In this paper, a discrete wavelet transform (DWT) based approach is proposed for power system frequency estimation. Unlike the existing frequency estimators mainly used for power system monitoring and control, the proposed approach is developed for fundamental frequency estimation in the field of energy metering of nonlinear loads. The characteristics of a nonlinear load is that the power signal is heavily distorted, composed of harmonics, inter-harmonics and corrupted by noise. The main idea is to predetermine a series of frequency points, and the mean value of two frequency points nearest to the power system frequency is accepted as the approximate solution. Firstly the input signal is modulated with a series of modulating signals, whose frequencies are those frequency points. Then the modulated signals are decomposed into individual frequency bands using DWT, and differences between the maximum and minimum wavelet coefficients in the lowest frequency band are calculated. Similarities among power system frequency and those frequency points are judged by the differences. Simulation results have proven high immunity to noise, harmonic and inter-harmonic interferences. The proposed method is applicable for real-time power system frequency estimation for electric energy measurement of nonlinear loads.
The shipping noise near channels and ports is an important contribution to the ambient noise level, and the depth of these sites is often less than 100 m. However less attention has been paid to the measurement in shallow water environments (Brooker, Humphrey, 2016). This paper presents extensive measurements made on the URN (underwater radiated noise) of a small fishing boat in the South China Sea with 87 m depth. The URN data showed that the noise below 30 Hz was dominated by the background noise. The transmission loss (TL) was modelled with FEM (finite element method) and ray tracing according to the realistic environmental parameters in situ. The discrepancy between the modelled results and the results using simple law demonstrates both sea surface and bottom have significant effect on TL for the shallow water, especially at low frequencies. Inspired by the modelling methodology in AQUO (Achieve QUieter Oceans) project (Audoly et al., 2015), a predicted model applied to a typical fishing boat was built, which showed that the URN at frequencies below and above 100 Hz was dominated by non-cavitation propeller noise and mechanical noise, respectively. The agreement between predicted results and measured results also demonstrates that this modelling methodology is effective to some extent.