1 - Derivation of generic equivalent models for distribution network analysis using artificial intelligence techniques

Abstract

The growing penetration of distributed energy resources and the use of new types of loads have emerged the need to develop robust and up-to-date equivalent models for distribution network (DN) analysis. In this aspect, several approaches have been proposed in the literature to derive generic sets of model parameters that can represent the DN for a wide range of operating conditions. In this chapter, conventional generalization techniques based on statistical analysis as well as modern ones, using artificial neural networks (ANNs) and clustering techniques, are developed. Both static models, which facilitate DN steady-state analysis and dynamic models suitable for transient analysis, are considered. The performance of the developed generalization approaches (GAs) is evaluated using measurements acquired from laboratory-scale active and passive DN configurations. This is dictated by the need to evaluate all examined GAs under practical conditions. A comparative assessment of all examined GAs is conducted, highlighting their distinct advantages and drawbacks.