Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/341429
Title: Transient stability constrained optimal power flow using cuckoo search algorithm with artificial intelligence technique
Researcher: Manjula, V
Guide(s): Mahabub Basha, A
Keywords: Engineering and Technology
Engineering
Engineering Electrical and Electronic
Artificial intelligence
Power system
University: Anna University
Completed Date: 2020
Abstract: Growth in load demand and the push to change the generation sources to smaller plants utilizing renewable energy sources along with uncertainty of transaction is likely to strain existing power systems. This will lead to transmission system functioning closer to their operating limits causing increased contingency. Contingency is overloading of one or more transmission lines and/or transformers in the power system. The power system is a complex network utilized for generating, transmitting and distributing electric power. It is required to operate by utilizing minimum resources to satisfy maximum security and reliability. Optimal Power Flow (OPF) problem is to optimize specific objectives by adjusting a few power system variables, provided that all equality and inequality constraints of the system are satisfied. OPF is a highly constrained non-linear problem with continuous and discrete variables. The techniques that have been implemented to optimize the OPF are partitioned into two categories such as deterministic methods and evolutionary methods. Deterministic methods include linear and non-linear programming, quadratic programming and interior point method. These methods have a problem in handling many local minima due to the non-convexity of OPF problems. Gradient-based methods overcome the convergence problem, but sometimes fail to meet inequality constraints. Due to the limitations of deterministic methods, evolutionary methods were introduced to remedy these limitations and optimize OPF problems effectively. Evolutionary methods include genetic algorithm, differential algorithm, evolutionary programming, simulated annealing, particle swarm optimization and shuffle frog leaping algorithm, improved the performance of the optimization technique to reach the global solution faster and easier. Genetic Algorithm (GA) with fuzzy logic was also implemented and has been used for OPF solution. A hybrid method of particle swarm optimization, genetic algorithm, and fuzzy logic techniques have been utilized in OPF. newline
Pagination: xviii,165p
URI: http://hdl.handle.net/10603/341429
Appears in Departments:Faculty of Electrical Engineering

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10_listofabbreviations.pdf185.8 kBAdobe PDFView/Open
11_chapter1.pdf249.5 kBAdobe PDFView/Open
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13_chapter3.pdf604.03 kBAdobe PDFView/Open
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80_recommendation.pdf48.59 kBAdobe PDFView/Open
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