Artificial Neural Network Based Droop-Control Technique for Accurate Power Sharing in an Islanded Microgrid

DOI

10.1080/18756891.2016.1237183How to use a DOI?

Keywords

Artificial neural network (ANN); Microgrid; Photovoltaic (PV); Battery energy storage system (BESS); Solid oxide fuel cell (SOFC); Droop control

Abstract

In an islanded microgrid, while considering the complex nature of line impedance, the generalized droop control fails to share the actual real/reactive power between the distributed generation (DG) units. To overcome this power sharing issue, in this paper a new approach based on feed forward neural network (FFNN) is proposed. Also, the proposed FFNN based droop control method simultaneously controls the microgrid voltage and frequency within the limits. The proposed microgrid consists of combination of photovoltaic (PV) system and battery energy storage system (BESS) as the first DG unit and solid oxide fuel cell (SOFC) as the second DG unit. The simulation of the proposed microgrid is carried out in Matlab/Simulink environment and necessary results are compared to show the effectiveness of the proposed method.

Copyright

© 2016. the authors. Co-published by Atlantis Press and Taylor & Francis

Open Access

This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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TY  - JOUR
AU  - T. Vigneysh
AU  - N. Kumarappan
PY  - 2016
DA  - 2016/09/01
TI  - Artificial Neural Network Based Droop-Control Technique for Accurate Power Sharing in an Islanded Microgrid
JO  - International Journal of Computational Intelligence Systems
SP  - 827
EP  - 838
VL  - 9
IS  - 5
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2016.1237183
DO  - 10.1080/18756891.2016.1237183
ID  - Vigneysh2016
ER  -