Alireza Shahsavari

TL;DR: A novel method is proposed to locate the source of events in power distribution systems by using distribution-level phasor measurement units, a.k.a., micro-PMUs, built upon the compensation theorem in circuit theory to generate an equivalent circuit to represent the event by using voltage and current synchrophasors that are captured by micro- PMUs.

TL;DR: A novel data-driven event detection technique is introduced to extract events from the extremely large collection of raw micro-PMU data and a data- driven event classifier is developed to effectively classify power quality events.

TL;DR: In this paper, a multi-objective solution approach is developed to simultaneously minimize indispensable economic and technical objectives, and the resultant optimization problem is solved through a multiobjective particle swarm optimization (MOPSO) based algorithm, accompanied by a fuzzy decision making method to select the best result among the obtained Pareto optimal set of solutions.

TL;DR: A new TI algorithm based on measurements from a few line current sensors, together with available pseudo-measurements for nodal power injections, which is able to identify all possible topologies, including radial, loop, and island configurations, which extends the application of TI to identify switch malfunctions and to detect outages.

TL;DR: This work develops a novel data-driven approach to analyze transient load behaviors, in form of a three-phase surge current profile, that could be induced on a distribution feeder once a group of loads responds to a regulation down event.