Power quality daily predictions in smart off-grids using differential, deep and statistics machine learning models processing NWP-data
Microgrid autonomous networks need an effective plan and control of power supply, energy storage, and retransmission. Prediction and monitoring of power quality (PQ) along with efficient utilization of Renewable Energy (RE) is unavoidable to optimize the system performance without abnormalities. Alt...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-05-01
|
| Series: | Energy Strategy Reviews |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2211467X23000263 |
| _version_ | 1797822029523582976 |
|---|---|
| author | Ladislav Zjavka |
| author_facet | Ladislav Zjavka |
| author_sort | Ladislav Zjavka |
| collection | DOAJ |
| description | Microgrid autonomous networks need an effective plan and control of power supply, energy storage, and retransmission. Prediction and monitoring of power quality (PQ) along with efficient utilization of Renewable Energy (RE) is unavoidable to optimize the system performance 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 measured completely 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. The AI models were evolved using weather data and the binary status of attached equipment in the test predetermined daily training periods. Daily statistical models process 24-h forecast data and definition load series of trained input variables to calculate the target PQ parameters at the same times. Optimal utilization, 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 h. A parametric C++ application software with applied PQ and weather data is free available to allow reproducibility of the results. |
| first_indexed | 2024-03-13T10:02:48Z |
| format | Article |
| id | doaj.art-014ddfe525a042cfaabbc195520a3483 |
| institution | Directory Open Access Journal |
| issn | 2211-467X |
| language | English |
| last_indexed | 2024-03-13T10:02:48Z |
| publishDate | 2023-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Strategy Reviews |
| spelling | doaj.art-014ddfe525a042cfaabbc195520a34832023-05-23T04:21:40ZengElsevierEnergy Strategy Reviews2211-467X2023-05-0147101076Power quality daily predictions in smart off-grids using differential, deep and statistics machine learning models processing NWP-dataLadislav Zjavka0Department of Computer Science, Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava, 17. Listopadu 15/2172, Ostrava, Czech RepublicMicrogrid autonomous networks need an effective plan and control of power supply, energy storage, and retransmission. Prediction and monitoring of power quality (PQ) along with efficient utilization of Renewable Energy (RE) is unavoidable to optimize the system performance 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 measured completely 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. The AI models were evolved using weather data and the binary status of attached equipment in the test predetermined daily training periods. Daily statistical models process 24-h forecast data and definition load series of trained input variables to calculate the target PQ parameters at the same times. Optimal utilization, 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 h. A parametric C++ application software with applied PQ and weather data is free available to allow reproducibility of the results.http://www.sciencedirect.com/science/article/pii/S2211467X23000263Power qualitySmart off-gridBinomial networkDeep learningPrediction modelDerivative transformation |
| spellingShingle | Ladislav Zjavka Power quality daily predictions in smart off-grids using differential, deep and statistics machine learning models processing NWP-data Energy Strategy Reviews Power quality Smart off-grid Binomial network Deep learning Prediction model Derivative transformation |
| title | Power quality daily predictions in smart off-grids using differential, deep and statistics machine learning models processing NWP-data |
| title_full | Power quality daily predictions in smart off-grids using differential, deep and statistics machine learning models processing NWP-data |
| title_fullStr | Power quality daily predictions in smart off-grids using differential, deep and statistics machine learning models processing NWP-data |
| title_full_unstemmed | Power quality daily predictions in smart off-grids using differential, deep and statistics machine learning models processing NWP-data |
| title_short | Power quality daily predictions in smart off-grids using differential, deep and statistics machine learning models processing NWP-data |
| title_sort | power quality daily predictions in smart off grids using differential deep and statistics machine learning models processing nwp data |
| topic | Power quality Smart off-grid Binomial network Deep learning Prediction model Derivative transformation |
| url | http://www.sciencedirect.com/science/article/pii/S2211467X23000263 |
| work_keys_str_mv | AT ladislavzjavka powerqualitydailypredictionsinsmartoffgridsusingdifferentialdeepandstatisticsmachinelearningmodelsprocessingnwpdata |