摘要：
In order to fully consider the influence of uncertain factors such as complex and changeable weather conditions and social events, a multi-channel LSTM-CNN (Long Short Term Memory-Convolutional Neural Network) power load multi-step forecasting model based on feature fusion is proposed for the first time, by modeling the sequential data with different time scales and using the neural network model composed of multiple LSTM networks in parallel, the multi-scale time feature representation is learned, the output time step of each LSTM is convoluted by CNN, and its output characteristics are extracted.
作者机构：
[屈星; 盛义发; 肖金凤; 徐祖华] School of Electrical Engineering, University of South China, Hengyang;421001, China;[李欣然] School of Electrical and Information Engineering, Hunan University, Changsha;410082, China;[屈星; 盛义发; 肖金凤; 徐祖华] 421001, China
通讯机构：
[Qu, X.] S;School of Electrical Engineering, China
关键词：
负荷建模;电池储能系统;广义负荷;广义综合负荷模型;电力系统
摘要：
针对含电池储能系统(batter energy storage system,BESS)的配电网参与电网仿真所需的广义综合负荷模型展开研究。阐述了BESS的仿真数学模型,搭建了基于MATLAB/Simulink的BESS仿真平台;通过对BESS暂态运行特性的仿真分析,提出了一种面向负荷的BESS等效模型并进行了检验;建立了考虑BESS的配电网广义综合负荷模型,该广义综合负荷模型由BESS等效模型和经典综合负荷模型(composite load model,CLM)并联构成;通过多场景的仿真分析,检验了提出的广义综合负荷模型的有效性和模型参数稳定性。
期刊：
Recent Advances in Electrical & Electronic Engineering,2018年11(2):97-102 ISSN：2352-0965
通讯作者：
Jin-Feng, Xiao(jinfengxiaosheng@163.com)
作者机构：
[Xiao Jin-Feng] School of Electrical Engineering, University of South China, Hengyang;Hunan;421001, China;[Xiao Qi-Ming] Foxconn Wireless Business Group, Hengyang;[Xiao Jin-Feng; Xiao Qi-Ming] Hunan
通讯机构：
School of Electrical Engineering, University of South China, Hengyang, Hunan, China
期刊：
2018 2nd IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC),2018年:1123-1129
作者机构：
[Jinfeng Xiao; Renxiong Zhuo] College of Electrical Engineering, University of South China
会议名称：
2018 2nd IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)
会议时间：
May 2018
会议地点：
Xi'an, China
会议论文集名称：
2018 2nd IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)
关键词：
fault classification;Support Vector Machine;Grey Wolf Optimization;Complete Ensemble Empirical Mode Decomposition with Adaptive Noise
摘要：
In the training of Support Vector Machine (SVM) classification model, some problems such as over-fitting, under-fitting, slow training speed and prediction affected by penalty and kernel function parameters are exposed. A fault classification method of rolling bearing based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Grey Wolf Optimization (GWO) -SVM is proposed. Firstly, the acquire signals are adaptively decomposed into a plurality of Intrinsic Mode Function(IMF) component by CEEMDAN, and the energy entropy of each IMF component is extracted to form a set of high dimensional feature vector. Then, the processed feature vectors are introduced into the diagnosis network of GWO-SVM algorithm to build a fault classification model of motor rolling bearing. The classification results show that the CEEMDAN and GWO-SVM fault classification network of motor rolling bearing has higher accuracy and shorter diagnosis time than Particle Swarm Optimization (PSO) -SVM and Cross Validation (CV) -SVM.
