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Maximum power point tracking technique based on variable step size with sliding mode controller in photovoltaic system

Soft Computing volume 27, pages 3829–3845 (2023)Cite this article

Abstract

Due to the economic and technical advantages, the use of solar energy is expanding in developed countries. The extraction of maximum power in solar power plants is an important issue that requires extensive research. Extracting the maximum possible power in solar power plants can increase the efficiency of this type of renewable energy sources (RESs). Climatic condition is a very important feature of solar systems. In fact, radiation and temperature are two important parameters that affect the efficiency of solar systems. This paper suggests a novel maximum power point tracking (MPPT) technique based on the sliding mode controller (SMC) to extract the maximum power of photovoltaic (PV) systems in different climatic circumstances. To obtain the optimal coefficients of the SMC online, the Grey wolf optimizer (GWO) algorithm is employed. SMC coefficients are applied for the variable perturb and observe (P&O) step of MPPT. The proposed GWO-SMC controller can eliminate oscillations in the transient mode and guarantee stability. The findings of the simulation indicate that with the use of an MPPT controller for the solar-PV system, such as P&O, Fuzzy Logic (FLC), Incremental Conductance (INC), the β method, and hill climbing (HC) MPPT, the system will operate more efficiently. The method that has been suggested is tested in a number of different climate conditions. The findings indicate that the proposed technique has an efficiency of 99%, which demonstrates a substantially superior response time when reaching the MPP in comparison to prevalent methods, which have an efficiency of 92 to 97%. The results of the simulations allow for the various approaches to be ranked as follows: 1. GWO-SMC, 2. FLC, 3. INC, 4. β method, 5. P&O, 6. HC with response times of 0.14 s, 0.17 s, 0.23 s, 0.25, 0.28 s and 0.35, respectively. The fluctuations using the combinatorial GWO-SMC technique is 4.31 W, while that of the P&O is 74.56 W. Through simulation and testing with the MATLAB software, the developed method's performance is evaluated to make a comparison.

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Data availability

Enquiries about data availability should be directed to the authors.

Abbreviations

MPPT:

Maximum power point tracking

SMC:

Sliding mode controller

PV:

Photovoltaic

GWO:

Grey wolf optimizer

P&O:

Perturb and observe

RES:

Renewable energy sources

INC:

Incremental conductance

HC:

Hill-climbing

FLCs:

Fuzzy logic controller

ANNs:

Artificial neural networks

GA:

Genetic algorithm

PSCs:

Partial shading conditions

PWM:

Pulse-width modulation

T :

Temperature

G :

Radiation

n :

Ideality factor

Eg :

Bandgap

PLL:

Phase lock loop

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Funding

This work was supported by the National Natural Science Foundation of China (No.61862051), the Science and Technology Foundation of Guizhou Province (No.ZK[2022]549, No. [2019]1299), the Top-notch Talent Program of Guizhou province (No.KY[2018]080), the Natural Science Foundation of Education of Guizhou province(No.[2019]203) and the Funds of Qiannan Normal University for Nationalities (No. qnsy2018003, No. qnsy2019rc09, No. qnsy2018JS013, No. qnsyrc201715).

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Authors and Affiliations

  1. School of Computer and Information, Qiannan Normal University for Nationalities, Duyun, Guizhou, 558000, China

    Tao Hai

  2. Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Province, Duyun, 558000, China

    Tao Hai

  3. Institute for Big Data Analytics and Artificial Intelligence (IBDAAI), Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia

    Tao Hai & Jasni Mohamad Zain

  4. School of Computing, College of Computing, Informatics and Media, Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia

    Jasni Mohamad Zain

  5. Solar Energy and Power Electronic Co., Ltd, Tokyo, Japan

    Hiroki Nakamura

Authors
  1. Tao Hai
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  2. Jasni Mohamad Zain
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  3. Hiroki Nakamura
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Correspondence to Jasni Mohamad Zain.

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Appendices

Appendix A


GWO parameters:

Number of search-agents = 100 and maximum number of iteration = 50.

Appendix B

See Table 8.

Table 8 PI coefficients
Full size table

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Hai, T., Zain, J.M. & Nakamura, H. Maximum power point tracking technique based on variable step size with sliding mode controller in photovoltaic system. Soft Comput 27, 3829–3845 (2023). https://doi.org/10.1007/s00500-022-07588-6

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Keywords

  • Photovoltaic (PV)
  • Maximum power point (MPP)
  • Stability
  • Dynamic
  • Grey wolf optimizer (GWO) algorithm