pmu placement methods in power systems based on evolutionary algorithms and gps receiver

Clicks: 162
ID: 239137
2013
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Popular

Ranked #17 of 30 articles by views in international journal of corrosion

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
In this paper, optimal placement of Phasor Measurement Unit (PMU) using Global Positioning System (GPS) is discussed. Ant Colony Optimization (ACO), Simulated Annealing (SA), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are used for this problem. Pheromone evaporation coefficient and the probability of moving from state x to state y by ant are introduced into the ACO. The modified algorithm overcomes the ACO in obtaining global optimal solution and convergence speed, when applied to optimizing the PMU placement problem. We also compare this simulink with SA, PSO and GA that to find capability of ACO in the search of optimal solution. The fitness function includes observability, redundancy and number of PMU. Logarithmic Least Square Method (LLSM) is used to calculate the weights of fitness function. The suggested optimization method is applied in 30-bus IEEE system and the simulation results show modified ACO find results better than PSO and SA, but same result with GA.
Reference Key
mosavi2013iranianpmu Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;M. R. Mosavi;A. Akhyani
Journal international journal of corrosion
Year 2013
DOI
DOI not found
URL
http://ijeee.iust.ac.ir/browse.php?a_code=A-10-78-6&slc_lang=en&sid=1
Keywords
evolutionary algorithms electronics nuclear engineering

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.