Analysis of Factors Affecting Electric Power Quality: PLS-SEM and Deep Learning Neural Network Analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10252681" target="_blank" >RIV/61989100:27240/23:10252681 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/abstract/document/10103865" target="_blank" >https://ieeexplore.ieee.org/abstract/document/10103865</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2023.3268037" target="_blank" >10.1109/ACCESS.2023.3268037</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Analysis of Factors Affecting Electric Power Quality: PLS-SEM and Deep Learning Neural Network Analysis

Popis výsledku v původním jazyce

The world today is increasingly dependent directly or indirectly on the power system. Ensuring the quality of power supplied to electrical equipment is essential. The national regulatory framework is for harmonic mitigation in the global power system. This paper discusses the relationship between Efficiency (E), Security (S), and Reliability (R) for Electric Power Quality (EPQ). We measure the harmonic mitigation regulations listed in the IEEE 519 standard. To evaluate the proposed E, S, and R constructs and their relationship to EPQ, a multi-planning approach the method of Partial Least Squares-Structural Equation Modeling (PLS-SEM) and Deep Learning Artificial Neural Network (ANN) analysis were performed. In it, deep Learning Artificial Neural Network (ANN) was performed to complement the PLS-SEM findings and higher prediction accuracy. The study shows that the aspects of efficiency (E), security (S), and reliability (R) have a significant relationship with Electric Power Quality (EPQ). Another result of the study indicates that science, technology, engineering and math (STEM) resource conditions have a significant and positive impact on EPQ. Author

Název v anglickém jazyce

Analysis of Factors Affecting Electric Power Quality: PLS-SEM and Deep Learning Neural Network Analysis

Popis výsledku anglicky

The world today is increasingly dependent directly or indirectly on the power system. Ensuring the quality of power supplied to electrical equipment is essential. The national regulatory framework is for harmonic mitigation in the global power system. This paper discusses the relationship between Efficiency (E), Security (S), and Reliability (R) for Electric Power Quality (EPQ). We measure the harmonic mitigation regulations listed in the IEEE 519 standard. To evaluate the proposed E, S, and R constructs and their relationship to EPQ, a multi-planning approach the method of Partial Least Squares-Structural Equation Modeling (PLS-SEM) and Deep Learning Artificial Neural Network (ANN) analysis were performed. In it, deep Learning Artificial Neural Network (ANN) was performed to complement the PLS-SEM findings and higher prediction accuracy. The study shows that the aspects of efficiency (E), security (S), and reliability (R) have a significant relationship with Electric Power Quality (EPQ). Another result of the study indicates that science, technology, engineering and math (STEM) resource conditions have a significant and positive impact on EPQ. Author

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

—

Svazek periodika

23038234

Číslo periodika v rámci svazku

11

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

1

Strana od-do

1

Kód UT WoS článku

001033183400001

EID výsledku v databázi Scopus

2-s2.0-85153513381