Wavelet multiscale principal components and traveling waves enabled machine learning technique for protection of MT-HVDC systems

Muzzammel, Raheel; Raza, Ali; Arshad, Rabia; Sobahi, Nebras; Attar, Eyad T.

doi:10.1016/j.egyr.2023.03.039

Cited by 4 publications

(1 citation statement)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tree fundamental approaches-AI-based, mechanismbased, and signal processing-based-can detect and classify faults in MMC-HVDC systems [21].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Fault Detection and Classification in MMC-HVDC Systems: Integrating Harris Hawks Optimization Algorithm with Machine Learning Methods

Hazim Hameed Hameed,

Kutbay,

Rahebi

et al. 2024

International Transactions on Electrical Energy Systems

View full text Add to dashboard Cite

Accurate fault detection in high-voltage direct current (HVDC) transmission lines plays a pivotal role in enhancing operational efficiency, reducing costs, and ensuring grid reliability. This research aims to develop a cost-effective and high-performance fault detection solution for HVDC systems. The primary objective is to accurately identify and localize faults within the power system. In pursuit of this goal, the paper presents a comparative analysis of current and voltage characteristics between the rectifier and inverter sides of the HVDC transmission system and their associated alternating current (AC) counterparts under various fault conditions. Voltage and current features are extracted and optimized using a metaheuristic approach, specifically Harris Hawk’s optimization method. Leveraging machine learning (ML) and artificial neural networks (ANN), this technique demonstrates its effectiveness in generating a fault locator with exceptional accuracy. With a substantial volume of data employed for learning and training, the Harris Hawks optimization method exhibits faster convergence compared to other metaheuristic methods examined in this study. The research findings are applied to simulate diverse fault types and unknown fault locations at multiple system points. Evaluating the fault detection system’s effectiveness, quantified through metrics such as specificity, accuracy, F1 score, and sensitivity, yields remarkable results, with percentages of 99.01%, 98.69%, 98.64%, and 98.67%, respectively. This research underscores the critical role of accurate fault detection in HVDC systems, offering valuable insights into optimizing grid performance and reliability.

show abstract

“…Tree fundamental approaches-AI-based, mechanismbased, and signal processing-based-can detect and classify faults in MMC-HVDC systems [21].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Fault Detection and Classification in MMC-HVDC Systems: Integrating Harris Hawks Optimization Algorithm with Machine Learning Methods

Hazim Hameed Hameed,

Kutbay,

Rahebi

et al. 2024

International Transactions on Electrical Energy Systems

View full text Add to dashboard Cite

show abstract

A systematic review of fault characteristics and protection schemes in hybrid AC/DC networks: Challenges and future directions

Mehdi,

Ul Hassan,

Haider

et al. 2024

Energy Reports

View full text Add to dashboard Cite

Fuzzy Logic Approach Based Fault Detection in Multi Terminal HVDC Transmission Systems

Aboelezz,

Sedhom,

Saeed

et al. 2023

2023 24th International Middle East Power System Conference (MEPCON)

View full text Add to dashboard Cite

Wavelet multiscale principal components and traveling waves enabled machine learning technique for protection of MT-HVDC systems

Cited by 4 publications

References 65 publications

Enhancing Fault Detection and Classification in MMC-HVDC Systems: Integrating Harris Hawks Optimization Algorithm with Machine Learning Methods

Enhancing Fault Detection and Classification in MMC-HVDC Systems: Integrating Harris Hawks Optimization Algorithm with Machine Learning Methods

A systematic review of fault characteristics and protection schemes in hybrid AC/DC networks: Challenges and future directions

Fuzzy Logic Approach Based Fault Detection in Multi Terminal HVDC Transmission Systems

Contact Info

Product

Resources

About