The Current State of the Art in Research on Predictive Maintenance in Smart Grid Distribution Network: Fault’s Types, Causes, and Prediction Methods—A Systematic Review

Mahmoud, Moamin A.; Nasir, Naziffa Raha Md; Gurunathan, Mathuri; Raj, P.K. Arul; Mostafa, Salama A.

doi:10.3390/en14165078

Cited by 52 publications

(14 citation statements)

References 91 publications

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“…Recent research [3] outlined how digital technologies may support electricity system decarbonization and process optimization. The report delineated various use cases employing big data (BD) [9,10], digital twins (DT) [11,12], artificial intelligence (AI) [13,14], internet of things (IoT) [15], distributed ledger (DL -incl. blockchain [16,17]), and cloud computing (CC) among others [18].…”

Section: Digitalization In the Electricity Systemmentioning

confidence: 99%

Digitalization in decarbonizing electricity systems – Phenomena, regional aspects, stakeholders, use cases, challenges and policy options

Heymann

Verma

Milojevic³

et al. 2023

Energy

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Section: Digitalization In the Electricity Systemmentioning

confidence: 99%

Digitalization in decarbonizing electricity systems – Phenomena, regional aspects, stakeholders, use cases, challenges and policy options

Heymann

Verma

Milojevic³

et al. 2023

Energy

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“…Various classification structures, such as hierarchy tree and faceted analysis [10], have been used recently to develop taxonomies in knowledge fields, such as education [11], psychology [12], computer science [13], and cyberattack [14]. Taxonomy has also been implemented to manage information and data in maintenance management [15] and for classifying business applications [16] and for smart grid predictive maintenance [17].…”

Section: Current State Of the Artmentioning

confidence: 99%

Taxonomy of Damage Patterns in Composite Materials, Measuring Signals, and Methods for Automated Damage Diagnostics

2022

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Due to the increasing use of the different composite materials in lightweight applications, such as in aerospace, it becomes crucial to understand the different damages occurring within them during life cycle and their possible inspection with different inspection techniques in different life cycle stages. A comprehensive classification of these damage patterns, measuring signals, and analysis methods using a taxonomical approach can help in this direction. In conjunction with the taxonomy, this work addresses damage diagnostics in hybrid and composite materials, such as fibre metal laminates (FMLs). A novel unified taxonomy atlas of damage patterns, measuring signals, and analysis methods is introduced. Analysis methods based on advanced supervised and unsupervised machine learning algorithms, such as autoencoders, self-organising maps, and convolutional neural networks, and a novel z-profiling method, are implemented. Besides formal aspects, an extended use case demonstrating damage identification in FML plates using X-ray computer tomography (X-ray CT) data is used to elaborate different data analysis techniques to amplify or detect damages and to show challenges.

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“…Predictive maintenance is a major area of applied research in the energy operations sector [27], [28]. The ability to determine areas of the electrical grid which are susceptible to failing in some pre-determined timespan has many obvious benefits for customers downstream from any grid infrastructure which may fail.…”

Section: A German Electricity Grid Datamentioning

confidence: 99%

Practical Quantum K-Means Clustering: Performance Analysis and Applications in Energy Grid Classification

DiAdamo¹,

O'Meara²,

Cortiana³

et al. 2022

IEEE Trans. Quantum Eng.

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In this work, we aim to solve a practical use-case of unsupervised clustering which has applications in predictive maintenance in the energy operations sector using quantum computers. Using only cloud access to quantum computers, we complete a thorough performance analysis of what some current quantum computing systems are capable of for practical applications involving non-trivial midto-high dimensional datasets. We first benchmark how well distance estimation can be performed using two different metrics based on the swap-test, using angle and amplitude data embedding. Next, for the clustering performance analysis, we generate sets of synthetic data with varying cluster variance and compare simulation to physical hardware results using the two metrics. From the results of this performance analysis, we propose a general, competitive, and parallelized version of quantum k-means clustering to avoid some pitfalls discovered due to noisy hardware and apply the approach to a real energy grid clustering scenario. Using real-world German electricity grid data, we show that the new approach improves the balanced accuracy of the standard quantum k-means clustering by 67.8% with respect to the labeling of the classical algorithm.INDEX TERMS Quantum clustering, quantum distance estimation, quantum computing, cloud quantum computing.

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The Current State of the Art in Research on Predictive Maintenance in Smart Grid Distribution Network: Fault’s Types, Causes, and Prediction Methods—A Systematic Review

Cited by 52 publications

References 91 publications

Digitalization in decarbonizing electricity systems – Phenomena, regional aspects, stakeholders, use cases, challenges and policy options

Digitalization in decarbonizing electricity systems – Phenomena, regional aspects, stakeholders, use cases, challenges and policy options

Taxonomy of Damage Patterns in Composite Materials, Measuring Signals, and Methods for Automated Damage Diagnostics

Practical Quantum K-Means Clustering: Performance Analysis and Applications in Energy Grid Classification

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