Advanced power theories and signal decomposition methods for controlling smart converters in smart grid applications

Abstract

During last two decades, the enormous level of aggregation of distributed generation units DGUs (widely known as the technology of Microgrids), in addition to increasing usage of nonlinear loads in power systems has raised new mathematical-conceptual challenges, specially in power electronics. Most of the traditional power theories and concepts therein, have been defined and formulated for simple balanced and linear systems. As a result, most of them are not directly applicable in case of new system structures with a considerable amount of uncertainty in the production and nonlinearity in the consumption. Due to uncertainties injected by the dynamic behavior of the DGUs (mostly renewable-based), the power components in the traditional power theories should be redefined under highly dynamic behavior of the power signals. Moreover, corresponding justifications need to be implemented to adapt all the related control strategies and compensation techniques. Renewable-based energies, such as wind and solar, are inherently uncertain power sources which can have unpredictable unwanted impacts on power flow, voltage regulation, and result in distribution losses. Microgrids that are quickly expanded through the power networks and power theories play a critical role in all the control strategies designed for these systems. When operating in the islanded mode, low-voltage Microgrids can exhibit considerable variation of amplitude and frequency of the voltage supplied to the loads, thus affecting power quality and network stability. Limited power capability in Microgrids can cause a voltage distortion which affects measurement accuracy, and possibly cause tripping of protections. Besides, the nonlinear and unbalanced loads obscure the traditional power definitions and equations. In such contexts, a reconsideration of power theories is required, since they form the basis for supply and load characterization and accountability. Moreover, developing new control techniques for harmonic and reactive compensators are mandatory, because they operate in a strongly interconnected environment and must perform cooperatively to face system dynamics, ensure power quality, and limit distribution losses. The main purpose of this research is to improve the quality, reliability and stability of future electrical power delivery by improving the overall performance of smart Microgrids through usage of advanced time-domain power theories (such as instantaneous power theory (PQ) and Conservative Power Theory (CPT)). Another major contribution of this work is the introduction of new mathematical power theory concepts (termed Enhanced Instantaneous Power Theory (EIPT)) in addition to implementation of adequate new control strategies. This work specially expanded based on a specific viewpoint which says that power theories can be interpreted as advanced signal decomposition techniques which are used as the initial step in electrical power signals analysis. This signal analysis step forms the fundamental headstock for power electronic interfaces controller design procedure. After describing the mathematical fundamentals of our modified power theory, EIPT; then this method is used as a time-domain signal decomposition approach for relevant applications. Exploiting the fine levels of information revealed through analysis of the power signals with the mentioned decomposition approaches, we provide more levels of freedom in the case of control frameworks. This research also investigate the interesting application of EIPT, besides other practical power theories such as CPT, in islanding detection problems, where a new instantaneous intelligent passive islanding detection strategy will be introduced. In a nutshell, developing new time-domain power theory concepts while exploiting the inherent capacities of the pre-existing power theories, the main goal of this work will be designing a reliable and smart multifunctional control scheme that can address all the aforementioned challenges.