BALTAS, NICHOLAS-GREGORY, LAI, NGOC BAO, Marin L. , Tarraso A. , RODRÍGUEZ CORTÉS, PEDRO
No
ECCE - IEEE Energy Convers. Congr. Expo.
Conference Paper
Científica
01/01/2020
2-s2.0-85097162989
The presence of power electronics in today's power systems strengthens due to the wider integration of renewable energy and energy storage systems. Subsequently, the dynamical response becomes harder to model and understand. As a possible solution, coherency identification, among other applications, can reduce complexity. However, conventional tools possess limitations related to the assumptions need to be taken beforehand. In this paper, we propose a fully unsupervised variation of neural networks called the growing self organising maps (GSOM). The main advantage of GSOM over traditional methods is that network structure is not fixed, thus previous assumptions about the number of coherent groups or data structure are not necessary. A spreading factor controls the growth rate of the network allowing the analyst to choose the level of granularity whilst ensuring topology preservation. The effectiveness of the proposed algorithm is tested on the Nordic 32 power system. © 2020 IEEE.
Digital storage; Energy conversion; Energy storage; Power electronics; Self organizing maps; Coherency identification; Dynamical response; Energy storage systems; Growing self-organising maps; Integration of renewable energies; Network structures; Spreading factor; Topology preservation; Renewable energy resources