Fuzzy rule extraction from dynamic data for voltage risk identification
This paper presents a methodology for performing on-line voltage risk identification (VRI) in power supply networks using hyper-rectangular 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.
fuzzy rule extraction voltage risk identification synchronized phasor measurements fuzzy hyperrectangular composite neural networks value-at-risk