WAMS Operations in Power Grids: A Track Fusion-Based Mixture Density Estimation-Driven Grid Resilient Approach Toward Cyberattacks

Khalid, Haris M.; Qasaymeh, M. M.; Khalid, Haris M.; Moursi, Mohamed Shawky El; Foley, Aoife; Sweidan, Tha’er O.; Padmanaban, Sanjeevikumar

doi:10.1109/jsyst.2023.3285492

Cited by 14 publications

(6 citation statements)

References 31 publications

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“…The Kullback-Leibler distance is used in Chaojun et al (2015) to calculate the difference of the probability distributions between online measurement and the historical data to identify cyberattacks against alternating current (AC) state estimation. Moreover, a mixture density-based maximum likelihood estimation algorithm is proposed in Khalid et al (2023b) to identify cyberattack vectors for WAMS. In Khalid et al (2023a), a median regression function-based state estimation is presented to mitigate the impact of cyberattacks in modern power grids that extensively utilize PMU measurements.…”

Section: Model-based Methodsmentioning

confidence: 99%

Cyber resilience methods for smart grids against false data injection attacks: categorization, review and future directions

Syrmakesis,

Hatziargyriou

2024

Front. Smart Grids

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For a more efficient monitoring and control of electrical energy, the physical components of conventional power systems are continuously integrated with information and communication technologies, converting them into smart grids. However, energy digitalization exposes power systems into a wide range of digital risks. The term cyber resilience for electrical grids expands the conventional resilience of power systems, which mainly refers to extreme weather phenomena. Since this is a relatively new term, there is a need for the establishment of a solid conceptual framework. This paper analyzes and classifies the state-of-the-art research methodologies proposed for strengthening the cyber resilience of smart grids. To this end, the proposed work categorizes the cyberattacks against smart grids, identifies the vulnerable spots of power system automation and establishes a common ground about the cyber resilience. The paper concludes with a discussion about the limitations of the proposed methods in order to extract useful suggestions for future directions.

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Section: Model-based Methodsmentioning

confidence: 99%

Cyber resilience methods for smart grids against false data injection attacks: categorization, review and future directions

Syrmakesis,

Hatziargyriou

2024

Front. Smart Grids

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“…A parallelized message passing interface (MPI)-based computing was processed to reduce its computational burden. This work adopted a mature application known as oscillation detection as an example of a monitoring candidate to demonstrate the proposed method (Khalid et al, 2023).…”

Section: Review Of Existing Literaturementioning

confidence: 99%

Electricity theft detection in smart grid using machine learning

Iftikhar,

Khan,

Raza

et al. 2024

Front. Energy Res.

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Nowadays, electricity theft is a major issue in many countries and poses a significant financial loss for global power utilities. Conventional Electricity Theft Detection (ETD) models face challenges such as the curse of dimensionality and highly imbalanced electricity consumption data distribution. To overcome these problems, a hybrid system Multi-Layer Perceptron (MLP) approach with Gated Recurrent Units (GRU) is proposed in this work. The proposed hybrid system is applied to analyze and solve electricity theft using data from the Chinese National Grid Corporation (CNGC). In the proposed hybrid system, first, preprocess the data; second, balance the data using the k-means Synthetic Minority Oversampling Technique (SMOTE) technique; third, apply the GTU model to the extracted purified data; fourth, apply the MLP model to the extracted purified data; and finally, evaluate the performance of the proposed system using different performance measures such as graphical analysis and a statistical test. To verify the consistency of our proposed hybrid system, we use three different ratios for training and testing the dataset. The outcomes show that the proposed hybrid system for ETD is highly accurate and efficient compared to the other models like Alexnet, GRU, Bidirectional Gated Recurrent Unit (BGRU) and Recurrent Neural Network (RNN).

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“…The integration of advanced technologies within the power grid infrastructure has revolutionized the way electricity is generated, transmitted, and distributed [1]. Smart grids, enabled by cuttingedge digital communication and control systems, promise enhanced efficiency, reliability, and sustainability [2].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Resilience: Smart Grid Cybersecurity and Fault Diagnosis Strategies

Basharat,

Huma

2024

AJRCoS

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The increasing integration of advanced technologies within the power grid infrastructure has led to significant advancements in efficiency, reliability, and sustainability. However, this integration also introduces new vulnerabilities, particularly in the realm of cybersecurity. This paper presents an overview of smart grid cybersecurity challenges and proposes strategies for enhancing resilience through fault diagnosis techniques. Firstly, the paper examines the evolving threat landscape facing smart grids, encompassing cyber-attacks, insider threats, and natural disasters. It highlights the critical need for robust cybersecurity measures to safeguard grid operations and prevent potentially catastrophic disruptions. Next, the paper delves into various cybersecurity frameworks and standards tailored specifically for smart grids, emphasizing the importance of comprehensive risk assessment, intrusion detection systems, and secure communication protocols. Additionally, it discusses the role of machine learning and artificial intelligence in augmenting cyber defense capabilities, enabling proactive threat detection and rapid response. Furthermore, the paper explores fault diagnosis strategies aimed at maintaining grid resilience in the face of cyber incidents or physical faults. It discusses the integration of data analytics, predictive modeling, and real-time monitoring to identify and mitigate potential grid disturbances swiftly.

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WAMS Operations in Power Grids: A Track Fusion-Based Mixture Density Estimation-Driven Grid Resilient Approach Toward Cyberattacks

Cited by 14 publications

References 31 publications

Cyber resilience methods for smart grids against false data injection attacks: categorization, review and future directions

Cyber resilience methods for smart grids against false data injection attacks: categorization, review and future directions

Electricity theft detection in smart grid using machine learning

Enhancing Resilience: Smart Grid Cybersecurity and Fault Diagnosis Strategies

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