Power Quality Estimations for Unknown Binary Combinations of Electrical Appliances Based on the Step-by-Step increasing Model Complexity

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Power Quality Estimations for Unknown Binary Combinations of Electrical Appliances Based on the Step-by-Step increasing Model Complexity

Popis výsledku v původním jazyce

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 &apos;1/0&apos; 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.

Název v anglickém jazyce

Power Quality Estimations for Unknown Binary Combinations of Electrical Appliances Based on the Step-by-Step increasing Model Complexity

Popis výsledku anglicky

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 &apos;1/0&apos; 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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Cybernetics and Systems

ISSN

0196-9722

e-ISSN

—

Svazek periodika

55

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

21

Strana od-do

1184-1204

Kód UT WoS článku

000875562400001

EID výsledku v databázi Scopus

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