Faculty Publications
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Item MAPE-An Alternative Fitness Metric for Prony Analysis of Power System Signals(De Gruyter peter.golla@degruyter.com, 2018) Rao, K.; Shubhanga, K.N.Phasor Measurement Units have facilitated tracking of oscillations in power system response signals. This has provided an impetus for identifying unstable component modes directly from oscillatory signals. Prony analysis, the earliest method proposed for this purpose, throws up some trivial modes. These not only distract the analyzer but also prolong processing time thereby delaying corrective action. Hence the fitness metric chosen should serve to minimize the number of trivial modes. The conventional fitness metric is Signal-To-Noise Ratio (SNR), which is actually Signal-To-Estimation error Ratio (SER). This paper proposes that Mean Absolute Percentage Error (MAPE) can also serve well as a fitness metric. It is shown through case studies carried out on well-known four-machine power system that there are a few cases where MAPE performs better than SER while in some instances SER works better. This inference is verified even in the presence of measurement noise. Hence a novel fitness metric is proposed combining MAPE with SER. Case studies on simulated signals obtained from New England-power system prove that this novel metric can achieve considerable reduction in processing time. Besides, an exponential binary search has been suggested for determining the optimal model order in minimum number of iterations. © 2018 Walter de Gruyter GmbH, Berlin/Boston 2018.Item Equivalence of Matrix Pencil and HTLS Ring-Down Electromechanical Mode Identification Algorithms(Institute of Electrical and Electronics Engineers Inc., 2023) Rao, K.; Shubhanga, K.N.Matrix pencil and Hankel total least squares (HTLS) are two popular ring-down electro- mechanical mode identification algorithms. The appeal of these algorithms can be attributed to faster execution due to the non-iterative procedure of model order determination based on singular value decomposition of the data matrix. In this paper, these two algorithms are shown to be equivalent - the data matrix in one being the transpose of that in the other. Although this equivalence is proved in the context of power systems, it is valid for other areas of system identification as well. Further, the performance of these algorithms is examined as noise level in the signal increases, and it is shown that these work right down to an SNR of 1 dB provided the signal has only poorly damped modes. © 2013 IEEE.Item A Comparison of SVD-Augmented Prony Algorithms for Noisy Power System Signals(Institute of Electrical and Electronics Engineers Inc., 2023) Rao, K.; Shubhanga, K.N.The conventional Prony algorithm, which is the most prominent power system ring-down mode identification method, fails if the test signal is noisy [with a signal-to-noise ratio (SNR) below 20 dB]. The performance of Prony algorithm can be improved through singular value decomposition (SVD)-based rank reduction of the data matrix. Principal eigenvector (PE)-Prony and total least squares (TLS)-Prony are two known formulations of SVD-augmented Prony algorithms. In both PE-Prony and TLS-Prony algorithms, the Toeplitz structure of the linear prediction data matrix is lost upon SVD-based noise filtering. On the other hand, structured total least squares (STLS)-Prony algorithm retains the Toeplitz structure even after SVD-based filtering and is hence expected to perform better. But a formulation of STLS-Prony algorithm for power systems is not available in the literature. Hence, the same is developed successfully in this paper. As a prelude to the formulation of STLS-Prony algorithm, PE-Prony and TLS-Prony analyses of power system signals are discussed in detail, bringing out their nuances. Further, case studies are carried out on some benchmark power systems to demonstrate that all the three algorithms work successfully even at an SNR of 1 dB when the test signal has only inter-area modes. It is also shown that the performance of STLS-Prony algorithm is superior when the test signal has a highly damped local mode. Further, it is illustrated that by virtue of structure-preserving property, STLS-Prony algorithm is endowed with a unique filtering attribute although it has a longer execution time. © 2013 IEEE.