Fuzzy System Optimization Using a Hierarchical Genetic Algorithm Applied to Pattern Recognition

Currently, ocean wave energy is a novel means of electricity generation that is projected to potentially serve as a primary energy source in coastal areas. However, for wave energy converters (WECs) to be applicable on a scale that allows for grid implementation, these devices will need to be placed in close relative proximity to each other. From what’s been learned in the wind industry of the U.S., the placement of these devices will require optimization considering both cost and power. However, current research regarding optimized WEC layouts only considers the power produced. This work explores the development of a genetic algorithm (GA) that will create optimized WEC layouts where the objective function considers both the economics involved in the array’s development as well as the power generated. The WEC optimization algorithm enables the user to either constrain the number of WECs to be included in the array, or allow the algorithm to define this number. To calculate the objective function, potential arrays are evaluated using cost information from Sandia National Labs Reference Model Project, and power development is calculated such that WEC interaction affects are considered. Results are presented for multiple test scenarios and are compared to previous literature, and the implications of a priori system optimization for offshore renewables are discussed.

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Application of genetic algorithm for broad learning system optimization

2020 7th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS) ◽

10.1109/iccss52145.2020.9336848 ◽

2020 ◽

Author(s):

Jiawei Li ◽

Yi Zuo ◽

Yiliang Li ◽

Yang Wang ◽

Tieshan Li ◽

...

Keyword(s):

Genetic Algorithm ◽

System Optimization ◽

Learning System

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A neural network used for PD pattern recognition in large turbine generators with genetic algorithm

Conference Record of the 2000 IEEE International Symposium on Electrical Insulation (Cat. No.00CH37075) ◽

10.1109/elinsl.2000.845406 ◽

2002 ◽

Cited By ~ 1

Author(s):

Guangning Wu

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Pattern Recognition ◽

Turbine Generators

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Optimum Selection of Hydraulic Devices for Water Hammer Control in the Pipeline Systems Using Genetic Algorithm

Volume 1: Fora, Parts A, B, C, and D ◽

10.1115/fedsm2003-45262 ◽

2003 ◽

Cited By ~ 3

Author(s):

Bong Seong Jung ◽

Bryan W. Karney

Keyword(s):

Genetic Algorithm ◽

Steady State ◽

Optimization Problems ◽

System Optimization ◽

Water Hammer ◽

Pipeline System ◽

Optimal Size ◽

Protection Strategy ◽

Hydraulic Devices ◽

Selection Of

Genetic algorithms have been used to solve many water distribution system optimization problems, but have generally been limited to steady state or quasi-steady state optimization. However, transient events within pipe system are inevitable and the effect of water hammer should not be overlooked. The purpose of this paper is to optimize the selection, sizing and placement of hydraulic devices in a pipeline system considering its transient response. A global optimal solution using genetic algorithm suggests optimal size, location and number of hydraulic devices to cope with water hammer. This study shows that the integration of a genetic algorithm code with a transient simulator can improve both the design and the response of a pipe network. This study also shows that the selection of optimum protection strategy is an integrated problem, involving consideration of loading condition, device and system characteristics, and protection strategy. Simpler transient control systems are often found to outperform more complex ones.

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