Fault Mapping in Multi-machine Power Systems by Principal Component Sensitivity-An Energy Function Perspective

Abstract

This paper envisages the mapping of particular faults associated with their real time occurring events in a large scale power system by exploring the underlying rich information embedded in the Structure Preserving Energy Function (SPEF) components. A sliding window based prediction is employed to continuously monitor the change in the SPEF components in pre-fault, during-fault and post-fault stages. A Stacked Potential Energy (SPE) matrix is introduced and subjected to Principal Component Sensitivity (PCS) to discern the effect of events by analyzing the upper and lower dimensional sub-spaces. The effectiveness of the proposed sequence of classification can be observed from the results on a 4-machine, 2-area test system, where a coherency can be noticed between types of events and the corresponding mappings in the respective SPEFs. Highly correlated energy functions can be sought-after by pointing at different events which may trigger some specific components of interconnected power systems.