Big data processing and analysis platform for condition monitoring of electric power system

This paper presents a preliminary study of developing a novel platform for big data management, processing and analysis of modern power systems. The framework comprises a big data acquisition subsystem, a big data analysis subsystem, a decision-making assistance subsystem and an information integration subsystem. For the big data management system, a novel structure is designed according to three different data resources, including database, data files and data stream. Further, powerful open-source computation algorithms and self-developed novel intelligent methods are integrated in the big data analysis system. To be specific, our early work on statistical processing monitoring (Principal Component Analysis (PCA)), advanced modelling methods (Fast Recursive Algorithm (FRA)) and newly developed optimization method (Teaching-Learning Based Optimization (TLBO)) are integrated into a self-developed analysis module. Thus, with the novel big data acquisition structure and data processing engine, the proposed platform can provide a powerful tool for big data analytic based Smart Grid monitoring.

[1]  Mesut Baran,et al.  Distribution system state estimation using AMI data , 2009, 2009 IEEE/PES Power Systems Conference and Exposition.

[2]  Joseph M. Hellerstein,et al.  Eddies: continuously adaptive query processing , 2000, SIGMOD '00.

[3]  R. Venkata Rao,et al.  Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems , 2011, Comput. Aided Des..

[4]  Reynold Xin,et al.  GraphX: a resilient distributed graph system on Spark , 2013, GRADES.

[5]  Michael Stonebraker,et al.  Monitoring Streams - A New Class of Data Management Applications , 2002, VLDB.

[6]  Sang Oh Park,et al.  An Adaptive Power Management System for multimedia applications using data stream partitioning , 2011, 2011 IEEE International Conference on Consumer Electronics (ICCE).

[7]  Shanlin Yang,et al.  Demand side management in China: The context of China’s power industry reform , 2015 .

[8]  Brian E. Granger,et al.  IPython: A System for Interactive Scientific Computing , 2007, Computing in Science & Engineering.

[9]  Y. Xue,et al.  Synchrophasor-Based Islanding Detection for Distributed Generation Systems Using Systematic Principal Component Analysis Approaches , 2015, IEEE Transactions on Power Delivery.

[10]  J. E. Jackson,et al.  Control Procedures for Residuals Associated With Principal Component Analysis , 1979 .

[11]  Eduardo Cotilla-Sanchez,et al.  Power system data management and analysis using synchrophasor data , 2014, 2014 IEEE Conference on Technologies for Sustainability (SusTech).

[12]  Ameet Talwalkar,et al.  MLlib: Machine Learning in Apache Spark , 2015, J. Mach. Learn. Res..

[13]  Piroska Haller,et al.  A survey on cloud-based software platforms to implement secure smart grids , 2014, 2014 49th International Universities Power Engineering Conference (UPEC).

[14]  Yusheng XUE,et al.  A self-learning TLBO based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads , 2014 .

[15]  Paul Zikopoulos,et al.  Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data , 2011 .

[16]  Mladen Kezunovic,et al.  Smart grid data analytics for digital protective relay event recordings , 2015, Inf. Syst. Frontiers.

[17]  Jianghai Hu.  Application of stochastic hybrid systems in power management of streaming data , 2006, 2006 American Control Conference.

[18]  Xin Wang,et al.  Dynamic Energy Management for Smart-Grid-Powered Coordinated Multipoint Systems , 2016, IEEE Journal on Selected Areas in Communications.

[19]  Alexandros Labrinidis,et al.  Challenges and Opportunities with Big Data , 2012, Proc. VLDB Endow..

[20]  George K. Karagiannidis,et al.  Big Data Analytics for Dynamic Energy Management in Smart Grids , 2015, Big Data Res..

[21]  Liu Yang,et al.  Overview of Big Data Research , 2013 .

[22]  Jignesh M. Patel,et al.  Storm@twitter , 2014, SIGMOD Conference.

[23]  Zhengping Qian,et al.  TimeStream: reliable stream computation in the cloud , 2013, EuroSys '13.

[24]  Luigi Vanfretti,et al.  The OpenPMU Platform for Open-Source Phasor Measurements , 2013, IEEE Transactions on Instrumentation and Measurement.

[25]  Yao Zhang,et al.  Application of Dynamic Equivalence Based on Coherency in South China Power Grid , 2011, 2011 Asia-Pacific Power and Energy Engineering Conference.

[26]  Mohamad Musavi,et al.  New Reduced Model approach for Power System State Estimation Using Artificial Neural Networks and Principal Component Analysis , 2014, 2014 IEEE Electrical Power and Energy Conference.

[27]  Kang Li,et al.  A statistical process control approach for automatic anti-islanding detection using synchrophasors , 2013, 2013 IEEE Power & Energy Society General Meeting.

[28]  Mladen Kezunovic,et al.  Measures of Value: Data Analytics for Automated Fault Analysis , 2012, IEEE Power and Energy Magazine.

[29]  Shanlin Yang,et al.  Big data driven smart energy management: From big data to big insights , 2016 .

[30]  E. Ortjohann,et al.  Integration of clustering power systems approach and data management infrastructure for smart grids , 2014, 2014 International Symposium on Power Electronics, Electrical Drives, Automation and Motion.

[31]  Shanlin Yang,et al.  A framework of service-oriented operation model of China׳s power system , 2015 .

[32]  George W. Irwin,et al.  A fast nonlinear model identification method , 2005, IEEE Transactions on Automatic Control.

[33]  Zhile Yang,et al.  A novel radial basis function neural network principal component analysis scheme for PMU-based wide-area power system monitoring , 2015 .

[34]  Kang Li,et al.  Two-Stage Mixed Discrete–Continuous Identification of Radial Basis Function (RBF) Neural Models for Nonlinear Systems , 2009, IEEE Transactions on Circuits and Systems I: Regular Papers.

[35]  Jun Luo,et al.  A Spatial-Temporal Analysis of Users' Geographical Patterns in Social Media: A Case Study on Microblogs , 2014, DASFAA Workshops.

[36]  A. Kala Karun,et al.  A review on hadoop — HDFS infrastructure extensions , 2013, 2013 IEEE CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGIES.

[37]  Kang Li,et al.  Loss-of-Main Monitoring and Detection for Distributed Generations Using Dynamic Principal Component Analysis , 2014 .

[38]  Murtaza Haider,et al.  Beyond the hype: Big data concepts, methods, and analytics , 2015, Int. J. Inf. Manag..

[39]  Yogesh L. Simmhan,et al.  Cloud-Based Software Platform for Big Data Analytics in Smart Grids , 2013, Computing in Science & Engineering.

[40]  Morteza Saberi,et al.  Improved estimation of electricity demand function by using of artificial neural network, principal component analysis and data envelopment analysis , 2013, Comput. Ind. Eng..