ReviewMicromachines2021
Artificial Neural Networks in MPPT Algorithms for Optimization of Photovoltaic Power Systems: A Review.
Review in Micromachines, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
12 citing papers in PubMed.
- Energy optimization of PV systems under partial shading conditions using various technique-based MPPT methods.Scientific reports · 2026Article
- Multilayer perceptron neural network approach for power quality improvement in a grid integrated PV and electric vehicle systems.PloS one · 2026Article
- Comparative analysis of reinforcement learning and artificial neural networks for inverter control in improving the performance of grid-connected photovoltaic systems.Scientific reports · 2025Article
- The Integration of Artificial Intelligence with Micro-Nano-Systems: Perspectives, Challenges and Future Prospects.Micromachines · 2025Article
- Improving photovoltaic water pumping system performance with ANN-based direct torque control using real-time simulation.Scientific reports · 2025Article
- A new MPPT mechanism based on multi-verse optimization algorithm tuned FLC for photovoltaic systems.Scientific reports · 2024Article
- Experimental validation of effective zebra optimization algorithm-based MPPT under partial shading conditions in photovoltaic systems.Scientific reports · 2024Article
- Experimental validation of a low-cost maximum power point tracking technique based on artificial neural network for photovoltaic systems.Scientific reports · 2024Article
- Training of Feed-Forward Neural Networks by Using Optimization Algorithms Based on Swarm-Intelligent for Maximum Power Point Tracking.Biomimetics (Basel, Switzerland) · 2023Article
- A Novel Method to Detect Partial Shadow Effects in Perovskite-Based Simulated Solar Cell System Faults.Micromachines · 2023Article
- Recursive bit assignment with neural reference adaptive step (RNA) MPPT algorithm for photovoltaic system.Scientific reports · 2023Article
- A unique novel-based FLC approach for enhancing MPPT operation of solar systems considering sudden/gradual variation in weather conditions.Science progressArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.