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Indirect matrix converters (IMC) feature an AC-AC configuration without an intermediate DC link and wide application prospects in wind power generation, AC transmission, and various other fields, known for their sinusoidal input/output, high power density, and superior operating efficiency. However, these converters inevitably generate common-mode voltages during modulation operations, causing motor damage and affecting the reliable functioning of the systems. This paper aims to enhance the operational reliability of IMCs by suppressing common-mode voltage output, and focuses on five classical modulation methods for suppressing common-mode voltage peaks by 42.3% in IMCs, including the modulation method for synthesizing reference output voltage vectors using adjacent four effective voltage vectors at the inverter stage, that for synthesizing reference output voltage vectors using adjacent three effective voltage vectors at the inverter stage, that for synthesizing reference current vectors using adjacent three effective current vectors at the rectifier stage, that for synthesizing reference current vectors using two adjacent 120° effective current vectors plus a zero-current vector at the rectifier stage, and that for synthesizing reference current vectors using weighted three effective current vectors at the rectifier stage. By modeling the effective value (root mean square, rms) of output common-mode voltages, which incorporates the influence of voltage transfer ratios on these values, a quantitative analysis was conducted to explore the variation patterns of the rms value of common-mode voltages. Following this, an evaluation was performed on the suppression capability of different modulation methods on common-mode voltages. The comparative analysis results indicated that the space vector modulation method of synthesizing reference voltage using adjacent four effective voltage vectors at the inverter stage exhibited the best suppression effect on the rms value of common-mode voltages in IMCs. Additionally, the correctness of the theoretical analysis results was verified through simulations and experiments.

For the problem that the abnormal circulation of bipolar double twelve-pulse ultra high voltage direct current (UHVDC) line permanent grounding fault can not be effectively isolated, an optimized scheme for suppressing the abnormal fault circulation is proposed. Firstly, the influencing factors of abnormal circulating current value for DC line permanent grounding fault are analyzed. Secondly, combined with the existing removal strategies of DC line fault in China, the causes of abnormal circulation are analyzed, which are caused by restarting the high-end valve group with communication condition, unsuccessful restart with communication condition or without communication condition after DC line grounding fault happens. Then, a circulating current suppression scheme for DC line permanent ground fault based on pole isolation and blocking after shifting phase 90° strategy is proposed accordingly. Finally, the simulation model is established through real time digital system (RTDS) to verify the effectiveness of the improved scheme. The results show that the scheme proposed in this paper can effectively solve the abnormal circulation problem of DC line permanent grounding fault under different working conditions.

Container technology has become a widely used virtualization technology in cloud platform due to its lightweight virtualization characteristics. However, it shares the kernel with the host, so it has poor security and isolation, and is vulnerable to flood, denial of service, and escape attacks. In order to effectively detect whether the container is attacked or not, an intrusion detection method based on host system call frequency was proposed. This method took advantage of the different frequency of system call between different attack behaviors, collected the system call generated when the container was running, extracted the system call features by combining the sliding window and TF-IDF algorithm, and classified by comparing the feature similarity. The experimental results show that the detection rate of this method can reach 97%, and the false alarm rate is less than 4%.