In this work, a fault detection methodology during power swing is demonstrated. Modern distance relays are embedded with power swing blocking (PSB) function to preserve the security and reliable operation of power system during swing. However, the operation of PSB should be unblocked and let the distance relay allow to operate for any fault during swing. However, sometimes, distance relay unable to make a proper discrimination between swing and fault event leading cascading failures. In order to accomplish the fault detection task, first online empirical mode decomposition is used and processed through Hilbert–Huang transform to compute the amplitude and instantaneous frequency of that signal. Next, discrete teager energy approach is applied to estimate the teager energy. The energy operator functions as a reliable index for fault detection task during power swing. To evaluate the performance of the proposed method, different power system structures are considered and simulated using EMTDC/PSCAD. Results for current transformer saturation, single-pole tripping, and in presence of noise are provided. The response of the proposed method is compared with the conventional and existing methods. Results and comparative assessment reports demonstrate that the method is more efficient and robust in maintaining both selectivity and dependability.

In this work, a single-mode resonant microwave cavity (2.45 GHz) is loaded with spherical particles. A weakly coupled electromagnetic (EM)–thermal solver is used iteratively to determine how the microscopic geometry (local curvatures between the particles, grain size and neck size) modifies the EM field, and in turn the thermal field in the particles and also to quantify the microwave effect for an experimental process. The modelling is performed with the conformal finite element solver COMSOL Multiphysics. Moreover, this study will show that the electric field norm increases for one spherical particle of ceria with different sizes and consequently the temperature increases. In addition, the electric field and heating behaviour have been studied in the case of three particles with different neck sizes. Finally, the effect of the configuration versus the E-field direction has been studied for three particles and has a significant result.

Electric vehicles (EVs) are becoming more popular due to ever increasing concern on the environmental effects and rising gasoline prices. However, the disadvantage of EV is their driving range and charging time. This can be overcome by a simple concept as inductive power transfer, which is an emerging EV battery charging technology. In static and dynamic charging of EV, the mutual inductance (MI) plays a key role in effective power transfer. This study proposes a new analysis concept for computation of MI between circular spiral inductive coils without core, with core and chassis. The MI is computed using Neumann's approximated equation for possible misalignments such as perfect, planar and angular misalignments. The computed values of MI for the above-mentioned alignments are verified using finite element modelling analysis and also validated through experimentation. The comparative analysis conveys that the computed values of MIs are accurate and relative error is negligible.

Study of alternate fluids for transformer and their combination with traditional ones is an interesting area of research. The intent of this work is to investigate the thermal ageing performance of ester oils (synthetic and natural), and mixed oil vis-à-vis mineral oil. All the samples were thermally stressed at 110, 140, 160, and 185°C for 2 weeks each. Dissolved decay contents in oil and linear thermal expansion coefficients of solid insulation have been studied using ultraviolet visible infrared spectroscopy and dilatometry analysis, respectively, as per ASTM standards. Change in functional groups of the insulation oils with ageing have been understood by Fourier transform infrared spectroscopy analysis. Additionally, diffusion of oil–moisture mixture into paper with ageing and the corresponding effect on dielectric strength of paper has been also examined using ASTM standards. It is observed that the use of synthetic ester (SE) and addition of SE to mineral insulating oil lead to improved performance of oil–paper insulation system with retarding the rate of degradation of the insulation system.

A novel, cost- effective and efficient wireless pressure measurement system is modeled for transmission of the signal in harsh environment. The sensing part involves rubber bellows with a capacitive sensor made of copper plates. For remote transmission, monitoring and controlling the mechanical displacement of the bellows is converted into dc output voltage using differentiator and precision half wave rectifier. A linearization circuit is also designed, which linearized the output voltage with a value of percentage deviation from linearity of ±1.8%. For further FSK mode of transmission, the obtained linearized voltage is converted into 1 to 5 volt with a signal conditioning circuit. The transmitted output voltage is recovered using FSK demodulator circuit, LPF and a Decision circuit at receiving end. The full scale percentage error of the measurement system lies within 5% which is in an acceptable range. The proposed method is an economic and efficient transmission technique in hazardous areas where wired transmission is not feasible. The mathematical equations explaining the functioning of the proposed transmitter have been derived. The operation of the proposed pressure transmitter has been experimentally tested. The design approach, mathematical analysis and experimental results of the proposed model are reported in this paper.

An interpolation algorithm of the eigenvalues and eigencurrents (eigenpairs) is proposed in this study to accelerate the broadband evaluation of characteristic modes. A bowtie antenna, an aircraft and a spherical shell are selected for numerical verification. To improve the stability and accuracy of the interpolation algorithm, a sampling scheme integrating non-uniform frequency step (NUFS) into adaptive mode tracking is adopted. For comparison, the same problems are computed also by the method of moments (MoM) and a uniform frequency step (UFS) interpolation scheme. It is found that the interpolated eigenvalues and eigenpatterns by the NUFS scheme are very close to those by MoM while the ones by the UFS scheme yield larger deviations. The peak memory and computation time have been also studied. Compared with the computation by MoM, the memory consumed by the interpolation with NUFS is less and up to 80% of the computational time can be saved.

Two of the main problems studied in protective current transformers (CTs) are the saturation and residual flux in the ferromagnetic core. To minimise the effects of high saturation levels and residual flux, a gapped core CT can be employed. Although solving both issues, research reports that the introduction of gaps in the ferromagnetic core can develop into a mechanical fragility when compared with a closed core. However, the fragility is not proven in the research. To investigate the mechanical fragility of a gapped core CT, this research aims to verify the influence of the electromagnetic forces in the gap borders of a protective CT under a short-circuit condition. The influence was verified in three different gap lengths: 8, 16 and 24 mm. On the basis of the results, the electromagnetic forces in the gap borders of a protective CT did not influence the CT response.

The development of nanofluids is necessary to replace the conventional transformer oil used in power and distribution transformers. A stable nanofluid has been prepared to verify its insulating and heat transfer performance in oxidative ageing environment. Due to the superior thermal and electrical properties of a nanoparticle (NP), its dispersion in mineral oil (MO) is expected to improve the dielectric and cooling properties of the nanofluid. This study presents a comparative assessment of the pre- and post-ageing effects on MO and nanofluids. An open beaker, single temperature oxidative thermal ageing experiment is performed at 115°C for different ageing times, i.e. 164, 328, and 492 h. A concentration of 0.01 wt% of NP for both titanium oxide and exfoliated hexagonal boron nitride (Eh-BN) is dispersed into a base fluid, i.e. MO, to prepare nanofluids. The important thermal, electrical and physicochemical properties of all the three insulating oils are analysed. The superior thermal, electrical and physicochemical performance of Eh-BN/MO-nanofluid is observed to be superior. Higher thermal conductivity, insulation and hydrophobic Eh-BN NPs on dispersion with MO provide better cooling and insulation even after an ageing, thus confirm its usability as a novel nanofluid-based transformer oil.

Electrostatic field distribution has a decisive influence on the strength of air insulation. To predict air-gap breakdown voltages according to electric field calculation results, a set of electrostatic field features is defined on the shortest interelectrode path of the rod–plane gap. These features are taken as the input parameters of a machine learning model established by support vector regression (SVR), thus to describe their relationships with the output breakdown voltage. Trained by small-sample data randomly selected according to the distribution ranges of the electric field non-uniform coefficient, the SVR model is applied to predict the breakdown voltages of rod–plane gaps with the rod diameter of 20, 25 and 30 mm, and the gap distance ranging from 1 to 9 cm. The predicted results agree well with the experimental data, while the mean absolute percentage errors of the six predictions are within 2.5%. Furthermore, the Φ27 mm rod–plane gap breakdown voltages are also predicted, and the results fall in the range between those of Φ25 and Φ30 mm rod–plane gaps, which conform to the actual law. The results validate the validity of the electrostatic field features and the generalisation performance of the SVR model.

Bearings are one of the most important components in many industrial machines. Effective bearing fault diagnosis and severity detection are critical for keeping the machines operate normally and safe. In this study, the problem of simultaneous bearing fault diagnosis and severity detection with deep learning is addressed. Existing solutions developed using deep learning rely on fault feature extraction using complicated signal processing techniques. They perform bearing fault diagnosis and severity detection separately and normally require extensive supervised fine tuning. This study presents an effective deep learning-based solution using a large memory storage and retrieval (LAMSTAR) neural network. The developed approach can automatically extract self-learned fault features and perform bearing fault diagnosis and severity detection simultaneously. The structure of the LAMSTAR network is determined by optimally selecting the sliding box size of the input time–frequency matrix. The effectiveness of the proposed approach is validated using data collected from rolling element bearing tests.

The study established a test platform for a long sphere-plane air gap in the test hall of the ultra-high voltage (UHV) direct current(DC) Testing Site. Under the sphere-plane gaps with 3, 5 and 7 m distance, the study carried out the experiment on discharge characteristics under switching impulses on a shield ball on which surface tips had different lengths at different locations. By comparing with the test data obtained under conditions with a smooth surface of the shield ball and the same gap distance, the discharge laws under the influence of surface tips on the shield ball are shown as follows: the presence of the tips greatly decreased the discharge voltage of the sphere-plane gap with the maximum decrease amplitude of 39.0%. Moreover, the decrease degree was increasingly significant with the growing length of tips. The influence of the same length of tips on the discharge voltage of the gap gradually declined with the growth of the gap distance. The presence of tips significantly reduced the discharge voltage, especially in the perpendicular line of the shield ball. The results indicate that it's necessary to strictly control the machining and installation to avoid the occurrence of tips in UHV DC engineering.

The pattern recognition of a partial discharge (PD) is critical to evaluate the insulation condition of electric equipment of high voltage. However, much attention had been paid to recognise PD types which are known, but it is ignored that the types which did not appear previously. To solve the above problems, a method to recognise unknown PD types based on improved support vector data description (SVDD) algorithm is introduced in this study. Tri-training algorithm and double thresholds set based on Otsu algorithm are used to improve the traditional SVDD classifiers. PD samples collected from different artificial defects models are finally classified by the improved fuzzy c-means clustering algorithm. Experiments compared the improved SVDD with existing one-class classification methods such as SVDD, one-class support vector machine and probability density function estimation. The results show that the proposed method has much higher recognition accuracy. It is verified that the improved SVDD is an efficient method which can be applied to the recognition of unknown PD types.

The practical performance of the electrical capacitance tomography (ECT) instrument largely relies on the accuracy and robustness of the image reconstruction algorithm. Varying from the common imaging methods, in this study, a regularisation-based loss function is proposed to transform the ECT imaging task into an optimisation problem, in which the least trimmed squares method is employed to measure the data fidelity, and the -norm of the variables is used as the regulariser. An efficient memetic algorithm (MA) that constitutes of the monkey king evolution algorithm and the Fletcher–Reeves conjugate gradient method is developed for solving the proposed loss function. The numerical simulations reveal that the feasibility and robustness of the proposed MA lead to a series of satisfactory results in the ECT field.

Powder flow abounds in industrial and agricultural production such as in chemical industry, food processing, and agricultural methods, where filling rate is an important operating parameter, especially for screw quantitative feeding of powder. In order to test the filling rate in screw feeding pipe with a special feed divider, a new capacitance sensor consisted of 12 spiral electrodes, 12 radial electrodes, and a shield cover is presented. Then to optimise the geometry of the capacitance sensor, the influence of parameters such as the radius of the sensor shield, radial electrode inserted depth, and electrode field angle on the sensor is analysed through finite-element analysis. The optimal geometry dimensions are as follows: radius of the shield is 27 mm, electrode field angle is 27° and radial electrode insert depth is 3 mm. On the basis of these parameters, the sensor is developed and its performance is evaluated. The results show that the relative errors of the measured values are no more than 7%. The developed capacitance sensor can be applied in filling rate measurement of screw feeding production.

The authors report the development of a high sensitive, 16 channel magnetic flux leakage (MFL) instrument for imaging of localised flaws in small diameter ferromagnetic steel tubes. The instrument consists of a magnetising unit of two bobbin coils wound on a ferrite core, a sensing unit of flexible giant magneto-resistive (GMR) sensor array and an image visualisation unit. 3D-non-linear finite-element modelling has been carried out to optimise the inter-coil spacing of the bobbin coils and to identify the number of GMR elements and their locations between the bobbin coils. A series of experiments has been carried out to evaluate the flaw detection performance of the MFL instrument. Studies reveal that the instrument can image as small as 1.1 mm diameter hole and 0.46 mm deep shallow outer diameter circumferential notch in 17.2 mm diameter Mod. 9Cr–1Mo steel tubes used as steam generator material in prototype fast breeder reactor, with a signal-to-noise ratio better than 8 dB. Studies also indicate that intensity of MFL signal measured by the instrument increases with increase in hole diameter and notch depth. The instrument is portable and easy to use for field inspection of ferromagnetic steel components.