An Economic Operation Analysis Method of Transformer Based on Clustering

The economic operation of power transformers is analyzed in the present paper, which is performed by the clustering analysis method. In order to overcome the disadvantages of the conventional k-means algorithm lacking the stability and accuracy, we propose a novel boost k-means algorithm by optimizing the choice of initial cluster centers, and no additional parameters are required. The proposed approach outperforms the conventional approach in most experiments, for the best one, the accuracy of the proposed approach is 20.37% higher than that of the traditional approach. More importantly, empirical research is conducted in the paper. The index system reflecting the load characteristics of power transformers is established, and using the boost k-means algorithm, the economic operation analysis of power transformers is conducted. The clustering results of different transformers are obtained and the relevant suggestions are given as well. The empirical analysis results prove the validity of the proposed approach, and it can be efficiently applied for the economic operation analysis of transformers.

[1]  Ming Zhong,et al.  I-nice: A new approach for identifying the number of clusters and initial cluster centres , 2018, Inf. Sci..

[2]  Christian Hennig,et al.  Recovering the number of clusters in data sets with noise features using feature rescaling factors , 2015, Inf. Sci..

[3]  Zhang Xuewei,et al.  Research on transformer fault diagnosis method and calculation model by using fuzzy data fusion in multi-sensor detection system , 2019, Optik.

[4]  Yunming Ye,et al.  Neighborhood Density Method for Selecting Initial Cluster Centers in K-Means Clustering , 2006, PAKDD.

[5]  Andrew Chi-Sing Leung,et al.  PSO-based K-Means clustering with enhanced cluster matching for gene expression data , 2012, Neural Computing and Applications.

[6]  Chun-Wei Tsai,et al.  A modified multiobjective EA-based clustering algorithm with automatic determination of the number of clusters , 2012, 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[7]  Zhang Yao,et al.  Economic operation of transformers in the area power network based on real-time analysis and control , 2008, 2008 China International Conference on Electricity Distribution.

[8]  F. Lemke,et al.  SELF-ORGANIZING DATA MINING BASED ON GMDH PRINCIPLE Dipl , 2022 .

[9]  Kotagiri Ramamohanarao,et al.  Automatically Determining the Number of Clusters in Unlabeled Data Sets , 2009, IEEE Transactions on Knowledge and Data Engineering.

[10]  F. Lemke,et al.  Self-Organising Data Mining , 2003 .

[11]  Sergei Vassilvitskii,et al.  k-means++: the advantages of careful seeding , 2007, SODA '07.

[12]  C. Hennig,et al.  How to find an appropriate clustering for mixed‐type variables with application to socio‐economic stratification , 2013 .

[13]  Xinwang Liu,et al.  K-Means Clustering With Incomplete Data , 2019, IEEE Access.

[14]  D. J. Newman,et al.  UCI Repository of Machine Learning Database , 1998 .

[15]  Tao Li,et al.  Study on A United Evaluation Algorithm and Its Applications for Economic Operation of Transformer , 2012 .

[16]  Albert K. W. Yeung,et al.  Concepts and Techniques of Geographic Information Systems (2nd Edition) (Ph Series in Geographic Information Science) , 2006 .

[17]  Adel El-Shahat,et al.  Smart Distributed Generation Systems Using Artificial Neural Network-Based Event Classification , 2018, IEEE Power and Energy Technology Systems Journal.

[18]  Whei-Min Lin,et al.  Transformer-fault diagnosis by integrating field data and standard codes with training enhancible adaptive probabilistic network , 2005 .

[19]  Hui Ma,et al.  Application of fuzzy support vector machine for determining the health index of the insulation system of in-service power transformers , 2013, IEEE Transactions on Dielectrics and Electrical Insulation.

[20]  Andrés Melgar,et al.  Converting data into knowledge for preventing failures in power transformers , 2019, Engineering Failure Analysis.

[21]  Michalis Vazirgiannis,et al.  On Clustering Validation Techniques , 2001, Journal of Intelligent Information Systems.

[22]  Wojciech Kwedlo,et al.  Using Genetic Algorithm for Selection of Initial Cluster Centers for the K-Means Method , 2010, ICAISC.

[23]  Murat Erisoglu,et al.  A new algorithm for initial cluster centers in k-means algorithm , 2011, Pattern Recognit. Lett..

[24]  Shutao Li,et al.  Gene selection using genetic algorithm and support vectors machines , 2008, Soft Comput..

[25]  Sami Sieranoja,et al.  How much can k-means be improved by using better initialization and repeats? , 2019, Pattern Recognit..

[26]  Fei Zhu,et al.  Spectral-Spatial Feature Extraction and Classification by ANN Supervised With Center Loss in Hyperspectral Imagery , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[27]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[28]  Y. C. Huang,et al.  A New Data Mining Approach to Dissolved Gas Analysis of Oil-Insulated Power Apparatus , 2002, IEEE Power Engineering Review.

[29]  Eiiti Kasuya,et al.  Wilcoxon signed-ranks test: symmetry should be confirmed before the test , 2010, Animal Behaviour.

[30]  Xiaokuo Kou,et al.  A Novel Maintenance Decision Making Model of Power Transformers Based on Reliability and Economy Assessment , 2019, IEEE Access.

[31]  Jian Pei,et al.  A rank sum test method for informative gene discovery , 2004, KDD.

[32]  A. G. Ivakhnenko.  Sorting Methods in Self-Organization of Models and Clusterizations (Review of New Basic Ideas) - Iterative (Multirow) Polynomial GMDH Algorithms , 2002 .

[33]  Xin Liu,et al.  Atherosclerotic Plaque Pathological Analysis by Unsupervised $K$ -Means Clustering , 2018, IEEE Access.