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. R2 was used for assessing the data trend.
Highlights
Due to the increasing demand for electric energy currently signaled by the market, generators are operating close to their maximum capacities for longer periods
An overview on partial discharges in high voltage equipment using PD raw data was performed in [11] using k-means techniques to cluster signals
In [17], used machine-learning techniques to classify a database composed of 352 phase-resolved partial discharge (PRPD) measurements obtained from a generator of 37.5 MW and 12.5 kV, in which 96 attributes were extracted for the computational process of obtaining clusters
Summary
Due to the increasing demand for electric energy currently signaled by the market, generators are operating close to their maximum capacities for longer periods. An overview on partial discharges in high voltage equipment using PD raw data was performed in [11] using k-means techniques to cluster signals In another approach, neural networks were applied to classify partial discharges’ signals into six types using real measurement data from high-voltage motors [12]. A proposal for automatic PD classification in hydro generators’ windings using NN based on the concept of image projection resulted in the identification of only four patterns [16] Another approach, in [17], used machine-learning techniques to classify a database composed of 352 PRPD measurements obtained from a generator of 37.5 MW and 12.5 kV, in which 96 attributes were extracted for the computational process of obtaining clusters.
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