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数据驱动的变频空调负荷模型参数在线辨识方法
作者:
作者单位:

1.上海交通大学电子信息与电气工程学院,上海市 200240;2.上海交通大学后勤保障中心能源保障部,上海市 200240;3.国网上海市电力公司电力调度控制中心,上海市200122;4.国网上海浦东供电公司张江科学城能源服务中心,上海市 201210

摘要:

准确辨识空调负荷模型的参数是挖掘其节能及需求响应潜力的重要基础,当前研究大多采用精度较差的离线辨识方法。为此,基于数据驱动思想,提出一种变频空调模型参数在线辨识方法。首先,建立了数据驱动的空调负荷模型参数在线辨识架构。然后,基于空调负荷模型提出数据驱动的在线辨识机制和方法。其中,数据驱动的在线辨识机制设计为基于参数显著变化事件驱动的参数更新判别机制和基于历史参数波动范围的参数动态阈值设定机制,在该机制下通过粒子群优化算法建立了快速在线辨识方法。最后,通过实测环境,验证了所提在线辨识方法的有效性,与离线辨识方法相比,所提方法极大地提高了计算速度及准确度,可满足在线应用需要。

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作者简介:

吴承鑫(1997—),男,硕士研究生,主要研究方向:Zigbee无线通信、空调节能。E-mail:jndwcx@sjtu.edu.cn
范帅(1993—),男,博士,主要研究方向:需求响应与需求侧调度。E-mail:fanshuai@sjtu.edu.cn
王治华(1977—),男,硕士,高级工程师,主要研究方向:电网调度自动化。E-mail:wangzh@sh.sgcc.com.cn
何光宇(1973—),男,通信作者,博士,教授,主要研究方向:智能电网、状态估计、智能用电、用户侧能量管理系统。E-mail:gyhe@sjtu.edu.cn


Data-driven Online Identification Method for Parameters of Inverter Air-conditioning Load Model
Author:
Affiliation:

1.School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;2.Energy Security Department, Logistics Support Center, Shanghai Jiao Tong University ,Shanghai 200240, China;3.Electric Power Dispatching and Communication Center of State Grid Shanghai Municipal Electric Power Company, Shanghai 200122, China;4.Zhangjiang Science City Energy Service Center of State Grid Shanghai Pudong Power Supply Company, Shanghai 201210, China

Abstract:

Accurately identifying the parameters of the air-conditioning load model is an important basis for tapping its energy saving and demand response potential. Current studies mostly adopt offline identification methods with low accuracy. For this, based on the idea of data-driven, an online identification method of model parameters for air-conditioning is proposed. Firstly, the framework of data-driven online identification for parameters of the air-conditioning load model is established. Secondly, based on the air-conditioning load model, the data-driven online identification mechanism and method are proposed. Among them, the data-driven online identification mechanism is designed as the parameter update discrimination mechanism based on event-driven significant changes in parameters, and the dynamic threshold setting mechanism for parameters based on fluctuation ranges of historical parameters. Under this mechanism, a fast online identification method is established through the particle swarm optimization algorithm. Finally, the effectiveness of the proposed online identification method is verified in a practical experimental environment. Compared with offline identification methods, the proposed method greatly improves the calculation speed and accuracy, and can meet the needs of online applications.

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引用本文
[1]吴承鑫,沈海军,王治华,等.数据驱动的变频空调负荷模型参数在线辨识方法[J].电力系统自动化,2022,46(1):120-129. DOI:10.7500/AEPS20210405003.
WU Chengxin, SHEN Haijun, WANG Zhihua, et al. Data-driven Online Identification Method for Parameters of Inverter Air-conditioning Load Model[J]. Automation of Electric Power Systems, 2022, 46(1):120-129. DOI:10.7500/AEPS20210405003.
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  • 收稿日期:2021-04-05
  • 最后修改日期:2021-08-01
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  • 在线发布日期: 2022-01-05
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