The virtual synchronous generator (VSG) exhibits high-dimensional complexity, necessitating a tradeoff between accuracy and complexity when constructing small-signal models. To address this issue, this article proposes a data-driven modeling approach based on long short-term memory (LSTM) neural networks. The focus is on mapping relationships between electrical quantities, considering the influence of irrational factors on model accuracy. A detailed data-driven modeling approach for VSG is proposed and verified in this article. Due to the time-series correlation in the electrical data generated during VSG operation, the LSTM algorithm, known for its excellent time-series prediction capabilities, is chosen to construct the VSG data-driven model. Several complex VSG application scenarios are used to validate the effectiveness and accuracy of the proposed modeling approach. In conclusion, the LSTM-based VSG data-driven modeling outperforms small-signal models and recurrent neural network data-driven models in terms of accuracy and stability.

The accurate calculation of electric field intensity under transmission lines is more and more important with the expansion of high voltage engineering, which largely determines the site selection and design of transmission projects. In order to get more accurate results, a methodology of a charge simulation method (CSM) combined with an intelligent optimization algorithm for a 3D calculation model is proposed in this paper. Three key points are emphasized for special mention in this optimized charge simulation method (OCSM). First, the number of sub-segments on the finite length conductor, the position and number of the simulating charge set on a sub-segment are taken as the optimization parameters for unified calculation. Secondly, the fitness function of optimization algorithm is constructed by two values, voltage relative error and electric field intensity relative error. Thirdly, a finite element method (FEM) was used to obtain the electric field intensities, which are compared with the results of the proposed algorithm. A simulation case is carried out on a 3D calculation model of 220 kV transmission lines, which verify the effectiveness of the optimization algorithm. The proposed OCSM solves the parameter optimization problem of CSM in the 3D computational model, which considers physical shape of wire span, and has the advantages of strong global search ability and higher calculation accuracy.

We present a simple and robust way to execute selective population transfer and creation of an arbitrary superposition between quantum states in a Λ-type four-level system with doublet ground states driven by a single linearly chirped pulse. It is demonstrated that the population in the initial state can be completely and flexibly transferred to either of the target states by manipulating the chirp rate and detuning of the laser pulse. Besides, the formation of an arbitrary superposition between the initial state and either of the target states through properly changing the chirp rate and detuning is also exhibited. The results of this method can be useful for selective quantum coherent control in systems with multiple target states.

With the application of distributed power sources, the stability of the power system has been dramatically affected. Therefore, scholars have proposed the concept of a virtual synchronous generator (VSG). However, after the system is disturbed, how to make it respond quickly and effectively to maintain the stability of the system becomes a complex problem.To address this problem, a frequency prediction component is incorporated into the control module of the VSG to enhance its performance. The Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model is used for frequency prediction, ensuring that the maximum energy capacity released by the storage system is maintained. Additionally, it guarantees that the inverter's output power does not exceed its rated capacity, based on the predicted frequency limit after the system experiences a disturbance. The advantage of real-time adjustment of inverter parameters is that the setting intervals for inertia and damping can be increased. The selection criteria for inertia and damping can be derived from the power angle oscillation curve of the synchronous generator. Consequently, an adaptive control strategy for VSG parameters is implemented to enhance the system's frequency restoration following disturbances. The validity and effectiveness of the model are verified through simulations in Matlab/Simulink.

Position-sensitive detector (PSD) is widely used in precision measurement fields such as flatness detection, auto-collimator systems, and degrees of freedom testing. However, due to factors such as uneven surface resistance and differences in electrode structures, the nonlinearity of PSD becomes increasingly severe as the photosensitive surface moves from the center toward the edges of the four electrodes. To address this issue, a PSD nonlinearity correction algorithm is proposed. The algorithm utilizes the particle swarm optimization (PSO) algorithm to determine the optimal weights and thresholds, providing better initial parameters for the back propagation (BP) neural network. The BP neural network then iterates continuously until the error conditions are met, completing the correction process. Furthermore, a PSD nonlinearity correction system was developed, and the influence of different spot sizes on PSD positioning accuracy was simulated based on the current equation under the Gaussian spot model. This validated the robustness of the correction algorithm under varying spot sizes. The results demonstrate that the overall optimized error is reduced by 84.51%, and for spot sizes smaller than 1 mm, the error reduction exceeds 93.89%. This method not only meets the measurement accuracy requirements but also extends the measurement range of PSD.