Abstract
A fault diagnosis framework for electrical power transmission networks, which combines Hybrid Bayesian Networks (HBN) and Wavelets is proposed. HBN is a probabilistic graphical model in which discrete and continuous data are analyzed. In this work, power network’s protection breakers are modeled as discrete nodes, and information extracted from voltages measured in every electrical network node represent the continuous nodes. Protection breakers are devices with the function to isolate faulty nodes by opening the circuit, and are considered to be working in one of three states: OK, OPEN, and FAIL. On the other hand, node voltages data are processed with wavelets, delivering specific coefficients patterns which are encoded into probability distributions of continuous HBN nodes. Experimental tests show a good performance of the diagnostic system when simultaneous multiple faults are simulated in a 24 nodes electrical network, in comparison with a previous approach in the same domain.
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Garza Castañón, L.E., Guillén, D.R., Morales-Menendez, R. (2011). Fault Diagnosis in Power Networks with Hybrid Bayesian Networks and Wavelets. In: Mehrotra, K.G., Mohan, C.K., Oh, J.C., Varshney, P.K., Ali, M. (eds) Modern Approaches in Applied Intelligence. IEA/AIE 2011. Lecture Notes in Computer Science(), vol 6703. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21822-4_4
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DOI: https://doi.org/10.1007/978-3-642-21822-4_4
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