Electrical steel shows a distinct non-linear and anisotropic behaviour even for non-oriented steel. The flux guidance in electrical machines is realised by ferromagnetic steel sheets, and therefore it is necessary to consider the anisotropy in the simulation to have reliable and accurate results. Due to the fact that the reduction of silicon steel waste in the production process of electrical machines is crucial, and to enhance the material utilisation the segmentation is employed in the construction of electrical machines. The segmentation further allows to enhance the magnetic behaviour of the machine by considering the easy magnetisation axis in the design process. Nowadays, consideration of ferromagnetic anisotropy in the simulation of electrical machines is not accurate. To cope with these inaccuracies, in this contribution, a vectorial anisotropy model based on two-dimensional measurements is employed to the simulation of a segmented electrical machine, which is not state of the art in current machine computation. With the presented methodology, it is possible to simulate and design segmented electrical machines in an accurate fashion taking the ferromagnetic anisotropy into account.

The uncertainty of an electromagnetic device is inherent in its manufacturing process. To consider the various uncertainties, several probabilistic design optimisation techniques, such as robust or reliability-based design optimisation, have been developed. Although a statistical model of uncertainties is extremely important in obtaining an accurate result from a probabilistic design optimisation, most studies on probabilistic design optimisation have assumed these uncertainties to follow normal distributions. However, this assumption may not be valid in several real-world applications. Therefore, this study presents an efficient uncertainty identification method that provides a systematic framework to select the fittest distribution and find its optimal statistical parameters using finite element analysis and experimental data from prototype testing. The Akaike information criterion and maximum likelihood estimation are used for model selection and parameter estimation, respectively. To reduce the computational cost, the kriging surrogate model is used to evaluate the response of the electromagnetic device. The proposed method is applied to a surface-mounted permanent magnet synchronous motor, to identify the uncertainties that produce the additional harmonic components of cogging torque. The results show that this method is a powerful tool in analysing the effect of uncertainties on the performance of an electromagnetic device.

The accurate computation of multi-coupled electric drive systems is a complex process. In order to consider a variety of physical effects, their interactions as well as the interactions of different components, multiple fields of science have to be addressed. Numerical simulations help to compute these complex systems and to design machines in the best possible way for its specific application. The numerical computation scheme proposed in this study consists of six main sectors: the rough machine design, the preparation of the finite element method including the characterisation and modelling of materials, the electromagnetic simulation, the mechanical simulation, the control and power electronics and the thermal design and simulation. In each of the main sectors, different effects affecting the machine and the system behaviour can be considered and calculated. Depending on the application, they can be added or removed modularly in order to adapt the simulation effort and the simulation accuracy to the application. A detailed insight into the computation methods and approaches in each of these main sectors and their coupling is given in this study.

A novel implementation of the stretched coordinate perfectly matched layer (PML) is presented for the two-dimensional (2D) transmission line modelling (TLM) method. The formulation offers a unified approach and is based on the mapping of the TLM node to a complex stretched domain for which the resulting transformation of the constituent RLC transmission line components is elaborated. The transformation is shown to modify the TLM connect-scatter algorithm. The absorption performance is demonstrated by simulating a canonical waveguide test case. Unlike the existing split-field based TLM-PML implementations, which are better suited to lossless media, the numerical results obtained show the proposed PML formulation is effective in the termination of both lossy and lossless media.

Validation and verification activities are essential enablers for the practical application of computational electromagnetics (CEM) models in industrial applications, but these terms are widely confused or merged. Furthermore, concepts such as the accuracy and precision of measurement results are often poorly understood. As model accuracy is most commonly judged against measured results there is a strong case to establish a well-defined vocabulary for describing the accuracy of CEM models that is both consistent with, and complementary to, the existing vocabulary associated with measurements. In order to help clarify the situation, this study identifies the difference between validation and verification in relation to CEM, suggests a mapping between CEM and measurement processes, collates a number of relevant definitions from existing measurement standards, and proposes new and complementary definitions that relate specifically to CEM simulation. It is considered that the proposed vocabulary could help to avoid misunderstandings between the test and modelling domains and eliminate ambiguity in standards relating to CEM validation and verification. In addition, the terminology presented here could also be readily adapted to help develop or clarify similar standards for other physics-based simulation domains, and perhaps even for more general mathematical modelling applications.

The eddy-current losses in litz-wire windings are mostly due to the proximity effect, which can be macroscopically modelled via a complex frequency-dependent permeability. In this study, the authors propose a new closed-form expression for this complex permeability. Results are compared with approximations found in the literature in a wide-frequency range. For the sake of validation, a two-dimensional)-axisymmetric gapped transformer with litz-wire primary and secondary coils is studied.

An essential requirement to improve the lightning performance of transmission line is the achievement of low impulse impedance of grounding system, which diminishes the occurrence of the external overvoltages. In this study, for the first time, a multi-cross structure used as transmission line tower-footing grounding system is investigated by means of hybrid continuous circuit-trapezoidal integration method (HCCTIM). It is discovered that the angle of minimum impulse impedance or the optimal structure of multi-cross grounding system is mainly dependent on the branch length compared to the effective length of a single horizontal conductor. Furthermore, in the current work, the trapezoidal integration method is used to solve the continuous circuit equations which elements are extracted from electromagnetic integral method, called HCCTIM. Coupling elements and soil ionisation are considered in this model. This method is an accurate, simple, and less time-consuming computational method. The comparison of the results with measurements, other recent simulations and CDEGS software, satisfactorily validate the proposed method.

Large amounts of labelled vibration data of rolling bearings are difficult to acquire in full during operating conditions under varying loads. Moreover, a large divergence in data distribution exists between source and target domains for the same state. A multiple-state identification method for rolling bearings under varying loads is proposed. The deep domain adaptation method integrates the convolutional and pooling theory with the deep belief network (DBN) that enables the construction of a convolutional Gaussian–Bernoulli DBN, which is used to extract the deep generalised features from the frequency-domain amplitudes of the rolling bearings. The weighted mixed kernel is then used instead of the single kernel to improve the joint distribution adaptation, which is used to process the features of both the labelled source domain and the unlabelled target domain for domain adaptation, and reduce the distribution divergence. Finally, the k-nearest neighbour algorithm is used for identification. Experimental results show that the proposed method can make full use of unlabelled data, mine the deep features of vibration signals, and reduce the divergence between data of the same state. In resolving the multiple-state identification of rolling bearings under varying loads, a higher accuracy is attained in the identification.

Partial discharge (PD) detection and location based on ultra-high frequency (UHF) sensor array have been employed for the power equipment in the whole substation for assessing the insulating condition of electrical equipment and determining a precondition for further implementing insulation diagnosis. At present, one of the widely used localisation methods is time difference of arrival (TDOA) based, localisation result of which is extremely sensitive to time delay estimation and usually time-consuming to solve. Motivated by this, a data-driven method using the deep neural network (DNN) is proposed in this study to significantly speed up the solving process of non-linear TDOA equations and simultaneously guarantee the accuracy of results. It works with sequences of time delay measured from the UHF sensor array as the input of the network and with the corresponding coordinates of PD source as output to train the network. Simulation results demonstrate that the proposed method shows relatively higher accuracy and efficiency in PD location. In addition, many factors such as array shape, error type added to time delay, and detailed structure and parameters of network are taken into consideration for error analysis, laying foundation to more reliable localisation of PD.

Due to the characteristics of measurement datum distribution, the altitude positioning error of GPS (Global Positioning System) is relatively large. This study discusses a differential barometric altimetry (DBA) system from systematic implementation level and provides a key technical solution for the continuous and precise altitude measurement from the DBA systematic implementation aspect, which can provide a good supplement and enhancement for GPS. The structure of signal message is designed; the power spectrum of spread spectrum ranging signal is given, and the fusion of navigation and communication is realised. The two dimensional curved surface fitting function of pressure and temperature (P, T) is constructed, and the fitting parameters are calculated by the least-square solution, which can realise calibration compensation and measurement correction of sensor. The influence of the system noise variance matrix Q _k and the measurement noise variance matrix R _k on the filtering effect in Kalman filter is analysed. An algorithm of multi-base station (BS) correction is proposed, which can realise the height information correction calculation in measuring station (MS) via seamless handover between multi-BSs. The experimental results show that the key technology solutions in DBA system can achieve a well systematic performance..

Multiple-level and up-and-down test methods are commonly employed in laboratories to obtain the breakdown voltage distribution of self-restoring insulation, which is applied for equipment evaluation and for risk calculation in insulation coordination studies. This study investigates the reliability of the methods and proposes a methodology for evaluating their performance. The accuracy and precision of the estimated distribution parameters were analysed considering the variation of the test parameters. Algorithms that simulate the random nature of the tests were developed and applied in simulations. Then, a case study of risk calculation was carried out. The results indicated that both methods estimate the critical flashover voltage with acceptable accuracy and precision, even for the minimum parameters defined by standards. However, estimating the standard deviation is the critical step of the tests. The proposed methodology allows parameter definition for a given reliability requirement in the case of the constant voltage method. In the case of the up-and-down method, however, high dispersion was observed for all analysed cases, which indicates that this method presents low reliability for calculating flashover risk and, therefore, its use for insulation coordination studies is not recommended.

The performance of measurement transformers especially in transient phenomena affects the monitoring, control and protective devices. Coupling capacitor voltage transformer (CCVT) is a measurement transformer that may experience ferroresonance phenomenon due to the presence of capacitors and non-linear reactors. This study provides a probabilistic framework for investigation of ferroresonance phenomenon in CCVT. The aim of this investigation is to find out the area of the power system in which a CCVT is highly prone to experience ferroresonance phenomenon. Also through this framework the behaviour of the different types of ferroresonance suppression circuits and possible solutions to avoid or reduce the chance of ferroresonance will be discussed. The effects of several factors such as switching moment, different fault type and the fault inception angle are investigated. The investigation will be conducted considering the effects of single-pole auto-reclosing, three-pole auto-reclosing and capacitor failure in the CCVT. As shown in the simulation results, the presented framework can provide some useful information regarding the exposure level and severity of CCVT ferroresonance in the system.

Background and Objective: Obstructive Sleep Apnea is a disease that causes respiratory arrest in sleep and reduces sleep quality. The diagnosis of the disease is made by the physician in two stages by examining the patient records taken with the polysomnography device. Because of the negative aspects of this process, new diagnostic processes and devices are needed. In this article, a new approach to sleep staging, which is one of the diagnostic steps of the disease, was proposed. An artificial intelligence-based sleep/awake system detection was developed for sleep staging processing. Photoplethysmography (PPG) signal and heart rate variable (HRV) were used in the study. PPG records taken from patient and control groups were cleaned by the digital filter. The HRV parameter was then derived from the PPG signal. Then, 40 features from HRV signal and 46 features from PPG signal were extracted. The extracted features were classified by reduced machine learning techniques with F-score feature selection method. In order to evaluate the performances of the classifiers, the sensitivity and specificity values, the accuracy rates for each class were computed in the test set and receiver operating characteristic curve prepared. In addition, area under the curve (AUC), Kappa coefficient and F-score were calculated. According to the results obtained, the system can be realised with 91.09% accuracy rate using 11 PPG and HRV and with 90.01% accuracy rate using 14 HRV features. These success rates are quite enough for the system to work. When all these values are taken into consideration, it is possible to realise a practical sleep/awake detection system. This article suggests that the PPG signal can be used to diagnose obstructive sleep apnea by processing with artificial intelligence and signal processing techniques.

In order to improve the quality of reconstructed images in electrical capacitance tomography (ECT), the image reconstruction method based on compressed sensing for ECT is studied. First, the traditional discrete Fourier transform and discrete cosine transform are used as a sparsity basis to make the grey vectors of the typical two-phase flow distributions sparse. The energy loss of the sparse signals under different sparsity degrees is calculated, and the effect of energy loss on the quality of reconstructed images is studied. Then, using the natural sparsity of the original signal, an improved orthogonal matching pursuit algorithm for ECT image reconstruction is proposed. There are two main improvements in the proposed algorithm. First, multiple columns instead of one column in each iteration are selected for improving the reconstruction speed. Second, a regularisation solution instead of the least-squares solution is used for improving the adaptability to ill-posed inverse problems. Simulation and experimental tests are carried out and the results show that the proposed method can effectively improve the reconstructed images quality, and on the whole, obtain better reconstruction results than the Landweber iteration algorithm, the Tikhonov regularisation algorithm, and the gradient projection for sparse reconstruction algorithm.

A modified design topology of planar submersible sensor, to realize the improved sensitivity, using the split ring resonator (SRR) loaded coupled microstrip line is presented. The designed sensor can easily be immersed in the liquid sample under test. The maximum numerical sensitivity of the designed sensor (11.58%) at the design frequency of 6.12 GHz is quite higher than the sensitivity of earlier reported submersible sensor (4.01%). The measured unloaded resonant frequency of the fabricated sensor shows a close match with the corresponding simulated data. The fabricated sensor prototype can distinguish between the test samples having nearly similar dielectric properties due to its improved sensitivity. Two significant aspects of petrochemical industries in the Indian continent, i.e. contamination/blending of petrol with kerosene/ethanol are mainly considered here. As the discrimination between petrol and kerosene using the commonly available microwave sensors is especially quite challenging due to the very similar dielectric behaviour of these two chemicals. The developed prototype can typically measure the minimum 2% level of kerosene adulteration in petrol corresponding to 7.5 MHz shift in the measured resonant frequency. The uncertainty in the measured frequency shift of the liquid samples using the proposed sensor is found to be ∼1 MHz.