Efficient parameter estimation and control in microgrids and power systems

Abstract

Methods for the efficient harvesting of renewable energy and minimizing energy loss are the subjects of large-scale research efforts. One widely accepted approach is to consider a cluster of renewable power generators, loads and control circuits as a small-scale network called a microgrid. Control circuits in microgrids are specially designed to meet certain requests. Voltage parameters, namely frequency, phase and amplitude, are important indexes in such control circuits. This thesis mainly explores voltage parameter estimators and their applications in microgrids. In this context, research is conducted to accomplish the following three objectives.

The first objective is to review parameter estimation algorithms in the literature and then identify the research gaps. Estimators in microgrids usually correspond to different voltage signal models. This thesis investigates both reliability and efficiency of various estimation algorithms in terms of different signal models. Through the extensive assessment of literature, most parametric estimators suffer three specific limitations. These shortcomings are: the trade-off between estimation accuracy and computational cost; the lack of estimators compatible with most voltage signals; and the harmonic distortion.

The second objective is to propose alternative approaches to deal with the identified three shortcomings in the literature. To accomplish this objective, we first develop an efficient spectral leakage subtraction (SLS) based estimator and then extend it into the harmonic case. Next, a novel weighted least squares refinement (WLSR) is employed as a post-processing step for both the SLS based estimator, along with its harmonic version to further improve their estimation accuracy. Meanwhile, a more reasonable weighted least squares (WLS) based data fusion method is proposed to replace the traditional Clarke's transformation in three-phase (3PH) power systems.

The third objective is to explore how to employ the estimator characteristics to optimize microgrid operation. In this thesis, a linear prediction (LP) based parameter estimation framework is developed to solve the inherent communication delays in the secondary level of control of microgrids.

The performance of all presented findings in this thesis has been verified using both simulation and experimental data.