A Review of Machine Learning Approaches in Synchrophasor Technology

Lal, M Dhinu; Ramesh, V.

doi:10.1109/access.2023.3263547

Cited by 10 publications

(2 citation statements)

References 187 publications

(216 reference statements)

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“…Machine learning-based error correction codes and communication protocols offer superior efficacy and efficiency in PLC systems compared to conventional approaches [110,111]. Nevertheless, several factors must be considered when employing and evaluating these techniques.…”

Section: Evaluation Of Machine Learning-based Methods In Plc Systemsmentioning

confidence: 99%

Machine Learning-Based Error Correction Codes and Communication Protocols for Power Line Communication: An Overview

Akinci,

Erdemir,

Zengin

et al. 2023

IEEE Access

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This study endeavors to investigate the effectiveness of machine learning-based methodologies in enhancing the performance and reliability of Power Line Communication (PLC) systems. PLC systems constitute a critical component within the domains of energy management, monitoring, and automation. The fundamental objective herein is to contribute significantly to the scholarly discourse by conducting a comprehensive review encompassing research investigations and practical applications documented in the extant literature. The primary motivation underpinning this research is predicated upon the necessity for a meticulous evaluation of machine learning techniques that hold the potential to enhance the efficacy and stability of PLC systems. The deployment of these techniques bears the promise of engendering heightened levels of efficiency across the spectrum of energy management, communication, and automation systems. Within this scholarly quest, the study posits a hypothesis: Machine learning-based methodologies possess the capacity to effect marked improvements in the performance and reliability of PLC systems. Methodological scrutiny is executed through a comprehensive evaluation of diverse machine learning techniques, including, but not limited to, deep learning, support vector machines, and random forests, facilitated by a series of empirical experiments and simulations. Empirical findings resoundingly corroborate the proposition, substantiating a significant enhancement in the operational performance of PLC systems when these machine learning methods are judiciously employed. In summation, this study assumes the role of a catalyst in exploring latent, untapped potential inherent within machine learning-based methodologies, customarily calibrated to resonate within the intricate matrix of PLC systems. The zenith of this rigorous investigation stands poised to illuminate the path toward transformative advancements in the domains of energy management, communication, monitoring, and automation systems. The findings contribute significantly to the academic discourse, offering a compass for future research inquiries and practical applications within this burgeoning and dynamic field. INDEX TERMSPower line communication, error correction codes, machine learning, transmission control protocols, communication protocols, power networks.

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Section: Evaluation Of Machine Learning-based Methods In Plc Systemsmentioning

confidence: 99%

Machine Learning-Based Error Correction Codes and Communication Protocols for Power Line Communication: An Overview

Akinci,

Erdemir,

Zengin

et al. 2023

IEEE Access

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“…Due to their crucial role in offering the robustness and security of smart grid condition monitoring, this study emphasizes the latter aspect of the design of cyberattack mitigation systems. In this context, by utilizing cutting-edge learning algorithms, deep learning is an efficient way to participate in mitigating the possible impacts of cyberattacks on PMUs [9]. Deep learning models are considered to be very effective in promptly sensing any signs of a cyberattack and evaluating enormous amounts of PMU data.…”

Section: Introductionmentioning

confidence: 99%

Towards Resilient and Secure Smart Grids against PMU Adversarial Attacks: A Deep Learning-Based Robust Data Engineering Approach

Berghout

Benbouzid

2023

Electronics

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In an attempt to provide reliable power distribution, smart grids integrate monitoring, communication, and control technologies for better energy consumption and management. As a result of such cyberphysical links, smart grids become vulnerable to cyberattacks, highlighting the significance of detecting and monitoring such attacks to uphold their security and dependability. Accordingly, the use of phasor measurement units (PMUs) enables real-time monitoring and control, providing informed-decisions data and making it possible to sense abnormal behavior indicative of cyberattacks. Similar to the ways it dominates other fields, deep learning has brought a lot of interest to the realm of cybersecurity. A common formulation for this issue is learning under data complexity, unavailability, and drift connected to increasing cardinality, imbalance brought on by data scarcity, and fast change in data characteristics, respectively. To address these challenges, this paper suggests a deep learning monitoring method based on robust feature engineering, using PMU data with greater accuracy, even within the presence of cyberattacks. The model is initially investigated using condition monitoring data to identify various disturbances in smart grids free from adversarial attacks. Then, a minimally disruptive experiment using adversarial attack injection with various reality-imitating techniques is conducted, inadvertently damaging the original data and using it to retrain the deep network, boosting its resistance to manipulations. Compared to previous studies, the proposed method demonstrated promising results and better accuracy, making it a potential option for smart grid condition monitoring. The full set of experimental scenarios performed in this study is available online.

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A Survey of Integrating Federated Learning with Smart Grids: Application Prospect, Privacy Preserving and Challenges Analysis

Tang

Yan

Wang

et al. 2023

Communications in Computer and Information Science

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A Review of Machine Learning Approaches in Synchrophasor Technology

Abstract: I. Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Cited by 10 publications

References 187 publications

Machine Learning-Based Error Correction Codes and Communication Protocols for Power Line Communication: An Overview

Machine Learning-Based Error Correction Codes and Communication Protocols for Power Line Communication: An Overview

Towards Resilient and Secure Smart Grids against PMU Adversarial Attacks: A Deep Learning-Based Robust Data Engineering Approach

A Survey of Integrating Federated Learning with Smart Grids: Application Prospect, Privacy Preserving and Challenges Analysis

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