Ravindra, B

Crosswinds often play a substantial role in traffic accidents on highways, bridges, and slopes. Accidents occur when the vehicle yaws and the driver loses control. This paper analyzes the passive crosswind response of various automobiles in static situations by neglecting the steering and driver inputs. First, this paper will explore the yawing of vehicles on a straight road using wind data from the Hudhud cyclone (that occurred in 2014), including wind direction, speed, density, and pressure. Second, this study will determine how a vehicle will react to a crosswind when driving on a grade, such as a hill or a mountain, using wind data with varying altitude. The impact of aerodynamic loads on vehicle dynamics is studied in this context. In a crosswind, a car’s suitable rearward center of pressure (C.P.) placement is needed to reduce lane deviations. However, changes in the vehicle’s axle loads result in a shift in the C.P. location, which turns the automobile. Hence, the yawing of cars moving on a straight road with different axle loads needs to be analyzed. Wind density variation is vital when a car/truck is moving on a road. The change in wind density (caused by pressure changes as elevation increases) will impact the vehicle’s response to crosswinds. This paper also examines how wind density variation changes the vehicle dynamics while driving on a hill.

Exhaust emission analysis from diesel vehicles has received a lot of attention in recent times in the context of implementation of Bharat Stage-IV norms and thermal signature analysis for civil and military applications. The exhaust emission thermal IR signatures of military diesel vehicles such as truck and bus using a gas analyser and thermal imager under idling and accelerating conditions of these vehicles is investigated. Concentration and temperature of diesel exhaust emission CO, NOx, and HC remains almost constant during engine running in idle condition and varies with the engine acceleration. Exhaust gases maximum temperature reaches in the range of 240 °C - 270 °C during engine acceleration. A detailed investigation of thermal signature in mid wave infrared, 3 µm - 5 µm waveband and long wave infrared, 8 µm - 14 µm waveband is also presented under the same engine running conditions. Thermal image analysis exhibited that the area of thermal IR image of diesel vehicles truck and bus has been increased 0.077 per cent and 0.594 per cent, respectively with the engine acceleration. It has been observed that thermal signature of exhaust gases is a good tool for vehicle exhaust emission visualisation and analysis.

An electricity consumer paying residential tariff and is willing to become a producer of power through photovoltaic (PV) panels is termed as a solar prosumer. The demand for distributed deployment of solar energy through such panels in Europe, USA and Japan arose due to the residential rooftop sector. In contrast, large scale power plants led the solar power growth in India. This article presents some of the efforts made to generate awareness regarding roof top PV plants in India and addresses the reasons behind the Indian residential (and even commercial) electricity consumers' unwillingness to become solar prosumers. In the recent times, the concept of adding energy storage capacity to solar panels is referred to as 'prosumage', still uneconomical at large. Going off-grid is a possible option in the Europe and USA, if an inexpensive storage option is provided to take care of intermittency of solar energy. An unreliable grid is one of the reasons for seeking energy storage in the Indian context. In addition there exists a significant captive power generation capacity through diesel generator sets, wind, biomass and other energy sources. Hybridization of solar with such sources can be a way forward to meet the goals of the solar mission.

Solar power generation has accelerated worldwide during the last decade. Often the regions of high intensity of solar radiation are in the desert areas which are prone to Haboobs (Arabic word meaning "wind"). Haboobs are sandstorms caused by strong horizontal winds. They occur regularly in arid regions throughout the world. Impact of these events has been examined from various angles such as health, aviation, transportation, agriculture as they affect a billion people around the world. Here it is shown that these events can decrease the yield of a solar photovoltaic plant significantly. Analysis of measured solar radiation during a sand storm is carried out to assess its impact on solar power generation.

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.

The primary aim of this paper is to analyze solar power variability. Ground-based measurements of solar photovoltaic power are used for the forecasting of 43-kW A-Si SPV system. In this study, we describe the variability in the power production of solar photovoltaic plant at IIT, Jodhpur. Solar PV generation forecasting is playing a key role in accurate solar power dispatchability as well as scheduling of PV power for hybrid power generation systems. The actual power produced by a PV power system varies according to variation in meteorological parameters and efficiency of PV system components. For the purpose of forecasting as per the schedule in the Indian power sector, a time slot of 15 min is considered for each forecasting. The proposed generalized neural network technique will be appropriated for modeling of solar power variability forecasting. In this paper, we used generalized neural network for forecasting the PV power variability.

The percentage of renewable energy sources such as solar, wind power and biomass in the energy mix of India is increasing every year. Solar power variability is an important issue for grid integration of solar photovoltaic power plants. The main objective of this paper is to forecast the power generated in a 5 MW solar PV plant owned by Gujarat Power Corporation Limited (GPCL) at Charanka solar park, Gujarat. Charanka is a location with an average of 320 sunny days in a year. Average solar insolation available here is 5.7-6.0 kWh/m2 per day. Data obtained from 1st March 2014-31st August 2014 is used for analysis purposes. In this paper a two stage procedure is used referred to as GNN (Generalized Neural Network) model. In the primary stage pre-processing is done on the raw data followed by neural network model for forecasting.