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Due to the relatively low operational reliability of the MMC valve, it is necessary to establish corresponding test methods to examine the tolerance of the valve to electrical and thermal stresses under engineering requirements through operational tests, to verify its operational reliability. To meet the operational testing requirements for higher voltage and larger current converter valves, an appropriate test topology is required. The paper first analyzes the voltage and current stress characteristics of capacitor components and MMC converter valves in ±800 kV/8 GW Flexible DC transmission system, providing a theoretical basis for the stress of operational testing valve, requiring that to be equivalent to actual operating conditions. Addressing the operational testing demands for high voltage and large current converter valves in the ±800 kV/8 GW flexible DC transmission MMC, a test topology is proposed. This topology can meet the operating conditions of higher voltage and larger current and simultaneously conduct tests on valve segments and modules, significantly improving operational testing efficiency and reducing testing costs. Additionally, a control method applicable to this test topology is proposed, enabling flexible adjustment of DC and AC in the test circuit, thereby enhancing the equivalency of operational tests. In this paper, the proposed topology and control method have enhanced testing efficiency, reduced testing costs, improved testing equivalence, and enhanced the precision of current and capacitor voltage control. Finally, simulations and experiments are constructed to validate the feasibility and effectiveness of the proposed operational test topology and its control method through simulations and experiments.

Specific Emitter Identification (SEI) detects the individual emitter according its varied signal characteristics. The method operates in the physical layer of the internet and can effectively improve the security of the Internet of Things (IoT). Generally, SEI identifies the uniqueness of the transmitting platform by using the unintentional modulation information of the emitter such as radar, which has “fingerprint” characteristics. Existing SEI methods are based on hand-crafted features to distinguish different emitters. In this paper, traditional feature extraction methods are studied and a new recognition method is proposed. To determine the effectiveness of the method, the output signals of eight amplifiers are collected as the research object. The power spectrum characteristics and adjacent channel power ratio (ACPR) of the signal are then extracted and eight amplifiers are distinguished. Finally, the quadrature-phase signals are converted into pictures, and convolutional neural networks are used to automatically extract features for classification and recognition. The results show that the recognition rate of converting signals into pictures can reach 95%, when SNR is 20dB.