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Induction heating applications aided by power electronic control have become very attractive in the recent past. For cooking applications, power electronics circuits are very suitable to feed power to multi loads with an appropriate control technique. The purpose of this paper is to develop a three leg inverter to feed power to three loads simultaneously and independently.

Pulse density modulation control technique is used to control the output power independently with constant switching frequency.

Multi-load handling converter with independent power control is achieved with reduced number of switching devices (two switches/per load) with simple control strategy.

The proposed system is simulated in MATLAB/Simulink, and the thermal analysis is carried out in COMSOL multi-physics software. The hardware realisation is performed for a 1 kW prototype with 20 kHz switching frequency and 10 kHz pulse density modulation frequency. PIC16F877A microcontroller is used to validate the experimental results for various values of control signals (D_PDM). The simulation and experimental results are in good agreement and validates the developed system.

This paper aims to investigate the efficacy of teeth flux sensors in detecting, locating and assessing the severity of short-circuit faults in the stator windings of induction machines.

The experimental study involves inducing short-circuit winding turn variations on the induction machine’s stator and continuously measuring the RMS values across teeth flux sensors. Two crucial steps are taken for machine diagnosis: measurements under load operating conditions for fault detection and measurements under no-load conditions to determine fault location and severity.

The experimental results demonstrate that the proposed approach using teeth flux sensors is reliable and effective in detecting, locating and evaluating the severity of stator winding faults.

While this study focuses on short-circuit faults, future research could explore other fault types and alternative sensor configurations to enhance the comprehensiveness of fault diagnosis.

The methodology outlined in this paper holds the potential to significantly reduce maintenance time and costs for induction machines, leading to substantial savings for companies.

This research contributes to the field by presenting an innovative approach that uses teeth flux sensors for a comprehensive fault diagnosis in induction machines. The originality lies in the effectiveness of this approach in providing reliable fault detection, location and severity evaluation.

In this study, the effects of strain rate on the bending strength of full-scale wide-flange steel beams have been examined at elevated temperatures. Both full-scale loaded heating tests under steady-state conditions and in-plane numerical analysis using a beam element have been employed.

The load–deformation relationships in 385 N/mm²-class steel beam specimens was examined using steady-state tests at two loading rate values (0.05 and 1.00 kN/s) and at two constant member temperatures (600 and 700 °C). Furthermore, the stress–strain relationships considering the strain rate effects were proposed based on tensile coupon test results under various strain rate values. The in-plane elastoplastic numerical analysis was conducted considering the strain rate effect.

The experimental test results of the full-scale steel beam specimens confirmed that the bending strength increased with increase in strain rate. In addition, the analytical results agreed relatively well with the test results, and both strain and strain rate behaviours of a heated steel member, which were difficult to evaluate from the test results, could be quantified numerically.

The novelty of this study is the quantification of the strain rate effect on the bending strength of steel beams at elevated temperatures. The results clarify that the load–deformation relationship of steel beams could be evaluated by using in-plane analysis using the tensile coupon test results. The numerical simulation method can increase the accuracy of evaluation of the actual behaviour of steel members in case of fire.

On the one hand, this paper is to further understand the residents' differentiated power consumption behaviors and tap the residential family characteristics labels from the perspective of electricity stability. On the other hand, this paper is to address the problem of lack of causal relationship in the existing research on the association analysis of residential electricity consumption behavior and basic information data.

First, the density-based spatial clustering of applications with noise method is used to extract the typical daily load curve of residents. Second, the degree of electricity consumption stability is described from three perspectives: daily minimum load rate, daily load rate and daily load fluctuation rate, and is evaluated comprehensively using the entropy weight method. Finally, residential customer labels are constructed from sociological characteristics, residential characteristics and energy use attitudes, and the enhanced FP-growth algorithm is employed to investigate any potential links between each factor and the stability of electricity consumption.

Compared with the original FP-growth algorithm, the improved algorithm can realize the excavation of rules containing specific attribute labels, which improves the excavation efficiency. In terms of factors influencing electricity stability, characteristics such as a large number of family members, being well employed, having children in the household and newer dwelling labels may all lead to poorer electricity stability, but residents' attitudes toward energy use and dwelling type are not significantly associated with electricity stability.

This paper aims to uncover household socioeconomic traits that influence the stability of home electricity use and to shed light on the intricate connections between them. Firstly, in this article, from the perspective of electricity stability, the characteristics of the power consumption of residents' users are refined. And the authors use the entropy weight method to comprehensively evaluate the stability of electricity usage. Secondly, the labels of residential users' household characteristics are screened and organized. Finally, the improved FP-growth algorithm is used to mine the residential household characteristic labels that are strongly associated with electricity consumption stability.

1. The stability of electricity consumption is important to the stable operation of the grid.

2. An improved FP-growth algorithm is employed to explore the influencing factors.

3. The improved algorithm enables the mining of rules containing specific attribute labels.

4. Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.

The stability of electricity consumption is important to the stable operation of the grid.

An improved FP-growth algorithm is employed to explore the influencing factors.

The improved algorithm enables the mining of rules containing specific attribute labels.

Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.

This study provides an analysis of patch repair for cracked aircraft structures. Delamination is a type of damage that affects the patch's behavior. The purpose of this study is to assess the influence of delamination on repair performance.

An analytical and numerical study using the finite element method was conducted for a cracked plate repaired with a patch containing a pre-existing delamination defect. The method for defining the contact pair surfaces and modeling the delamination interaction within the patch interface is specified using the virtual crack closure technique (VCCT) approach.

The efficiency of the repair is measured in terms of the J-integral. The effects of delamination initiation, mechanical loading, crack length and patch stacking sequences are presented. It is noted that in mode I, delamination propagation is only significant at node A. The numerical results are in good agreement with those of the analytical solution found in the literature. It is observed that the patch's behavior is strongly dependent on loading, crack size and stacking sequences in terms of reducing the structure's lifespan, especially in the presence of delamination.

The numerical modeling presented by the VCCT approach is highly valuable for studying delamination evolution. The influence of loading, crack size and stacking sequences on repair performance is discussed in this work.

This paper aims to reduce the impact of disordered charging of large-scale electric vehicles (EVs) on the grid. EV is great significance for environmental protection, energy conservation and emission reduction to replace fuel vehicles with EVs. However, as a kind of random mobile load, large-scale integration into the power grid may lead to power quality problems such as line overload, line loss increase and voltage reduction. This paper realizes the orderly charging of electric vehicles and the safe operation of the distribution network by optimizing the dispatching scheme.

This paper takes the typical IEEE-33 node distribution system as the research object, adopts the improved particle swarm optimization algorithm and takes the minimum operation cost, the minimum environmental pollution, the minimum standard deviation of daily load, the minimum peak valley difference of load, the minimum node voltage offset rate and the minimum system grid loss rate as the optimization objectives.

Controlling the disordered charging of large-scale electric vehicles by optimizing the dispatching algorithm can realize the full consumption of renewable energy and the safe operation of the power grid.

Results show that the proposed scheme can realize the transfer of charging load in time and space, so as to stabilize the load fluctuation of distribution grid, improve the operation quality of power grid, reduce the charging cost of users and achieve the expected research objectives.

This paper aims to overcome the limitations of low efficiency, low power density and strong electromagnetic interference (EMI) of the existing pulsed melt inert gas (MIG) welding power supply. So a novel and simplified implementation of digital high-power pulsed MIG welding power supply with LLC resonant converter is proposed in this work.

A simple parallel full-bridge LLC resonant converter structure is used to design the digital power supply with high welding current, low arc voltage, high open-circuit voltage and a wide range of arc loads, by effectively exploiting the variable load and high-power applications of LLC resonant converter.

The efficiency of each converter can reach up to 92.3%, under the rated operating condition. Notably, with proposed scheme, a short-circuit current mutation of 300 A can stabilize at 60 A within 8 ms. Furthermore, the pulsed MIG welding test shows that a stable welding process with 280 A peak current can be realized and a well-formed weld bead can be obtained, thereby verifying the feasibility of LLC resonant converter for pulsed MIG welding power supply.

The high efficiency, high power density and weak EMI of LLC resonant converter are conducive to the further optimization of pulsed MIG welding power supply. Consequently, a high performance welding power supply is implemented by taking adequate advantages of LLC resonant converter, which can provide equipment support for exploring better pulsed MIG welding processes.

Health risks associated with coronavirus disease 2019 (COVID-19) have severely affected the financial stability of airline companies globally. Recapturing financial stability following this crisis depends heavily on these companies’ ability to attain efficient and productive operations. This study uses several empirical approaches to examine key factors contributing to carriers sustaining high productivity prior to, during and after a major recession. Findings suggest, regardless of economic conditions, that social distancing which requires airline companies in the Asia Pacific region to fly with a significant percentage of unfilled seats weakens the performance of those companies. Furthermore, efficient operations do not guarantee the avoidance of productivity declines, especially during a recession.

The purpose of this paper is to study Al-based green composite. To make composite samples of aluminium alloy (AA3105) with different weight percentages of rice husk ash (RHA) and eggshell (ES) particles as reinforcement, stir casting method was used.

Several other aspects, including the weight percent of reinforcing agent particles, the applied stress and the sliding speed, were taken into consideration. During the course of the wear test, the sliding distance that was recorded varied from a minimum of 1,000 m all the way up to a maximum of 3,135 m (10, 15, 20, 25 and 30 min). The typical range for normal loads is 8–24 N, and their speed is 1.58 m/s.

With the AA/ES/RHA composite, the wear rates decreases when the grain size of the reinforcing particles enhanced. Scanning electron microscopy images of worn surfaces show that at low speeds, delaminating and ploughing are the main causes of wear. At high speeds, ploughing is major cause of wear. Composites with better wear-resistant properties can be used in wide range of tribological applications, especially in the automotive industry. It was found that hardness increases at the same time as the weight of the reinforcement increases. Tensile and hardness were maximized at 10% reinforcement mix in Al3105.

In this work, ES and RHA has been used to develop green metal matrix composite to support green revolution as promoted/suggested by United Nations thus reducing the environmental pollution.

Disruptions at ports may destroy the planned ship schedules profoundly, which is an imperative operation problem that shipping companies need to overcome. This paper attempts to help shipping companies cope with port disruptions through recovery scheduling.

This paper studies the ship coping strategies for the port disruptions caused by severe weather. A novel mixed-integer nonlinear programming model is proposed to solve the ship schedule recovery problem (SSRP). A distributionally robust mean conditional value-at-risk (CVaR) optimization model was constructed to handle the SSRP with port disruption uncertainties, for which we derive tractable counterparts under the polyhedral ambiguity sets.

The results show that the size of ambiguity set, confidence level and risk-aversion parameter can significantly affect the optimal values, decision-makers should choose a reasonable parameter combination. Besides, sailing speed adjustment and handling rate adjustment are effective strategies in SSRP but may not be sufficient to recover the schedule; therefore, port skipping and swapping are necessary when multiple or longer disruptions occur at ports.

Since the port disruption is difficult to forecast, we attempt to take the uncertainties into account to achieve more meaningful results. To the best of our knowledge, there is barely a research study focusing on the uncertain port disruptions in the SSRP. Moreover, this is the first paper that applies distributionally robust optimization (DRO) to deal with uncertain port disruptions through the equivalent counterpart of DRO with polyhedral ambiguity set, in which a robust mean-CVaR optimization formulation is adopted as the objective function for a trade-off between the expected total costs and the risk.