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标题：Fuzzy entropy type II method for optimizing clean and renewable solar energy
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作者：M. Ramli ; M. Mardlijah ; M. Ikhwan 等
期刊名称：Global Journal of Environmental Science and Management
印刷版ISSN：2383-3572
电子版ISSN：2383-3866
出版年度：2022
卷号：8
期号：3
页码：389-402
DOI：10.22034/GJESM.2022.03.07
语种：English
出版社：Iran Solid Waste Association
摘要：BACKGROUND AND OBJECTIVES: A solar panel is a device that converts solar rays into electricity. It is a step to reduce emissions from fossil energy, which is to replace it with renewable energy. It requires a control system to ensure that the position of the solar panel is always perpendicular to the sun''s rays. This study aims to modify the fuzzy set based on fuzzy entropy in the control system that has been developed. The modifications made are expected to increase the efficiency of solar panels in harvesting energy.METHODS: Type II fuzzy sliding mode control is used, along with a modified fuzzy set based on the entropy value. Before modification, the system containing the fuzzy set generates a histogram of entropy and voltage performance, which is the initial value and the comparison value. The algorithm alters the footprint of the uncertainty limit. This change results in a new fuzzy set, which results in a new histogram and voltage. The final step is to compare the initial and final parameters based on the results of the modifications.FINDINGS: The solar panels require only 7.3x10-5 degrees of movement per second. This is a very slow movement for a dc motor with a maximum voltage of 12 volts. The simulation produced a stable speed of 7.297x10-5 on the unmodified system and 7.295x10-5 on the modified system. The modified system experiences a slight delay towards the stable point because the fuzzy entropy method reduces the dominance of set point positions in the system.CONCLUSION: The modified fuzzy set is good at controlling the solar panel driving motor based on the output voltage value. On both controllers under consideration, the voltages follow the same pattern. However, it experienced a control mismatch at the point towards the set point. Finally, by changing the foot of uncertainty and adjusting it proportionally according to control needs, the control system based on fuzzy sets with fuzzy entropy can be further developed.