Power Quality Estimations for Unknown Binary Combinations of Electrical Appliances Based on the Step-by-Step increasing Model Complexity
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10250536" target="_blank" >RIV/61989100:27240/24:10250536 - isvavai.cz</a>
Result on the web
<a href="https://www.tandfonline.com/doi/full/10.1080/01969722.2022.2137633" target="_blank" >https://www.tandfonline.com/doi/full/10.1080/01969722.2022.2137633</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1080/01969722.2022.2137633" target="_blank" >10.1080/01969722.2022.2137633</a>
Alternative languages
Result language
angličtina
Original language name
Power Quality Estimations for Unknown Binary Combinations of Electrical Appliances Based on the Step-by-Step increasing Model Complexity
Original language description
Smart detached houses, contingent on Renewable Energy (RE), are subjected to an unstable power supply of the intermitted nature. The power quality (PQ) norms define allowable variances in the characteristics of electrical systems to ensure their functioning without malfunction. The estimation and optimization of PQ parameters on day bases are inevitable in the regulation of systems to comply with the specified standards and allow the fault-free operation of electrical equipment. Measurements of all PQ states are impossible for dozens of eventual grid-attached power consumers defined by their binary load patterns. Specific demands and uncertain RE can lead to system instability and unacceptable PQ. Self-optimizing models based on Artificial Intelligence (AI) can estimate the next PQ states in real off-grids where power is induced only by chaotic RE sources. A new proposed multistage prediction scheme allows incremental improvements in the accuracy of AI models beginning their development with binary coded data only. The number of selected PQ inputs gradually increased in the next estimate for the initial equipment in demand. Historical records include complete training PQ data for all parameters, but only '1/0' switch-on load sequences are available at prediction times. The most valuable PQ outputs are modeled in the previous stages to process their supplementary series in the next prediction. More capable models, applied to previously approximated PQ data, are able to better compute the PQ output in the secondary steps. Complementary PQ inputs are supplied with the new processing data, which were unknown in the previous stage. The growing number of input features enables a more complex representation of the target quantity in each iteration. Advanced input selection and data re-evaluation can additionally improve model discriminability for unseen active load patterns. It can be applied in modeling unknown states of various dynamical systems, initially defined only by series of binary or inadequate input data, to improve the results.
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
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Cybernetics and Systems
ISSN
0196-9722
e-ISSN
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Volume of the periodical
55
Issue of the periodical within the volume
5
Country of publishing house
US - UNITED STATES
Number of pages
21
Pages from-to
1184-1204
UT code for WoS article
000875562400001
EID of the result in the Scopus database
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