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Performance Enhancement of MPPT Controller to Tune Optimal Voltage for PV-BES System Using Converged Barnacles Mating Optimizer Algorithm Based ANFIS

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Abstract

Research into renewable energies is expanding quickly, especially photovoltaic (PV) systems. PV systems are employed extensively in several renewable energy applications. The primary challenge with PV systems is maximizing electricity output. Consequently, a significant amount of research into modeling PV continues to focus on maximizing the generated power. Maximum power point tracking (MPPT) refers to the optimization of PV power generation. Accordingly, an effective MPPT approach deploying a converged barnacles mating optimizer (CBMO)-based adaptive neuro-fuzzy inference system (ANFIS) is introduced in this paper. The mentioned strategy is utilized to detect and track the maximum power point (MPP) in two phases. At the initial stage, ideal voltages are determined using the CBMO algorithm in various temperatures and irradiances in the offline mode. After being trained, the ANFIS calculates the ideal voltage depending on the radiation conditions on solar panels. It then enters the tracking cycle and attempts to identify the MPP. To evaluate the behavior of the suggested technique, a Matlab/Simulink-based MPPT model is created. The proposed approach is evaluated under various weather conditions. The results demonstrate that the suggested methodology for tracking is efficacious under all environmental circumstances. Simulation of the suggested technique is carried out, and the results demonstrate that the introduced MPPT algorithm will effectively give the global maximum under a variety of climatic circumstances. Additionally, this approach is exceedingly effective, rapid, and stable. The findings demonstrate that the suggested technique properly identifies the optimum MPP at 99.3% efficiency.

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Abbreviations

PV:

Photovoltaic

RMSE:

Root mean square error

MPPT:

Maximum power point tracking

I PH :

Cell’s photocurrent

CBMO:

Converged barnacles mating optimizer

I S :

Saturation current

ANFIS:

Adaptive neuro-fuzzy inference system

I SH :

Shunt current

MPP:

Maximum power point

Q :

Electron charge

BES:

Battery energy storage

K :

Boltzman constant

HC:

Hill climbing

n :

Solar cell’s ideality factor

P&O:

Perturb and observe

Np :

Number of series

AI:

Artificial intelligence

Ns :

Parallel cells

MFs:

Membership functions

E G :

Band gap energy

GA:

Genetic algorithm

I S0 :

Reverse saturation current

PSCs:

Partial shading conditions

V OC :

Open-circuit voltage

PLL:

Phase-locked loop

l b/u b :

Lower bound and upper bound

PT:

Park transform

\(X_{{b_{\rm M} }}^{N}\) :

Mum variable

E:

Relative error

\(X_{{b_{\rm D} }}^{N}\) :

Dad variable

MAE:

Mean absolute error

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Acknowledgements

The authors would like to express their profound gratitude to King Abdullah City for Atomic and Renewable Energy (K.A.CARE) for their financial support in accomplishing this work.

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Authors and Affiliations

  1. Control and Instrumentation Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia

    Mujahed Al-Dhaifallah

  2. Interdisciplinary Research Center (IRC) for Renewable Energy and Power Systems, King Fahd University of Petroleum & Minerals, KFUPM Box 120, 31261, Dhahran, Saudi Arabia

    Mujahed Al-Dhaifallah

  3. Department of Computer, College of Science and Arts in Ar Rass, Qassim University, Ar Rass, Saudi Arabia

    Salem Alkhalaf

  4. Solar Energy and Power Electronic Co, Ltd, Ankara, Turkey

    Hitoshi Oikawa

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  1. Mujahed Al-Dhaifallah
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  2. Salem Alkhalaf
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  3. Hitoshi Oikawa
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Correspondence to Hitoshi Oikawa.

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Al-Dhaifallah, M., Alkhalaf, S. & Oikawa, H. Performance Enhancement of MPPT Controller to Tune Optimal Voltage for PV-BES System Using Converged Barnacles Mating Optimizer Algorithm Based ANFIS. Int. J. Fuzzy Syst. 26, 625–644 (2024). https://doi.org/10.1007/s40815-023-01622-x

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  • DOI: https://doi.org/10.1007/s40815-023-01622-x

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