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Due to the lack of flexible interconnection devices, power imbalances between networks cannot be relieved effectively. Meanwhile, increasing the penetration of distributed generators exacerbates the temporal power imbalances caused by large peak-valley load differences. To improve the operational economy lowered by spatiotemporal power imbalances, this paper proposes a two-stage optimization strategy for active distribution networks (ADNs) interconnected by soft open points (SOPs). The SOPs and energy storage system (ESS) are adopted to transfer power spatially and temporally, respectively. In the day-ahead scheduling stage, massive stochastic scenarios against the uncertainty of wind turbine output are generated first. To improve computational efficiency in massive stochastic scenarios, an equivalent model between networks considering sensitivities of node power to node voltage and branch current is established. The introduction of sensitivities prevents violations of voltage and current. Then, the operating ranges (ORs) of the active power of SOPs and the state of charge (SOC) of ESS are obtained from models between networks and within the networks, respectively. In the intraday corrective control stage, based on day-ahead ORs, a receding-horizon model that minimizes the purchase cost of electricity and voltage deviations is established hour by hour. Case studies on two modified ADNs show that the proposed strategy achieves spatiotemporal power balance with lower cost compared with traditional strategies.

Sulfur dioxide (SO2) is a key indicator for fault diagnosis in sulfur hexafluoride (SF6) gas-insulated equipment. In this work, an in situ photoacoustic detection system using an ultraviolet (UV) LED light as the excitation source was established to detect SO2 in high-pressure SF6 buffer gas. The selection of the SO2 absorption band is discussed in detail in the UV spectral regions. Based on the result of the spectrum selection, a UV LED with a nominal wavelength of 285 nm and a bandwidth of 13 nm was selected. A photoacoustic cell, as well as a high-pressure sealed gas vessel containing it, were designed to match the output optical beam and to generate a PA signal in the high-pressure SF6 buffer gas. The performance of the proposed system was assessed in terms of linearity and detection limit. An SO2 detection limit (1σ) of 0.17 ppm was achieved. Additionally, a correction method was supplied to solve PA signal derivation induced by pressure fluctuation. The method can reduce the derivation from about 5% to 1% in the confirmation experiment.

While encryption ensures the confidentiality and integrity of user data, more and more attackers try to hide attack behaviours through encryption, which brings new challenges to malicious traffic identification. How to effectively detect encrypted malicious traffic without decrypting traffic and protecting user privacy has become an urgent problem to be solved. Most of the current research only uses a single CNN, RNN, and SAE network to detect encrypted malicious traffic, which does not consider the forward and backward correlation between data packets, so it is difficult to effectively identify malicious features in encrypted traffic. This study proposes an approach that combines spatial-temporal feature with dual-attention mechanism, which is called TLARNN. Specifically, first we use 1D-CNN and BiGRU to extract spatial features in encrypted traffic packets and temporal features between encrypted streams, respectively, which enriches the features of different dimensions, and then, the soft attention mechanism is focused on the encrypted data packets to extract features. Ultimately, the second layer of the soft attention mechanism is used for aggregating malicious features. Several comparative experiments are designed to prove the effectiveness of the proposed scheme. The experimental results demonstrate that the proposed scheme has a significant performance improvement compared to existing ones.