Summary
Occurring in transmission or distribution networks, voltage sags (transient reductions of voltage magnitude) can cause serious damage to end-use equipment (domestic appliances, precision instruments, etc.) and industrial processes (PLC and controller resets, time life reduction, etc.) resulting in important economic losses. We present a statistical method to determine whether these disturbances originate upstream (transmission system) or downstream (distribution system) of the registering point located in distribution substations. The method uses only information from the recorded disturbances and exploits their statistical properties of them in terms of covariance. Multiway Principal Component Analysis (MPCA) is proposed to model classes of sags according to their origin upstream/downstream using the RMS values of voltages and currents. New, not-yet-seen sags are projected to these models and classified based on statistical criteria measuring their consistency with the models. the successful classification of real sags recorded in electric substations is compared with other methodologies.
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Khosravi, A., Melendez, J., Colomer, J., Sanchez, J. (2008). Multiway Principal Component Analysis (MPCA) for Upstream/Downstream Classification of Voltage Sags Gathered in Distribution Substations. In: Liu, Y., Sun, A., Loh, H.T., Lu, W.F., Lim, EP. (eds) Advances of Computational Intelligence in Industrial Systems. Studies in Computational Intelligence, vol 116. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78297-1_14
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DOI: https://doi.org/10.1007/978-3-540-78297-1_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78296-4
Online ISBN: 978-3-540-78297-1
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