Fawaz M. Al Hadi

Harmonics forecasting stands as a crucial approach in the development of devices aimed at minimizing harmonics disturbances. The primary objective of this study is to create a hybrid forecasting model that can deliver precise and dependable forecasts for harmonics in Renewable Energy Systems (RES). To achieve this goal, the Adaptive Neuro Fuzzy Inference System (ANFIS) with the Long Short-Term Memory Network (LSTM) are combined in two distinct structured models. In the first model, LSTM is employed in the initial stage and ANFIS in the subsequent one, while the second model follows the reverse order. Additionally, for the generation of harmonics, two renewable generator models are utilized. The first model encompasses a grid-connected Double-Fed Induction Generator (DFIG) driven by a wind turbine and integrated with a Solar Photovoltaic (PV)-based power generator. The second generator model combines a Solar-PV generator with a wind turbine-linked Permanent Magnet Synchronized Generator (PMSG) connected to a shared grid. The harmonics produced by these generator models are used to construct training and testing datasets, which are subsequently employed for generating forecasts using the proposed hybrid forecasting models. The accuracy of forecasting results is verified through a comparison with benchmark studies in the literature. The findings reveal that the model employing ANFIS in the initial stage and LSTM in the second stage (referred to as the ANFIS-LSTM model) consistently yields the best forecasts among all the models tested in this study with RMSE of 0.0287, 0.0372, 0.0396 and 0.0311 for THD, h7, h11 and h13 respectively. Moreover, it exhibits a significant improvement over any of the techniques used in previous literature. Ultimately, this research establishes that both hybrid models proposed outperform the individual forecasting techniques used as benchmarks in terms of accuracy and precision.

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.