Photovoltaic Power Quality Analysis Based on the Modulation Broadband Mode Decomposition Algorithm

被引:2
作者
Wang, Zucheng [1 ]
Peng, Yanfeng [1 ]
Liu, Yanfei [2 ]
Guo, Yong [1 ]
Liu, Yi [3 ]
Geng, Hongyan [1 ]
Li, Sai [1 ]
Fan, Chao [1 ]
机构
[1] Hunan Univ Sci & Technol, Hunan Prov Key Lab Hlth Maintenance Mech Equipmen, Xiangtan 411201, Peoples R China
[2] Hunan Univ, Coll Mech & Vehicle Engn, Changsha 410082, Peoples R China
[3] Natl Innovat Ctr Adv Rail Transit Equipment, Zhuzhou 412000, Peoples R China
基金
中国国家自然科学基金;
关键词
modulated broadband mode decomposition; BP neural network; signal feature extraction; disturbance identification; photovoltaic power quality; MULTISCALE FUZZY ENTROPY; FEATURE-EXTRACTION;
D O I
10.3390/en14237948
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
The Broadband Mode Decomposition (BMD) method was previously proposed to solve the Gibbs phenomenon that occurs during photovoltaic signal decomposition; its main idea is to build a dictionary which contains signal features, and to search in the dictionary to solve the problem. However, BMD has some shortcomings; especially if the relative bandwidth of the decomposed signal is not small enough, it may treat a square wave signal as several narrowband signals, resulting in a deviation in the decomposition effect. In order to solve the problem of relative bandwidth, the original signal is multiplied by a high-frequency, single-frequency signal, and the wideband signal is processed as an approximate wideband signal. This is the modulation broadband mode decomposition algorithm (MBMD) proposed in this article. In order to further identify and classify the disturbances in the photovoltaic direct current (DC) signal, the experiment uses composite multi-scale fuzzy entropy (CMFE) to calculate the components after MBMD decomposition, and then uses the calculated value in combination with the back propagation (BP) neural network algorithm. Simulation and experimental signals verify that the method can effectively extract the characteristics of the square wave component in the DC signal, and can successfully identify various disturbance signals in the photovoltaic DC signal.
引用
收藏
页数:26
相关论文
共 25 条
  • [1] Hybrid multiscale wind speed forecasting based on variational mode decomposition
    Ali, Mumtaz
    Khan, Asif
    Rehman, Naveed Ur
    [J]. INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS, 2018, 28 (01):
  • [2] Real-time recognition of power quality disturbance-based deep belief network using embedded parallel computing platform
    Chen, Ziming
    Li, Mengshi
    Ji, Tianyao
    Wu, Qinghua
    [J]. IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, 2020, 15 (04) : 519 - 526
  • [3] Islanding and power quality disturbance monitoring in microgrid using adaptive cross variational mode decomposition and reduced kernel ridge regression
    Dash, Pradipta Kishore
    Satapathy, Prachitara
    Nayak, Pravati
    Sahani, Mrutyunjaya
    [J]. INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS, 2020, 30 (06)
  • [4] Reduction of the Gibbs phenomenon applied on nonharmonic time base distortions
    De Ridder, F
    Pintelon, R
    Schoukens, J
    Verheyden, A
    [J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2005, 54 (03) : 1118 - 1125
  • [5] Variational Mode Decomposition
    Dragomiretskiy, Konstantin
    Zosso, Dominique
    [J]. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2014, 62 (03) : 531 - 544
  • [6] Leakage Signal Analysis of Urban Gas Pipeline Based on Improved Variational Mode Decomposition
    Hao, Yongmei
    Du, Zhanghao
    Xing, Zhixiang
    Mao, Xiaohu
    [J]. INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 2020, 34 (13)
  • [7] Feature extraction of AC square wave.SAW arc characteristics using improved Hilbert-Huang transformation and energy entropy
    He, Kuanfang
    Zhang, Zhuojie
    Xiao, Siwen
    Li, Xuejun
    [J]. MEASUREMENT, 2013, 46 (04) : 1385 - 1392
  • [8] The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
    Huang, NE
    Shen, Z
    Long, SR
    Wu, MLC
    Shih, HH
    Zheng, QN
    Yen, NC
    Tung, CC
    Liu, HH
    [J]. PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 1998, 454 (1971): : 903 - 995
  • [9] Feature Extraction Based on the Non-Negative Matrix Factorization of Convolutional Neural Networks for Monitoring Domestic Activity With Acoustic Signals
    Lee, Seokjin
    Pang, Hee-Suk
    [J]. IEEE ACCESS, 2020, 8 : 122384 - 122395
  • [10] Leite D., 2020, 2020 IEEE INT C FUZZ, P1, DOI [10.1109/FUZZ48607.2020.9177847, DOI 10.1109/FUZZ48607.2020.9177847]