Partial discharge (PD) pattern identification is an important tool in high-voltage (HV) insulation diagnosis of power systems. Based on an extension method, a PD identification system for HV power transformers is proposed in this paper. A PD detector is used to measure the raw three-dimensional (3D) PD patterns of epoxy resin power transformers using an L sensor, according to which two fractal features (the fractal dimension and the lacunarity) and the mean discharge are extracted as critical PD features that form the cluster domains of defect types. The matter–element models of the PD defect types are then built according to the PD features derived from practical experimental results. The PD defect type can be directly identified by the correlation degrees between a tested pattern and the matter–element models. To demonstrate the effectiveness of the PD features extraction and the extension method, the identification ability is investigated on 144 sets of field-test PD patterns of epoxy resin power transformers. Compared with a multilayer neural network and K-means methods, the results show that a high accuracy together with a high tolerance in the presence of noise interference is reached by use of the extension method.

One of the most important parameters indicating the state of aerosol presence in the atmosphere is their size distribution. It can be used several distribution functions for characterising aerosol size distribution, depending on their size range. Also there are available many instruments that make measurements of aerosol number concentrations, like multi-channel aerosol/particle counters or aerosol spectrometers. In this study, the author have set the focus on a method to determine coefficients of power law distribution, based only on the measurement results taken by these aerosol/particle counters. The determination of these coefficients is based only on the measurement results taken by the aerosol/particle counters. Measurement instruments give aerosol concentrations in different measuring channels, and then using this algorithm it can be possible to estimate Junge coefficients, and so on to determine the size distribution of aerosol particles. This method facilitates the process for determination of aerosol size distribution, because it does not require several parameters as in other methods, however, this determination can be realised knowing only aerosol number concentrations in several bins of the measurement instrument. The accuracy of this method depends on the size of the channels of the measurement instrument. In short, the larger is the number of channels that have one measurement instrument, the more accurate can be the application of above mentioned method.

A simple method of parameter-free Paretian optimisation is proposed in order to explore shapes of the unknown boundary of a synthesis region, in the search for the family of optimal shapes fulfilling a prescribed set of conflicting objective functions. To this end, a velocity-time law, related to the objective functions, governs the motion of a set of nodes located along the synthesis region boundary. The proposed method of optimal shape design conserves domain connectivity; moreover, because of the kinematic formulation, there is no need to solve the motion equation of the moving boundary. This way, the computational cost of the proposed method is low, and the advantage of a broad search space – inherent in the parameter-free approach – is preserved. To assess the method, a case study of inverse induction-heating is considered; the associated field problem is solved by means of finite-element analysis.

Time-domain features of partial discharge (PD) signals are often used to classify PD patterns. This paper proposes spectral features that are extracted using a filter bank, consisting of band-pass filters. By applying the fast Fourier transform to the PD signal, the resulting frequency bins are grouped into L octave frequency sub-bands. Two new features called the octave frequency moment coefficients (OFMC) and octave frequency Cepstral coefficients (OFCC) are defined in this paper. In addition, time–frequency domain coefficients (TFDC) obtained via wavelet analysis are also analysed. A PD signal can now be represented as an L-dimensional feature vector of OFMC, OFCC or TFDC. These features are compared with discrete wavelet transform-based higher-order statistical features (HOSF) using three different classifiers: probabilistic neural network, support vector machine and the recently emerged sparse representation classifier. Results show that the proposed spectral features are robust and provide a better classification accuracy of PD signals, compared with HOSF.

In partial discharge (PD) analyses, there is a need to compare the noise level of measured signals in different conditions and/or after applying certain signal processing algorithms (e.g. noise reduction). Moreover, in PD analyses through modelling approaches, whenever noisy signals should be constructed by adding noise with a desired level to the simulated PD signal and/or where de-noising process is employed, there is a need to evaluate the strength of noise. In all these cases, signal-to-noise ratio (SNR) is already used as an indicator of noise level, whereas it is shown that SNR can be deceptive and misleading. Instead, this study recommends employing the proposed peak SNR which is more robust than SNR. The results can be extended to the analysis of any non-periodic transient signal as well.

One of the main methods for partial discharge (PD) source localisation in power transformers is acoustic emission measurements. This study describes a new method for detection and location of two simultaneous partial discharge sources in three-phase power transformer. In this method, acoustic signals are detected by sensors first and are then denoised using a wavelet transform. Finally, the two PD sources are localised using the modified binary partial swarm optimisation (MBPSO) method. To prove the efficiency of the two simultaneous PD localisations, the proposed algorithm is used to localise PD sources of the arc furnace transformer at Isfahan's Mobarakeh steel company. For this purpose, the PD localisation problem converts to an optimisation problem. To prove the efficiency of the MBPSO algorithm, its performance is compared with a genetic algorithm. The PD localisation results confirm the efficiency of the proposed method for the detection and location of PD sources.

This study concerns the strapdown inertial navigation system (SINS) initial alignment under marine mooring condition with large initial error. The ten-dimensional state initial alignment error functions of the SINS with inclusion of non-linear characteristics have been derived. It is pointed out for the first time that the non-linear functions are applied to only a subset of the elements of the state vector, that is, the velocities error and the misalignment angles. Then a computationally efficient refinement of the unscented transformation (UT) called marginalised UT (MUT) is investigated in these special non-linear systems with a linear substructure. A performance comparison between the extended Kalman filter (EKF), the UT-based Kalman filter (UKF) and the MUT-based Kalman filter (MUKF) demonstrates that both the UKF and the MUKF can outperform the EKF and the MUKF and can achieve, if not better, at least a comparable performance to the UKF, at a significantly lower expense.