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RISS 인기검색어
- 검색키워드
- 주제어 : Deep learning based MPPT (검색결과 2 건)
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Real-Time Simulation of a Wind-Solar-Battery Based Microgrid System
Roy Bappa,Adhikari Shuma,Datta Subir,Lalngaihawma Samuel,Samanta Soumya 대한전기학회 2023 Journal of Electrical Engineering & Technology Vol.18 No.3
The increasing demand for electrical energy with the knowledge of clean technologies has attracted researchers to generate electric power utilizing renewable sources of energy. Therefore, in this article, a wind-batter-solar based microgrid model is considered for studying its performances under various real-time scenarios such as (i) non-availability of wind power, (ii) non-availability of solar power, and (iii) availability of all sources. In this system, vector control schemes are applied to manage the power flow for tracking maximum power. The deep learning-based maximum power point tracking (MPPT) system is used for both solar and wind systems. For real-time validation, the studied model is designed for the RT_LAB environment. The system behavior is also investigated in real-time with OPAL-RT (OP 4510) simulator. Results are investigated thoroughly and it can be concluded that the proposed technique can be operated and implemented in real-time.
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Raj G. Chauhan,Saurabh K. Rajput,Himmat Singh Techno-Press 2022 Advances in energy research Vol.8 No.3
This research work focuses to design and simulate a 200W solar power system with electrical power conservation scheme as well as thermal power conservation modeling to improve power extraction from solar power plant. Many researchers have been already designed and developed different methods to extract maximum power while there were very researches are available on improving solar power thermally and mechanically. Thermal parameters are also important while discussing about maximizing power extraction of any power plant. A specific type of coolant which have very high boiling point is proposed to be use at the bottom surface of solar panel to reduce the temperature of panel in summer. A comparison between different maximum power point tracking (MPPT) technique and proposed MPPT technique is performed. Using this proposed Thermo-electrical MPPT (TE-MPPT) with Deep Learning Algorithm model 40% power is conserved as compared to traditional solar power system models.
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