Numerical methods for scientists and engineers (2nd ed.)
Numerical methods for scientists and engineers (2nd ed.)
Neural network learning and expert systems
Neural network learning and expert systems
Rule-based neural networks for classification and probability estimation
Neural Computation
A novel neural network approach to knowledge acquisition
A novel neural network approach to knowledge acquisition
Matrix computations (3rd ed.)
Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing
Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing
Neuro-fuzzy rule generation: survey in soft computing framework
IEEE Transactions on Neural Networks
A Real-Time Decision Support System for Voltage Collapse Avoidance in Power Supply Networks
IEICE - Transactions on Information and Systems
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This paper presents a methodology for performing on-line voltage risk identification (VRI) in power supply networks using hyperrectangular composite neural networks (HRCNNs) and synchronized phasor measurements. The FHRCNN presented in this study integrates the paradigm of neural networks with the concept of knowledge-based approaches, rendering them both more useful than when applied alone. The fuzzy rules extracted from the dynamic data relating to the power system formalize the knowledge applied by experts when conducting the voltage risk assessment procedure. The efficiency of the proposed technique is demonstrated via its application to the Taiwan Power Provider System (Tai-Power System) under various operating conditions. Overall, the results indicated that the proposed scheme achieves a minimum 97% success rate in determining the current voltage security level.