Mean–Variance Mapping Optimization Algorithm for Power System Applications in DIgSILENT PowerFactory

The development and application of heuristic optimization algorithms have gained a renewed interest due to the limitations of classical optimization tools for tackling several hard-to-solve problems in different engineering fields

Jaime C. Cepeda; José Luis Rueda; István Erlich; Abdul W. Korai; Francisco M. Gonzalez-Longatt

2014

Key concepts

Scholarcy highlights

  • The development and application of heuristic optimization algorithms have gained a renewed interest due to the limitations of classical optimization tools for tackling several hard-to-solve problems in different engineering fields
  • Due to the complex nature of power system dynamics, electrical engineering optimization problems usually present a discontinuous multimodal and non-convex landscape that necessarily has to be handled by heuristic optimization algorithms
  • Its novel search mechanism performs within a normalized range of the search space for all optimization variables and follows a single parent–offspring pair approach
  • The algorithm proceeds by projecting randomly selected variables onto the corresponding mapping function that guides the solution toward the best set achieved so far
  • This chapter addresses key aspects concerning the implementation of mean–variance mapping optimization by using DIgSILENT programming language
  • An exemplary application on the coordinated tuning of power system supplementary damping controllers is presented and discussed in order to highlight the feasibility and effectiveness of structuring mean–variance mapping optimization-based applications in DIgSILENT PowerFactory environment

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