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.

This article addresses the suitable approaches for empowering energy citizens and smart energy communities through the development of community-based microgrid (C-MG) solutions while taking into consideration the functional architectural layers and system integration topologies, interoperability issues, strategies for consumer-centric energy trading under the local electricity market (LEM) mechanism, and socio-economic aspects. Thus, this article presents state-of-the-art microgrid solutions for the smart energy community along with their motivation, advantages and challenges, comprehensibly contrasted between the recommended generic architecture and every other reported structure. The notion of LEM for peer-to-peer (P2P) energy exchange inside a transactive energy system based on a flexible consumer-centric and bottom-up perspective towards the participation in the wholesale electricity market (WEM) is also reviewed and critically explored. Furthermore, the article reviews the interoperability issues in relation to the development of C-MG including energy trading facilities. The article’s overall contribution is that it paves the path for advanced research and industrialisation in the field of smart energy communities through the analytical recommendations of the C-MG architecture and DER (distributed energy resource) integration structure, considering the future trend of local energy markets and socio-economic aspects.

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.