Self-Sustainable Model for Unorganized Sector in Indian Context

Robust economic development will be difficult without sustaining the unorganized sector. Sustainability is one of the important indicators of Indian economy and sustainability challenges are gaining lot of increasingly weightage & recognition as a powerful tool for integration of unorganized sector and public communication in providing information on countries in fields such as environment, economic, social or technological improvement. This work offers system and information content of the following economic categories: development, sustainable economic development. This paper discusses data analysis, the most technical part of performance measurements. The aim is to address issues that are problematic in undertaking research on indicators and are related to a fundamental yet often neglected area of unorganized sector. Keywords: self-sustainable model, unorganized sector, developing country, Indi

Performance Analysis Of Neuro Genetic Algorithm Applied On Detecting Proportion Of Components In Manhole Gas Mixture

The article presents performance analysis of a real valued neuro genetic algorithm applied for the detection of proportion of the gases found in manhole gas mixture. The neural network (NN) trained using genetic algorithm (GA) leads to concept of neuro genetic algorithm, which is used for implementing an intelligent sensory system for the detection of component gases present in manhole gas mixture Usually a manhole gas mixture contains several toxic gases like Hydrogen Sulfide, Ammonia, Methane, Carbon Dioxide, Nitrogen Oxide, and Carbon Monoxide. A semiconductor based gas sensor array used for sensing manhole gas components is an integral part of the proposed intelligent system. It consists of many sensor elements, where each sensor element is responsible for sensing particular gas component. Multiple sensors of different gases used for detecting gas mixture of multiple gases, results in cross-sensitivity. The cross-sensitivity is a major issue and the problem is viewed as pattern recognition problem. The objective of this article is to present performance analysis of the real valued neuro genetic algorithm which is applied for multiple gas detection.Comment: 16 pages,11 figure

Electrical Characterization of a Nanoporous Silicon Sensor for Low ppm Gas Moisture Sensing

A nanoporous silicon sensor prepared by electrochemical etching of p type single crystal silicon in HF electrolyte has been characterized for measuring gas moisture in the range of 6 to 100 ppmV. Impedance characteristics show that PS may also be useful for developing CMOS compatible trace moisture sensor. The behavior of the capacitive sensor has also been inverse modeled using multilayer perceptron neural network to determine the concentration of the soft sensor. The simulation results closely follow the actual sensor response

Modeling of an Aged Porous Silicon Humidity Sensor Using ANN Technique

Porous silicon (PS) sensor based on capacitive technique used for measuring relative humidity has the advantages of low cost, ease of fabrication with controlled structure and CMOS compatibility. But the response of the sensor is nonlinear function of humidity and suffers from errors due to aging and stability. One adaptive linear (ADALINE) ANN model has been developed to model the behavior of the sensor with a view to estimate these errors and compensate them. The response of the sensor is represented by third order polynomial basis function whose coefficients are determined by the ANN technique. The drift in sensor output due to aging of PS layer is also modeled by adapting the weights of the polynomial function. ANN based modeling is found to be more suitable than conventional physical modeling of PS humidity sensor in changing environment and drift due to aging. It helps online estimation of nonlinearity as well as monitoring of the fault of the PS humidity sensor using the coefficients of the model

Porous Silicon Hydrogen Sensor at Room Temperature: The Effect of Surface Modification and Noble Metal Contacts

Porous silicon (PS) was fabricated by anodization of p-type crystalline silicon of resistivity 2-5 Ω cm. After formation, the PS surface was modified by the solution containing noble metal like Pd. Pd-Ag catalytic contact electrodes were deposited on porous silicon and on p-Silicon to fabricate Pd-Ag/PS/p-Si/Pd-Ag sensor structure to carry out the hydrogen sensing experiments. The Sensor was exposed to 1% hydrogen in nitrogen as carrier gas at room temperature (270C). Pd modified sensor showed minimum fluctuations and consistent performance with 86% response, response time and recovery time of 24 sec and 264 sec respectively. The stability experiments were studied for both unmodified and Pd modified sensor structures for a period of about 24 hours and the modified sensors showed excellent durability with no drift in response behavior

Physics based modeling of dust accumulation on a bifacial solar PV module for generation loss estimation due to soiling

In this paper, a physics-based model for dust accumulation on the front and rear surfaces of a bifacial module is presented. The accumulation on both the surfaces is assessed considering deposition, rebound and resuspension phenomena. The lift-off phenomenon of dust particle from ground is included additionally for the rear surface. This composite model is utilized to estimate soiling on both sides, which is extended to analytically assess the energy loss of the bifacial module. Experimentation has been carried out in four phases on a 10 kWp rooftop solar PV power plant. In the first phase, result shows soiling is less on glass than on the transparent back sheet-based rear surface. In the second phase, it is observed that surface density of dust on back surface for 34 days is 0.08 g/m2, for 79 days 0.6 g/m2 and for 126 days 1.8 g/m2 which are deviated from model based calculated ones by 10%, 33.33% and 4.4% respectively. The surface density of dust accumulated on the glass-based rear surface is about (1/6)th of the front glass surface, which is validated by the model also. The measured transmittance reduction is 3.2% for the back glass substrate and 29.6% for the front glass without manual cleaning for the test period. The model leads to the interesting result that the average energy generation loss for the bifacial plant is 1.4%/day compared to 1.7%/day for the monofacial plant since the generation enhancement from the rear surface more than compensates for the soiling loss from the back surface

Field-Validated Communication Systems for Smart Microgrid Energy Management in a Rural Microgrid Cluster

This paper demonstrates a smart energy management scheme for solar photovoltaic-biomass integrated grid-interactive microgrid cluster system. Three interconnected microgrids were chosen as a cluster of microgrids for validation of the proposed community energy management scheme. In this work, a Global System for Mobile (GSM)-based bidirectional communication technique was adopted for real-time coordination among the renewable energy sources and loads. To realize the common phenomenon of local grid outage in rural distribution networks, a practical case study is designed in this work. The optimized scheduling of the energy sources and loadsof different microgrids and the distribution grid were implemented to ensure zero loss of power supply probability (LPSP) for dynamic load profiles. The laboratory-scale prototype of the proposed microgrid clustering was first developed in this work by establishing real-time communication among multiple energy sources and loads through different energymeters located at different places inside the academic campus. The field validation was performed with a microgrid cluster consisting of 45 kWP solar photovoltaic, 50 kVA biogas plant, community loads in a village. The developed smart energy management solution is a generalized one and applicable to satisfy scalable community energy demands as well