Date of Award

January 2015

Document Type


Degree Name

Master of Science (MS)


Electrical Engineering

First Advisor

Prakash Ranganathan


Synchrophasors, or also known as Phasor Measurement Units (PMUs), are the state- of-the-art measurement sensor that gather key sensor parameters such as voltage, frequency (f), current (i), and phase angle (ϕ) to monitor the state of an electric grid. The significant feature of a synchrophasor is in its ability to provide real-time streaming data from smart grid. The sampling rate of PMUs ranges from 30 samples to a maximum of 120 samples per second. With such large date-rate, the operations of the power-grid is known with high granularity. However, utilities face certain challenges with synchrophasor measurements. One of the common challenge with synchrophasor is the selection of location to place them in the grid. A synchrophasor placed on a bus is capable of measuring currents, voltages, phasor and frequency information on the entire transmission line incident to that bus. Furthermore, neighboring buses also become observable (i.e. adjacent bus voltage equations are solvable) using Ohm’s law, Kirchhoff’s Voltage Law (KVL) and Kirchhoff’s Current Law (KCL). Thus, it is not necessary to place PMUs on every single bus of the power-grid.

Synchrophasors are expensive units and depending on vendor type, the number of measurement channels and features, the cost per unit can increase. There are several optimal solutions proposed to minimize the cost function to place the synchrophasors. Studies often ignored other metrics such as reliability, and security. This can jeopardize the reliability of the power-grid.

Thus, this thesis work focus on a multi-objective problem that include reliability, cost, energy, and distance. This research proposes a criteria called as Optimal Redundancy Criterion (ORC) based on Linear Programming (LP) methods to find an optimal solution for the placement problem. Although, synchrophasors provide real-time information about the grid, the system operators need to identify, classify and analyze fault or anomalies in the power-grid. Such detection of the faults will improve the situational awareness of the power-grid. This research addresses such challenges by developing data mining algorithms for effective visualization and control of data. The secondary goal is accomplished by implementing a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to IEEE test system and phasor data from openPDC framework. The scalability and decision making process for large scale utility test systems using DBSCAN is also investigated.