Neural-network-based adaptive UPFC for improving transient stability performance of power system
IEEE Transactions on Neural Networks
Acute lymphoblastic leukemia identification using blood smear images and a neural classifier
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
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Real-life applications of intelligent systems that use neural networks require a high degree of success, usability and reliability. Power systems applications can benefit from such intelligent systems; particularly for voltage stabilization. Voltage instability in power distribution systems could lead to voltage collapse and thus power blackouts. This paper presents an intelligent system which detects voltage instability and classifies voltage output of an assumed power distribution system (PDS) as: stable, unstable or overload. The novelty of our work is the use of voltage output images as the input patterns to the neural network for training and generalizing purposes, thus providing a faster instability detection system that simulates a trained operator controlling and monitoring the 3-phase voltage output of the assumed PDS. Experimental results suggest that our method performs well and provides a fast and efficient system for voltage instability detection.