Stationary supercapacitor energy storage operation algorithm based on neural network learning system

• Autorzy: Jefimowski W. , Nikitenko A. , Drążek Z. , Wieczorek M.

Identyfikatory

DOI

10.24425/bpasts.2020.134176

• Języki publikacji: EN

Abstrakty

EN

The paper proposes to apply an algorithm for predicting the minimum level of the state of charge (SoC) of stationary supercapacitor energy storage system operating in a DC traction substation, and for changing it over time. This is done to insure maximum energy recovery for trains while braking. The model of a supercapacitor energy storage system, its algorithms of operation and prediction of the minimum state of charge are described in detail; the main formulae, graphs and results of simulation are also provided. It is proposed to divide the SoC curve into equal periods of time during which the minimum states of charge remain constant. To predict the SoC level for the subsequent period, the learning algorithm based on the neural network could be used. Then, the minimum SoC could be based on two basic types of data: the first one is the time profile of the energy storage load during the previous period with the constant minimum SoC retained, while the second one relies on the trains’ locations and speed values in the previous period. It is proved that the use of variable minimum SoC ensures an increase of the energy volume recovered by approximately 10%. Optimum architecture and activation function of the neural network are also found.

Słowa kluczowe

EN

stationary energy storage; operation algorithms; machine learning; supervised learning; prediction

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2020
• Tom: Vol. 68, nr 4
• Strony: 733--738
• Opis fizyczny: Bibliogr. 22 poz., rys.

Twórcy

autor

Jefimowski W.

• Warsaw University of Technology, Electrical Power Engineering Institute, Koszykowa Street 75, Mechanics Building, 00-662 Warsaw, Poland

autor

Nikitenko A.

• Warsaw University of Technology, Electrical Power Engineering Institute, Koszykowa Street 75, Mechanics Building, 00-662 Warsaw, Poland

autor

Drążek Z.

• Warsaw University of Technology, Electrical Power Engineering Institute, Koszykowa Street 75, Mechanics Building, 00-662 Warsaw, Poland

autor

Wieczorek M.

• Warsaw University of Technology, Electrical Power Engineering Institute, Koszykowa Street 75, Mechanics Building, 00-662 Warsaw, Poland

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).