Particle swarm optimization for enhanced maximum power point tracking: design and implementation in Proteus

Murugesan Vishnu Priya, Gopal Anandha Kumar
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Abstract

This study introduces a photovoltaic (PV) system model tailored for PV design, incorporating a particle swarm optimization (PSO) MPPT technique to achieve optimal efficiency, swift responsiveness, and cost-effectiveness. To initiate, a PV module model is formulated within Proteus using SPICE coding. Subsequently, an experimental test setup is deployed to authenticate and validate the model. Following this, a PSO-based MPPT algorithm is proposed, which overcomes the limitations of conventional perturb and observe (P&O) and incremental conductance MPPT methods, notably reducing the reliance on mathematical divisions. To substantiate the effectiveness of the proposed approach, both methodologies are implemented on an affordable Arduino Uno platform utilizing the simulated PV module model. The outcomes highlight that the PSO-based MPPT algorithm excels in terms of rapid response (0.09 s), minimal steady-state oscillation, and an impressive 99 percent efficiency.
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用于增强最大功率点跟踪的粒子群优化:Proteus 的设计与实现
本研究介绍了一种为光伏设计量身定制的光伏(PV)系统模型,该模型结合了粒子群优化(PSO)MPPT 技术,以实现最佳效率、快速响应和成本效益。首先,在 Proteus 中使用 SPICE 编码建立光伏模块模型。随后,利用实验测试装置对模型进行认证和验证。随后,提出了一种基于 PSO 的 MPPT 算法,该算法克服了传统的扰动和观测 (P&O) 以及增量电导 MPPT 方法的局限性,显著减少了对数学划分的依赖。为了证实所提方法的有效性,我们利用模拟的光伏组件模型,在经济实惠的 Arduino Uno 平台上实施了这两种方法。结果表明,基于 PSO 的 MPPT 算法在快速响应(0.09 秒)、最小稳态振荡和令人印象深刻的 99% 效率方面表现出色。
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