Title :
Neural-network-based fuzzy model and its application to transient stability prediction in power systems
Author :
Su, Mu-Chun ; Liu, Chih-Wen ; Tsay, Shuenn-Shing
Author_Institution :
Dept. of Electr. Eng., Tamkang Univ., Tamsui, Taiwan
fDate :
2/1/1999 12:00:00 AM
Abstract :
We present a general approach to deriving a new type of neural network-based fuzzy model for a complex system from numerical and/or linguistic information. To efficiently identify the structure and the parameters of the new fuzzy model, we first partition the output space instead of the input space. As a result, the input space itself induces corresponding partitions within each of which inputs would have similar outputs. Then we use a set of hyperrectangles to fit the partitions of the input space. Consequently, the premise of an implication in the new type of fuzzy rule is represented by a hyperrectangle and the consequence is represented by a fuzzy singleton. A novel two-layer fuzzy hyperrectangular composite neural network (FHRCNN) can be shown to be computationally equivalent to such a special fuzzy model. The process of presenting input data to each hidden node in a FHRCNN is equivalent to firing a fuzzy rule. An efficient learning algorithm was developed to adjust the weights of an FHRCNN. Finally, we apply FHRCNNs to provide real-time transient stability prediction for use with high-speed control in power systems. From simulation tests on the IEEE 39-bus system, it reveals that the proposed novel FHRCNN can yield a much better performance than that of conventional multilayer perceptrons (MLP´s) in terms of computational burden and classification rate
Keywords :
fuzzy neural nets; learning (artificial intelligence); multilayer perceptrons; performance evaluation; power engineering computing; power system transient stability; real-time systems; IEEE 39-bus system; classification rate; complex system; computational burden; fuzzy rule; fuzzy singleton; high-speed control; hyperrectangles; learning algorithm; linguistic information; multilayer perceptrons; neural network-based fuzzy model; numerical information; output space partitioning; performance; power systems; simulation; transient stability prediction; two-layer fuzzy hyperrectangular composite neural network; Computer networks; Control systems; Fuzzy neural networks; Fuzzy systems; Neural networks; Power system control; Power system simulation; Power system stability; Power system transients; Real time systems;
Journal_Title :
Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on
DOI :
10.1109/5326.740677