Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/224964
Title: Cost Function Optimization for Maintaining the Quality of Service in a Distributed Power System
Researcher: Raghavendra G
Guide(s): R. Manjunath
Keywords: quality of the power distribution, High amount of time for training and processing of data, proposed CODES-MM
University: Jain University
Completed Date: 23/08/2018
Abstract: The area of the power system is widely known for its applicability in almost all the sectors of research, industries, etc. The problem associated with the quality of the power distribution is extremely essential as it equally affects both the consumer as well as power service providers. The recent researchers were interested in giving a better way of power generation which can provide an important solution against dynamic power demand. The recently adopted techniques to do so are neural networks, machine learning, genetic algorithms, etc., and these algorithms yield optimizing results but consume more time for training and processing of data. Also, the existing distributed energy storage (DES) systems exploit the deliverable functions linked with nonlinearity of power generation from utility grid aspects. This research introduces a computational model for distributed energy system (DES) to achieve efficient as well as optimal power supply to its end user. The design of computational model involves parameters design for DES (i.e., failure models, fuel availability, outage cost etc.). Then, a stochastic modelling based on probabilistic approach is introduced against dynamic load demands. Finally, an optimization principle is cost optimization of distributive energy system using Markov model (CODES-MM) given which reduces the cost of distributed power consumption to great extent. To make the perfect remark on the effectiveness of the proposed model, its outcomes are compared with recently implemented technique, i.e., fuzzy logic based methods and is found that the proposed probability-based approach presents the accurate results of fault tolerance estimation with less error rate of 2.21 with higher power capacity of 0.764. The performance of the proposed CODES-MM is improved by integrating the pricing policy and distributed energy storage power allocation using a Markov decision process to achieve a significant allocation of power distribution over a network. The outcomes of CODES-MM are compared with existing researches and it is found that the proposed model reduces the financial cost for the same DES capacity compared to other methods. newline
Pagination: 158 p.,
URI: http://hdl.handle.net/10603/224964
Appears in Departments:Department of Electrical Engineering

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