Code and data from an ADALINE network trained with the RTRL and LMS algorithms for an MPPT controller in a photovoltaic system

Viloria-Porto, Julie; Robles-Algarín, Carlos; Restrepo-Leal, Diego

doi:10.1016/j.dib.2020.106296

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…Combining Equations ( 23), ( 27) and ( 28), the following bilinear switched model of the global system is expressed as Equation (42).…”

Section: Nonlinear Controlmentioning

confidence: 99%

“…The recharge time is improved by using the PV generator at its maximum power. For this goal, different MPPT algorithms are suggested in the literature: perturb and observe (P & O) [33][34][35], the incremental inductance (IC) [36][37][38], fuzzy logic (FL) [39][40][41], neuronal networks (NN) [42][43][44], particle swarm optimization (PSO) [45][46][47], and sliding mode [48,49] etc. High oscillation remains the major weakness of these MPPT approaches.…”

Section: Introductionmentioning

confidence: 99%

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Robust Optimization and Power Management of a Triple Junction Photovoltaic Electric Vehicle with Battery Storage

Hamed

Sbita

et al. 2022

Sensors

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This paper highlights a robust optimization and power management algorithm that supervises the energy transfer flow to meet the photovoltaic (PV) electric vehicle demand, even when the traction system is in motion. The power stage of the studied system consists of a triple-junction PV generator as the main energy source, a lithium-ion battery as an auxiliary energy source, and an electric vehicle. The input–output signal adaptation is made by using a stage of energy conversion. A bidirectional DC-DC buck-boost connects the battery to the DC-link. Two unidirectional boost converters interface between the PV generator and the DC link. One is controlled with a maximum power point tracking (MPPT) algorithm to reach the maximum power points. The other is used to control the voltage across the DC-link. The converters are connected to the electric vehicle via a three-phase inverter via the same DC-link. By considering the nonlinear behavior of these elements, dynamic models are developed. A robust nonlinear MPPT algorithm has been developed owing to the nonlinear dynamics of the PV generator, metrological condition variations, and load changes. The high performance of the MPPT algorithm is effectively highlighted over a comparative study with two classical P & O and the fuzzy logic MPPT algorithms. A nonlinear control based on the Lyapunov function has been developed to simultaneously regulate the DC-link voltage and control battery charging and discharging operations. An energy management rule-based strategy is presented to effectively supervise the power flow. The conceived system, energy management, and control algorithms are implemented and verified in the Matlab/Simulink environment. Obtained results are presented and discussed under different operating conditions.

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“…Combining Equations ( 23), ( 27) and ( 28), the following bilinear switched model of the global system is expressed as Equation (42).…”

Section: Nonlinear Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Optimization and Power Management of a Triple Junction Photovoltaic Electric Vehicle with Battery Storage

Hamed

Sbita

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…To overcome this limitation, other strategies based on intelligent optimization, such as the fuzzy logic technique or the neural network method, have been designed in the same sector [22,23]. The two techniques lead to higher profitability, but their main issue is the database required to adapt these algorithms to PV systems.…”

Section: Introductionmentioning

confidence: 99%

A Comparison between Particle Swarm and Grey Wolf Optimization Algorithms for Improving the Battery Autonomy in a Photovoltaic System

et al. 2021

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This research focuses on a photovoltaic system that powers an Electric Vehicle when moving in realistic scenarios with partial shading conditions. The main goal is to find an efficient control scheme to allow the solar generator producing the maximum amount of power achievable. The first contribution of this paper is the mathematical modelling of the photovoltaic system, its function and its features, considering the synthesis of the step-up converter and the maximum power point tracking analysis. This research looks at two intelligent control strategies to get the most power out, even with shading areas. Specifically, we show how to apply two evolutionary algorithms for this control. They are the “particle swarm optimization method” and the “grey wolf optimization method”. These algorithms were tested and evaluated when a battery storage system in an Electric Vehicle is fed through a photovoltaic system. The Simulink/Matlab tool is used to execute the simulation phases and to quantify the performances of each of these control systems. Based on our simulation tests, the best method is identified.

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Pl/sql design to determine the training data pattern on the Adaline neural network

Hastono

Syah

2021

J. Phys.: Conf. Ser.

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Inventories of goods in business activities greatly affect the smooth running of business activities. The durian fruit sales business is also affected by inventory. Durian fruit sellers must be able to predict the needs of durian fruit customers so that sales business activities run stable. For sellers with low durian fruit sales, they will have no difficulty predicting demand for durian fruit. But for sellers of durian fruit in large quantities, it will take a long time and is prone to errors. An error in the prediction causes the sales business to lose. It takes high accuracy and fast time to make predictions. Adaline neural network has been proven to be able to solve prediction problems precisely. Adaline network performance is affected by training data. Durian fruit sales data is used as network training data to improve network performance. The focus of this research is the design of the pl/sql database to determine the training data input pattern in the android application to predict the number of durian fruit customer needs. Types of pl/sql used in this study are procedures and functions. Meanwhile, the method used to design pl/sql is an extreme method.

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Code and data from an ADALINE network trained with the RTRL and LMS algorithms for an MPPT controller in a photovoltaic system

Cited by 3 publications

References 3 publications

Robust Optimization and Power Management of a Triple Junction Photovoltaic Electric Vehicle with Battery Storage

Robust Optimization and Power Management of a Triple Junction Photovoltaic Electric Vehicle with Battery Storage

A Comparison between Particle Swarm and Grey Wolf Optimization Algorithms for Improving the Battery Autonomy in a Photovoltaic System

Pl/sql design to determine the training data pattern on the Adaline neural network

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