AIMC Topic: Electric Power Supplies

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Extended DEMATEL method with intuitionistic fuzzy information: A case of electric vehicles.

PloS one Dec 19, 2024
The Decision-Making Trial and Laboratory (DEMATEL) methodology excels in the analysis of interdependent factors within complex systems, with correlation data typically presented in crisp values. Nevertheless, the judgments made by decision-makers oft...
Intuition Decision Making Electricity Algorithms Electric Power Supplies Fuzzy Logic Motor Vehicles Humans
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A novel strategy for the MPPT in a photovoltaic system via sliding modes control.

PloS one Dec 13, 2024
This paper proposes a robust maximum power point tracking algorithm based on a super twisting sliding modes controller. The underlying idea is solving the classical trajectory tracking control problem where the maximum power point defines the referen...
Electric Power Supplies Solar Energy Neural Networks, Computer Models, Theoretical Algorithms Bayes Theorem Computer Simulation Temperature
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New approach for accurate discrimination and location of power transformers with different internal winding faults.

PloS one Oct 11, 2024
Power transformers are essential elements in power systems and thus their protection schemes have critical importance. In this paper, a scheme is proposed for accurate discrimination and location of internal faults in power transformers using convent...
Algorithms Neural Networks, Computer Electric Power Supplies
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Novel glassbox based explainable boosting machine for fault detection in electrical power transmission system.

PloS one Aug 28, 2024
The reliable operation of electrical power transmission systems is crucial for ensuring consumer's stable and uninterrupted electricity supply. Faults in electrical power transmission systems can lead to significant disruptions, economic losses, and ...
Neural Networks, Computer Machine Learning Electric Power Supplies Equipment Failure Algorithms Electricity
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Machine-learning based control of bi-modular multilevel PWM inverter for high power applications.

PloS one Jul 22, 2024
This paper presents the topology and machine learning-based intelligent control of high-power PV inverter for maximum power extraction and optimal energy utilization. Modular converters with reduced components economic and reliable for high power app...
Electric Power Supplies Machine Learning Algorithms Computer Simulation
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Decoupling ion concentrations from effluent conductivity profiles in capacitive and battery electrode deionizations using an artificial intelligence model.

Water research Jul 14, 2024
Owing to its simplicity of measurement, effluent conductivity is one of the most studied factors in evaluations of desalination performance based on the ion concentrations in various ion adsorption processes such as capacitive deionization (CDI) or b...
Water Purification Models, Theoretical Electric Conductivity Artificial Intelligence Electric Power Supplies Electrodes Ions
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Research on short-term power load forecasting based on VMD and GRU.

PloS one Jul 11, 2024
The traditional method for power load forecasting is susceptible to various factors, including holidays, seasonal variations, weather conditions, and more. These factors make it challenging to ensure the accuracy of forecasting results. Additionally,...
Forecasting Electric Power Supplies Neural Networks, Computer Algorithms
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Ultralow-Power Single-Sensor-Based E-Nose System Powered by Duty Cycling and Deep Learning for Real-Time Gas Identification.

ACS sensors Jun 10, 2024
This study presents a novel, ultralow-power single-sensor-based electronic nose (e-nose) system for real-time gas identification, distinguishing itself from conventional sensor-array-based e-nose systems, whose power consumption and cost increase wit...
Electronic Nose Deep Learning Semiconductors Gases Electric Power Supplies
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Economic benefit analysis of lithium battery recycling based on machine learning algorithm.

PloS one Jun 7, 2024
Lithium batteries, as an important energy storage device, are widely used in the fields of renewable vehicles and renewable energy. The related lithium battery recycling industry has also ushered in a golden period of development. However, the high c...
Lithium Cost-Benefit Analysis Algorithms Recycling Electric Power Supplies Neural Networks, Computer Machine Learning
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GraphKM: machine and deep learning for K prediction of wildtype and mutant enzymes.

BMC bioinformatics Mar 28, 2024
Michaelis constant (K) is one of essential parameters for enzymes kinetics in the fields of protein engineering, enzyme engineering, and synthetic biology. As overwhelming experimental measurements of K are difficult and time-consuming, prediction of...
Neural Networks, Computer Language Protein Engineering Electric Power Supplies Deep Learning
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