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

被引：2

作者：

Janiga, Klara ^{[1
]}

Miller, Piotr ^{[1
]}

Malkowski, Robert ^{[2
]}

Izdebski, Michal ^{[3
]}

机构：

[1] Lublin Univ Technol, Fac Elect Engn & Comp Sci, Dept Elect Power Engn, PL-20618 Lublin, Poland

[2] Gdansk Univ Technol, Fac Elect & Control Engn, Dept Power Elect & Elect Machines, PL-80233 Gdansk, Poland

[3] Inst Power Engn, Gdansk Div, PL-80870 Gdansk, Poland

来源：

ENERGIES | 2024年 / 17卷 / 22期

关键词：

on-load tap changer; artificial neural networks; low-voltage networks; photovoltaics; voltage regulation; HIGH PV PENETRATION; VOLTAGE REGULATION; DISTRIBUTION-SYSTEM; COORDINATED CONTROL; ENERGY-STORAGE; OLTC; MITIGATION; INVERTERS; TIME;

D O I：

10.3390/en17225749

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

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.

引用

页数：25

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