Axisymmetric finite element models have been developed for the simulation of negative discharges in air without and with the presence of dielectrics. The models are based on the hydrodynamic drift-diffusion approximation. A set of continuity equations accounting for the movement, generation and loss of charge carriers (electrons, positive and negative ions) is coupled with Poisson's equation to take into account the effect of space and surface charges on the electric field. The model of a negative corona discharge (without dielectric barriers) in a needle-plane geometry is analysed first. The results obtained show good agreement with experimental observations for various Trichel pulse characteristics. With dielectric barriers introduced into the discharge system, the surface discharge exhibits some similarities and differences to the corona case. The model studies the dynamics of volume charge generation, electric field variations and charge accumulation over the dielectric surface. The predicted surface charge density is consistent with experimental results obtained from the Pockels experiment in terms of distribution form and magnitude.

Modeling of the partial discharge (PD) process allows a better understanding of the phenomena. In this paper, a simulation model for spherical cavities within a homogeneous dielectric material has been developed. The model is implemented using Finite Element Analysis (FEA) software in parallel with a mathematical package. This method provides many advantages over previous PD models because discharge events can be simulated dynamically and the electric field in the cavity can be calculated numerically. The model has been used to study the effect of different amplitudes and frequencies of the applied voltage and simulation results have been compared with experimental measurement results. It is found that certain model parameters are dependent on the applied stress and parameters that clearly affect PD activity can be readily identified, these parameters include; the electron detrapping time constant, the cavity surface conductivity, the initial electron generation rate and the extinction voltage. The influence of surface charge decay through conduction along the cavity wall on PD activity has also been studied.

Partial discharge (PD) signals generated within electrical power equipment can be used to assess the condition of the insulation. In practice, testing often results in multiple PD sources. In order to assess the impact of individual PD sources, signals must first be discriminated from one another. This paper presents a procedure for the identification of PD signals generated by multiple sources. Starting with the assumption that different PD sources will display unique signal profiles this will be manifested in the distribution of energies with respect to frequency and time. Therefore the technique presented is based on the comparison of signal energies associated with particular wavelet-decomposition levels. Principal component analysis is adopted to reduce the dimensionality of the data, whilst minimizing lost information in the data concentration step. Physical parameters are extracted from individual PD pulses and projected into 3-dimensional space to allow clustering of data from specific PD sources. The density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm is chosen for its ability to discover clusters of arbitrary shape in n-dimension space. PD data from individual clusters can then be further analyzed by projecting the clustered data with respect to the original phase relationship. Results and analysis of the technique are compared for experimentally measured PD data from a range of sources commonly found in three different types of high voltage (HV) equipment; ac synchronous generators, induction motors and distribution cables. These experiments collect data using varied test arrangements including sensors with different bandwidths to demonstrate the robustness and indicate the potential for wide applicability of the technique to PD analysis for a range of insulation systems.

This paper considers a general class of linear iterative learning control (ILC) algorithm applied to tracking tasks which require the plant output to reach given points at predetermined time instants, without the specification of intervening reference points. A framework is developed in the frequency-domain in which the reference is updated between trials. It is shown that superior convergence and robustness properties are obtained compared with those associated with using the original class of ILC algorithm to track a prescribed arbitrary reference trajectory satisfying the point-to-point output constraints. Experimental results using a non-minimum phase test facility are presented to illustrate the theoretical findings.

In this work, a model representing partial discharge (PD) behaviour of a spherical cavity within a homogeneous dielectric material has been developed to study the influence of cavity surface charge distribution on the electric field distribution in both the cavity and the material itself. The charge accumulation on the cavity surface after a PD event and charge movement along the cavity wall under the influence of electric field magnitude and direction has been found to affect the electric field distribution in the whole cavity and in the material. This in turn affects the likelihood of any subsequent PD activity in the cavity and the whole sequence of PD events. The model parameters influencing cavity surface charge distribution can be readily identified; they are the cavity surface conductivity, the inception field and the extinction field. Comparison of measurement and simulation results has been undertaken to validate the model.