PROJECT TITLE :

Adaptive Learning in Time-Variant Processes With Application to Wind Power Systems

ABSTRACT:

This study develops new adaptive learning ways for a dynamic system where the dependency among variables changes over time. Generally, many statistical methods specialize in characterizing a system or process with historical data and predicting future observations based on a developed time-invariant model. However, for a nonstationary method with time-varying input-to-output relationship, a single baseline curve might not accurately characterize the system’s dynamic behavior. This study develops kernel-primarily based nonparametric regression models that enable the baseline curve to evolve over time. Applying the proposed approach to a real wind Power System, we have a tendency to investigate the nonstationary nature of wind result on the turbine response. The results show that the proposed methods will dynamically update the time-varying dependency pattern and will track changes in the operational wind Power System.