Neuro - Fuzzy System Based Augmentation for Transient Stability

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Arvind Parwal
Manaullah,Arvind K Sharma, Gaurav Parwal


Computational Intelligence combines neural network, fuzzy systems and evolutional computing. Neurofuzzy integrated system utilizes features of both Neural and Fuzzy networks together for better results by which generalization of the unseen data from seen data by forming the fuzzy rules and training. In this project training of the system with training data which usually is 70% of the whole available data, rest 30% data is used for testing. The algorithm used in work is hybrid algorithm. A characteristic inherent to Electric power system is that they operate under the influence of disturbance. With the growing stress on today’s power system, the potential impact of faults and other disturbances on their security is increasing. Here an Adaptive Network based fuzzy system is used. The data with power speed and fault clearing time is used in order to train network with rule based structure.


Keywords: ANN, Fixed Control Logic (FLC), Hybrid Algorithm.


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