Settings of protection are essential to ensure the sensitivity and selectivity needed to detect defects. Making the correct settings requires the calculation of the fault currents with as little error as possible. Fault currents are influenced by the parameters of the electrical networks, including the state of the insulation and the Petersen coil, which changes during their operation electrical networks. This paper analyzes how the insulation parameters of medium voltage power lines, the parameters of the Petersen coil used to treat the neutral of the medium voltage electrical network and the value of the resistance at the fault location influence the fault current in the case of a single-phase fault. The large number of single-phase faults that occur in medium voltage electrical networks justifies this analysis. The symmetrical components method was used to calculate the fault current. The results obtained by calculation were verified experimentally by causing a single-phase-to-ground fault in a real medium voltage network. The paper presents the situations in which the analytical calculation of the single-phase-to-ground fault current can lead to inadmissibly large errors, even over 50%, but also the situations in which the errors fall below 3%.

Energy communities represent an important step towards clean energy; however, their management is a complex task due to various factors such as fluctuating demand and energy prices, variable renewable generation, and external factors such as power outages. This paper investigates the effectiveness of a Reinforcement Learning agent, based on the Proximal Policy Optimisation (PPO) algorithm, for energy management across three different energy community configurations. The performance of the PPO agent is compared against a Rule-Based Controller (RBC) and a baseline scenario using solar generation but no active management. Simulations were run in the CityLearn framework to simulate real world data. Across the three evaluated community configurations, the PPO agent achieved its greatest improvement over a single run in the scenario where all participants were prosumers (Schema 3), with a reduction of 9.2% in annual costs and carbon emissions. The main contribution of this work is demonstrating the viability of Reinforcement Learning agents in energy optimization problems, providing an alternative to traditional RBCs for energy communities.

One important problem in the operation of medium voltage networks is the detection of a single-line-to-ground fault in its incipient state, when the fault resistance values are very high. In a medium voltage (MV) distribution network with a neutral grounding resistor (NGR), one of the methods employed to discriminate a single line-to-ground fault is the use of an overcurrent relay with an operating characteristic adjusted according to the effective value of the current flowing through the limiting resistor. In case of a single line-to-ground fault with a high fault resistance value, the correct tripping settings of the protective relay require the precise computation of this current. In comparison to the assumptions made by the models from the literature—the three-phase voltage system of the medium voltage busbars is symmetrical and there are no active power losses in the network insulation—the model proposed in this paper considers the pre-fault zero-sequence voltage of the medium voltage busbars and the active power losses in the network insulation, which is necessary in certain fault conditions where the use of the former leads to unacceptable errors.

The paper analyses how the characteristics of the medium-voltage network with insulated neutral and the conditions under which the single line-to-ground fault occurs (insulation condition and value of the fault resistance) influence the zero-sequence components of the voltage of the transformer station medium-voltage bus bar, the fault current, the currents of the faulted line and the currents of the other non-faulted power lines connected to the transformer station bus bars. Assuming the waveform of the medium voltage bus voltages in the transformer station is known, the influence of the fault resistance on the total distortion coefficient of the fault current is analysed. This establishes the conditions under which a single line-to-ground fault can be detected by controlling the total distortion coefficient of the fault current and the currents of the fault-free lines. It also examines the conditions under which a single line-to-ground fault can be selectively detected by checking the effective value and direction of flow of the zero-sequence currents of the medium-voltage lines during the fault. The results obtained by the calculation were verified experimentally in the medium voltage network considered in the study. The acceptable differences, considering the degree of accuracy with which the parameters of a medium voltage network are known, between the results obtained by calculation and those obtained experimentally show that the simplifying assumptions accepted for the mathematical models used in the calculation are correct.

Multiple distribution network models have been developed and made openly available in recent years to examine the challenges faced by grid operators. Models common to North America or the United Kingdom do not highlight aspects of grid operation and construction specific to Eastern Europe. This paper provides guidance on how to model a real distribution network with resonant grounding from Romania and how to simulate simultaneous faults using the MATLAB/Simulink environment. Additionally, this paper addresses specific challenges, such as neutral grounding using either a grounding transformer with windings connected in zigzag or an artificial zigzag coil. The purpose of this paper is to provide a framework that lays the groundwork for researchers and grid operators for further distribution network analysis.