Doctor of Philosophy
School of Electrical, Computer and Telecommunication Engineering
Afroni, Mohammad Jasa, Analysis of non-stationary power quality waveforms using iterative empirical mode decomposition methods and SAX algorithm, Doctor of Philosophy thesis, School of Electrical, Computer and Telecommunication Engineering, University of Wollongong, 2015. https://ro.uow.edu.au/theses/4482
The nonstationary nature of power-quality (PQ) waveforms requires a tool that can accurately analyze and visually identify the instants of transitions. One of the recently reported tools available to analyze nonstationary complex waveforms with a very good time resolution is the Hilbert Huang Transform (HHT) which consists of two steps - the Empirical Mode Decomposition (EMD) and the Hilbert Transform (HT). The EMD process decomposes the signal into Intrinsic Mode Functions (IMFs), each of which represents the identified signal component. Once the IMFs are obtained, the HT can then be applied to find the instantaneous amplitude, frequency and phase of each IMF.
However, HHT has difficulty in resolving waveforms containing components with close frequencies with a ratio of less than two, and similar to other waveform classification techniques, it has difficulty in resolving the instants of sudden changes in the waveform. To overcome the problem with components containing close frequencies, a novel Iterative Hilbert Huang transform (IHHT) is proposed in this thesis. The problem in identifying instants of sudden changes in the waveform is resolved by proposing the use of Symbolic Aggregate ApproXimation (SAX) method. SAX converts the signal into symbols that can be utilized by a pattern detector algorithm to identify the boundaries of the stationary signals within a nonstationary signal. Results from IHHT and SAX method to analyze and visually identify simulated and measured nonstationary PQ waveforms will be provided and discussed. The proposed method is particularly useful when investigating the behavior of the harmonic components of a particular PQ waveform of interest in a given interval of time, following a data-mining search of a large database of PQ events. It can be used to both identify, and later isolate unique signatures within a provider's distribution system.
The Hilbert Huang Transform (HHT) can also decompose noisy Power Quality (PQ) waveforms, however, when the noise level becomes relatively high, the EMD process may generate mixed IMFs which give inaccurate results when the HT is applied to the IMFs. To resolve the mode mixing issue, an improved EMD method, referred to as the Ensemble Empirical Mode Decomposition (EEMD), is then used. Results from the decomposition process of the signals demonstrate the ability of the EEMD method in resolving mode-mixing issues resulting from the presence of noise.
However, the EEMD method may not produce accurate instantaneous amplitude values of the detected IMFs and hence the Iterative EEMD (IEEMD) method is proposed in this thesis together with the SAX (Symbolic Aggregate approXimation) based pattern detector algorithm, to determine the boundaries of the segments in the non-stationary signal. Results from simulated signals show that the proposed methods are effective in decomposing noisy non-stationary signal.
The Iterative HHT and the Iterative EEMD methods have also been tested with real single and three phase signals from measurement using Dranetz PQ analyzer and from Pqube cloud storage of PQ events. The decomposition results have shown the accuracy of the Iterative HHT over the standard / traditional HHT for less noisy signals. However, for more noisy signals, the EMD process will fail to identify the correct frequency of the signal components due to the mode mixing issue and therefore the standard HHT as well as the iterative HHT can not decompose the signal accurately. The EEMD method can resolve the mode mixing issue in the decomposition process of the more noisy signal. However, the MSE of the reconstructed signal to the original is still high and therefore, the proposed Iterative EEMD is used to decompose the signals and it is found that the accuracy of the IEEMD is better than the traditional EEMD method.