[1]葛夕武[],朱超[],马骏毅[],等. 基于耦合隐马尔可夫模型的输电线路状态评估[J].计算机技术与发展,2017,27(04):154-169.
　GE Xi-wu[],ZHU Chao[],MA Jun-yi[],et al. State Evaluation of Transmission Line Based on CoupledHidden Markov Model[J].,2017,27(04):154-169.
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 基于耦合隐马尔可夫模型的输电线路状态评估()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
27
期数:
2017年04期
页码:
154-169
栏目:
应用开发研究
出版日期:
2017-04-10

文章信息/Info

Title:
 State Evaluation of Transmission Line Based on CoupledHidden Markov Model
文章编号:
1673-629X（2017)04-0164-06
作者:
 葛夕武[1]; 朱超[2]; 马骏毅[3]; 刘强[1]; 梁晟杰[1]; 吴国梁[1]
1. 南京供电公司；2.河海大学 能源与电气学院；3.镇江供电公司
Author(s):
GE Xi-wu[1]; ZHU Chao[2]; MA Jun-yi[3]; LIU Qiang[1]; LIANG Sheng-jie[1; WU Guo-liang[1]
关键词:
 ; 人工智能; 机器学习; 耦合隐马尔可夫模型; 状态评估
Keywords:
 ; artificial intelligence; machine learning; coupled hidden Markov model; state evaluation
分类号:
TP302
文献标志码:
A
摘要:
 架空输电线路是输电网络的重要组成部分,其运行状态将直接影响整个输电系统的运行可靠性.为了更好地掌握架空输电线路的运行状态,需要准确地对其进行状态评估.为此,提出了一种基于人工智能的机器学习方法-耦合隐马尔可夫模型来对架空输电线路进行状态评估.根据评估要求收集架空输电线路在正常、注意、异常、严重四种状态下的历史评估数据并将数据进行归一化处理.利用该人工智能算法对归一化后的四类数据进行模型训练,得到四组不同状态下的模型参数,建立起正常、注意、异常、严重四个状态的耦合隐马尔可夫模型.将归一化后的评估数据带入建立好的四组模型当中,得到四个状态评估值,其中评估值最大的该组模型所对应的状态组别就是评估数据所反映的线路状态.采用该机器学习模型对某条架空输电线路进行实证分析和评估,并将评估结果与实际的监控情况进行比较.分析和评估结果表明,所提出的方法具有一定的实用性和可行性.
Abstract:
 Overhead transmission line is an important part of the transmission network.The running state will directly affect the reliability of the operation of the whole power system.In order to learn the running state of the transmission line exactly,it is needed to evaluate the line accurately.A machine learning method based on artificial intelligence called Coupled Coupled Hidden Markov Model (CHMM) has been proposed for the assessment of the state of the overhead transmission line.According to the assessment demand,the historical assessment data of overhead transmission line under normal,abnormal,attention,serious four kinds of state are collected,and the data are normalized.The artificial intelligence algorithm is used to train the normalized four kinds of data to obtain the model parameters of the four groups and four states have been established for the CHMM.The normalized test data are brought into the four sets of models,and the four state evaluation values are obtained,in which the maximum value of the model is the state of the test data.This machine learning model is applied to conduct the empirical analysis and an overhead transmission line is evaluated,compared with the result of the assessment and the actual monitoring.The result of analysis and estimation shows that the method is effective and feasible.

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更新日期/Last Update: 2017-06-19