No abstract

To improve the precision of wind farm multi-machine equivalence and multi-scene generalization, this paper proposes a method for wind turbine clustering and equivalent parameter identification in multi-time scales based on the deep migration of multi-view features. The proposed technique carries out multi-machine equivalence by leveraging the multi-view information from each turbine in a wind farm. Specifically, a deep spatio-temporal Improved Auto-Encoder is designed, jointly trained with the target clustering layer. IAE is used for mining multi-view latent characteristics of wind turbines orienting to grouping turbines to improve the model's adaptability to multiple scenarios and divide turbines in an unsupervised manner. This method generates a visual heat map to represent the attended area of characteristics based on transfer learning and Class Activation Map to enable interpretability. In the next phase, this technique constructs a multi-objective optimization model by synthesizing the equivalent deviation of voltage, current, active power, and reactive power to further improve accuracy. It can identify the equivalent parameters of collector lines, the mechanical structure, and the control system at different time scales simultaneously via the black-box paralleled optimization method based on Bayes and Multi-arm Bandit. The proposed approach is evaluated on a typical double-fed wind farm with grid-side faults under various conditions of disturbing winds. Also, an ablation study is conducted to make analysis according to the two phases, i.e., turbine division and parameter identification. The results validate the accuracy and robustness of this method.

Recent proliferation and integration of tissue-clearing methods and light-sheet fluorescence microscopy has created new opportunities to achieve mesoscale 3D whole-brain connectivity mapping with exceptionally high throughput. With the rapid generation of massive high-quality imaging datasets, downstream analysis is becoming the major technical bottleneck for mesoscale connectomics. Current computational solutions are labour-intensive with limited applications because of the exhaustive manual annotation and heavily customised training. Meanwhile, whole-brain data analysis always requires combining multiple packages and secondary development by users. To address these challenges, we developed D-LMBmap, an end-to-end package providing an integrated workflow containing three modules based on novel deep-learning algorithms for whole-brain connectivity mapping: axon segmentation, brain region segmentation and whole-brain registration. D-LMBmap is the first method that does not require manual annotation for axon segmentation, and achieves quantitative analysis of whole-brain projectome in a single workflow with superior accuracy for multiple cell types in all of the modalities tested.

In order to improve the wind power consumption level of the new power system with new energy as the main body, it is necessary to accurately predict the wind speed. The key is to refine the dynamic trend of the wind power system and the potential physical structure in the wind speed sequence. Firstly, on the basis of the library Koopman dynamics theory and the encoder structures, a physically constrained spatio-temporal neural network is built, which generates the linear evolution moment of the nonlinear variables of the wind farm. Secondly, the system trend is approximated by the linear evolution matrix, and the forward and backward dynamics are fully considered in the prediction process. Then, the bidirectional correlation prediction mechanism and the cost function adapted to different objects are set to reduce the requirements for the reversibility and stability of the prediction sequence. Meanwhile, the hidden vector of feature space is visualized to show the feature interval dependency in the system. Finally, the effectiveness of the proposed method is verified by the wind speed measurement data of Prince Mountain in Beipiao. The results show that the proposed method has high prediction accuracy, strong generalization ability, and high interpretability for strong random and strong fluctuation wind speed series.

No abstract