IoT-based data-driven fault allocation in microgrids using advanced µPMUs

Kavousi‐Fard, Abdollah; Nikkhah, Saeed; Pourbehzadi, Motahareh; Dabbaghjamanesh, Morteza; Farughian, Amir

doi:10.1016/j.adhoc.2021.102520

Cited by 21 publications

(8 citation statements)

References 27 publications

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“…Furthermore, the data transmission and computation complexity have been greatly reduced due to the use of IoT infrastructure by the µPMUs. [32] Faults must be identified in real time and with high precision using the real-time highresolution measurements made possible by the deployment of µPMUs. Debottam Mukherjee et al presented comparisons of several machine learning algorithms to classify faults such as L-G (Line to ground), LL-G (Double line to ground), and LLL-G (line to line to line to ground).…”

Section: Event Detectionmentioning

confidence: 99%

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A Comprehensive review on the current trends in Micro-Phasor Measurement Units

Annamalai

Saranathan

Achuthan

et al. 2022

IOP Conf. Ser.: Mater. Sci. Eng.

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Micro-Phasor Measurement Units are invaluable data sources that improve visibility in a variety of domains of application, including power generation, transmission, and distribution. It started out with the objective of acquiring phasor values at specific points across the network and synchronizing them to a standard time frame. The technology has evolved into an all-encompassing unit with applications in the fields of protection, control, and automation. In light of the fact that these units are now being used as a standard benchmark, it is imperative that they be improved. This study presents a comprehensive review into the world of Synchro-Phasor technology, covering all aspects of the production of μPMUs and their role in the modern power system. In addition, the current deployment status of this technology has also been discussed to further emphasize its emergence.

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Section: Event Detectionmentioning

confidence: 99%

“…On the other hand, µPMUs have an angle measurement accuracy of about ±0.01 % with ±0.002 % of angle resolution. In contrast to PMUs which have a data rate of 15 readings per second, µPMUs have a data rate of 100 -120 readings per second [32].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive review on the current trends in Micro-Phasor Measurement Units

Annamalai

Saranathan

Achuthan

et al. 2022

IOP Conf. Ser.: Mater. Sci. Eng.

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“…Via computing the ΔV / ΔI for the μPMUs that have been used in the upstream and the downstream of the DG, the upstream and downstream impedances Z up and Z down are calculated [24]:…”

Section: Problem Formulationmentioning

confidence: 99%

“…Via computing the Δ V / Δ I for the μPMUs that have been used in the upstream and the downstream of the DG, the upstream and downstream impedances Z up and Z down are calculated [24]:

\begin{equation}{Z_{up}} = \frac{{\Delta {V_{up}}}}{{\Delta {I_{up}}}}\end{equation}

\begin{equation}{Z_{down}} = \frac{{\Delta {V_{down}}}}{{\Delta {I_{down}}}}\end{equation}

…”

Section: Problem Formulationmentioning

confidence: 99%

“…In this framework, UT would be deployed for handling the uncertainty effects of the loads. Assuming Δ V m,f as the voltage change of forward voltage calculation of m th bus and Z m‐ 1 ,m as the impedance between buses ‘ m ‐1′ and ‘ m ’, the computations of forward bus voltage have been performed as [24]:

\fontsize{8}{10}\begin{eqnarray} \def\eqcellsep{&}\begin{array}{l} E(\Delta V_{m,f}) = \\[6pt]\quad\quad\quad E(\Delta V_{m - 1,f}) + (E(\Delta {I_{^{up}}}) + E(\Delta {I_1}) + \cdots + E(\Delta I_{m - 1,f}))\\[6pt]\quad\quad\quad{Z_{^{m - 1,m}}}\quad \forall m \in {\Omega ^{bus}} \end{array} \end{eqnarray}

…”

Section: Problem Formulationmentioning

confidence: 99%

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A blockchain‐enabled secured fault allocation in smart grids based on μ PMUs and UT

Almutairi

Alrumayh

Alyami

et al. 2021

IET Renewable Power Gen

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Improvements in the high accuracy of micro‐phasor measurement unit (μPMU) and synchrophasor unit provide the distribution network a special perspective to access full observability in the presence of renewable distribution generations (DGs). In this regard, modelling the uncertainty effects for accurate fault detection in smart grids has become a novel turning point. This research work attempts to develop a novel stochastic architecture based on unscented transform (UT), which requires a very few number of μPMUs, to communicate in a secured blockchain‐enabled structure to allocate and detect the smart grid irregularities and faults. Such a blockchain‐based technique can be beneficial for encrypting the data transmitted among the μPMUs in the smart grid consisting of a variety of distributed energy sources (DERs) and high impedance fault events, while incorporating the uncertainties associated with the load and individual installation sensors. The proposed stochastic structure benefits from voltage measurement and uses its angle in event placement that performs more efficiently than the conventional sensors based on high‐voltage and current metering. The simulation results show the great precision and effectiveness of the proposed model during various fault scenarios in smart grid.

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Recent trends and developments in protection systems for microgrids incorporating distributed generation

Ashraf,

Hasan,

Evkay

et al. 2024

WIREs Energy & Environment

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Microgrids are integral to power grids; they enhance grid reliability by integrating distributed generators (DGs) to fulfill the local load requirements, lowering energy generation costs, and providing eco‐friendly energy resources to reduce carbon emissions. Despite their benefits, new capabilities also introduce protection‐related complexities, including bidirectional power flow, varying fault currents, false tripping, relay reach limitations, coordination issues, and the requirement for proper grounding. This article offers a detailed review of protection issues in AC, DC, and hybrid AC–DC microgrids, investigating existing approaches to address these issues. Furthermore, the constraints and hurdles associated with these approaches and the future trends and intelligent approaches required to develop a reliable and efficient protection strategy are also identified in this article.This article is categorized under: Energy and Power Systems > Microgrids Energy and Power Systems > Distributed Generation

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IoT-based data-driven fault allocation in microgrids using advanced µPMUs

Cited by 21 publications

References 27 publications

A Comprehensive review on the current trends in Micro-Phasor Measurement Units

A Comprehensive review on the current trends in Micro-Phasor Measurement Units

A blockchain‐enabled secured fault allocation in smart grids based on μ PMUs and UT

Recent trends and developments in protection systems for microgrids incorporating distributed generation

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