A learning intelligent System for classification and characterization of localized faults in Smart Grids

Santis, Enrico De; Rizzi, Antonello; Sadeghian, Alireza

doi:10.1109/cec.2017.7969631

Cited by 11 publications

(5 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the decision tree algorithm, based on the feature value decision is made on data instances, thus grid data can be classified as faulty or anomalous. Santisa et al [45] and [59] considered it as a one-class classification problem and proposed a combined method of dissimilarity measures learning by evolutionary learning and clustering techniques. Then they analyzed the results using a fuzzy set-based decision rule.…”

Section: ) Power Data Analyticsmentioning

confidence: 99%

See 1 more Smart Citation

A Comprehensive Review on Big Data for Industries: Challenges and Opportunities

et al. 2023

View full text Add to dashboard Cite

Technological advancements in large industries like power, minerals, and manufacturing are generating massive data every second. Big data techniques have opened up numerous opportunities to utilize massive datasets in several effective ways to improve the efficacy of related industries. This paper presents a review of big data technologies used in the power, mineral, and manufacturing industries for various purposes. We analyze the meta-data of the collected papers before reviewing and selecting papers by applying selection criteria and paper quality assessment strategy. Then we propose a taxonomy of big data application areas in the power, mineral, and manufacturing industries. We have studied current big data architectures and techniques implemented in industry sectors and have uncovered the big data research gaps in industry sectors. To address the gaps, we point out some relevant research questions and, to answer the questions, we make some future research recommendations that might explore interesting research ideas for building a big data-driven industry. As the careful use of big data benefits every other industry sector; hence, supportive big data frameworks need to be developed to speed up the big data analysis process. Proper multi-dimensional big data assessment is also needed to take into account for serving effective data analysis tasks.INDEX TERMS Big data for industry, smart grid, power system, oil and gas industry, minerals, big data technology, manufacturing industry, big-data-driven industry.

show abstract

Section: ) Power Data Analyticsmentioning

confidence: 99%

“…Power fault [45], [59], power quality event [31] detection Transmission fault [92] detection Power swing fault, islanding [76], [16] detection [92],…”

Section: Malbasa Proposed Activementioning

confidence: 99%

A Comprehensive Review on Big Data for Industries: Challenges and Opportunities

et al. 2023

View full text Add to dashboard Cite

show abstract

“…However, the approach was limited to collecting the security system information in a distribution system with DGs for fault recognition or failure detection. Likewise, De Santis et al [134] developed a method to detect faults in the grid activity of medium-voltage feeders in Rome, Italy. In addition, the encryption solution for an advanced metering network (AMI) of the SG has been suggested and introduced in [135] for a specific AMI encryption method.…”

Section: Challenges and Future Prospectsmentioning

confidence: 99%

Cyber–physical security for on‐going smart grid initiatives: a survey

Hossain

Peng

2020

IET cyber-phys. syst.

View full text Add to dashboard Cite

The smart grid is an upgraded concept of electricity network with tight coupling among information, control, and bidirectional communication technologies. Along with the silent features of the ongoing smart grid, cyber-physical security appears to be a deep concern due to its significant dependence on sensing technologies, complex networks of computers, intelligence, and wide-area communication infrastructures. Moreover, the smart grid is an extensive critical infrastructure and vulnerable to coordinated cyber-physical attacks. As a result, cyber-physical attacks cause significant threats to the confidentiality and integrity of the power data, including power outages, cascading failures, and unnecessary expenditure. In this study, security issues of smart grid, including cyber-physical interdependency, attack varieties, detection methods, requirements, standards, challenges, and future prospects, are taken into consideration for both cyber and physical systems, aiming to provide an extensive understanding of vulnerabilities and solutions for the smart power grid. By revealing the inherent features of cyber-physical security in the smart grid, this survey study is addressed to facilitate future research in these two areas.

show abstract

“…Thanks to the development of ICT technology in power systems, a huge volume of data can be collected via AMI and communication infrastructures. Power system operating data, weather information and log data of relay protection devices are processed as the input of a one class classification system, which is a data-driven model of fault phenomena based on a hybridization of evolutionary learning and clustering techniques in (De Santis et al, 2015;De Santis et al, 2017). This fault recognition system is validated in the medium voltage power grid in Rome.…”

Section: Predictive Maintenance/condition Based Maintenancementioning

confidence: 99%

Big data analytics in smart grids: a review

2018

View full text Add to dashboard Cite

Data analytics are now playing a more important role in the modern industrial systems. Driven by the development of information and communication technology, an information layer is now added to the conventional electricity transmission and distribution network for data collection, storage and analysis with the help of wide installation of smart meters and sensors. This paper introduces the big data analytics and corresponding applications in smart grids. The characterizations of big data, smart grids as well as huge amount of data collection are firstly discussed as a prelude to illustrating the motivation and potential advantages of implementing advanced data analytics in smart grids. Basic concepts and the procedures of the typical data analytics for general problems are also discussed. The advanced applications of different data analytics in smart grids are addressed as the main part of this paper. By dealing with huge amount of data from electricity network, meteorological information system, geographical information system etc., many benefits can be brought to the existing power system and improve the customer service as well as the social welfare in the era of big data. However, to advance the applications of the big data analytics in real smart grids, many issues such as techniques, awareness, synergies, etc., have to be overcome.

show abstract

A learning intelligent System for classification and characterization of localized faults in Smart Grids

Cited by 11 publications

References 10 publications

A Comprehensive Review on Big Data for Industries: Challenges and Opportunities

A Comprehensive Review on Big Data for Industries: Challenges and Opportunities

Cyber–physical security for on‐going smart grid initiatives: a survey

Big data analytics in smart grids: a review

Contact Info

Product

Resources

About