Adaptive control of a single source reduced switch MLI-based DSTATCOM for wind energy conversion system

The escalating integration of renewable energy sources along with the prevalence of nonlinear loads in electric power systems have heightened power quality (PQ) issues. In response, this research proposes a novel distribution static compensator (DSTATCOM) design featuring a three-phase single-source five-level inverter for distributed power systems based on wind energy conversion systems. The multi-level inverter (MLI) adopts a single DC source and minimum switch count, prioritizing simplicity. A radial basis function neural network (RBFNN) assisted p-q theory-based control, coupled with a genetic algorithm tuned proportional integral (PI) controller, ensures effective compensation and stable dc-link voltage. Simulation models and a laboratory prototype confirm the effectiveness and dynamic compensation capabilities of the presented DSTATCOM design across various load conditions. Results reveal stable grid-side parameters, low total harmonic distortion (THD), and the maintenance of a unity power factor. The hardware analysis confirms reduced THD values post-compensation, 2.174% for voltage and 1.898% for current, attesting to the DSTATCOM's efficacy in improving PQ. The proposed design outperforms conventional MLI topologies in terms of component count, reliability, and simplicity. The introduced RBFNN exhibits superior harmonic extraction, achieving a minimal prediction error of 1.3 x10(-8). This work establishes the effectiveness of the proposed DSTATCOM in enhancing PQ in distributed power systems, setting the stage for further optimization and integration of advanced control strategies.