Historical Multi-Station SCADA Data Compression of Distribution Management System Based on Tensor Tucker Decomposition

Zhao, Huiru; Ma, Libo; Yan, Xihui; Zhao, Yang

doi:10.1109/access.2019.2937383

Cited by 6 publications

(5 citation statements)

References 15 publications

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“…For example, BT Sport uses Aspera technology to power its intense file-based production workflows. Furthermore, other technologies, such as data compression, are available to facilitate the transfer of large amounts of data [27,28].…”

Section: Deploying Data and Software In Fog Nodes And Cloud Serversmentioning

confidence: 99%

Fog Computing for Smart Cities’ Big Data Management and Analytics: A Review

Badidi¹,

Mahrez

Sabir

2020

Future Internet

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Demographic growth in urban areas means that modern cities face challenges in ensuring a steady supply of water and electricity, smart transport, livable space, better health services, and citizens’ safety. Advances in sensing, communication, and digital technologies promise to mitigate these challenges. Hence, many smart cities have taken a new step in moving away from internal information technology (IT) infrastructure to utility-supplied IT delivered over The Internet. The benefit of this move is to manage The vast amounts of data generated by The various city systems, including water and electricity systems, The waste management system, transportation system, public space management systems, health and education systems, and many more. Furthermore, many smart city applications are time-sensitive and need to quickly analyze data to react promptly to The various events occurring in a city. The new and emerging paradigms of edge and fog computing promise to address big data storage and analysis in The field of smart cities. Here, we review existing service delivery models in smart cities and present our perspective on adopting these two emerging paradigms. We specifically describe The design of a fog-based data pipeline to address The issues of latency and network bandwidth required by time-sensitive smart city applications.

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Section: Deploying Data and Software In Fog Nodes And Cloud Serversmentioning

confidence: 99%

Fog Computing for Smart Cities’ Big Data Management and Analytics: A Review

Badidi¹,

Mahrez

Sabir

2020

Future Internet

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“…Dalam mengontrol aliran daya listrik melalui sistem SCADA pasti akan terjadi kejadian kegagalan dalam sistem seperti Downtime peralatan ataupun gangguan lain darisistem SCADA seperti kegagalan server, workstation, dan Link Telekomunikasi. [3] Hal ini berpengaruh pada parameter kinerja sistem SCADA seperti daya yang harusnya di transfer dari gardu induk lain yang tidak terjadi gangguan ke gardu induk yang sedang terjadi gangguan hal ini dilakukan untuk mengcover ketersediaan listrik dari gardu induk yang mengalami kegagalan untuk menghindari terjadinya Blackout (Pemadamanlistrik). [4] Untuk meminimalisir keadaan tersebut maka dilakukan perhitungan Availability dari sistem SCADA agar nilai keandalan dari masingmasing infrastruktur pendukung sistem SCADA dapat memenuhi standar kinerja yang telah di tentukan oleh PT.PLN (Persero) sebagai perusahaan penyedia listrik nasional.…”

Section: Pendahuluanunclassified

Penentuan Kinerja Sistem SCADA PT.PLN (Persero) Unit Pelaksana Pengatur Beban Area Jakarta dan Banten Berdasarkan Nilai Mean Time To Repair

Astuti

Fayakun

Ramza

2020

Prosid Sem Nas Teknoka

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Sistem SCADA dan Telekomunikasi yang dibentuk dari sub sistem teleinformasi data yang merupakan rangkaian perangkat dan dapat menghasilkan fungsi power sistem untuk dikontrololeh sub sistem master station pada yang terdapat pada kontrol center. Dalam menjalankan fungsinya setiap peralatan pada masing – masing sub sistem harus dapat memberikan layanyan dari segi kualitas dan kuantitas hal tersebut dapat diukur melalui kinerja. Target kinerja pengusahaan yang telah ditetapkan merupakan kesanggupan unit pelaksana pengatur beban yang memberikan hasil layanan (availability) berupa fungsi sub sistem yang di dapat melalui perhitungan nilai MTTR(Mean Time To Repair) pada masing – masing sub sistem selama periode triwulan (satu tahun anggaran). Dari hasil penentuan kinerja sistem SCADA PT.PLN (Persero) unit pelaksana pengatur beban area Jakarta dan Banten untuk periode III (Juli, Agustus, September) tahun 2020 diperoleh tingkat availability teleinformasi data sebesar 99,27%, dan nilai availability master station sebesar 99,99% Serta nilai Availability sistem SCADA dan Telekomunikasi sebesar 99.63%. Dimana hasil yang dicapai dari periode triwulan III telah mencapai tolak ukur dari persyaratan perusahaan yaitu 99,5%.

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“…Many scholars have conducted in-depth research on intelligent alarm technology, by using K-nearest neighbours algorithm (KNN), convolutional neural network (CNN), and other machine learning or artificial intelligence algorithms. Progressive purposes include cleaning power grid big data, analysing security incidents with typical modelling methods, and finally achieving the intelligent processing of power grid data [2][3][4][5]. However, implementing these algorithms takes a decent amount of computing space.…”

Section: Introductionmentioning

confidence: 99%

An analysis of power grid alarm data with clustering algorithms and the associated processing model

Su,

Wang,

Wang

et al. 2023

Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023)

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Alarm data of power grids can be classified into various groups according to different criteria, therefore making it extremely hard to be understood. A clustering algorithm based on timestamps and processed keywords is proposed to solve these problems. First, the K-Means algorithm is conducted to cluster the latest occurrence time. The original data set is divided into multiple groups. Secondly, the secondary K-Means clustering is also performed based on the start time of the alarm data for further corrections. Next, we extract the keywords features, and then vectorize them. Then we apply the density-based spatial clustering of applications with noise (DBSCAN) algorithm for distinguishing these keyword features. Finally, results with both the K-Means and the DBSCAN methods are integrated and presented, and their descriptions on correlation are provided. Moreover, classifications of the alarm events in the original data are also specified. The experimental results show that the proposed model can achieve an average compression rate of 23.62% and an average accuracy rate of 93.41%. Consequently, the proposed model can effectively improve the ability to visualize the data and reduce the complexity of alarm data.

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Historical Multi-Station SCADA Data Compression of Distribution Management System Based on Tensor Tucker Decomposition

Cited by 6 publications

References 15 publications

Fog Computing for Smart Cities’ Big Data Management and Analytics: A Review

Fog Computing for Smart Cities’ Big Data Management and Analytics: A Review

Penentuan Kinerja Sistem SCADA PT.PLN (Persero) Unit Pelaksana Pengatur Beban Area Jakarta dan Banten Berdasarkan Nilai Mean Time To Repair

An analysis of power grid alarm data with clustering algorithms and the associated processing model

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