Detection of Sources of Instability in Smart Grids Using Machine Learning Techniques

The prediction of smart grid stability represents a challenging research problem because this information can be very useful for the identification of the participants that lead to instabilities in the system and consequently it is very useful to determine configurations in which the system is stable even if some participants present abnormalities. The main contributions of this research article are: (1) the presentation of a machine learning methodology for predicting the smart grid stability which is based on features extraction using the tsfresh package from Python, (2) the selection of features using three methods namely, Binary Particle Swarm optimization Features Selection (BPSOFS), Binary Kangaroo Mob optimization Features Selection (BKMOFS) and Multivariate Adaptive Regression Splines (MARS), (3) the prediction of the system’s stability using four classifiers namely, Logistic Regression (LR), Random Forest (RF), Gradient Boosted Trees (GBT) and Multilayer Perceptron Classifier (MPC) and (4) the detection of instability sources using a method based on machine learning and statistics. The best prediction results are obtained when MPC is applied (93.8%) and when the features are selected using BPSOFS.

[1]  Hsing-Kuo Kenneth Pao,et al.  Compressed learning for time series classification , 2016, 2016 IEEE International Conference on Big Data (Big Data).

[2]  A. Jovic,et al.  Feature Extraction for ECG Time-Series Mining Based on Chaos Theory , 2007, International Conference on Information Technology Interfaces.

[3]  Ž. Eleschová,et al.  Smart Grids analysis - View of the transmission systems voltage stability , 2018, 2018 19th International Scientific Conference on Electric Power Engineering (EPE).

[4]  Kesheng Wu,et al.  Extracting Baseline Electricity Usage Using Gradient Tree Boosting , 2015, 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity).

[5]  Bingbing Li.  A Principal Component Analysis Approach to Noise Removal for Speech Denoising , 2018, 2018 International Conference on Virtual Reality and Intelligent Systems (ICVRIS).

[6]  Chien-Liang Liu,et al.  Time Series Classification With Multivariate Convolutional Neural Network , 2019, IEEE Transactions on Industrial Electronics.

[7]  Adilson E. Motter,et al.  Comparative analysis of existing models for power-grid synchronization , 2015, 1501.06926.

[8]  Kemal Leblebicioglu,et al.  Time series classification using point-wise features , 2017, 2017 25th Signal Processing and Communications Applications Conference (SIU).

[9]  Wu Yang,et al.  The distributed storage strategy research of remote sensing image based on Mongo DB , 2014 .

[10]  Girijesh Prasad,et al.  Optimising frequency band selection with forward-addition and backward-elimination algorithms in EEG-based brain-computer interfaces , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

[11]  Dorin Moldovan,et al.  SWAG: Semantic web application generator - a library for using ontologies as web services , 2016, 2016 IEEE 12th International Conference on Intelligent Computer Communication and Processing (ICCP).

[12]  Brian McBride,et al.  Jena: A Semantic Web Toolkit , 2002, IEEE Internet Comput..

[13]  Bhole Rahul Hiraman,et al.  A Study of Apache Kafka in Big Data Stream Processing , 2018, 2018 International Conference on Information , Communication, Engineering and Technology (ICICET).

[14]  Oliver Kamps,et al.  Characterizing the Fluctuations of Wind Power Production by Multi-time Statistics , 2014 .

[15]  Leonardo Feltrin.  KNIME an Open Source Solution for Predictive Analytics in the Geosciences [Software and Data Sets] , 2015, IEEE Geoscience and Remote Sensing Magazine.

[16]  Jian Ma,et al.  A comprehensive software suite for power grid stability monitoring based on synchrophasor measurements , 2017, 2017 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT).

[17]  Tong Zheng,et al.  Logistic Regression Analysis on Learning Behavior and Learning Effect Based on SPOC Data , 2018, 2018 13th International Conference on Computer Science & Education (ICCSE).

[18]  M. L. Dewal,et al.  Features extraction of EEG signals using approximate and sample entropy , 2012, 2012 IEEE Students' Conference on Electrical, Electronics and Computer Science.

[19]  Hamdi Abdi,et al.  Voltage Stability Analysis in Radial Smart Distribution Grids , 2017 .

[20]  Junliang Liu,et al.  Convolutional neural networks for time series classification , 2017 .

[21]  Yuan Lin,et al.  Analysis of Influence With Connected Wind Farm Power Changing and Improvement Strategies on Grid Voltage Stability , 2018, 2018 China International Conference on Electricity Distribution (CICED).

[22]  Klemens Böhm,et al.  Towards Concise Models of Grid Stability , 2018, 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm).

[23]  José Meseguer,et al.  Formal Analysis of Fault-tolerant Group Key Management Using ZooKeeper , 2013, 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing.

[24]  Elçin Kartal-Koç,et al.  Model selection in multivariate adaptive regression splines (MARS) using information complexity as the fitness function , 2014, Machine Learning.

[25]  Tamara Radivilova,et al.  Classification of Fractal Time Series Using Recurrence Plots , 2018, 2018 International Scientific-Practical Conference Problems of Infocommunications. Science and Technology (PIC S&T).

[26]  Yang Hao,et al.  The integration on-line system of prediction and control for transient stability in smart power grids , 2013, Proceedings of the 32nd Chinese Control Conference.

[27]  A. Zabidi,et al.  Pattern classification in recognizing Qalqalah Kubra pronuncation using multilayer perceptrons , 2012, 2012 International Symposium on Computer Applications and Industrial Electronics (ISCAIE).

[28]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[29]  Faouzi Mhamdi,et al.  A Filter Correlation Method for Feature Selection , 2014, 2014 25th International Workshop on Database and Expert Systems Applications.

[30]  J. Kurths,et al.  Taming instabilities in power grid networks by decentralized control , 2015, 1508.02217.

[31]  Jian Fu,et al.  SPARK – A Big Data Processing Platform for Machine Learning , 2016, 2016 International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII).

[32]  Dipti P. Rana,et al.  Review of random forest classification techniques to resolve data imbalance , 2017, 2017 1st International Conference on Intelligent Systems and Information Management (ICISIM).

[33]  Liu Xinyuan,et al.  Voltage stability analysis based on short circuit capacity , 2018, 2018 Chinese Control And Decision Conference (CCDC).

[34]  Andreas W. Kempa-Liehr,et al.  Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh - A Python package) , 2018, Neurocomputing.

[35]  Inder Monga,et al.  Lambda architecture for cost-effective batch and speed big data processing , 2015, 2015 IEEE International Conference on Big Data (Big Data).

[36]  Ganesh Kumar Venayagamoorthy,et al.  Intelligent sense-making for smart grid stability , 2011, 2011 IEEE Power and Energy Society General Meeting.

[37]  Shouvik kumar Samanta,et al.  Smart Grid Stability Analysis on Smart Demand Load Response in Coordinated Network , 2018, 2018 2nd International Conference on Power, Energy and Environment: Towards Smart Technology (ICEPE).

[38]  Yuanqing Li,et al.  Feature extraction with multiscale autoregression of multichannel time series for P300 speller BCI , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[39]  Noureddine Zerhouni,et al.  A feature extraction procedure based on trigonometric functions and cumulative descriptors to enhance prognostics modeling , 2013, 2013 IEEE Conference on Prognostics and Health Management (PHM).

[40]  Bandana Mahapatra,et al.  Data reduction in MANETs using forward feature construction technique , 2015, 2015 International Conference on Man and Machine Interfacing (MAMI).

[41]  Jinhai Liu,et al.  Markov chain-based feature extraction for anomaly detection in time series and its industrial application , 2018, 2018 Chinese Control And Decision Conference (CCDC).

[42]  G. Filatrella,et al.  Analysis of a power grid using a Kuramoto-like model , 2007, 0705.1305.

[43]  Goran Andersson,et al.  Impact of Low Rotational Inertia on Power System Stability and Operation , 2013, 1312.6435.

[44]  A. Asuncion,et al.  UCI Machine Learning Repository, University of California, Irvine, School of Information and Computer Sciences , 2007 .

[45]  Michael Massoth,et al.  Stability Improvement Solution of the Smart Power Grid by an Analysis of Voltage Variation in Intelligent Buildings , 2014 .

[46]  Chandan Kumar Chanda,et al.  Investigate the impact of smart grid stability analysis on synchronous generator , 2017, 2017 IEEE Calcutta Conference (CALCON).

[47]  Andrey Kashlev,et al.  A Big Data Modeling Methodology for Apache Cassandra , 2015, 2015 IEEE International Congress on Big Data.