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Faiz Majeed,Dania Batool,Jonghoon Kim 전력전자학회 2021 전력전자학술대회 논문집 Vol.2021 No.11
The existing energy resources offer a huge amount of potential to threaten the survival of planet Earth. To overcome such disasters as climate change, solar energy offers a solution. In this regard, many types of research have been done to produce cost-effective and efficient solar energy. There are lot of factors on which efficiency of solar panel depends, finding the maximum power point angle is one of those factors and the presented work is also related to it. This paper proposes a system that is intelligent enough to find the angle of the solar panel where maximum energy is produced. The maximum energy producing angle is measured on only one solar panel, the information of the maximum energy producing angle is shared with the whole system and all the solar panels are moved accordingly. This paper proposes to use the proportional integral differential controller to move the solar panels to maximize energy producing angle. This design helps in saving computational load from all solar panels and in the production of maximum energy.
Dania Batool,Faiz Majeed,Jonghoon Kim 전력전자학회 2021 전력전자학술대회 논문집 Vol.2021 No.11
A battery-based inverter converts the DC source from the batteries to AC. These batteries are the basis for the entire system. This paper proposed a novel architecture for the inverter which requires fewer semiconductor switching devices. This will help to reduce the size, switching losses, and improve system efficiency. Herein, we suggest a new pulse width modulation technique to implement the concept of equal DC sources discharge. DC sources must be balanced in terms of their switching angles and voltage in order to reduce the total harmonic distortion by the maximum possible voltage sinusoidal. Additionally, a state-of-charge balancing technique is implemented by using a particle swarm optimization algorithm. A switch utilization comparison for traditional and proposed cascaded H-bridge is also presented for high number of output voltage levels.
클라우드 컴퓨팅 플랫폼을 활용한 머신러닝 기반 배터리 온도예측
최창기(Changki Choi),이평연(Pyeongyeon Lee),김재영(Jaeyeong Kim),조상우(Sangwoo Cho),Faiz Majeed,김종훈(Jonghoon Kim) 한국통신학회 2022 한국통신학회 학술대회논문집 Vol.2022 No.2
본 논문은 클라우드 컴퓨팅 플랫폼을 활용하여 배터리 전압데이터를 통한 온도예측을 수행하였다. 배터리에 대한 데이터는 IoT 환경을 통해 클라우드 서버에 송신되는 것을 가정하였고, 클라우드 컴퓨팅 플랫폼에서의 머신러닝 기법을 활용하였다. 머신러닝에서 필요한 특성(Feature)을 산출하기 위해 탐색적 데이터 분석을 수행하였고, 머신러닝 모델을 통해 예측한 온도 값을 실제 데이터와 비교함으로써 모델에 대한 신뢰성을 평가하였다.