Combined Approach Using Clustering-Random Forest to Evaluate Partial Discharge Patterns in Hydro Generators

Ana C. N. Pardauil, Thiago P. Nascimento, Marcelo R. S. Siqueira, Ubiratan H. Bezerra and Werbeston D. Oliveira
Additional contact information
Ana C. N. Pardauil: Institute of Technology, Electrical Engineering Faculty, Federal University of Pará, Belém, Pará 66075-110, Brazil
Thiago P. Nascimento: Computer Science Department, Federal University of Amapá, Macapá, Amapá 68903-419, Brazil
Marcelo R. S. Siqueira: Physics Department, Federal University of Amapá, Macapá, Amapá 68903-419, Brazil
Ubiratan H. Bezerra: Institute of Technology, Electrical Engineering Faculty, Federal University of Pará, Belém, Pará 66075-110, Brazil
Werbeston D. Oliveira: Electrical Engineering Department, Federal University of Amapá, Macapá, Amapá 68903-419, Brazil

Energies, 2020, vol. 13, issue 22, 1-18

Abstract: The measurement and analysis of partial discharges (PD) are like medical examinations, such as Electrocardiogram (ECG), in which there are preestablished criteria. However, each patient will present his particularities that will not necessarily imply his condemnation. The consolidated method for PD processing has high qualifications in the statistical analysis of insulation status of electric generators. However, although the IEEE 1434 standard has well-established standards, it will not always be simple to classify signals obtained in the measurement of the hydro generator coupler due to variations in the same type of PD incidence that may occur as a result of the uniqueness of each machine subject to staff evaluation. In order to streamline the machine diagnostic process, a tool is suggested in this article that will provide this signal classification feature. These measurements will be established in groups that represent each known form of partial discharge established by the literature. It was combined with supervised and unsupervised techniques to create a hybrid method that identified the patterns and classified the measurement signals, with a high degree of precision. This paper proposes the use of data-mining techniques based on clustering to group the characteristic patterns of PD in hydro generators, defined in standards. Then, random forest decision trees were trained to classify cases from new measurements. A comparative analysis was performed among eight clustering algorithms and random forest for choosing which is the superior combination to make a better classification of the equipment diagnosis. R 2 was used for assessing the data trend.

Keywords: partial discharges; data-mining techniques; hydro generators; clustering algorithms; random forest (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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