Date of Defense
10-6-2025 3:00 PM
Location
Online
Document Type
Dissertation Defense
Degree Name
Doctor of Philosophy in Electrical Engineering
College
COE
Department
Electrical and Communication Engineering
First Advisor
Prof. Hussain Shareef
Keywords
High Impact Low Probability; Prediction; Distribution System, Service Restoration; Power Systems Resilience.
Abstract
In this dissertation, the overhead line failure due to extreme winds was investigated. The objective of the overhead line failure analysis was to evaluate the failure probability of line due to hurricane. The standard IEEE 33 bus test system was utilized to analyze the impact of hurricane wind speed intensity at distinct time and location on the grid. The test system was divided into four regions based on the number of buses and hurricane category (according to Saffire Simpson Hurricane Wind Scale). Regions 1, 2, and 3 cover 8 buses while region 4 covers 9 buses. Regions 1, 2, 3, and 4 were affected by a hurricane category of 4, 3, 2, and 1 respectively. The Dynamic Bayesian network (DBN) based failure model was developed using Genie software for asynchronous hurricane scenarios to determine the line failure of overhead lines. The developed model was validated using fragility curve Monte Carlo simulation based (FC-MCS-SCENRED) model. A service restoration model was formulated with the aim of maximizing restored loads and minimizing the power losses using DG integration and system reconfiguration. Three different cases depicting minor, major and worst-case scenarios were investigated. Case 1 (Faulted lines: 18, 21) representing minor scenario corresponds to region 2 affected by category 3 hurricane at time instant t = 0 while case 2 (Faulted lines: 5, 6, 7, 8, 18, 19, 20, 21, 25, 33) representing major scenario corresponds to region 2 affected by category 3 hurricane at time instant t = 1, and case 3 (Faulted lines: 1, 2, 3, 4, 18, 22, 23, 24) representing worst-case scenario (blackout) corresponds to region 1 affected by category 4 hurricane at time instant t = 0. Using system reconfiguration and optimal DG placement, the percentage load restored for every case and scenario was computed. Finally, three resilience indicators ๐ 1, ๐ 2, and ๐ 3 were used to quantify the resilience of the restoration model outcomes. The findings of the overhead line failure model and the service restoration model were used to calculate resilience metrics. While ๐ 2 and ๐ 3 were derived from the resilience trapezoid frameworkโevaluating recovery efficiency and phased performance, ๐ 1 provides a complementary perspective by quantifying cumulative losses across all nodes. Together, these metrics holistically assess resilience in terms of severity (๐ 1), restoration success (๐ 2), and phased adaptability (๐ 3). The integration of DG and reconfiguration restored the load from 90.3% to 100% for Case 1 (t = 0). For Case 2 (t = 1) reconfiguration and DG placement restored the load from 34.994% to 80.35% and 100% respectively. For Case 3 (t = 0) reconfiguration was insufficient in restoring the load while DG placement restored the load from 0% to 100%. Thus, the DBN based overhead line failure analysis together with reconfiguration and optimal DG placement-based service restoration resulted in improved load recovery and power losses.
Included in
RESILIENCE ORIENTED DYNAMIC BAYESIAN NETWORK FOR TIME DEPENDENT POWER DISTRIBUTION SYSTEM DURING HURRICANES
Online
In this dissertation, the overhead line failure due to extreme winds was investigated. The objective of the overhead line failure analysis was to evaluate the failure probability of line due to hurricane. The standard IEEE 33 bus test system was utilized to analyze the impact of hurricane wind speed intensity at distinct time and location on the grid. The test system was divided into four regions based on the number of buses and hurricane category (according to Saffire Simpson Hurricane Wind Scale). Regions 1, 2, and 3 cover 8 buses while region 4 covers 9 buses. Regions 1, 2, 3, and 4 were affected by a hurricane category of 4, 3, 2, and 1 respectively. The Dynamic Bayesian network (DBN) based failure model was developed using Genie software for asynchronous hurricane scenarios to determine the line failure of overhead lines. The developed model was validated using fragility curve Monte Carlo simulation based (FC-MCS-SCENRED) model. A service restoration model was formulated with the aim of maximizing restored loads and minimizing the power losses using DG integration and system reconfiguration. Three different cases depicting minor, major and worst-case scenarios were investigated. Case 1 (Faulted lines: 18, 21) representing minor scenario corresponds to region 2 affected by category 3 hurricane at time instant t = 0 while case 2 (Faulted lines: 5, 6, 7, 8, 18, 19, 20, 21, 25, 33) representing major scenario corresponds to region 2 affected by category 3 hurricane at time instant t = 1, and case 3 (Faulted lines: 1, 2, 3, 4, 18, 22, 23, 24) representing worst-case scenario (blackout) corresponds to region 1 affected by category 4 hurricane at time instant t = 0. Using system reconfiguration and optimal DG placement, the percentage load restored for every case and scenario was computed. Finally, three resilience indicators ๐ 1, ๐ 2, and ๐ 3 were used to quantify the resilience of the restoration model outcomes. The findings of the overhead line failure model and the service restoration model were used to calculate resilience metrics. While ๐ 2 and ๐ 3 were derived from the resilience trapezoid frameworkโevaluating recovery efficiency and phased performance, ๐ 1 provides a complementary perspective by quantifying cumulative losses across all nodes. Together, these metrics holistically assess resilience in terms of severity (๐ 1), restoration success (๐ 2), and phased adaptability (๐ 3). The integration of DG and reconfiguration restored the load from 90.3% to 100% for Case 1 (t = 0). For Case 2 (t = 1) reconfiguration and DG placement restored the load from 34.994% to 80.35% and 100% respectively. For Case 3 (t = 0) reconfiguration was insufficient in restoring the load while DG placement restored the load from 0% to 100%. Thus, the DBN based overhead line failure analysis together with reconfiguration and optimal DG placement-based service restoration resulted in improved load recovery and power losses.