TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Wenzel, Adrian
A1  - Linke, Manuela
A1  - Messmer, Tobias
A1  - Micard, Gabriel
A1  - Schubert, Gunnar
A1  - Minde, Adrian
A1  - Kindl, Matthias
T1  - Innovative grid optimization approach based on artificial neural networks
T2  - IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), 29 Sept.-2 Oct. 2019, Bucharest, Romania
N2  - We present an innovative decision support system (DSS) for distribution system operators (DSO) based on an artificial neural network (ANN). A trained ANN has the ability to recognize problem patterns and to propose solutions that can be implemented directly in real time grid management. The principle functionality of this ANN based optimizer has been demonstrated by means of a simple virtual electrical grid. For this grid, the trained ANN predicted the solution minimizing the total line power dissipation in 98 percent of the cases considered. In 99 percent of the cases, a valid solution in compliance with the specified operating conditions was found. First ANN tests on a more realistic grid, calibrated with household load measurements, revealed a prediction rate between 88 and 90 percent depending on the optimization criteria. This approach promises a faster, more cost-efficient and potentially secure method to support distribution system operators in grid management.
KW  - Artificial Neural Network
KW  - Decision Support System
KW  - Distribution Grid Management
KW  - Smart Grid
Y1  - 2019
SN  - 978-1-5386-8218-0
SB  - 978-1-5386-8218-0
SN  - 978-1-5386-8219-7
SB  - 978-1-5386-8219-7
U6  - https://doi.org/10.1109/ISGTEurope.2019.8905733
DO  - https://doi.org/10.1109/ISGTEurope.2019.8905733
N1  - Volltextzugang für Angehörige der Hochschule Konstanz möglich.
SP  - 4
S1  - 4
PB  - IEEE
ER  -