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PSO-AFSA Global Maximum Power Point Tracking Algorithm with Adaptive Evolutionary Strategy for PV System

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Recent Advances in Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2018)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 109))

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Abstract

The P-U curve of the PV (photovoltaic) system has multi-peak characteristics under non-uniform irradiance conditions (NUIC). The conventional MPPT algorithm can only track the local maximum power points, therefore, PV system fails to work at the global optimum, causing serious energy loss. How to track its global maximum power point is of great significance for the PV system to maintain an efficient output state. PSO-AFSA (Particle Swarm Optimization Artificial Fish Swarm Algorithm) is a global maximum power point tracking (GMPPT) algorithm with strong global search capability, but the convergence speed and accuracy of the algorithm are limited. To solve the mentioned problems, a modified AESPSO-AFSA GMPPT algorithm is proposed in this paper by introducing the evolution strategy into PSO-AFSA algorithm. Simulation results show that under NUIC, compared with the conventional P&O and PSO-AFSA algorithm, the proposed algorithm has well performance on getting out of the local optimal solution and improving the global optimal solution of the individual neighborhood, the convergence speed and convergence accuracy are also increased.

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References

  1. Hu, P.: Geothermal-solar combined organic Rankine cycle power generation technology research. J. Northeast. Dianli Univ. 35(5), 41–45 (2015)

    Google Scholar 

  2. Chen, K.: An improved MPPT controller for photovoltaic system under partial shading condition. IEEE Trans. Sustain. Energy 5(3), 978–985 (2017)

    Article  Google Scholar 

  3. Sundareswaran, K.: Development of an improved P&O algorithm assisted through a colony of foraging ants for MPPT in PV system. IEEE Trans. Ind. Inform. 12(1), 187–200 (2016)

    Article  MathSciNet  Google Scholar 

  4. Li, X.: A comparative study of the main incremental conductance-based MPPT techniques for PV applications. Power Electron. 50(12), 91–98 (2016)

    Google Scholar 

  5. Pragallapati, N.: Distributed PV power extraction based on a modified interleaved SEPIC for nonuniform irradiance conditions. IEEE J. PVs 5(5), 1442–1453 (2015)

    Google Scholar 

  6. Ishaque, K.: A deterministic particle swarm optimization maximum power point tracker for PV system under partial shading condition. IEEE Trans. Ind. Electron. 60(8), 3195–3206 (2013)

    Google Scholar 

  7. Belghith, O.: MPPT design using PSO technique for photovoltaic system control comparing to fuzzy logic and P&O controllers. Energy Power Eng. 08(11), 349–366 (2016)

    Article  Google Scholar 

  8. Lian, K.: A maximum power point tracking method based on Perturb-and-Observe combined with particle swarm optimization. IEEE J. PVs 4(2), 626–633 (2014)

    Google Scholar 

  9. Dang, K.: Simulation study of improved single neuron in photovoltaic power generation system. J. Northeast. Dianli Univ. 33(Z1), 99–102 (2013)

    Google Scholar 

  10. Yin, L.: Three-step MPPT algorithm for photovoltaic arrays with local shadows. J. Northeast. Electr. Power Univ. 37(6), 15–20 (2017)

    Google Scholar 

  11. Lyden, S.: A simulated annealing global maximum power point tracking approach for PV modules under partial shading conditions. IEEE Trans. Power Electron. 31(6), 4171–4181 (2016)

    Article  Google Scholar 

  12. Duan, Q.: An intelligent algorithm for maximum power point tracking in PV system under partial shading conditions. Trans. Inst. Meas. Control. 39(2), 1–13 (2016)

    Google Scholar 

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Acknowledgments

This work was supported by National Natural Science Foundation of China (No. 61501106), Science and Technology Foundation of Jilin Province (No. 20180101039JC and JJKH20170102KJ).

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Authors and Affiliations

  1. School of Computer Science, Northeast Electric Power University, Jilin, 132012, China

    Jianpo Li & Pengwei Dong

  2. Global Energy Interconnection Group Co., Ltd., Beijing, 100031, China

    Cong Zheng, Fuxin Liu & Songjun Pan

  3. Beijing China-Power Information Technology Co., Ltd., Beijing, 110000, China

    Baochun Mu

  4. State Grid Information & Telecommunication Branch, Beijing, 100142, China

    Ziqi Dong

Authors
  1. Jianpo Li
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  2. Pengwei Dong
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  3. Cong Zheng
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  4. Fuxin Liu
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  5. Songjun Pan
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  6. Baochun Mu
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  7. Ziqi Dong
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Corresponding author

Correspondence to Jianpo Li .

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Editors and Affiliations

  1. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, China

    Jeng-Shyang Pan

  2. Graduate School of Engineering, Tohoku University, Sendai, Miyagi, Japan

    Akinori Ito

  3. Swinburne University of Technology, Hawthorn, VIC, Australia

    Pei-Wei Tsai

  4. Centre for Artificial Intelligence, Faculty of Engineering and Information Technology, University of Technology Sydney, Canberra, ACT, Australia

    Lakhmi C. Jain

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Li, J. et al. (2019). PSO-AFSA Global Maximum Power Point Tracking Algorithm with Adaptive Evolutionary Strategy for PV System. In: Pan, JS., Ito, A., Tsai, PW., Jain, L. (eds) Recent Advances in Intelligent Information Hiding and Multimedia Signal Processing. IIH-MSP 2018. Smart Innovation, Systems and Technologies, vol 109. Springer, Cham. https://doi.org/10.1007/978-3-030-03745-1_8

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