Electrical Power System Dynamic Stability Evaluation using a Knowledge-Based Approach

In this paper, dynamic stability assessment of electrical power systems is formulated as a pattern recognition problem so that it is fast and accurate enough for on-line applications. The problem concerned is the potentially hazardous dynamic instability of the interconnected power system between Hong Kong and China. New techniques, such as the Branch and Bound Search, are employed to achieve optimality in Feature Selection. In Feature Extraction where further dimensionality reduction is made, matrix augmentation is used to extract information content in class-centralised vectors as well as in class-mean vectors. Finally, for improvement of classification efficiency, an innovative approach making use of partitioning and decision tree search is pursued to tackle the non- linearly separable patterns which exist uniquely in the study of dynamic stability.