Improved Receding Horizon Fourier Analysis for Quasi-periodic Signals

In this paper, an efficient short-time Fourier analysis method for the quasi-periodic signals is proposed via an optimal fixed-lag finite impulse response (FIR) smoother approach using a receding horizon scheme. In order to deal with time-varying Fourier coefficients (FCs) of quasi-periodic signals, a state space model including FCs as state variables is augmented with the variants of FCs. Through an optimal fixed-lag FIR smoother, FCs and their increments are estimated simultaneously and combined to produce final estimates. A lag size of the optimal fixed-lag FIR smoother is chosen to minimize the estimation error. Since the proposed estimation scheme carries out the correction process with the estimated variants of FCs, it is highly probable that the smaller estimation error is achieved compared with existing approaches not making use of such a process. It is shown through numerical simulation that the proposed scheme has better tracking ability for estimating time-varying FCs compared with existing ones.111Ysciescopuskc

Selection of the Most Suitable Decomposition Filter for the Measurement of Fluctuating Harmonics

The proliferation of nonlinear loads in both industrial and residential distribution grids leads to undesirable nonsinusoidal and fluctuating harmonic pollution on voltage and current waveforms. New analysis tools, such as wavelets, are being used to overcome the problems posed by the use of the Fourier transform when analyzing complex waveforms. Nevertheless, the selection of the wavelet basis must be done carefully to minimize spectral leakage due to the nonexact frequency discrimination. In this context, this paper proposes an objective method for comparing different wavelet families for the measurement of harmonic contents. This methodology is applicable for determining the best filter among the 53 preselected structures according to the following requirements: frequency selectivity, computational complexity, convolution results, and observed spectral leakage. With all these considerations, the Butterworth infinite-impulse response filter of order 29 was found to be the best wavelet decomposition structure to achieve an effective harmonic analysis up to the 50th order

Induction motor diagnosis by advanced notch FIR filters and the wigner-ville distribution

During the last years, several time-frequency decomposition tools have been applied for the diagnosis of induction motors, for those cases in which the traditional procedures, such as motor current signature analysis, cannot yield the necessary response. Among them, the Cohen distributions have been widely selected to study transient and even stationary operation due to their high-resolution and detailed information provided at all frequencies. Their main drawback, the cross-terms, has been tackled either modifying the distribution, or carrying out a pretreatment of the signal before computing its time-frequency decomposition. In this paper, a filtering process is proposed that uses advanced notch filters in order to remove constant frequency components present in the current of an induction motor, prior to the computation of its distribution, to study rotor asymmetries and mixed eccentricities. In transient operation of machines directly connected to the grid, this procedure effectively eliminates most of the artifacts that have prevented the use of these tools, allowing a wideband analysis and the definition of a precise quantification parameter able to follow the evolution of their state. © 1982-2012 IEEE

Measurement, control and protection of microgrids at low frame rates supporting security of supply

Increasing penetrations of distributed generation at low power levels within electricity networks leads to the requirement for cheap, integrated, protection and control systems. To minimise unit cost, algorithms for the measurement of AC voltage and current waveforms should be implemented on a single microcontroller, which also carries out all other protection and control tasks, including communication and data logging. This limits the frame rate of the major algorithms, although ADCs can be over-sampled using peripheral control processors on suitable microcontrollers. Measurement algorithms also have to be tolerant of poor power quality which may arise, even transiently, within a microgrid, battlefield, or disaster-relief scenario. This thesis analyses the potential magnitude of these interfering signals, and presents suitably tolerant architectures and algorithms for measurements of AC waveforms (amplitude, phase and frequency). These algorithms are shown to be robust and accurate, with harmonic content up to the level of 53% THD, and with the major algorithms executing at only 500 samples per second. This is achieved by the careful optimisation and cascaded use of exact-time averaging techniques, which prove to be useful at all stages of the measurements: from DC bias removal to low-sample-rate Fourier analysis to sub-harmonic ripple removal. Algorithms for three-phase nodal power flow analysis are benchmarked on the Infineon TC1796 microcontroller and require less than 8% of the 2000μs frame time, leaving the remainder free for other algorithms. Furthermore, to optimise security of supply in a microgrid scenario, loss-of-mains must be detected quickly even when there is an accidental or deliberate balance between local active power generation and demand. The measurement techniques are extended to the detection of loss-of-mains using a new Phase Offset relay, in combination with a novel reactive power control technique to avoid the non-detection-zone. These techniques are tested using simulation, captured network transient events, and a real hardware microgrid including a synchronous generator and inverter

A survey on tidal analysis and forecasting methods for Tsunami detection

Accurate analysis and forecasting of tidal level are very important tasks for human activities in oceanic and coastal areas. They can be crucial in catastrophic situations like occurrences of Tsunamis in order to provide a rapid alerting to the human population involved and to save lives. Conventional tidal forecasting methods are based on harmonic analysis using the least squares method to determine harmonic parameters. However, a large number of parameters and long-term measured data are required for precise tidal level predictions with harmonic analysis. Furthermore, traditional harmonic methods rely on models based on the analysis of astronomical components and they can be inadequate when the contribution of non-astronomical components, such as the weather, is significant. Other alternative approaches have been developed in the literature in order to deal with these situations and provide predictions with the desired accuracy, with respect also to the length of the available tidal record. These methods include standard high or band pass filtering techniques, although the relatively deterministic character and large amplitude of tidal signals make special techniques, like artificial neural networks and wavelets transform analysis methods, more effective. This paper is intended to provide the communities of both researchers and practitioners with a broadly applicable, up to date coverage of tidal analysis and forecasting methodologies that have proven to be successful in a variety of circumstances, and that hold particular promise for success in the future. Classical and novel methods are reviewed in a systematic and consistent way, outlining their main concepts and components, similarities and differences, advantages and disadvantages