A robust experimental-based artificial neural network approach for photovoltaic maximum power point identification considering electrical, thermal and meteorological impact

Gowid, Samer; Massoud, Ahmed

doi:10.1016/j.aej.2020.06.024

Cited by 21 publications

(11 citation statements)

References 28 publications

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“…Meteonorm ® software containing climatological data for solar engineering applications at every location. It represents an average year of the selected climatological period based on the user's settings [20], [22][23][24][25][26][27][28][29]. The Meteonorm ® insolation database is based on 20-year measurement periods, the other meteorological Parameters mainly from 1991-2010.…”

Section: B Selection Of Tilt Angle Of Solar Pv Module At a Particular Locationmentioning

confidence: 99%

Global Solar Insolation Estimation and Investigation: A Case Study of Various Nations and Cities

Jayaswal

Sharma²,

Bhaskar

et al. 2021

IEEE Access

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Solar insolation is sun radiation converted to electrical energy and injected into the utility network using grid-connected solar power plants. Recently, in many nations' electricity grids, the share of power generated using solar photovoltaic (PV) power plants is rapidly increasing. In this case study, solar insolation levels on different latitudes with fix & two seasonal tilt angles have been measured using Meteonorm ® software. For these latitudes, fourteen airport locations have been chosen across the globe. From the case study, it has been evident that a double seasonal tilt angle provides more global insolation as compared to fix tilt angle with a suitable azimuth angle. There is also a reduction in insolation level at fixed and two seasonal tilt angles if going away from the equator. Further, up to three seasonal tilt angles, there is sufficient enhancement has been observed in solar insolation. It is also analyzed that for multiple seasonal tilt angle based solar PV Plant, month-wise tilt angle selection is more important. Further Average, Rectangular, and Trapezoidal methods have been used for estimation of cumulative inclined insolation for a specific period and compared. From the comparison, the trapezoidal method has been found optimal for accurate estimation of cumulative inclined insolation.

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Section: B Selection Of Tilt Angle Of Solar Pv Module At a Particular Locationmentioning

confidence: 99%

Global Solar Insolation Estimation and Investigation: A Case Study of Various Nations and Cities

Jayaswal

Sharma²,

Bhaskar

et al. 2021

IEEE Access

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“…Así mismo, se aplican arquitecturas de redes profundas, las cuales generan modelos composicionales, donde el patrón a reconocer, es expresado como una composición de niveles, lo que se ha considerado como punto de partida de esta investigación. Para la obtención del modelo de optimización, se considera el modelado, de forma semi-empírica, cuyos coeficientes pueden ser obtenidos por datos experimentales, en relación a las principales variables o parámetros con mayor impacto [10], un análisis de correspondencia a través de un método de generalización e identificación con las características físicas.…”

Section: Antecedentes En Modelado Neuronal De Sistemas De Erncunclassified

“…5 Potencial Estimado con Herramienta: Explorador Solar. coincidiendo con el esquema LFSR [10][11][12] (Linear Feedback Shift Register). Esto orientado capas paralelas de elementos de configuración del sistema, donde la creciente demanda de digitalización de la infraestructura energética da espacio a desarrollos basados en tecnologías FPGA (Field Programmable Gates Array), como dispositivos inteligentes para la definición de parámetros de control para máxima eficiencia y arquitectura dinámica de los arreglos en ERNC.…”

Section: Aerounclassified

Modelo de optimización LFSR basado en método de coeficientes adaptativos para sistemas reconfigurables de ERNC

Sandoval-Ruiz

2021

Ingeniare. Rev. chil. ing.

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En esta investigación se presenta un modelo de optimización para sistemas de energías renovables, considerando la configuración híbrida de conversión, etapas de almacenamiento y esquema de realimentación, basada en coeficientes adaptativos de la arquitectura LFSR (Linear Feedback Shift Register). El método de correlación entre los módulos de conversión, para tecnología fotovoltaica bifacial y eólica, comprende el modelado neuronal y un análisis de las estructuras componentes, para establecer las ecuaciones descriptivas. Entre los resultados se reporta la caracterización de los módulos para un esquema concatenado, simplificación del nivel de abstracción y correspondencia entre los modelos del sistema híbrido realimentado. Entre los aportes se presenta una descripción matemática generalizada, con operadores de convolución LFC (n, k) y una propuesta de optimización, a partir de eficiencia energética. Se obtiene como conclusión que el modelo reconfigurable con realimentación selectiva, cumple con los criterios de recuperación de energía regenerativa y reutilización de componentes, para la adaptación de las tecnologías a modelos de desarrollo sostenible.Palabras clave: Modelo de optimización, esquemas reconfigurables LFSR, coeficientes adaptativos, energías renovables, fotovoltaica bifacial.

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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%

“…The proposed technique can provide accurate results, but at the cost of the increased computational complexity. The algorithm that predict the MPP voltage by using the multilayer feedforward NN is proposed in [33].…”

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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A robust experimental-based artificial neural network approach for photovoltaic maximum power point identification considering electrical, thermal and meteorological impact

Cited by 21 publications

References 28 publications

Global Solar Insolation Estimation and Investigation: A Case Study of Various Nations and Cities

Global Solar Insolation Estimation and Investigation: A Case Study of Various Nations and Cities

Modelo de optimización LFSR basado en método de coeficientes adaptativos para sistemas reconfigurables de ERNC

Neural Network Approach to MPPT Control and Irradiance Estimation

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