Electromagnetic simulators for all types of applications have matured to tools that are routinely used in industry and research. Over roughly five decades many different methods have been developed. Commercial solvers offer today a rich portfolio of algorithms that cover most of the needs. State-of-the-art software tools contain dozens of different solver technologies to be able to cover a wide range of applications. Usability and the ability to integrate tools into industrial workflows are aspects of increasing relevance. On the algorithmic side the progress seems to slow down and hardly any completely new algorithm was proposed for years. Possible trends for the future will be presented and illustrated with practical examples.

Condition monitoring and fault diagnosis are important for maintaining the system performance and guaranteeing the operational safety. The traditional data-driven approaches mostly incorporate well-defined features and methodologies such as supervised artificial intelligence algorithms. Prior knowledge of possible features and a large quantity of labelled condition data are needed. Besides, many traditional approaches require rebuilding or a retraining of the original model to diagnosis new conditions. The present study proposes an intelligent fault diagnosis approach that uses a deep neural network (DNN) based on stacked denoising autoencoder. Representative features are learned by applying the denoising autoencoder to the unlabelled data in an unsupervised manner. A DNN is then constructed and fine-tuned with just a few items of labelled data. The trained DNN achieves high performance in fault classification. Furthermore, new conditions can be correctly classified by simply fine-tuning the trained DNN model using a small amount of labelled data under the new conditions. The effectiveness of the proposed approach is evaluated using a case study of fault diagnosis of a bearing unit. The results indicate that the proposed method can extract representative features from massive unlabelled data on the system condition and achieve high performance in fault diagnosis.

The authors have recently developed a time-domain reflectometry (TDR)-based leak-localisation system which, being based on an electromagnetic technique, overcomes the well-known downsides (sensitivity to environmental noise, to pipe material and geometry etc.) of traditional, electro-acoustic leak-localisation techniques. Starting from the positive results obtained so far, in this work, the authors investigate the possibility of implementing an integrated TDR/ground penetrating radar (TDR/GPR) approach. The TDR would allow the preliminary leak localisation, while the GPR technique would be used to have a countercheck for possible false positives. Also, GPR would allow to assess the quantity of the leaked water (thus scheduling a priority order for repair) and to obtain a three-dimensional view of the subsurface soil around the pipes, so as to assess their health status. In this work, the combined TDR/GPR approach is tested on a pipe section in the presence of two leaks. In addition to this, in this study, also a different configuration of the TDR sensing element (SE) is experimented. As described in what follows, this new SE configuration is expected to expedite considerably the implementation and the use of the TDR-based leak detection system.

This study presents a backup protection scheme for transmission line compensated with unified power flow controller (UPFC) using power differential protection algorithm. In this scheme, power differential feature is evaluated from the apparent power flow in each phase at the local and remote bus to discriminate between the fault and normal situation. The proposed protection algorithm can be integrated with the conventional distance protection to supervise its operation during stressed conditions. Extensive fault simulation studies have been performed for different types of fault with several conditions and critical dynamic situations/issues such as load encroachment, power swing and high impedance fault (HIF). A 500 kV three machines four bus transmission system with UPFC has been used to validate the performance of the proposed scheme using MATLAB software. The test results depict that the proposed scheme is robust against HIF, power swing and load encroachment. It is not affected by the variation in pre-fault power flow angle and synchronisation delay. The proposed scheme has also been tested for current transformer (CT) and coupling capacitor voltage transformer (CCVT) transient error, which brings out the superiority of the scheme.

Integration of distributed generations into distribution systems has various impacts on power networks such as reconstruction of distribution networks and increase in short-circuit capacity. Reconstruction of distribution networks magnifies importance of detecting faults directions, which is relegated to directional relay. On the other hand, one of the most attractive approaches to mitigate the increased short-circuit capacity is utilising fault current limiters (FCLs). Since FCLs can drastically affect voltage and current signals during the first quarter cycle after fault occurrence and due to overlap between operation time of FCLs and directional relays, the affected voltage and current signals can lead to mal-operation of directional relays. To investigate this issue, performance of some well-known directional relay algorithms is investigated with and without the presence of FCL using plenty of simulations and some experiments. It is concluded that the FCL has different impacts on the investigated algorithms, so that some of them are not affected and some others are considerably affected.

This study presents the design of a piezoelectric vibration energy generator with power conditioning circuit to power CMOTE wireless sensor node. The test result shows that the energy harvester produces a maximum AC output voltage of 3 V with an optimal resistive load of 200 Ω for 1.5 m/s² acceleration at the resonant frequency of 275 Hz. The output power from the energy harvester is 65.9 mW. The characterisation analysis of energy harvester without tip mass and effect caused by the addition of mass at different positions have been analysed. Additional mass reduces the device frequency to 21.5 Hz and improves the output voltage up to 3.632 V. A single-stage AC–DC power converter which integrates the rectification and boosting circuit is designed, simulated and implemented in hardware to extract the maximum power from harvester to attain higher efficiency. The designed circuit will operate at a minimum AC voltage of 0.5 V. The minimum output from the harvester is rectified, boosted to 7V _DC output and regulated to 3.3 V to power wireless sensor node. The conversion efficiency of the circuit is improved to 70.03% with reduced loss of 19.76 mW by size reduction.

This study solves the attitude determination problem based on a single sensor observation. The rotation equation is transformed into a quadratic quaternion form and is then derived to a linear matrix equation with pseudoinverse matrices. The analytic solutions to the equation are computed via elementary row operations. The solutions show that the attitude determination from a single sensor observation has infinite solutions and the general one is governed by two limiting quaternions. Accordingly, the variance analysis is given in view of probabilistic characters. The authors explore the experimental results via the accelerometer attitude determination system. The properties of the two limiting quaternions are investigated in the experiment. The results show that the gravity-determination abilities of the two limiting quaternions are quite different. Using the rotation vector and eigenvalue decomposition of the attitude matrix, the authors prove that one limiting quaternion is better than another one geometrically. The singularity analysis is also performed revealing the non-existence of singularities for limiting quaternions. The above findings are novel, which are quite different from the conclusions made in a previously published study.

Microwave-based measurement can provide new methods and practical solutions for the challenging task of measurement inside large industrial combustion chambers. In order to measure or communicate inside a combustion chamber using microwaves, the spectrum of background noise at microwave frequencies must be known. This study investigates the basic electromagnetic spectrum in flames, concentrating on microwave frequencies from 0.1 to 19 GHz. The study introduces, as reference values, known results from forest flames and flames in controlled environments. The focus of the work is the level of background noise at microwave frequencies and the impact of this noise on radio communication inside the combustion chamber. The effects of the microwave radiation levels are compared with the requirements of practical communication systems such as Bluetooth and narrowband ISM (industrial, scientific, medical) systems at 800–950 MHz.

Magnetic vector potential (MVP) formulations are widely used in low-frequency eddy-current computation. For full-wave Maxwell problems with coupled resistive, inductive and capacitive effects, existing MVP formulations are either ungauged or with too complicated stabilisation procedure or with non-symmetric system matrix. Since sophisticated preconditioners are necessary to ensure good convergence rates of iterative solvers when used to solve the resultant linear system, it is also important to investigate symmetric gauged formulations which can be solved by more robust state-of-the-art sparse direct solvers. Traditional Coulomb gauged formulation using penalty technique is not feasible for edge elements since the divergence of the edge element basis function is zero within each element. In this study, a novel MVP formulation with Coulomb Gauge for full-wave Maxwell problems using edge elements is proposed. The proposed formulation is symmetric and stable for all frequencies, easy to implement and uniquely solvable. Numerical results obtained using the proposed MVP formulation are demonstrated to showcase its stability and accuracy. The method is very promising in further engineering applications.

The ultra-high frequency (UHF) partial discharge (PD) monitoring is one of the most effective ways to detect the insulation failure in electrical equipment. Although the UHF method avoids the intensive low-frequency noise, various electromagnetic interferences in substation can still influence its effectiveness and reliability. Most existing denoising algorithms, however, are unable to suppress different kinds of noise simultaneously. Meanwhile, the main focus of these algorithms is on the degree of noise reduction, while pay little attention to feature preservation. Therefore, a novel denoising method namely optimised variation mode decomposition and wavelet (OVMDW) is developed in this paper. In OVMDW, an optimised variational mode decomposition (VMD) algorithm is presented firstly to decompose the original signal into several band-limited modes. Then, a determination rule is designed to identify the effective components from these modes. Finally, the wavelet-based denoising method is employed to remove the residual white noise. Denoising results for simulative and experimental data show that the proposed method can remove various interferences from UHF PD signals. Additionally, new indexes are introduced to evaluate the performance of denoising algorithms in practical situations. Comparison results based on these indexes reveal that the proposed method using the VMD approach can preserve quite well the features of original signals.

Measuring electrical conductivity makes it possible to determine the concentration of dissolved ionic compounds in the water. Presented is a sensor developed for easily measuring the electrical conductivity of aqueous solutions. This sensor is comprised of two planar electrodes integrated onto a printed circuit board (PCB). PCB technology enables this sensor to be manufactured at high volumes for a modest cost. However, when the sensor is integrated into a PCB, it becomes difficult to analytically determine the influence the dimensions of the sensor's electrodes have on its measurements. In this research, a genetic algorithm was used to derive an equation predicting the behaviour of a PCB sensor with any reasonable electrode dimensions. Afterwards, the accuracy of this equation was evaluated by comparing its predictions to the measurements taken using actual sensors.

The inductive coupling between high-voltage power lines (HVPL) and buried pipelines has been an important research subject over the last decades. This coupling may result in alternating current (AC) on pipelines that may pose a serious threat to the pipelines due to corrosive effects and the cathodic protection (CP) performance. A method to investigate the effects of the induced AC density on the corrosion and the CP performances of the X70 steel buried pipeline due to the inductive interference caused by HVPL is proposed in this study. The method is based on the corrosion parameters of the X70 steel pipeline obtained by electrochemical measurements such as Tafel slopes, corrosion current densities, and corrosion potentials. These parameters were used as boundary conditions in the elaborated CP model. The results showed that, firstly, the induced AC density affects the electrochemical characteristic of the X70 steel and accelerates the corrosion of the pipeline. On the other hand, the impressed current cathodic protection is incapable of maintaining the CP potential level. However, some technical solutions were proposed to prevent the pipelines from AC corrosion and maintain the CP potential to an acceptable level.

Frequency-domain spectroscopy (FDS) is a widely accepted method for the estimation of moisture content in oil–paper insulation of power transformer. Researchers have shown that measurement temperature is an important factor that not only shifts the tan δ curve both horizontally and vertically with respect to frequency axis but also affects the value of paper-moisture content of transformer insulation. This implies FDS data measured at different temperatures yields different results even if the insulation under consideration remains more or less unaffected. Availability and construction of master curve for in-service real-life units might not always be available. This study proposes a method that is capable of predicting the profile of tan δ curve at different measurement temperatures. Existing expressions available for predicting paper-moisture does not consider the effect of change in measurement temperature. This study also proposes modification of existing expression to predict the moisture content of insulation paper at any measurement temperature. In addition, this study outlines a method to evaluate the value of activation energy (E _a) from the insulation response. The discussed technique is first tested successfully on data recorded from a laboratory sample. Thereafter, the method is applied on data collected from a real-life power transformer.

Large-scale three-dimensional (3D) electromagnetic simulations are commonly used as design and investigation tools in a wide variety of technological fields. It is not uncommon for both excitations and observation quantities to be expressed in terms of particular field profiles of feed waveguides. These may then be used to evaluate, e.g. scattering parameters. These field profiles must be obtained as a pre-processing task before the main simulation. Use of a theoretical field profile as an excitation to a discretised structure will typically cause a non-physical reflection. It is therefore more desirable in practice to use a field profile that is consistent with both the discretisation of the geometry and the 3D method of simulation. The authors present an approach to extracting these 2D field profiles from large-scale 3D unstructured meshes which are to be simulated with the unstructured transmission line modelling method. Discretised slices from the 3D mesh are extracted and incrementally extruded into a form suitable for consistent pre-processing. The impacts of all the parameters of the approach are investigated. Benchmarking is undertaken on both coaxial cable and microstrip waveguide feed structures showing that good quality results can be obtained straightforwardly.

A method for determination of attitude of the airborne remote sensing platform is proposed. In this method, the attitude of airborne platform can be obtained on-line by following four steps: (i) recording the attitude of the platform at time t ₀, which serves as the initial attitude; (ii) starting from t ₀, taking aerial images using the dedicated camera fixed on the airborne platform at time t_k (k = 0, 1, 2, …) regularly and noting as the kth (k = 0, 1, 2, …) aerial image; (iii) calculating the attitude change between every two adjacent images; (iv) determining the attitude of the platform at a time according to the initial attitude and the related attitude change. Simulation results proves the theoretical feasibility of the method. The attitude error sources are also be analysed. The attitude accuracy is proportional to focal length, field angle and matching accuracy of the corresponding points. Physical experiments were carried out to illustrate the feasibility and effectiveness of the proposed method.

Loss modelling for induction machines operated as variable speed drives (VSD) is indispensable for designing highly utilised traction machines. Accurate loss modelling can be performed using finite-element analysis (FEA). Since VSD have to deliver torque at different values of mechanical speed, the torque-speed operating points are sought. For those torque-speed operating points, the drive should be operated in the highest efficiency points. Finding these points from transient FEA requires the simulation of several ten to hundreds operating points in terms of stator current and rotor fundamental frequency. For that reasons, local loss modelling using FEA is very time consuming. This study will present a hybrid simulation approach coupling numerical and analytical models to reduce simulation time for simulation of the torque-speed operating points. The simulation approach takes into account the inverter voltage and current limitations and finds the highest efficiency operating point for every torque-speed operating point.

Under practical conditions, the power quality disturbances have a complex nature, and are often corrupted with external noise. This calls for robust detection and classification using appropriate signal processing and classification techniques. In this study, a time–frequency-scale transform is presented as a detection tool with high-noise immunity. It is a variant of chirplet transform adapted for power quality studies, which incorporates a Hann window and is capable of shifting and scaling operations. A number of simulated and real power quality disturbances are detected and classified under various noise levels for performance assessment. Performance of the time–frequency-scale transform is dependent on window length, and hence three different classifiers are employed to study the effect of window length variation on classification accuracy. Detection and feature extraction in time–frequency-scale transform is somewhat similar to Stockwell transform; therefore, a suitable comparison is shown wherever required. Results of the proposed methodology are found to be appreciable. Moreover, observations from this work can serve as a foundation for formulation of optimised classification problems involving manoeuvrable window signal processing techniques.

In this study, the authors investigate joint iterative frequency-domain channel estimation (FD-CE) and turbo equalisation for doubly selective high-frequency channel. The performance of time and FD turbo equalisers (TD/FD-TE) by applying them to Appendix C of military standard (MIL-STD)-188-110C is compared. Compared to TD-TE, FD-TE has reduced computational complexity; however, its performance degrades for time-varying channels. To improve the performance of FD-TE, we propose a new iterative CE scheme which can be used jointly with FD-TE. We employ low-complexity FD least mean-square algorithm in iterative process. By the use of soft information fed back from the decoder from the previous iteration, we improve the quality of the channel estimate over iterations. Simulation results show that at bit error rate (BER) of 1×10⁻⁵ the proposed algorithm improves the performance of FD-TE ∼2.5 and 3.5 dB for 8 phase shift keying (PSK) and 16 quadrature-amplitude modulation (QAM), respectively. Also, we study the impact of CE error on the performance of FD-TE. For 16QAM when SNR values are more than 20 dB, there is a gain between 1 and 3 dB when we consider the variance of CE error.