数据挖掘技术在配电网报警.doc
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数据挖掘技术在配电网报警,摘要在配电网调度自动化系统中，报警系统信息量巨大，其中往往含有大量的噪声信息。传统的报警系统只是单纯地上传告警信息，未经处理且快速变化的海量报警信息容易造成调度员忽略真正重要的报警信息，延时处理故障，危害电网安全。针对以上问题，本文将数据挖掘技术引入到配电网报警信息的噪声处理中，应用改进后的id3算法构造对配电网中的报...
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摘 要
在配电网调度自动化系统中，报警系统信息量巨大，其中往往含有大量的噪声信息。传统的报警系统只是单纯地上传告警信息，未经处理且快速变化的海量报警信息容易造成调度员忽略真正重要的报警信息，延时处理故障，危害电网安全。
针对以上问题，本文将数据挖掘技术引入到配电网报警信息的噪声处理中，应用改进后的ID3算法构造对配电网中的报警信息进行噪声过滤的分类器。该分类器可以将报警信息分为噪声信息和非噪声信息两类，很好的解决了报警信息不加识别的上传给调度人员的缺点。本文所做的工作主要如下：
(1) 深入研究了数据挖掘技术，重点对数据挖掘中的决策树算法进行了详细探讨；概括总结了ID3算法、C4.5算法、CART算法、SLIQ算法及SPRINT算法的特点及优缺点。
(2) 本文探讨了经典的ID3算法的两个突出缺点：运算复杂和在选择属性时偏向于取值较多的属性，并对这两个方面进行改进。ID3算法是以信息熵理论为基础构建决策树的，每次在选择分裂属性时都要对其进行多次对数运算，在数据量较多的时候，运算速度明显变慢，本文针对这一问题，提出了相应的简化方法；再则，ID3应用信息增益作为最佳属性的选择标准，这样就会导致该算法偏向于选择取值较多的属性，而有时候取值较多的属性并不是最优属性，针对这一问题，本文引入一乘积因子到简化后的信息熵中，来克服ID3算法的取值的偏向性。
(3) 本文通过实例分析对改进后的ID3算法的优越性进行实验验证，并实现了用MATLAB语言来构造决策树。
(4) 本文在研究配电网中报警信息特点的基础上，实现了配电网中的报警信息的预处理及主要归纳关系的形成，只有对形式复杂的报警信息进行预处理并且得到主要归纳关系后才可以将它们用于数据挖掘任务中。
(5) 用改进后的ID3决策树算法设计了用于配电网报警信息中噪声识别的分类器。该分类器可以从报警信息中识别出噪声信息。
关键词 数据挖掘；决策树；ID3算法；报警信息
Abstract
In dispatching automation system of distribution power grid, there is a large amount of information which usually contains a lot of noisy information. The traditional alarm system simply uploads alarm information, so the untreated and fast changing mass alarm information will result in ignoring really important information for dispatcher, and it will also delay the processing of the fault and endanger the safety of the power grid.
Aming at the problems above, this paper introduces data mining technology to the noise processing of alarm information in power grid，using the improved ID3 algorithm to construct noise filtering classifier dealing with alarm information in power grid. The classifer constructed in this paper can divide the alarm information into two kinds: noisy information and normal alarm information, and it can resolve the defect that the alarm information is uploaded to dispatcher without recognition. The main work done by this paper is as follows:
(1) This paper studies the data mining technology deeply, mainly discusses decision tree algorithm in detail; This paper sums up the characteristics, strengths and weaknesses of the follow algorithm: ID3 algorithm, C4.5 algorithm, CART algorithm, SLIQ algorithm, and SPRINT algorithm.
(2) This paper discusses two shortcomings of the classical ID3 algorithm: too complicated in calculating and leaning on the attribute which have more value while selecting the optimum attribute, aiming at these two aspects, this paper puts forward corresponding improved method. ID3 algorithm which bases on information entropy theory has multiple logarithm operation in choosing the splitting attribute when building the decision tree, so the computing speed will slows down especially when the data is large, aiming at this problem, this paper puts forward corresponding simplified method. Furthermore, the ID3 algorithm applicates the information gain as the criteria of seleting splitting attribute, so it often leans on the attribute which has more value, aiming at this problem, we introduce a divisor to the simplified ID3 algorithm to overcome the bias on attribute.
(3) Through the analysis of examples, the superiority of improved ID3 algorithm is verified and this paper carries out the construction of decision tree by MATLAB language.
(4) Based on the study of the alarm information’s characteristics, this paper realizes the preprocessing and relationship induction of the alarm information. The alarm information has complex forms, so it’s necessary to preprocess it and summarize relationship from it before data mining.
(5) Constructing noise information recognition classifier used in alarm information of distribution power grid with improved ID3 algorithm, this classifier can recognize noise information from alarm information.
Key words data mining; decision tree; ID3 algorithm; alarm information
目 录
摘 要 I
Abstract III
第1章 绪 论 1
1.1 课题的研究背景 1
1.2 国内外研究现状 3
1.3 本文的主要工作 5
1.4 本文的组织结构 5
第2章 数据挖掘技术及其在电力系统中的应用 7
2.1 数据挖掘简介 7
2.2 数据挖掘的任务及步骤 8
2.3 数据挖掘中常用的算法和技术 10
2.4 电力系统中的数据挖掘 12
2.5 配电网报警信息处理中的数据挖掘 13
2.5.1 远动系统的特点 13
2.5.2 报警信息的分类 14
2.5.3 报警信息的特点 14
2.5.4配电网报警信息中的数据挖掘 15
2.6..
在配电网调度自动化系统中，报警系统信息量巨大，其中往往含有大量的噪声信息。传统的报警系统只是单纯地上传告警信息，未经处理且快速变化的海量报警信息容易造成调度员忽略真正重要的报警信息，延时处理故障，危害电网安全。
针对以上问题，本文将数据挖掘技术引入到配电网报警信息的噪声处理中，应用改进后的ID3算法构造对配电网中的报警信息进行噪声过滤的分类器。该分类器可以将报警信息分为噪声信息和非噪声信息两类，很好的解决了报警信息不加识别的上传给调度人员的缺点。本文所做的工作主要如下：
(1) 深入研究了数据挖掘技术，重点对数据挖掘中的决策树算法进行了详细探讨；概括总结了ID3算法、C4.5算法、CART算法、SLIQ算法及SPRINT算法的特点及优缺点。
(2) 本文探讨了经典的ID3算法的两个突出缺点：运算复杂和在选择属性时偏向于取值较多的属性，并对这两个方面进行改进。ID3算法是以信息熵理论为基础构建决策树的，每次在选择分裂属性时都要对其进行多次对数运算，在数据量较多的时候，运算速度明显变慢，本文针对这一问题，提出了相应的简化方法；再则，ID3应用信息增益作为最佳属性的选择标准，这样就会导致该算法偏向于选择取值较多的属性，而有时候取值较多的属性并不是最优属性，针对这一问题，本文引入一乘积因子到简化后的信息熵中，来克服ID3算法的取值的偏向性。
(3) 本文通过实例分析对改进后的ID3算法的优越性进行实验验证，并实现了用MATLAB语言来构造决策树。
(4) 本文在研究配电网中报警信息特点的基础上，实现了配电网中的报警信息的预处理及主要归纳关系的形成，只有对形式复杂的报警信息进行预处理并且得到主要归纳关系后才可以将它们用于数据挖掘任务中。
(5) 用改进后的ID3决策树算法设计了用于配电网报警信息中噪声识别的分类器。该分类器可以从报警信息中识别出噪声信息。
关键词 数据挖掘；决策树；ID3算法；报警信息
Abstract
In dispatching automation system of distribution power grid, there is a large amount of information which usually contains a lot of noisy information. The traditional alarm system simply uploads alarm information, so the untreated and fast changing mass alarm information will result in ignoring really important information for dispatcher, and it will also delay the processing of the fault and endanger the safety of the power grid.
Aming at the problems above, this paper introduces data mining technology to the noise processing of alarm information in power grid，using the improved ID3 algorithm to construct noise filtering classifier dealing with alarm information in power grid. The classifer constructed in this paper can divide the alarm information into two kinds: noisy information and normal alarm information, and it can resolve the defect that the alarm information is uploaded to dispatcher without recognition. The main work done by this paper is as follows:
(1) This paper studies the data mining technology deeply, mainly discusses decision tree algorithm in detail; This paper sums up the characteristics, strengths and weaknesses of the follow algorithm: ID3 algorithm, C4.5 algorithm, CART algorithm, SLIQ algorithm, and SPRINT algorithm.
(2) This paper discusses two shortcomings of the classical ID3 algorithm: too complicated in calculating and leaning on the attribute which have more value while selecting the optimum attribute, aiming at these two aspects, this paper puts forward corresponding improved method. ID3 algorithm which bases on information entropy theory has multiple logarithm operation in choosing the splitting attribute when building the decision tree, so the computing speed will slows down especially when the data is large, aiming at this problem, this paper puts forward corresponding simplified method. Furthermore, the ID3 algorithm applicates the information gain as the criteria of seleting splitting attribute, so it often leans on the attribute which has more value, aiming at this problem, we introduce a divisor to the simplified ID3 algorithm to overcome the bias on attribute.
(3) Through the analysis of examples, the superiority of improved ID3 algorithm is verified and this paper carries out the construction of decision tree by MATLAB language.
(4) Based on the study of the alarm information’s characteristics, this paper realizes the preprocessing and relationship induction of the alarm information. The alarm information has complex forms, so it’s necessary to preprocess it and summarize relationship from it before data mining.
(5) Constructing noise information recognition classifier used in alarm information of distribution power grid with improved ID3 algorithm, this classifier can recognize noise information from alarm information.
Key words data mining; decision tree; ID3 algorithm; alarm information
目 录
摘 要 I
Abstract III
第1章 绪 论 1
1.1 课题的研究背景 1
1.2 国内外研究现状 3
1.3 本文的主要工作 5
1.4 本文的组织结构 5
第2章 数据挖掘技术及其在电力系统中的应用 7
2.1 数据挖掘简介 7
2.2 数据挖掘的任务及步骤 8
2.3 数据挖掘中常用的算法和技术 10
2.4 电力系统中的数据挖掘 12
2.5 配电网报警信息处理中的数据挖掘 13
2.5.1 远动系统的特点 13
2.5.2 报警信息的分类 14
2.5.3 报警信息的特点 14
2.5.4配电网报警信息中的数据挖掘 15
2.6..
