A Dual-view Attention Neural Network for Assigning Industrial Categories to Academic Patents

Industrial technology matching events are held by governmental institutions worldwide to promote patent transfer from universities to industries. When collecting academic patents for the matching events, governmental institutions lack professional knowledge for identifying academic patents suitable for various industries. Therefore, previous studies adopted International Patent Classification (IPC) codes assigned by patent examiners to represent patents and mined the industry-related cues through the mapping link between IPC codes and industry categories. However, IPC codes are too general to specifically represent the complex patents, leading to inaccurate tagging. The view of patent inventors (e.g., patent titles and abstracts) contains rich industry-related cues that benefit assigning industrial categories to academic patents. Therefore, we propose a dual-view attention neural network that learns low-dimensional patent representations from the views of patent examiners and inventors and merges the representations for classifying academic patents into suitable industrial categories. Experiments show that the proposed method outperforms benchmark methods

Resilient distributed control of islanded microgrids under hybrid attacks

In this paper, a resilient control strategy is proposed to improve the stability of frequency and voltage recovery for the islanded microgrid (MG) under hybrid cyber attacks. To deal with the common false data injection attacks (FDI) and denial of service attacks (DoS) in MGs, the proposed resilient control strategy utilizes the observers to accurately estimate the potential FDI signals on both the sensors and actuators of each distributed generation unit (DG) and reconstruct the unavailable states in the system to enhance the system’s ability actively. The ultimate uniform boundedness (UUB) of the system under hybrid cyber attacks is proved by the Lyapunov stability theory. Finally, an islanded MG system is established in MATLAB/SIMULINK, and multiple scenarios are simulated to verify the effectiveness of the method

An evolution strategy of GAN for the generation of high impedance fault samples based on Reptile algorithm

In a distribution system, sparse reliable samples and inconsistent fault characteristics always appear in the dataset of neural network fault detection models because of high impedance fault (HIF) and system structural changes. In this paper, we present an algorithm called Generative Adversarial Networks (GAN) based on the Reptile Algorithm (GANRA) for generating fault data and propose an evolution strategy based on GANRA to assist the fault detection of neural networks. First, the GANRA generates enough high-quality analogous fault data to solve a shortage of realistic fault data for the fault detection model’s training. Second, an evolution strategy is proposed to help the GANRA improve the fault detection neural network’s accuracy and generalization by searching for GAN’s initial parameters. Finally, Convolutional Neural Network (CNN) is considered as the identification fault model in simulation experiments to verify the validity of the evolution strategy and the GANRA under the HIF environment. The results show that the GANRA can optimize the initial parameters of GAN and effectively reduce the calculation time, the sample size, and the number of learning iterations needed for dataset generation in the new grid structures

Effective mass of composite fermion: a phenomenological fit in with anomalous propagation of surface acoustic wave

We calculate the conductivity associated with the anomalous propagation of a surface acoustic wave above a two-dimensional electron gas at $\nu=1/2$. Murthy-Shankar's middle representation is adopted and a contribution to the response functions beyond the random phase approximation has been taken into account. We give a phenomenological fit for the effective mass of composite fermion in with the experimental data of the anomalous propagation of surface acoustic wave at $\nu=1/2$ and find the phenomenological value of the effective mass is several times larger than the theoretical value $m_{th}^*=6\epsilon/e^2l_{1/2}$ derived from the Hartree-Fock approximation. We compare our phenomenologically fitting composite fermion effective mass with those appeared in the measurements of the activation energy and the Shubnikov-de Haas effect and find that our result is fairly reasonable.Comment: 8 pages, 5 figures, the longer version of cond-mat/9801131 with crucial corrections, accepted for publication by PR

Area COI-based slow frequency dynamics modeling, analysis and emergency control for interconnected power systems

published_or_final_versionElectrical and Electronic EngineeringDoctoralDoctor of Philosoph

Household load forecasting model based on Markov state transition

International audienceAbstract：The development of smart grid improves the emphasis on family load forecasting. Based on the theory of state transition, a Monte Carlo Markov Chain load forecasting model of single equipment based on the selection of similar days is proposed and the bottom⁃up analysis method to obtain the comprehensive load level of a single family is used. For the temperature control equipment, Pearson correlation coefficient is used to study the correlation between the ambient temperature and the operation cycle of the equipment, and the hidden Markov model is used to predictthe operation state of the compressor of the temperature control equipment according to the external environment information of the day. With the operation state predicted, the average power in different time periods is calculated to reflect the user load level. The simulation results show that the predicted error of the Monte Carlo Markov chain model based on the similar day selection is about 2% ~8% for the daily load expectation of different equipment, while the predicted accuracy of the hidden Markov model for the temperature control equipment is about 70%

A Lyapunov approach based on gravitational search algorithm for transient stability assessment of AC/DC systems with wind power

Abstract With large‐scale wind farms connected to AC/DC network, the transient stability assessment (TSA) of the power system becomes more and more difficult. Among them, estimating the region of attraction (ROA) of the equilibrium point is a traditional but still challenging problem. Based on the Lyapunov stability theory, this paper proposes a new approach to obtain the enlarged estimation of the ROA. The optimization and updating strategy of shape function in the sum of squares (SOS) optimization problem are studied to reduce the conservatism of estimation result. In the proposed method, the gravitational search algorithm (GSA) is employed to optimize the coefficients of initial shape function to improve estimation performance. Based on the time‐domain simulation (TDS) of expected faults, the fitness value for shape function optimization is calculated using the values of state variables at the fault clearing time and the system stability information. Furthermore, the optimal Lyapunov function is computed by introducing the update condition of shape function and adjusting the iteration strategy of the ROA estimation algorithm. Finally, the proposed method is applied to a two‐machine‐infinite‐bus system and a more complex nine‐bus AC/DC system with wind power. And the effectiveness of the proposed method is verified by comparing with the existing ROA estimation methods

Analysis of the influence of dynamic components of AC/DC system on transient voltage stability based on energy function method

With the interconnection of large power networks, transient voltage stability of AC/DC power systems has been paid more attention in recent years. To investigate main influence of generating facility, high-voltage direct current (HVDC) and motor load for transient voltage stability, from the perspective of the generalised branch potential energy, the new indexes, and analysis method is proposed here. First, an energy function for AC/DC system is constructed with major dynamic components, namely classical HVDC control systems and motors in third-order model. Accordingly, the influence effects of the above major dynamic elements are investigated based on indexes from the corresponding generalised branch potential energy. With the rule of change in transient potential energy distribution in the network and information in stability margins, an evaluating procedure is established. Here, the judgment of transient voltage stability employs a heuristic energy function method. Finally, simulation tests on three-machine system with a HVDC transmission line are conducted in PSAT package, and the obtained results demonstrate the feasibility and effectiveness of the proposed method

Influence of DVR on Adjacent Load and Its Compensation Strategy Design Based on Externality Theory

Dynamic voltage restorer (DVR) is a compensation device that can effectively improve power quality, and it is widely used to compensate the voltage sag on sensitive loads. However, DVR will affect the operation of the sensitive loads in parallel at the same busbar (such parallel are defined as adjacent loads) in the process of improving sensitive load voltage quality. With the externality theory, this paper proposes a solution to clarify the influence of DVR on the adjacent load and handle the possible controversy. This paper first establishes a simple system that consists of a DVR, sensitive loads, and adjacent loads. Based on the equivalent network, the influence of the DVR on the adjacent load is analyzed in terms of voltage change and power consumed. The externality boundaries are clearly defined when considering network parameters in order to fairly deal with the interaction of multi entities. Afterwards, an optimization compensation strategy that is based on externality theory is proposed. Finally, the simulation on compensation strategy is tested on the MATLAB/Simulink platform to demonstrate the feasibility and effectiveness of the proposed method