MPPT controller based on the neural network model of the photovoltaic panel

Rolevski, Maja; Zečević, Žarko

doi:10.1109/it48810.2020.9070299

Cited by 3 publications

(5 citation statements)

References 11 publications

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“…The current source (Iph) represents the cell photocurrent connected in parallel with a diode. Ns is the number of series cells, Np is the number of parallel cells, Rsh and Rs are the shunt and series resistances of the cell, respectively [15,16]. The current source (Iph) is described by [17] 𝐼 𝑝ℎ = 𝐼 𝑠𝑐 + 𝐾 𝑖 (𝑇 − 298) • 𝐺 1000 ⁄…”

Section: Solar Array Mathematical Modelmentioning

confidence: 99%

“…where Tr is the nominal temperature (298 K), Eg0 is the bandgap energy of the semiconductor (1.1 eV). The PV module output current (I) is [15]…”

Section: Solar Array Mathematical Modelmentioning

confidence: 99%

“…Moreover, the lookup table data must be increased to get highly accurate tracking and cover all operating points, thus increasing the calculation difficulties. To solve the complexity problem, the authors in [15] and [16] combined P&O MPPT and Lookup Table MPPT. The method recorded the irradiance and duty cycle values from the P&O MPPT and sent the data into a table to use the next time.…”

Section: Introductionmentioning

confidence: 99%

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Enhancement of MPPT Module for Partial Shading Photovoltaic System Under Uniform Irradiance Conditions

Mohammed¹,

Mohd‐Mokhtar²,

Ali

2023

IIUMEJ

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Maximum Power Point Tracking (MPPT) algorithms play a critical role in maximizing the output power of solar panels. Different MPPT techniques are evaluated based on several criteria, such as tracking speed, simplicity, and accuracy with changes in solar irradiance and ambient temperature. Under partial shading conditions (PSCs), conventional techniques fail to track global maximum power points (GMPP). This paper aims to present an automatic and accurate method to fix the complexity of determining the accurate lookup table data in an automatic and fast process under uniform irradiance conditions (UICs) and PSCs. The proposed method runs the photovoltaic (PV) module with all potential irradiance and temperature. It automatically calculates the perfect voltage reference (Vref) for all potential PV system cases. The Vref is collected in an array, sent into a two-dimensional lookup table, and used for controlling the boost converter. Simulation results verify the effectiveness of the proposed method. In addition, a comparison was also made with the conventional perturb and observe (P&O) method. Under UICs, the proposed method takes less time than the conventional P&O algorithm to reach the MPP. The time difference between them is ?t = 0.133 sec and ?t = 0.04 sec for the first scanning process at t = 0 sec and sudden change irradiance at t = 1.5 sec, respectively. As for PSCs, the proposed method reached the GMPP during pattern 104 (first peak) without any power loss, while the P&O MPPT was able to track the GMPP but with power losses of 2729.97 watts. ABSTRAK: Algoritma Penjejakan Titik Kuasa Maksimum (MPPT) memainkan peranan penting dalam memaksimumkan kuasa keluaran panel solar. Teknik MPPT yang berbeza dinilai berdasarkan beberapa kriteria seperti kelajuan pengesanan, kesederhanaan, dan ketepatan dengan perubahan dalam sinaran suria dan suhu ambien. Di bawah keadaan teduhan separa (PSC), teknik konvensional gagal menjejak titik kuasa maksimum global (GMPP). Kajian ini bertujuan bagi membentangkan kaedah automatik dan tepat bagi membetulkan kesusahan dalam menentukan carian data berjadual secara tepat, automatik dan pantas di bawah keadaan sinaran seragam (UIC) dan PSC. Kaedah yang dicadangkan menjalankan modul fotovoltaik (PV) dengan semua potensi sinaran dan suhu dan mengira rujukan voltan sempurna (Vref) secara automatik bagi semua kes yang berpotensi dalam sebarang jenis sistem PV. Vref dikumpul dalam tata susunan, dihantar ke dalam jadual carian dua dimensi, dan digunakan bagi mengawal penukar rangsangan. Keputusan simulasi mengesahkan keberkesanan kaedah yang dicadangkan. Perbandingan juga dibuat dengan kaedah konvensional perhati dan ganggu (P&O). Di bawah UIC, kaedah yang dicadangkan mengambil masa yang lebih singkat berbanding algoritma konvensional P&O bagi mencapai MPP. Perbezaan masa antara keduanya adalah masing-masing, ?t = 0.133 saat dan ?t = 0.04 saat bagi proses pengimbasan pertama iaitu pada t = 0 saat dan sinaran perubahan mendadak pada t = 1.5 saat. Bagi PSC, kaedah yang dicadangkan mencapai GMPP semasa corak 104 (puncak pertama) tanpa kehilangan kuasa manakala MPPT P&O dapat mengesan GMPP tetapi dengan pengurangan kuasa sebanyak 2729.97 watt.

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Section: Solar Array Mathematical Modelmentioning

confidence: 99%

“…where Tr is the nominal temperature (298 K), Eg0 is the bandgap energy of the semiconductor (1.1 eV). The PV module output current (I) is [15]…”

Section: Solar Array Mathematical Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enhancement of MPPT Module for Partial Shading Photovoltaic System Under Uniform Irradiance Conditions

Mohammed¹,

Mohd‐Mokhtar²,

Ali

2023

IIUMEJ

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“…Comprehensive review of the NN-based MPPT algorithms can be found in [30]. The NN-based algorithms provide the V mpp voltage at the output, while their inputs can be datasheet information [31][32][33], irradiance and temperature [34][35][36], voltage and current measurements [37,38]. Moreover, some authors focuses on the design of both the MPPT algorithm and control unit [42,43].…”

Section: Overview Of Mptt Algorithmsmentioning

confidence: 99%

“…Among the proposed algorithms, the most precise are those that use irradiance and temperature as input information [34][35][36]. Figure 5a shows an example of the feedforward NN in which G and T measurement are used to predict V mpp .…”

Section: Overview Of Mptt Algorithmsmentioning

confidence: 99%

Neural Network Approach to MPPT Control and Irradiance Estimation

Zečević

Rolevski

2020

Applied Sciences

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Photovoltaic (PV) modules require maximum power point tracking (MPPT) algorithms to ensure that the amount of power extracted is maximized. In this paper, we propose a low-complexity MPPT algorithm that is based on the neural network (NN) model of the photovoltaic module. Namely, the expression for the output current of the NN model is used to derive the analytical, iterative rules for determining the maximal power point (MPP) voltage and irradiance estimation. In this way, the computational complexity is reduced compared to the other NN-based MPPT methods, in which the optimal voltage is predicted directly from the measurements. The proposed algorithm cannot instantaneously determine the optimal voltage, but it contains a tunable parameter for controlling the trade-off between the tracking speed and computational complexity. Numerical results indicate that the relative error between the actual maximum power and the one obtained by the proposed algorithm is less than 0.1%, which is up to ten times smaller than in the available algorithms.

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