An ANN-Based Method for On-Load Tap Changer Control in LV Networks with a Large Share of Photovoltaics—Comparative Analysis

Klara Janiga (), Piotr Miller, Robert Małkowski and Michał Izdebski
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Klara Janiga: Department of Electrical Power Engineering, Faculty of Electrical Engineering and Computer Science, Lublin University of Technology, 20-618 Lublin, Poland
Piotr Miller: Department of Electrical Power Engineering, Faculty of Electrical Engineering and Computer Science, Lublin University of Technology, 20-618 Lublin, Poland
Robert Małkowski: Department of Power Electronics and Electrical Machines, Faculty of Electrical and Control Engineering, Gdansk University of Technology, 80-233 Gdansk, Poland
Michał Izdebski: Gdansk Division, Institute of Power Engineering, 80-870 Gdansk, Poland

Energies, 2024, vol. 17, issue 22, 1-25

Abstract: The paper proposes a new local method of controlling the on-load tap changer (OLTC) of a transformer to mitigate negative voltage phenomena in low-voltage (LV) networks with a high penetration of photovoltaic (PV) installations. The essence of the method is the use of the load compensation (LC) function with settings determined via artificial neural network (ANN) algorithms. The proposed method was compared with other selected local methods recommended in European regulations, in particular with those currently required by Polish distribution system operators (DSOs). Comparative studies were performed using the model of the 116-bus IEEE test network, taking into account the unbalance in the network and the voltage variation on the medium voltage (MV) side.

Keywords: on-load tap changer; artificial neural networks; low-voltage networks; photovoltaics; voltage regulation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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