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The swivel clevis (SC) is a vulnerable part of the Overhead Catenary System (OCS). Regular inspection using computer vision technology is an effective way to detect SC defects and improve the OCS operation safety. However, achieving full automation of SC defects detection is still a difficult task due to defective sample scarcity and data distribution shift. To overcome these problems, this paper proposes a novel defects detection method that combines an adaptive SC segmentation network (Adaptive SSN) and local operators. During the inspection process, an unreliability index defined by the model uncertainty and prior knowledge is used to monitor the reliability of the Adaptive SSN. When data distribution shift causes the Adaptive SSN to be unreliable, human annotator will be asked to update the training set and retrain the Adaptive SSN to adapt to the new data distribution. Then the geometric features obtained from segmentation masks and the local features extracted by local operators are used to detect the SC defects. Effectiveness of the proposed method is demonstrated by the experimental results on the data from several high-speed railway lines.

Metal oxide varistors (MOVs) are usually applied to clamp the voltage across a dc circuit breaker (DCCB) after fault current elimination. This letter focuses on the transient process of a DCCB turn- off and has three contributions. First, it reveals that snubber circuit inductance, the internal and external stray inductances of an MOV could cause a significant overvoltage across a DCCB, which exceeds 47.5% of its nominal clamping voltage. The overvoltage mechanism is clearly analyzed based on the working principle of a DCCB, providing theoretical equations to estimate the overvoltage magnitude. Second, a nonlinear model of an MOV is proposed to simplify the calculation and provide an accurate estimation result. Third, a new arrangement of series submodules with discrete MOVs is proposed to suppress the overvoltage. A hybrid 500 kV/25 kA DCCB prototype with series insulated gate bipolar transistors (IGBTs) is implemented to validate the proposed analysis. The series-IGBT rating voltage is 1440 kV, and the MOV with a nominal clamping voltage of 800 kV is used to protect IGBTs. However, a 25 kA turn- off experiment clearly shows that the overvoltage increased to 1203 kV by an external stray inductance of 39.5 μ H and d i /d t of 1480 A/ μ s. The relative error between measurements and simulations is within 3%. Besides, simulations on the new arrangement show the overvoltage is decreased by 23.4% than before.

As a key component of the high-speed railway catenary, the insulator plays an important role in supporting the catenary and maintaining the insulation between the catenary and earth. Regular inspection using computer vision technology is an effective way to detect insulator defects and improve the catenary operation safety. However, achieving full automation of insulator defects detection is still a difficult task due to the defective sample scarcity and subtle defect features. To overcome the problem, this article proposes defect detection GANs (DefGANs) that consist of a denoising autoencoder, a discriminator, and a classifier to detect defects, which can generate defective samples from pitted latent representations, thereby improving the reliability of defect detection classifier. The proposed DefGAN can pit the latent representations of normal samples to generate defective samples. Our proposal mainly consists of two stages. In the first stage, we use cascaded deep segmentation networks (CDSNets) to extract the insulator from the catenary images. Then, the insulator area is sampled into small patches. In the second stage, the patches are fed into the proposed DefGAN to detect defects. Moreover, the defect score is determined by the anomaly probability of the classifier and reconstruction error of the denoising autoencoder. The effectiveness of our work is measured across the catenary data sets.

Extraction of the current commutation loop (CCL) inductance is necessary for the overvoltage analysis of power devices. In general, each transient process of a converter corresponds to a specific CCL. However, there is no such single CCL in the full-bridge module in hybrid dc circuit breakers during the turn- off transient. First, this article proposes a coupled inductance model of full-bridge modules. The model has less number of inductance compared to the traditional partial inductance model. Second, this article proposes the extraction method of inductances that can be easily obtained by both simulation and measurement. Moreover, the measured overvoltage waveforms of insulated gate bipolar translators (IGBTs) in the full-bridge module agree with the simulation results. Third, we propose an equivalent circuit of the series full-bridge modules in a 500 kV hybrid circuit breaker based on the proposed coupled inductance model. The mutual inductances among different modules are considered due to the compact structure of the series modules. The measured results show that the overvoltage of the IGBT in the series module could be well-predicted by the proposed equivalent circuit.

Load redistribution (LR) attacks represent a major cyber threat to the power grid that can result in branch overloads and even cascading failures in the transmission network by injecting false data to load bus power injection and line power flow measurements. While the mechanism and defense strategies have been well studied for single-occurrence (i.e., discrete) LR attacks, this paper focuses on a yet unexplored category of LR attacks, namely, the continuous LR (CLR) attack, which continuously injects time-varying false data to one or more bus and branch measurements within the system for a certain period of time. To cope with the potentially ever-changing targeted branches (i.e., the branches under attack) and system vulnerability features caused by CLR attacks, this paper takes a different route from the traditional LR defense and mitigation approaches. We propose a novel correlation chain model to capture the vulnerability correlations among branches under the CLR attack. Then, a dynamic sequential-mining algorithm (DSMA) with top-k structure is proposed based on the specific nature of the CLR attack, to mine the historical SCADA measurement database, extract vulnerable branch sequences, and identify the most suitable branches to defend preventively. Simulation studies are conducted on IEEE RTS-96 system to evaluate the effectiveness of the proposed approach in terms of increasing attack costs for the attackers and reducing the potential impacts of CLR attacks.

The load redistribution (LR) attack, as a special type of false data injection attacks, aims to distort the results of the security-constrained economic dispatch (SCED) and drive the system to non-optimal or even unsecure operating conditions by injecting false data to the node power injection and branch power flow measurements. The LR attack represents a great danger to the safe and reliable operation of the current power grid where the physical infrastructure is increasingly intertwined with cyber components. In this paper, we propose a novel pre-overload vulnerability graph (POV-graph) approach to systematically assess, evaluate, and quantify the system vulnerability under an LR attack. The proposed approach is formulated based on the specific overloading mechanism of the LR attack as well as their cascading patterns. A set of case studies are performed on the IEEE 39-bus, 118-bus, and RTS-96 systems to validate the effectiveness of the proposed approach. Simulation results have shown, for the first time that, by revealing the vulnerability correlations among branches, the proposed approach allows the system operator to better utilize the defense resources and focus on strengthening a limited number of vulnerable branches to enhance the system’s overall security against LR attacks.

OBJECTIVE: : To compare the genotypes of Bordetella pertussis isolated from infants in Xi’an and Shanghai. METHODS: : Samples were collected by nasopharyngeal swab from infants aged

To analyse macrolide resistance and molecular characteristics ofSusceptibilities ofOf 58

A pantograph–catenary system (PCS) is an important part of a railway power supply system, which is an interface of the power supply system and the electric locomotive. The quality of a traction power supply depends on the stability of the contact between a pantograph and a catenary. Therefore, it is necessary to monitor the contact state by detecting the contact point (CPT) between the pantograph and the catenary. Recently, automatic CPT detection methods based on video monitoring have been introduced to improve the railway operation safety. However, the existing methods were still not stable enough in complex backgrounds. To improve the stability of CPT detection, we proposed a method combining a deep convolutional network with handcrafted features to detect the CPT. The proposed method consists of two stages. First, a deep pantograph network (DPN) was adopted to segment the pantograph strip. The DPN was mainly composed of a deep pantograph detection network (DPDN) and a deep pantograph segmentation network (DPSN). Then, the edge detection and the Hough transform were used to detect the contact line above the pantograph. Concretely, the CPT was obtained by finding the intersection of the contact line and the upper surface of the pantograph strip. The experimental results demonstrated the robustness and the accuracy of the proposed method.

The imbalance of data samples and fluctuating operating conditions are the two main challenges faced by vibration data-driven fault diagnosis for the iron core looseness of saturable reactors in UHVDC thyristor valves. This paper proposes a vibration data-driven saturable reactor iron core looseness fault diagnosis strategy named CVG-MFICNN based on CVAE-GAN and MFICNN to overcome the two challenges. This strategy uses a novel 1-D CVAE-GAN model to produce generated samples and expand the training set based on imbalanced training samples. An MFICNN model structure is designed to allow the simultaneous processing of multimodal features such as the SST time-frequency spectrum, time-domain vibration sequence, frequency-domain power spectrum sequence, and time-domain statistics. Using these multimodal features and the MFICNN model, the hidden fault information in vibration data can be effectively mined. An experiment is conducted to collect vibration data of saturable reactors with different faults. Models based on the proposed strategy and other methods are trained and tested using the collected data. The comparison results show that the performance of the proposed CVG-MFICNN approach is significantly superior to that of single-feature CNNs, traditional machine learning methods, and classical image classification CNNs in the application of UHVDC thyristor valve saturable reactor iron core looseness fault diagnosis.

ObjectiveTo compare the macrolide resistance and molecular characteristics of clinical isolated Bordetella pertussis, and explore the relationship between the macrolide-resistance and genotypes. MethodsErythromycin、azithromycin and clarithromycin susceptibility of clinical isolates during 2018-2020 was determined by E-test. The A2047G of the 23S rRNA genes was sequenced for drug-resistance mutation. Multilocus antigen sequence typing (MAST)、Multiple Locus Variable-number tandem repeat Analysis (MLVA) and Pulsed field gel electrophoresis (PFGE) methods were employed to do molecular typing for the strains. Results58 strains were isolated in this study, 46 of them were macrolide-resistant and 12 sensitive. All macrolide-resistant strains carried a genetic mutation at the A2047G site, genotype was prn1/ptxP1/ptxA1/fim3-1/fim2-1, the MLVA types were identified as MT195、MT55 and MT104, PFGE profiles were classified into BPSR23 and BpFINR9 types. However, no mutations were found in all macrolide-sensitive strains whose genotypes were (prn9 or prn2)/ptxP1/ptxA1/fim3-1/fim2-1 and MT27, and PFGE classified other profiles. Conclusions The clinical isolated Bordetella pertussis has serious resistance to erythromycin and began to spread to other macrolides. There were differences between macrolide-resistant and -sensitive Bordetella pertussis in genotypes. The acquisition of macrolide resistance may be associated with change of specific molecular types.

This paper aims to identify the vulnerability of electrical cyber-physical systems (CPSs) through fault propagation under cyber attacks. First, we propose a fault propagation model mainly considering the impact of interruptions on some nodes of the cyber network on the electrical physical systems. Secondly, two graphs, i.e. propagation graph and attack graph are proposed to reveal the physical fault propagation mechanisms and analyze the attack intensity of combinations of different communication nodes, respectively. Thirdly, a set of traditional vulnerable indices based on the propagation and attack graphs are employed to identify both the critical physical branches and communication nodes in the CPS. Finally, comparative analyses with and without considering the CPS on both IEEE 118- and 300- bus systems show that the fault propagation among are more sophisticated and the wrong decisions that the control center makes causes the higher vulnerability of the electrical network due to the interruption of the transmission information in the cyber system under cyber attacks.

Communication networks as smart infrastructure systems play an important role in smart girds to monitor, control, and manage the operation of electrical networks. However, due to the interdependencies between communication networks and electrical networks, once communication networks fail (or are attacked), the faults can be easily propagated to electrical networks which even lead to cascading blackout; therefore it is crucial to investigate the impacts of failures of communication networks on the operation of electrical networks. This paper focuses on cascading failures in interdependent systems fromthe perspective of cyberphysical security. In the interdependent fault propagation model, the complex network-based virus propagation model is used to describe virus infection in the scale-free and small-world topologically structured communication networks. Meanwhile, in the electrical network, dynamic power flow is employed to reproduce the behaviors of the electrical networks after a fault. In addition, two time windows, i.e., the virus infection cycle and the tripping time of overloaded branches, are considered to analyze the fault characteristics of both electrical branches and communication nodes along time under virus propagation.The proposed model is applied to the IEEE 118-bus system and the French grid coupled with different communication network structures. The results show that the scale-free communication network is more vulnerable to virus propagation in smart cyber-physical grids.

Objective To analyse macrolide resistance and molecular characteristics of Bordetella pertussis clinical isolates from western China, and to explore the relationship between macrolide-resistance and genotypes. Methods Susceptibilities of B. pertussis clinical isolates to erythromycin, azithromycin and clarithromycin were determined by epsilometer test (E-test). Isolated strains were sequenced to ascertain the presence of the 23S rRNA gene A2047G mutation. Strains were typed using multilocus antigen sequence typing, multilocus variable-number tandem-repeat analysis (MLVA) and pulsed-field gel electrophoresis (PFGE). Results Of 58 B. pertussis strains isolated in this study, 46 were macrolide-resistant and 12 were macrolide sensitive. All macrolide-resistant strains carried the A2047G mutation and were the prn1/ ptxP1/ ptxA1/ fim3-1/ fim2-1 genotype; the MLVA types were MT195 (19/58), MT55 (13/58) and MT104 (14/58), and the PFGE profiles were classified into BpSR23 (17/58) and BpFINR9 (29/58) types. None of the macrolide-sensitive strains carried the A2047G mutation; genotypes were ( prn9 or prn2)/ ptxP3/ ptxA1/ fim3-1/ fim2-1, and all were MT27. PFGE profiles differed from the macrolide-resistant strains. Conclusions B. pertussis clinical isolates from western China were severely resistant to macrolides. Genotypes differed between macrolide-resistant and macrolide-sensitive strains, and there may be a correlation between acquisition of macrolide resistance and changes in specific molecular types.

In the recent years, the automatous operation of All Electric Ships (AES) has gained a lot of attention from both academia and industrial researchers as one of the most promising prospects for the transformational control development of new generation electric ships. In this paper, the reconfiguration problem of shipboard power system (SPS) considering external threats is investigated from the perspective of hybrid and distributed. By constructing a two-layer distributed control framework, mission commands are interpreted by coordinator and then send to zonal controllers, while reconfiguration actions can be performed in-zone by zonal controllers or among multi-zones by coordinator to complete the mission. Based on the extended hybrid model of SPS developed, the subsystem reconfiguration and coordinate algorithm are proposed. Finally the practical example of SPS facing threats is provided to illustrate the process to choose the optimal reconfiguration strategy.

The current interruption test based on an LC resonance circuit for hybrid DC circuit breakers (HVDC CBs) is widely employed to characterize the current interruption capability of CBs. In order to ensure a high-fidelity replica of the fault current in a high voltage application, this paper firstly proposes an equivalent model of the test circuit, where not only parasitic resistances but also the threshold voltages and on-state resistances of various semiconductor devices are considered. Moreover, the analytical formula of the test current is derived by including the working principle of the HVDC CB. Secondly, the parameter extraction method, which combines finite element analysis and measurements by impedance analyzer, is given in this paper. The extracted result implies that, in current interruption transient, equivalent resistances of the 500 kV CB is as large as 535 mΩ, having a significant influence on current waveforms. Thirdly, the 34 kV/25 kA current interruption test for the 500 kV CB is conducted. The measured results are proved to be consistent with the analytical results obtained from the proposed model, and the relative error is less than 2%.

Power transmission networks play an important role in smart girds. Fast and accurate faulty-equipment identification is critical for fault diagnosis of power systems; however, it is rather difficult due to uncertain and incomplete fault alarm messages in fault events. This paper proposes a new fault diagnosis method of transmission networks in the framework of membrane computing. We first propose a class of spiking neural P systems with self-updating rules (srSNPS) considering biological apoptosis mechanism and its self-updating matrix reasoning algorithm. The srSNPS, for the first time, effectively unitizes the attribute reduction ability of rough sets and the apoptosis mechanism of biological neurons in a P system, where the apoptosis algorithm for condition neurons is devised to delete redundant information in fault messages. This simplifies the complexity of the srSNPS model and allows us to deal with the uncertainty and incompleteness of fault information in an objective way without using historical statistics and expertise. Then, the srSNPS-based fault diagnosis method is proposed. It is composed of the transmission network partition, the SNPS model establishment, the pulse value correction and computing, and the protection device behavior evaluation, where the first two components can be finished before failures to save diagnosis time. Finally, case studies based on the IEEE 14- and IEEE 118-bus systems verify the effectiveness and superiority of the proposed method.

In the Urban Energy Internet (UEI), any partial and weak faults may be transmitted to other subsystems through cyber and energy spaces, which may increase the risk of system catastrophe, ranging from small scale to large scale, from single system to multi-system, and from minor accidents to system crashed. Therefore, the analysis of the catastrophe in UEI is urgently needed. In this paper, a research framework of UEI catastrophe analysis and its dynamic early warning mechanism are constructed, based on the integration of human, cyber and energy in UEI, which aims at quantifying the potential catastrophic risks of the human-cyber-energy coupling system. First, the interaction mechanism among human, cyber and energy are analyzed. Secondly, the hybrid model of UEI calculation of probabilistic energy flow based on the dynamic probability method is developed. Furthermore, based on the analysis of catastrophe propagation and evolution mechanism of the human-cyber-energy space, the catastrophe analysis method for coupling system is analyzed, and indicators and assessment methods for characterizing the local and overall disaster-resistant status of UEI. On this foundation, the dynamic early warning mechanism of catastrophe based on artificial immune system is constructed. Finally, the comprehensive simulation architecture of catastrophe analysis and early warning mechanism for UEI are developed to verify the validity of the theoretical methods. The expected achievements of this framework proposed in this paper can provide theoretical support for the planning and operation of UEI.

This paper proposes a novel cascading faults graph (CFG) based on fault chain theory to assess the transmission network vulnerability. The proposed CFG is able to effectively reveal the mechanism of fault propagation in a transmission network by transforming an electrical network with spatial information into a fault propagation graph with temporal information, thus providing a better way for transmission network vulnerability assessment. Numerical results on IEEE 39-, 118-bus systems and French grid demonstrate the effectiveness of the proposed method.

Periodic switching processes of transistors may cause high-level electromagnetic interference (EMI), which will conduct along magnetic components in the system and threaten electromagnetically sensitive facilities. In order to evaluate impacts of magnetic components on propagation of the EMI, it is necessary to investigate capacitive effects on the whole component, i.e., the global capacitive effects. In this case, detailed partial capacitance (PC) networks based on turn-to-turn and turn-to-ground capacitances may be impractical due to their size and the resultant computational burden. A terminal capacitance (TC) method characterized by explicit physical mechanism and high computation efficiency is proposed to analyze global capacitive effects of magnetic components. By defining coefficient matrix accounting for potential distribution of each winding, PCs corresponding to turns are decoupled to TCs corresponding to external terminals, and quantificational links between TCs and PCs are established with rigorous equations. Furthermore, TCs are directly deduced based on energy computation without having to compute PCs beforehand, making the TC method convenient and efficient. An air-core inductor and a two-winding transformer are used to verify the proposed TC method, and a smoothing reactor employed in the practical UHVdc engineering is used as an application of the method.

Bacterial foodborne diseases remain major threats to food safety and public health, especially in developing countries. In this study a novel assay, combining gold nanoparticle (GNP)-based multiplex oligonucleotide ligation-PCR and universal oligonucleotide microarray technology, was developed for inexpensive, specific, sensitive, and multiplex detection of eight common foodborne pathogens, including Shigella spp., Campylobacter jejuni, Bacillus cereus, Escherichia coli O157:H7, Listeria monocytogenes, Salmonella enterica, Staphylococcus aureus, and Vibrio parahaemolyticus. The target fragments of the eight pathogens were enriched by multiplex PCR and subjected to multiplex ligase detection reaction. Ligation products were enriched and labeled with GNPs by universal asymmetric PCR, using excess GNP-conjugated primers. The labeled single-stranded amplicons containing complementary tag sequences were captured by the corresponding tag sequences immobilized on microarrays, followed by silver staining for signal enhancement. Black images of microarray spots were visualized by naked eyes or scanned on a simple flatbed scanner, and quantified. The results indicated that this assay could unambiguously discriminate all eight pathogens in single and multiple infections, with detection sensitivity of 3.3-85CFU/mL for pure cultures. Microarray results of ninety-five artificially contaminated and retail food samples were consistent with traditional culture, biochemical and real-time PCR findings. Therefore, the novel assay has the potential to be used for routine detection due to rapidity, low cost, and high specificity and sensitivity.

As the key equipment of ultra-high-voltage direct-current (UHVDC) transmission systems, the converter valve would bear different forms of overvoltage. Consequently, a wideband model for the $\pm $ 1100 kV UHVDC converter valve is essential for the converter system to make overvoltage analysis, insulation design, and electromagnetic disturbance prediction. This paper proposed a wideband modeling method for the $\pm $ 1100 kV converter valve. First, the physics-based wideband model for the key component in the converter valve module is built, of which the parameters could be obtained through genetic algorithm. Second, the parasitic capacitances are obtained through a simplified boundary-element method according to the given structure of the $\pm $ 1100 kV converter valve. By combining wideband models for key components in the converter valve module and the parasitic capacitances of the valve tower, the integrated wideband model for the converter valve could be obtained. The systematic modelling method proposed in this paper could also be of reference value to the modelling of other large-scale complex high-voltage power-electronic equipment. Finally, the experiments for converter valve module and converter tower are carried out, and the corresponding results justified the effectiveness and correctness of the proposed wideband model for the converter valve tower.

Parasitic capacitances of the high-frequency (HF) transformer used in dc–dc converter impact harmonic analysis of the transformer and interactions with the converter. Resonant frequencies of open- and short-circuit impedances characteristics (ICs) have been widely used to extract parasitic capacitances of HF transformers, but it was reported that some resonant frequencies were unobtainable as they were too high where the ICs fluctuated heavily, and parasitic capacitances remained unevaluated. In this paper, resonance features of ICs and voltage transfer characteristics (TCs) are investigated, and a new frequency of voltage TC is used for extracting parasitic capacitances. By analyzing inner links of known impedance resonant frequencies, why and which resonant frequencies are likely to be unobtainable is revealed. Furthermore, a new frequency corresponding to zero of TC, accounting for parallel resonance between leakage inductance and mutual capacitance of the two windings, is used to extract parasitic capacitances. With this new frequency mutual capacitance of the windings is evaluated, and missing equation due to unobtainable impedance resonant frequency is added. Besides, merits and limitations of extracting parasitic capacitances based on ICs and TCs are analyzed. These techniques are verified with several HF transformer prototypes in the laboratory.

The dc-dc modular multilevel converter (dc-dc MMC) with a medium-frequency ac-link transformer is a promising candidate for the modern electric ship medium voltage dc power system to provide galvanic isolation, flexible voltage regulation, and fault ride through ability. However, the steady states and dynamics of such an isolated dc-dc MMC are rather complicated because it contains many state variables. Moreover, the on-ship requirements further complicate the system design. This paper thus proposes the modeling and control of such a dc-dc MMC. Based on the fact that such a dc-dc MMC is controlled by the phase-shifted operation, the fundamental period averaging (FPA) method is applied to obtain the steady-state and small-signal models, and based on the on-ship requirements, the modulation index control as well as the simple but efficient competitive dual closed loops (a voltage loop for normal condition and a current loop for overload condition) are designed. It is proved that the FPA method can preserve the necessary model information about the dc-dc MMC, and the proposed controllers can be well designed based on the steady-state and small-signal FPA models by compromising the on-ship requirements. The analysis and design results are verified by the simulations and experiments.

Security issues related to vulnerability assessment in electrical networks are necessary for operators to identify the critical branches. At present, using complex network theory to assess the structural vulnerability of the electrical network is a popular method. However, the complex network theory cannot be comprehensively applicable to the operational vulnerability assessment of the electrical network because the network operation is closely dependent on the physical rules not only on the topological structure. To overcome the problem, an adjacent graph (AG) considering the topological, physical, and operational features of the electrical network is constructed to replace the original network. Through the AG, a branch importance index that considers both the importance of a branch and the fault adjacent relationships among branches is constructed to evaluate the electrical network vulnerability. The IEEE 118-bus system and the French grid are employed to validate the effectiveness of the proposed method. National Natural Science Foundation of China under Grant U1734202 National Key Research and Development Plan of China under Grant 2017YFB1200802-12 National Natural Science Foundation of China under Grant 51877181 National Natural Science Foundation of China under Grant 61703345 Chinese Academy of Sciences, under Grant 2018-2019-01

We, the Editor and Publisher of the Journal of Receptors and Signal Transduction, have retracted the following article: Ultrasound microbubble-mediated miR-150-5p inhibits gastric cancer cell growth by targeting the expression of NR2F2; Wei Wei, Xiaoguang Wei, Minyu ZhangCheng Peng; https://doi/10.1080/10799893.2021.1884260Since publication, concerns have been raised about the integrity of the following figures in the article. 2(A) miR-NC and 2(D) miR-NC-MBs;2(A) Control and 5(C) NR2F2-MBs + miR-NC-MBsWhen approached for an explanation, the authors were unable to provide original data in a suitable format that would confirm the authenticity of the data. The authors have been notified of the retraction but have not responded.We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions.The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as 'Retracted'.

The high-power high-voltage high-frequency transformer (H3T) is a key equipment to develop the isolated high-power dc–dc converter. In this paper, a wideband mechanism model is proposed to analyze the external behaviors of the H3T. This model is characterized by elaborate considerations on magnetic and capacitive effects of the H3T, including electrostatic couplings between windings, magnetic core, and the enclosure, with parameters which have explicit physical meanings. In terms of large number of turns and racetrack windings of the H3T, a finite-element method based on weighted algorithm is used to extract model inductance and capacitance parameters. To verify the proposed model and corresponding parameter extraction method, impedance characteristics of an H3T prototype under open circuit, short circuit, and load conditions are measured and simulated over a wide frequency band. The results show that the proposed model is valid up to 300 kHz. Besides, when one or two windings of the H3T are grounded, the proposed model may be reduced to traditional six or three capacitance models due to changes of winding-to-ground capacitances, which may account for shift of resonant frequencies. The proposed model and corresponding analyses are helpful to electromagnetic design and external behavior investigations of the H3T.

DC–DC modular multilevel converter (DC–DC MMC) with the medium-frequency or high-frequency interlinked transformer is an attractive candidate for high-voltage-direct-current system and DC grid. This study proposes the phase-shifted modulation for DC–DC MMC, aiming to reduce the switching losses, minimise the computational burdens and obtain the satisfactory performance. According to the features of DC–DC MMC, the least but necessary control parameters are extracted, and their control cycles are also properly designed to adapt to the digital implementation. Then, the modulation algorithm is designed and the parameter mapping is realised by the simplest algorithm. Finally, the voltage fluctuations of the submodules (SMs) under such a modulation are predicted off-line, and the adapted sort-and-selection strategy is designed for voltage balance of the SMs. With the proposed modulation, the switching frequencies of all SMs are strictly equivalent to the fundamental frequency; the sampling, parameter mapping and sort-and-selection frequencies are also the fundamental frequency. These features alleviate not only the switching loss in the power stage, but also the computational burden in the modulation and control systems. The analysis and design of the proposed modulation will be given in this study, and verified by the experiments.

This paper focuses on power system fault diagnosis based on Weighted Corrective Fuzzy Reasoning Spiking Neural P Systems with real numbers (rWCFRSNPSs) to propose a graphic fault diagnosis method, called FDWCFRSNPS. In the FD-WCFRSNPS, an rWCFRSNPS is proposed to model the logical relationships between faults and potential warning messages triggered by the corresponding protective devices. In addition, a matrixbased reasoning algorithm for the rWCFRSNPS is devised to reason about the fault alarm messages using parallel representations. Besides, a layered modeling method based on rWCFRSNPSs is developed to adapt to topological changes in power systems and a Temporal Order Information Processing Method based on Cause– Effect Networks is designed to correct fault alarm messages before the fault reasoning. Finally, in a case study considering a local subsystem of a 220kV power system, the diagnosis results of five test cases prove that the proposed FD-WCFRSNPS is viable and effective. Ministerio de Economía, Industria y Competitividad TIN2017-89842-P (MABICAP)