False data injection threats in active distribution systems: A comprehensive survey

Husnoo, Muhammad Akbar; Anwar, Adnan; Hosseinzadeh, Nasser; Islam, Shama Naz; Mahmood, Abdun Naser; Doss, Robin

doi:10.1016/j.future.2022.10.021

Cited by 17 publications

(5 citation statements)

References 76 publications

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“…The development cost of μ-PMU is depended on three factors. 40 Also, the development cost of μ-PMU is given by, 1. The processing and communication unit of the system.…”

Section: Development Costmentioning

confidence: 99%

“…In these figs there are totally 2 μ-PMUs are located in IEEE-13 bus-system nodes, such as 2 and 7. And 12 μ-PMUs are placed at the nodes of IEEE-34 bus systems, such as 02, 10,14,20,24,34,40,38,54,88, 64, and 46.…”

Section: Placement Of 𝛍-Pmus In Different Busesmentioning

confidence: 99%

“…The development cost of μ‐PMU is depended on three factors 40 . Also, the development cost of μ‐PMU is given by, The processing and communication unit of the system. Current transformer (CT) Voltage transformer (VT)

{\mathrm{DC}}_{\upmu \hbox{-} \mathrm{pmu}}=\mathrm{Cos}{t}_{\mathrm{design}}+{\phi}_{\mathrm{VT}}\cdot \mathrm{Cos}{t}_{\mathrm{VT}}+{\phi}_{\mathrm{CT}}\cdot \mathrm{Cos}{t}_{\mathrm{CT}}\cdot h,

where,

{\mathrm{Cost}}_{\mathrm{design}}

as PMU cost main bored component based on design,

{\phi}_{\mathrm{VT}}

as count of phases, which is calculated by voltage transformer that is, almost three,

{\mathrm{Cost}}_{\mathrm{VT}}

as cost of single VT,

{\phi}_{\mathrm{CT}}

count of phases need to measure by current transformer that is, almost three and

{\mathrm{Cost}}_{\mathrm{CT}}

as single current transformer cost.…”

Section: Configuration Of μ‐Pmus With Hybrid Techniquesmentioning

confidence: 99%

See 2 more Smart Citations

Optimal location of micro‐phasor measurement units in distribution system control, monitoring, and protection using hybrid technique

Hussain¹,

Elango²

2023

Optim Control Appl Methods

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This manuscript proposes a hybrid technique for the optimum location of micro‐phasor measurement units (μ‐PMUs) in the distribution system (DS).The proposed hybrid approach is the wrapper of dwarf mongoose optimizer (DMO) and pelican optimization algorithm (POA). The updating behavior of DMO is improved by the POA; therefore, it is called DMO‐POA approach. The main aim of the proposed approach is to avoid discontinuity of supply and minimizes the count of PMDs to reduce cost and attain the maximal count of measurement‐redundancy that is limited to full‐system observability for IEEE bus‐system in zero injection nodes (ZINs) and tie‐switches. The proposed approach considered the optimum count of μ‐PMUs is needed for the thorough distribution system economy, observability, and the reliability measure. The proposed approach considers the distribution of PMUs in the reconfiguration of smart‐distribution‐grids, ZINs and communication system requirements. Here, the objective‐function is aiming at minimizing the costs of reliability and capital. The proposed method uses the infrastructure of communication cost to simultaneously co‐optimize the system switching strategy and find the optimal sites for PMUs. The performance of the proposed technique is tested in different test‐systems, like IEEE‐14 bus, 13, 34, 37, and 123 buses and the performance is executed by comparing it with the existing approaches. Furthermore, the proposed system is effectual to find an optimal solution with less computation and decreases the complexity of the algorithm.

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“…The development cost of μ-PMU is depended on three factors. 40 Also, the development cost of μ-PMU is given by, 1. The processing and communication unit of the system.…”

Section: Development Costmentioning

confidence: 99%

Section: Placement Of 𝛍-Pmus In Different Busesmentioning

confidence: 99%

{\mathrm{DC}}_{\upmu \hbox{-} \mathrm{pmu}}=\mathrm{Cos}{t}_{\mathrm{design}}+{\phi}_{\mathrm{VT}}\cdot \mathrm{Cos}{t}_{\mathrm{VT}}+{\phi}_{\mathrm{CT}}\cdot \mathrm{Cos}{t}_{\mathrm{CT}}\cdot h,

where,

{\mathrm{Cost}}_{\mathrm{design}}

as PMU cost main bored component based on design,

{\phi}_{\mathrm{VT}}

as count of phases, which is calculated by voltage transformer that is, almost three,

{\mathrm{Cost}}_{\mathrm{VT}}

as cost of single VT,

{\phi}_{\mathrm{CT}}

count of phases need to measure by current transformer that is, almost three and

{\mathrm{Cost}}_{\mathrm{CT}}

as single current transformer cost.…”

Section: Configuration Of μ‐Pmus With Hybrid Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

Optimal location of micro‐phasor measurement units in distribution system control, monitoring, and protection using hybrid technique

Hussain¹,

Elango²

2023

Optim Control Appl Methods

View full text Add to dashboard Cite

show abstract

“…Despite various detection approaches, IoT devices face challenges due to limited hardware resources specialized for specific capabilities. Intelligent and rapidly evolving IoT malware poses a challenge, as does evaluating the vast behavioral data created by IoT malware 11 . To address these challenges, Machine Learning (ML) emerges as a potential solution, offering a comprehensive approach to data preparation, evaluation, and cross-validation using algorithm-based learning curves 12 .…”

Section: Introductionmentioning

confidence: 99%

Deep learning hybridization for improved malware detection in smart Internet of Things

Almazroi,

Ayub

2024

Sci Rep

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The rapid expansion of AI-enabled Internet of Things (IoT) devices presents significant security challenges, impacting both privacy and organizational resources. The dynamic increase in big data generated by IoT devices poses a persistent problem, particularly in making decisions based on the continuously growing data. To address this challenge in a dynamic environment, this study introduces a specialized BERT-based Feed Forward Neural Network Framework (BEFNet) designed for IoT scenarios. In this evaluation, a novel framework with distinct modules is employed for a thorough analysis of 8 datasets, each representing a different type of malware. BEFSONet is optimized using the Spotted Hyena Optimizer (SO), highlighting its adaptability to diverse shapes of malware data. Thorough exploratory analyses and comparative evaluations underscore BEFSONet’s exceptional performance metrics, achieving 97.99% accuracy, 97.96 Matthews Correlation Coefficient, 97% F1-Score, 98.37% Area under the ROC Curve(AUC-ROC), and 95.89 Cohen’s Kappa. This research positions BEFSONet as a robust defense mechanism in the era of IoT security, offering an effective solution to evolving challenges in dynamic decision-making environments.

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“…The main causes of electromagnetic interference include excessive geomagnetic disturbances (GMDs), high altitude electromagnetic pulse (HEMP), intentional electromagnetic interference (IEMI), and others. The industry knows better how to deal with more common EMC threats [5][6][7][8], whereas bringing GMDs as another threat into the picture highlights new facets of reliable power grid operation.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Geomagnetically Induced Currents Impact on Power Grid Modelling

et al. 2023

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Recent history demonstrates that threat has no borders, though risk does, due to national and regional differences in vulnerabilities and exposure landscapes. The difference between well and poorly managed threat is striking. Inequalities in preparing for threats as a function of their type are still apparent. Compared to more concerning electromagnetic interference threats, the impact of geomagnetic disturbance (GMD) on power grid operation is not well studied. The need for detailed research of GMD negative impacts is expected to broaden awareness. The amplitude of geomagnetically induced currents (GICs) is treated as a uniform measure of danger that can be processed by various stakeholders. Hence, methods for increasing the accuracy of GIC representation are presented in this paper. A low-entropy signal is defined and it is shown that the feature of low signal entropy can be used for increasing the accuracy of the measurement equipment. At the end, a full-system view of GMD impact on power grid operation is given.

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False data injection threats in active distribution systems: A comprehensive survey

Cited by 17 publications

References 76 publications

Optimal location of micro‐phasor measurement units in distribution system control, monitoring, and protection using hybrid technique

Optimal location of micro‐phasor measurement units in distribution system control, monitoring, and protection using hybrid technique

Deep learning hybridization for improved malware detection in smart Internet of Things

Assessment of Geomagnetically Induced Currents Impact on Power Grid Modelling

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