Indirect Power-System Contingency Screening for Real-Time Applications Based on PCA

Dimitrovska, Teodora; Rudež, Urban; Mihalič, Rafael

doi:10.1109/tpwrs.2017.2691556

Cited by 14 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several approaches based on PCA have been recently proposed for similar applications including the identification of critical locations in power systems [24], prioritisation of localisation for PMU placement [25], monitoring system oscillations [26, 27], detection of islanding conditions [28, 29], dynamic event detection and classification [30, 31], detection of bad data in PMU measurements using PCA [32], and coherency identification [33]. Other techniques include optimal sensor locations for signal reconstruction [14].…”

Section: Conceptual Frameworkmentioning

confidence: 99%

Data‐driven sensor placement for state reconstruction via POD analysis

Castillo

Messina

2020

IET Generation, Transmission & Distribution

View full text Add to dashboard Cite

Section: Conceptual Frameworkmentioning

confidence: 99%

Data‐driven sensor placement for state reconstruction via POD analysis

Castillo

Messina

2020

IET Generation, Transmission & Distribution

View full text Add to dashboard Cite

“…Meanwhile, data-driven techniques have become prevalent in the real-time operation and control of bulk power systems [7], [8], and studies have been presented regarding VOLUME 11, 2023 This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.…”

Section: Introductionmentioning

confidence: 99%

Assessing Bulk Power System Reliability by End-to-End Line Maintenance-Aware Learning

Zhu

Singh

2023

IEEE Access

View full text Add to dashboard Cite

To overcome the slow-running drawback of the Monte Carlo simulation method for bulk power system reliability assessment, this paper develops an end-to-end machine learning approach to directly predict the targeted reliability index considering grid topology changes caused by emergent line maintenance. Three machine learning models, i.e., Support Vector Machine, Boosting Trees, and Graph Neural Network, are considered and compared. The grid topology information is embedded into the above models via two feature engineering schemes. Dataset creation and data preprocessing are also described. Then, two case studies with different experimental settings and prediction targets are performed on the IEEE RTS-79 system to inspect the proposed approach's adaptability. Results demonstrate the proposed approach's effectiveness and speed advantage. Finally, an analysis is presented regarding the Support Vector Machine's generalizability against the varying dataset size based on the empirical-risk theory from the machine learning community.

show abstract

The design and experimentation of the new cascaded DC-DC boost converter for renewable energy

Can

2019

International Journal of Electronics

View full text Add to dashboard Cite

Indirect Power-System Contingency Screening for Real-Time Applications Based on PCA

Cited by 14 publications

References 3 publications

Data‐driven sensor placement for state reconstruction via POD analysis

Data‐driven sensor placement for state reconstruction via POD analysis

Assessing Bulk Power System Reliability by End-to-End Line Maintenance-Aware Learning

The design and experimentation of the new cascaded DC-DC boost converter for renewable energy

Contact Info

Product

Resources

About