Yeshwantrao Chavan College of Engineering

About: Yeshwantrao Chavan College of Engineering is a based out in . It is known for research contribution in the topics: Inverter & Microstrip antenna. The organization has 632 authors who have published 586 publications receiving 4037 citations.

A Simplified Nearest Level Control (NLC) Voltage Balancing Method for Modular Multilevel Converter (MMC)

Abstract: In this paper, a simplified nearest level control balancing method for modular multilevel converter is presented. The proposed method neither requires individual sorting of the submodule voltages nor the redundancy of the switching states. Once the sorting of the submodules is done on the basis of the number of the submodules to be switched on, the identifications of the submodules can be carried out throughout the stages of the implementation of the this method. The proposed method also does not require the individual submodule status in the gate pulse generation stage. The gate logic in the presented method can be implemented with the help of the switching states of the voltage levels. Those simplifications and removing of the some of the stages by the proposed balancing method may ease and lead to the less processor time at the implementation level. The pictorial presentation further helps in consolidating the understanding of the different stages of the method. Rigorous simulations are carried out for open and one of the prominent closed-loop applications, i.e., modular multilevel converter-based high-voltage direct current to demonstrate the validity and effectiveness of the proposed simplified balancing method under normal and emergency conditions.

A Three-Phase Hybrid Cascaded Modular Multilevel Inverter for Renewable Energy Environment

Abstract: This paper presents a three-phase hybrid cascaded modular multilevel inverter topology which is derived from the proposed modified H-bridge module. This topology results in the reduction of number of power switches, losses, installation area, voltage stress and converter cost. For renewable energy environment such as photovoltaic (PV) connected to the microgrid system, it enables the tranformerless operation and enhances the power quality. This multilevel inverter is an effective and efficient power electronic interface strategy for renewable energy systems. The basic operation of single module and the proposed cascaded hybrid topology is explained. The ability to operate in both symmetrical and asymmetrical modes is analyzed. The comparative analysis is done with classical cascaded H-bridge and flying capacitor multilevel inverters. The nearest level control method is employed to generate the gating signals for the power semiconductor switches. To verify the applicability and performance of the proposed structure in PV renewable energy environment, simulation results are carried out by MATLAB/Simulink under both steady-state and dynamic conditions. Experimental results are presented to validate the simulation results.

Critical success factors for Lean Six Sigma in SMEs (small and medium enterprises)

Abstract: Purpose – The purpose of this paper is to identify and list critical success factors (CSFs) of Lean Six Sigma (LSS) framework affecting and influencing quality, operational and financial performance of small and medium enterprises (SMEs). It also intends to guide researchers and practitioners in selecting appropriate set of CSFs for empirical studies, developing frameworks and to ensure effective implementation experience of LSS. Design/methodology/approach – It systematically reviews literature on CSFs and Indian experience regarding LSS. It uses exploratory approach for data collection and documents various studies depicting both manufacturing and service experiences by using time tested statistical tools to prioritize CSFs, which critically influence LSS implementation. Findings – The study guides and facilitates researchers and practitioners in using the most appropriate set of CSFs for empirical studies and in developing/modifying/reviewing application frameworks. It also guides implementation experi...

Fresnel lens: A promising alternative of reflectors in concentrated solar power

Abstract: Modern solar energy harnessing technology demands high grade energy to achieve efficient power generation with compact plant size and least payback period. But readily available solar energy is low grade energy. Concentrated Solar Power (CSP) technology is capable to cater the demand. Reflector or mirror in CSP contributes 50% of total cost of installation, hence huge initial investment and high life cycle costs are the major challenges associated with such plants. Moreover due to adverse service conditions and environmental impacts, it gets degraded early, causing substantial drop in efficiency and consequently reduction in life cycle. Fresnel lens as solar concentrator in Photovoltaic/Thermal (PV/T) applications may prove to be a promising alternative due to its potential to overcome techno-commercial constraints associated with conventional reflector based CSP. A critical review covering global CSP deployment, operational requirement and failure mechanism in mirrors/reflectors is being presented. As an alternative, design considerations of Fresnel lens and its effect on various efficiencies, reflectance, transmittance and associated losses are discussed. Learning from early research work, innovative and emerging trends, economics, challenges and advantages are also presented.

Unhealthy region of citrus leaf detection using image processing techniques

Abstract: Producing agricultural products are difficult task as the plant comes to an attack from various micro-organisms, pests and bacterial diseases. The symptoms of the attacks are generally distinguished through the leaves, steams or fruit inspection. The present paper discusses the image processing techniques used in performing early detection of plant diseases through leaf features inspection. The objective of this work is to implement image analysis and classification techniques for extraction and classification of leaf diseases. Leaf image is captured and then processed to determine the status of each plant. Proposed framework is model into four parts image preprocessing including RGB to different color space conversion, image enhancement; segment the region of interest using K-mean clustering for statistical usage to determine the defect and severity areas of plant leaves, feature extraction and classification. texture feature extraction using statistical GLCM and color feature by means of mean values. Finally classification achieved using SVM. This technique will ensure that chemicals only applied when plant leaves are detected to be effected with the disease.