Application of Multiattention Mechanism in Power System Branch Parameter Identification

Abstract

Maintaining accuracy and robustness has always been an unsolved problem in the task of power grid branch parameter identification. Therefore, many researchers have participated in the research of branch parameter identification. The existing methods of power grid branch parameter identification suffer from two limitations. (1) Traditional methods only use manual experience or instruments to complete parameter identification of single branch characteristics, but they are only used to identify a single target and cannot make full use of the historical information of power grid data. (2) Deep learning methods can complete model training through historical data, but these methods cannot consider the constraints of power grid topological structure, which is equivalent to identifying connected power grid branches separately. To overcome these limitations, we propose a novel multitask Graph Transformer Network (GTN), which combines a graph neural network and a multiattention mechanism to construct our model. Specifically, we input the global features and topology information of branch nodes into our GTN model. In the process of parameter identification, the multihead attention mechanism is used to fuse the branch feature information of different subspaces, which highlights the importance of different branches and enhances the ability of local feature extraction. Finally, the fitting and prediction of each branch feature are completed through the decoding layer. The experiment shows that our proposed GTN is superior to other machine learning methods and deep learning methods and can still realize accurate branch parameter identification under various noise conditions.

Multi Model Establishment of Enterprise Case Aided Decision-making Based on Data Analysis

“…Aided decision-making is a computer supported verification technology [4][5][6], which can make enterprise decisions quickly, stably and safely, and make the decision results meet different constraints. In the process of enterprise case aided decision making, the scheduling plan [7][8][9][10] can be implemented through computer simulation to correct the final decision result and support the decision-making scheme of the enterprise.…”

Active identification method of power network topology anomaly based on graph database

“…In order to meet the demand for distributed energy power supply and distribution, most distribution networks have set up a new distributed control mode [1][2][3] . The distribution network actively obtains access information to ensure the reliability of distribution network operation.…”

An Extensible Gradient-Based Optimization Method for Parameter Identification in Power Distribution Network

“…Tey can be classifed into various methods, such as the fullscale approach [3], PSOSR [4], normalized Lagrange multiplier (NLM) test [5], fnite-time algorithm (FTA) [6], residual method, sensitivity analysis method, Lagrange multiplier method [7], Hefron-Phillips method [8], and specialized Newton-Raphson iteration [9]. Additionally, recent advancements in machine learning and deep learning techniques have led to the proposal of smart methods, including artifcial neural network [10], graph convolution network (GCN) [11], support vector machine (SVM) [12], multihead attention network [13], deep reinforcement learning [14], estimation using synchrophasor data [15], PSCAD simulation [16], multimodal long short-term memory deep learning [17], and edge computing [18]. While these methods show efectiveness with simulation data, they often require specialized measuring devices.…”