Over the years, power systems have been severely affected by extreme events. This situation has worsened given that climate change has proven to exacerbate their frequency and magnitude. In this context, resilience assessments have proved crucial to prevent and tackle the effects of these events on power systems. Some resilience studies have taken advantage of the so-called fragility curves (FCs) to evaluate the vulnerability of the system components against these natural hazards. Conceptually, FCs provide the failure probability of a particular grid asset according to the intensity of an extreme event, which can be determined based on the hazard intensity inherently dictated by the nature of the event. The probability of failure can be obtained following diverse methodologies and criteria. Thus, the resilience assessment of the event may vary significantly depending on how the probability of failure was determined. This paper provides, for the first time, a comprehensive review of the FCs used to model the vulnerability of the power system components, classifying them according to the physical magnitude and the system element subject to each type of event. Furthermore, a comparison of results obtained applying different FCs is developed to show the relevance of their modelling. The content of this paper can be used as a hands-on guide for researchers and power systems engineers to perform resilience studies.

The connection of renewable energy sources (RESs) to the distribution network has been rising at a steady pace over the past decades. The great penetration of RESs such as grid‐connected photovoltaic system brings new technical challenges to the distribution networks such as unintentional islanding. Conceptually, this situation occurs when a portion of the network that has been isolated from the main grid remains energised by the embedded RESs. This unexpected scenario should be thereby identified effectively to avoid frequency and voltage deviations and their hazardous effects. The aim of this paper is to provide a comprehensive review on the recently developed islanding detection methods for grid‐following/grid‐connected photovoltaic system, analyse their existing limitations, and suggest possible future research implementations. In this context, an in‐depth comparison is provided considering the main features used in islanding detection methods such as non‐detection zone, detection time, implementation cost and complexity, and power quality degradation. Finally, the main technical requirements established by the current grid codes are recalled identifying potential multi‐functional approaches to expand the current islanding detection capabilities.

This paper presents a novel voltage sag topology that occurs during an unintentional islanding operation (IO) within a distribution network (DN) due to large induction motors (IMs). When a fault occurs, following the circuit breaker (CB) fault clearing, transiently, the IMs act as generators due to their remanent kinetic energy until the CB reclosing takes place. This paper primarily contributes to voltage sag characterization. Therefore, this novel topology is presented, analytically modelled and further validated. It is worth mentioning that this voltage sag has been identified in a real DN in which events have been recorded for two years. The model validation of the proposed voltage sag is done via digital simulations with a model of the real DN implemented in Matlab considering a wide range of scenarios. Both simulations and field measurements confirm the voltage sag analytical expression presented in this paper as well as exhibiting the high accuracy achieved in the three-phase model adopted.

In this article, three evolutionary search algorithms: particle swarm optimization (PSO), simulated annealing (SA) and genetic algorithms (GA), have been employed to determine the optimal parameter values of the fractional-order (FO)-PI controllers implemented in the dual active bridge-based (DAB) DC microgrid. The optimum strategy to obtain the parameters of these FO-PI controllers is still a major challenge for many power systems applications. The FO-PI controllers implemented in the DAB are used to control the DC link voltage to the desired value and limit the current flowing through the converter. Accordingly, the investigated control system has six parameters to be tuned simultaneously; K_p1, K_i1, λ₁ for FO-PI voltage controller and K_p2, K_i2, λ₂ for FO-PI current controller. Crucially, this tuning optimization process has been developed to enhance the voltage stability of a DC microgrid. By observing the frequency-domain analysis of the closed-loop and the results of the subsequent time-domain simulations, it has been demonstrated that the evolutionary algorithms have provided optimal controller gains, which ensures the voltage stability of the DC microgrid. The main contribution of the article can be considered in the successful application of evolutionary search algorithms to tune the parameters of FO-based dual loop controllers of a DC microgrid scheme whose power conditioner is a DAB topology.

This paper is aimed at assessing the impact of unintentional islanding operations (IOs) in the presence of large induction motors (IMs) within distribution networks (DNs). When a fault occurs, following the circuit breaker (CB) fault clearing, the IMs act transiently as generators, due to its inertia, until the CB reclosing takes place. The present work is the outcome of a project carried out in a small DN, where field measurements were recorded over two years. This paper provides a detailed description of the test system, a selected list of field measurements, and a discussion on modeling guidelines used to create the model of the actual power system. The main goal is to validate the system model by comparing field measurements with simulation results. The comparison of simulations and field measurements prove the appropriateness of the modeling guidelines used in this work and highlight the high accuracy achieved in the implemented three-phase Matlab/Simulink model.