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Identification of Low Frequency Oscillations Based on Multidimensional Features and ReliefF-mRMR

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  • Shuang Feng

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China
    Jiangsu Provincial Key Laboratory of Smart Grid Technology and Equipment, Southeast University, Nanjing 210096, China)

  • Jianing Chen

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China
    Jiangsu Provincial Key Laboratory of Smart Grid Technology and Equipment, Southeast University, Nanjing 210096, China)

  • Yi Tang

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China
    Jiangsu Provincial Key Laboratory of Smart Grid Technology and Equipment, Southeast University, Nanjing 210096, China)

Abstract

Low frequency oscillations (LFOs) in power systems usually fall into two types, i.e., forced oscillations and natural oscillations. Waveforms of the two are similar, but the suppression methods are different. Therefore, it is important to accurately identify LFO type. In this paper, a method for discriminating LFO type based on multi-dimensional features and a feature selection algorithm combining ReliefF and minimum redundancy maximum relevance algorithm (mRMR) is proposed. Firstly, 53 features are constructed from six aspects—time domain, frequency domain, energy, correlation, complexity, and modal analysis—which comprehensively characterize the multidimensional features of LFO. Then, the optimal feature subset with greater relevance and less redundancy is extracted by ReliefF-mRMR. In order to improve the classification performance, a modified Support Vector Machine (SVM) with Genetic Algorithm (GA) optimizing the key parameters is adopted, which is conducted in MATLAB. Finally, in 179-bus system, the samples of LFOs are generated by the Power System Analysis Toolbox (PSAT) and the accuracy of the LFO type identification model is verified. In ISO New England and East China power grid, it is proven that the proposed method can accurately identify LFO type considering the influences of noise, oscillation mode, and data incompletion. Hence, it has good robustness, noise immunity, and practicability.

Suggested Citation

  • Shuang Feng & Jianing Chen & Yi Tang, 2019. "Identification of Low Frequency Oscillations Based on Multidimensional Features and ReliefF-mRMR," Energies, MDPI, vol. 12(14), pages 1-18, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:14:p:2762-:d:249617
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    References listed on IDEAS

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    1. Yan Zhao & Zhimin Li & Yonghui Nie, 2016. "A Time-Frequency Analysis Method for Low Frequency Oscillation Signals Using Resonance-Based Sparse Signal Decomposition and a Frequency Slice Wavelet Transform," Energies, MDPI, vol. 9(3), pages 1-18, March.
    2. Ju Liu & Wei Yao & Jinyu Wen & Haibo He & Xueyang Zheng, 2014. "Active Power Oscillation Property Classification of Electric Power Systems Based on SVM," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-9, May.
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    Cited by:

    1. Akilu Yunusa-Kaltungo & Ruifeng Cao, 2020. "Towards Developing an Automated Faults Characterisation Framework for Rotating Machines. Part 1: Rotor-Related Faults," Energies, MDPI, vol. 13(6), pages 1-20, March.

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