Hybrid Firefly-PSO MPPT Based Single Stage Induction Motor for PV Water Pumping With Deep Fuzzy-Neural Network Learning

Neeraj Priyadarshi, M. Bhaskar, Prabhakar Modak, Niraj Kumar
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引用次数: 2

Abstract

The presented research explains a hybrid firefly algorithm (FA)-particle swarm optimization (PSO) based maximum power point tracking (MPPT) for single stage induction motor run photovoltaic (PV) water pump architecture. The exploration and exploitation equivalence is achieved using the hybrid FA-PSO technique, which provides high convergence speed, accurate PV power tracking, fewer oscillations closer to the global maximum power point (GMPP), and improved global and local search performance under varying operating conditions. The proposed induction motor driven PV system has been made without mechanical sensors and has a low cost which is regulated using a field oriented controller (FOC) with a deep fuzzy neural network algorithm. The consummation of the introduced PV based water pumping is justified over steady and transient variations of sun irradiation in which MPPT and DC-link voltage utilization are regulated through VSI (voltage source inverter).
基于深度模糊神经网络学习的混合萤火虫- pso - MPPT单级感应电机用于光伏水泵
提出了一种基于混合萤火虫算法(FA)和粒子群优化(PSO)的单级感应电机运行的光伏(PV)水泵结构最大功率点跟踪(MPPT)。采用混合FA-PSO技术实现等效的探测和开发,具有收敛速度快、光伏功率跟踪准确、振荡更少、更接近全局最大功率点(GMPP)的特点,提高了不同运行条件下的全局和局部搜索性能。本文提出的感应电机驱动光伏系统不需要机械传感器,且成本低,采用基于深度模糊神经网络算法的场定向控制器(FOC)进行调节。通过太阳辐照的稳态和瞬态变化,通过VSI(电压源逆变器)调节MPPT和直流链路电压利用率,证明了所介绍的基于PV的抽水系统的完善。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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