Titan's cold case files - Outstanding questions after Cassini-Huygens

Abstract The entry of the Cassini-Huygens spacecraft into orbit around Saturn in July 2004 marked the start of a golden era in the exploration of Titan, Saturn's giant moon. During the Prime Mission (2004–2008), ground-breaking discoveries were made by the Cassini orbiter including the equatorial dune fields (flyby T3, 2005), northern lakes and seas (T16, 2006), and the large positive and negative ions (T16 & T18, 2006), to name a few. In 2005 the Huygens probe descended through Titan's atmosphere, taking the first close-up pictures of the surface, including large networks of dendritic channels leading to a dried-up seabed, and also obtaining detailed profiles of temperature and gas composition during the atmospheric descent. The discoveries continued through the Equinox Mission (2008–2010) and Solstice Mission (2010–2017) totaling 127 targeted flybys of Titan in all. Now at the end of the mission, we are able to look back on the high-level scientific questions from the start of the mission, and assess the progress that has been made towards answering these. At the same time, new scientific questions regarding Titan have emerged from the discoveries that have been made. In this paper we review a cross-section of important scientific questions that remain partially or completely unanswered, ranging from Titan's deep interior to the exosphere. Our intention is to help formulate the science goals for the next generation of planetary missions to Titan, and to stimulate new experimental, observational and theoretical investigations in the interim

Methods for wavelet-based autonomous discrimination of multiple partial discharge sources

Recent years have seen increased interest in the application of on-line condition monitoring of medium voltage networks as the need to maintain and operate ageing cable networks increases. Detection and analysis of partial discharge (PD) activity is generally used as an indicator of pre-breakdown processes that may be indicative of insulation degradation over time. A significant challenge for on-line monitoring is discrimination between multiple partial discharge sources that will often naturally exist in the data. To discriminate between PD sources each PD signal is represented as a feature vector and a clustering algorithm is used to identify clusters in the resulting feature vector space, often after dimensional reduction. Each cluster identified in the data corresponds to a distinct PD source. In this work a comparison of clustering algorithms and dimensional reduction techniques is performed to identify clusters for a variety of PD data sets, in all cases the feature vector is created using wavelet decomposition energies. The three clustering algorithms used were Density Based Spatial Clustering of Applications with Noise (DBSCAN), Ordering Points to Identify Clustering Structure (OPTICS) and Simple Statistics-based Near Neighbour clustering technique (SSNN). The two dimensional reduction techniques considered were Principal Component Analysis (PCA) and t Distributed Stochastic Neighbour Embedding (t SNE). At present the most commonly used combination of dimensional reduction technique and clustering algorithm for PD data are PCA and DBSCAN respectively. From the comparison performed in this work it was found that t SNE combined with OPTICS or SSNN were the most successful at clustering PD data. For use in practical situations SSNN is preferred over OPTICS as it requires only a single input parameter, which generally be hardcoded, leading to a completely autonomous technique. It is therefore suggested that a combination of t SNE and SSNN is particularly appropriate for discriminating PD sources

The deterioration of frequency selectivity with age

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Classification and localisation of multiple partial discharge sources within a high voltage transformer winding

Partial discharge (PD) analysis is widely adopted for assessing the condition of the insulation systems within high voltage (HV) transformers. Different PD sources have different effects on the insulation condition of HV transformers. In a typical field environment, multiple PD sources may occur in HV transformer simultaneously. Therefore, source classification is very important to identify the types of defects causing discharges in a HV transformer. In recent years, several classification techniques have been proposed for application in PD analysis. This paper proposes automatic techniques to classify and localize multiple PD sources within a HV transformer winding. The proposed processing technique relies on the assumption that the PD pulses generated from different defects exhibit unique waveform characteristics. Surface and void discharges which are the common types of defect events that may occur within HV transformer windings have been experimentally generated. Each pair combination was injected simultaneously into different locations along the HV transformer winding with analysis of two wideband radio frequency current transformers (RFCTs) data captured from each end of the winding. After PD pulses extraction and wavelet analysis, this paper presents two approaches using two different methods to accurately locate multiple PD sources within an HV transformer winding. The performances of the two approaches for this type of application are presented