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
2014
Mean–Variance Mapping Optimization Algorithm for Power System Applications in DIgSILENT PowerFactory
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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