Design and validation of a single-phase buck–boost inverter with Grey Wolf optimization algorithm under partial shaded conditions

Sreedhar, R.; Chandrasekar, P.; Karunanithi, K.; Vijayakumar, S. C.; Raja, S. P.

doi:10.1007/s41870-022-00948-3

Cited by 10 publications

(1 citation statement)

References 21 publications

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“…Additionally, metaheuristic algorithms are less affected by the parameters and aging of solar system [13]. Some of the recently used metaheuristic algorithms for solving MPPT problems are Particle Swarm Optimization (PSO) [14], Salp-Swarm Algorithm (SSA) [15], Grey Wolf Optimizer (GWO) [16], Improved Grey Wolf Optimizer (IGWO) [17] and Improved Team Game Optimization Algorithm (ITGA) [18]. Another potential MPPT strategy is by combining two different MPPT approaches [19][20][21][22][23][24], where both algorithms complement each other to optimize the exploration and exploitation processes.…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Deterministic Peak Hopping Maximum Power Point Tracking Algorithm for Complex Partial Shading Condition

Koh,

Tan,

Lim

et al. 2024

IEEE Access

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Conventional perturb and observe (P&O) algorithm fails to track global maximum power point (GMPP) under complex partial shading conditions (PSC). While many of the latest maximum power point tracking (MPPT) algorithms are designed to handle simpler PSCs with fewer peaks, their capability to handle highly complex PSCs remains uncertain. This study presented more practical, challenging, and complex PSCs that have over five peaks and extremely close peak values. A new deterministic peak hopping (PH)-based MPPT algorithm with simple mechanisms is proposed to address these complex PSCs. An agent is utilized to scan and hop between the lower and higher duty cycle regions of P-V curve with optimum step size, thereby effectively narrowing down the tracking region, moving towards the GMPP. Additionally, the proposed algorithm utilizes an adjustable sampling time during scanning and hopping process to accelerate the convergence. Extensive simulation studies have revealed the effectiveness of the proposed algorithm in tracking GMPP. Moreover, the proposed algorithm outperforms five of the latest MPPT algorithms. In experimental setup, the proposed algorithm is successfully implemented into real-time TI C2000 microcontroller and performed robustly using ITECH IT6012C-800-40 PV simulator, achieving tracking time shorter than 0.83s and tracking accuracy over 98.70%.INDEX TERMS complex partial shading, maximum power point tracking, peak hopping algorithm, photovoltaic

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