An Improved Gray Wolf Optimizer MPPT Algorithm for PV System With BFBIC Converter Under Partial Shading

Abstract

Based on the boost full bridge isolated converter (BFBIC) topology and considering the sudden changes in the external environment, a global maximum power point tracking (GMPPT) control strategy based on an improved gray wolf optimizer (IGWO) algorithm is proposed in this paper. In the strategy, a nonlinear tangent trigonometric function as a convergence factor is integrated into the gray wolf optimizer (GWO) algorithm. In addition, the active-clamp circuit and phase-shift are used to implement the soft switch technology for BFBIC converter in photovoltage (PV) system. Finally, the maximum power point tracking (MPPT) performance on PV system with the proposed IGWO algorithm under static and dynamic partial shading conditions (PSCs) was investigated and compared with other common perturb and observe(P&O), particle swarm optimization (PSO), artificial bee colony (ABC), adapt inertia weight salp swarm algorithm (WSSA), salp swarm algorithm with grey wolf optimizer (SSA-GWO), SSA with PSO (SSA-PSO), enhanced GWO (EGWO) MPPT algorithms. The effectiveness and stability of the proposed control strategy are validated, especially tracking speed under PSCs. Simulation results show that the BFBIC topology with the proposed IGWO algorithm outperforms other algorithms on most cases, especially only takes the tracking time of 0.24s and reaches the efficiency of 98.54% under the most severe PSCs. INDEX TERMS Boost full bridge isolated converter (BFBIC), improved grey wolf optimizer (IGWO), global maximum power point tracking (GMPPT), partial shading conditions (PSCs)

A Multi-Producer Group-Search-Optimization Method-Based Maximum-Power-Point-Tracking for Uniform and Partial Shading Condition

“…2) Conduct the producer cycle: conduct the search cycle of the ith producer Pri, and set the ith producer Pri searches at the range of (i-1)×UOC_module to i×UOC_module, at this time the position of the ith producer is X z i . The producer search strategy is conducted by (2). The scroungers search toward the producer by (5).…”

“…Photovoltaic (PV) power, as one of the key renewable energies, has attracted universal attention all over the world [1]. One of the key challenges is to improve the efficiency of PV power by tracking the maximum power point, which is the basis for improving PV utilization and prosumer energy management [2]. A series of maximum power point tracking control algorithms (such as hill-climbing [3], perturbation method [4], and observation method [5]) are proposed to improve the efficiency, which demonstrate good performance mainly under single peak condition.…”

Optimized Global Maximum Power Point Tracking of Photovoltaic Systems Based on Rectangular Power Comparison

“…The next class of GMPPT methods involve artificial neural networks (ANN) [19]- [22]. Many of the bioinspired methods such as artificial bee colony [23], particle swarm optimization (PSO) [24], [25], gray wolfe optimization [26], [27], etc. suffer from slow tracking speeds.…”

Modified deterministic Jaya (DM‐Jaya)‐based MPPT algorithm under partially shaded conditions for PV system

“…Besides, a high data set is required for training the ANN, which needs a large memory for implementation as well as fuzzification and defuzzification are very complicated tasks. Therefore, the MPPT methods based on swarm intelligence and bio‐inspired have been proposed in the literature as alternative methods to optimise the drawbacks of the aforementioned methods such as deterministic particle swarm optimisation (PSO) [15], Leader particle swarm optimisation (LPSO) [16], distributed PSO [17], Lipschitz optimisation [18], fusion firefly [19], Harris hawk optimisation [20], hybrid evolutionary [21], firefly algorithm [22], simulated annealing [23], flower pollination algorithm [24], improved grey wolf optimiser (GWO) [25], whale optimisation algorithm [26], moth‐flame optimisation [27], human psychology optimisation [28], a novel bat [29], new Cuck–Sepic converter with a hybrid GSA–PSO [30] etc. These algorithms mimic animals’ natural behaviour, such as birds, fish, and other animals.…”