CHENG Ying;DONG Wei;JIANG Zhentao;TANG Yi;FENG Changyou;

• 1:State Grid Zhejiang Electric Power Co.
• 2: Ltd. Research Institute
• 3:School of Electrical Engineering
• 4: Southeast University

Abstract：

Real-time and accurate identification of load composition is of great significance for power system simulation and analysis. Current identification processes based on conventional optimization methods struggle to handle multidimensional temporal characteristics and are computationally intensive, leading to insufficient identification accuracy and an inability to meet the demands of online applications. To address this challenge, an online load composition identification method suitable for active integrated load models is proposed, integrating the feature weighting capability of attention mechanisms with the feature extraction capability of convolutional neural network(CNN). Firstly, an active integrated load model incorporating photovoltaics and energy storage is proposed from a mechanistic perspective. Subsequently, a feature extraction network integrating multi-scale convolution and attention mechanisms is constructed to capture heterogeneous load features in parallel and highlight critical information. Finally, key load nodes are screened based on the ratio of global parameter sensitivity among load nodes as an evaluation metric, and target nodes are identified accordingly. Case study results demonstrate that, compared to existing methods, the proposed approach achieves higher identification accuracy and robustness, meeting the requirements for online security analysis in most power system operational scenarios.

Key Words： integrated load model;composition identification;CNN;attention mechanism;deep learning

Abstract:

Keywords:

Foundation: 国家电网有限公司科技项目（5100-202419015A-1-1-ZN）

Authors: CHENG Ying;DONG Wei;JIANG Zhentao;TANG Yi;FENG Changyou;

DOI: 10.19585/j.zjdl.202603008

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