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 producing clean electrical energy. Grid integration RES involves various types power electronics-based converters inverters. These electronic devices produce harmonics at their terminals, which transferred to grid. Harmonic forecasting is techniques used design harmonic mitigation in order reduce harmonics. The core objective this work develop hybrid model accurate reliable forecasts for RES. Six novel models proposed perform forecasting. on different combinations multi-layered Artificial Neural Networks (ANN) Adaptive Neuro Fuzzy Inference System (ANFIS). two-staged architecture. Three (model-1, 2 & 3) use ANN first stage ANFIS second while other three (model-4, 5 6) designed vice versa prior. Two renewable generators generate generator combines Double-Fed Induction Generator (DFIG) driven by wind turbine with solar photovoltaic (PV) panels whereas, Permanent Magnet Synchronous (PMSG) panels. purpose these voltage current waveforms using real-world data (Wind Speed Irradiation). Harmonics extracted from create training testing datasets models. forecasted six results validated comparing them benchmark done literature. show that model-3 model-6 best consistent performing