D
D. Rama Prabha
Researcher at VIT University
Publications - 16
Citations - 283
D. Rama Prabha is an academic researcher from VIT University. The author has contributed to research in topics: Fault (power engineering) & Glass fiber. The author has an hindex of 5, co-authored 16 publications receiving 218 citations.
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Journal ArticleDOI
Optimal placement and sizing of multiple distributed generating units in distribution networks by invasive weed optimization algorithm
D. Rama Prabha,T. Jayabarathi +1 more
TL;DR: A multi-objective technique for optimally determining the location and sizing of multiple distributed generation units in the distribution network with different load models and the loss sensitivity factor (LSF) determines the optimal placement of DGs.
Journal ArticleDOI
Optimal location and sizing of distributed generation unit using intelligent water drop algorithm
TL;DR: In this paper, the authors presented a novel method for optimal location and sizing of distributed generation (DG) unit, which is a nature inspired optimization approach in which a natural river always finds an easier path to flow from source to destination when the entire possible paths are available.
Proceedings ArticleDOI
Human detector and counter using raspberry Pi microcontroller
TL;DR: A novel initiative towards the digital image processing technique by the application of histogram of oriented gradients (HOG) feature descriptor using the OpenCV library coded with the High-level programming language Python, booted with the help of Raspberry Pi microcontroller fitted with a RaspiCam to capture moving images of objects passing under it.
Proceedings ArticleDOI
Determining the optimal location and sizing of distributed generation Unit using Particle Swarm Optimization algorithm
TL;DR: A robust stochastic optimization technique based on the movement and intelligence of swarms called Particle Swarm Optimization has been implemented to obtain optimal solution of the DG placement problem.
Journal ArticleDOI
Application of EMD, ANN and DNN for Self-Aligning Bearing Fault Diagnosis
TL;DR: This study aims at developing a novel method for the analysis of the various faults in self-aligning roller bearings as well as the automatic classification of faults using artificial neural network (ANN) and deep neuralnetwork (DNN).