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Dynamic behaviors of distribution networks are of great importance for the power system analysis. Nowadays, due to the integration of the renewable energy generation, energy storage, plug-in electric vehicles, and distribution networks turn from passive systems to active ones. Hence, the dynamic behaviors of active distribution networks (ADNs) are much more complex than the traditional ones. The research interests how to establish an accurate model of ADNs in modern power systems are drawing a great deal of attention. In this paper, motivated by the similarities between power system differential algebraic equations and the forward calculation flows of recurrent neural networks (RNNs), a long short-term memory (LSTM) RNN-based equivalent model is proposed to accurately represent the ADNs. First, the adoption reasons of the proposed LSTM RNN-based equivalent model are explained, and its advantages are analyzed from the mathematical point of view. Then, the accuracy and generalization performance of the proposed model is evaluated using the IEEE 39-Bus New England system integrated with ADNs in the study cases. It reveals that the proposed LSTM RNN-based equivalent model has a generalization capability to capture the dynamic behaviors of ADNs with high accuracy.

The mechanism of gas excitation for wheel eccentricity and to calculate Alford's force are introduced. On the basis of the blade-and-flow parameters a new formulation is derived and validated. The calculation results are consistent with current theory and experimental conclusions. The physical meaning of the ranges of numerical values of the efficiency factor are discussed. This gets rid of the difficulty of selecting the efficiency factor in Alford's formulation and lays a theoretical foundation for the stability analysis to increase turbine rotor stability.

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Cybercriminals use Malicious Uniform Resource Locators (URLs) as the entry to implement a variety of web attacks, such as phishing, spamming, and malware distribution, which may lead to huge finance and data loss. Thus, malicious URLs should be detected as accurately and quickly as possible. Heuristic-based detection approaches are one of the most popular methods to achieve the above goals. The detection results come from the usage of many heuristic features in this approach. However, tremendous new pages and meaningless tokens lead to the explosion of feature sets, and exhaust memory space finally.In this paper, we try to address the problem by selecting some representative members from the initial feature set, which should have the best predictive ability among the same number of selected features. For each feature, we give an evaluation method of O(1) complexity to measure its predictive ability. Then we make the selection based on all the measured values with linear complexity. Experimental results show that our approach can achieve almost the same false negative rate using only 8.3% features for malicious URLs detection, comparing with prior approaches. Moreover, our approach may work efficiently in the big data era, as it can handle 20 thousand URLs per second in our experiments on average.