Modeling of the work of geothermal energy with the support of the maximum power value with the help of intellectual networks
DOI:
https://doi.org/10.31548/energiya2019.06.133Abstract
Abstract. It is suggested to look at some of the features of geothermal power and consider a technological complex that will include a compressor, sensors, CPU and data acquisition boards. In such a system it is necessary to ensure maximum power take-off under various external factors. It is suggested to do this through the use of intelligent systems, namely the neural network, and it must be created and trained for a specific result.
The purpose of the study is to develop and integrate a neural network that would have a structure for controlling the power in the compressor power system to ensure maximum efficiency. A neural network can act as a logical device and will support the maximum power point (value) in a system with an alternative energy source.
This article provides an example of the use of a neural network in order to find the value of the maximum power point in a power supply system with a geothermal energy source. One of the main features of the constructed neural network is its structure. The neural network is written in the form of a script code and has a multilayer structure (where the first (input) layer has an activation function - a hyperbolic tangent, and the second (output) layer has one - a linear function).
Also, the work considers already known methods of controlling alternative sources and pumping devices, compressors. After that, it was concluded that the use of artificial intelligence (fuzzy logic or neural networks) will have a certain expediency. It appears as a result of the fact that the networks do not require any additional equipment other than a PC for their work, however they constantly retrain themselves, which subsequently will make it possible to take into account the error.
For successful integration of the neural network into the system, the learning process of the neural netwirk was considered, and the construction itself was performed in the Matlab application package.
Key words: geotermal energy, MPPT, MatLab, neural networks, compressor, geotremal stations, turbines, modeling, ventiles, weight functions, automote
References
Heotermalni elektrostantsii: perevahy i nedoliky [Geothermal power plants: advantages and disadvantages]. Available at: https://avenston.com/ru/articles/geothermal-pp-pros-cons/.
Heotermalna enerhiia [Geothermal energy]. Available at: http://www.altenergo-nii.ru/renewable/geothermal/.
Chornyi, O. P., Luhovyi, A. V., Rodkin, D. I. (2001). Modeliuvannia elektromekhanichnykh system [Modeling of electromechanical systems]. Kremenchuk, 374
Herman-Halkin, S. H. (2008). Matlab&Simulink. Proektuvannia mekhatronykh system na PK [Matlab & Simulink. Designing mechatronic systems on a PC]. Korona, 368.
Stychynskyi, Z. A., Voropai, N. I. (2010). Vidnovliuvani dzherela enerhii: Teoretychni osnovy, tekhnolohii, tekhnichna kharakterystyka, ekonomika [renewable energy sources: Theoretical foundations, technologies, technical characteristics, economics]. Knyha 2010, 223.
Golitsin, M. V., Golitsin, A. M., Pronina, N.M. (2004). Al’ternativnyye energonositeli [Alternative energy carriers]. Moskow: Nauka, 159.
Downloads
Published
Issue
Section
License
Relationship between right holders and users shall be governed by the terms of the license Creative Commons Attribution – non-commercial – Distribution On Same Conditions 4.0 international (CC BY-NC-SA 4.0):https://creativecommons.org/licenses/by-nc-sa/4.0/deed.uk
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
