PowerFactory Applications for Power System Analysis

Part of the series Power Systems pp 267-295


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

  • Jaime C. CepedaAffiliated withCorporación Centro Nacional de Control de Energía—CENACE
  • , José Luis RuedaAffiliated withDepartment of Electrical Sustainable Energy, Delft University of Technology
  • , István ErlichAffiliated withDepartment of Electrical Engineering and Information Technologies, University Duisburg-Essen
  • , Abdul W. KoraiAffiliated withInstitute of Electrical Power Systems, University Duisburg-Essen
  • , Francisco M. Gonzalez-LongattAffiliated withSchool of Electronic, Electrical and Systems Engineering, Loughborough University Email author 

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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. While most of the pioneer heuristic optimization approaches, such as genetic algorithms, particle swarm optimization, and differential evolution, are undergoing different types of modifications and extensions in order to improve their performance, great focus is also being put into the development of new approaches aiming at conceptual simplicity, easy adaptability for a variety of optimization-based applications, and outstanding performance. The mean–variance mapping optimization (MVMO) is a recent contribution to the family of evolutionary 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. Besides, MVMO is characterized by a continuously updated knowledge archive storing the n-best solutions achieved so far, from which a special mapping function, which accounts for the mean and variance of the optimization variables, is applied for mutation operation. In this way, the algorithm proceeds by projecting randomly selected variables onto the corresponding mapping function that guides the solution toward the best set achieved so far. Despite the orientation on the best solution, the algorithm keeps on searching globally. This chapter addresses key aspects concerning the implementation of MVMO by using DIgSILENT programming language (DPL). 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 MVMO-based applications in DIgSILENT PowerFactory environment.


DIgSILENT programming language Damping control Heuristic optimization Mean–variance mapping optimization Power system stability