Shekha Rai
ABSTRACT
This paper presents an online detection method of small signal oscillations using an enhanced Prony algorithm. The proposed method has considered the affect of missing measure-ments of phasor measurement units (PMUs) which occurs as a result of network congestion or defect in PMUs or phasor data concentrators (PDCs). In this context, at first, a sequential K nearest neighbours (SKNN) classifier is utilized to provide a robust data set to address such issue. In the second step, improved Prony algorithm is used to identify the oscillatory modes. The proposed approach has been compared to Matrix Pencil, Eigen Realization algorithm (ERA) and improved Prony for generated test signals with missing data at different noise levels. The suitability of the proposed monitoring scheme is further demonstrated on two area network and real PMU measurements derived from the Western Electricity Coordinating Council (WECC).
- Byerly, R. T., Bennon, R. J., and Sherman, D.E. 1982. Eigenvalue Analysis of Synchronizing Power Flow Oscillations in Large Electric Power Systems. IEEE Trans. Power App. Syst. PAS-101, 1 (Jan 1982), 235--243.Google Scholar
Cross Ref
- Phadke, A. G., and Thorp, J. S. 2008. Synchronized Phasor Measurements and Their Applications. New York, Springer.Google Scholar
- Girgis, A. A., and Ham, F. M. 1980. A Quantitative Study of Pitfalls in the FFT. IEEE Trans. Aerosp. Electron. Syst. AES-16, 4 (Jul 1980), 434--439.Google ScholarCross Ref
- Grant, L. L., and Crow, L. L. 2011. Comparison of matrix pencil and prony methods for power system modal analysis of noisy signals. in 2011 North American Power Symposium. IEEE (Aug 2011). 1--7.Google ScholarCross Ref
- Prony, R. De. 1795. Essai experimentale et analytique. J. Ecole Polytechnique. (Paris), 24--76.Google Scholar
- Kumaresan, R., and Tufts, D. 1982. Estimating the parameters of exponentially damped sinusoids and pole-zero modeling in noise. IEEE Trans. Acoust., Speech, Signal Processing, 30, 6, 833--840.Google ScholarCross Ref
- Tripathy, P., Srivastava, S. C., and Singh, S. N. 2009. An improved Prony method for identifying low frequency oscillations using synchrophasor measurements. In International conference on Power Systems (Dec.2009). 1--5.Google Scholar
- Rai, S., Lalani, D., Nayak, S. K., Jacob, T., and Tripathy, P. 2016. Estimation of low-frequency modes in power system using robust modified Prony. IET Gener. Transm. Distrib, 10, 6, 1401--1409.Google ScholarCross Ref
- Wang, Y., Wang, J., Meng, Q., and Yu, H. 2012. Power system oscillation modes identification based on Eigen system realization algorithm via empirical mode decomposition. Energy Procedia, 17, 12, 189--195.Google ScholarCross Ref
- Tripathy, P., Srivastava, S. C., and Singh, S. N. 2011. A modified TLS-ESPRIT-based method for low-frequency mode identification in power systems utilizing synchrophasor measurements. IEEE Trans. Power Syst., 26, 2 (May 2011), 719--727.Google ScholarCross Ref
- Rai, S., Tripathy, P., and Nayak, S. K. 2014. A robust TLS-ESPIRIT method using covariance approach for identification of low-frequency oscillatory mode in power systems. in 2014 Eighteenth National Power Systems Conference (NPSC) of IEEE (Dec 2014). 1--6.Google ScholarCross Ref
- He, M., Vittal, V., and Zhang, J. 2013. Online dynamic security assessment with missing pmu measurements: A data mining approach. IEEE Trans. Power Syst., 28, 2 (May 2013), 1969--1977.Google ScholarCross Ref
- Hildebrand, F. 1956. Introduction to Numerical Analysis. Dover Books on Advanced Mathematics, Dover Publications, Incorporated.Google ScholarDigital Library
- Scharf, L. L. 1991. The SVD and reduced rank signal processing. IEEE. Trans. Signal. Process., 25, 2, 113--133.Google ScholarDigital Library
- Xiao, J., Xie, X., Han, Y., and Wu, J. 2004. Dynamic tracking of low-frequency oscillations with improved Prony method in wide-area measurement system. in Proc. IEEE Power Eng. Soc. General Meeting. 1(Jun 2004). 1104--1109.Google Scholar
- Kim, K., Kim, B., and Yi, G. 2004. Reuse of imputed data in microarray analysis increases imputation efficiency. BMC Bioinformatics, 5, 1(Oct 2004), 243--252.Google Scholar
- Samet, H. 2008. K-nearest neighbor finding using max nearest dist. IEEE Trans. Pattern Analysis and Machine Intelligence, 30, 2 (Feb 2008), 243--252.Google ScholarDigital Library
- Kundur, P. 1994. Power System Stability and Control. New York: McGraw-Hill.Google Scholar
- PDCI Probe Testing Plan, 2005 [Online]. Available:http://www.transmission.bpa.gov/business/operations/SystemNews/.Google Scholar
- Report and data of WECC. [Online]. Available: ftp://ftp.bpa.gov/pub/WAMSInformation/.Google Scholar
Index Terms
- An Enhanced Prony Algorithm for On-line Detection of Small Signal Oscillations for Synchrophasor Application
Recommendations
Fast and accurate power estimation method based on a PMU counter
ICUIMC '14: Proceedings of the 8th International Conference on Ubiquitous Information Management and CommunicationMost mobile computing devices use a battery as their main power source. Efficient power use and management are very important in mobile devices. It is necessary to understand the power consumption of the processor in a mobile device. In this paper, we ...
An ultra low power low voltage linear PMU for portable applications
SBCCI '11: Proceedings of the 24th symposium on Integrated circuits and systems designThis paper describes the implementation of a linear power management unit (PMU) for portable devices. The system includes ultra low power current and voltage references, oscillator and 8-bit analog-to-digital converter for power supply monitoring, a ...
Identification of topology changes in power grids using phasor measurements
Phasor measurement units PMUs are increasingly important for monitoring the state of an electrical power grid and quickly detecting topology changes caused by events such as lines going down or large loads being dropped. Phasors are complex?valued ...
Comments