Power system disturbance detection and classification based on wide area phasor measurements

The current drive to a more dependable "Smart Grid" requires an increased level of stability, which should be achieved by quicker recognition of disturbances so that response can either be automated or implemented more quickly by operators. Current practices involve operator interpretation of inadequate unsynchronized data gathered from supervisory control and data acquisition system (SCADA) presented via an energy management system (EMS). There is a need for real-time interpretation of large quantities of synchronized data to automatically analyze and detect power system disturbances. This research proposes a process that can be a viable solution to this aspect of the modern "Smart Grid".;In this research work, the continuum system model is first introduced into wide-area disturbance detection study. Unlike the conventional electrical grid modeling and simulation, the continuum model treats power network as distributed equivalent generators with finite rotor inertia and transmission line impedance per unit length. Secondly, analytic solutions of continuum model based system are first revealed in this research. For a given continuum system, the proposed solving method calculates system states in any time period, which significantly improves the situational awareness. Thirdly, in the proposed on-line disturbance detection process, the system exploits gathered synchronized wide area measurement system (WAMS) data, preprocesses it using appropriately chosen wavelet filter and then detects event via sophisticatedly designed detection methods. Further analysis on detected disturbances will elicit disturbance location, type, and time of occurrence based on optimization and data mining technique. An entire generation trip and line trip disturbance real-time detection and event analysis methods are developed. Finally, for online disturbance classification, the artificial neural network (ANN) is introduced into WAMS data mining and on-line system to improve automation analysis process and successful rate of disturbance event detection and fault location estimation.;The implementation feasibility of proposed generation trip and line trip detection and location estimation methods is validated using both simulation results and actual event data. In addition, ANN is successfully implemented in online event classification, and generates satisfactory results.