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

S. Asha Kiranmai; A. Jaya Laxmi

doi:10.1186/s41601-018-0103-3

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

Original Research | 更新时间：2026-09-30

- Data mining for classification of powerquality problems using WEKA and theeffect of attributes on classificationaccuracy
- Protection and Control of Modern Power Systems Vol. 3, Issue 3, Pages: 1-12(2018)
- 作者机构：
  
  1. Department of Electrical Engineering, University College of Engineering, Osmania University, Hyderabad, Telangana,India
  2. Department of Electrical and Electronics Engineering, Jawaharlal Nehru Technological University Hyderabad College of Engineering, Hyderabad, Telangana,India
- 作者简介：
- 基金信息：
- DOI：10.1186/s41601-018-0103-3
  CLC：
- Published：01 July 2018
- 稿件说明：
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S. Asha Kiranmai, A. Jaya Laxmi. Data mining for classification of powerquality problems using WEKA and theeffect of attributes on classificationaccuracy[J]. Protection and Control of Modern Power Systems, 2018, 3(3): 1-12.
DOI：

S. Asha Kiranmai, A. Jaya Laxmi. Data mining for classification of powerquality problems using WEKA and theeffect of attributes on classificationaccuracy[J]. Protection and Control of Modern Power Systems, 2018, 3(3): 1-12. DOI： 10.1186/s41601-018-0103-3.

摘要

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.

Abstract

There is growing interest in power quality issues due to wider developments in power delivery engineering. In order to maintain good power quality

swell

interruption and unbalance using data mining algorithms: J48

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.

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