A multi-agent-based for fault location in distribution networks with wind power generator

The integration of distributed generation (DG) units such as wind power into the distribution network are one of the most viable technique to meet the energy demand increases. But, the integration of these DG units into power systems can change the dynamic performances of the systems and create new challenges that are necessary to be taken care of in the operation of the network. The fault location and diagnosis are the most significant technical challenges that can improve power systems’ reliability and stability. In this paper, a Multi-Agent System (MAS) based on current amplitude and current direction measured proposed for fault location, isolation, and power restoration in a smart distribution system with the presence of a wind power generator. The agents can communicate and collaborate to locate the faulted line, then send trips signal to corresponding circuit breakers accordingly. The simulation results show the performance of the proposed techniques.

Low Power Restoration Circuits Reduce Swing Voltages of SRAM Cell With Improved Read and Write Margins

This paper examines the factors that affect the static noise margin (SNM) of static random access memories which focus on optimizing read and write operation of 8T SRAM cell which is better than 6T SRAM cell using swing restoration for dual node voltage. New 8T SRAM technique on the circuit or architecture level is required. In this paper, comparative analysis of 6T and 8T SRAM cells with improved read and write margin is done for 130nm technology with cadence virtuoso schematics tool.

Advanced Edge-Cloud Computing Framework for Automated PMU-Based Fault Localization in Distribution Networks

The detection and localization of faults plays a huge role in every electric power system, be it a transmission network (TN) or a distribution network (DN), as it ensures quick power restoration and thus enhances the system’s reliability and availability. In this paper, a framework that supports phasor measurement unit (PMU)-based fault detection and localization is presented. Besides making the process of fault detecting, localizing and reporting to the control center fully automated, the aim was to make the framework viable also for DNs, which normally do not have dedicated fiber-optic connectivity at their disposal. The quality of service (QoS) for PMU data transmission, using the widespread long-term evolution (LTE) technology, was evaluated and the conclusions of the evaluation were used in the development of the proposed edge-cloud framework. The main advantages of the proposed framework can be summarized as: (a) fault detection is performed at the edge nodes, thus bypassing communication delay and availability issues, (b) potential packet losses are eliminated by temporally storing data at the edge nodes, (c) since the detection of faults is no longer centralized, but rather takes place locally at the edge, the amount of data transferred to the control center during the steady-state conditions of the network can be significantly reduced.

Reconfiguring Distribution Grids

Optimizing the grid structure becomes necessary in case of post-emergency power restoration or when overloaded. In normal operation, distribution grid parameters are adjusted to reduce the overload and the electricity losses; reconfiguration is not considered. The grid topology is reconfigurable by solving the optimization problem by the branch-and-bound algorithm subject to keeping the parameters within their acceptable limits in the context of line resistance, electricity losses, and power flows for all possible grid layouts. Simulation of a 10-kV distribution grid with four power centers shows that reconfiguring reduces power consumption by 2.9% and the inline active power losses by 256 kW. In this case study, reconfiguring has helped keep the voltage within the acceptable range while not overloading the lines. This study shows the proposed approach is suitable for dynamic grid reconfiguration as a way to reduce the overload and the electricity losses; at the same time, the approach does not require calculating nonlinear steady-state equations for each possible grid topology.