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Non-overlapping moving compressive measurement algorithm for electrical energy estimation of distorted m-sequence dynamic test signal

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  • Wang, Xuewei
  • Wang, Jing
  • Wang, Lin
  • Yuan, Ruiming

Abstract

The complex random characteristics in smart grid lead to inaccuracy of smart electricity metering. This is caused by the power filter and energy accumulation algorithm under dynamic signal conditions. By analyzing the typical intrinsic characteristics of large power electrical loads, this paper proposes a distorted m-sequence dynamic test (DmDT) signal model to reflect the characteristics and summarizes the parameter set that relates to the characteristics. In addition, based on the compressive measurement (CM) theory, a novel non-overlapping moving compressive measurement (NOLM-CM) algorithm is proposed to accurately estimate electrical energy. The performance of the non-overlapping moving compressive measurement (NOLM-CM) algorithm is tested under representative distorted dynamic random conditions. Simulation and experimental results indicate that the non-overlapping moving compressive measurement (NOLM-CM) algorithm achieves accurate estimation of electrical energy. Furthermore, the comparisons with five popular window-based estimation algorithms by simulations verify the higher performance of the non-overlapping moving compressive measurement (NOLM-CM) algorithm.

Suggested Citation

  • Wang, Xuewei & Wang, Jing & Wang, Lin & Yuan, Ruiming, 2019. "Non-overlapping moving compressive measurement algorithm for electrical energy estimation of distorted m-sequence dynamic test signal," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
  • Handle: RePEc:eee:appene:v:251:y:2019:i:c:43
    DOI: 10.1016/j.apenergy.2019.05.037
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    1. McLoughlin, Fintan & Duffy, Aidan & Conlon, Michael, 2015. "A clustering approach to domestic electricity load profile characterisation using smart metering data," Applied Energy, Elsevier, vol. 141(C), pages 190-199.
    2. Berrada, Asmae & Loudiyi, Khalid & Garde, Raquel, 2017. "Dynamic modeling of gravity energy storage coupled with a PV energy plant," Energy, Elsevier, vol. 134(C), pages 323-335.
    3. Yang, Ting & Pen, Haibo & Wang, Dan & Wang, Zhaoxia, 2016. "Harmonic analysis in integrated energy system based on compressed sensing," Applied Energy, Elsevier, vol. 165(C), pages 583-591.
    4. Lebotsa, Moshoko Emily & Sigauke, Caston & Bere, Alphonce & Fildes, Robert & Boylan, John E., 2018. "Short term electricity demand forecasting using partially linear additive quantile regression with an application to the unit commitment problem," Applied Energy, Elsevier, vol. 222(C), pages 104-118.
    5. Barros, Julio & Diego, Ramón I., 2016. "A review of measurement and analysis of electric power quality on shipboard power system networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 665-672.
    6. Blaifi, S. & Moulahoum, S. & Colak, I. & Merrouche, W., 2016. "An enhanced dynamic model of battery using genetic algorithm suitable for photovoltaic applications," Applied Energy, Elsevier, vol. 169(C), pages 888-898.
    7. Yang, Yandong & Li, Shufang & Li, Wenqi & Qu, Meijun, 2018. "Power load probability density forecasting using Gaussian process quantile regression," Applied Energy, Elsevier, vol. 213(C), pages 499-509.
    8. Schopfer, S. & Tiefenbeck, V. & Staake, T., 2018. "Economic assessment of photovoltaic battery systems based on household load profiles," Applied Energy, Elsevier, vol. 223(C), pages 229-248.
    9. Nardin, Gioacchino & Meneghetti, Antonella & Dal Magro, Fabio & Benedetti, Nicole, 2014. "PCM-based energy recovery from electric arc furnaces," Applied Energy, Elsevier, vol. 136(C), pages 947-955.
    10. Yang, Fangfang & Xing, Yinjiao & Wang, Dong & Tsui, Kwok-Leung, 2016. "A comparative study of three model-based algorithms for estimating state-of-charge of lithium-ion batteries under a new combined dynamic loading profile," Applied Energy, Elsevier, vol. 164(C), pages 387-399.
    11. Sun, Bing & Yu, Yixin & Qin, Chao, 2017. "Should China focus on the distributed development of wind and solar photovoltaic power generation? A comparative study," Applied Energy, Elsevier, vol. 185(P1), pages 421-439.
    12. Lin, Yi-Pin & Wang, Wen-Hsian & Pan, Shu-Yuan & Ho, Chang-Ching & Hou, Chin-Jen & Chiang, Pen-Chi, 2016. "Environmental impacts and benefits of organic Rankine cycle power generation technology and wood pellet fuel exemplified by electric arc furnace steel industry," Applied Energy, Elsevier, vol. 183(C), pages 369-379.
    13. Spagnuolo, Antonio & Petraglia, Antonio & Vetromile, Carmela & Formosi, Roberto & Lubritto, Carmine, 2015. "Monitoring and optimization of energy consumption of base transceiver stations," Energy, Elsevier, vol. 81(C), pages 286-293.
    14. He, Yaoyao & Liu, Rui & Li, Haiyan & Wang, Shuo & Lu, Xiaofen, 2017. "Short-term power load probability density forecasting method using kernel-based support vector quantile regression and Copula theory," Applied Energy, Elsevier, vol. 185(P1), pages 254-266.
    15. Bhattacharjee, Vikram & Khan, Irfan, 2018. "A non-linear convex cost model for economic dispatch in microgrids," Applied Energy, Elsevier, vol. 222(C), pages 637-648.
    16. Zheng, Yanchong & Shang, Yitong & Shao, Ziyun & Jian, Linni, 2018. "A novel real-time scheduling strategy with near-linear complexity for integrating large-scale electric vehicles into smart grid," Applied Energy, Elsevier, vol. 217(C), pages 1-13.
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