Name
Abstract | ||
|---|---|---|
The present paper describes a methodology based on Evolutionary Algorithms (EAs) that defines the configuration required for a monitoring system, in order to monitor voltage and current state variables from a power network. The methodology defines not only the sites where the meters should be installed, but also how their transducers (PTs and CTs) should be connected. The monitoring system's observability is verified through three different rules based on Kirchhoff's laws. A branch-and-bound algorithm and a modified Genetic Algorithm (GA) are used to solve the optimization problem. The objective is to reduce the cost of the whole monitoring system. It is also shown why intelligent searching methods are required for solving the optimization problem. Three different networks were used to assess the methodology's performance: IEEE 14-bus system, IEEE 30-bus system and a real power distribution feeder. The results were compared with the ones obtained through other methodologies that have already been published before. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/TSG.2012.2235472 | Smart Grid, IEEE Transactions |
Keywords | Field | DocType |
electric current measurement,genetic algorithms,piezoelectric transducers,power system harmonics,power system state estimation,tree searching,voltage measurement,EA,IEEE 14-bus system,IEEE 30-bus system,Kirchhoff law,branch-and-bound algorithm,cost reduction,current state variable monitoring,evolutionary algorithm,harmonic state estimation,intelligent searching method,modified GA,modified genetic algorithm,monitoring system observability,optimal monitoring systems,optimization problem,real power distribution feeder,transducers,voltage state variable monitoring,Evolutionary algorithms,harmonic distortions,monitoring systems and state estimation | Mathematical optimization,Observability,Metre,Evolutionary algorithm,Voltage,Harmonic,Control engineering,Electronic engineering,State variable,Engineering,Optimization problem,Genetic algorithm | Journal |
Volume | Issue | ISSN |
4 | 1 | 1949-3053 |
Citations | PageRank | References |
5 | 0.57 | 1 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carlos Frederico M. Almeida | 1 | 5 | 0.57 |
| Nelson Kagan | 2 | 15 | 3.47 |