Fuzzy Reliability Assessment of Distribution System with Wind Farms and Plug-in Electric Vehicles

Abstract

The equipment failures are highly uncertain in nature and simple average failure rate will not reflect this uncertainty. The uncertainty level further increases in reliability evaluation due to the integration of wind farm (WF) because of the intermittent nature of wind speed and random charging patterns of plug-in electric vehicles (PEVs). In this work, the uncertain variables in the distribution system (failure rate, repair time, WF output, PEVs charging and system load factor) are represented as fuzzy numbers to handle the uncertainty. The available uncertain data are used to find the probability distribution function (PDF) of that parameter and is converted into fuzzy membership function using transformation techniques. Failure rate of equipment is converted into failure probability using Monte Carlo simulation (MCS) method. Sampling method is applied to create the PDF of a variable which has average value. Fuzzy severity index (FSI) is proposed to find the importance of an equipment on reliability and is evaluated by measuring the fuzzy distance between the fuzzy reliability indices. The proposed assessment method is validated on modified RBTS bus 2 by comparing with analytical and MCS methods. The proposed method has been tested with integration of WFs and PEVs.     

Impatient customers in an M/M/1 queue with single and multiple working vacations

We consider an M/M/1 queue with impatient customers and two different types of working vacations. The working vacation policy is the one in which the server serves at a lower rate during a vacation period rather than completely stop serving. The customer’s impatience is due to its arrival during a working vacation period, in which the customer service rate is lower than the normal busy period. We analyze the queue for two different working vacation termination policies, a multiple working vacation policy and a single working vacation policy. Closed-form solutions and various performance measures like, the mean queue lengths and the mean waiting times are derived. The stochastic decomposition properties are verified for both multiple and single working vacation cases. A comparison of both the models is carried out to capture their performances with the change in system parameters.     

Determination of land surface temperature and its lapse rate in the Satluj River basin using NOAA data

Temperature lapse rate (TLR), an essential parameter for snowmelt runoff analysis, was determined for the Satluj River basin in the Western Himalayas. National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) data sets were used to determine the land surface temperature (LST) of the region using the split‐window algorithm proposed by Coll and Caselles (Journal of Geophysical Research, 1997, 102, pp. 16697–16713). The LST was correlated with the elevation values obtained from a US Geological Survey digital elevation model (USGS‐DEM) of the same area and the trend showed an inverse relationship between LST and elevation. The TLRs for the study area on 2 February, 1 March, 26 March, 16 October, 1 November and 20 November 2004 were in the range 0.6–0.74°C/100 m. The results obtained were compared with lapse rates determined using Moderate Resolution Imaging Spectroradiometer (MODIS) LST maps. TLR determination in the past was based on air temperature data available from meteorological stations that are sparsely located in rugged terrain such as the Himalayas. As these measurements were point data and had been measured manually, they may have led to erroneous results. Satellite data, however, provide continuous and potentially unbiased recording provided an accurate radiometric calibration and atmospheric correction can be achieved. A previous TLR calculation using air temperature from meteorological stations for the western Himalayan region was found to be 0.65°C/100 m. Air temperature and LST from NOAA‐AVHRR and MODIS‐Terra data were found to be in good agreement. This type of study will be useful for snowmelt runoff modelling studies for the Himalayan region.     

Accuracy assessment of MODIS,NOAA and IRS data in snow cover mapping under Himalayan conditions

Snow cover information is an essential parameter for a wide variety of scientific studies and management applications, especially in snowmelt runoff modelling. Until now NOAA and IRS data were widely and effectively used for snow‐covered area (SCA) estimation in several Himalayan basins. The suit of snow cover products produced from MODIS data had not previously been used in SCA estimation and snowmelt runoff modelling in any Himalayan basin. The present study was conducted with the aim of assessing the accuracy of MODIS, NOAA and IRS data in snow cover mapping under Himalayan conditions. The total SCA was estimated using these three datasets for 15 dates spread over 4 years. The results were compared with ground‐based estimation of snow cover. A good agreement was observed between satellite‐based estimation and ground‐based estimation. The influence of aspect in SCA estimation was analysed for the three satellite datasets and it was observed that MODIS produced better results. Snow mapping accuracy with respect to elevation was tested and it was observed that at higher elevation MODIS sensed more snow and proved better at mapping snow under mountain shadow conditions. At lower elevation, IRS proved better in mapping patchy snow cover due to higher spatial resolution. The temporal resolution of MODIS and NOAA data is better than IRS data, which means that the chances of getting cloud‐free scenes is higher. In addition, MODIS has an automated snow‐mapping algorithm, which reduces the time and errors incorporated during processing satellite data manually. Considering all these factors, it was concluded that MODIS data could be effectively used for SCA estimation under Himalayan conditions, which is a vital parameter for snowmelt runoff estimation.     

Identification of drought‐vulnerable areas using NOAA AVHRR data

Drought is a recurring phenomenon in many parts of India, bringing significant water shortages, economic losses and adverse social consequences. The western regions of India (Rajasthan and Gujarat provinces) have suffered with severe droughts several times in the past. In this study meteorological and satellite data were used for monitoring drought in the southern part of Rajasthan. Monthly rainfall data from six stations were used to derive the Standardized Precipitation Index (SPI). The Advanced Very High Resolution Radiometer (AVHRR) onboard the National Oceanic and Atmospheric Administration (NOAA) series of satellite was used for calculating brightness temperature (BT), the Normalized Difference Vegetative Index (NDVI) and the Water Supplying Vegetation Index (WSVI). BT was converted to the Vegetation Condition Index (VCI) and the Temperature Condition Index (TCI), which are useful indices for the estimation of vegetation health and drought monitoring. The analysis was carried out for a period of four years (2002–2005) and from the SPI analysis it was observed that 2002 was a drought year. On the basis of the satellite‐based indices, the study area was divided into categories of extreme, severe, moderate and slight drought and normal condition. We found that in 2002 all of the area under study was affected by drought with greater intensity, mostly classed as extreme and severe drought conditions. An analysis was carried out of the study area divided into four zones on the basis of rainfall distribution, soil characteristics, cropping patterns and other physical characteristics. This analysis revealed that zone 1 was the most drought‐prone area in all four years; zone 4 was the next area most affected by severe drought, followed by zones 2 and 3, which were less affected by drought conditions.     

Transient Bimodality and Catastrophic Jumps in Innovation Diffusion

A new phenomenon in innovation diffusion that exhibits a transient bimodality familiar in physical sciences is predicted in this paper for the first time on the basis of an extended Bass model. This accounts for the population heterogeneity, with the parameters characterizing the ldquoword of mouthrdquo and ldquomass mediardquo processes as random. A theoretical framework based on a nonlinear random differential equation is thus introduced to develop a stochastic model for new product diffusion with parametric uncertainty. The analytical investigation of the model establishes the existence of transient bimodality, which manifests through a cusp catastrophe. Illustrations based on analytical and simulation studies are presented. Empirical validation of the proposed model based on well-known data sets where the Bass model is known to fit very well has been carried out.     

Formation of Nanodimensional 3C-SiC Structures from Rice Husks

We have demonstrated that large quantities of β-SiC nanostructures can be obtained from rice husk agricultural waste by using controlled conditions in a thermogravimetric setup. This simple and inexpensive method of producing these structures on a large scale is critical for applications in nanoelectronics, nanosensors, and biotechnology. The temperature and atmosphere are two critical elements in forming either α-cristobalite (SiO₂) or β-SiC. Using different characterization methods (x-ray diffraction, scanning electron microscopy, transmission electron microscopy, and Raman spectroscopy), we have shown that pyrolysis of rice husks in argon atmosphere at 1375°C results in simultaneous formation of carbon nanotubes, β-SiC nanowires/nanorods, and β-SiC powder.     

Structural and electrical properties of TlLiWO4 ceramics

Abstracts are not published in this journal     

Influence of sorption kinetics for zirconia sensitization in solid oxide fuel cell functional anode prepared by electroless technique

For preparation of solid oxide fuel cell functional anode (Ni-YSZ) by electroless deposition technique, surface adsorption of metallic palladium (Pd⁰) on zirconia is the most important step during the sensitization process. For the sensitization process, the initial reaction kinetics are based on external mass transfer followed by intra-particle mass transfer phenomena. A kinetic model for the reversible sorption of Pd⁰ on zirconia is developed that incorporates an effectiveness factor (η) which estimates the extent of intra-particle mass transfer. Based on the proposed model, an expression for Pd⁰ uptake at equilibrium (p), an important property of YSZ, is developed. The theoretical kinetic model proposed is verified with experimental parameters like electrical properties of these functional anodes prepared under various sensitization conditions, e.g., agitation frequencies, equilibration time etc. Due to intra-particle mass transfer, the concentration gradient of Pd⁰ from the surface of YSZ to bulk is minimized which favours uniform deposition of Ni on YSZ. Consequently, during subsequent electroless deposition of metallic Ni, the concentration gradient of the same is reduced from the bulk to the YSZ surface and results in enhanced functionality in the cermet anode. The validation is correlated with the electrical properties and surface morphologies of these functional cermets.     

Degradation kinetics of poly(HDDA‐co‐MMA)

1,6‐hexanediol diacrylate (HDDA) and methyl methacrylate (MMA) were copolymerized in different weight ratios using UV light induced photo‐polymerization to give poly(HDDA‐co‐MMA). Differential scanning calorimetry shows that copolymer was formed. The thermogravimetric and differential scanning calorimetric studies with different heating rates were carried out on these copolymers to understand the nature of degradation and to determine its kinetics. Different kinetic models were adopted to evaluate various parameters like the activation energy, the order, and the frequency factor. These analyses are important to study the binder removal from 3D‐shaped ceramic objects made by techniques like Solid free form fabrication. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2010