Security Of Electric Power System Research Articles

Combined use of unsupervised and supervised learning for dynamic security assessment

It is desirable to assess the security and stability of electric power systems after exposure to large disturbances. In this connection, the critical clearing time (CCT) is an attribute which provides significant information about the quality of the postfault system behavior. It may be regarded as a complex mapping of the prefault, fault-on, and postfault system conditions into the time domain. High prediction and generalization capabilities of artificial neural networks provide the basis for synthesis of such a complex mapping carrying input pattern attributes into the single valued space of the CCT. The authors consider the possibility of using unsupervised and supervised learning programs to discover what combination of raw measurements is significant in determining CCT. Correlation analysis and a Euclidean metric are used to specify interfeature dependencies. An example of a four-machine power system is used to illustrate the suggested approach.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Comparison of microprocessor systems for real-time phasor measurements for large-scale power systems: a case study

Recent progress in microprocessor-based relaying systems and the technology involved in the new measurement systems for assessment of on-line dynamic security of electric power systems are described. The technique of obtaining the measurements from selected locations on interconnected electric power systems is presented. The measurements require a number of algorithms that have been developed. A comparison of the different approaches has also been conducted using a realistic system for confirmation of the developed technique.