Intelligent control of the power generation system using DSPACE

Abstract

Abstract Wind power systems (WPs) are complex non‐linear systems with varying parameters affected by environmental changes, including wind speed fluctuations. Extracting maximum power from WPs poses a significant challenge due to these factors. Direct power control (DPC) is a highly effective technique known for its simplicity and ease of implementation. However, it suffers from power ripples caused by the use of hysteresis comparators and switching tables that operate at variable frequencies. To address this issue, this paper presents the robust neural controller (NC) based on DPC, which replaces the switching tables. The Double‐Fed Induction Generator (DFIG) is the chosen generator for the studied WP system due to its advantageous features. The NC‐DPC effectively regulates the exchange of active and reactive powers between the DFIG and the system, maximizing power extraction from the WP system while reducing Total Harmonic Distortion and enhancing overall system quality. The effectiveness of the NC‐DPC is evaluated through MATLAB simulations and further supported by experimental data obtained using the Real‐Time Interface of the dSPACE‐DS1104 Controller card.

Keywords

• intelligent control
• neural network
• power control
• power generation control