Parameter Identification of Interarea Oscillations in Electrical Power Systems via an Improved Hilbert Transform Method

The paper deals with a novel method for the analysis of low-frequency inter-area oscillations, a harmful phenomenon that can be more and more frequent in electrical power systems due to the expansion of interconnected power grid. In particular, inter-area oscillations are electromechanical oscillations involving groups of generators geographically far from each other; correctly and timely estimating their parameters is a fundamental task to preserve the stability of the electrical power system. Phasor Measurement Unit (PMU) technology has become the key element in Monitoring and Control systems of Transmission System Operators offering new opportunities for improving their situational awareness. Proposed method processes measures provided by the PMUs to extract the oscillating components and, for each of them, estimates characteristic parameters such as frequency and damping. To this aim, the method exploits a combination of the Hilbert Transform and some optimization algorithms, thus gaining the following improvements: (a) enhanced capability of separating oscillatory components, thanks to a new strategy for the choice of bisecting frequency, (b) possibility of separating components characterized by frequency difference lower than the spectral resolution, (c) remarkable accuracy associated with the damping estimates of each oscillation and (d) robustness to the noise affecting the input signal. Results obtained in tests involving either synthesized signals or simulated electrical systems finally assessed the promising performance of the proposed method.

[1]  H. Boche,et al.  A new algorithm for the reconstruction of bandlimited functions and their Hilbert-transformed , 1996, Proceedings of 20th Biennial Conference on Precision Electromagnetic Measurements.

[2]  A.R. Messina,et al.  Interpretation and Visualization of Wide-Area PMU Measurements Using Hilbert Analysis , 2006, IEEE Transactions on Power Systems.

[3]  Song Han,et al.  Dynamic characteristic analysis of power system low frequency oscillation using Hilbert-Huang transform , 2009, 2009 IEEE/PES Power Systems Conference and Exposition.

[4]  D. Lauria,et al.  On Hilbert transform methods for low frequency oscillations detection , 2014 .

[5]  Behnam Mohammadi-Ivatloo,et al.  Online small signal stability analysis of multi-machine systems based on synchronized phasor measurements , 2011 .

[6]  Christian Rehtanz,et al.  Analysis of low frequency oscillations in power system based on HHT technique , 2010, 2010 9th International Conference on Environment and Electrical Engineering.

[7]  Xiaorong Xie,et al.  WAMS-based detection and early-warning of low-frequency oscillations in large-scale power systems , 2008 .

[8]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[9]  Petr Korba Real-time monitoring of electromechanical oscillations in power systems: first findings , 2007 .

[11]  Annalisa Liccardo,et al.  The Huang Hilbert Transform for evaluating the instantaneous frequency evolution of transient signals in non-linear systems , 2016 .

[12]  Davide Lauria,et al.  An Optimized HT-Based Method for the Analysis of Inter-Area Oscillations on Electrical Systems , 2019, Energies.

[13]  Babu Narayanan,et al.  POWER SYSTEM STABILITY AND CONTROL , 2015 .

[14]  Bin Sun,et al.  Dynamic characteristic analysis of power system interarea oscillations using HHT , 2010 .

[15]  Graham Rogers,et al.  Power System Oscillations , 1999 .

[16]  Danna Zhou,et al.  d. , 1934, Microbial pathogenesis.

[17]  E. M. Carlini,et al.  An integrated approach to improve the networks security in presence of high penetration of RES , 2014, 2014 International Symposium on Power Electronics, Electrical Drives, Automation and Motion.

[18]  Davide Lauria,et al.  Improved non-linear least squares method for estimating the damping levels of electromechanical oscillations , 2015 .

[19]  Davide Lauria,et al.  Transient Stability Margins Evaluation Based Upon Probabilistic Approach , 2013 .

[20]  Bo Wang,et al.  Research on forced oscillations disturbance source locating through an energy approach , 2016 .

[21]  Bogdan Marinescu,et al.  Dynamic equivalent of neighbor power system for day-ahead stability studies , 2010, 2010 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe).

[22]  Davide Lauria,et al.  Real time generator coherency evaluation via Hilbert transform and signals morphological similarity , 2014, 2014 International Symposium on Power Electronics, Electrical Drives, Automation and Motion.

[23]  Yilu Liu,et al.  Identification of Interarea Modes From Ringdown Data by Curve-Fitting in the Frequency Domain , 2017, IEEE Transactions on Power Systems.