Frame-Angle Controlled Wavelet Modulated Inverter and Self-Recurrent Wavelet Neural Network-Based Maximum Power Point Tracking for Wind Energy Conversion System

Abstract

In this work, a new control methodology is proposed for Type-IV wind energy conversion system (WECS) using a self-recurrent wavelet neural network (SRWNN) control with a Vienna rectifier as the machine side converter (MSC). A SRWNN combines excellent dynamic properties of recurrent neural networks and the fast convergence speed of wavelet neural network. Hidden neurons of SRWNN contains local self-feedback loops, which provide the memory feature and the necessary information of past values of the signals, allowing it to track maximum power from WECS under varying wind speeds. The Vienna rectifier allows unity power factor operation to increase electrical efficiency. Frame angle-controlled wavelet modulation is proposed for the grid side converter (GSC). Wavelet modulated inverter produces output voltage fundamental components with higher magnitudes than those obtained from the pulse width modulated inverters. The non-linear load compensation and power quality enhancement are achieved by executing frame angle control for WM inverter. The overall system is modeled, and performance is verified in MATLAB Simulink. The hardware prototype is developed, and the switching pulses for the rectifier and inverter are generated using dSPACE1104 controller. The results prove that the system provides low harmonic content and high magnitude of the fundamental current component at the machine and grid sides and ensures maximum power operation at various wind speeds. INDEX TERMS Wind Energy Conversion System (WECS), Wavelet modulation (WM), Maximum power point tracking (MPPT), Self recurrent wavelet neural network (SRWNN)

Reference Model Adaptive Control Scheme on PMVG-Based WECS for MPPT under a Real Wind Speed

“…This method presented the multi-layer cascade forward backpropagation technique rather than the most basic and widely used feed-forward multi-layer perceptron scheme. Following that, the authors in [24] have introduced a self-recurrent wavelet NN control approach for WECS and it has local self-feedback loops in a self-recurrent wavelet neural network, which give the memory function and the essential knowledge of historical signal values. The authors in [25] have proposed an upgraded gray BP NN and a modified ensemble empirical mode decomposition auto-regressive integrated moving average for real-time wind speed estimation.…”

Study and Implementation of a High-Quality True Sine Wave DC-to-AC Inverter for Solar Power Generation Systems

“…In order to realize the maximum power tracking of the solar system, it is necessary to adjust the output of the solar cell through the power conversion circuit with the maximum power tracking control function, so that the solar panel can output the maximum power and realize fast and accurate tracking. Various maximum power point tracking (MPPT) methods are proposed throughout the literature, such as bang-bang control, wavelet control, and the Fourier series method [ 6 , 7 , 8 , 9 ]. However, the solar illumination and ambient temperature are closely related to the change of the maximum output power of solar panels.…”