Harmonics Forecasting of Wind and Solar Hybrid Model Based on Deep Machine Learning
Solar and Wind energy based Renewable Energy Systems (RES) are one of the most rapidly growing technologies as a means of producing clean electrical energy. Grid integration of RES involves various types of power electronics-based converters and inverters. These electronic devices produce harmonics...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10250797/ |
| _version_ | 1797680917168259072 |
|---|---|
| author | Fawaz M. Al Hadi Hamed H. Aly Timothy Little |
| author_facet | Fawaz M. Al Hadi Hamed H. Aly Timothy Little |
| author_sort | Fawaz M. Al Hadi |
| collection | DOAJ |
| description | Solar and Wind energy based Renewable Energy Systems (RES) are one of the most rapidly growing technologies as a means of producing clean electrical energy. Grid integration of RES involves various types of power electronics-based converters and inverters. These electronic devices produce harmonics at their terminals, which are transferred to the grid. Harmonics forecasting is one of the techniques used to design harmonics mitigation devices in order to reduce harmonics. The core objective of this work is to develop a hybrid forecasting model to produce accurate and reliable harmonics forecasts for RES. Six novel hybrid forecasting models are proposed in this work to perform harmonics forecasting. These models are based on different combinations of multi-layered Artificial Neural Networks (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS). The forecasting models proposed are two-staged architecture. Three hybrid forecasting models (model-1, 2 & 3) use ANN in the first stage and ANFIS in the second while the other three models (model-4, 5 & 6) are designed vice versa of prior. Two renewable generators are used to generate harmonics. The first generator combines Double-Fed Induction Generator (DFIG) driven by wind turbine with solar photovoltaic (PV) panels whereas, the second generator combines wind turbine driven Permanent Magnet Synchronous Generator (PMSG) with solar panels. The purpose of these generators is to produce voltage and current waveforms using real-world data (Wind Speed & Solar Irradiation). Harmonics are extracted from these waveforms which are used to create training and testing datasets for the forecasting models. Harmonics are forecasted using the six forecasting models proposed and results are validated by comparing them to benchmark work done in the literature. The results show that model-3 and model-6 are the best and most consistent performing models. |
| first_indexed | 2024-03-11T23:37:13Z |
| format | Article |
| id | doaj.art-b60627a75cd844309d63ce9a765622dc |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T23:37:13Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b60627a75cd844309d63ce9a765622dc2023-09-19T23:00:58ZengIEEEIEEE Access2169-35362023-01-011110043810045710.1109/ACCESS.2023.331474210250797Harmonics Forecasting of Wind and Solar Hybrid Model Based on Deep Machine LearningFawaz M. Al Hadi0https://orcid.org/0009-0002-0611-7552Hamed H. Aly1https://orcid.org/0000-0003-2676-081XTimothy Little2Electrical and Computer Engineering Department, Dalhousie University, Halifax, CanadaElectrical and Computer Engineering Department, Dalhousie University, Halifax, CanadaElectrical and Computer Engineering Department, Dalhousie University, Halifax, CanadaSolar and Wind energy based Renewable Energy Systems (RES) are one of the most rapidly growing technologies as a means of producing clean electrical energy. Grid integration of RES involves various types of power electronics-based converters and inverters. These electronic devices produce harmonics at their terminals, which are transferred to the grid. Harmonics forecasting is one of the techniques used to design harmonics mitigation devices in order to reduce harmonics. The core objective of this work is to develop a hybrid forecasting model to produce accurate and reliable harmonics forecasts for RES. Six novel hybrid forecasting models are proposed in this work to perform harmonics forecasting. These models are based on different combinations of multi-layered Artificial Neural Networks (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS). The forecasting models proposed are two-staged architecture. Three hybrid forecasting models (model-1, 2 & 3) use ANN in the first stage and ANFIS in the second while the other three models (model-4, 5 & 6) are designed vice versa of prior. Two renewable generators are used to generate harmonics. The first generator combines Double-Fed Induction Generator (DFIG) driven by wind turbine with solar photovoltaic (PV) panels whereas, the second generator combines wind turbine driven Permanent Magnet Synchronous Generator (PMSG) with solar panels. The purpose of these generators is to produce voltage and current waveforms using real-world data (Wind Speed & Solar Irradiation). Harmonics are extracted from these waveforms which are used to create training and testing datasets for the forecasting models. Harmonics are forecasted using the six forecasting models proposed and results are validated by comparing them to benchmark work done in the literature. The results show that model-3 and model-6 are the best and most consistent performing models.https://ieeexplore.ieee.org/document/10250797/Harmonicsrenewable energy systemspower qualityartificial neural networksadvanced neuro fuzzy inference system |
| spellingShingle | Fawaz M. Al Hadi Hamed H. Aly Timothy Little Harmonics Forecasting of Wind and Solar Hybrid Model Based on Deep Machine Learning IEEE Access Harmonics renewable energy systems power quality artificial neural networks advanced neuro fuzzy inference system |
| title | Harmonics Forecasting of Wind and Solar Hybrid Model Based on Deep Machine Learning |
| title_full | Harmonics Forecasting of Wind and Solar Hybrid Model Based on Deep Machine Learning |
| title_fullStr | Harmonics Forecasting of Wind and Solar Hybrid Model Based on Deep Machine Learning |
| title_full_unstemmed | Harmonics Forecasting of Wind and Solar Hybrid Model Based on Deep Machine Learning |
| title_short | Harmonics Forecasting of Wind and Solar Hybrid Model Based on Deep Machine Learning |
| title_sort | harmonics forecasting of wind and solar hybrid model based on deep machine learning |
| topic | Harmonics renewable energy systems power quality artificial neural networks advanced neuro fuzzy inference system |
| url | https://ieeexplore.ieee.org/document/10250797/ |
| work_keys_str_mv | AT fawazmalhadi harmonicsforecastingofwindandsolarhybridmodelbasedondeepmachinelearning AT hamedhaly harmonicsforecastingofwindandsolarhybridmodelbasedondeepmachinelearning AT timothylittle harmonicsforecastingofwindandsolarhybridmodelbasedondeepmachinelearning |