A Novel Maximum Power Point Tracking Strategy Based on Enhanced Real-Time Adaptive Step-Size Modified Control for Photovoltaic Systems

Author:

Zhou Junfeng,Zhang Yubo,Zhang Shuxiao,Guo Yuanjun,Yang Zhile,Feng Wei,Zhang Yanhui

Abstract

With the development of society, the demand for energy keeps increasing. Solar energy has received widespread concern for its renewable and environmentally friendly advantages. As one of the most efficient solar energy devices, the output power of photovoltaic (PV) cells is easily affected by the external environment. In order to solve the problem of the maximum power output of PV cells, this paper proposed a maximum power point tracking (MPPT) method. Based on the online particle swarm optimization (PSO) variable step length algorithm, the pulse width modulation (PWM) control module parameters are set according to the parameters of the PV cells’ output voltage. By dynamically adjusting the output voltage step of the PV cells online, the output of the PV cells is stabilized near the maximum power point (MPP). The simulation results concluded that the method and model could accurately adjust the output voltage according to the external environment changes in real time and reduce the voltage fluctuation at the MPP, providing a new idea to solve the problem of MPPT of PV cells.

Publisher

Frontiers Media SA

Subject

Economics and Econometrics,Energy Engineering and Power Technology,Fuel Technology,Renewable Energy, Sustainability and the Environment

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Harnessing Solar Energy: Enhancing Maximum Power Output of Parallel Solar Panels through External Voltage Analysis;2023 International Conference on Smart-Green Technology in Electrical and Information Systems (ICSGTEIS);2023-11-02

2. Power quality improvement by using photovoltaic based PSO as MPPT with Space vector modulation (SVM) control strategy of Quasi Z-Source inverter;2023 1st International Conference on Renewable Solutions for Ecosystems: Towards a Sustainable Energy Transition (ICRSEtoSET);2023-05-06

3. Parameters Identification of Battery Model Using a Novel Differential Evolution Algorithm Variant;Frontiers in Energy Research;2022-05-11

4. Parameters identification of photovoltaic models using a differential evolution algorithm based on elite and obsolete dynamic learning;Applied Energy;2022-05

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