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In this study, a series arc fault detection method combining load recognition and multilayer perceptron-support vector machine (MLP-SVM) is proposed. The method addresses the issue of interfering loads on series arc fault detection and the lack of significant arc fault features in some loads. Initially, the eigenvalues of the line currents for single and mixed loads are extracted in the time domain, both during arc fault and normal conditions. Subsequently, load recognition is performed using a complex matrix calculation method. Then, a feature matrix and history matrix are created for each load. The history matrix is then used to compare the data in the feature matrix to detect any abnormalities in the eigenvalues of each in the presence of any irregularity, the line current flowing through this load will be consistently gathered throughout several cycles, and processed to obtain the eigenvalues, then fed into the MLP-SVM model for training. The classification outcomes will be achieved by means of model train. The results demonstrate that the method effectively prevents misclassification of interfering loads, resulting in improved accuracy in series arc fault detection.

Achieving early warning of electrical fires is of great importance in preventing their occurrence. The overheating diagnosis of electrical equipment by detecting volatiles is an effective way to achieve early warning of electrical fires. This paper proposes a machine olfaction-based overheating diagnosis method for electrical equipment. First, the primary materials commonly used for non-metallic elements in electrical equipment are determined. Second, a semiconductor oxide sensor array is designed based on the volatiles generated by these materials during overheating. Next, the output curves of the sensor array at different heating temperatures for these materials are obtained through experiments. Subsequently, the output data of the sensor array at multiple moments in the period of each output curve entering the plateau are extracted to construct the output vectors of the sensor array. Then, the Principal Component Analysis (PCA) algorithm and Linear Discriminant Analysis (LDA) algorithm are used to extract the characteristic quantities in the output vectors to construct the feature vectors. Finally, a second-order BP neural network model is designed based on the feature vector to determine whether the electrical equipment is overheated and what the overheated material is. The experimental results show that the accuracy of the proposed overheating diagnosis method can reach 94.58%.

In recent years, DC fault arc detection has been an electrical engineering research hotspot. At present, most proposed detection methods do not analyze the effects of fault arc electrical characteristics on both line current and supply voltage. Therefore, this study extensively analyzes variations of the line current and supply voltage because of DC arc faults based on the volt-ampere characteristics of DC arc faults. Then, a DC series arc fault detection method is proposed that comprehensively uses information on line current and supply voltage. An experimental platform for DC fault arc generation and detection was established using a DC-DC converter and a photovoltaic power supply as DC power supplies, and the proposed method was confirmed by experiments using this platform. Experimental results demonstrate that the proposed method can effectively distinguish arc faults and has the characteristics of clear physical meaning while maintaining a low amount of calculation.

In a cascaded H-bridge rectifier (CHBR), the dc capacitor voltages need to be controlled to balance by the voltage balance controller when the loads are unbalanced. However, the voltage balance controller will fail to balance the dc capacitor voltages when the load unbalancing range exceeds a certain limit under unity power factor; that limit is constrained by the AC voltage and transmission power of each H-bridge. First, the relationships in CHBR were analyzed. Then, the expression between AC voltage and the power of each H-bridge was derived. Then the load unbalancing limits to maintain the dc capacitor voltage balance of each H-bridge were further deduced. In addition, the influence of ripple power and total load power on the load unbalancing limits was investigated. Finally, an experimental prototype of a single-phase three-cell cascaded H-bridge rectifier was built and the correctness of the proposed load unbalancing limits was verified by the experiment. The proposed load unbalancing limits are universal, which provides theoretical support for the stable operation and power transmission of CHBR.

This paper proposes a centralized-decentralized control strategy for regenerative braking energy utilization and power quality improvement in the modified AC-fed railway system with energy-storage-based smart electrical infrastructure. The proposal of a centralized-decentralized control strategy can enhance the ability to withstand and rapidly recover from disruptions, thus providing further guarantees for safe and reliable operation and energy conservation for railway systems. First of all, the description and control strategy of the modified railway system are outlined, and then the control principles and implementation process of the centralized control and decentralized control strategies are given. Moreover, a method of load power detection and regulated power reference calculation is proposed. Finally, the effectiveness of the proposed strategy is verified in a case of a modified railway system consisting of four traction substations and eight power supply sections. The results demonstrate that regenerative braking energy can be efficiently utilized in railways and that power quality can be improved using the proposed centralized-decentralized control strategy.

With respect to the problems of great impact on the utility grid, the increasing catenary voltage, the limited utilization rate of the regenerative braking energy (RBE) and the irreducible cost of electricity for locomotives caused by the RBE generated by electric locomotives of the whole railway line cannot be fully utilized in traction power supply systems. An energy management system (EMS) integrating electrical energy and information is proposed in this paper. A corresponding optimization algorithm is also proposed to calculate the distribution of the regenerative braking power in the whole railway line. The structure and working principle of the EMS are introduced. The basic principle and detailed steps of the proposed algorithm are given. Finally, the optimization calculation and simulation are carried out, the effectiveness and feasibility of the proposed EMS and the optimization algorithm are verified.

This paper presents a modified power supply system based on the current alternating current (AC)-fed railways with neutral zones that can further improve the eco-friendliness and smart level of railways. The modified system complements the existing infrastructure with additional energy-storage-based smart electrical infrastructure. This infrastructure comprises power electronic devices with energy storage system connected in parallel to both sides of each neutral zone in the traction substations, power electronic devices connected in parallel to both sides of each neutral zone in section posts, and an energy management system. The description and functions of such a modified system are outlined in this paper. The system allows for the centralized- and distributed-control of different functions via an energy management system. In addition, a control algorithm is proposed, based on the modified system for regenerative braking energy utilization. This would ensure that all the regenerative braking energy in the whole railway electrical system is used more efficiently. Finally, a modified power supply system with eight power supply sections is considered to be a case study; furthermore, the advantages of the proposed system and the effectiveness of the proposed control algorithm are verified.

Owing to the shortcomings of existing series arc fault detection methods, based on a summary of arc volt⁻ampere characteristics, the change rule of the line current and the relationship between the voltage and current are deeply analyzed and theoretically explained under different loads when series arc faults occur. A series arc fault detection method is proposed, and the software flowchart and principles of the applied hardware implementation are given. Finally, a prototype of an arc fault detection device (AFDD) with a rated voltage of 220 V and a rated current of 40 A is developed. The prototype was tested according to experimental methods provided by the Chinese national standard, GB/T 31143-2014. The experimental results show that the proposed detection method is simple and practical, and can be implemented using a low-cost microprocessor. The proposed method will provide good theoretical guidance in promoting the research and development of an AFDD.

AC heavy-haul trains produce a huge amount of regenerative braking energy when they run on long downhill sections. If this energy can be used by uphill trains in the same power supply section, a reduction in coal transportation cost and an improvement in power quality would result. To accurately predict the energy consumption and regenerative braking energy of heavy-haul trains on large long slopes, a single-particle model of train dynamics was used. According to the theory of railway longitudinal section simplification, the energy consumption and the regenerative braking energy model of a single train based on the train attributes, line conditions, and running speed was established. The model was applied and verified on the Shenshuo Railway. The results indicate that the percentage error of the proposed model is generally less than 10%. The model is a convenient and simple research alternative, with strong engineering feasibility. Based on this foundation, a model of train energy consumption was established under different interval lengths by considering the situation of regenerative braking energy in the multi-train operation mode. The model provides a theoretical foundation for future train diagram layout work with the goal of reducing the total train energy consumption.

Structural disparities (eg, food insecurities, housing, and lack of transportation) at different social levels (eg, personal, family, and community) are strong determinants of health, influencing individuals’ and population well-being worldwide. Research is scarce examining how clinical communication can mitigate the negative impact of social disparities obstructing the reception of quality healthcare. In this study, we explore the mediation role of patient-centered communication (PCC) between social determinants of health (SDH) and quality of care. Using a sample of 5437 adult who visited a healthcare provider in the past 12 months from the sixth Health Information National Trends Survey (HINTS 6), our key points of findings included that the models showed PCC partially mediating the connections from (a) “skipped meals” (effect = −.08, 95%CI = [−.12, −.04]), (b) “unaffordable meals” (effect = −.08, 95%CI = [−.11, −.05]), (c) “fear of eviction” (effect = −.1, 95%CI = [−.14, −.06]), and (d) “lack of transportation” (effect = −.12, 95%CI = [−.16, −.08]) to quality of care (QoC). Specifically, better communication had a positive impact on mediating the disparities; poor communication did not. Demonstrating in a nationally representative sample, our findings indicate the key role of patient-centered clinical communication in effectively alleviating the inherent challenges faced by people with low health literacy and socioeconomic status. Theoretical and practical implications are discussed.

Objective: COVID-19 has exacerbated pre-existing rates of overweight and obesity in the United States. mHealth technologies are gaining in popularity for its potential to reduce obesity, if facilitated by patient-centered communication. This study explores predictors of overweight and obese individuals' exercise levels during COVID-19. Methods: 2191 respondents who visited a doctor in the past year and self-reported being overweight were selected from the 2020 Health Information National Trends Survey (HINTS). Respondents reported their physical activity, beliefs about obesity, health tracking behaviors, and communication with providers during the pandemic. Structural equation modeling was used to explore connections among the variables. Results: Patient-provider (e-)communication was significantly associated with changes in people's obesity-related beliefs and mHealth tracking usage, predicting moderate exercise during the pandemic. Conclusion: The findings illustrate the need for patient-centered communication encounters to include discussions on mHealth technologies and accessible methods of engaging in physical activity.Innovation: This study examined secondary data provided by overweight and obese individuals from the early days of the COVID-19 pandemic; this population may benefit from targeted health interventions using mHealth technologies. Our findings suggest that healthcare providers should engage patients through mHealth technology and seek to improve digital health literacy to progress physical activity nationwide.

As China launches its second COVID-19 booster campaign and races to bring new vaccine technologies to protect against severe COVID-19 infections, there is limited research on how Chinese residents search for vaccine-related information. This study examined the factors influencing Chinese residents’ information-seeking behaviors regarding COVID-19 boosters with a sample of 616 respondents with a mean age of 31.53 from a research panel. Structural equation modeling was used to report factors that influenced respondents’ seeking intent. The results indicated that seeking-related subjective norms (β = −0.55, p < 0.001), negative affect (β = 0.08, p < 0.05), positive affect (β = 0.18, p < 0.001), and perceived knowledge insufficiency (β = 0.10, p < 0.001) are strong predictors of one’s seeking intent. We also discovered that there was an inverse relationship between risk perception and positive affect (β = −0.55, p < 0.001) and between negative and positive affect (β = −0.19, p < 0.01), while all measurements were either directly or indirectly related to information-seeking intent. A few more indirect but important relationships were also included in our discussion. In conclusion, the present study helps understand what motivates Chinese residents to seek COVID-19 booster information when limited information is available.