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Zinc Metal Solubilization by Gluconacetobacter diazotrophicus and Induction of Pleomorphic Cells
( Saravanan ),( Venkatakrishnan Sivaraj ),( Jabez Osborne ),( Munusamy Madhaiyan ),( Lazar Mathew ),( Jong Bae Chung ),( Ki Sup Ahn ),( Tong Min Sa ) 한국미생물생명공학회 2007 Journal of microbiology and biotechnology Vol.17 No.9
Analysis of Neural Network Approaches for Nonlinear Modeling of Switched Reluctance Motor Drive
Saravanan P,Balaji M,Balaji Nagaraj K,Arumugam R 대한전기학회 2017 Journal of Electrical Engineering & Technology Vol.12 No.4
This paper attempts to employ and investigate neural based approaches as interpolation tools for modeling of Switched Reluctance Motor (SRM) drive. Precise modeling of SRM is essential to analyse the performance of control strategies for variable speed drive application. In this work the suitability of Generalized Regression Neural Network (GRNN) and Extreme Learning Machine (ELM) in addition to conventional neural network are explored for improving the modeling accuracy of SRM. The neural structures are trained with the data obtained by modeling of SRM using Finite Element Analysis (FEA) and the trained neural network is incorporated in the model of SRM drive. The results signify the modeling accuracy with GRNN model. The closed loop drive simulation is performed in MATLAB/Simulink environment and the closeness of the results in comparison with the experimental prototype validates the modeling approach.
Saravanan Subramanian,Thangavel Subbaiyan 대한전기학회 2014 Journal of Electrical Engineering & Technology Vol.9 No.5
This paper propose a new power conditioner topology with intelligent power management controller that integrates multiple renewable energy sources such as solar energy, wind energy and fuel cell energy with battery backup to make best use of their operating characteristics and obtain better reliability than that could be obtained by single renewable energy based power supply. The proposed embedded controller is programmed for maintaining a constant voltage at PCC, maximum power point tracking for solar PV panel and WTG and power flow control by regulating the reference currents of the controller on instantaneous basis based on the power delivered by the sources and load demand. Instantaneous variation in reference currents of the controller enhances the controller response as it accommodates the effect of continuously varying solar insolation and wind speed in the power management. The power conditioner uses a battery bank with embedded controller based online SOC estimation and battery charging system to suitably sink or source the input power based on the load demand. The simulation results of the proposed power management system for a standalone solar/WTG/ fuel cell fed hybrid power supply with real time solar radiation and wind velocity data collected from solar centre, KEC for a sporadically varying load demand is presented in this paper and the results are encouraging in reliability and stability perspective.
Optimization of Wear Behavior on Cenosphere -Aluminium Composite
Saravanan, V.,Thyla, P.R.,Balakrishnan, S.R. Materials Research Society of Korea 2015 한국재료학회지 Vol.25 No.7
The magnitude of wear should be at a minimum for numerous automobile and aeronautical components. In the current work, composites were prepared by varying the cenosphere content using the conventional stir casting method. A uniform distribution of particles was ensured with the help of scanning electron microscopy (SEM). Three major parameters were chosen from various factors that affect the wear. A wear test was conducted with a pin-on-disc apparatus; the controlling parameters were volume percentages of reinforcement of 5, 10, 15, and 20%, applied loads of 9.8, 29.42, and 49.03 N, and sliding speeds of 1.26, 2.51, and 3.77 m/s. The design of the experiments (DOE) was performed by varying the different influencing parameters using the full factorial method. An analysis of variance (ANOVA) was used to analyze the effects of the parameters on the wear rate. Using regression analysis, a response curve was obtained based on the experimental results. The parameters in the resulting curve were optimized using the Genetic Algorithm (GA). The GA results were compared with those of an alternate efficient algorithm called Neural Networks (NNs).