Power Quality daily Predictions in Smart Off-grids using Differential, Deep and Statistics Machine Learning models processing NWP-data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10252107" target="_blank" >RIV/61989100:27240/23:10252107 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1016/j.esr.2023.101076" target="_blank" >https://doi.org/10.1016/j.esr.2023.101076</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.esr.2023.101076" target="_blank" >10.1016/j.esr.2023.101076</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Power Quality daily Predictions in Smart Off-grids using Differential, Deep and Statistics Machine Learning models processing NWP-data

Popis výsledku v původním jazyce

Autonomous microgrid networks need an effective plan and control of power supply, energy storage, and retransmission. Prediction and monitoring of power quality (PQ) along with efficient utilisation of renewable energy (RE) is unavoidable to optimise the performance of the system without abnormalities. Alterations and irregularities in PQ must remain within the prescribed norm ranges and characteristics to allow fault-tolerant operation of the detached system in various modes of attached equipment. The PQ data for all possible combinations of grid-attached household appliances and different inside/outside conditions cannot be completely measured or described exactly by physical equations. PQ predictions on a daily basis using Artificial Intelligence (AI) models are needed because atmospheric fluctuations and anomalies in local weather with uncertainties in system states primarily influence the induced power and operation of real off-grids. A novel soft-computing method using Differential Learning, which allows modelling of complex dynamics of weather-dependent systems, is presented and compared with the recent standard deep and probabilistic machine learning. AI models were developed using weather data and the binary status of the attached equipment during the predetermined daily training periods of the test. Daily statistical models process 24-hour forecast data and definition load series of trained input variables to calculate the target PQ parameters at the same times. Optimal utilisation, efficiency, and failure-free operation of smart grids can be planned according to the suggested operable power consumption scenarios based on their PQ verification on a day-horizon. Executable load sequences can be automatically combined and scheduled in the system to be adapted to user needs, considering the RE production potential, charge state, and optimal PQ characteristics over the next 24 hours. Parametric C++ application software with applied PQ and weather data is available for free to allow reproducibility of the results.

Název v anglickém jazyce

Power Quality daily Predictions in Smart Off-grids using Differential, Deep and Statistics Machine Learning models processing NWP-data

Popis výsledku anglicky

Autonomous microgrid networks need an effective plan and control of power supply, energy storage, and retransmission. Prediction and monitoring of power quality (PQ) along with efficient utilisation of renewable energy (RE) is unavoidable to optimise the performance of the system without abnormalities. Alterations and irregularities in PQ must remain within the prescribed norm ranges and characteristics to allow fault-tolerant operation of the detached system in various modes of attached equipment. The PQ data for all possible combinations of grid-attached household appliances and different inside/outside conditions cannot be completely measured or described exactly by physical equations. PQ predictions on a daily basis using Artificial Intelligence (AI) models are needed because atmospheric fluctuations and anomalies in local weather with uncertainties in system states primarily influence the induced power and operation of real off-grids. A novel soft-computing method using Differential Learning, which allows modelling of complex dynamics of weather-dependent systems, is presented and compared with the recent standard deep and probabilistic machine learning. AI models were developed using weather data and the binary status of the attached equipment during the predetermined daily training periods of the test. Daily statistical models process 24-hour forecast data and definition load series of trained input variables to calculate the target PQ parameters at the same times. Optimal utilisation, efficiency, and failure-free operation of smart grids can be planned according to the suggested operable power consumption scenarios based on their PQ verification on a day-horizon. Executable load sequences can be automatically combined and scheduled in the system to be adapted to user needs, considering the RE production potential, charge state, and optimal PQ characteristics over the next 24 hours. Parametric C++ application software with applied PQ and weather data is available for free to allow reproducibility of 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

2023

Kód důvěrnosti údajů

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

Název periodika

Energy Strategy Reviews

ISSN

2211-467X

e-ISSN

—

Svazek periodika

47

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

13

Strana od-do

1-13

Kód UT WoS článku

000966684100001

EID výsledku v databázi Scopus

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