $$$ is denoted by $$$ {\beta}_0 $$$. For any pair of fireflies $$$ i $$$ and $$$ j $$$, located at $$$ {x}_i $$$ and $$$ {x}_j $$$, respectively, the Euclidean distance is described by Equation () 59 $$$$ {r}_{ij}=\left\Vert {X}_i-{X}_j\right\Vert =\sqrt{\sum \limits_{k=1}^d{\left({x}_{i,k}-{x}_{j,k}\right)}^2}.$$…”

Global Maximum Power Point Tracking of Photovoltaic Module Arrays Based on an Improved Intelligent Bat Algorithm

“…Based on the reasons given above for enhancing the performance of a photovoltaic power generation system and realizing better efficiency in energy conversion, the IIBA was proposed in this paper, which was applied in MPPT when multiple peak values occurred in P-V curves with a PVMA under partial shading, so the GMPP could be tracked swiftly and, in turn, provide better performance in steady and dynamic responses compared to the conventional BA. In [24] the author first searched for the maximum power point (MPP) using the intelligent bee colony algorithm. Then, using traditional P&O algorithms, the next tracking direction was validated to track the global maximum point.…”

A Hybrid Artificial Ecosystem Optimizer and Incremental-Conductance Maximum-Power-Point-Tracking-Controlled Grid-Connected Photovoltaic System

“…Most of the maximum power point tracking technologies are unable to track the GP during PS conditions. Firefly [3,4], flower pollination [5], particle swarm optimization (PSO) [6,7], improved PSO [8,9], ant colony [10,11], Cuckoo search [12], the sensor-less artificial vision algorithm [13], the improved rat swarm optimizer algorithm [14], and whale optimization [15] are all bioinspired soft computing/optimization algorithms that are used to locate the GP. Although the GP moves in space and time as the shading pattern (SP) varies, research has confirmed that optimization approaches could generally follow it [16].These optimization algorithms have proven to be efficient in tracking the MPP during normal circumstances, but during PS, the process becomes complex, as it involves many iterations and is not suitable for dynamic environments individually.…”