Deep Learning-Based PMU Cyber Security Scheme Against Data Manipulation Attacks With WADC Application

Elimam, Mohamed; Isbeih, Younes J.; Azman, Syafiq Kamarul; Moursi, Mohamed Shawky El; Hosani, Khalifa Al

doi:10.1109/tpwrs.2022.3181353

Cited by 12 publications

(4 citation statements)

References 33 publications

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“…1) Accuracy: It is a metric to measure the percentage of correctly classified samples in the testing dataset [45]:…”

Section: Evaluating Customized Deep Cnnmentioning

confidence: 99%

“…3) Precision and Recall: These metrics are useful for evaluating the performance of a model when the classes are imbalanced. Precision measures the proportion of true positive predictions out of all positive predictions, while recall measures the proportion of true positives out of all actual positive samples [45]:…”

Section: Evaluating Customized Deep Cnnmentioning

confidence: 99%

“…4) F1 score: The F1 score is a harmonic mean of precision and recall and is useful when both measures are equally important [45]:…”

Section: Evaluating Customized Deep Cnnmentioning

confidence: 99%

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A Deep Convolutional Neural Network-Based Approach to Detect False Data Injection Attacks on PV-Integrated Distribution Systems

Ahmadzadeh,

Abazari,

Ghafouri

et al. 2024

IEEE Access

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The integration of photovoltaic (PV) panels has allowed power distribution systems (PDSs) to regulate their voltage through the injection/absorption of reactive power. The deployment of information and communication technologies (ICTs), which is required for this scheme, has made the PDS prone to various cyber threats, e.g., false data injection (FDI) attacks. To counter these attacks, this paper proposes a data-driven framework to detect FDI attacks against voltage regulation of PV-integrated PDS. Initially, an attack-free system is modeled along with its voltage regulation scheme, where the grid measurements are sent to a centralized controller and the control signals are transmitted back to PVs to be used by their local controllers. Then, a convolutional neural network (CNN) framework is proposed to detect FDI attacks. To train this framework-which should be able to distinguish between normal grid behaviors and attacks-a complete and realistic dataset is formed to cover all normal conditions and unpredictable changes of a PDS during a year. Since normal variations and fluctuations in power consumption lead to changes in the voltage profile, this dataset is enriched using features such as season, weekdays, weekends, load conditions, and PV generation power. The performance of the trained framework has been compared with other supervised Machine Learning-based and deep-learning techniques for FDI attacks against modified IEEE 33-and 141bus PDSs. Simulation results demonstrate the superior performance of the proposed framework in detecting FDI attacks.

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“…1) Accuracy: It is a metric to measure the percentage of correctly classified samples in the testing dataset [45]:…”

Section: Evaluating Customized Deep Cnnmentioning

confidence: 99%

Section: Evaluating Customized Deep Cnnmentioning

confidence: 99%

See 1 more Smart Citation

A Deep Convolutional Neural Network-Based Approach to Detect False Data Injection Attacks on PV-Integrated Distribution Systems

Ahmadzadeh,

Abazari,

Ghafouri

et al. 2024

IEEE Access

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show abstract

“…[217] This proposed method is efficient during false data detection. [218] This proposed technique includes a delayed alarm triggering mechanism to ensure reliable PMU-based data manipulation attack detection. This suggested technique enhances the system's noise immunity.…”

Section: Ref No Contributionmentioning

confidence: 99%

Real-Time Grid Monitoring and Protection: A Comprehensive Survey on the Advantages of Phasor Measurement Units

et al. 2023

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The emerging smart-grid and microgrid concept implementation into the conventional power system brings complexity due to the incorporation of various renewable energy sources and non-linear inverter-based devices. The occurrence of frequent power outages may have a significant negative impact on a nation’s economic, societal, and fiscal standing. As a result, it is essential to employ sophisticated monitoring and measuring technology. Implementing phasor measurement units (PMUs) in modern power systems brings about substantial improvement and beneficial solutions, mainly to protection issues and challenges. PMU-assisted state estimation, phase angle monitoring, power oscillation monitoring, voltage stability monitoring, fault detection, and cyberattack identification are a few prominent applications. Although substantial research has been carried out on the aspects of PMU applications to power system protection, it can be evolved from its current infancy stage and become an open domain of research to achieve further improvements and novel approaches. The three principal objectives are emphasized in this review. The first objective is to present all the methods on the synchro-phasor-based PMU application to estimate the power system states and dynamic phenomena in frequent time intervals to observe centrally, which helps to make appropriate decisions for better protection. The second is to discuss and analyze the post-disturbance scenarios adopted through better protection schemes based on accurate and synchronized measurements through GPS synchronization. Thirdly, this review summarizes current research on PMU applications for power system protection, showcasing innovative breakthroughs, addressing existing challenges, and highlighting areas for future research to enhance system resilience against catastrophic events.

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Design of a resilient wide-area damping controller using time delays

Bento

2024

Electr Eng

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Deep Learning-Based PMU Cyber Security Scheme Against Data Manipulation Attacks With WADC Application

Cited by 12 publications

References 33 publications

A Deep Convolutional Neural Network-Based Approach to Detect False Data Injection Attacks on PV-Integrated Distribution Systems

A Deep Convolutional Neural Network-Based Approach to Detect False Data Injection Attacks on PV-Integrated Distribution Systems

Real-Time Grid Monitoring and Protection: A Comprehensive Survey on the Advantages of Phasor Measurement Units

Design of a resilient wide-area damping controller using time delays

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