An international research team has used, for the first time, a powerful metaheuristic and a nature-inspired algorithm to improve the estimation of the parameters of PV cells and modules. The algorithm imitates the natural phenomenon of growth and uses the diffusion process, based on random fractals.
A new algorithm identifies five kinds of faults in PV systems, while also detecting when faults have been resolved to prevent false detections. It is based on the least significant difference test, which is a set of individual t-tests comparing the means of two or more pre-determined groups.
Liten, a research institute of the French Alternative Energies and Atomic Energy Commission, is developing a method of assessing losses at every stage from the reception of solar rays to the injection of electricity into the grid, to ‘make it possible to optimize the maintenance of the power plants to guarantee their performance’.
Researchers at the U.S. Argonne National Laboratory have applied a combination of machine learning and artificial intelligence to help narrow down a list of 166 billion molecules that could be used to form the basis of a battery electrolyte. The technique, say the researchers, offers a way to greatly reduce the cost of narrowing down such an enormous data set, while still providing a precise understanding of each molecule and its likely suitability.
Mathematicians at Canada’s University of Waterloo who turned their attention to solar power have developed an algorithm they say offers better control over PV plant output. The researchers estimate the algorithm could improve the output of a 100 MW power plant by almost a million kilowatt-hours per year.
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