## 1 Introduction
### Literature Review  
Most research has evaluated the effects of PV prosumers focusing on the different aspects of the voltage profile from LVEDNs; see Figure 1. However, the results have depended on the type of LVEDNs, test (for example, IEEE EDNs) or real associated with the pilot projects of different DNOs, and the working assumptions considered. Various definitions regarding the penetration degree of the prosumers from a LVEDN have been given in the literature. The first definition refers to the ratio of roof space needed to install the PV panels to the total surface available [20]. Another definition considers the ratio of annual energy from PV systems to the total energy consumption [21]. Some researchers have attempted to define it concerning the transformer capacity [22], whereas others have calculated it as the ratio of installed PV peak capacity to the maximum load of the feeder [1]. Some definitions have even replaced the \"maximum load of feeder\" with \"minimum load of feeder\" [23]. These included the ratio between the actual PV output and the active power load. However, the most accessible, clear, and easy-to-understand definition refers to the number of PV prosumers from the end-users connected in the LVEDNs [19; 24]. The studies differ through the factors that influence the design and operation of LVEDNs. They refer to the type of network (rural/urban or aerial/underground), the weather conditions, the generation/load profiles, and the capacity and spread of PV prosumers. Their extension to other networks or areas is difficult since the results are influenced by the analysed LVEDNs. This suggests that further studies must include the multiple aspects of voltage control. Many voltage control solutions have been developed based on the constant voltage (CV) method and the line-drop compensation (LDC) method. The CV method [25; 26] is represented by fixing the tap position for all time slots regardless of the load variation. It is widely used by DNOs through power transformers with NLTC to prevent voltage issues in the LVEDNs. However, an optimal solution is not easy to identify due to the variations in the load and intermittent nature of the PV prosumers. The LDC method [26; 27; 28] solves the decreased voltage issues based on the data performed by the voltage and current measuring devices placed on the LV side of the EDS. The current measured is multiplied by the impedance calculated against a virtual load centre (VLC). The voltage of the VLC is computed using the difference against the voltage of the LV bus of the EDS. The OLTC modifies the tap to ensure that the voltage of the VLC remains at the same value as the reference voltage. It also uses a time delay and dead band to prevent sudden voltage changes. The limits of both methods have been identified in the case of the PV-rich LVEDNs, being unable to respond to the intermittent nature of prosumers as they only consider the load's influence. The value of the current at the LV side of the EDS decreases as the PV capacity increases. The small voltages can occur in the phase when only consumers are connected. Thus, identification of new solutions to improve voltage control in PV-rich LVEDNs represents nowadays a challenge in the LVEDNs. The experience of the decision-makers in the voltage control process from the LVEDNs integrated in a rule-based expert system and real-time communication solutions can represent the poles of new voltage control solutions. Figure 1: The evolution of the voltage in the LV EDNs without and with PV prosumers integrated. Thus, artificial intelligence (AI) techniques can play a vital role in the integration of PV prosumers by providing a more seamless and accurate voltage control in the LVEDNs. However, despite the progress that AI techniques have made in the field of PV integration, expert systems are still not widely used [29; 30]. There are few approaches that integrate an expert system to solve the technical issues (including voltage control) of EDNs.