## 1 Introduction
### Literature Review
A comprehensive control system has been presented for UHV EDNs by [PERSON] et al. [31]. It uses an expert system that integrates a numerical subsystem containing power flow software to obtain a sensitivity matrix. The tree space of this parameter depends on the variable sensitivity of different power sources. The system has been implemented at the UHV level, where the control capabilities are more numerous than at the low voltage level. An expert system that can control the voltage level of a 20 kV MVEDN using a supervisory control and data acquisition (SCADA) system has been proposed by [PERSON] and [PERSON] [32]. This system can reduce the multiple voltage violations in the EDS and detect abnormal conditions at the buses from the EDN. The expert system focused on the control of the shunt reactive compensating devices and transformer tap change. The pursued objective is to identify the most effective control strategy for keeping the bus voltage within limits. The weak point of this system is represented by a lack of distributed generation sources in the distribution network (this is a passive EDN). An expert system using the fuzzy-logic-based remote monitoring system for a low voltage network has been proposed in [33]. The system provides real-time monitoring of the over-voltage and under-voltage conditions at the end-user level. The system is designed based on a Proteus simulator to communicate with devices that measure the voltage in passive LVEDNs, without the prosumers connected. The use of the LDC method to fix the reference voltage represents the weak point of this system. In addition, the system has been tested only in passive LVEDNs, without prosumers. [PERSON] et al. [34] proposed an expert system for the reconfiguration process of a MVEDN using a hybrid Fuzzy-Flower Pollination Algorithm. The aim is to decrease the power losses and improve the voltage level. This system can perform optimization in abnormal and normal operating conditions. The weak point identified for this system is associated with the use only in the passive MVEDNs. [PERSON] et al. proposed an optimal design and control method for an energy hub consisting of a battery bank, wind turbines, and solar photovoltaic panels [35]. Their structure can analyse the power flows in the hub based on an expert system. The optimal operating regime is also determined considering the cost of electricity generated by renewable energy sources and the battery bank's state of charge. The expert system has not been tested in the case of a LVEDNs. The generalization process from an energy hub to a LVEDN can lead to technical issues due to the higher complexity of an active LVEDN which includes more end-users. [PERSON] et al. developed a hybrid expert system to handle the complexity of the LV network demand [36]. It combines a set of modules, such as clustering, correlation, and neural network, to forecast and analyse future energy demand. The system cannot be used in the voltage control of active LVEDNs. [PERSON] et al. presented in [37] a reactive power compensation solution based on the real-time data collected from the distribution networks. The suggested solution, integrated into an expert system, uses fuzzy logic and heuristic algorithms. The priority of the devices/equipment needing the reactive power compensation is determined based on fuzzy logic. The application allows the expert to take the appropriate steps to establish the optimal location of the devices. The system does not integrate the OLTC-fitted distribution transformers. [PERSON] and [PERSON] proposed in [38] an expert-system-based decision-making framework to identify the most appropriate replacement transformer fleet solution. The expert system determines the order of the ageing transformers in a replacement ranking according to the different priority categories. The system is used only in replacing the process of the OLTC-fitted MV/LV distribution transformers in the planning stage of active LVDNs.