Operation pattern recognition via Mass data in bulk transmission grid

In bulk transmission grid, big quantity of data is accumulated, but is not effectively utilized. In this paper, a data-driven analysis framework for operation pattern recognition is proposed, which makes use of mass data in bulk transmission grid to dig deeper underlying information. The concept of operation pattern is put forward, and the two sub-patterns of the operation pattern are defined. Then procedure for operation pattern recognition is introduced, which adopts clustering as the main algorithm. Furthermore, the algorithm details are discussed. Finally the proposed method is tested on a real grid, visualized results are given.

[1]  T. Sasaki,et al.  Statistical and Dynamic Analysis of Generation Control Performance Standards , 2002, IEEE Power Engineering Review.

[2]  Viktor Mayer-Schnberger,et al.  Big Data: A Revolution That Will Transform How We Live, Work, and Think , 2013 .

[3]  A. N. de Souza,et al.  Detection and Identification of Abnormalities in Customer Consumptions in Power Distribution Systems , 2011, IEEE Transactions on Power Delivery.

[4]  Wei Hu,et al.  Raw Wind Data Preprocessing: A Data-Mining Approach , 2015, IEEE Transactions on Sustainable Energy.

[5]  Hsiao-Dong Chiang,et al.  Hierarchical K-means Method for Clustering Large-Scale Advanced Metering Infrastructure Data , 2017, IEEE Transactions on Power Delivery.

[6]  M. Sami Fadali,et al.  A Sparse-Data-Driven Approach for Fault Location in Transmission Networks , 2017, IEEE Transactions on Smart Grid.

[7]  Thomas J. Overbye,et al.  Feature Extraction and Visualization of Power System Transient Stability Results , 2014, IEEE Transactions on Power Systems.

[8]  Goran Strbac,et al.  C-Vine copula mixture model for clustering of residential electrical load pattern data , 2017 .

[9]  S. Dolnicar,et al.  An examination of indexes for determining the number of clusters in binary data sets , 2002, Psychometrika.

[10]  Frank Englert,et al.  Enhancing user privacy by data driven selection mechanisms for finding transmission-relevant data samples in energy recommender systems , 2015, 2015 International Conference and Workshops on Networked Systems (NetSys).

[11]  Shi-bing Zhou,et al.  Method for determining optimal number of clusters in K -means clustering algorithm: Method for determining optimal number of clusters in K -means clustering algorithm , 2010 .

[12]  A.H. Nizar,et al.  Power Utility Nontechnical Loss Analysis With Extreme Learning Machine Method , 2008, IEEE Transactions on Power Systems.

[13]  G. Chicco,et al.  Support Vector Clustering of Electrical Load Pattern Data , 2009, IEEE Transactions on Power Systems.

[14]  A. H. Nizar,et al.  Identification and detection of electricity customer behaviour irregularities , 2009, 2009 IEEE/PES Power Systems Conference and Exposition.

[15]  Kankar Bhattacharya,et al.  Clustering Technique Applied to Nodal Reliability Indices for Optimal Planning of Energy Resources , 2016, IEEE Transactions on Power Systems.

[16]  Katharine Armstrong,et al.  Big data: a revolution that will transform how we live, work, and think , 2014 .

[17]  Yi Wang,et al.  Clustering of Electricity Consumption Behavior Dynamics Toward Big Data Applications , 2016, IEEE Transactions on Smart Grid.

[18]  Zhiyong Yuan,et al.  Visualization of wide area measurement information from the FNET system , 2011, 2011 IEEE Power and Energy Society General Meeting.

[19]  Gianfranco Chicco,et al.  Electrical Load Pattern Grouping Based on Centroid Model With Ant Colony Clustering , 2013, IEEE Transactions on Power Systems.

[20]  Daniel T. Larose,et al.  Discovering Knowledge in Data: An Introduction to Data Mining , 2005 .

[21]  Chris Develder,et al.  Two-Stage Load Pattern Clustering Using Fast Wavelet Transformation , 2016, IEEE Transactions on Smart Grid.

[22]  Shen Chunhui,et al.  Distributed Affinity Propagation Clustering Based on MapReduce , 2012 .

[23]  J. Morales,et al.  Point Estimate Schemes to Solve the Probabilistic Power Flow , 2007, IEEE Transactions on Power Systems.