The Power Quality Forecasting Model for Off-Grid System Supported by Multi-objective Optimization
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F17%3A10235852" target="_blank" >RIV/61989100:27240/17:10235852 - isvavai.cz</a>
Nalezeny alternativní kódy
RIV/61989100:27730/17:10235852 RIV/61989100:27740/17:10235852
Výsledek na webu
<a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7938383" target="_blank" >http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7938383</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TIE.2017.2711540" target="_blank" >10.1109/TIE.2017.2711540</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
The Power Quality Forecasting Model for Off-Grid System Supported by Multi-objective Optimization
Popis výsledku v původním jazyce
Measurement and control of electric power quality (PQ) parameters in Off-Grid systems has played an important role in recent years. The purpose is to detect or forecast the presence of PQ parameter disturbances to be able to suppress or to avoid their negative effects on the power grid and appliances. This paper focuses on several PQ parameters in Off-Grid systems and it defines three evaluation criteria that are supposed to estimate the performance of a new forecasting model combining all the involved PQ parameters. These criteria are based on common statistical evaluations of computational models from the machine learning field of study. The studied PQ parameters are voltage, power frequency, total harmonic distortion and flicker severity. The approach presented in this paper also applies a machine learning based model of Random Decision Forest for PQ forecasting. The database applied in this task contains real Off-Grid data from long-term one-minute measurements. The hyper-parameters of the model are optimized by Multi-objective optimization (MOO) towards the defined evaluation criteria.
Název v anglickém jazyce
The Power Quality Forecasting Model for Off-Grid System Supported by Multi-objective Optimization
Popis výsledku anglicky
Measurement and control of electric power quality (PQ) parameters in Off-Grid systems has played an important role in recent years. The purpose is to detect or forecast the presence of PQ parameter disturbances to be able to suppress or to avoid their negative effects on the power grid and appliances. This paper focuses on several PQ parameters in Off-Grid systems and it defines three evaluation criteria that are supposed to estimate the performance of a new forecasting model combining all the involved PQ parameters. These criteria are based on common statistical evaluations of computational models from the machine learning field of study. The studied PQ parameters are voltage, power frequency, total harmonic distortion and flicker severity. The approach presented in this paper also applies a machine learning based model of Random Decision Forest for PQ forecasting. The database applied in this task contains real Off-Grid data from long-term one-minute measurements. The hyper-parameters of the model are optimized by Multi-objective optimization (MOO) towards the defined evaluation criteria.
Klasifikace
Druh
J<sub>imp</sub> - 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)
Návaznosti výsledku
Projekt
Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2017
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
IEEE Transactions on Industrial Electronics
ISSN
0278-0046
e-ISSN
—
Svazek periodika
64
Číslo periodika v rámci svazku
12
Stát vydavatele periodika
US - Spojené státy americké
Počet stran výsledku
9
Strana od-do
"9507 "- 9516
Kód UT WoS článku
000413946800033
EID výsledku v databázi Scopus
2-s2.0-85033231272