## 1 Introduction  
The increase in distributed generation (DG) has changed the traditional direction of power flows in medium voltage (MV) networks. Up until recently, power flew from higher voltage towards the loads which are connected on the low voltage (LV). Now, in instances when the generation from plants that are connected on the LV side is higher than the load itself, the power will flow upstream [1; 2]. There are several reasons for the popularization of distributed generation (DG). The first one is the rapid decline of costs of devices and technologies that are needed to build and operate plants based on Renewable Energy Sources (RESs). One of the significant price declines is that of the photovoltaic panels, which became 99% cheaper in 40 years, as the price dropped from 105.7 USD/Watt in 1975 to only 0.2 USD/Watt in 2020 [3]. Other reasons are the public's raised interest and awareness towards climate change, governmental incentives, and common goals from various international organizations [4; 5; 6]. Without doubt, RESs have their advantages, the main one being the fact that they decrease the overall CO\({}_{2}\) emissions. RESs, however, also have their disadvantages. Their unpredictable nature poses new challenges for the operation of the power system [7]. One of the issues this causes is over-voltages [8]. Due to that, this paper investigates two voltage regulation mechanisms that will be controlled by a machine-learning (ML) algorithm. Although voltage is closely related to reactive power, no reactive power methods of voltage regulation such as reactors or capacitors are considered in this paper. Instead, the voltage regulation mechanisms considered are the line voltage regulator (LVR) and the electronic on-load tap changer (E-OLTC). The intention is to use an ML algorithm as the controller of the correct tap positions of E-OLTCs and LVRs, in such a manner that voltages are kept within \(\pm 10\%\) of the nominal voltage. Different geographical areas have different Distribution Codes that decide the allowed voltage range. In the feeder used in this study case, the allowed voltage range is \(\pm 0.1\) Vn in normal operation and down to \(-0.15\) Vn in critical conditions. The effectiveness of LVRs and E-OLTCs depends greatly on the topology of the feeder and their positioning in it. Since LVRs are installed longitudinally, they become useful in long feeders that do not have many side branches, while E-OLTCs perform better in branchy feeders. In cases where feeders seem to have a main long axis, LVRs can prove to be an excellent choice if installed at a point along the axis where there are significant voltage drops or under-voltages [9; 10]. That usually is valid for villages and other extra urban feeders that go on for tens of kilometers. On the other hand, for feeders in cities, LVRs may not be helpful due to these feeders typically having several branches. In the latter case, E-OLTCs that are part of the usual MV/LV transformers are better suited. E-OLTCs make it possible to directly regulate the voltage of their busbars to which the loads are connected, without affecting a larger portion of the feeder [11; 12]. This paper does compare their individual and combined effects on voltage regulation. ML algorithms are receiving increasing attention in many fields, with no exception for power systems [13]. For instance, in [14], the authors used an ML and geographic information systems (GIS) approach for electrical load assessment to increase resilience in distribution networks. In [15], the authors conducted research on the ML-based application to predict cascading failure propagation. Paper [16] proposes a bottom-up data-driven multistage adaptive robust optimization (MARO) framework to address the power systems' renewable transition under uncertainty, while paper [17] proposes a machine-learning method to evaluate the low-frequency oscillation stability of the power system accurately and efficiently considering the random response data containing the uncertainties of the power grid.