Advertisement

Multi crowd fast power control algorithm based on neighborhood opportunistic learning

  • Xiaobo Xue
  • Yonghong Tan
Original Research
  • 21 Downloads

Abstract

For improving the energy-saving power generation scheduling in the electric power industry energy saving and emission reduction capability and enhance the safety and economy of power energy structure, we based on neighborhood opportunity learning multi intelligent power quickly swarm intelligent control algorithm is presented in this paper. Firstly, the proposed algorithm is based on the characteristic value of each neighborhood characteristic value which is obtained by chance mining. The system can obtain the real-time sensing data of each neighborhood power station. This algorithm establishes a neighborhood modular architecture. The algorithm in the opportunity to automate the operation of the module information on the opportunity to learn. Secondly, based on the opportunity to learn and fast variation of the power vector, the algorithm is derived to control the power generation fast group. The algorithm of the multiple power generation control system through the opportunity to control and quickly set the mapping link. Finally, the experimental results show that the proposed algorithm has a significant advantage in real-time, reliability and cost of intelligent management and fast control of power generation to adapt to a variety of power generation devices.

Keywords

Multi intelligent fast power Neighborhood set Opportunistic learning Crowd control 

Notes

Acknowledgements

This work is supported in part by the National Natural Science Foundation of China (51405498) and Natural Science Foundation of Shaanxi Province, China (2013JQ8023).

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interests regarding the publication of this paper.

References

  1. Costantino N, Serventi R, Tinfena F et al (2011) Design and test of an HV-CMOS intelligent power switch with integrated protections and self-diagnostic for harsh automotive applications. IEEE Trans Ind Electron 58(7):2715–2727CrossRefGoogle Scholar
  2. Fu M, Yin H, Liu M et al (2016) Loading and power control for a high-efficiency class E PA driven megahertz WPT system. IEEE Trans Ind Electron 63(11):6867–6876CrossRefGoogle Scholar
  3. Gai Y, Krishnamachari B (2014) Distributed stochastic online learning policies for opportunistic spectrum access. IEEE Trans Signal Process 62(23):6184–6193MathSciNetCrossRefGoogle Scholar
  4. Graham J, Starzyk JA, Jachyra D (2015) Opportunistic behavior in motivated learning agents. IEEE Trans Neural Netw Learn Syst 26(8):1735–1746MathSciNetCrossRefGoogle Scholar
  5. Lee M, Uhm Y, Yong K et al (2009) Intelligent power management device with middleware based living pattern learning for power reduction. IEEE Trans Consum Electron 55(4):2081–2089CrossRefGoogle Scholar
  6. Lin S, Salles D, Freitas W et al (2012) An intelligent control strategy for power factor compensation on distorted low voltage power systems. IEEE Trans Smart Grid 3(3):1562–1570CrossRefGoogle Scholar
  7. Lin FJ, Tan KH, Fang DY et al (2013) Intelligent controlled three-phase squirrel–cage induction generator system using wavelet fuzzy neural network for wind power. IET Renew Power Gen 7(5):552–564CrossRefGoogle Scholar
  8. Logan KP (2007) Intelligent diagnostic requirements of future all-electric ship integrated power system. IEEE Trans Ind Appl 43(1):139–149CrossRefGoogle Scholar
  9. Mainali K, Tripathi A, Madhusoodhanan S et al (2015) A transformerless intelligent power substation: a three-phase SST enabled by a 15-kV SiC IGBT. IEEE Power Electron Magn 2(3):31–43CrossRefGoogle Scholar
  10. Murphey YL, Park J, Kiliaris L et al (2013) Intelligent hybrid vehicle power control—Part II: online intelligent energy management. IEEE Trans Veh Technol 62(1):69–79CrossRefGoogle Scholar
  11. Rouger N, Crebier JC, Catellani S (2008) High-efficiency and fully integrated self-powering technique for intelligent switch-based flyback converters. IEEE Trans Ind Appl 44(3):826–835CrossRefGoogle Scholar
  12. Sikkabut S, Mungporn P, Ekkaravarodome C et al (2016) Control of high-energy high-power densities storage devices by Li–ion battery and supercapacitor for fuel cell/photovoltaic hybrid power plant for autonomous system applications. IEEE Trans Ind Appl 52(5):4395–4407CrossRefGoogle Scholar
  13. Wang Z, Chen B, Wang J et al (2017) Networked microgrids for self-healing power systems. IEEE Trans Smart Grid 7(1):310–319CrossRefGoogle Scholar
  14. Winkler J, Dueñas-Osorio L, Stein R et al (2017) Performance assessment of topologically diverse power systems subjected to hurricane events. Reliab Eng Syst Saf 95(4):323–336CrossRefGoogle Scholar
  15. Xu Y, Wang J, Wu Q et al (2012) Opportunistic spectrum access in unknown dynamic environment: a game-theoretic stochastic learning solution. IEEE Trans Wireless Commun 11(4):1380–1391CrossRefGoogle Scholar
  16. Xu Y, Wu Q, Shen L et al (2013) Opportunistic spectrum access with spatial reuse: graphical game and uncoupled learning solutions. IEEE Trans Wireless Commun 12(10):4814–4826CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Electronics and Information EngineeringHunan University of Science and EngineeringYongzhouChina

Personalised recommendations