Stability and Dynamics of Active Distribution Networks (ADNs) With D-PMU Technology: A Review

Bu, Siqi; Meegahapola, Lasantha; Wadduwage, D. Prasad; Foley, Aoife

doi:10.1109/tpwrs.2022.3179488

Cited by 23 publications

(2 citation statements)

References 113 publications

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“…The use of smart grids entails two-way monitoring, communication, and control, boosting the efficiency and stability of the energy supply while enabling more flexibility and better control over energy consumption [1]. A more sustainable energy system and generation mix can potentially be implemented by means of a smart grid, which also enables the greater management of renewable energy sources [2]. However, owing to their heavy reliance on such technology, smart grid components are all vulnerable to cyberattacks, where cybercriminals can target them individually and manipulate data throughout the network [3].…”

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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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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“…ote that the phasor measurement unit (PMU) technology developed for transmission grids cannot measure small phase angles in distribution systems. Consequently, the 'DPMU', also known as 'micro-PMU (µPMU)', has been developed with high resolution [5,6]. DPMUs can provide accurate synchronized measurements of phasors and amplitudes for voltage and current [7,8].…”

Section: Introductionmentioning

confidence: 99%

Optimal placement of distribution-level synchrophasor sensors for distribution system

Chen¹,

Liu²,

Chen³

et al. 2022

Meas. Sci. Technol.

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This paper proposes a new method for the placement of the important synchrophasor sensors, the Distribution-level Phasor Measurement Units (DPMUs). The new objective function and constraints are developed to achieve the minimization of distribution system state estimation error, in the presence of the limited number of DPMUs, the zero injection buses, the limited DPMU channels and the Supervisory Control And Data Acquisition (SCADA) measurements. The A-optimal experimental design criterion is utilized to formulate the proposed objective function and constraints. The state estimation error is obtained using the maximum likelihood estimator under non-Gaussian measurement noise since the traditional assumption of Gaussian noise may violate the reality. The performance of the proposed method is demonstrated by simulations on the IEEE 34-bus and 123-bus distribution systems.

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Anti-islanding image detection and optimization of distributed power supply using neural network architecture search

Xiong,

Li,

Wang

et al. 2024

Discov Computing

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Stability and Dynamics of Active Distribution Networks (ADNs) With D-PMU Technology: A Review

Cited by 23 publications

References 113 publications

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

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

Optimal placement of distribution-level synchrophasor sensors for distribution system

Anti-islanding image detection and optimization of distributed power supply using neural network architecture search

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