Preprint Article Version 1 This version is not peer-reviewed

A model free control based on machine learning for energy converters in an array

Version 1 : Received: 31 October 2018 / Approved: 2 November 2018 / Online: 2 November 2018 (12:35:44 CET)

A peer-reviewed article of this Preprint also exists.

Thomas, S.; Giassi, M.; Eriksson, M.; Göteman, M.; Isberg, J.; Ransley, E.; Hann, M.; Engström, J. A Model Free Control Based on Machine Learning for Energy Converters in an Array. Big Data Cogn. Comput. 2018, 2, 36. Thomas, S.; Giassi, M.; Eriksson, M.; Göteman, M.; Isberg, J.; Ransley, E.; Hann, M.; Engström, J. A Model Free Control Based on Machine Learning for Energy Converters in an Array. Big Data Cogn. Comput. 2018, 2, 36.

Journal reference: Big Data Cogn. Comput. 2018, 2, 36
DOI: 10.3390/bdcc2040036

Abstract

This paper introduces a model-free, "on-the-fly" learning control strategy for arrays of energy converters with adjustable generator damping. The devices are arranged so that they are affected simultaneously by the energy medium. Each device uses a different control strategy, of which at least one has to be the machine learning approach presented in this paper. During operation all energy converters record the absorbed power and control output; the machine learning device gets the data from the converter with the highest power absorption and so learns the best performing control strategy for each state. Consequently, the overall network has a better overall performance than each individual strategy. This concept is evaluated for wave energy converter (WEC) with numerical simulations and experiments with physical scale models in a wave tank. In the first of two numerical simulations, the learnable WEC works in an array with four WECs applying a constant damping factor. In the second simulation, two learnable WECs were learning with each other. It showed that in the first test the WEC was able to absorb as much as the best constant damping WEC, while in the second run it could absorb even slightly more. During the physical model test, the ANN showed its ability to select the better of two possible damping coefficients based on real world input data.

Subject Areas

machine learning; wave energy; power take-off; artificial neural network; wave tank test; physical scale model; floating point absorber; damping; control; collaborative

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