Power System Stability Analysis and Control Using Statistical Machine Learning Techniques

点击翻译按钮获取中文摘要

Simulation methods based on conventional offline models have been used widely in power system stability analysis and control design and they constitute effective tools for ensuring system stability. Effectiveness of these stability analysis and design methods is declining steadily because of the constant evolution of the power grid environment, the changes being largely attributable to increased variations in power flow and the difficulties in acquiring accurate offline models for various power-electronics-based devices. Advancement in information and communications technologies have facilitated transfer of massive data in real time and implies an opportunity for wider applications of advanced real time monitoring systems, allowing the acquisition of data of real time conditions and dynamics of various components of power systems. This makes the whole system more observable. Meanwhile, data-driven methods such as statistical machine learning techniques have developed significantly in recent times and have been successfully applied in various areas. Therefore, real time stability analysis and control using statistical machine learning techniques has become an important research direction since it aims to perceive the system’s operational situation directly through real time data and provide insights into optimal operations and controls. This has the potential to resolve the problems of biased parameters when using offline models which, in most cases, do not fit real time operating conditions in the power grid. The significance of this research motivates this research program to combine statistical machine learning with domain knowledge in power systems and make them applicable to stability analysis and control in real power systems. The long-term goal of this research program is to develop new approaches for power system stability analysis and effective online model-free and self-optimization control strategies. To achieve this ultimate goal, the short-term goals are (i) to develop new approaches for prediction, control and optimization of power systems to resolve the problem of bias in offline models used in the conventional power system simulation; and (ii) to apply the general approaches proposed in this program to various problems related to power system stability and develop new online control strategies for the same. The outcomes of this research are expected to not only constitute milestones in power system stability analysis and control, but also contribute to the development of a more reliable and stable power system in the future.