首页|人工智能赋能电力电容器运行状态感知及优化控制技术综述

人工智能赋能电力电容器运行状态感知及优化控制技术综述

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电力电容器作为新型电力系统重要的无功补偿设备,具有安装数量多、故障类型复杂、运行状态多变等特点,给电力企业在电力电容器的运行管理方面带来巨大的挑战.传统的感知方法存在需要大量人力资源、数据分析困难和预测能力有限的缺点,已经远远不能满足现代智能电网的要求.为有效实现对电力电容器的高效运维和智慧化管理,从而降低电力系统的运行风险,结合新一代人工智能技术的优势,从顶层设计的思路系统梳理人工智能赋能电力电容器的运行数据获取、运行状态研判以及运行态势预测等关键技术,同时阐述近年来电力电容器协同控制和优化技术的最新进展,并对人工智能赋能电力电容器状态感知及优化控制技术的未来研究方向进行展望,旨在为我国相关研究工作提供参考和思路.
Overview of Operation State Sensing and Optimal Control Technology of Artificial Intelligence Empowered Power Capacitor
As an important reactive power compensation equipment in new power system,power capacitor has many installation numbers,complex fault types and changeable operation state,which brings great challenges to power enterprises in the operation and management of power capacitors.Traditional perception methods have the disadvantages of requiring a large number of human resources,difficult data analysis and limited prediction capability,which are far from meeting the requirements of modern smart grids.In order to effectively realize the effective operation and maintenance,control,and intelligent management of massive power capacitors,combined with the advantages of next-generation artificial intelligence technology,key technologies such as the operation data acquisition,operation state judgment and operation situation prediction technologies of artificial intelligence enabled power capacitors are systematically combed from the top-level design ideas.At the same time,the latest progress of incooperative control and optimization technology for power capacitors in recent years is expounded,and the future research direction of state awareness and optimization control technology of artificial intelligence-enabled power capacitors is prospected,aiming at providing reference and ideas for related research work in China.

Artificial intelligencepower capacitorstate awarenesscollaborative control

葛磊蛟、金雨含、李小平、陈艳波

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天津大学电气自动化与信息工程学院 天津 300072

兰州交通大学自动化与电气工程学院 兰州 730070

华北电力大学电气与电子工程学院 北京 100096

人工智能 电力电容器 状态感知 协同控制

2024

电气工程学报
机械工业信息研究院

电气工程学报

CSTPCD北大核心
影响因子:0.121
ISSN:2095-9524
年,卷(期):2024.19(4)