The Power Quality Forecasting Model for Off-Grid System Supported by Multi-objective Optimization

Result code in 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>
Alternative codes found
RIV/61989100:27730/17:10235852 RIV/61989100:27740/17:10235852
Result on the web
<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>

Result language
angličtina
Original language name
The Power Quality Forecasting Model for Off-Grid System Supported by Multi-objective Optimization
Original language description
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.
Czech name
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Czech description
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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)

Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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