Transient security assessment of power systems by pattern recognition and Liapunov's direct method
Technical Report
·
OSTI ID:7136226
Transient security assessment is becoming an important part of power system operation. On-line assessment of the transient security will be a must for reliable operation of the system before too long. Two different methods are presented here which can meet all the requirements of this problem. Considerable amount of work has been done in the area of transient security assessment by Liapunov's direct method. The major difficulty in the application of this method has been the determination of the stability region around the postfault equilibrium point. An algorithm is developed which overcomes this difficulty. This algorithm also brings out and overcomes the weaknesses of some earlier works. Examples given bring out the salient points of the algorithm and also focus on the great potentials of this approach. Pattern recognition provides an alternative to Liapunov's method for transient security assessment. This is a relatively new concept and its basic elements are described. Because of the high dimensionality of power systems, feature selection becomes a very difficult task. Two different concepts are used for feature selection. The first approach is purely statistical and the feature selection is achieved as a result of the principal component analysis of the sample vector. A classifier based on the non-parametric estimation of the probability density function is used for classification. The results obtained are a considerable improvement over those previously reported. The second approach is a pragmatic one. Here the understanding of the system behavior is applied for getting effective features. A simple piecewise linear classifier gives better than 98% correct recognition.
- Research Organization:
- Purdue Univ., Lafayette, Ind. (USA). School of Electrical Engineering
- OSTI ID:
- 7136226
- Report Number(s):
- TR-EE-76-35
- Country of Publication:
- United States
- Language:
- English
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