VFT performance of a 180/110 mm co-axial gap in SF6 up to 4 bar absolute

Disconnector switch operation in GIS generates VFT voltages in the system. It is important, for insulation co-ordination purposes, to obtain accurate VFT V-t data for typical gap geometries found in GIS. This paper presents experimentally obtained VFT V-t data for a 180/1 lOmm co-axial gap. The VFT has a time to first peak of 35 ns and a oscillation frequency of 13,6 MHz. Due to the location of the voltage divider in a compartment adjacent to the gap, a correction factor of 1.1 is used to relate the measured breakdown voltage to that in the gap. Positive polarity VFT V-t data is presented for 1, 2, 3 and 4 bar absolute and negative polarity VFT data for 3 and 4 bar absolute. Two methods of generating the VFT's are used. The first is to power up the test transformer at power frequency. The second is to generate a switching impulse by discharging a capacitor into the primary of the test transformer.

Impacts of wind power integration on small signal stability and low frequency oscillation characteristics of interconnected power systems

为研究风电并网对互联系统低频振荡的影响,基于完整的双馈风电机组模型,定性分析了两区域互联系统在风电机组并网前后阻尼特性的变化情况.从双馈风电机组并网输送距离、并网容量、互联系统联络线传送功率、是否加装电力系统稳定器等多个方面,多角度分析了风电场并网对互联系统小干扰稳定及低频振荡特性的影响.之后,以两个包括两个区域的电力系统为例,进行了系统的计算分析和比较.结果表明,有双馈风电机组接入的互联电力系统,在不同运行模式下,双馈风电机组的并网输送距离、出力水平、联络线传送功率对低频振荡模式的影响在趋势和程度上均有显著差异,这样在对风电场进行入网规划、设计和运行时就需要综合考虑这些因素的影响.

Dust charge and ion drag forces in a high-voltage, capacitive radio frequency sheath

The charge of an isolated dust grain and ion drag forces on the grain in a collisionless, high-voltage, capacitive rf sheath are studied theoretically. The studies are carried out assuming that the positive ions are monoenergetic, as well as in more realistic approximation, assuming that the time-averaged energy distribution of ions impinging on the dust grain has a double-peaked hollow profile. For the nonmonoenergetic case, an analytical expression for the ion flux to the dust grain is obtained. It is studied how the dust charge and ion drag forces depend on the rf frequency, electron density at plasma-sheath boundary, electron temperature and ratio of the effective oscillation amplitude of rf current to the electron Debye length. It is shown that the dust charge and ion drag forces obtained in the monoenergetic ion approximation may differ from those calculated assuming that the ions are nonmonoenergetic. The difference increases with increasing the width of the ion energy spread in the ion distribution. © 2009 American Institute of Physics.

Adaptive frequency scaled wavelet packet decomposition for frog call classification

Environmental changes have put great pressure on biological systems leading to the rapid decline of biodiversity. To monitor this change and protect biodiversity, animal vocalizations have been widely explored by the aid of deploying acoustic sensors in the field. Consequently, large volumes of acoustic data are collected. However, traditional manual methods that require ecologists to physically visit sites to collect biodiversity data are both costly and time consuming. Therefore it is essential to develop new semi-automated and automated methods to identify species in automated audio recordings. In this study, a novel feature extraction method based on wavelet packet decomposition is proposed for frog call classification. After syllable segmentation, the advertisement call of each frog syllable is represented by a spectral peak track, from which track duration, dominant frequency and oscillation rate are calculated. Then, a k-means clustering algorithm is applied to the dominant frequency, and the centroids of clustering results are used to generate the frequency scale for wavelet packet decomposition (WPD). Next, a new feature set named adaptive frequency scaled wavelet packet decomposition sub-band cepstral coefficients is extracted by performing WPD on the windowed frog calls. Furthermore, the statistics of all feature vectors over each windowed signal are calculated for producing the final feature set. Finally, two well-known classifiers, a k-nearest neighbour classifier and a support vector machine classifier, are used for classification. In our experiments, we use two different datasets from Queensland, Australia (18 frog species from commercial recordings and field recordings of 8 frog species from James Cook University recordings). The weighted classification accuracy with our proposed method is 99.5% and 97.4% for 18 frog species and 8 frog species respectively, which outperforms all other comparable methods.