Vidya Academy of Science and Technology

About: Vidya Academy of Science and Technology is a based out in . It is known for research contribution in the topics: Deep learning & Integer. The organization has 169 authors who have published 185 publications receiving 969 citations.

Microgrid Energy Management Combining Sensitivities, Interval and Probabilistic Uncertainties of Renewable Generation and Loads

Abstract: This paper proposes an optimal energy management approach combining sensitivities, interval, and probabilistic uncertainties of wind and solar power sources and loads in microgrid. Affine arithmetic (AA) is used to model the interval uncertainties and sensitivities in nodal power injections. However, all the elements in the interval solutions of AA-optimal power flow may not be significant in view of the probabilistic nature of statistical data. So, those elements which are significant with a desired confidence level are boxed using probability boxes obtained by deriving best fitting discrete state probability distribution functions (PDFs) for load and renewable power injections. Thus, the original hard bounded affine intervals are made soft bounded using the derived joint PDFs, forming new less conservative and more feasible intervals of cost and power flow variables. The minimization of the operational cost is taken care of by stochastic weight tradeoff particle swarm optimization. The method is tested in CIGRE LV benchmark microgrid with fuel cell, microturbine, diesel generator, wind, and solar power sources.

Improved strain sensing property of functionalised multiwalled carbon nanotube/polyaniline composites in TPU matrix

Abstract: Polyaniline (PANI)/functionalised multiwalled carbon nanotube (FMWCNT) based conductive thermoplastic polyurethane (TPU) films were prepared to study their strain sensing property. The composites were prepared by solution casting method using FMWCNT coated with polyaniline. The coating was done by in-situ and ex-situ polymerization of aniline. The composites thus prepared were designated as FMWCNT-PANI/TPU (I) and FMWCNT-PANI/TPU (E), respectively. The electrical resistivity and resistivity – strain behaviour of these composites were measured. The percolation threshold and the strain sensitivity of these films depended on the dispersion of conductive fillers in the polymer matrix. The well-dispersed filler in FMWCNT-PANI/TPU (I) composites resulted in low percolation threshold and improved strain sensitivity. These composites with 2 weight% filler content, showed a gauge factor of 1075 at 100% strain and exhibited high reversibility in resistivity upon elongating to 20%. A coating of PANI on FMWCNT reduced its entanglement and enhanced the interfacial interaction between the nano fillers and TPU, leading to improved strain sensitivity. The experimental data for strain sensing was in good agreement with the theoretical equations derived from a model based on the tunneling theory by Simmons.

Detection of unhealthy plant leaves using image processing and genetic algorithm with Arduino

Abstract: India is an agricultural country and about seventy percent of our population depends on agriculture. One-third of our national income comes from agriculture. So the disease detection of plants plays an important role in the agricultural field. Majority of the plant diseases are caused by the attack of bacteria, fungi, virus etc. If proper care is not taken in this area, it may lead to serious effects on plants and adversely affects the productivity and quality. To detect, the plant diseases we need a fast automatic way. The main approach adopted in practice for detection and identification of plant diseases is naked eye observation through experts. The decision making capability of an expert also depends on his/her physical condition, such as fatigue and eye sight, work pressure, climate etc. So this method is time consuming and less efficient. Here, a project is proposed with an idea of detecting plant diseases using image processing. Image processing toolbox of Matlab is used for measuring affected area of disease and to determine the difference in the color of the disease affected area. This concept can be extended to detect the symptoms of any type of plant diseases that is affected on different horticulture crops. The algorithm can be used to classify the leaves and the classified outcomes are separated using Arduino based conveyor belt system. This reduces an important task of monitoring of farms crops at very early stage itself to detect the symptom of diseases appear on plant leaves.