Performance Enhancement of MAF based PLL with Phase Error Compensation in the Pre-Filtering Stage

被引:0
|
作者
Ali, Zunaib [1 ]
Christofides, Nicholas [1 ]
Hadjidemetriou, Lenos [2 ]
Kyriakides, Elias [2 ]
机构
[1] Frederick Univ, Dept Elect Engn Dept, Nicosia, Cyprus
[2] Univ Cyprus, KIOS Res & Innovat Ctr Excellence, Dept Elect & Comp Engn, Nicosia, Cyprus
来源
2017 IEEE MANCHESTER POWERTECH | 2017年
关键词
Phase Lock Loop (PLL); Unbalanced Faults; harmonic distortion; Phase error; Frequency Overshoot; moving average filter (MAF); LOCKED LOOP SYSTEM; POWER CONVERTERS; OPERATION;
D O I
暂无
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
The large scale integration of Renewable Energy Sources (RES) requires sophisticated control techniques for efficient power transfer under faults and/or off-nominal grid conditions. A RES is efficiently integrated to the grid via proper control of the Grid Side Converter (GSC) by accurately estimating the grid voltage phase angle. Moving Average Filter (MAF) based Phase Lock Loop (PLL) techniques provide reduced complexity, however, they present disadvantages under specific grid fault conditions. The most recent MAF based technique is the EPMAFPLL, which provides improved dynamic response and reduces the phase error under off-nominal grid frequencies. However, the EPMAFPLL presents high phase and frequency overshoot at the time of fault. Furthermore, inaccurate harmonic mitigation under off-nominal grid frequencies was not investigated in EPMAFPLL. A modified EPMAFPLL (EPMAFPLL Type 2) is proposed in this paper. The modified EPMAFPLL accurately compensates the offset errors under offnominal grid frequencies, offers lower frequency overshoot and faster dynamics under faults. In addition, it provides accurate compensation of grid voltage harmonics under off-nominal grid frequencies.
引用
收藏
页数:6
相关论文
共 50 条
  • [41] Efficient block-based coding of noise images by combining pre-filtering and DCT
    Kim, SD
    Jang, SK
    Kim, MJ
    Ra, JB
    ISCAS '99: PROCEEDINGS OF THE 1999 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, VOL 4: IMAGE AND VIDEO PROCESSING, MULTIMEDIA, AND COMMUNICATIONS, 1999, : 37 - 40
  • [42] Efficient block-based coding of noisy images by combining pre-filtering and DCT
    Kim, SD
    Jang, SK
    Kim, MJ
    Ra, JB
    ELECTRONICS LETTERS, 1999, 35 (20) : 1717 - 1719
  • [43] Performance and Analysis of Pre-Filtering Techniques for MISO Downlink TDD MC-CDMA Systems
    Silva, Adao
    Gameiro, Atilio
    2006 IEEE 63RD VEHICULAR TECHNOLOGY CONFERENCE, VOLS 1-6, 2006, : 269 - 273
  • [44] A Pre-Filtering Approach for Incorporating Contextual Information Into Deep Learning Based Recommender Systems
    Al Jawarneh, Isam Mashhour
    Bellavista, Paolo
    Corradi, Antonio
    Foschini, Luca
    Montanari, Rebecca
    Berrocal, Javier
    Manuel Murillo, Juan
    IEEE ACCESS, 2020, 8 : 40485 - 40498
  • [45] A Unique-Pattern based Pre-Filtering Method for Rule Matching of Network Security
    Huang, Nen-Fu
    Hung, Hsien-Wei
    Tsai, Wen-Yen
    18TH ASIA-PACIFIC CONFERENCE ON COMMUNICATIONS (APCC 2012): GREEN AND SMART COMMUNICATIONS FOR IT INNOVATION, 2012, : 744 - 748
  • [47] Fast and robust two-frame random phase-shifting interferometry without pre-filtering
    Zhang, Hangying
    Yang, Feng
    Zhao, Hong
    Cao, Liangcai
    OPTICS EXPRESS, 2022, 30 (15) : 26426 - 26439
  • [48] An Efficient Pre-filtering Mechanism for Parallel Intrusion Detection Based on Many-Core GPU
    Wu, Chengkun
    Yin, Jianping
    Cai, Zhiping
    Zhu, En
    Cheng, Jieren
    SECURITY TECHNOLOGY, PROCEEDINGS, 2009, 58 : 298 - 305
  • [49] Phase Estimation Error Detection and Compensation Method of DDSRF-PLL and DSOGI-PLL under Three-Phase Voltage Unbalance
    Qi Y.
    Li K.
    Gao C.
    Xue T.
    You X.
    Diangong Jishu Xuebao/Transactions of China Electrotechnical Society, 2024, 39 (02): : 567 - 579
  • [50] A Dictionary Based Survival Error Compensation for Robust Adaptive Filtering
    Sun, Lei
    Chen, Badong
    Yang, Jie
    Zhou, Ronghua
    Nie, Qing
    Wang, Aihua
    2016 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2016, : 1408 - 1414