A simulation study for dust accumulation on the surface of the photovoltaic panels based on discrete element method

Cao, Shengxian; He, Mingyue; Fan, Siyuan; Zhao, Bo

doi:10.1109/eiecs53707.2021.9588023

Cited by 1 publication

(1 citation statement)

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“…In this paper, a dual-axis solar tracking system is designed and developed based on light intensity comparison between four LDR sensors. Fuzzy logic algorithm is designed to give the best accuracy in positioning the solar PV panels [7,8]. In addition, the power efficiency of solar irradiance on the PV panels between solar-tracking [12] and non-tracking mode are compared under the same location and environment.…”

Section: Introductionmentioning

confidence: 99%

Automated Dual-axis Solar Tracking System Using Fuzzy Logic Control

Yang,

Yunus,

Sahlan

et al. 2023

2023 IEEE 9th International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA)

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Utilization of solar powered system as renewable energy alternatives plays a dominant role in generating electricity. Throughout the years, solar tracking system has been continuously improved by researchers globally to maximize the power efficiency of a system. In this paper, a Fuzzy Logic Controller (FLC) is integrated into a large scale solar tracking system with photovoltaic (PV) panels to optimize the system's performance. In this project, a dual-axis solar tracking based on light intensity comparison using Fuzzy Logic Control method is utilized. Arduino Uno microcontroller is used as the main controller of this system and Light Dependent Resistors (LDR) are used to sense the light intensity at different altitude angles and sun azimuth. Two NEMA 34 stepper motors are used to actuate the Photovoltaic (PV) system. FLC is utilized to help the microcontroller to decide which direction and position of the PV panel should rotate to or stay. From the experiment conducted, it was observed that the developed system has an increase of output power efficiency of 96% when compared to the conventional solar tracking system.

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Section: Introductionmentioning

confidence: 99%