First Advisor

Robert B. Bass

Term of Graduation

Summer 2022

Date of Publication

7-22-2022

Document Type

Thesis

Degree Name

Master of Science (M.S.) in Electrical and Computer Engineering

Department

Electrical and Computer Engineering

Language

English

Subjects

Mathematical optimization, Electric power systems, Computer algorithms

DOI

10.15760/etd.7968

Physical Description

1 online resource (ix, 87 pages)

Abstract

Modern power systems characterized by complex topologies require accurate situational awareness to maintain an adequate level of reliability. Since they are large and spread over wide geographical areas, occurrence of failures is inevitable in power systems. Various generation and transmission disturbances give rise to a mismatch between generation and demand, which manifest as frequency events. These events can take the form of negligible frequency deviations or more severe emergencies that can precipitate cascading outages, depending on the severity of the disturbance and efficacy of remedial action schema. The impacts of such events have become more critical with recent decline in system inertia as they tend to exhibit larger frequency deviations and higher Rate of Change of Frequency in low inertia systems. The susceptibility of different Balancing Authorities to these events varies depending on their inertia levels. Due to the repercussions, it is indispensable to arrest such disturbances on time by activating responsive frequency control measures.

This study developed a configurable event detection framework using linear regression-based event detection algorithm with tunable parameters. Two swarm intelligence-based optimization algorithms, Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO), were developed to dictate the algorithm parameter adjustments and make it adaptable to system conditions. The optimization algorithms tune the detection parameters according to the definition of frequency events specified by experts and enable event detection as desired by system operators. The performance of GWO and PSO algorithms are analyzed using actual Phasor Measurement Unit (PMU) data, and the efficacy of the proposed system is demonstrated using a set of performance evaluation metrics. The proposed event detection framework is shown to be capable of detecting events with high accuracy and speed.

Rights

© 2022 Umar Farooq

In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).

Persistent Identifier

https://archives.pdx.edu/ds/psu/38311

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