Synchrophasor technology opens a new window for power system observability. Phasor measurement units (PMUs) are able to provide synchronized and accurate data such as frequency, voltage and current phasors, vibration, and temperature for power systems. Thus, the utilization of PMUs has become quite important in the fast monitoring, protection, and even the control of new and complicated distribution systems. However, data quality and communication are the main concerns for synchrophasor applications. This study presents a comprehensive survey on wide-area monitoring systems (WAMSs), PMUs, data quality, and communication requirements for the main applications of PMUs in a modern and smart distribution system with a variety of energy resources and loads. In addition, the main challenges for PMU applications as well as opportunities for the future use of this intelligent device in distribution systems will be presented in this paper.

Faults on individual modules within a photovoltaic (PV) array can have a significant detrimental effect on the power efficiency and reliability of the entire PV system. In addition, PV module faults can create risks to personnel safety and fire hazards if they are not detected quickly. As IoT hardware capabilities increase and machine learning frameworks mature, better fault detection performance may be possible using low-cost sensors running machine learning (ML) models that monitor electrical and thermal parameters at an individual module level. In this paper, to evaluate the performance of ML models that are suitable for embedding in low-cost hardware at the module level, eight different PV module faults and their impacts on PV module output are discussed based on a literature review and simulation. The faults are emulated and applied to a real PV system, allowing the collection and labelling of panel-level measurement data. Then, different ML methods are used to classify these faults in comparison to the normal condition. Results confirm that NN obtain 93% classification accuracy for seven selected classes.

The accurate detection and identification of intermittent cable faults are helpful in improving the reliability of the distribution system. This paper proposes intermittent fault detection and identification for distribution networks based on machine-learning (ML) techniques. For this reason, the IEEE 33 bus system is simulated in the radial and mesh topologies by considering all possible single- and three-phase electrical faults and limitations to collect high-resolution voltage and current waveforms. Moreover, this simulation investigates and considers various cases including low-impedance faults (LIFs) and high-impedance faults (HIFs) with a short and long duration. The collected data from the simulation are used for high-impedance intermittent fault detection, classification, and branch identification using eight supervised learning methods. A comparison between the accuracy and error of these ML classifiers shows that gradient booster (GB) and K-nearest neighbors (KNN) have the best performance for all three objectives. However, GB has a very high computation time compared to KNN.

In this paper, the virtual synchronous generator (VSG) concept is utilized in the controller of the grid-connected dual two-level voltage source inverter (DTL VSI). First, the topology of the VSG and the DTL VSI are presented. Then, the state-space equations of the DTL VSI and the grid-connected two-level voltage source inverter (TL VSI), regarding the presence of the phase-locked loop (PLL) and the VSG, are given. Next, the small-signal modeling of the DTL VSI and the TL VSI is realized. Eventually, the stability enhancement in the DTL VSI compared with the TL VSI is demonstrated. In the TL VSI, large values of virtual inertia could result in oscillations in the power system. However, the ability of the DTL VSI in damping oscillations is deduced. Furthermore, in the presence of nonlinear loads, the potentiality of the DTL VSI in reducing grid current Total Harmonic Distortion (THD) is evaluated. Finally, by using a proper reference current command signal, the abilities of the DTL VSI and the TL VSI in supplying nonlinear loads and providing virtual inertia are assessed simultaneously. The simulation results prove the advantages of the DTL VSI compared with the TL VSI in virtual inertia emulation and oscillation damping, which are realized by small-signal analysis.

Ferroresonance, as an undesirable disturbance, leads to significant overvoltage and distorted waveforms. This phenomenon can be highly damaging to voltage transformers and other parallel-connected equipment and can entail catastrophic consequences. This paper aims to design and study a solid-state ferroresonance-suppressing circuit (SSFSC) to protect voltage transformers (VTs) together with other parallel-connected equipment in wind generation systems from the adverse effects of the ferroresonance phenomenon. The proposed structure consists of low-voltage circuits, including power IGBTs. The excellent performance of the proposed SSFSC in suppressing ferroresonance overvoltage in wind generation VTs has been authenticated by analyses conducted utilizing a wind generation system model. In order to validate the performance of the proposed SSFSC, detailed analytical studies and time-domain simulations have been carried out employing a MATLAB/Simulink environment. The results verify that the proposed SSFSC can effectively suppress ferroresonance phenomena in VTs and mitigate their accompanying overvoltages with a high operational speed.

With the ongoing expansion and interconnection of electrical power systems, alongside the rapid proliferation of renewable distributed generations (DGs), the short-circuit extent in the power grid is experiencing a significant rise. Fault current limiters (FCLs) have been introduced in an effort to address this issue, ensuring the robustness and sustainability of expensive power system components when confronted with short-circuit faults. Among the various types of FCLs, bridge-type DC reactor fault current limiters (BDCR-FCLs) have emerged as one of the most promising options. While BDCR-FCLs have shown excellent properties in limiting harmful short-circuit currents, they are also advantageous in other respects. This paper investigates the supplementary functionalities of BDCR-FCLs as a multifaceted device towards the enhancement of the quality of supplied energy in terms of total harmonic distortion (THD) reduction, power factor (PF) correction, peak current reduction for nonlinear loads, and soft load variation effects, as well as their capability to limit fault current. To this aim, the capabilities of BDCR-FCLs have been studied through various simulated case studies in PSCAD/EMTDC software V5.0.1, in addition to experimental tests considering an AC microgrid connected to a DC system. The experimental and simulation investigations verify the superior multifaceted functionalities BDCR-FCLs introduce in addition to their excellent fault current-limiting capabilities. The results show that PF improved by 6.7% and 7%, respectively, in simulation and experimental tests. Furthermore, the current THD decreased by 20% and 18% in the simulation and experiment, respectively.