scholarly journals Optimization of modes of the electric power systems by genetic algorithms

2020 ◽  
Vol 216 ◽  
pp. 01099
Author(s):  
Behzod Pulatov ◽  
Shanazarov Alisher
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Power Systems ◽  
Electric Power ◽  
Shortest Path ◽  
Optimization Problems ◽  
Electric Power Systems ◽  
Data Filtering ◽  
Path Search ◽  
Shortest Path Search

In article discusses issues for solving optimization problems based on the use of genetic algorithms. Nowadays, the genetic algorithms for solving various problems. This includes the shortest path search, approximation, data filtering and others. In particular, data is being examined regarding the use of a genetic algorithm to solve problems of optimizing the modes of electric power systems. Imagine an algorithm for developing the development of mathematical models, which includes developing the structure of the chromosome, creating a started population, creating a directing force for the population, etc.

scholarly journals OPTIMIZATION OF MODES OF ELECTR POWER SYSTEMS BY GENETIC ALGOROTHMS

2019 ◽  
Vol 2019 (3) ◽  
pp. 224-229
Author(s):  
B Pulatov
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Power Systems ◽  
Electric Power ◽  
Optimization Problems ◽  
Block Diagram ◽  
Electric Power Systems ◽  
Data Filtering ◽  
Path Search ◽  
Shortest Path Search

In article discusses issues for solving optimization problems based on the use of genetic algorithms. To date, the genetic use algorithm for solving various problems. Which includes the shortest path search, approximation, data filtering and others. In particular, data is being examined regarding the use of a genetic algorithm to solve problems of optimizing the modes of electric power systems. Imagine an algorithm for developing the development of mathematical models, which includes developing the structure of the chromosome, creating a started population, creating a directing force for the population, etc. As well as the presentation, the selected structure should take into account all the features and limitations imposed on the desired solution, as well as the fact that the implementation of crossоver and mutation algorithms directly depends on its choice. To solve optimization problems, a block diagram of the genetic algorithm is given.

scholarly journals Locating High-Impedance Fault Section in Electric Power Systems Using Wavelet Transform,k-Means, Genetic Algorithms, and Support Vector Machine

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Ying-Yi Hong ◽  
Wei-Shun Huang
Keyword(s):  
Support Vector Machine ◽  
Genetic Algorithms ◽  
Wavelet Transform ◽  
Power Systems ◽  
Electric Power ◽  
Fire Hazard ◽  
Electric Power Systems ◽  
Support Vector ◽  
Discrete Wavelet ◽  
High Impedance

High-impedance faults (HIFs) caused by downed conductors in electric power systems are in general difficult to be detected using traditional protection relays due to small fault currents. The energized downed conductor can pose a safety risk to the public and cause a fire hazard. This paper presents a new method for locating the line (feeder) section of the HIF with the help of limited measurements in electric power systems. The discrete wavelet transform is used to extract the features of transients caused by HIFs. A modifiedk-means algorithm associated with genetic algorithms is then utilized to determine the placement of measurement facilities. The signal energies attained by wavelet coefficients serve as inputs to the support vector machine for locating the HIF line section. The simulation results obtained from an 18-busbar distribution system show the applicability of the proposed method.

scholarly journals Optimization Methods for Electric Power Systems: An Overview

2005 ◽  
Vol 2 (1) ◽  
Author(s):  
R. C. Bansal
Keyword(s):  
Artificial Intelligence ◽  
Power Systems ◽  
Electric Power ◽  
Power Optimization ◽  
Optimization Problems ◽  
Mathematical Optimization ◽  
Optimization Methods ◽  
Electric Power Systems ◽  
Unexpected Events ◽  
Large Complex

Power systems optimization problems are very difficult to solve because power systems are very large, complex, geographically widely distributed and are influenced by many unexpected events. It is therefore necessary to employ most efficient optimization methods to take full advantages in simplifying the formulation and implementation of the problem. This article presents an overview of important mathematical optimization and artificial intelligence (AI) techniques used in power optimization problems. Applications of hybrid AI techniques have also been discussed in this article.

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