No abstract

The objective of the paper is to discuss a tool which is proving extremely efficient in partial discharge measurement studies. Though the technique itself is not new, its application to partial discharge studies is. I t will he demonstrated in this paper that it has tremendous power a n d this accounts for its rapid growth a s a n application in this field. The paper begins with the description of the fundamentals of wavelet analysis, wavelet categories a n d the properties of the associated wavelet transforms. PD pulses a s acquired from detectors composed of different detection circuits a r e investigated a n d numerically simulated, a n d a method on how to select optimally the wavelet corresponding to the representative forms of I'D pulse is then presented. Finally, applications of wavelet analysis to partial discharge studies a r e explored. The paper demonstrates that the wavelet based denoising method proposed in the paper can he employed in 'separating I'D pulses from electrical noise successfully a n d can be used in pulse propagation studies of partial discharge in distributed impedance plant to provide enhanced information a n d further infer the original site of the I'D pulse.

A digital method for the discrimination of neutron and-ray events from an organic scintillator has been investigated by using frequency gradient analysis (FGA) based on the Fourier transform. Since the scintillation process and the photomultiplier tube (PMT) anode signal are often very noisy, most pulse-shape discrimination methods in a scintillation detection system (e.g., the charge comparison (CC) method or pulse gradient analysis (PGA)) using time-domain features of the signal depend greatly on the associated de-noising algorithm. In this research, the performance of the new FGA method and the PGA method have been studied and compared on a theoretical basis and then verified by time-offlight (TOF). The frequency-domain features extracted by the FGA method exhibit a strong insensitivity to the variation in pulse response of the photomultiplier tube (PMT) and can be used to discriminate neutron and-ray events in a mixed radiation field. It is shown that the FGA method results in an increased figure of merit (FOM) which corresponds to a reduction in the area of overlap between neutron and-ray events. The FGA method has the potential to be implemented in current embedded electronic systems to provide real-time discrimination in standalone instruments.

Eddy-current techniques can be used to create electrical conductivity mapping of an object. The eddy-current imaging system in this paper is a magnetic induction tomography (MIT) system. MIT images the electrical conductivity of the target based on impedance measurements from pairs of excitation and detection coils. The inverse problem here is ill-posed and nonlinear. Current state-of-the-art image reconstruction methods in MIT are generally based on linear algorithms. In this paper, a regularized Gauss-Newton scheme has been implemented based on an edge finite-element forward solver and an efficient formula for the Jacobian matrix. Applications of Tikhonov and total variation regularization have been studied. Results are presented from experimental data collected from a newly developed MIT system. The paper also presents further progress in using an MIT system for molten metal flow visualization in continuous casting by applying the proposed algorithm in a real experiment in a continuous casting pilot plant of Corus RD&T, Teesside Technology Centre.

No abstract

This paper presents the latest development of an EMT system designed for use in the metal production industry such as imaging molten steel flow profiles during continuous casting. The system that has been developed is based on a commercial data acquisition board residing in a PC host computer and programmed in the LabView graphical language. The paper reviews the new EMT hardware electronics and software. The noise effects and the detectability limits of the system are given in the paper followed by the system sensitivity map analysis. Optimal image reconstructions, including the simultaneous iterative reconstruction technique (SIRT) and non-iterative Tikhonov regularization, truncated singular value decomposition (TSVD), are also discussed and applied for the system. The system has been demonstrated in real time (10 frames s−1 for 5 kHz excitation) with test phantoms that represent typical metal flow profiles such as central, annular stream and multiple streams.

Today's electricity grid is rapidly evolving, with increased penetration of renewable energy sources (RES). Conventional Optimal Power Flow (OPF) has non-linear constraints that make it a highly non-linear, non-convex optimisation problem. This complex problem escalates further with the integration of RES, which are generally intermittent in nature. In this article, an optimal power flow model combines three types of energy resources, including conventional thermal power generators, solar photovoltaic generators (SPGs) and wind power generators (WPGs). Uncertain power outputs from SPGs and WPGs are forecasted with the help of lognormal and Weibull probability distribution functions, respectively. The over and underestimation output power of RES are considered in the objective function i.e. as a reserve and penalty cost, respectively. Furthermore, to reduce carbon emissions, a carbon tax is imposed while formulating the objective function. A grey wolf optimisation technique (GWO) is employed to achieve optimisation in modified IEEE-30 and IEEE-57 bus test systems to demonstrate its feasibility. Hence, novel contributions of this work include the new objective functions and associated framework for optimising generation cost while considering RES; and, secondly, computational efficiency is improved by the use of GWO to address the non-convex OPF problem. To investigate the effectiveness of the proposed GWObased approach, it is compared in simulation to five other nature-inspired global optimisation algorithms and two well-established hybrid algorithms. For the simulation scenarios considered in this article, the GWO outperforms the other algorithms in terms of total cost minimisation and convergence time reduction.

No abstract