摘要
随着智能电网和大数据技术的发展,用电器的识别和检测对规划用电器使用和参与需求响应具有越来越重要的意义。针对用电器识别,提出了一种基于FFT与遗传算法的用电器识别方法。分析了典型用电器负荷的电流波形频谱,并以其谐波作为负荷识别的最优特征参量,通过快速傅里叶变换对波形数据进行处理,以欧式距离最小作为优化指标,采用遗传算法搜寻最优解,最终实现对用电器类别的精确识别。在MATLAB/Simulink中搭建用电器识别模型,并以TI公司的TM S320F28335数字控制器为核心搭建了硬件系统,进行了实验验证。仿真结果与实际测试表明,该算法能够准确识别用电器的类别。
With the development of smart grid and big data technology,the identification and detection of electrical appliances have become more and more important for planning the use of electrical appliances and participating in demand response.This paper proposes a method for identifying consumers based on FFT and genetic algorithm.The current waveform spectrum of a typical electrical appliance load is analyzed,and its harmonics were used as the optimal characteristic parameters for load identification.The waveform data is processed by fast Fourier transform,taking the minimum Euclidean distance as the optimization indicator,and the genetic algorithm is used to search for the optimal solution,and finally the precise identification of the consumer category is achieved.An electrical appliance identification model is built in MATLAB/Simulink,and a hardware system is built with TI’s TMS320 F28335 digital controller as the core for actual verification.Simulation and practical tests show that the algorithm can accurately identify the type of consumer electronics.
作者
曹以龙
涂少博
帅禄玮
CAO Yilong;TU Shaobo;SHUAI Luwei(School of Electronics and Information Engineering,Shanghai University of Electric Power,Shanghai 200090,China)
出处
《上海电力大学学报》
CAS
2020年第5期436-440,450,共6页
Journal of Shanghai University of Electric Power
关键词
负荷识别
特征参量
快速傅里叶变换
欧式距离
遗传算法
load identification
characteristic parameters
fast fourier transform
euclidean distance
genetic algorithm