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To ensure their sound and continuous operation to the greatest extent, VSC-based DC girds have extremely stringent requirements for transmission line relay protection. In terms of guaranteeing their reliability, accurate identification of lightning strikes on DC transmission lines is one of the urgent key problems to be solved. An effective ultra-high-speed identification scheme of lightning strikes suitable for the VSC-based DC grid is proposed in this paper. First, an 1-mode reverse voltage traveling wave (RVTW) is constructed applying the pole-mode transformation theory. Next, fault traveling wave propagation characteristics along the DC transmission line are analyzed in depth utilizing Peterson's law. Then, differences of time-frequency electromagnetic transient characteristics of 1-mode RVTWs between disturbances and faults caused by lightning strikes are distinguished in detail by means of the classical wavelet transformation multi-resolution analysis theory. Finally, extensive simulations are carried out to evaluate the performance of the proposed identification scheme, and by which its excellent rapidity, reliability and robustness are validated.

Abstract High‐frequency voltage oscillation occurs at the initial stage of DC short‐circuit fault in the modular multilevel converter (MMC)‐based high‐voltage direct‐current systems, which directly threatens the reliable operation of the secondary equipment in DC circuit breakers (DCCBs). Therefore, this paper focusses on the voltage oscillation characteristic at DCCB terminals, considered the stray parameters of connecting lines, the parasitic parameters of main equipment and the impedance of grounding grid, established equivalent circuit models of MMC‐HVDC converter station at the initial stage of pole‐to‐pole and pole‐to‐ground fault, and obtained the analytical formula for the voltage oscillation frequency. Then, the comparison with the DC short‐circuit test data of ±200 kV MMC‐HVDC converter station verified the validity of the models and revealed the mechanism of voltage oscillation. Finally, the influence of the parameters of the converter station on the oscillation characteristics was discussed. It was proposed to install the DCCB at the MMC valve's terminal near the DC side, and the oscillation frequency is about 10–60 kHz, which is more than 5 times lower than that at the DC reactor's terminal near the DC pole bus, and the disturbance of oscillation to DCCB was alleviated.

Abstract Saturable reactor is the key equipment in converter valves; it is important to evaluate the core condition. In this paper, a recognition model of saturable reactor core loosening based on variational mode decomposition (VMD)‐symmetrized dot pattern (SDP) feature fusion images and scale‐invariant feature transform (SIFT) was proposed. Firstly, the saturable reactor vibration test under high frequency pulse excitation was carried out, and the vibration signals in different core loosening degrees were collected. Secondly, the VMD algorithm was used to decompose the broadband vibration signal into multiple narrowband functions, which were used to reflect the characteristics of each frequency band. Thirdly, each function and the original signal were transformed by SDP, the generated spiral arms were fused into a new image, and the typical templates in different loosening degrees were selected. Finally, the improved SIFT algorithm was used to obtain the matching results between test sets and templates. The results show that the recognition accuracy of the proposed model for core loosening is 97.5%, which is better than traditional algorithms. It can find the core loosening defect early and avoid further failures such as water pipe break and discharge, which can provide an important basis for saturable reactor monitoring.

During the long-term operation of thyristor converter valves, the saturable reactor vibration (mainly caused by magnetostriction) will lead to core looseness faults. In order to accurately evaluate the fault degradation degree, this paper proposes a vibration signal recognition model for iron core looseness based on synchrosqueezed wavelet transforms and a convolutional neural network. Firstly, vibration experiments are conducted on saturable reactors to obtain signals under different core looseness degrees. Then, the spectrogram features of vibration signals are extracted using synchrosqueezed wavelet transform. Finally, based on the high-dimensional learning ability of convolutional neural networks, the fault characteristics of the spectrogram are mined to accurately identify the core looseness degree. The research results indicate that the model in the paper has higher recognition accuracy than some other methods, which provides convenience for the monitoring and maintenance of saturable reactors.

Abstract The high current of hybrid DC circuit breaker (HCB) in the process of breaking generates strong transient magnetic field (MF), which may interfere with the normal operation of driver control units (DCUs). Therefore, the analysis for transient MF disturbance is of great significance. Due to the large space scale of HCB, with the large number of power electronic devices, the numerical calculation method has the disadvantages of large consumption of resources and slow calculation speed in solving the transient MF inside the HCB. In this study, the current generation mechanism of a 500 kV HCB is analysed, and the equivalent current path is obtained by considering the skin effect. Combined with the temporal and spatial distribution of transient current, the multi‐process transient equivalent model of transient MF calculation is established, and the model has the advantages of high precision and high speed. Then, the breaking experiment of HCB is carried out, and the transient MF near the DCU is measured. The experiment results verify the validity of the model. Furthermore, the disturbance of transient MF at the DCU of the transfer branch during breaking 25 kA current is analysed.

Abstract An insulation board is an important insulator used between damping capacitors in a converter valve. The potential creeping discharge phenomenon on the insulation board will affect the insulation between the damping capacitors and even the safe operation of the converter valve; therefore, a modified chaos particle swarm optimization multiple signal classification (MCSPO‐MUSIC) localization algorithm based on a sparse array was proposed. The performance of the localization algorithm and the sparse array was analyzed by MATLAB simulation, and a test platform was established to detect the insulation board discharge position localization. The simulation results showed that the calculation time of this algorithm is about 1.5 s, which is an order of magnitude less than traditional MUSIC algorithm, and it is found that when the sparsity of the 4 × 4 array is 4 (the sparse array elements are 5, 9, 14 and 15), the localization accuracy remains high. Ten groups of experimental data were put into the MCSPO‐MUSIC algorithm; the root mean square errors (RMSE) of the localization errors are 1.91° (non‐sparse array) and 3.12° (sparse array), respectively. Finally, the blind source separation algorithm was used to remove the field noise, which verifies the algorithm and sparse concept accurate and economical in practical application.

In practical application, there are many parallel insulated gate bipolar transistor (IGBT) chips and anti-parallel fast recovery diode (FRD) chips in the IGBT devices. Because of the interactions between parasitic inductance and chip capacitance, plasma extraction transit time (PETT) oscillation might raise during the operation of IGBT device. The PETT oscillation, whose frequency ranges from 1 MHz to 700 MHz, may induce interference on driver circuits, and then cause the device failure. Moreover, the electromagnetic disturbance will affect the secondary devices. Therefore, it should be seriously considered in the design of the packaging to suppress PETT oscillation. The mechanism of PETT oscillation is complex, and its electrical characteristics are sensitive to the chip's parameters, load current, and DC link voltage. Hence, PETT oscillation is an important problem to be solved in device packaging design. In this paper, the characteristics, mechanism, and suppression methods of PETT oscillation are summarized. Based on the systematic summary, various suppression methods are compared and analyzed. Furthermore, the future research issues are prospected.

The increasing factors of uncertainty faced by the system are due to the deep coupling of the electric power cyber network and the physical network. Consequently, ensuring the efficient, secure, and stable operation of the cyber–physical power system (CPPS) has become a key concern. To achieve this, vulnerability assessment plays a crucial role, as it identifies and protects the vulnerable points of the system. The application of complex network theory to assess the vulnerability of CPPSs has garnered significant attention from scholars. This paper delves into the research connotation of vulnerability assessment for CPPSs, starting with the origin, definition, and classification of vulnerability. Subsequently, the assessment framework of vulnerability based on complex network theory is presented, and the status of current domestic and international research in this field is summarized. Furthermore, the interrelationship between system vulnerability and cascading failures is analyzed from the perspective of complex network theory. In conclusion, the ideas of CPPS coupling modeling in vulnerability assessment are summarized, the concept of situation awareness is introduced, and a prospective approach for dynamic vulnerability assessment is proposed. This approach is based on situation awareness combined with complex network theory. Security protection and optimal operation of CPPSs based on vulnerability assessment are also discussed, along with the assessment of vulnerability within integrated energy cyber–physical systems (IECPSs).

Abstract The direct‐current circuit breaker (DCCB) is the most ideal choice for DC fault isolation in DC grids. Despite a late start, China's research and development on the DCCB have made outstanding achievements. This article provides a brief glance of current China's DCCB development status. It begins by sorting out the technical route according to the topology of DCCB. Then it systematically summarises both mechanical and hybrid DCCBs with focussing on the aspects of topology structure and principle, key technology and characteristics, prototype development and application. It is apparent that Chinese scientists and engineers confronted the worldwide problem of large capacity DC breaking, and put forward a comprehensive solution which consists of an innovative topology structure based on coupled negative voltage circuit, breaking throughs on the key technologies such as highly controllable and reliable fault current commutation, millisecond‐level ultra‐fast and efficient electromagnetic repulsion mechanism, high tolerance and high stability power electronic switch, low residual voltage and fast response energy consumption device, etc. The article states that the world's first set of hybrid high‐voltage (HV) DCCB, and the first set of mechanical HV DCCB have been developed. These DCCBs will soon be deployed to the DC grids which have the highest voltage levels therefore require the strongest breaking capacity. These achievements are leading the world in the development and application of DCCB. The article also discusses the overall development trends of DCCB in the areas of new topologies, key technological breakthroughs and application scenarios, etc. These discussions serve as references for DCCB's future technological advancement and its ever‐expanding applications.

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.

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.

To reveal fault propagation paths is one of the most critical studies for the analysis of power system security; however, it is rather difficult. This paper proposes a new framework for the fault propagation path modeling method of power systems based on membrane computing. We first model the fault propagation paths by proposing the event spiking neural P systems (Ev-SNP systems) with neurotransmitter concentration, which can intuitively reveal the fault propagation path due to the ability of its graphics models and parallel knowledge reasoning. The neurotransmitter concentration is used to represent the probability and gravity degree of fault propagation among synapses. Then, to reduce the dimension of the Ev-SNP system and make them suitable for large-scale power systems, we propose a model reduction method for the Ev-SNP system and devise its simplified model by constructing single-input and single-output neurons, called reduction-SNP system (RSNP system). Moreover, we apply the RSNP system to the IEEE 14- and 118-bus systems to study their fault propagation paths. The proposed approach first extends the SNP systems to a large-scaled application in critical infrastructures from a single element to a system-wise investigation as well as from the post-ante fault diagnosis to a new ex-ante fault propagation path prediction, and the simulation results show a new success and promising approach to the engineering domain.

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.

Assessment of electrical network vulnerability based on complex network theory (CNT) has attracted increasing attention. However, CNT focuses on analyzing the structural vulnerability and has significant limitations regarding operational vulnerability. To address the lack of a comprehensive CNT-based framework to assess operational vulnerability, a temporal-spatial correlation graph (TSCG) that considers the topological, physical, and operational characteristics of electrical networks is proposed. To better assess vulnerability, two metrics, i.e., impact ability and susceptibility of branches, based on symmetric entropy from the load redistribution mechanism of electrical networks and their corresponding TSCGs are proposed. Applications to IEEE 39-bus system, IEEE 118-bus system, and French grid demonstrate that the proposed TSCGs have distinctive features that can intuitively and simply reveal the features of impact ability and susceptibility in CNT.

The contact wire support (CWS) is an important catenary component that maintains the contact wire height and stagger. The direct impact of the pantographs makes the CWS a vulnerable part of the catenary. Recently, automatic catenary inspection using computer vision and pattern recognition has been introduced to improve railway operation safety. However, the automated detection of CWS defects remains to be further studied. This paper proposes a novel CWS defect detection system that consists of three stages. First, the Faster R-CNN network is adopted to localize the key catenary components, and the image areas that contain CWS components are obtained. Then, the CWS components are segmented using a Bayesian fully convolutional catenary components segmentation network (CCSN) that fuses different level features of the backbone network. The CCSN is not only able to perform accurate CWS components segmentation, but also capable of evaluating the model uncertainty by Monte Carlo dropout. Finally, the defect status is determined using the proposed criteria, which are defined according to the geometries of the components. Experiments on the Hefei-Fuzhou high-speed railway line indicate that this approach can be applied to the CWS defect detection.

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.

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 from the perspective of cyber-physical 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.

This paper proposes a new framework to analyze two vulnerability features, impactability and susceptibility, in electrical networks under deliberate attacks based on complex network theory: these two features are overlooked but vital in vulnerability analyses. To analyze these features, metrics are proposed based on correlation graphs constructed via critical paths, which replace the original physical network. Moreover, we analyze the relationship between the proposed metrics according to degree from the perspective of load redistribution mechanisms by adjusting parameters associated with the metrics, which can change the load redistribution rules. Finally, IEEE 118- and 300-bus systems and a realistic large-scale French grid are used to validate the effectiveness of the proposed metrics.