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Singh V.P.,CoE System Science | Ravindra B.,CoE System Science | Vijay V.,CoE System Science | Bhatt M.S.,CPRI Bangalore
3rd International Conference on Renewable Energy Research and Applications, ICRERA 2014 | Year: 2014

The Indian Institute of Technology, Jodhpur (IITJ) has been monitoring and recording all the parameter of 101 kW (43.30 kW A-Si PV system located in Block 1 and 58.08 kW C-Si PV system in Block 2) grid tied solar photovoltaic system over the 4 year. The paper present the operational data 43.30 kW amorphous silicon (A-Si) based PV system located at Block 1 and 58.08 kW crystalline silicon based PV system at Block 2 of IIT Jodhpur. This paper helps in a study of the performance and consistency of this system. This paper will estimate the theoretical and actual Power output, Energy yield of the both PV systems. During the year, the PV systems in Jodhpur, India have generated a 74922 kWh by C-Si PV and 55910 kWh solar energy by A-Si PV system. As a whole, the location of Solar PV system is the primary reason of energy variability and system production. © 2014 IEEE. Source


Singh V.P.,CoE Energy | Vijay V.,CoE Energy | Siddhartha Bhatt M.,CPRI Bangalore | Chaturvedi D.K.,DEI Agra
2013 13th International Conference on Environment and Electrical Engineering, EEEIC 2013 - Conference Proceedings | Year: 2013

The main objective of this paper is to perform data analysis of ground based measurement and review the state of the art of IIT Jodhpur Rooftop solar photovoltaic installed 101 kW system. Solar power forecasting is playing a key role in solar PV park installation, operation and accurate solar power dispatchability as well as scheduling. Solar Power varies with time and geographical locations and meteorological conditions such as ambient temperature, wind velocity, solar radiation and module temperature. The location of Solar PV system is the main reason of solar power variability. Solar variability totally depends on system losses (deterministic losses) and weather parameter (stochastic losses). In the case of solar power, deterministic losses can be found out accurately but stochastic losses are very uncertain and unpredicted in nature. The proposed soft computing technique will be suitable for solar power forecasting modeling. In this paper Fuzzy theory, Adaptive Neuro-fuzzy interface system, artificial neural network and generalized neural network are used as powerful tool of solar power Forecasting. This soft computing cum nature inspired techniques are able to accurately and fast forecasting compared to conventional methods of forecasting. This is done analyzing the operational data of 101 kW PV systems (43.30 kW located in Block 1 and 58.08 kW in Block 2), during the year 2011. © 2013 IEEE. Source


Jayalekshmi B.R.,National Institute of Technology Karnataka | Shivashankar R.,National Institute of Technology Karnataka | Venkataramana K.,National Institute of Technology Karnataka | Ramesh Babu R.,CPRI Bangalore | And 4 more authors.
Computer Methods for Geomechanics: Frontiers and New Applications | Year: 2011

Generally a base isolator shifts the natural period of the building away from that of the predominant period of the most probable earthquakes and provides additional damping to absorb the energy. The present study focuses on the effi cacy of soil, geofi bre reinforced soil and a layer of smooth geosynthetic membrane placed in soil in reducing the seismic response of a structure. Shake table tests are carried out in a tri-axial shaker system on a 1/3rd scaled model of a single storey, single bay RC space frame. A steel tank fi xed to the shake table is used as a container for soil and reinforced soil. The structure with different base conditions is subjected to sine sweep tests and the motion corresponding to the response spectrum of Zone III as per IS 1893(Part1):2002. Analysis of results shows that smooth geomembrane in sand can be effectively used to reduce the seismic response of the structure. Source

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