【论文推荐】中国电力科学研究院王晓辉等：面向配用电网运行数据相似性匹配的神经信息检索技术研究

摘要

随着配用电系统规模和复杂度的增加，以及可再生能源的广泛接入，电力系统运行控制难度随之加大，亟需提升其在数据驱动运行管理和智能分析挖掘方面的能力。为深入挖掘配用电系统历史运行断面数据相似性规律，辅助电网准确获取高价值的历史运维经验和知识，基于图数据计算技术，提出融合注意力机制的神经信息检索模型。根据配用电系统运行数据处理流程，建立了神经信息检索技术框架；结合配用电系统天然具备的图特征，构建了数据接入、数据补齐和多源数据的统一图数据结构与数据融合方法；进一步，构建了图节点特征嵌入表示学习算法和神经信息检索算法模型，利用生成的图节点特征表示向量集进行神经信息检索算法模型训练与测试。利用该模型在某省公司配用电系统运行断面数据上进行验证，结果表明，所提方法对历史运行特征的相似性匹配具有较高的准确性，可有效支撑配用电系统故障智能诊断与运维消缺工作。

Similarity matching method of power distribution system operating data based on neural information retrieval

面向配用电网运行数据相似性匹配的神经信息检索技术研究

Kai Xiao¹, Daoxing Li¹, Pengtian Guo¹, Xiaohui Wang¹, Yong Chen¹

(1. China Electric Power Research Institute Co. Ltd., Beijing 100192, P. R. China)

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02 语音介绍-面向配用电系统运行数据相似性匹配的神经信息检索技

Abstract

Operation control of power systems has become challenging with an increase in the scale and complexity of power distribution systems and extensive access to renewable energy. Therefore, improvement of the ability of data-driven operation management, intelligent analysis, and mining is urgently required. To investigate and explore similar regularities of the historical operating section of the power distribution system and assist the power grid in obtaining high-value historical operation, maintenance experience, and knowledge by rule and line, a neural information retrieval model with an attention mechanism is proposed based on graph data computing technology. Based on the processing flow of the operating data of the power distribution system, a technical framework of neural information retrieval is established. Combined with the natural graph characteristics of the power distribution system, a unified graph data structure and a data fusion method of data access, data complement, and multi-source data are constructed. Further, a graph node feature-embedding representation learning algorithm and a neural information retrieval algorithm model are constructed. The neural information retrieval algorithm model is trained and tested using the generated graph node feature representation vector set. The model is verified on the operating section of the power distribution system of a provincial grid area. The results show that the proposed method demonstrates high accuracy in the similarity matching of historical operation characteristics and effectively supports intelligent fault diagnosis and elimination in power distribution systems.

Keywords

Neural information retrieval, Power distribution, Graph data, Operating section, Similarity matching.

Fig.1 Neural information retrieval technology framework for operating section of the power distribution system

Fig.2 Neural information retrieval model framework

Fig.3 Retrieval process of power distribution historical operating section

本文引文信息

Xiao K, Li DX, Guo PT, et al (2023) Similarity matching method of power distribution system operating data based on neural information retrieval. Global Energy Interconnection, 6(1): 15-25

肖凯，李道兴，郭鹏天等 (2023) 面向配用电网运行数据相似性匹配的神经信息检索技术研究. 全球能源互联网（英文）, 6(1): 15-25

Biographies

Kai Xiao

Kai Xiao received master degree at North China Electric Power University, Baoding, in 2013. He is working in China Electric Power Research Institute Co., Ltd. His research interests include power big data technology, power graph computing and power marketing business.

Daoxing Li

Daoxing Li received master degree at North China Electric Power University, Beijing, in 2021. He is working in China Electric Power Research Institute Co., Ltd. His research interests include artificial intelligence and graph computing.

Pengtian Guo

Pengtian Guo received master degree at North China Electric Power University, Beijing, in 2020. He is working in China Electric Power Research Institute Co., Ltd. His research interests include power Internet of things and artificial intelligence.

Xiaohui Wang

Xiaohui Wang received the Doctor’s degree from North China Electric Power University, Beijing, 2012.He is currently working at the China Electric Power Research Institute Co., Ltd. Beijing. His research interests include power big data technology, artificial intelligence, active distributed network, energy internet.

Yong Chen

Yong Chen received the Doctor’s degree from Huazhong University of Science and Technology, Wuhan. He is working in China Electric Power Research Institute Co., Ltd. His research interests include high performance computing, artificial intelligence.

编辑：刘通明

审核：王伟

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