The temperature gradient and mismatching between the thermal expansion of the core and flange readily lead to cracks and discharges on the core surface of the dry-type valve-side bushing, which severely impact the safety of power systems. It is vital to clarify the cracking risk of bushing cores under temperature gradients and establish corresponding control methods. The mechanical properties of epoxy resin impregnated paper (ERIP) material were measured in this study at different temperatures, and a thermal–mechanical coupling simulation model was established. The thermal and stress distributions of the core were obtained and the cracking risk was defined accordingly. The crack development mode was explored as it relates to the phase-field mode. Various elastic cushion materials affecting the stress distribution of the core were investigated. The results show that the mechanical properties of the ERIP material decrease rapidly as the temperature increases. When under severe working conditions, the maximum first principal stress of the core may be significantly higher than the tensile strength of the ERIP material resulting in significant axial cracks. Adding an elastic cushion layer made of polyurethane rubber can effectively relax the interface stress and reduce the cracking risk.

This paper takes the vehicles scheduling of hazardous materials as the research object. First, considered the four objectives of risk minimization, cost minimization, risk equilibrium value minimization, and duration minimization, the vehicles robust scheduling of hazardous materials in multiple distribution centers with risk balance is constructed. Then, uncertain models are transformed into peer-to-peer models through the idea of robust discrete optimization. By introducing the crossover and mutation operators of genetic algorithm into particle swarm algorithm, a hybrid particle swarm algorithm is constructed, and three-stage coding rules are adopted. Finally, case study is exemplified to prove the feasibility of the model and algorithm. The results show that the obtained scheduling scheme for the vehicles of hazardous materials minimizes the risk value and makes the risk distribution more balanced for the government regulatory department, and the vehicle can avoid certain road sections with relatively large risk values. For transportation enterprises, the vehicle scheduling scheme for transporting hazardous materials minimizes the cost and shortens the task duration at the same time. In the case of risk uncertainty, different robust control parameters are introduced to make the vehicles robust scheduling of hazardous materials have different degrees of stability.

A novel method for observer-based control of disturbed polytopic linear parameter varying (LPV) systems with inexactly measured scheduling parameters is investigated in this study. Despite the imperfect scheduling parameters knowledge, the proposed control method ensures the closed-loop induced L ₂-norm performance criterion. Unlike the previous methods, the inexact scheduling parameters are not assumed to be proportional to the original scheduling parameters of the LPV system. Employing both cases of parameter-dependent and parameter-independent Lyapunov functions, sufficient design conditions in terms of linear matrix inequalities are provided using Finsler's lemma. Numerical examples are included to demonstrate the efficiency of the proposed method and its superiority over the previous techniques. Moreover, a practical case study, i.e. clutchless automated manual transmission system is considered and controlled by the developed approach numerically.

The Reynolds-averaged Navier–Stokes (RANS) method coupling with the actuator disc model (ADM) is considered as a promising numerical simulation technology of wind turbine wake, and it is widely utilised in the aerodynamics of wind turbines and optimal layout of wind farms. The turbulence model is widely adopted, among the RANS-based turbulence models. However, the turbulence model easily overestimates the turbulence viscosity in the wake, which results in fast recovery of wake velocity and failure in wake forecasting. In addition, ADM with the oversimplified geometrical structure ignores the effects of nacelle and tower on the wind turbine wake, which further lowers the accuracy of wake simulation. Therefore, the numerical simulation of wind turbine wake based on the extended turbulence model of EI Kasmi coupling with ADM considering nacelle and tower is proposed. Comparing the results of Marchwood Engineering Laboratories (MEL) ABL wind tunnel measurements and TNO wind tunnel experiments, it has been found that the proposed model improves the simulation effect for the near wake and has a certain contribution to the wake prediction accuracy overall.

The accumulation of insulator surface contamination is closely related to the distribution of flow field around the insulator. In order to study the unevenness of the insulator surface contamination, a three-unit lxy-160 insulator is selected. Based on the principle of fluid mechanics, the fluid movement state on the insulator surface is determined by the calculation of Reynolds number and boundary layer, then the flow field distribution around the insulator is obtained. On this basis, the artificial contamination test and numerical simulation are carried out to study the characteristics of contamination uneven distribution on the insulator surface. The results show that when the wind speed is 3–7 m/s and the particle diameter is 1–60 µm, there is obvious boundary layer separation phenomenon, and the flow state before the separation point is laminar, while the flow state behind the separation point is turbulent. From top to bottom, the degree of accumulation on each insulator is different, and the contamination on the windward and leeward sides are unevenly distributed in a fan shape. The increment of wind speed as well as particle diameter will aggravate the unevenness of the contamination.

Electromagnetic interference (EMI) diagnostics aid in identifying insulation and mechanical faults arising in high voltage (HV) electrical power assets. EMI frequency scans are analysed to detect the frequencies associated with these faults. Time-resolved signals at these key frequencies provide important information for fault type identification and trending. An end-to-end fault classification approach based on real-world EMI time-resolved signals was developed which consists of two classification stages each based on 1D-convolutional neural networks (1D-CNNs) trained using transfer learning techniques. The first stage filters the in-distribution signals relevant to faults from out-of-distribution signals that may be collected during the EMI measurement. The fault signals are then passed to the second stage for fault type classification. The proposed analysis exploits the raw measured time-resolved signals directly into the 1D-CNN which eliminates the need for engineered feature extraction and reduces computation time. These results are compared to previously proposed CNN-based classification of EMI data. The results demonstrate high classification performance for a computationally efficient inference model. Furthermore, the inference model is implemented in an industrial instrument for HV condition monitoring and its performance is successfully demonstrated in tested in both a HV laboratory and an operational power generating site.

The artificial bee colony (ABC) algorithm is a biological-inspired optimisation algorithm proposed by Karaboga. Since its solution search equation is good at exploration but poor at exploitation, the ABC algorithm converges slowly and is easy to fall into local optimum. Inspired by opposition-based learning (OBL), the authors propose an improved ABC algorithm called opposition-based learning ABC (OLABC). In OLABC, firstly, the population would be initialised using OBL. Secondly, to ensure the diversity of the population during the iterative process, the solution search equation is employed to bee phase would be improved. Generate the opposite solution when the fitness value of the newly generated solution is smaller than the current solution, and then apply the greedy selection strategy to update the solution. Thirdly, the adaptive weight strategy is used to dynamically adjust the weight, balancing the global exploration and local exploitation capabilities of the algorithm. Experiments on a set of benchmark functions show that OLABC has better convergence speed and optimisation precision than the compared algorithms.

The transformer is one of the main components in the power network and transformer windings are one of the most expensive elements in the power transformer. Optimisation of winding distances is one of the most important parameters during the manufacturing of transformer. The distance between the windings in the two winding transformers is well known to transformer designers and manufacturers. However, insulation of the high voltage transformer with additional winding and tap winding is still a major problem for transformer designers. In this study, the additional winding to tap winding distance optimisation is made for a high voltage power transformer. Optimisation of the transformer's windings just not minimises the cost of the transformer but also increases the lifetime of the transformer. With additional winding and tap winding in high-voltage transformers, insulation distance is a major concern for minimising the cost and size of the high voltage transformer. In this study, an approach is made to balance the cost, size, and safety of high voltage transformers. The optimised distance and position between tap winding and additional winding are determined by using the finite element analysis. The finite element method results were also verified by making a prototype transformer.

Defect detection is now an active research area for production quality assurance. Traditional visual inspection systems are developed by human beings, which is a time-consuming, labour-intensive, and highly error-prone process, and are therefore unreliable. To overcome these problems, the authors proposed a new method for detecting defects when printing on a 3D micro-textured surface. They utilise an orientation code as the basis to resist the fluctuations in illumination. Based on the consistency of the pixel pairs, they developed a model called multiple paired pixel consistency to represent the statistical relationship between each pixel pair in defect-free images. Finally, based on this model, they designed a defect detection method. Even with different defect sizes, illumination conditions, noise intensities, and other characteristics, the performance of the proposed algorithm is extremely stable and highly accurate, and the recall, precision, and F-measure in most of the results can reach 0.85,0.93, and 0.9, respectively. In addition, the defect detection rate can reach almost 100%. This demonstrates that the authors' approach can achieve state-of-the-art accuracy in real industrial applications.

An eccentric position diagnosis method of static eccentricity (SE) fault of external rotor permanent magnet synchronous motor (ER-PMSM) is presented. Firstly, an analytical model of no-load radial magnetic field of ER-PMSM is established. Analytical models of no-load Back-EMF of both unit motors and the whole motor are carried out and are verified by finite element method (FEM) and experimental measurements. Then, the influences of SE ratio, SE circumferential angle, winding distribution mode and number of parallel branches on no-load radial magnetic field and no-load Back-EMF are analyzed based on these analytical models. The results show that SE does not affect the frequency characteristics of no-load radial magnetic field, but changes space order characteristics. On one hand, for ER-PMSM, of which the number of unit motors is equal to 1, SE causes no-load Back-EMF distortion. On the other hand, for ER-PMSM, of which the number of unit motors is greater than 1, SE does not affect no-load Back-EMF of the whole motor, but it still leads to no-load Back-EMF distortion of unit motors. Therefore, based on total harmonic distortion (THD) of no-load Back-EMF of unit motor, a projection method of intersection lines for SE fault diagnosis of ER-PMSM is proposed finally.