Data-driven Fault Diagnosis of Power Transformers using Dissolved Gas Analysis (DGA)
Corresponding email: arian@ie.ui.ac.id
Published at : 21 Apr 2020
Volume : IJtech
Vol 11, No 2 (2020)
DOI : https://doi.org/10.14716/ijtech.v11i2.3625
Dhini, A., Faqih, A., Kusumoputro, B., Surjandari, I., Kusiak, A., 2020. Data-driven Fault Diagnosis of Power Transformers using Dissolved Gas Analysis (DGA). International Journal of Technology. Volume 11(2), pp. 388–399
| Arian Dhini | Laboratory Industrial Engineering Department, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia |
| Akhmad Faqih | Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia |
| Benyamin Kusumoputro | Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia |
| Isti Surjandari | Laboratory Industrial Engineering Department, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia |
| Andrew Kusiak | Department of Industrial and Systems Engineering, College of Engineering, University of Iowa, 4627 Seamans Center for the Engineering Arts and Sciences, Iowa City -Iowa, 52242, USA |
A power transformer is a critical piece of equipment in a
power plant for distributing electricity, and it experiences thermal and
electrical stresses during operation. Dissolved gas analysis (DGA) remains one
of the most effective techniques to monitor the health of oil-filled
transformers. Some traditional approaches for interpreting DGAs have been
introduced. Occasionally, such approaches leave the state of the transformer
uncategorized. This study proposed data-driven approaches for a fault diagnosis
system based on DGA data using support vector machine (SVM). SVM is known for
its robustness, good generalization capability, and unique global optimum
solutions, particularly when data is limited. Backpropagation neural networks
(BPNN) and extreme learning machine-radial basis function (ELM-RBF), a recent
Neural Networks (NN)-based method with extremely fast computation time, were
compared to SVM. An advanced technique to overcome the imbalanced data and
synthetic minority oversampling technique (SMOTE) was proposed to investigate
the effect on classifier performance. The model was trained and tested using
IEC TC 10 databases and transformer DGA monitoring data of a thermal power
plant in Jakarta. The results indicated that SVM displayed the best performance
compared to ELM-RBF and BPNN. It demonstrated extremely high accuracy, while
still maintaining fast computation time for all stages in the proposed
multistage fault diagnosis system.
Condition monitoring; Dissolved gas analysis; Fault diagnosis; Support vector machine; Transformer
Thermal power plants remain main electricity providers; however, most of them are highly aged. According to Ulum et al. (2017), plant equipment, which degrades over time, leads to electricity production loss. Therefore, the industrial process measurements are imperative to monitor and assure the quality and safety in operations (Kusiak and Song, 2009). Those measurements are the bases to determine the maintenance activities. Such maintenance will raise the reliability and availability of equipment (Pariaman et al., 2017), including the reliability and availability of power transformers, one of the key pieces of equipment in a power distributing system. Despite the fact that faults in a transformer occur infrequently, once they do occur, their impact is significant in terms of safety, downtime, and equipment loss (Hernandez and Labib, 2017). DGA has been known to be a popular, sensitive, and reliable technique to monitor the insulation condition of transformers (Saha, 2003; Chakravorti et al., 2013). However, conventional DGA interpretation methods include certain drawbacks in terms of accuracy and uncertainty (Shintemirov et al., 2009; Ghoneim and Taha, 2016). These approaches require the manual interpretation of experts, and some measurements may be unidentifiable when using any interpretation method (Lin et al., 1993; Yang et al., 2009; Abu-Siada and Islam, 2012).
Faults should be diagnosed accurately and in a
timely manner, since ignoring them degrades the safety and security of the
process, and it could lead to catastrophic failures and loss of material and
even life (Gao
et al., 2015). Therefore, machine learning
methods have been recently applied in studies for transformer fault detection
and diagnosis (FDD) using DGA data. Support vector machine (SVM) has become an
increasingly popular technique in machine learning. SVM is known for its
robustness, good generalization capability, and unique global optimum solution.
Shin and Cho (2006) explained that its good generalization capability was
due to structural risk minimization, which is employed by SVM, rather than
empirical risk minimization, as in NN. SVM also performs well with small
samples and high dimension data, while still maintaining short computational
times (Lv et al., 2005; Bacha et al., 2012;
Sahri and Yusof, 2015; Souza and Ramachandran, 2016). Some studies have compared SVM with other methods, such
as kNN, ANN (Shintemirov et al., 2009), the expert system (Lv
et al., 2005), fuzzy logic, multilayer
perceptron (MLP), and radial basis function neural networks (RBFNN) (Fei et al., 2009; Bacha et al. 2012). These studies found that SVM performed better. Ghoneim and Taha (2016)
conducted a fault diagnosis study in the same area, but they only applied limit
rules to categorize faults.
In reality, the availability of
fault data is usually significantly lower than the normal operating condition
(NOC). This situation may lead to a decrease in classifier performance. Fault
diagnosis focuses on how to detect a fault, the minority class. Sahri
and Yusof (2015) tested some scenarios of input features without
considering how to handle imbalanced data. Only a few studies have considered
this imbalanced data in FDD. Chawla et al. (2002) proposed
the use of the synthetic minority oversampling technique (SMOTE) to overcome
the imbalanced data problem by creating new data through an interpolation process.
No previous studies have applied SMOTE in the case of transformers for fault
diagnosis.
This study aims to develop an
accurate and fast transformer fault diagnosis system from DGA data using SVM
and SMOTE for data balancing. Other recent classifier methods, extreme learning
machine-radial basis function (ELM-RBF), which is known for its extremely fast
computation time, is applied. The high computation of ELM-RBF, while still
maintains good accuracy, will result in a number advantages, such as fast fault
diagnosis and lower hardware investment costs. In addition,
the classical approach of the NN-based technique, backpropagation neural
networks (BPNN), was also tested. He and Kusiak (2017) employed SVM, Multi-layer perceptron (MLP) and ELM based
methods, which performed satisfactorily, to predict wind power. This
study contributes to the possibility of lengthening transformer life by
proposing fault diagnosis systems using aforementioned data-driven approaches. The proposed models were trained and tested using the
fault and NOC DGA data from IEC TC 10 and NOC DGA data from a thermal power
plant in Jakarta. Ghoneim and Taha (2016)
and Sahri and Yusof (2015) used IEC TC 10 as
a data source as well. Only Ghoneim and Taha
(2016) and Sahri and Yusof (2015) used real
data, whereas Bacha et al. (2012) used simulated data from the Tunisian company of
Electricity and Gas.
The remainder of this article is
organized as follows. Section 2 describes the study methods used in this research,
from the proposed research framework to the methods applied. The following
section, Section 3, elaborates the results and discusses the fault diagnosis
based on certain performance measures. Some concluding remarks and areas of
future research are provided in Section 4.
Based
on the two performance measures of accuracy and computation time, it can be
concluded that the SVM outperforms other NN-based methods, ELM-RBF and BPNN,
for the proposed multistage fault diagnosis of power transformers using DGA
data. SVM performs satisfactorily with high accuracy and fast computation time.
It has proven to be an effective method in terms of classification with a
better generalization. Despite its poorer results compared to SVM, ELM-RBF also
performed the best in terms of computation time. In terms of accuracy, BPNN
exhibited the best accuracy. In the future, research to
improve accuracy, data quantity, and data quality should be enhanced by adding
more training data, selecting optimum features, and combining other types of
input data, such as gas ratio. More comparisons with other data-driven
methods, such as decision trees, kNN, or even deep learning, and applying
optimization in parameter selections, represent additional options.
| Filename | Description |
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| R1-IE-3625-20200207193846.jpg | Figure 1 |
| R1-IE-3625-20200207193907.jpg | Figure 2 |
| R1-IE-3625-20200207193931.jpg | Figure 3 (revised) |
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