Effectiveness of artificial neural networks for first swing stability determination of practical systems

The paper presents an evaluation of the effectiveness of artificial neural networks for rapid determination of critical clearing times for practical networks with varying line outages and load patterns. Studies are reported on the performance of artificial neural networks which have been trained using previously proposed and new training items. It is concluded that artificial neural networks have difficulty in returning consistently accurate answers under varying network conditions. >

[1]  R. Dhifaoui,et al.  Fast Transient Stability Assessment Revisited , 1986, IEEE Transactions on Power Systems.

[2]  R.J. Marks,et al.  Preliminary results on using artificial neural networks for security assessment (of power systems) , 1989, Conference Papers Power Industry Computer Application Conference.

[3]  Mohamed A. El-Sharkawi,et al.  Correction to `Preliminary results on using artificial neural networks for security assessment' (May , 1991 .

[4]  D.G. Schwartz,et al.  Fuzzy logic flowers in Japan , 1992, IEEE Spectrum.

[5]  Chao-Rong Chen,et al.  Synchronous machine steady-state stability analysis using an artificial neural network , 1991 .

[6]  G. S. Hope,et al.  Expert Systems in Electric Power Systems a Bibliographical Survey , 1989, IEEE Power Engineering Review.

[7]  Antti J. Koivo,et al.  Security Evaluation in Power Systems Using Pattern Recognition , 1974 .

[8]  Q. Henry Wu,et al.  A neural network regulator for turbogenerators , 1992, IEEE Trans. Neural Networks.

[9]  Kwang Y. Lee,et al.  A study on neural networks for short-term load forecasting , 1991, Proceedings of the First International Forum on Applications of Neural Networks to Power Systems.

[10]  Edward Wilson Kimbark,et al.  Power System Stability , 1948 .

[11]  Robert J. Marks,et al.  Electric load forecasting using an artificial neural network , 1991 .

[12]  Solomon Lefschetz,et al.  Differential Equations: Geometric Theory , 1958 .

[13]  M.A. El-Sharkawi,et al.  An adaptively trainable neural network algorithm and its application to electric load forecasting , 1991, Proceedings of the First International Forum on Applications of Neural Networks to Power Systems.

[14]  Leon Lapidus,et al.  A GUIDE TO METHODS FOR THE GENERATION OF LIAPUNOV FUNCTIONS , 1969 .

[15]  G. Lambert-Torres,et al.  Short-term load forecasting using a fuzzy engineering tool , 1991, Proceedings of the First International Forum on Applications of Neural Networks to Power Systems.

[16]  Lars Kai Hansen,et al.  Neural Network Ensembles , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  S. S. Venkata,et al.  A hybrid artificial neural network/artificial intelligence approach for voltage stability enhancement , 1991, Proceedings of the First International Forum on Applications of Neural Networks to Power Systems.

[18]  G. G. Richards,et al.  Harmonic source monitoring and identification using neural networks , 1990 .

[19]  Hiroyuki Mori An artificial neural-net based method for estimating power system dynamic stability index , 1991, Proceedings of the First International Forum on Applications of Neural Networks to Power Systems.

[20]  William D. Stevenson,et al.  Elements of Power System Analysis , 1962 .

[21]  V. Brandwajn,et al.  Neural networks for dynamic security assessment of large-scale power systems: requirements overview , 1991, Proceedings of the First International Forum on Applications of Neural Networks to Power Systems.

[22]  Dejan J. Sobajic,et al.  Artificial Neural-Net Based Dynamic Security Assessment for Electric Power Systems , 1989, IEEE Power Engineering Review.

[23]  Mark W. White,et al.  A neural network approach to the detection of incipient faults on power distribution feeders , 1990 .

[24]  G. T. Heydt,et al.  Transient stability assessment by pattern recognition in the frequency domain , 1991 .

[25]  B. Stott,et al.  Power system dynamic response calculations , 1979, Proceedings of the IEEE.

[26]  Hiroyuki Mori,et al.  An artificial neural-net based technique for power system dynamic stability with the Kohonen model , 1991 .

[27]  Yoshiakira Akimoto,et al.  Genetic algorithms approach to voltage optimization , 1991, Proceedings of the First International Forum on Applications of Neural Networks to Power Systems.

[28]  L. L. Lai,et al.  Fault Diagnosis in HVDC Systems with Neural Networks , 1993 .

[29]  P. T. Moseley,et al.  Techniques and Mechanisms in Gas Sensing , 1991 .

[30]  T. Tsuji,et al.  Security monitoring systems including fast transient stability studies , 1975, IEEE Transactions on Power Apparatus and Systems.