Power quality multi-step predictions with the gradually increasing selected input parameters using machine-learning and regression
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F21%3A10247187" target="_blank" >RIV/61989100:27240/21:10247187 - isvavai.cz</a>
Result on the web
<a href="http://www.sciencedirect.com/science/article/pii/S2352467721000138" target="_blank" >http://www.sciencedirect.com/science/article/pii/S2352467721000138</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.segan.2021.100442" target="_blank" >10.1016/j.segan.2021.100442</a>
Alternative languages
Result language
angličtina
Original language name
Power quality multi-step predictions with the gradually increasing selected input parameters using machine-learning and regression
Original language description
Autonomous off-grid systems dependent upon Renewable Energy (RE) sources are characterized by stochastic supplies of the fluctuating low short-circuit power. Power Quality (PQ) standards define load characteristics in electric power systems and their ability to function properly without failures. Monitoring, prediction and optimization of PQ parameters are necessary to maintain their alterations steady within the prescribed range, which allow fault-tolerant operation of various electrical devices. It is not possible to measure complete PQ data for all possible combinations of dozens of grid-connected appliances, whose load specifics and collisions primarily determine the course of PQ parameters and their eventual disturbances. Self-adapting PQ prediction models based on Artificial Intelligence (AI) are required as induced power is influenced particularly by changeable weather conditions in real off-grid operation mode of systems using RE. A novel multi-step PQ prediction algorithm is proposed, which develops AI models with the gradually increasing number of selected input PQ-parameters. In each next step a more complex model is formed, using an additional co-related PQ-input to calculate its target PQ-output with a better accuracy. PQ-models with the progressively growing PQ-inputs, using their data predicted in the previous step, can better approximate and estimate the target quantity. The presented results show this training and feature selection procedure can step by step improve accuracy of PQ-models for unknown combinations of off-grid connected household appliances. (C) 2021 Elsevier Ltd
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Sustainable Energy Grids & Networks
ISSN
2352-4677
e-ISSN
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Volume of the periodical
26
Issue of the periodical within the volume
26
Country of publishing house
US - UNITED STATES
Number of pages
10
Pages from-to
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UT code for WoS article
000645076400010
EID of the result in the Scopus database
2-s2.0-85101138456