Optimizing Photovoltaic Power Production in Partial Shading Conditions Using Dandelion Optimizer (DO)-Based MPPT Method

Abstract

This research proposes the dandelion optimizer (DO), a bioinspired stochastic optimization technique, as a solution for achieving maximum power point tracking (MPPT) in photovoltaic (PV) arrays under partial shading (PS) conditions. In such scenarios, the overall power output of the PV array is adversely affected, with shaded cells generating less power and consuming power themselves, resulting in reduced efficiency and local hotspots. While bypass diodes can be employed to mitigate these effects by redirecting current around shaded cells, they may cause multiple peaks, making MPPT challenging. Therefore, metaheuristic algorithms are suggested to effectively optimize power output and handle multiple peaks. The DO algorithm draws inspiration from the long-distance movement of a dandelion seed, which relies on the force of the wind. By utilizing this bioinspired approach, the DO algorithm can successfully capture the maximum power point (MPP) under different partial shading scenarios, where traditional MPPT algorithms often struggle. An essential contribution of this research lies in the examination of the performance of the proposed algorithm through simulation and real-time hardware-in-the-loop (HIL) results. Comparing the DO algorithm with the state-of-the-art algorithms, including particle swarm optimization (PSO) and cuckoo search (CS), the DO algorithm outperforms them in terms of power tracking efficiency, tracking duration, and the maximum power tracked. Based on the real-time HIL results, the DO algorithm achieves the highest average efficiency at 99.60%, surpassing CS at 96.46% and PSO at 94.74%. These findings demonstrate the effectiveness of the DO algorithm in enhancing the performance of MPPT in PV arrays, particularly in challenging partial shading conditions.

A Harmony Search Switching Matrix Algorithm for Enhanced Performance of Solar PV Arrays During Non-Uniform Irradiance Scenarios

“…where, IM, i, j, Sij and So denote the maximum current of the module at STC, row index, column index, irradiance received by i th row and j th column, and standard irradiance level (1000W/m 2 ) respectively. (10) where Va and Vmk represent the array voltage and voltage of the k th row respectively.…”

“…Similarly, a salp-swarm MPPT algorithm has been proposed in [9] resulting in unparalleled global exploration and local search capabilities, yielding heightened energy of 43.75%. Another Dandelion Optimizer MPPT technique has been proposed that achieved the highest average efficiency at 99.60% surpassing other techniques [10]. Some other techniques include SA-P&O [11], voltage scanning method [12], marine predator [13], modified honey badger [14] algorithms, etc.…”

A Particle Swarm Optimization–Adaptive Weighted Delay Velocity-Based Fast-Converging Maximum Power Point Tracking Algorithm for Solar PV Generation System

“…In Ref. [22], a dandelion optimizer (DO) algorithm was presented for maximum power point tracking (MPPT) in photovoltaic (PV) arrays under partial shading conditions. The DO algorithm outperforms other state-ofthe-art algorithms in terms of power tracking efficiency, tracking duration, and maximum power tracking.…”

A smart and intelligence based controlling algorithms for maximum power extraction and effective battery management in EV systems

“…In the present time, the solar‐powered operated EVs (SPEVs) gain popularity and significantly grow in the automotive sector. To reduce pollution and save money on the importation of fossil fuels, the government accords SPEVs a significant amount of attention 8‐10 . In EVs, the power electronic converters are crucial for delivering the necessary amount of power during acceleration as well as for storing, using and gaining energy during the process of regenerative braking.…”