Smart Grid using Big Data Analytics

Author(s):  
Robert C. Qiu ◽  
Paul Antonik
IEEE Access ◽  
2020 ◽  
pp. 1-1
Author(s):  
Dabeeruddin Syed ◽  
Ameema Zainab ◽  
Shady S. Refaat ◽  
Haitham Abu-Rub ◽  
Othmane Bouhali

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Mukhtaj Khan ◽  
Zhengwen Huang ◽  
Maozhen Li ◽  
Gareth A. Taylor ◽  
Phillip M. Ashton ◽  
...  

The rapid deployment of Phasor Measurement Units (PMUs) in power systems globally is leading to Big Data challenges. New high performance computing techniques are now required to process an ever increasing volume of data from PMUs. To that extent the Hadoop framework, an open source implementation of the MapReduce computing model, is gaining momentum for Big Data analytics in smart grid applications. However, Hadoop has over 190 configuration parameters, which can have a significant impact on the performance of the Hadoop framework. This paper presents an Enhanced Parallel Detrended Fluctuation Analysis (EPDFA) algorithm for scalable analytics on massive volumes of PMU data. The novel EPDFA algorithm builds on an enhanced Hadoop platform whose configuration parameters are optimized by Gene Expression Programming. Experimental results show that the EPDFA is 29 times faster than the sequential DFA in processing PMU data and 1.87 times faster than a parallel DFA, which utilizes the default Hadoop configuration settings.


2021 ◽  
Vol 13 (23) ◽  
pp. 13322
Author(s):  
Vinoth Kumar Ponnusamy ◽  
Padmanathan Kasinathan ◽  
Rajvikram Madurai Elavarasan ◽  
Vinoth Ramanathan ◽  
Ranjith Kumar Anandan ◽  
...  

The role of energy is cardinal for achieving the Sustainable Development Goals (SDGs) through the enhancement and modernization of energy generation and management practices. The smart grid enables efficient communication between utilities and the end- users, and enhances the user experience by monitoring and controlling the energy transmission. The smart grid deals with an enormous amount of energy data, and the absence of proper techniques for data collection, processing, monitoring and decision-making ultimately makes the system ineffective. Big data analytics, in association with the smart grid, enable better grid visualization and contribute toward the attainment of sustainability. The current research work deals with the achievement of sustainability in the smart grid and efficient data management using big data analytics, that has social, economic, technical and political impacts. This study provides clear insights into energy data generated in the grid and the possibilities of energy theft affecting the sustainable future. The paper provides insights about the importance of big data analytics, with their effects on the smart grids’ performance towards the achievement of SDGs. The work highlights efficient real-time energy data management involving artificial intelligence and machine learning for a better future, to short out the effects of the conventional smart grid without big data analytics. Finally, the work discusses the challenges and future directions to improve smart grid technologies with big data analytics in action.


2019 ◽  
Vol 5 (1) ◽  
pp. 34-45 ◽  
Author(s):  
Kun Wang ◽  
Chenhan Xu ◽  
Yan Zhang ◽  
Song Guo ◽  
Albert Y. Zomaya

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