Detection and classification of micro-grid faults based on HHT and machine learning techniques

被引:186
作者
Mishra, Manohar [1 ]
Rout, Pravat Kumar [2 ]
机构
[1] Siksha O Anusandhan Univ, Elect Engn Dept, Bhubaneswar 751030, Odisha, India
[2] Siksha O Anusandhan Univ, Inst Tech Educ & Res, Elect & Elect Engn Dept, Bhubaneswar 751030, Odisha, India
关键词
distributed power generation; fault diagnosis; learning (artificial intelligence); power generation protection; Hilbert transforms; decomposition; feature extraction; power generation faults; support vector machines; Bayes methods; power generation reliability; power engineering computing; microgrid fault detection; microgrid fault event classification; HHT; extreme machine learning technique; microgrid protection scheme; Hilbert-Huang transform; three-phase current signal extraction; empirical mode decomposition method; intrinsic mode function; IMF; input vector; symmetrical fault; asymmetrical fault; high impedance fault; islanding; grid connection; naive Bayes classifier; support vector machine; IEC microgrid model; reliability; PROTECTION; COORDINATION; TRANSFORM; SCHEME;
D O I
10.1049/iet-gtd.2017.0502
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This study presents a novel micro-grid protection scheme based on Hilbert-Huang transform (HHT) and machine learning techniques. Initialisation of the proposed approach is done by extracting the three-phase current signals at the targeted buses of different feeders. The obtained non-stationary signals are passed through the empirical mode decomposition method to extract different intrinsic mode functions (IMFs). In the next step using HHT to the selected IMFs component, different needful differential features are computed. The extracted features are further used as an input vector to the machine learning models to classify the fault events. The proposed micro-grid protection scheme is tested for different protection scenarios, such as the type of fault (symmetrical, asymmetrical and high impedance fault), micro-grid structure (radial and mesh) and mode of operation (islanded and grid connected) and so on. Three different machine learning models are tested and compared in this framework: Naive Bayes classifier, support vector machine and extreme learning machine. The extensive simulated results from a standard IEC micro-grid model prove the effectiveness and reliability of the proposed micro-grid protection scheme.
引用
收藏
页码:388 / 397
页数:10
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