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.
Keywords
- Root Mean Square
- Power Quality
- Principal Component Analysis Model
- Voltage Magnitude
- Distribution Substation
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References
Bollen MHJ (2000) Understanding Power Quality problems. IEEE Press, New York
Leborgne CR (2007) Voltage sags: single event characterisation, system performance and source location. PhD Thesis, Department of Energy and Enviroment, Chalmers University of Technology
Parsons AC, Grady WM, Powers EJ, Soward JC (2000) A direction finder for power quality disturbances based upon disturbance power and energy. IEEE Transactions on Power Delivery, 15(3):1081–1086
Li C, Tayjasanant T, Xu W, Li X (2003) Method for voltage-sag-source detection by investigatig slope of the system trajectory. In IEE Proc. Gen., Trans. & Distrib., 150(3):367–372
Tayjasanant T, Li C, Xu W (2005) A resistance sign-based method for voltage sag source detection. IEEE Transactions on Power Delivery, 20(4):2544–2551
Hamzah N, Mohamed A, Hussain A (2004) A new approach to locate the voltage sag source using real current component. Electric Power Systems Research, 72:113–123
Pradhan AK, Routray A (2005) Applying distance relay for voltage sag-source detection. IEEE Transactions on Power Delivery, 20(1):529–531
Leborgne RC, Karlsson D, Daalder J (2006) Voltage sag source location methods performance under symmetrical and asymmetrical fault conditions. In IEEE PES Transmission and Distribution Conference and Exposition Latin America, Venezuela
Mora J, Llanos D, Melendez J, Colomer J, Sanchez J, Corbella X (2003) Classification of Sags Measured in a Distribution Substation based on Qualitative and Temporal Descriptors. In 17th International Conference on Electricity Distribution, Barcelona, Spain
Wise BM, Gallagher NB, Watts S, White DD, Barna GG (1999) A comparison of pca, multiway pca, trilinear decomposition and parallel factor analysis for fault detection in a semiconductor etch process. Journal of Chemometrics, 13:379–396
Kourti T, MacGregor J (1996) Multivariate spc methods for process and product monitoring, Journal of Quality Technology, 28(4):409–428
Westerhuis JA, Kourti T, MacGregor JF (1999) Comparing alternative approaches for multivariate statistical analysis of batch process data. Journal of Chemometrics, 13:397–413
Russell EL, Chiang LH, Braatz RD (2000) Data-Driven Methods for Fault Detection and Diagnosis in Chemical Processes. Springer, Berlin Heidelberg New York
Wise BM, Gallagher NS (1996) The process chemometrics approach to process monitoring and fault detection. Journal of Process Control, 6(6):329–348
Nomikos P, MacGregor JF (1994) Monitoring batch processes using multi-way principal component analysis. AIChE Journal, 40(8):1361–1375
Lee DS,, Park JM, Vanrolleghem PA (2005)Adaptive multiscale principal component analysis for on-line monitoring of a sequencing batch reactor. Journal of Biotechnology, 116:195–210
Yoon S, MacGregor JF (2004) Principal-component analysis of multiscale data for process monitoring and fault diagnosis. AIChE Journal, 50(11):2991–2903
Witten IH, Frank E (2005) Data Mining: Practical Machine Learning Tools and Techniques, Second Edition. Morgan Kaufmann Publishers
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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
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