New Hybrid Maximum Power Point Tracking Methods for Fuel Cell using Artificial Intelligent
원문 초록
In this paper, two maximum power point tracking (MPPT) methods for Fuel Cell (FC) systems based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Imperialist Competitive Algorithm trained Neural Network (ICANN) are presented. The first operation voltage of the fuel cell corresponding to maximum power point is determined based on the data, and then the duty cycle of a DC/DC converter is adjusted using fuzzy logic controller to force the system that operates in conditions which match up with its maximum power point, in order to minimize the fuel consumption. The proposed systems and conventional fuzzy controller system are simulated in the MATLAB environment and results show acceptable operation under fast variation of conditions as well as normal conditions in minimum time.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
SpatialTrackerV2: 3D Point Tracking Made Easy
CharaConsist: Fine-Grained Consistent Character Generation
DragLoRA: Online Optimization of LoRA Adapters for Drag-based Image Editing in Diffusion Model
Seurat: From Moving Points to Depth
TAPIP3D: Tracking Any Point in Persistent 3D Geometry
POMATO: Marrying Pointmap Matching with Temporal Motion for Dynamic 3D Reconstruction
토론
이 논문과 연결된 포럼 글이 아직 없습니다.