厨房电器设备非侵入式粒子群搜索辨识及参数优化方法研究
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摘要
为了挖掘居民负荷参与需求侧响应的潜力,避免侵入式量测在居民负荷采集难以推广的问题,提出基于非侵入量测通过粒子群算法对非侵入量测关口的电气量稳态特征进行搜索辨识的方法。为解决多特征量中以单一特征量辨识准确率低,而多特征量综合辨识的系数难以确定的问题,提出了综合特征量适应度函数和辨识系数优化方法。最后,基于一组算例验证了所提方法的有效性,能够有效提高通过稳态量进行厨房设备辨识的准确率。
In order to explore the potential of residents' load to participate the demand side response and avoid the problem that intrusive load monitoring(ILM) is difficult to popularize in residential load collection,a method based on non-intrusive load monitoring(NILM) was proposed to search and identify the steady-state characteristics of the gate through particle swarm optimization(PSO).Furthermore,an optimization method of identification coefficient was proposed,to solve the problem of low identification accuracy rate based on single in multi-feature and difficult to determine the identification coefficient of comprehensive of multi-feature quantity.Finally,the effectiveness of the proposed method was verified based on a set of examples,which can effectively improve the accuracy rate of kitchen equipment identification through electrical steady-state characteristics.
In order to explore the potential of residents' load to participate the demand side response and avoid the problem that intrusive load monitoring(ILM) is difficult to popularize in residential load collection,a method based on non-intrusive load monitoring(NILM) was proposed to search and identify the steady-state characteristics of the gate through particle swarm optimization(PSO).Furthermore,an optimization method of identification coefficient was proposed,to solve the problem of low identification accuracy rate based on single in multi-feature and difficult to determine the identification coefficient of comprehensive of multi-feature quantity.Finally,the effectiveness of the proposed method was verified based on a set of examples,which can effectively improve the accuracy rate of kitchen equipment identification through electrical steady-state characteristics.
引文
[1] 李昌,徐建枫,郁弘.基于需求侧管理的用户端用电负荷最优需量控制[J].电器与能效管理技术,2015(15):62-66.
[2] 王静.非侵入式量测技术在自动需求响应系统中的应用研究[D].天津:天津大学,2017.
[3] 程祥,李林芝,吴浩,等.非侵入式负荷监测与分解研究综述[J].电网技术,2016,40(10):3108-3117.
[4] 徐彤,李永超,杨宇全,等.非侵入负荷监测装置及其工程应用研究[J].电子测量技术,2018,41(3):93-97.
[5] MARCHIORI A,HAKKARINEN D,HAN Q,et al.Circuit-level load monitoring for household energy management[J].IEEE Pervasive Computing,2011,10(1):40-48.
[6] SUZUKI K,INAGAKI S,SUZUKI T,et al.Nonintrusive appliance load monitoring based on integer programming[C]//IEEE Sice Conference,2008:2742-2747.
[7] LAI Y X,LAI C F,HUANG Y M,et al.Multi-appliance recognition system with hybrid SVM/GMM classifier in ubiquitous smart home[J].Information Sciences,2013,230(4):39-55.
[8] 徐青山,娄藕蝶,郑爱霞,等.基于近邻传播聚类和遗传优化的非侵入式负荷分解方法[J].电工技术学报,2018,33(16):3868-3878.
[9] 李如意,黄明山,周东国,等.基于粒子群算法搜索的非侵入式电力负荷分解方法[J].电力系统保护与控制,2016,44(8):30-36.
[10] 刘兴杰,曹美晗,许月娟.基于改进鸡群算法的非侵入式负荷监测[J].电力自动化设备,2018,38(5):235-240.
[11] 祁兵,韩璐.基于遗传优化的非侵入式居民负荷辨识算法[J].电测与仪表,2017,54(17):11-17.
[12] 孙毅,崔灿,陆俊,等.基于差量特征提取与模糊聚类的非侵入式负荷监测方法[J].电力系统自动化,2017,41(4):86-91.
[13] 孙毅,张璐,赵洪磊,等.基于动态自适应粒子群算法的非侵入式家居负荷分解方法[J].电网技术,2018,42(6):1819-1826.
[14] 夏磊,李泽滔,夏懿.电器辨识算法与系统研究[J].自动化仪表,2018,39(3):26-30.
[15] 曲禾笛.基于暂态特征的负荷非侵入式辨识方法研究与实现[D].成都:电子科技大学,2018.
[2] 王静.非侵入式量测技术在自动需求响应系统中的应用研究[D].天津:天津大学,2017.
[3] 程祥,李林芝,吴浩,等.非侵入式负荷监测与分解研究综述[J].电网技术,2016,40(10):3108-3117.
[4] 徐彤,李永超,杨宇全,等.非侵入负荷监测装置及其工程应用研究[J].电子测量技术,2018,41(3):93-97.
[5] MARCHIORI A,HAKKARINEN D,HAN Q,et al.Circuit-level load monitoring for household energy management[J].IEEE Pervasive Computing,2011,10(1):40-48.
[6] SUZUKI K,INAGAKI S,SUZUKI T,et al.Nonintrusive appliance load monitoring based on integer programming[C]//IEEE Sice Conference,2008:2742-2747.
[7] LAI Y X,LAI C F,HUANG Y M,et al.Multi-appliance recognition system with hybrid SVM/GMM classifier in ubiquitous smart home[J].Information Sciences,2013,230(4):39-55.
[8] 徐青山,娄藕蝶,郑爱霞,等.基于近邻传播聚类和遗传优化的非侵入式负荷分解方法[J].电工技术学报,2018,33(16):3868-3878.
[9] 李如意,黄明山,周东国,等.基于粒子群算法搜索的非侵入式电力负荷分解方法[J].电力系统保护与控制,2016,44(8):30-36.
[10] 刘兴杰,曹美晗,许月娟.基于改进鸡群算法的非侵入式负荷监测[J].电力自动化设备,2018,38(5):235-240.
[11] 祁兵,韩璐.基于遗传优化的非侵入式居民负荷辨识算法[J].电测与仪表,2017,54(17):11-17.
[12] 孙毅,崔灿,陆俊,等.基于差量特征提取与模糊聚类的非侵入式负荷监测方法[J].电力系统自动化,2017,41(4):86-91.
[13] 孙毅,张璐,赵洪磊,等.基于动态自适应粒子群算法的非侵入式家居负荷分解方法[J].电网技术,2018,42(6):1819-1826.
[14] 夏磊,李泽滔,夏懿.电器辨识算法与系统研究[J].自动化仪表,2018,39(3):26-30.
[15] 曲禾笛.基于暂态特征的负荷非侵入式辨识方法研究与实现[D].成都:电子科技大学,2018.