基于关联规则数据挖掘技术的电网故障诊断

在实际的电网故障诊断中,面临如何从海量数据找到真正对于诊断结果有帮助的关键数据以及当故障信息存在不完整或不确定性,甚至关键信息丢失时,会导致故障诊断难以得出正确结论的问题。针对此问题,将关联规则数据挖掘DLG(Direct Large temsests Generation）算法引入到电网故障诊断中。首先以保护、断路器作为条件属性,故障区域作为决策属性,考察各种故障情况并建立原始决策表,然后利用关联规则挖掘进行属性约简,通过修改阈值进行交互式挖掘,直接提取最佳属性约简组合,然后利用最佳属性约简组合形成的约简决策表和关联规则交互式挖掘,针对各种情况的故障信息进行诊断推理。运用C编写了基于该方法的故障诊断软件, 采用四母线配电网系统作为仿真对象,算例结果表明该算法在一定电网规模和保护动作信息不完备的情况下,故障诊断正确性高、容错性好,实用性强。     

一种改进的FP-Growth关联规则挖掘算法

挖掘频繁项目集是关联规则挖掘中的重点之一.Jiawei Han等人提出了FP-Growth算法,该算法不产生候选项目集.但当数据库较大时,生成PT-Tree需遍历的树的节点数目很多.本文通过对FP-Growth算法分析,提出的改进算法能有效地减少需遍历的树的节点数,从而降低了时间开销.实验结果表明,改进算法能够比较明显地提高挖掘效率.     

基于关联规则挖掘的气象观测设备一致性检测算法

为了保证气象观测设备采集数据的稳定性,从而需要对观测设备进行一致性检测。提出了一种基于兴趣度的关联规则的算法。并将该兴趣度关联规则挖掘算法应用于气象观测设备一致性检测上,可以形成关联规则气象观测设备一致性的模型。通过真实数据验证表明,该算法不仅能够挖掘出所有相关性很强的规则,同时与同类非Apriori类的算法相比,在时间性能上更加优越。通过该关联规则算法挖掘出所有关联项对形成范例库,利用规则匹配的方法对设备之间进行一致性检测,对算法实验优化,得到最优参数解,从而判定设备一致性。     

基于FP-network关联规则挖掘算法的配电网薄弱点分析研究

针对配电网运行时经常发生故障的情况,如何快速高效地寻找出配电网中的薄弱点成为了当下配电网安全运行的一大难题.文中采用频繁模式网络(FP-network)模型,建立事务-项目的关联矩阵,并且将所需要进行关联规则挖掘的数据储存在关联矩阵中,从而进行关联规则的数据挖掘.通过算例分析证实了FP-network关联规则挖掘算法可...     

基于遗传算法的模糊控制器规则优化

模糊控制器设计的关键问题就是模糊控制规则的选择。基于遗传算法的模糊控制规则表优化,是为模糊控制提供一种更加方便、有效的查表法。为了提高模糊控制器的性能,提出了基于遗传算法的模糊控制器规则表的优化方法,并进行了仿真实验。仿真结果表明,在模糊控制中采用遗传算法使控制系统最终达到所要求的控制效果,证明了该方法的可行性和有效性。     

变权重系数和关联规则挖掘方法在风光储运行优化中的应用

制定合理的风光储联合发电系统协调运行策略可以提高新能源电力企业的运行经济性。提出一种变权重系数和关联规则挖掘混合算法(Variable Weight Coefficients and Association Rules,VWC-AR),对风光储联合发电系统协调运行策略进行优化。以历史运行数据为基础,挖掘全工况下最优机组组合以及单台机组在全工况下的综合性能指标值,得到工况条件到最优机组组合之间的推理规则集。根据具体的调度需求和工况环境,利用均衡函数的变权综合模式,计算潜在目标机组组态模式的变权重系数,结合既有的综合性能评估方法,动态切换最优组态模式,从而建立一套客观、实时、准确的协调运行策略,优化系统运行成本。通过算例证明,该算法能够充分利用风光互补特性,延长蓄电池使用寿命,提高系统经济效益。     

软计算方法和数据挖掘理论在电力系统负荷预测中的应用

数据挖掘技术能够从大量数据中发现潜在知识，软计算是创建智能系统的有效方法，本文将两者结合，完成电力预测过程的两个主要任务：负荷坏数据处理和多因素负荷预测模型的建立。通过对Kohonen网聚类挖掘和BP网分类挖掘的效果分析，设计由这两种网络组合而成的神经网络模型，完成坏数据辨识和调整的任务；以模糊推理系统为基础构建多因素负荷预测模型，本文采用CART分类挖掘技术解决模糊结构辨识中的两个难点问题：输入空间划分和输入变量选择，在此基础上设计ANFIS网络进行参数辩识。良好的实例分析效果说明，数据挖掘思想和软计算方法相结合，是电力系统负荷预测的一种有效的思路和方法。     

基于关联分析算法的配电网多级冗余数据校验与挖掘技术研究

随着电网的快速建设与发展,电网的设备数据、运行数据以及管理数据等相关业务数据具有规模大、数据结构繁杂的特点,而且数据涉及到电网公司的多个部门、多个系统,彼此之间的数据会出现大量冗余及不一致性。因此,文中基于数据挖掘技术,对配电网多级冗余数据的校验的方法进行了研究,通过对照各项冗余数据与其他基础数据之间的关联关系,建立了数据相关性指标模型及冗余数据校验规则,并基于此对配电网多级冗余数据进行校验;并基于关联分析算法,对缺陷数据的挖掘与分析方法进行了研究。以某省级配电网的设备数据为例,进行了冗余数据的校验及缺陷数据的挖掘与分析,算例结果验证了文中所提方法的有效性及可行性。     

Data mining for classification of powerquality problems using WEKA and theeffect of attributes on classificationaccuracy

There is growing interest in power quality issues due to wider developments in power delivery engineering. In order to maintain good power quality, it is necessary to detect and monitor power quality problems. The power quality monitoring requires storing large amount of data for analysis. This rapid increase in the size of databases has demanded new technique such as data mining to assist in the analysis and understanding of the data. This paper presents the classification of power quality problems such as voltage sag, swell, interruption and unbalance using data mining algorithms: J48, Random Tree and Random Forest decision trees. These algorithms are implemented on two sets of voltage data using WEKA software. The numeric attributes in first data set include 3-phase RMS voltages at the point of common coupling. In second data set, three more numeric attributes such as minimum, maximum and average voltages, are added along with 3-phase RMS voltages. The performance of the algorithms is evaluated in both the cases to determine the best classification algorithm, and the effect of addition of the three attributes in the second case is studied, which depicts the advantages in terms of classification accuracy and training time of the decision trees.     

基于粗糙集的关联规则挖掘方法的研究与应用

基于粗糙集理论知识,对关联规则挖掘算法作出一定的改进。该算法的主要思想是把集合的近似质量作为迭代准则,初始约简集是所有的条件属性集合,在保证近似质量不变的前提下通过逐步缩减的方式来求取约简集,保证了所求的约简不会减弱对问题的分类决策能力。约简后得到新的决策表,在此基础上应用基于贪心思想的Apriori算法挖掘关联规则。算法的主要优势是在不影响对问题分类决策能力的前提下,以较小的属性和候选项集数目以及有限的扫描次数生成决策规则。通过应用实例和实验分析验证了算法的有效性。     

Evolutionary selection of interesting class association rules using genetic relation algorithm

During the last years, several association rule‐based classification methods have been proposed, these algorithms may quickly generate accurate rules. However, the generated rules are often very large in terms of the number of rules and usually complex and hardly understandable for users. Among all the rules generated by the algorithms, only some of them are likely to be of any interest to the domain expert analyzing the data. Most of the rules are either redundant, irrelevant or obvious. In this paper, a new method for selecting the interesting class association rules is proposed by an evolutionary method named genetic relation algorithm. The algorithm evaluates the relevance and interestingness of the discovered association rules by the relationships between the rules in each generation using a specific measure of distance among them giving a reduced set of rules as the result in the final generation. This small rule set has the following properties: (i) accurate as it has at least the same classification accuracy as the complete association rule set, (ii) interesting because of the diversity of rules and (iii) comprehensible because it is more understandable for the users as the number of attributes involved in the rules is also small. The efficiency of the proposed method is compared with other conventional methods including genetic network programming‐based mining using ten databases and the experimental results show that it outperforms others keeping a good balance between the classification accuracy and the comprehensibility of the rules. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Data mining methodology for disaggregation of load demand

This paper presents a new method in data mining to analyze the composition of the electric demand among the different consumption and the behavior of each type of load. The proposed method uses a heuristic optimization algorithm (Tabu Search) for minimizing the error between the real demand and the calculated approximation to this demand. This search is adaptative because the algorithm changes the relative weight of each load as well as the profile of each load. The obtained results show the good operation of the proposed methodology. Also, it is possible to observe that this approach to the knowledge of the demand is better than the classic approach in which “a picture” of the consumption can be obtained, while this methodology obtains the evolution of this consumption in time; that is to say, it shows “a movie” of the behavior of the loads.     

A nonlinear model to rank association rules based on semantic similarity and genetic network programing

Many methods have been studied for mining association rules efficiently. However, because these methods usually generate a large number of rules, it is still a heavy burden for the users to find the most interesting ones. In this paper, we propose a novel method for finding what the user is interested in by assigning several keywords, like searching documents on the Web using search engines. By considering both the semantic similarity between the rules and keywords, and the statistical information like support, confidence, chi-squared value, etc. we could rank the rules by a new method named RuleRank, where evolutionary methods are applied to find the optimal ranking model. Experiments show that our approach is effective for the users to find what they want. Copyright © 2009 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

MBFP generalized association rule mining and classification in traffic volume prediction

Genetic network programming (GNP)‐based time‐related association rules mining method provides a useful mean to investigate future traffic volume of road networks and hence helps us to develop traffic navigation system. Further improvements have been proposed in this paper about the time‐related association rule mining using generalized GNP with multibranches and full‐paths (MBFP) algorithm. For fully utilizing the potential ability of GNP structure, the mechanism of generalized GNP with MBFP is studied. The aim of this algorithm is to better handle association rule extraction from the databases with high efficiency in a variety of time‐related applications, especially in the traffic volume prediction problems. The generalized algorithm which can find the important time‐related association rules is described, and experimental results are presented considering a traffic prediction problem. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

基于关联规则挖掘的输电线路缺陷状态预测

对输电线路缺陷状态进行关联因素的分析和预测工作,可以为输电线路的巡维工作提供重要的技术支持。在现有输电线路状态分析和影响因素研究的基础上,提出了基于关联规则挖掘的输电线路缺陷状态预测方法。首先根据历史缺陷数据评价得到输电线路缺陷状态。结合各种影响因素,构建线路缺陷状态与相关因素的特征库。然后引入FP-Growth算法挖掘各因素与缺陷状态间的关联规则,并将得到的规则用于预测线路的缺陷状态。最后以某地区架空输电线路为例,通过历史缺陷等数据评价得到缺陷状态样本,提取相关条件特征作为输入特征,并用于预测线路的缺陷状态。结果验证了该方法的有效性,对输电线路的巡维检修有一定的参考价值。     

基于粗糙集与模糊规则的变压器故障诊断方法

引入了一种基于粗糙集约简并结合模糊规则的方法进行变压器故障诊断。该方法从变压器故障判别表出发,首先使用粗糙集理论进行决策表约简,在保持故障判别表分类能力不变的条件下,去除了变压器故障诊断知识中大量的冗余特征,然后结合模糊集合理论和模糊推理,计算出各个约简后的决策规则的模糊隶属度,最终得到故障类型的判断。实例表明,本方法可以有效地进行模糊推理并得到正确的诊断结果。     

基于关联规则挖掘的电力生产安全事故事件关键诱因筛选

随着智能电网、通信网络及电力生产安全事故事件分析水平的提高和发展,电力生产安全事故事件数据量快速增长、复杂性不断增大,逐步构成了电力生产安全事故事件大数据。为在先验事故事件大数据的基础上高效、可靠地对事故诱因进行分类和识别,基于关联规则挖掘进行电力生产安全事故事件关键诱因筛选。根据事故事件的特点,建立电力生产安全事故诱因分析体系,对不同类型的事故进行布尔离散化,并基于关联规则挖掘提出事故诱因的诱发度计算方法,运用Apriori算法进行深度关联规则挖掘,并根据强关联规则对关键诱因进行筛选和分析。以某区域近5年的事故实例分析验证了该方法的有效性。     

面向智能电网大数据关联规则挖掘的频繁模式网络模型

针对目前关联规则挖掘频繁树(FP-Tree)算法实现较困难以及难以处理数据库更新的缺点,提出了频繁模式网络(FP-network)模型,将关联规则挖掘所需要的信息压缩到一个无向网络图上,并建立事务项目关联矩阵,从而进行数据存储和数据挖掘。FP-network模型适用于智能电网大数据的关联规则挖掘。以关联规则挖掘在输电线路故障分析领域的应用为例进行算例分析,结果表明所提出的FP-network关联规则挖掘算法不仅继承了FP-Tree算法的优点,而且只需扫描一次数据库,也便于数据库的维护和更新,从而提高了智能电网大数据关联规则挖掘的效率。     

定量关联规则挖掘电站凝汽器运行数据研究

对凝汽器传热端差的各个主要影响因素及它们之间的关系进行了分析.将关联规则挖掘技术应用于凝汽器运行数据的分析,获得了有益的分析结果.对挖掘结果的分析表明,该技术可用于凝汽器的性能分析、状态监测、故障诊断和状态检修等方面,很有意义.