A hybrid modified FA-ANFIS-P&amp;O approach for MPPT in photovoltaic systems under PSCs

Farzaneh, Javad

doi:10.1080/00207217.2019.1672808

Cited by 19 publications

(7 citation statements)

References 23 publications

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“…To efficiently track the MPP, the authors in [138] proposed an FA-trained ANFIS technique that is combined with the P and O technique. This method identifies and tracks the MPP in two stages.…”

Section: Anfis With P and Omentioning

confidence: 99%

Hybrid Maximum Power Extraction Methods for Photovoltaic Systems: A Comprehensive Review

2023

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To efficiently and accurately track the Global Maximum Power Point (GMPP) of the PV system under Varying Environmental Conditions (VECs), numerous hybrid Maximum Power Point Tracking (MPPT) techniques were developed. In this research work, different hybrid MPPT techniques are categorized into three types: a combination of conventional algorithms, a combination of soft computing algorithms, and a combination of conventional and soft computing algorithms are discussed in detail. Particularly, about 90 hybrid MPPT techniques are presented, and their key specifications, such as accuracy, speed, cost, complexity, etc., are summarized. Along with these specifications, numerous other parameters, such as the PV panel’s location, season, tilt, orientation, etc., are also discussed, which makes its selection easier according to the requirements. This research work is organized in such a manner that it provides a valuable path for energy engineers and researchers to select an appropriate MPPT technique based on the projects’ limitations and objectives.

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Section: Anfis With P and Omentioning

confidence: 99%

Hybrid Maximum Power Extraction Methods for Photovoltaic Systems: A Comprehensive Review

2023

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“…Ref. [ 83 ] presents a hybrid algorithm for MPPT, using ANFIS combining P&O in a first stage and modified firefly optimization (MFA) in a second stage. The proposed method takes as input different levels of G, where it is processed by the ANFIS and yields an optimal output, which is then given to the P&O algorithm to search for the global MPP.…”

Section: Mppt Control Using Ann and Hybrid Metaherustic Algorithmsmentioning

confidence: 99%

Artificial Neural Networks in MPPT Algorithms for Optimization of Photovoltaic Power Systems: A Review

et al. 2021

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The use of photovoltaic systems for clean electrical energy has increased. However, due to their low efficiency, researchers have looked for ways to increase their effectiveness and improve their efficiency. The Maximum Power Point Tracking (MPPT) inverters allow us to maximize the extraction of as much energy as possible from PV panels, and they require algorithms to extract the Maximum Power Point (MPP). Several intelligent algorithms show acceptable performance; however, few consider using Artificial Neural Networks (ANN). These have the advantage of giving a fast and accurate tracking of the MPP. The controller effectiveness depends on the algorithm used in the hidden layer and how well the neural network has been trained. Articles over the last six years were studied. A review of different papers, reports, and other documents using ANN for MPPT control is presented. The algorithms are based on ANN or in a hybrid combination with FL or a metaheuristic algorithm. ANN MPPT algorithms deliver an average performance of 98% in uniform conditions, exhibit a faster convergence speed, and have fewer oscillations around the MPP, according to this research.

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“…In particular, some papers present results comparing the value of the power that the proposed solution allows reaching with the value of the power of the global MPP [77,[93][94][95]. In these cases, to compare results obtained in the different papers, the ratio between the reached power, P reached , and that one that should be obtained, P GMPP , has been calculated as:…”

Section: [90]mentioning

confidence: 99%

A State-of-Art-Review on Machine-Learning Based Methods for PV

et al. 2021

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In the current era, Artificial Intelligence (AI) is becoming increasingly pervasive with applications in several applicative fields effectively changing our daily life. In this scenario, machine learning (ML), a subset of AI techniques, provides machines with the ability to programmatically learn from data to model a system while adapting to new situations as they learn more by data they are ingesting (on-line training). During the last several years, many papers have been published concerning ML applications in the field of solar systems. This paper presents the state of the art ML models applied in solar energy’s forecasting field i.e., for solar irradiance and power production forecasting (both point and interval or probabilistic forecasting), electricity price forecasting and energy demand forecasting. Other applications of ML into the photovoltaic (PV) field taken into account are the modelling of PV modules, PV design parameter extraction, tracking the maximum power point (MPP), PV systems efficiency optimization, PV/Thermal (PV/T) and Concentrating PV (CPV) system design parameters’ optimization and efficiency improvement, anomaly detection and energy management of PV’s storage systems. While many review papers already exist in this regard, they are usually focused only on one specific topic, while in this paper are gathered all the most relevant applications of ML for solar systems in many different fields. The paper gives an overview of the most recent and promising applications of machine learning used in the field of photovoltaic systems.

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A hybrid modified FA-ANFIS-P&O approach for MPPT in photovoltaic systems under PSCs

Cited by 19 publications

References 23 publications

Hybrid Maximum Power Extraction Methods for Photovoltaic Systems: A Comprehensive Review

Hybrid Maximum Power Extraction Methods for Photovoltaic Systems: A Comprehensive Review

Artificial Neural Networks in MPPT Algorithms for Optimization of Photovoltaic Power Systems: A Review

A State-of-Art-Review on Machine-Learning Based Methods for PV

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