Modern economies run on the backbone of electricity as one of major factors behind industrial development. India is endowed with plenty of natural resources and the majority of electricity within the country is generated from thermal and hydro-electric plants. A few nuclear plants assist in meeting the national requirements for electricity but still many rural areas remain uncovered. As India is primarily a rural agrarian economy, providing electricity to the remote, undeveloped regions of the country remains a top priority of the government. A vital, untapped source is livestock generated biomass which to some extent has been utilized to generate electricity in small scale biogas based plants under the government's thrust on rural development. This study is a preliminary attempt to correlate developments in this arena in the Asian region, as well as the developed world, to explore the possibilities of harnessing this resource in a better manner. The current potential of 2600 million tons of livestock dung generated per year, capable of yielding 263,702 million m 3 of biogas is exploited. Our estimates suggest that if this resource is utilized judiciously, it possesses the potential of generating 477 TWh (Terawatt hour) of electrical energy per annum.
Effort Estimation has always been a challenging task for the Project managers. Many researchers have tried to help them by creating different types of models. This has been already proved that none is successful for all types of projects and every type of environment. Analytic Hierarchy Process (AHP) has been identified as the tool that would help in Multi Criteria Decision Making. Researchers have identified that AHP can be used for the comparison of effort estimation of different models and techniques. But the problem with traditional AHP is its inability to deal with the imprecision and subjectivity in the pairwise comparison process. The motive of this paper is to propose Fuzzy Analytic Hierarchy Process, which can be used to rectify the subjectivity and imprecision of AHP and can be used for selecting the type of Model best suited for estimating the effort for a given problem type or environment. Instead of single crisp value, Fuzzy AHP uses a range of values to incorporate decision maker"s uncertainty. From this range, decision maker can select the value that reflects his confidence and also he can specify his attitude like optimistic, pessimistic or moderate. In this work, the comparison of AHP and Fuzzy AHP is concluded using a case study of selection of effort estimation model.
This paper discuss the application of artificial neural network-based algorithms to identify different types of faults in a power transformer, particularly using DGA (Dissolved Gas Analysis) test. The analysis of Random Neural Network (RNN) using Levenberg-Marquardt (LM) and Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithms has been done using the data of dissolved gases of power transformers collected from Punjab State Transmission Corporation Ltd.(PSTCL), Ludhiana, India. Sorting of the preprocessed data have been done using dimensionality reduction technique, i.e., principal component analysis. The sorted data is used as inputs to the Random Neural Networks (RNN) classifier. It has been seen from the results obtained that BFGS has better performance for the diagnosis of fault in transformer as compared to LM.
In this paper a genetic algorithm (GA) has been applied to search for the active and reactive power scheduling for a multiobjective load dispatch problem. Fuzzy decisionmaking methodology has been applied to decide the fitness function corresponding to each string of active and reactive power generations. All the participating objectives are minimized simultaneously if they attain the maximum satisfaction of their respective membership function and act as fitness function for the schedule represented by the string of population. Fuzzy based number of functional operating constraints, such as equality constraints for real and reactive powers and inequality constraints for real and reactive power flow through transmission lines are included as penalties in the fitness function, which guarantee the optimal solution searched by genetic-fuzzy method. The real and reactive power transmission line flows are obtained with the help of generalized Z-bus distribution factors (GZBDF). The validity of the proposed method has been demonstrated on an 11-nodes sample system comprising five generators.
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