Detection and classification of power quality disturbances in wind‐grid integrated system using fast time‐time transform and small residual‐extreme learning machine

[1]  Thomas Ackermann,et al.  Wind Power in Power Systems: Ackermann/Wind Power in Power Systems , 2005 .

[2]  Han Zou,et al.  Robust Extreme Learning Machine With its Application to Indoor Positioning , 2016, IEEE Transactions on Cybernetics.

[3]  Xiaobin Liu,et al.  Identification of power quality disturbances based on improved TT transform and support vector classifier , 2016, 2016 IEEE 8th International Power Electronics and Motion Control Conference (IPEMC-ECCE Asia).

[4]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[5]  Jinyue Yan,et al.  Towards a Green Energy Economy? Assessing policy choices, strategies and transitional pathways , 2016 .

[6]  Azah Mohamed,et al.  Power Quality Impact of Renewable Energy based Generators and Electric Vehicles on Distribution Systems , 2013 .

[7]  Lin Li,et al.  自動車の部品製造のためのAC-170PXアルミ合金のフィレットエッジとフランジコーチレーザ溶接における多孔性生成に及ぼす熱入力とシートギャップの影響の把握 | 文献情報 | J-GLOBAL 科学技術総合リンクセンター , 2015 .

[8]  C. Robert Pinnegar,et al.  A method of time-time analysis: The TT-transform , 2003, Digit. Signal Process..

[9]  S. R. Mohanty,et al.  Classification of Power Quality Disturbances Due to Environmental Characteristics in Distributed Generation System , 2013, IEEE Transactions on Sustainable Energy.

[10]  V. Sadasivam,et al.  S Transform based Extreme Learning Machine for Power System Disturbances Classification , 2013 .

[11]  Chun-Yao Lee,et al.  Optimal Feature Selection for Power-Quality Disturbances Classification , 2011, IEEE Transactions on Power Delivery.

[12]  Mário Oleskovicz,et al.  Adaptive threshold based on wavelet transform applied to the segmentation of single and combined power quality disturbances , 2016, Appl. Soft Comput..

[13]  Khaled M. Abo-Al-Ez,et al.  Probabilistic power quality indices for electric grids with increased penetration level of wind power generation , 2016 .

[14]  Pradipta Kishore Dash,et al.  A new fast discrete S‐transform and decision tree for the classification and monitoring of power quality disturbance waveforms , 2014 .

[15]  Mohammad Mohammadi,et al.  Islanding detection approach with negligible non‐detection zone based on feature extraction discrete wavelet transform and artificial neural network , 2016 .

[16]  Pan Li,et al.  Modified S transform and ELM algorithms and their applications in power quality analysis , 2016, Neurocomputing.

[17]  J. Aguado,et al.  Classification of power quality disturbances using S-transform and Artificial Neural Networks , 2011, 2011 International Conference on Power Engineering, Energy and Electrical Drives.

[18]  Om Prakash Mahela,et al.  Power Quality Detection in Distribution System with Wind Energy Penetration Using Discrete Wavelet Transform , 2015, 2015 Second International Conference on Advances in Computing and Communication Engineering.

[19]  Xia Zhao,et al.  Analysis of transient waveform based on combined short time Fourier transform and wavelet transform , 2004, 2004 International Conference on Power System Technology, 2004. PowerCon 2004..

[20]  Peng LI,et al.  Hilbert-Huang transform with adaptive waveform matching extension and its application in power quality disturbance detection for microgrid , 2016 .

[21]  Shichang Du,et al.  An Optimal Ensemble Empirical Mode Decomposition Method for Vibration Signal Decomposition , 2017 .

[22]  Rajiv Kapoor,et al.  Classification of power quality events – A review , 2012 .

[23]  Bijaya Ketan Panigrahi,et al.  Classification of disturbances in hybrid DG system using modular PNN and SVM , 2013 .

[24]  Rajiv Kapoor,et al.  Hybrid demodulation concept and harmonic analysis for single/multiple power quality events detection and classification , 2011 .

[25]  Manish Kumar Saini,et al.  Optimum fractionally delayed wavelet design for PQ event detection and classification , 2017 .

[26]  Nand Kishor,et al.  Optimal feature and decision tree based classification of power quality disturbances in distributed generation systems , 2014, 2014 IEEE PES General Meeting | Conference & Exposition.

[27]  J.T. Bialasiewicz,et al.  The Wind Farm Aggregation Impact on Power Quality , 2006, IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics.

[28]  R. Ramakumar,et al.  Control and Operation of Grid-Connected Wind Farms , 2016 .

[29]  Joao P. S. Catalao,et al.  Comparative Study of Advanced Signal Processing Techniques for Islanding Detection in a Hybrid Distributed Generation System , 2015, IEEE Transactions on Sustainable Energy.

[30]  Hui Wang,et al.  An Adaptive Support Vector Machine-Based Workpiece Surface Classification System Using High-Definition Metrology , 2015, IEEE Transactions on Instrumentation and Measurement.

[31]  Nirmal-Kumar C. Nair,et al.  Power quality analysis for building integrated PV and micro wind turbine in New Zealand , 2013 .

[32]  M. L. Lauzon,et al.  A General Description of Linear Time-Frequency Transforms and Formulation of a Fast, Invertible Transform That Samples the Continuous S-Transform Spectrum Nonredundantly , 2010, IEEE Transactions on Signal Processing.

[33]  A.R. Abdullah,et al.  Power quality analysis using linear time-frequency distribution , 2008, 2008 IEEE 2nd International Power and Energy Conference.

[34]  Mike E. Davies,et al.  Measured energy use and indoor environment quality in green office buildings in China , 2016 .

[35]  Nalanie Mithraratne,et al.  Roof-top wind turbines for microgeneration in urban houses in New Zealand , 2009 .

[36]  Pradipta Kishore Dash,et al.  Nonstationary signal pattern recognition using fast time-time filtering and decision tree , 2014, J. Intell. Fuzzy Syst..

[37]  Chang Feng,et al.  Meta-ELM: ELM with ELM hidden nodes , 2014, Neurocomputing.

[38]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[39]  R. K. Patnaik,et al.  Impact of wind farms on disturbance detection and classification in distributed generation using modified Adaline network and an adaptive neuro-fuzzy information system , 2015, Appl. Soft Comput..

[40]  Radu Dogaru,et al.  A comparison of Extreme Learning Machine and Support Vector Machine classifiers , 2015, 2015 IEEE International Conference on Intelligent Computer Communication and Processing (ICCP).

[41]  Mrutyunjaya Sahani,et al.  Detection and classification of power quality event using wavelet transform and weighted extreme learning machine , 2016, 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT).

[42]  Manohar Mishra,et al.  Study the performance of S-transform based extreme learning Machine for islanding detection in distributed generation , 2016, 2016 National Power Systems Conference (NPSC).