A new approach for df/dt and active power imbalance in power system estimation using Huang’s Empirical Mode decomposition
| Autor: | Maja Muftić Dedović, Samir Avdakovic |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Discrete wavelet transform
Computer science Noise (signal processing) 020209 energy Noise reduction 020208 electrical & electronic engineering Phasor Energy Engineering and Power Technology 02 engineering and technology AC power Hilbert–Huang transform Electric power system Control theory 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering MATLAB computer computer.programming_language |
| Zdroj: | International Journal of Electrical Power & Energy Systems. 110:62-71 |
| ISSN: | 0142-0615 |
| DOI: | 10.1016/j.ijepes.2019.02.044 |
| Popis: | In this paper, Huang’s Empirical Mode Decomposition approach is proposed for df / d t and active power imbalance in power system estimation. Applied approach implies availability of synchronized phasor measurement units. In addition to the successful applications in the analysis of nonstationary dynamic behavior of power system, identifications and analyses of low-frequency electromechanical oscillations and signals denoising, this approach also enables direct estimation of rate of change of a weighted average frequency (frequency of the center of inertia), as well as assessment of the overall imbalance in the power system. This demonstration is performed using computer simulation testing on the 39 Bus New England System and Western System Coordinating Council 118 bus test systems in the DigSILENT PowerFactory power system analysis software package. To validate the proposed approach the actual frequency information are used. Empirical Mode Decomposition approach is compared with Discrete Wavelet Transform, Method of Least Squares and the results from the DigSILENT PowerFactory. Also, performance of the empirical mode decomposition are compared with performances of the multivariate empirical mode decomposition and noise assisted multivariate empirical mode decomposition on both, simulated signals and field measurements. Applied approach is implemented in the MATLAB environment and results show very high accuracy. |
| Databáze: | OpenAIRE |
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