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Revolutionizing Power Generation: Unleashing the Potential of PV Systems with Cutting-Edge FWA–ANN Adaptive Strategy

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Abstract

Developing a photovoltaic system that successfully harvests the sun’s power is one of the most difficult aspects of boosting power generation. Consequently, our study focuses on the development of a hybrid intelligent maximum power point tracking (MPPT) regulator that combines an innovative optimization approach based on the fireworks algorithm (FWA) and an artificial neural network (ANN) model. The proposed approach is examined in the first part of this study for various ANN configuration scenarios to identify the appropriate hyperparameters values and training algorithm required to converge toward the ideal objective function. After the optimization and training processes are completed, the selected FWA–ANN model is simulated as an MPPT controller using MATLAB/Simulink software and compared to the most commonly used MPPT techniques to demonstrate its effectiveness under various partial shading conditions. The results show that our proposed FWA–ANN technique using the BR and four neurons in the hidden layer reaches an excellent mean-squared error value equal to 0.1159 in the optimization process and 2.74e\(-\)13 in the training phase. In addition, our suggested model is compared to the most widely used MPPT techniques notably, perturb and observe, particle swarm optimization, grey wolf optimization, whale optimization algorithm, and fuzzy logic controller. The obtained outcomes prove that the FWA–ANN overpasses the applied techniques by tracking the maximum power point faster with a response time between 0.0048 and 0.0261 s, a power efficiency higher than 99.5749%, that can up to 99.9977%, and an extreme reduction of oscillations in the transient and steady states for all tested cases.

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Data availability

The datasets used in this study are available upon request from the corresponding author.

Abbreviations

T :

Temperature in \((^{\circ }\)C)

G :

Irradiance in \(\hbox {W}/\hbox {m}^{2}\)

\(N_{\textrm{cell}}\) :

Cells per module

\(P_{\textrm{mpp}}\) :

Power at the MPP in (W)

\(V_{\textrm{mpp}}\) :

Voltage at the MPP in (V)

\(I_{\textrm{mpp}}\) :

Current at the MPP in (A)

\(V_{\textrm{oc}}\) :

Nominal open-circuit voltage in (V)

\(I_{\textrm{sc}}\) :

Nominal short circuit current in (A)

\(N_{s}\) :

Number of cells per module

\(N_{p}\) :

Number of strings in parallel

\(R_{\textrm{s}}\) :

Series resistance

\(R_{\textrm{sh}}\) :

Shunt resistance

\(I_{L}\) :

Light generated current

\(I_{0}\) :

Diode saturation current

\(K_{I}\) :

Temperature coefficient of \(I_{\textrm{sc}}\)

\(K_{V}\) :

Temperature coefficient of \(V_{\textrm{oc}}\)

N :

Number of samples

\({y_{i}}\) :

True value for ith sample

\(\hat{y_{i}}\) :

True predicted for ith sample

\(p^{k}\) :

The parameter vector

\(p^{k+1}\) :

Updated estimate of the parameter vector

\(X^{k}\) :

The partial derivatives of the model

\(X^{Tk}\) :

The transpose of the design matrix \(X^{k}\)

W :

The weight matrix

i :

The predicted values vector

\(i^{-k}\) :

The i vector at previous iteration

\(\mu ^{k}\) :

The damping parameter at k-th iteration

\(\omega ^{k}\) :

Regularization diagonal matrix

E(w):

Global function of weights and bias

\(E'(w)\) :

Gradient of the global function

E"(w):

Hessian matrix

\(E_{W}\) :

Sum of all weights

\(E_{D}\) :

MSE cost function

\(Z_{F}\) :

The normalization factor

M :

The used ANN model

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  1. Faculty of Science and Technology Al Hoceima, Abdelmalek Essaadi University, Tangier-Tetouan-Al Hoceima, Morocco

    Noamane Ncir & Nabil El Akchioui

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  1. Noamane Ncir
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  2. Nabil El Akchioui
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Contributions

NN conceived and designed the proposed PV system modeling, collected and analyzed the data for training the neural network, conducted statistical analyses, performed simulation experiments, contributed to the interpretation of the results, provided expertise in the theoretical framework, conducted literature reviews, and contributed to the writing and editing of the manuscript. NEA supervised the project, provided guidance throughout the study, and critically reviewed the manuscript. All authors read and approved the final version of the manuscript.

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Correspondence to Noamane Ncir.

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Ncir, N., Akchioui, N.E. Revolutionizing Power Generation: Unleashing the Potential of PV Systems with Cutting-Edge FWA–ANN Adaptive Strategy. J Control Autom Electr Syst 35, 144–162 (2024). https://doi.org/10.1007/s40313-023-01057-7

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