Power Systems Stability and Security Assessments Using      Artifical Neural Networks
{'Code': '07010000', 'Directorate': {'Abbreviation': 'ENG', 'LongName': 'Directorate For Engineering'}, 'Division': {'Abbreviation': 'ECCS', 'LongName': 'Div Of Electrical, Commun & Cyber Sys'}}
{'SignBlockName': 'Vijay Vittal', 'PO_EMAI': None, 'PO_PHON': None}
This research is investigating the potential use of the emerging                technology of artificial neural networks as a powerful tool for on              line stability diagnostics.  Because the neural networks are                    trainable, the technique is adaptive and suitable for on-line                   applications as well as for planning.   With adequately trained neural          networks, the stability of the power system can be quickly assessed             with relatively little computational time.  The technological concept           being investigated is whether a large, complex neural net can be                trained to indicate whether a power system is stable or unstable when           the net is provided with input data on the system conditions (loads,            generation, etc.).  With present analytical tools, this stability               assessment requires massive computational time.  Successful                     implementation of a trainable artificial neural network will result in          real time assessment of system stability and security.  The ability is          very much needed in the power industry.
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