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Partial discharge (PD) testing of high voltage (HV) cables and cable accessories has been implemented predominantly using high frequency current transformers (HFCTs) as PD sensors. PD currents initiating at PD sources are coupled onto cable conductors and travel away from the PD sources and will be detected by HFCTs installed at cable terminations. In this paper, based on combining finite-difference time-domain (FDTD) modeling and transfer function theory, a hybrid modeling approach is proposed to investigate the processes of PD coupling and detection involved in HFCT-based PD testing of HV cables. This approach allows exciting a PD event anywhere in FDTD models of the cables and predicting output from HFCTs some distance away. Implementation of the method is illustrated using an 11 kV XLPE cable. Moreover, a “direct measurement” method to obtain original PD pulses as the excitation source waveform is presented. The modeling approach introduced here will facilitate studies on the relationship between measured PD signals and those excited at PD sources, which can potentially give useful insight into the basic mechanisms behind PD detection in cables.

Metal Additive Manufacturing (AM) is an emerging technology for rapid prototype manufacturing, and the structural integrity of printed structures is extremely important and should meet the specifications and high standards of the above industries. In several metal AM techniques, residual stresses and micro-cracks that occur during the manufacturing procedure can result in irreversible damage and structural failure of the object after its manufacturing. Thus effective quality control of AM is highly required. Most Non-Destructive Testing (NDT) techniques (X-Ray, Computed Tomography, Thermography) are ineffective in detecting residual stresses. Bulk, cost, and resolution are limitations of such technologies. These methods are time consuming both for data acquisition and data analysis and have not yet been successfully integrated into AM technology. However two sets of NDT techniques: Electromagnetic Acoustic Transducers (EMAT) and Eddy Current (EC) Testing, can be applied for residual stress detection for AM techniques. Therefore a crucial and novel extension system incorporation of big data collection from sensors of the both techniques and analysis through machine learning (ML) can estimate the likelihood of the AM techniques to introduce anomalies into the printed structures, which can be used as an on-line monitoring and detection system to control the quality of AM.