ADALINE Based MPPT With Indirect Control Mode for Photovoltaic Systems

Triki, Yacine; Bechouche, Alı; Seddiki, Hamid; Abdeslam, Djaffar Ould

doi:10.1109/isie.2019.8781337

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…In order to obtain the maximum available power from a PV system and to enhance the installation efficiency, MPPT methods are used [23]. The most popular conventional MPPT in full methods are perturb and observe (P&O), incremental conductance, fractional open circuit voltage and fractional short circuit current [24]. These algorithms are very popular because of its simplicity and fast convergence [31].…”

Section: Maximum Power Point Tracking Methodsmentioning

confidence: 99%

A Review of Control Techniques in Photovoltaic Systems

et al. 2020

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Complex control structures are required for the operation of photovoltaic electrical energy systems. In this paper, a general review of the controllers used for photovoltaic systems is presented. This review is based on the most recent papers presented in the literature. The control architectures considered are complex hybrid systems that combine classical and modern techniques, such as artificial intelligence and statistical models. The main contribution of this paper is the synthesis of a generalized control structure and the identification of the latest trends. The main findings are summarized in the development of increasingly robust controllers for operation with improved efficiency, power quality, stability, safety, and economics.

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Section: Maximum Power Point Tracking Methodsmentioning

confidence: 99%

A Review of Control Techniques in Photovoltaic Systems

et al. 2020

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“…Numerous studies demonstrate the capability of ANNs as MPPT techniques [43]. In this manner, the input signals to the ANN can be electrical (voltage and current) [13,[44][45][46], non-electrical (temperature and irradiance) [47][48][49][50], or any combination of these [8,[51][52][53]. Generally, electrical inputs are preferred to non-electrical inputs from cost and robustness points of view [8].…”

Section: Introductionmentioning

confidence: 99%

System-Independent Irradiance Sensorless ANN-Based MPPT for Photovoltaic Systems in Electric Vehicles

2021

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The integration of photovoltaic systems (PVS) in electric vehicles (EV) increases the vehicle’s autonomy by providing an additional energy source other than the battery. However, current solar cell technology generates around 200 W for a 1.4 m2 panel (to be installed on the roof of the EV) at stable irradiance conditions. This limitation in production and the sudden changes in irradiance produced by shadows of clouds, buildings, and other structures make developing a fast and efficient maximum power point tracking (MPPT) technique in this area necessary. This article proposes an artificial neural network (ANN)-based MPPT, called DS-ANN, that uses manufacturer datasheet parameters as inputs to the network to address this problem. The Bayesian backpropagation-regularization performs the training, ensuring that the MPPT technique operates satisfactorily on different PVS without retraining. We simulated the response of 20 commercial modules against actual irradiance data to validate the proposed method. The results show that our method achieves an average tracking efficiency of 99.66%, improving by 1.21% over an enhanced P&O method.

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