Harmonics Forecasting of Wind and Solar Hybrid Model Based on Deep Machine Learning
Bibliographic record
Abstract
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. Harmonic forecasting is one of the techniques used to design harmonic mitigation devices in order to reduce harmonics. The core objective of this work is to develop hybrid forecasting model to produce accurate and reliable harmonic forecasts for RES. Six novel hybrid forecasting models are proposed in this work to perform harmonic 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 first stage and ANFIS in 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 the 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".