Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

Real-time dynamic security assessment of power system using strategic PMU measurements and control technique

Ensaf, Mohammad (2024) Real-time dynamic security assessment of power system using strategic PMU measurements and control technique. Master's Thesis, University of Pittsburgh. (Unpublished)

[img] PDF
Restricted to University of Pittsburgh users only until 11 January 2026.

Download (4MB) | Request a Copy


This research presents a comprehensive exploration of adaptive control strategies and data-driven anomaly detection within power systems. At the core of the study lies the investigation of adaptive finite-time tracking control for strict-feedback nonlinear continuous-time
systems. These systems, when influenced by full-state constraints and dead zones, present significant challenges. By harnessing the principles of finite-time stability theory combined with barrier Lyapunov functions, the study introduces a groundbreaking adaptive tracking control strategy. Coupled with the adaptive backstepping method, this approach guarantees that the closed-loop system’s signals remain bounded. Furthermore, it ensures that outputs
adeptly track reference signals, while all system states are confined within predefined compact sets, enhancing system reliability and performance. In parallel, the research unveils an innovative approach to tackle the intricate challenge of anomaly detection in Phasor Measurement Unit (PMU) data. Recognizing the high-dimensional nature of PMU data, an ensemble model, synthesizing the strengths of Gaussian Process Regression (GPR) and Autoencoders,
is proposed. This ensemble not only boasts superior data reconstruction fidelity but also features a Bayesian optimization-driven threshold determination. Such a methodology fosters an adaptive, data-driven anomaly detection process, resulting in heightened specificity and sensitivity. Validation tests conducted on a synthetic dataset, infused with 84 frequency events, attest to the ensemble model’s superior capability in discerning nuanced
anomalies. This superiority is evident both visually and through rigorous quantitative metrics, underscoring the ensemble’s edge over traditional models.


Social Networking:
Share |


Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Ensaf, Mohammadmoe31@pitt.edumoe31
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Thesis AdvisorBarati,
Committee MemberMao,
Committee MemberDallal,
Date: 11 January 2024
Date Type: Publication
Defense Date: 23 October 2023
Approval Date: 11 January 2024
Submission Date: 24 October 2023
Access Restriction: 2 year -- Restrict access to University of Pittsburgh for a period of 2 years.
Number of Pages: 5
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Electrical and Computer Engineering
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: Power System, Adaptive Control, Power Swing Equation, Ensemble Model, Autoencoder, Gaussian Process, Kernel Regression, Event Detection, Bayesian Optimization
Related URLs:
Date Deposited: 11 Jan 2024 19:39
Last Modified: 11 Jan 2024 19:39


Monthly Views for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item