A power component detection (PCD) method and an improved current order limiter control (improved COL) are proposed to mitigate the commutation failure in a line-commutated converter-based high-voltage direct current (LCC-HVDC). Integrating the instantaneous voltage and instantaneous current characteristics under single-phase and three-phase-to-ground faults, a fault detection method based on the power component is proposed, and the corresponding setting principle is presented. Then, an improved dc current order limiter is introduced. To evaluate the effectiveness of the proposed methods, a dual-infeed HVDC system is developed in PSCAD/EMTDC, and the presented methods are also implemented. The transient performances of dual-infeed HVDC, under single-phase and three-phase-to-ground faults at the inverter ac busbar of one LCC-HVDC link, are investigated. Simulation results show that the PCD method, with the voltage and current components as an additional judgment criterion, is more sensitive to detect the single-phase and three phase faults, and even more reliable than the detect method taking only voltage or current components for faults recognition. It can also be concluded that the improved COL that is integrated with the advancing firing angle control has the ability to make the LCC-HVDC less susceptible to commutation failure. Finally, the improvement of commutation process by improved COL is further demonstrated.

This letter 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-and 118-bus systems demonstrate the effectiveness of the proposed method.

Transmission network vulnerability (TNV) assessment is a key issue in power systems to identify the vulnerable components against accidents or malicious threats. Recently, constructing the topological vulnerability indices, particularly extended topological indices, is a popular method to evaluate the network vulnerability. However, the topological vulnerability indices cannot reveal the mechanism of fault propagation. To overcome the shortcomings, this paper proposes a new method to assess the TNV through the cascading faults graph (CFG) based on fault chains, which is a statistical graph that comprehensively considers the physical, operational and structural features of electrical networks. Based on the complex network theory (CNT), the scale-free properties of the CFG are revealed through simulations on various transmission networks by corresponding the degree distribution of the CFG; then, the model constancy of the CFG is analyzed. Resorting to the CFG, a set of indices from the CNT is used to identify the vulnerable branches of transmission networks. Illustrative applications are applied to the IEEE 39-bus and 118-bus test systems to demonstrate the effectiveness of the proposed method.

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 paper proposes a defect detection GANs (DefGAN) that consists of a denoising auto-encoder, 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 is mainly consists of two stages. In the first stage, we use a 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 are determined by the anomaly probability of the classifier and reconstruction error of the denoising auto-encoder. The effectiveness of our work is measured across the catenary datasets.

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.

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.