电力系统自动化2019，Vol.43Issue(1)：133-140,8.DOI:10.7500/AEPS20180629012
基于多层极限学习机的电力系统频率安全评估方法
Frequency Safety Assessment of Power System Based on Multi-layer Extreme Learning Machine
摘要
Abstract
The random, intermittent and weak inertia characteristics of renewable energy generation have led to a prominent problem in the frequency safety of high-rate renewable energy power systems.The use of time-domain simulation for frequency safety assessment has the disadvantages of large amount of calculation and long time.It is difficult to meet the rapid assessment requirement of frequency safety under thecombined explosion"of multiple complex uncertainties.In order to realize online analysis and prediction of frequency safety, a method based on multi-layer extreme learning machine (ML-ELM) is applied.The non-linear mapping relationship between the input layer and the hidden layer is built by the deep structure theory and in the layer-wise unsupervised training, automatic encoder algorithms and regularization coefficients are introduced to optimize the weight matrix between the input layer and the hidden layer, so that the ML-ELM can effectively represent complex functions and improve predictive accuracy and generalization ability.Case studies of the IEEE RTS-79 system demonstrate the rapidity, high accuracy and well generalization ability of the proposed method.关键词
频率安全/极限学习机/低惯性系统/机器学习/人工智能/大数据Key words
frequency safety/extreme learning machine (ELM)/low inertia system/machine learning/artificial intelligence (AI)/big data引用本文复制引用
文云峰,赵荣臻,肖友强,刘祯斌..基于多层极限学习机的电力系统频率安全评估方法[J].电力系统自动化,2019,43(1):133-140,8.基金项目
国家自然科学基金资助项目(51707017) （51707017）
重庆市基础科学与前沿技术研究项目(cstc2017jcyjAX0422) （cstc2017jcyjAX0422）
中央高校基本科研业务费专项资金资助项目 This work is supported by National Natural Science Foundation of China (No. 51707017) , Chongqing Research Program of Basic Research and Frontier Technology (No. cstc2017jcyjAX0422) and Fundamental Research Funds for the Central Universities. （No. 51707017）
