Harmonics Forecasting of Wind and Solar Hybrid Model Driven by DFIG and PMSG Using ANN and ANFIS

Abstract

Grid integration of Renewable Energy Systems (RES) involves various types of power electronics-based converters and inverters. The use of these electronic-based devices results in inducing both current and voltage harmonics to the grid. In order to reduce harmonics, especially for large-scale integration of RES, harmonic forecasting is one of many techniques used to design harmonic mitigation devices. The core objective of this work is to develop a novel forecasting model for accurate and reliable harmonic estimation for RES. To achieve this two hybrid generator models are used. First model consists of wind turbine coupled with Doubly Fed Induction Generator (DFIG) combined with Solar Photo Voltaic (PV) based power generator which are connected to common grid. The second model uses Permanent Magnet Synchronous Generator (PMSG) with wind turbine in conjunction with Solar-PV generator. With real world meteorological data (wind speed and solar irradiation) as inputs, these generators simulate and produce output power with current and voltage waveforms. Harmonics are extracted from these waveforms to record, analyze, arrange and forecast future harmonics. Three parameters namely, Total Harmonics Distortion (THD) and the dominant individual harmonic contents, 11 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> harmonic (h11) and 13 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> harmonic (h13) are forecasted for both voltage and current waveforms. Artificial Neural Networks (ANN) and Adaptive Neuro Fuzzy Inference Systems (ANFIS) are the prominent methods used for forecasting. Three types of three-layered ANN structures namely Cascaded Neural Network with Recurrent Local feedback (3LCRNNL), Cascaded Neural Network with Recurrent Global feedback (3LCRNNG) and Cascaded Neural Network with Local and Global feedback (CRNNGL) have been proposed and utilized in this work with hyperbolic tangent as transfer function to adjust weights and scaled conjugate gradient method as optimizer to reduce training error. ANFIS is also employed with subtractive clustering method to improve adaptability and accuracy of forecasts. The results are compared and presented which shows that ANFIS recorded the best performance for THDV and h13 and 3LCRNNGL for h11 for the Wind DFIG-PV model voltage harmonics forecast. For forecasts of the current harmonics, 3LCRNNGL performed best for THDI and h11, whereas ANFIS performed best for h13. For Wind PMSG-PV generator model, ANFIS yields the best results in every scenario involving voltage and current harmonics.