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内蒙古自治区呼和浩特市赛罕区大学西街235号 邮编: 010021
作者机构:Agni Coll Technol Dept Elect & Elect Engn Chennai 600130 Tamil Nadu India SSN Coll Engn Dept Elect & Commun Engn Chennai 603110 Tamil Nadu India
出 版 物:《JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING》 (情境智能与人性化计算杂志)
年 卷 期:2021年第12卷第9期
页 面:8637-8645页
核心收录:
学科分类:0810[工学-信息与通信工程] 0711[理学-系统科学] 07[理学] 08[工学] 070105[理学-运筹学与控制论] 081101[工学-控制理论与控制工程] 0701[理学-数学] 071101[理学-系统理论] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术(可授工学、理学学位)]
主 题:Photovoltaic systems Maximum power point tracking Perturb and observe Particle swarm optimization Grasshopper optimization algorithm
摘 要:Solar Photovoltaic (PV) system is an excellent renewable energy solution in today s scenario. Harvesting maximum power from the solar PV system under dynamic meteorological conditions is a challenging task. Numerous bio-inspired Maximum Power Point Tracking (MPPT) strategies have been proposed in the literature. The conventional methods of MPPT control are easy and simple to implement, but has drawbacks such as steady state oscillations and inability to track the maximum power under swiftly varying irradiances and partial shading conditions. This paper proposes a Grasshopper Optimization Algorithm (GOA) tuned MPPT technique with the objective of obtaining optimal duty cycle,D, to control a DC-DC boost converter. The efficacy of the proposed system under start up transients, line disturbances, load disturbances, servo conditions and partial shading conditions are evaluated and compared with the conventional Perturb and Observe (P&O) based MPPT and the familiar Particle Swarm Optimization (PSO) based MPPT algorithm using MATLAB Simulink platform. It is observed that the proposed GOA tuned MPPT technique gives good steady state and dynamic response compared to P&O and PSO based MPPT algorithms, verified in terms of rise time, settling time, percentage maximum overshoot, Integral Squared Error and Integral Absolute Error.